�ݺ�ߣshows by User: j0mega

�ݺ�ߣshows by User: j0mega / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: j0mega / Mon, 20 Apr 2020 21:45:33 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: j0mega ASSESSMENT OF VOLTAGE FLUCTUATION AND REACTIVE POWER CONTROL WITH SVC USING PSO /j0mega/assessment-of-voltage-fluctuation-and-reactive-power-control-with-svc-using-pso jreactivepowercontrolwithsvcusingpso-200420214533
Smart grid novel growth in the arena of power, this is a new scheme and apparatus for producing and distributing electricity. Smart grids are very important part of the electrical circuit. Distribution Management System (DMS) used by the utilities for the state Estimation (SE). Basic application distribution control system, evaluation (SE) and the control reactive power). There SE principally used to monitor and to control the entire distributed network. In the distribution network has a problem voltage profile. It is controlled by distributed generators (DG), which are located in diverse positions in the system to maintain the voltage within certain limits. Validation through the implementation on the IEEE 14-bus radial transmission system indicated that PSO is reasonable to achieve the task. MATLAB software is used for results simulation]]>
Smart grid novel growth in the arena of power, this is a new scheme and apparatus for producing and distributing electricity. Smart grids are very important part of the electrical circuit. Distribution Management System (DMS) used by the utilities for the state Estimation (SE). Basic application distribution control system, evaluation (SE) and the control reactive power). There SE principally used to monitor and to control the entire distributed network. In the distribution network has a problem voltage profile. It is controlled by distributed generators (DG), which are located in diverse positions in the system to maintain the voltage within certain limits. Validation through the implementation on the IEEE 14-bus radial transmission system indicated that PSO is reasonable to achieve the task. MATLAB software is used for results simulation]]> Mon, 20 Apr 2020 21:45:33 GMT /j0mega/assessment-of-voltage-fluctuation-and-reactive-power-control-with-svc-using-pso j0mega@slideshare.net(j0mega) ASSESSMENT OF VOLTAGE FLUCTUATION AND REACTIVE POWER CONTROL WITH SVC USING PSO j0mega Smart grid novel growth in the arena of power, this is a new scheme and apparatus for producing and distributing electricity. Smart grids are very important part of the electrical circuit. Distribution Management System (DMS) used by the utilities for the state Estimation (SE). Basic application distribution control system, evaluation (SE) and the control reactive power). There SE principally used to monitor and to control the entire distributed network. In the distribution network has a problem voltage profile. It is controlled by distributed generators (DG), which are located in diverse positions in the system to maintain the voltage within certain limits. Validation through the implementation on the IEEE 14-bus radial transmission system indicated that PSO is reasonable to achieve the task. MATLAB software is used for results simulation <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/jreactivepowercontrolwithsvcusingpso-200420214533-thumbnail.jpg?width=120&height=120&fit=bounds" /> Smart grid novel growth in the arena of power, this is a new scheme and apparatus for producing and distributing electricity. Smart grids are very important part of the electrical circuit. Distribution Management System (DMS) used by the utilities for the state Estimation (SE). Basic application distribution control system, evaluation (SE) and the control reactive power). There SE principally used to monitor and to control the entire distributed network. In the distribution network has a problem voltage profile. It is controlled by distributed generators (DG), which are located in diverse positions in the system to maintain the voltage within certain limits. Validation through the implementation on the IEEE 14-bus radial transmission system indicated that PSO is reasonable to achieve the task. MATLAB software is used for results simulation

ASSESSMENT OF VOLTAGE FLUCTUATION AND REACTIVE POWER CONTROL WITH SVC USING PSO from Kashif Mehmood

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0 Short Term Load Forecasting Using Bootstrap Aggregating Based Ensemble Artificial Neural Network /slideshow/short-term-load-forecasting-using-bootstrap-aggregating-based-ensemble-artificial-neural-network/232330668 jraeeshorttermloadforecastingusingbootstrapaggregatingbased-200420214532
Short Term Load Forecasting (STLF) can predict load from several minutes to week plays the vital role to address challenges such as optimal generation, economic scheduling, dispatching and contingency analysis. This paper uses Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN) technique to perform STFL but long training time and convergence issues caused by bias, variance and less generalization ability, unable this algorithm to accurately predict future loads. This issue can be resolved by various methods of Bootstraps Aggregating (Bagging) (like disjoint partitions, small bags, replica small bags and disjoint bags) which helps in reducing variance and increasing generalization ability of ANN. Moreover, it results in reducing error in the learning process of ANN. Disjoint partition proves to be the most accurate Bagging method and combining outputs of this method by taking mean improves the overall performance. This method of combining several predictors known as Ensemble Artificial Neural Network (EANN) outperform the ANN and Bagging method by further increasing the generalization ability and STLF accuracy.]]>
Short Term Load Forecasting (STLF) can predict load from several minutes to week plays the vital role to address challenges such as optimal generation, economic scheduling, dispatching and contingency analysis. This paper uses Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN) technique to perform STFL but long training time and convergence issues caused by bias, variance and less generalization ability, unable this algorithm to accurately predict future loads. This issue can be resolved by various methods of Bootstraps Aggregating (Bagging) (like disjoint partitions, small bags, replica small bags and disjoint bags) which helps in reducing variance and increasing generalization ability of ANN. Moreover, it results in reducing error in the learning process of ANN. Disjoint partition proves to be the most accurate Bagging method and combining outputs of this method by taking mean improves the overall performance. This method of combining several predictors known as Ensemble Artificial Neural Network (EANN) outperform the ANN and Bagging method by further increasing the generalization ability and STLF accuracy.]]> Mon, 20 Apr 2020 21:45:32 GMT /slideshow/short-term-load-forecasting-using-bootstrap-aggregating-based-ensemble-artificial-neural-network/232330668 j0mega@slideshare.net(j0mega) Short Term Load Forecasting Using Bootstrap Aggregating Based Ensemble Artificial Neural Network j0mega Short Term Load Forecasting (STLF) can predict load from several minutes to week plays the vital role to address challenges such as optimal generation, economic scheduling, dispatching and contingency analysis. This paper uses Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN) technique to perform STFL but long training time and convergence issues caused by bias, variance and less generalization ability, unable this algorithm to accurately predict future loads. This issue can be resolved by various methods of Bootstraps Aggregating (Bagging) (like disjoint partitions, small bags, replica small bags and disjoint bags) which helps in reducing variance and increasing generalization ability of ANN. Moreover, it results in reducing error in the learning process of ANN. Disjoint partition proves to be the most accurate Bagging method and combining outputs of this method by taking mean improves the overall performance. This method of combining several predictors known as Ensemble Artificial Neural Network (EANN) outperform the ANN and Bagging method by further increasing the generalization ability and STLF accuracy. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/jraeeshorttermloadforecastingusingbootstrapaggregatingbased-200420214532-thumbnail.jpg?width=120&height=120&fit=bounds" /> Short Term Load Forecasting (STLF) can predict load from several minutes to week plays the vital role to address challenges such as optimal generation, economic scheduling, dispatching and contingency analysis. This paper uses Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN) technique to perform STFL but long training time and convergence issues caused by bias, variance and less generalization ability, unable this algorithm to accurately predict future loads. This issue can be resolved by various methods of Bootstraps Aggregating (Bagging) (like disjoint partitions, small bags, replica small bags and disjoint bags) which helps in reducing variance and increasing generalization ability of ANN. Moreover, it results in reducing error in the learning process of ANN. Disjoint partition proves to be the most accurate Bagging method and combining outputs of this method by taking mean improves the overall performance. This method of combining several predictors known as Ensemble Artificial Neural Network (EANN) outperform the ANN and Bagging method by further increasing the generalization ability and STLF accuracy.

