Controller design for DFIG-based WT using gravitational search algorithm for wind power generation - Bhari, O.P.; SarIta, K.; Vardhan, A. S. S.; Saket, R.K. (John Wiley and Sons Inc, 2021)
This paper describes the controller design aspects of DFIG-based wind turbine system (WTS) using gravitational search algorithm (GSA). The appropriate control schemes are required for efficient and reliable functioning of the DFIG-based wind energy conversion system (WECS). The control algorithms are implemented in converters which are placed in the rotor end and grid side of the WECS. The controller design schemes are optimized for accurate, reliable and stable operations of WECS using GSA. The most commonly used other design techniques are bacterial foraging optimization (BFO), and particle swarm optimization (PSO). Moreover, the transfer function modeling of DFIG is also described in this paper. The results show that the proposed GSA technique with sixths order transfer function model of DFIG improves the transient performance including time of rising the response to 90%, settling time, and amplitude of peak overshoot. The proposed GSA technique is compared with the techniques already implemented in the previous research works including PSO and BFO. The DFIG-based WTS's output waveforms of voltage at dc-link, reactive power, and active power are improved using GSA based design technique. Finally, it is concluded that the GSA technique gives better results as compared with the PSO and BFO techniques. © 2021 The Authors. IET Renewable Power Generation published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.This paper describes the controller design aspects of DFIG-based wind turbine system (WTS) using gravitational search algorithm (GSA). The appropriate control schemes are required for efficient and reliable functioning of the DFIG-based wind energy conversion system (WECS). The control algorithms are implemented in converters which are placed in the rotor end and grid side of the WECS. The controller design schemes are optimized for accurate, reliable and stable operations of WECS using GSA. The most commonly used other design techniques are bacterial foraging optimization (BFO), and particle swarm optimization (PSO). Moreover, the transfer function modeling of DFIG is also described in this paper. The results show that the proposed GSA technique with sixths order transfer function model of DFIG improves the transient performance including time of rising the response to 90%, settling time, and amplitude of peak overshoot. The proposed GSA technique is compared with the techniques already implemented in the previous research works including PSO and BFO. The DFIG-based WTS's output waveforms of voltage at dc-link, reactive power, and active power are improved using GSA based design technique. Finally, it is concluded that the GSA technique gives better results as compared with the PSO and BFO techniques. © 2021 The Authors. IET Renewable Power Generation published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.This paper describes the controller design aspects of DFIG-based wind turbine system (WTS) using gravitational search algorithm (GSA). The appropriate control schemes are required for efficient and reliable functioning of the DFIG-based wind energy conversion system (WECS). The control algorithms are implemented in converters which are placed in the rotor end and grid side of the WECS. The controller design schemes are optimized for accurate, reliable and stable operations of WECS using GSA. The most commonly used other design techniques are bacterial foraging optimization (BFO), and particle swarm optimization (PSO). Moreover, the transfer function modeling of DFIG is also described in this paper. The results show that the proposed GSA technique with sixths order transfer function model of DFIG improves the transient performance including time of rising the response to 90%, settling time, and amplitude of peak overshoot. The proposed GSA technique is compared with the techniques already implemented in the previous research works including PSO and BFO. The DFIG-based WTS's output waveforms of voltage at dc-link, reactive power, and active power are improved using GSA based design technique. Finally, it is concluded that the GSA technique gives better results as compared with the PSO and BFO techniques. © 2021 The Authors. IET Renewable Power Generation published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.This paper describes the controller design aspects of DFIG-based wind turbine system (WTS) using gravitational search algorithm (GSA). The appropriate control schemes are required for efficient and reliable functioning of the DFIG-based wind energy conversion system (WECS). The control algorithms are implemented in converters which are placed in the rotor end and grid side of the WECS. The controller design schemes are optimized for accurate, reliable and stable operations of WECS using GSA. The most commonly used other design techniques are bacterial foraging optimization (BFO), and particle swarm optimization (PSO). Moreover, the transfer function modeling of DFIG is also described in this paper. The results show that the proposed GSA technique with sixths order transfer function model of DFIG improves the transient performance including