Short Term Load Forecasting Using Bootstrap Aggregating Based Ensemble Artificial Neural Network from Kashif Mehmood

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0 Optimizing Size of Variable Renewable Energy Sources by Incorporating Energy Storage and Demand Response /slideshow/optimizing-size-of-variable-renewable-energy-sources-by-incorporating-energy-storage-and-demand-response/232330666 joptimizingsizeofvariablerenewableenergy-200420214531
The electricity sector contributes to most of the global warming emissions generated from fossil fuel resources which are becoming rare and expensive due to geological extinction and climate change. It urges the need for less carbon-intensive, inexhaustible Renewable Energy Sources (RES) that are economically sound, easy to access and improve public health. The carbon-free salient feature is the driving motive that propels widespread utilization of wind and solar RES in comparisons to rest of RES. However, stochastic nature makes these sources, variable renewable energy sources (VRES) because it brings uncertainty and variability that disrupt power system stability. This problem is mitigated by adding energy storage (ES) or introducing the demand response (DR) in the system. In this paper, an electricity generation network of China by the year 2017 is modeled using EnergyPLAN software to determine annual costs, primary energy supply (PES) and CO2 emissions. The VRES size is optimized by adding ES and DR (daily, weekly, or monthly) while maintaining critical excess electricity production (CEEP) to zero. The results substantiate that ES and DR increase wind and solar share up to 1000 and 874 GW. In addition, it also reduces annual costs and emissions up to 4.36 % and 45.17 %]]>
The electricity sector contributes to most of the global warming emissions generated from fossil fuel resources which are becoming rare and expensive due to geological extinction and climate change. It urges the need for less carbon-intensive, inexhaustible Renewable Energy Sources (RES) that are economically sound, easy to access and improve public health. The carbon-free salient feature is the driving motive that propels widespread utilization of wind and solar RES in comparisons to rest of RES. However, stochastic nature makes these sources, variable renewable energy sources (VRES) because it brings uncertainty and variability that disrupt power system stability. This problem is mitigated by adding energy storage (ES) or introducing the demand response (DR) in the system. In this paper, an electricity generation network of China by the year 2017 is modeled using EnergyPLAN software to determine annual costs, primary energy supply (PES) and CO2 emissions. The VRES size is optimized by adding ES and DR (daily, weekly, or monthly) while maintaining critical excess electricity production (CEEP) to zero. The results substantiate that ES and DR increase wind and solar share up to 1000 and 874 GW. In addition, it also reduces annual costs and emissions up to 4.36 % and 45.17 %]]> Mon, 20 Apr 2020 21:45:31 GMT /slideshow/optimizing-size-of-variable-renewable-energy-sources-by-incorporating-energy-storage-and-demand-response/232330666 j0mega@slideshare.net(j0mega) Optimizing Size of Variable Renewable Energy Sources by Incorporating Energy Storage and Demand Response j0mega The electricity sector contributes to most of the global warming emissions generated from fossil fuel resources which are becoming rare and expensive due to geological extinction and climate change. It urges the need for less carbon-intensive, inexhaustible Renewable Energy Sources (RES) that are economically sound, easy to access and improve public health. The carbon-free salient feature is the driving motive that propels widespread utilization of wind and solar RES in comparisons to rest of RES. However, stochastic nature makes these sources, variable renewable energy sources (VRES) because it brings uncertainty and variability that disrupt power system stability. This problem is mitigated by adding energy storage (ES) or introducing the demand response (DR) in the system. In this paper, an electricity generation network of China by the year 2017 is modeled using EnergyPLAN software to determine annual costs, primary energy supply (PES) and CO2 emissions. The VRES size is optimized by adding ES and DR (daily, weekly, or monthly) while maintaining critical excess electricity production (CEEP) to zero. The results substantiate that ES and DR increase wind and solar share up to 1000 and 874 GW. In addition, it also reduces annual costs and emissions up to 4.36 % and 45.17 % <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/joptimizingsizeofvariablerenewableenergy-200420214531-thumbnail.jpg?width=120&height=120&fit=bounds" /> The electricity sector contributes to most of the global warming emissions generated from fossil fuel resources which are becoming rare and expensive due to geological extinction and climate change. It urges the need for less carbon-intensive, inexhaustible Renewable Energy Sources (RES) that are economically sound, easy to access and improve public health. The carbon-free salient feature is the driving motive that propels widespread utilization of wind and solar RES in comparisons to rest of RES. However, stochastic nature makes these sources, variable renewable energy sources (VRES) because it brings uncertainty and variability that disrupt power system stability. This problem is mitigated by adding energy storage (ES) or introducing the demand response (DR) in the system. In this paper, an electricity generation network of China by the year 2017 is modeled using EnergyPLAN software to determine annual costs, primary energy supply (PES) and CO2 emissions. The VRES size is optimized by adding ES and DR (daily, weekly, or monthly) while maintaining critical excess electricity production (CEEP) to zero. The results substantiate that ES and DR increase wind and solar share up to 1000 and 874 GW. In addition, it also reduces annual costs and emissions up to 4.36 % and 45.17 %

Optimizing Size of Variable Renewable Energy Sources by Incorporating Energy Storage and Demand Response from Kashif Mehmood

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0 Optimal Load Shedding Using an Ensemble of Artifcial Neural Networks /slideshow/optimal-load-shedding-using-an-ensemble-of-artifcial-neural-networks/232330662 jlaodsheddingusingeannijeces-200420214526
Optimal load shedding is a very critical issue in power systems. It plays a vital role, especially in third world countries. A sudden increase in load can aﬀect the important parameters of the power system like voltage, frequency and phase angle. This paper presents a case study of Pakistan’s power system, where the generated power, the load demand, frequency deviation and load shedding during a 24-hour period have been provided. An artifcial neural network ensemble is aimed for optimal load shedding. The objective of this paper is to maintain power system frequency stability by shedding an accurate amount of load. Due to its fast convergence and improved generalization ability, the proposed algorithm helps to deal with load shedding in an efcient manner.]]>
Optimal load shedding is a very critical issue in power systems. It plays a vital role, especially in third world countries. A sudden increase in load can aﬀect the important parameters of the power system like voltage, frequency and phase angle. This paper presents a case study of Pakistan’s power system, where the generated power, the load demand, frequency deviation and load shedding during a 24-hour period have been provided. An artifcial neural network ensemble is aimed for optimal load shedding. The objective of this paper is to maintain power system frequency stability by shedding an accurate amount of load. Due to its fast convergence and improved generalization ability, the proposed algorithm helps to deal with load shedding in an efcient manner.]]> Mon, 20 Apr 2020 21:45:26 GMT /slideshow/optimal-load-shedding-using-an-ensemble-of-artifcial-neural-networks/232330662 j0mega@slideshare.net(j0mega) Optimal Load Shedding Using an Ensemble of Artifcial Neural Networks j0mega Optimal load shedding is a very critical issue in power systems. It plays a vital role, especially in third world countries. A sudden increase in load can aﬀect the important parameters of the power system like voltage, frequency and phase angle. This paper presents a case study of Pakistan’s power system, where the generated power, the load demand, frequency deviation and load shedding during a 24-hour period have been provided. An artifcial neural network ensemble is aimed for optimal load shedding. The objective of this paper is to maintain power system frequency stability by shedding an accurate amount of load. Due to its fast convergence and improved generalization ability, the proposed algorithm helps to deal with load shedding in an efcient manner. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/jlaodsheddingusingeannijeces-200420214526-thumbnail.jpg?width=120&height=120&fit=bounds" /> Optimal load shedding is a very critical issue in power systems. It plays a vital role, especially in third world countries. A sudden increase in load can aﬀect the important parameters of the power system like voltage, frequency and phase angle. This paper presents a case study of Pakistan’s power system, where the generated power, the load demand, frequency deviation and load shedding during a 24-hour period have been provided. An artifcial neural network ensemble is aimed for optimal load shedding. The objective of this paper is to maintain power system frequency stability by shedding an accurate amount of load. Due to its fast convergence and improved generalization ability, the proposed algorithm helps to deal with load shedding in an efcient manner.

Optimal Load Shedding Using an Ensemble of Artifcial Neural Networks from Kashif Mehmood

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0 Modelling and Implementation of Microprocessor Based Numerical Relay for Protection Against Over/Under Current, Over/Under Voltage /slideshow/modelling-and-implementation-of-microprocessor-based-numerical-relay-for-protection-against-overunder-current-overunder-voltage/232330660 jjctnmodellingandimplementationofmicroprocessor-200420214525
This paper includes the design and implementation of Numerical Relay that can protect the equipment against over-voltage, over-current and under voltage. Although, every power system is subjected to faults and these faults can severe damage to the power system. Therefore, it is necessary to observe and resolve in time to avoid a large damage such as blackouts. For this purpose, there should be some sensing devices, which give signals to the circuit breakers for preventing of power system damages. The multipurpose relays have much importance role in power system for sensing and measuring the amplitude of faults. Numerical relay provides settings of over-current, overvoltage and under voltage values. Simulations have been carried out using Proteus software along with tested on hardware with Arduino Uno Microcontroller that proves the working and operation of numerical relay.]]>
This paper includes the design and implementation of Numerical Relay that can protect the equipment against over-voltage, over-current and under voltage. Although, every power system is subjected to faults and these faults can severe damage to the power system. Therefore, it is necessary to observe and resolve in time to avoid a large damage such as blackouts. For this purpose, there should be some sensing devices, which give signals to the circuit breakers for preventing of power system damages. The multipurpose relays have much importance role in power system for sensing and measuring the amplitude of faults. Numerical relay provides settings of over-current, overvoltage and under voltage values. Simulations have been carried out using Proteus software along with tested on hardware with Arduino Uno Microcontroller that proves the working and operation of numerical relay.]]> Mon, 20 Apr 2020 21:45:25 GMT /slideshow/modelling-and-implementation-of-microprocessor-based-numerical-relay-for-protection-against-overunder-current-overunder-voltage/232330660 j0mega@slideshare.net(j0mega) Modelling and Implementation of Microprocessor Based Numerical Relay for Protection Against Over/Under Current, Over/Under Voltage j0mega This paper includes the design and implementation of Numerical Relay that can protect the equipment against over-voltage, over-current and under voltage. Although, every power system is subjected to faults and these faults can severe damage to the power system. Therefore, it is necessary to observe and resolve in time to avoid a large damage such as blackouts. For this purpose, there should be some sensing devices, which give signals to the circuit breakers for preventing of power system damages. The multipurpose relays have much importance role in power system for sensing and measuring the amplitude of faults. Numerical relay provides settings of over-current, overvoltage and under voltage values. Simulations have been carried out using Proteus software along with tested on hardware with Arduino Uno Microcontroller that proves the working and operation of numerical relay. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/jjctnmodellingandimplementationofmicroprocessor-200420214525-thumbnail.jpg?width=120&height=120&fit=bounds" /> This paper includes the design and implementation of Numerical Relay that can protect the equipment against over-voltage, over-current and under voltage. Although, every power system is subjected to faults and these faults can severe damage to the power system. Therefore, it is necessary to observe and resolve in time to avoid a large damage such as blackouts. For this purpose, there should be some sensing devices, which give signals to the circuit breakers for preventing of power system damages. The multipurpose relays have much importance role in power system for sensing and measuring the amplitude of faults. Numerical relay provides settings of over-current, overvoltage and under voltage values. Simulations have been carried out using Proteus software along with tested on hardware with Arduino Uno Microcontroller that proves the working and operation of numerical relay.