time of rising the response to 90%, settling time, and amplitude of peak overshoot. The proposed GSA technique is compared with the techniques already implemented in the previous research works including PSO and BFO. The DFIG-based WTS's output waveforms of voltage at dc-link, reactive power, and active power are improved using GSA based design technique. Finally, it is concluded that the GSA technique gives better results as compared with the PSO and BFO techniques. © 2021 The Authors. IET Renewable Power Generation published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.This paper describes the controller design aspects of DFIG-based wind turbine system (WTS) using gravitational search algorithm (GSA). The appropriate control schemes are required for efficient and reliable functioning of the DFIG-based wind energy conversion system (WECS). The control algorithms are implemented in converters which are placed in the rotor end and grid side of the WECS. The controller design schemes are optimized for accurate, reliable and stable operations of WECS using GSA. The most commonly used other design techniques are bacterial foraging optimization (BFO), and particle swarm optimization (PSO). Moreover, the transfer function modeling of DFIG is also described in this paper. The results show that the proposed GSA technique with sixths order transfer function model of DFIG improves the transient performance including time of rising the response to 90%, settling time, and amplitude of peak overshoot. The proposed GSA technique is compared with the techniques already implemented in the previous research works including PSO and BFO. The DFIG-based WTS's output waveforms of voltage at dc-link, reactive power, and active power are improved using GSA based design technique. Finally, it is concluded that the GSA technique gives better results as compared with the PSO and BFO techniques. © 2021 The Authors. IET Renewable Power Generation published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.This paper describes the controller design aspects of DFIG-based wind turbine system (WTS) using gravitational search algorithm (GSA). The appropriate control schemes are required for efficient and reliable functioning of the DFIG-based wind energy conversion system (WECS). The control algorithms are implemented in converters which are placed in the rotor end and grid side of the WECS. The controller design schemes are optimized for accurate, reliable and stable operations of WECS using GSA. The most commonly used other design techniques are bacterial foraging optimization (BFO), and particle swarm optimization (PSO). Moreover, the transfer function modeling of DFIG is also described in this paper. The results show that the proposed GSA technique with sixths order transfer function model of DFIG improves the transient performance including time of rising the response to 90%, settling time, and amplitude of peak overshoot. The proposed GSA technique is compared with the techniques already implemented in the previous research works including PSO and BFO. The DFIG-based WTS's output waveforms of voltage at dc-link, reactive power, and active power are improved using GSA based design technique. Finally, it is concluded that the GSA technique gives better results as compared with the PSO and BFO techniques. © 2021 The Authors. IET Renewable Power Generation published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.This paper describes the controller design aspects of DFIG-based wind turbine system (WTS) using gravitational search algorithm (GSA). The appropriate control schemes are required for efficient and reliable functioning of the DFIG-based wind energy conversion system (WECS). The control algorithms are implemented in converters which are placed in the rotor end and grid side of the WECS. The controller design schemes are optimized for accurate, reliable and stable operations of WECS using GSA. The most commonly used other design techniques are bacterial foraging optimization (BFO), and particle swarm optimization (PSO). Moreover, the transfer function modeling of DFIG is also described in this paper. The results show that the proposed GSA technique with sixths order transfer function model of DFIG improves the transient performance including time of rising the response to 90%, settling time, and amplitude of peak overshoot. The proposed GSA technique is compared with the techniques already implemented in the previous research works including PSO and BFO. The DFIG-based WTS's output waveforms of voltage at dc-link, reactive power, and active power are improved using GSA based design technique. Finally, it is concluded that the GSA technique gives better results as compared with the PSO and BFO techniques. © 2021 The Authors. IET Renewable Power Generation published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.This paper describes the controller design aspects of DFIG-based wind turbine system (WTS) using gravitational search algorithm (GSA). The appropriate control schemes are required for efficient and reliable functioning of the DFIG-based wind energy conversion system (WECS). The control algorithms are implemented in converters which are placed in the rotor end and grid side of the WECS. The controller design schemes are optimized for accurate, reliable