Modelling and Implementation of Microprocessor Based Numerical Relay for Protection Against Over/Under Current, Over/Under Voltage from Kashif Mehmood

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0 Comparative Performance Analysis of RPL for Low Power and Lossy Networks based on Different Objective Functions /slideshow/comparative-performance-analysis-of-rpl-for-low-power-and-lossy-networks-based-on-different-objective-functions/232330658 jijacsacomparativeperformanceanalysisofrpl-200420214525
The Internet of Things (IoT) is an extensive network between people-people, people-things and things-things. With the overgrown opportunities, then it also comes with a lot of challenges proportional to the number of connected things to the network. The IPv6 allows us to connect a huge number of things. For resource-constrained IoT devices, the routing issues are very thought-provoking and for this purpose an IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) is proposed. There are multi-HOP paths connecting nodes to the root node. Destination Oriented Directed Acyclic Graph (DODAG) is created taking into account different parameters such as link costs, nodes attribute and objective functions. RPL is flexible and it can be tuned as per application demands, therefore, the network can be optimized by using different objective functions. This paper presents a novel energy efficient analysis of RPL by performing a set of simulations in COOJA simulator. The performance evaluation of RPL is compared by introducing different Objective functions (OF) with multiple metrics for the network.]]>
The Internet of Things (IoT) is an extensive network between people-people, people-things and things-things. With the overgrown opportunities, then it also comes with a lot of challenges proportional to the number of connected things to the network. The IPv6 allows us to connect a huge number of things. For resource-constrained IoT devices, the routing issues are very thought-provoking and for this purpose an IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) is proposed. There are multi-HOP paths connecting nodes to the root node. Destination Oriented Directed Acyclic Graph (DODAG) is created taking into account different parameters such as link costs, nodes attribute and objective functions. RPL is flexible and it can be tuned as per application demands, therefore, the network can be optimized by using different objective functions. This paper presents a novel energy efficient analysis of RPL by performing a set of simulations in COOJA simulator. The performance evaluation of RPL is compared by introducing different Objective functions (OF) with multiple metrics for the network.]]> Mon, 20 Apr 2020 21:45:25 GMT /slideshow/comparative-performance-analysis-of-rpl-for-low-power-and-lossy-networks-based-on-different-objective-functions/232330658 j0mega@slideshare.net(j0mega) Comparative Performance Analysis of RPL for Low Power and Lossy Networks based on Different Objective Functions j0mega The Internet of Things (IoT) is an extensive network between people-people, people-things and things-things. With the overgrown opportunities, then it also comes with a lot of challenges proportional to the number of connected things to the network. The IPv6 allows us to connect a huge number of things. For resource-constrained IoT devices, the routing issues are very thought-provoking and for this purpose an IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) is proposed. There are multi-HOP paths connecting nodes to the root node. Destination Oriented Directed Acyclic Graph (DODAG) is created taking into account different parameters such as link costs, nodes attribute and objective functions. RPL is flexible and it can be tuned as per application demands, therefore, the network can be optimized by using different objective functions. This paper presents a novel energy efficient analysis of RPL by performing a set of simulations in COOJA simulator. The performance evaluation of RPL is compared by introducing different Objective functions (OF) with multiple metrics for the network. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/jijacsacomparativeperformanceanalysisofrpl-200420214525-thumbnail.jpg?width=120&height=120&fit=bounds" /> The Internet of Things (IoT) is an extensive network between people-people, people-things and things-things. With the overgrown opportunities, then it also comes with a lot of challenges proportional to the number of connected things to the network. The IPv6 allows us to connect a huge number of things. For resource-constrained IoT devices, the routing issues are very thought-provoking and for this purpose an IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) is proposed. There are multi-HOP paths connecting nodes to the root node. Destination Oriented Directed Acyclic Graph (DODAG) is created taking into account different parameters such as link costs, nodes attribute and objective functions. RPL is flexible and it can be tuned as per application demands, therefore, the network can be optimized by using different objective functions. This paper presents a novel energy efficient analysis of RPL by performing a set of simulations in COOJA simulator. The performance evaluation of RPL is compared by introducing different Objective functions (OF) with multiple metrics for the network.

Comparative Performance Analysis of RPL for Low Power and Lossy Networks based on Different Objective Functions from Kashif Mehmood

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0 Improved Virtual Synchronous Generator Control to Analyse and Enhance the Transient Stability of Microgrid /slideshow/improved-virtual-synchronous-generator-control-to-analyse-and-enhance-the-transient-stability-of-microgrid/232330656 jiet-improvedvirtualsynchronousgenerator-200420214524
In recent years, the integration of renewable energy resource (RES) into the power system is growing rapidly, and it is necessary to analyse and evaluate the effect of RES on transient stability of the power system. In this paper, centre of inertia (COI) concept is implemented to analyse and evaluate the integration effects of an auxiliary damping control (ADC) based virtual synchronous generator (VSG) consisting an improved governor. The impact of VSG integration is divided into synchronous generator (SG) linked parts and COI associated parts. Due to VSG integration into the power system, the significant elements which disturb the COI dynamic motion and rotor dynamics of SG are examined in detail. Different cases are considered to evaluate the effectiveness of the proposed method, i.e., VSG’s different integrating location and different power capacities. It is observed in simulation results that COI dynamic motion and rotor dynamics of SG are positively affected by VSG integration and transient stability improves significantly]]>
In recent years, the integration of renewable energy resource (RES) into the power system is growing rapidly, and it is necessary to analyse and evaluate the effect of RES on transient stability of the power system. In this paper, centre of inertia (COI) concept is implemented to analyse and evaluate the integration effects of an auxiliary damping control (ADC) based virtual synchronous generator (VSG) consisting an improved governor. The impact of VSG integration is divided into synchronous generator (SG) linked parts and COI associated parts. Due to VSG integration into the power system, the significant elements which disturb the COI dynamic motion and rotor dynamics of SG are examined in detail. Different cases are considered to evaluate the effectiveness of the proposed method, i.e., VSG’s different integrating location and different power capacities. It is observed in simulation results that COI dynamic motion and rotor dynamics of SG are positively affected by VSG integration and transient stability improves significantly]]> Mon, 20 Apr 2020 21:45:24 GMT /slideshow/improved-virtual-synchronous-generator-control-to-analyse-and-enhance-the-transient-stability-of-microgrid/232330656 j0mega@slideshare.net(j0mega) Improved Virtual Synchronous Generator Control to Analyse and Enhance the Transient Stability of Microgrid j0mega In recent years, the integration of renewable energy resource (RES) into the power system is growing rapidly, and it is necessary to analyse and evaluate the effect of RES on transient stability of the power system. In this paper, centre of inertia (COI) concept is implemented to analyse and evaluate the integration effects of an auxiliary damping control (ADC) based virtual synchronous generator (VSG) consisting an improved governor. The impact of VSG integration is divided into synchronous generator (SG) linked parts and COI associated parts. Due to VSG integration into the power system, the significant elements which disturb the COI dynamic motion and rotor dynamics of SG are examined in detail. Different cases are considered to evaluate the effectiveness of the proposed method, i.e., VSG’s different integrating location and different power capacities. It is observed in simulation results that COI dynamic motion and rotor dynamics of SG are positively affected by VSG integration and transient stability improves significantly <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/jiet-improvedvirtualsynchronousgenerator-200420214524-thumbnail.jpg?width=120&height=120&fit=bounds" /> In recent years, the integration of renewable energy resource (RES) into the power system is growing rapidly, and it is necessary to analyse and evaluate the effect of RES on transient stability of the power system. In this paper, centre of inertia (COI) concept is implemented to analyse and evaluate the integration effects of an auxiliary damping control (ADC) based virtual synchronous generator (VSG) consisting an improved governor. The impact of VSG integration is divided into synchronous generator (SG) linked parts and COI associated parts. Due to VSG integration into the power system, the significant elements which disturb the COI dynamic motion and rotor dynamics of SG are examined in detail. Different cases are considered to evaluate the effectiveness of the proposed method, i.e., VSG’s different integrating location and different power capacities. It is observed in simulation results that COI dynamic motion and rotor dynamics of SG are positively affected by VSG integration and transient stability improves significantly

Improved Virtual Synchronous Generator Control to Analyse and Enhance the Transient Stability of Microgrid from Kashif Mehmood

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0 Optimal Power Generation in Energy-Deficient Scenarios Using Bagging Ensembles /slideshow/optimal-power-generation-in-energydeficient-scenarios-using-bagging-ensembles/232330650 jieeeaccessoptimalpowergenerationinenergy-deficient-200420214517
This paper presents an improved technique for optimal power generation using ensemble artificial neural networks (EANN). The motive for using EANN is to benefit from multiple parallel processor computing rather than traditional serial computation to reduce bias and variance in machine learning. The load data is obtained from the load regulation authority of Pakistan for 24 hours. The data is analyzed on an IEEE 30-bus test system by implementing two approaches; the conventional artificial neural network (ANN) with feed-forward back-propagation model and a Bagging algorithm. To improve the training of ANN and authenticate its result, first the Load Flow Analysis (LFA) on IEEE 30 bus is performed using Newton Raphson Method and then the program is developed in MATLAB using Lagrange relaxation (LR) framework to solve a power-generator scheduling problem. The bootstraps for the EANN are obtained through a disjoint partition Bagging algorithm to handle the fluctuating power demand and is used to forecast the power generation. The results of MATLAB simulations are analyzed and compared along with computational complexity, therein showing the dominance of the EANN over the traditional ANN strategy that closed to LR]]>
This paper presents an improved technique for optimal power generation using ensemble artificial neural networks (EANN). The motive for using EANN is to benefit from multiple parallel processor computing rather than traditional serial computation to reduce bias and variance in machine learning. The load data is obtained from the load regulation authority of Pakistan for 24 hours. The data is analyzed on an IEEE 30-bus test system by implementing two approaches; the conventional artificial neural network (ANN) with feed-forward back-propagation model and a Bagging algorithm. To improve the training of ANN and authenticate its result, first the Load Flow Analysis (LFA) on IEEE 30 bus is performed using Newton Raphson Method and then the program is developed in MATLAB using Lagrange relaxation (LR) framework to solve a power-generator scheduling problem. The bootstraps for the EANN are obtained through a disjoint partition Bagging algorithm to handle the fluctuating power demand and is used to forecast the power generation. The results of MATLAB simulations are analyzed and compared along with computational complexity, therein showing the dominance of the EANN over the traditional ANN strategy that closed to LR]]> Mon, 20 Apr 2020 21:45:17 GMT /slideshow/optimal-power-generation-in-energydeficient-scenarios-using-bagging-ensembles/232330650 j0mega@slideshare.net(j0mega) Optimal Power Generation in Energy-Deficient Scenarios Using Bagging Ensembles j0mega This paper presents an improved technique for optimal power generation using ensemble artificial neural networks (EANN). The motive for using EANN is to benefit from multiple parallel processor computing rather than traditional serial computation to reduce bias and variance in machine learning. The load data is obtained from the load regulation authority of Pakistan for 24 hours. The data is analyzed on an IEEE 30-bus test system by implementing two approaches; the conventional artificial neural network (ANN) with feed-forward back-propagation model and a Bagging algorithm. To improve the training of ANN and authenticate its result, first the Load Flow Analysis (LFA) on IEEE 30 bus is performed using Newton Raphson Method and then the program is developed in MATLAB using Lagrange relaxation (LR) framework to solve a power-generator scheduling problem. The bootstraps for the EANN are obtained through a disjoint partition Bagging algorithm to handle the fluctuating power demand and is used to forecast the power generation. The results of MATLAB simulations are analyzed and compared along with computational complexity, therein showing the dominance of the EANN over the traditional ANN strategy that closed to LR <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/jieeeaccessoptimalpowergenerationinenergy-deficient-200420214517-thumbnail.jpg?width=120&height=120&fit=bounds" /> This paper presents an improved technique for optimal power generation using ensemble artificial neural networks (EANN). The motive for using EANN is to benefit from multiple parallel processor computing rather than traditional serial computation to reduce bias and variance in machine learning. The load data is obtained from the load regulation authority of Pakistan for 24 hours. The data is analyzed on an IEEE 30-bus test system by implementing two approaches; the conventional artificial neural network (ANN) with feed-forward back-propagation model and a Bagging algorithm. To improve the training of ANN and authenticate its result, first the Load Flow Analysis (LFA) on IEEE 30 bus is performed using Newton Raphson Method and then the program is developed in MATLAB using Lagrange relaxation (LR) framework to solve a power-generator scheduling problem. The bootstraps for the EANN are obtained through a disjoint partition Bagging algorithm to handle the fluctuating power demand and is used to forecast the power generation. The results of MATLAB simulations are analyzed and compared along with computational complexity, therein showing the dominance of the EANN over the traditional ANN strategy that closed to LR

Optimal Power Generation in Energy-Deficient Scenarios Using Bagging Ensembles from Kashif Mehmood