and stable operations of WECS using GSA. The most commonly used other design techniques are bacterial foraging optimization (BFO), and particle swarm optimization (PSO). Moreover, the transfer function modeling of DFIG is also described in this paper. The results show that the proposed GSA technique with sixths order transfer function model of DFIG improves the transient performance including time of rising the response to 90%, settling time, and amplitude of peak overshoot. The proposed GSA technique is compared with the techniques already implemented in the previous research works including PSO and BFO. The DFIG-based WTS's output waveforms of voltage at dc-link, reactive power, and active power are improved using GSA based design technique. Finally, it is concluded that the GSA technique gives better results as compared with the PSO and BFO techniques. © 2021 The Authors. IET Renewable Power Generation published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.This paper describes the controller design aspects of DFIG-based wind turbine system (WTS) using gravitational search algorithm (GSA). The appropriate control schemes are required for efficient and reliable functioning of the DFIG-based wind energy conversion system (WECS). The control algorithms are implemented in converters which are placed in the rotor end and grid side of the WECS. The controller design schemes are optimized for accurate, reliable and stable operations of WECS using GSA. The most commonly used other design techniques are bacterial foraging optimization (BFO), and particle swarm optimization (PSO). Moreover, the transfer function modeling of DFIG is also described in this paper. The results show that the proposed GSA technique with sixths order transfer function model of DFIG improves the transient performance including time of rising the response to 90%, settling time, and amplitude of peak overshoot. The proposed GSA technique is compared with the techniques already implemented in the previous research works including PSO and BFO. The DFIG-based WTS's output waveforms of voltage at dc-link, reactive power, and active power are improved using GSA based design technique. Finally, it is concluded that the GSA technique gives better results as compared with the PSO and BFO techniques. © 2021 The Authors. IET Renewable Power Generation published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.This paper describes the controller design aspects of DFIG-based wind turbine system (WTS) using gravitational search algorithm (GSA). The appropriate control schemes are required for efficient and reliable functioning of the DFIG-based wind energy conversion system (WECS). The control algorithms are implemented in converters which are placed in the rotor end and grid side of the WECS. The controller design schemes are optimized for accurate, reliable and stable operations of WECS using GSA. The most commonly used other design techniques are bacterial foraging optimization (BFO), and particle swarm optimization (PSO). Moreover, the transfer function modeling of DFIG is also described in this paper. The results show that the proposed GSA technique with sixths order transfer function model of DFIG improves the transient performance including time of rising the response to 90%, settling time, and amplitude of peak overshoot. The proposed GSA technique is compared with the techniques already implemented in the previous research works including PSO and BFO. The DFIG-based WTS's output waveforms of voltage at dc-link, reactive power, and active power are improved using GSA based design technique. Finally, it is concluded that the GSA technique gives better results as compared with the PSO and BFO techniques. © 2021 The Authors. IET Renewable Power Generation published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.This paper describes the controller design aspects of DFIG-based wind turbine system (WTS) using gravitational search algorithm (GSA). The appropriate control schemes are required for efficient and reliable functioning of the DFIG-based wind energy conversion system (WECS). The control algorithms are implemented in converters which are placed in the rotor end and grid side of the WECS. The controller design schemes are optimized for accurate, reliable and stable operations of WECS using GSA. The most commonly used other design techniques are bacterial foraging optimization (BFO), and particle swarm optimization (PSO). Moreover, the transfer function modeling of DFIG is also described in this paper. The results show that the proposed GSA technique with sixths order transfer function model of DFIG improves the transient performance including time of rising the response to 90%, settling time, and amplitude of peak overshoot. The proposed GSA technique is compared with the techniques already implemented in the previous research works including PSO and BFO. The DFIG-based WTS's output waveforms of voltage at dc-link, reactive power, and active power are improved using GSA based design technique. Finally, it is concluded that the GSA technique gives better results as compared with the PSO and BFO techniques. © 2021 The Authors. IET Renewable Power Generation published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.
Keywords: Controller design; wind power generation; algorithm