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0 Integrated Energy System Modeling of China for 2020 by Incorporating Demand Response, Heat Pump and Thermal Storage /j0mega/integrated-energy-system-modeling-of-china-for-2020-by-incorporating-demand-response-heat-pump-and-thermal-storage jieeeaccessdemandresponse-200420214516
Electricity and heat energy carriers are mostly produced by the fossil fuel sources that are conventionally operated independently, but these carriers have low efficiency due to heat losses. Moreover, a high share of variable renewable energy sources disrupts the power system reliability and flexibility. Therefore, the coupling of multiple energy carriers is underlined to address the above-mentioned issues that are supported by the latest technologies, such as combined heat and power, heat pumps, demand response, and energy storages. These coupling nodes in energy hubs stimulate the conversion of the electric power system into the integrated energy system that proves to be cost-effective, flexible, and carbon-free. The proposed work uses EnergyPLAN to model electricity, district, and individual heating integrated energy system of China for the year 2020. Furthermore, the addition of heat pumps, thermal storage, and demand response is analyzed in different scenarios to minimize the annual costs, fuel consumption, and CO2 emissions. Technical simulation strategy is conducted for optimal operation of production components that result in the reduction of the above-mentioned prominent factors while calculating the critical and exportable excess electricity production. The simulation results demonstrate that demand response and thermal storage significantly enhance the share of variable renewable energy sources. In addition, it substantially reduces the annual costs and fuel consumption, while heat pump increases the system efficiency]]>
Electricity and heat energy carriers are mostly produced by the fossil fuel sources that are conventionally operated independently, but these carriers have low efficiency due to heat losses. Moreover, a high share of variable renewable energy sources disrupts the power system reliability and flexibility. Therefore, the coupling of multiple energy carriers is underlined to address the above-mentioned issues that are supported by the latest technologies, such as combined heat and power, heat pumps, demand response, and energy storages. These coupling nodes in energy hubs stimulate the conversion of the electric power system into the integrated energy system that proves to be cost-effective, flexible, and carbon-free. The proposed work uses EnergyPLAN to model electricity, district, and individual heating integrated energy system of China for the year 2020. Furthermore, the addition of heat pumps, thermal storage, and demand response is analyzed in different scenarios to minimize the annual costs, fuel consumption, and CO2 emissions. Technical simulation strategy is conducted for optimal operation of production components that result in the reduction of the above-mentioned prominent factors while calculating the critical and exportable excess electricity production. The simulation results demonstrate that demand response and thermal storage significantly enhance the share of variable renewable energy sources. In addition, it substantially reduces the annual costs and fuel consumption, while heat pump increases the system efficiency]]> Mon, 20 Apr 2020 21:45:16 GMT /j0mega/integrated-energy-system-modeling-of-china-for-2020-by-incorporating-demand-response-heat-pump-and-thermal-storage j0mega@slideshare.net(j0mega) Integrated Energy System Modeling of China for 2020 by Incorporating Demand Response, Heat Pump and Thermal Storage j0mega Electricity and heat energy carriers are mostly produced by the fossil fuel sources that are conventionally operated independently, but these carriers have low efficiency due to heat losses. Moreover, a high share of variable renewable energy sources disrupts the power system reliability and flexibility. Therefore, the coupling of multiple energy carriers is underlined to address the above-mentioned issues that are supported by the latest technologies, such as combined heat and power, heat pumps, demand response, and energy storages. These coupling nodes in energy hubs stimulate the conversion of the electric power system into the integrated energy system that proves to be cost-effective, flexible, and carbon-free. The proposed work uses EnergyPLAN to model electricity, district, and individual heating integrated energy system of China for the year 2020. Furthermore, the addition of heat pumps, thermal storage, and demand response is analyzed in different scenarios to minimize the annual costs, fuel consumption, and CO2 emissions. Technical simulation strategy is conducted for optimal operation of production components that result in the reduction of the above-mentioned prominent factors while calculating the critical and exportable excess electricity production. The simulation results demonstrate that demand response and thermal storage significantly enhance the share of variable renewable energy sources. In addition, it substantially reduces the annual costs and fuel consumption, while heat pump increases the system efficiency <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/jieeeaccessdemandresponse-200420214516-thumbnail.jpg?width=120&height=120&fit=bounds" /> Electricity and heat energy carriers are mostly produced by the fossil fuel sources that are conventionally operated independently, but these carriers have low efficiency due to heat losses. Moreover, a high share of variable renewable energy sources disrupts the power system reliability and flexibility. Therefore, the coupling of multiple energy carriers is underlined to address the above-mentioned issues that are supported by the latest technologies, such as combined heat and power, heat pumps, demand response, and energy storages. These coupling nodes in energy hubs stimulate the conversion of the electric power system into the integrated energy system that proves to be cost-effective, flexible, and carbon-free. The proposed work uses EnergyPLAN to model electricity, district, and individual heating integrated energy system of China for the year 2020. Furthermore, the addition of heat pumps, thermal storage, and demand response is analyzed in different scenarios to minimize the annual costs, fuel consumption, and CO2 emissions. Technical simulation strategy is conducted for optimal operation of production components that result in the reduction of the above-mentioned prominent factors while calculating the critical and exportable excess electricity production. The simulation results demonstrate that demand response and thermal storage significantly enhance the share of variable renewable energy sources. In addition, it substantially reduces the annual costs and fuel consumption, while heat pump increases the system efficiency

Integrated Energy System Modeling of China for 2020 by Incorporating Demand Response, Heat Pump and Thermal Storage from Kashif Mehmood

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0 The Efficiency of Solar PV System /slideshow/the-efficiency-of-solar-pv-system/232330648 cpakistantheefficiencyofsolarsystem-200420214514
the need of electrical energy is essential part of any state to stable itself as well as to promote itself. The most reliable resources of electrical energy are Renewable resources. In these renewable resources, Sun is the biggest resource of energy. In active solar technique, electrical energy is produced by the phenomenon of Photoelectric effect. The Reliability and efficiency of solar power system can be improved by making sure that we are using this system properly. First of all, the main factor of solar power generation is the efficiency of solar cell that is made of Crystalline Silicon cell mostly. The efficiency of solar cell is not good yet, but the capability of solar cell to produce power is excellent. Secondly, there are many factors affecting the efficiency of PV system during installation and maintenance. This paper emphasizes on the efficiency of PV module affected by direction, angle, irradiance, shade, load and temperature. This paper describes the conceptual design of a smart battery health monitoring system along with protection of battery from over charging & over discharging using an embedded system. The working mechanism of this system is based on the input voltages to the embedded system from the battery which are further processed using ADC to convert them into Digital form which are then used to observe the state of battery’s condition The deeply study of these factors is essential before using this system and implementation of these results after study, enhance the efficiency of this system.]]>
the need of electrical energy is essential part of any state to stable itself as well as to promote itself. The most reliable resources of electrical energy are Renewable resources. In these renewable resources, Sun is the biggest resource of energy. In active solar technique, electrical energy is produced by the phenomenon of Photoelectric effect. The Reliability and efficiency of solar power system can be improved by making sure that we are using this system properly. First of all, the main factor of solar power generation is the efficiency of solar cell that is made of Crystalline Silicon cell mostly. The efficiency of solar cell is not good yet, but the capability of solar cell to produce power is excellent. Secondly, there are many factors affecting the efficiency of PV system during installation and maintenance. This paper emphasizes on the efficiency of PV module affected by direction, angle, irradiance, shade, load and temperature. This paper describes the conceptual design of a smart battery health monitoring system along with protection of battery from over charging & over discharging using an embedded system. The working mechanism of this system is based on the input voltages to the embedded system from the battery which are further processed using ADC to convert them into Digital form which are then used to observe the state of battery’s condition The deeply study of these factors is essential before using this system and implementation of these results after study, enhance the efficiency of this system.]]> Mon, 20 Apr 2020 21:45:14 GMT /slideshow/the-efficiency-of-solar-pv-system/232330648 j0mega@slideshare.net(j0mega) The Efficiency of Solar PV System j0mega the need of electrical energy is essential part of any state to stable itself as well as to promote itself. The most reliable resources of electrical energy are Renewable resources. In these renewable resources, Sun is the biggest resource of energy. In active solar technique, electrical energy is produced by the phenomenon of Photoelectric effect. The Reliability and efficiency of solar power system can be improved by making sure that we are using this system properly. First of all, the main factor of solar power generation is the efficiency of solar cell that is made of Crystalline Silicon cell mostly. The efficiency of solar cell is not good yet, but the capability of solar cell to produce power is excellent. Secondly, there are many factors affecting the efficiency of PV system during installation and maintenance. This paper emphasizes on the efficiency of PV module affected by direction, angle, irradiance, shade, load and temperature. This paper describes the conceptual design of a smart battery health monitoring system along with protection of battery from over charging & over discharging using an embedded system. The working mechanism of this system is based on the input voltages to the embedded system from the battery which are further processed using ADC to convert them into Digital form which are then used to observe the state of battery’s condition The deeply study of these factors is essential before using this system and implementation of these results after study, enhance the efficiency of this system. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/cpakistantheefficiencyofsolarsystem-200420214514-thumbnail.jpg?width=120&height=120&fit=bounds" /> the need of electrical energy is essential part of any state to stable itself as well as to promote itself. The most reliable resources of electrical energy are Renewable resources. In these renewable resources, Sun is the biggest resource of energy. In active solar technique, electrical energy is produced by the phenomenon of Photoelectric effect. The Reliability and efficiency of solar power system can be improved by making sure that we are using this system properly. First of all, the main factor of solar power generation is the efficiency of solar cell that is made of Crystalline Silicon cell mostly. The efficiency of solar cell is not good yet, but the capability of solar cell to produce power is excellent. Secondly, there are many factors affecting the efficiency of PV system during installation and maintenance. This paper emphasizes on the efficiency of PV module affected by direction, angle, irradiance, shade, load and temperature. This paper describes the conceptual design of a smart battery health monitoring system along with protection of battery from over charging & over discharging using an embedded system. The working mechanism of this system is based on the input voltages to the embedded system from the battery which are further processed using ADC to convert them into Digital form which are then used to observe the state of battery’s condition The deeply study of these factors is essential before using this system and implementation of these results after study, enhance the efficiency of this system.

The Efficiency of Solar PV System from Kashif Mehmood

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0 Voltage-current Double Loop Control Strategy for Magnetically Controllable Reactor Based Reactive Power Compensation /slideshow/voltagecurrent-double-loop-control-strategy-for-magnetically-controllable-reactor-based-reactive-power-compensation/232330647 cieeespec2019chinavoltage-currentdoubleloopcontrolstrategyfor-200420214513
Voltage regulation depending on reactive power compensation is the main feature of the AC power supply system. Magnetically controllable reactors (MCR) are becoming a growing demand for this purpose. The structure and working principles of MCR are analysed in this paper while a simulation model is established. The reactive power compensation strategy based on a single loop voltage control system (SLVCS) is presented and a double loop voltage-current control system (DLVCCS) is proposed. A comprehensive scenario is developed to mitigate reactive power compensation by using the proposed controls. Simulation results substantiate that proposed controls of MCR has a faster response in comparison to the traditional control, and it provides the least voltage variation at the line end. It also shows that the proposed control on MCR meet the desired objective of the voltage regulation and provides flexibility to ac transmission system]]>
Voltage regulation depending on reactive power compensation is the main feature of the AC power supply system. Magnetically controllable reactors (MCR) are becoming a growing demand for this purpose. The structure and working principles of MCR are analysed in this paper while a simulation model is established. The reactive power compensation strategy based on a single loop voltage control system (SLVCS) is presented and a double loop voltage-current control system (DLVCCS) is proposed. A comprehensive scenario is developed to mitigate reactive power compensation by using the proposed controls. Simulation results substantiate that proposed controls of MCR has a faster response in comparison to the traditional control, and it provides the least voltage variation at the line end. It also shows that the proposed control on MCR meet the desired objective of the voltage regulation and provides flexibility to ac transmission system]]> Mon, 20 Apr 2020 21:45:13 GMT /slideshow/voltagecurrent-double-loop-control-strategy-for-magnetically-controllable-reactor-based-reactive-power-compensation/232330647 j0mega@slideshare.net(j0mega) Voltage-current Double Loop Control Strategy for Magnetically Controllable Reactor Based Reactive Power Compensation j0mega Voltage regulation depending on reactive power compensation is the main feature of the AC power supply system. Magnetically controllable reactors (MCR) are becoming a growing demand for this purpose. The structure and working principles of MCR are analysed in this paper while a simulation model is established. The reactive power compensation strategy based on a single loop voltage control system (SLVCS) is presented and a double loop voltage-current control system (DLVCCS) is proposed. A comprehensive scenario is developed to mitigate reactive power compensation by using the proposed controls. Simulation results substantiate that proposed controls of MCR has a faster response in comparison to the traditional control, and it provides the least voltage variation at the line end. It also shows that the proposed control on MCR meet the desired objective of the voltage regulation and provides flexibility to ac transmission system <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/cieeespec2019chinavoltage-currentdoubleloopcontrolstrategyfor-200420214513-thumbnail.jpg?width=120&height=120&fit=bounds" /> Voltage regulation depending on reactive power compensation is the main feature of the AC power supply system. Magnetically controllable reactors (MCR) are becoming a growing demand for this purpose. The structure and working principles of MCR are analysed in this paper while a simulation model is established. The reactive power compensation strategy based on a single loop voltage control system (SLVCS) is presented and a double loop voltage-current control system (DLVCCS) is proposed. A comprehensive scenario is developed to mitigate reactive power compensation by using the proposed controls. Simulation results substantiate that proposed controls of MCR has a faster response in comparison to the traditional control, and it provides the least voltage variation at the line end. It also shows that the proposed control on MCR meet the desired objective of the voltage regulation and provides flexibility to ac transmission system

Voltage-current Double Loop Control Strategy for Magnetically Controllable Reactor Based Reactive Power Compensation from Kashif Mehmood

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0 Effcacious pitch angle control of variable-speed wind turbine using fuzzy based predictive controller /slideshow/effcacious-pitch-angle-control-of-variablespeed-wind-turbine-using-fuzzy-based-predictive-controller/232330646 celseviercpese2019japanefficaciouspitchanglecontrolofvariable-speed-200420214513
The Wind energy is more reliable and speedier growing, among the renewable energy resources, due to the world environment challenges as well as increasing demand for energy. The wind turbine system’s stability is cumbersome due to the uneven distribution of wind. Centrifugal and gravitational loads on the blades of a wind turbine creates weariness, resultantly decreases the power output along with the life of the equipment. Therefore, the need for a pitch angle control that can reduce the loading effect in addition to provide maximal power output. This paper proposes the fuzzy based model-predictive controller of pitch angle control to minimize the loading effect on wind turbine by limiting power output and rotor speed to its rated value as well as to maximize the extracted power output as compared to other techniques. The fuzzy logic controller works very effciently by encountering the system’s non-linearity while model predictive controller helps the system to become more stable and effcient. The superiority of prescribed controller is verifed by comparing it with PI controller. The proposed model has been tested in MATLAB/Simulink using a 3MW wind turbine system.]]>
The Wind energy is more reliable and speedier growing, among the renewable energy resources, due to the world environment challenges as well as increasing demand for energy. The wind turbine system’s stability is cumbersome due to the uneven distribution of wind. Centrifugal and gravitational loads on the blades of a wind turbine creates weariness, resultantly decreases the power output along with the life of the equipment. Therefore, the need for a pitch angle control that can reduce the loading effect in addition to provide maximal power output. This paper proposes the fuzzy based model-predictive controller of pitch angle control to minimize the loading effect on wind turbine by limiting power output and rotor speed to its rated value as well as to maximize the extracted power output as compared to other techniques. The fuzzy logic controller works very effciently by encountering the system’s non-linearity while model predictive controller helps the system to become more stable and effcient. The superiority of prescribed controller is verifed by comparing it with PI controller. The proposed model has been tested in MATLAB/Simulink using a 3MW wind turbine system.]]> Mon, 20 Apr 2020 21:45:13 GMT /slideshow/effcacious-pitch-angle-control-of-variablespeed-wind-turbine-using-fuzzy-based-predictive-controller/232330646 j0mega@slideshare.net(j0mega) Effcacious pitch angle control of variable-speed wind turbine using fuzzy based predictive controller j0mega The Wind energy is more reliable and speedier growing, among the renewable energy resources, due to the world environment challenges as well as increasing demand for energy. The wind turbine system’s stability is cumbersome due to the uneven distribution of wind. Centrifugal and gravitational loads on the blades of a wind turbine creates weariness, resultantly decreases the power output along with the life of the equipment. Therefore, the need for a pitch angle control that can reduce the loading effect in addition to provide maximal power output. This paper proposes the fuzzy based model-predictive controller of pitch angle control to minimize the loading effect on wind turbine by limiting power output and rotor speed to its rated value as well as to maximize the extracted power output as compared to other techniques. The fuzzy logic controller works very effciently by encountering the system’s non-linearity while model predictive controller helps the system to become more stable and effcient. The superiority of prescribed controller is verifed by comparing it with PI controller. The proposed model has been tested in MATLAB/Simulink using a 3MW wind turbine system. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/celseviercpese2019japanefficaciouspitchanglecontrolofvariable-speed-200420214513-thumbnail.jpg?width=120&height=120&fit=bounds" /> The Wind energy is more reliable and speedier growing, among the renewable energy resources, due to the world environment challenges as well as increasing demand for energy. The wind turbine system’s stability is cumbersome due to the uneven distribution of wind. Centrifugal and gravitational loads on the blades of a wind turbine creates weariness, resultantly decreases the power output along with the life of the equipment. Therefore, the need for a pitch angle control that can reduce the loading effect in addition to provide maximal power output. This paper proposes the fuzzy based model-predictive controller of pitch angle control to minimize the loading effect on wind turbine by limiting power output and rotor speed to its rated value as well as to maximize the extracted power output as compared to other techniques. The fuzzy logic controller works very effciently by encountering the system’s non-linearity while model predictive controller helps the system to become more stable and effcient. The superiority of prescribed controller is verifed by comparing it with PI controller. The proposed model has been tested in MATLAB/Simulink using a 3MW wind turbine system.

Effcacious pitch angle control of variable-speed wind turbine using fuzzy based predictive controller from Kashif Mehmood

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0 https://cdn.slidesharecdn.com/profile-photo-j0mega-48x48.jpg?cb=1587419113 https://cdn.slidesharecdn.com/ss_thumbnails/jreactivepowercontrolwithsvcusingpso-200420214533-thumbnail.jpg?width=320&height=320&fit=bounds j0mega/assessment-of-voltage-fluctuation-and-reactive-power-control-with-svc-using-pso ASSESSMENT OF VOLTAGE ... https://cdn.slidesharecdn.com/ss_thumbnails/jraeeshorttermloadforecastingusingbootstrapaggregatingbased-200420214532-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/short-term-load-forecasting-using-bootstrap-aggregating-based-ensemble-artificial-neural-network/232330668 Short Term Load Foreca... https://cdn.slidesharecdn.com/ss_thumbnails/joptimizingsizeofvariablerenewableenergy-200420214531-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/optimizing-size-of-variable-renewable-energy-sources-by-incorporating-energy-storage-and-demand-response/232330666 Optimizing Size of Var...