In this paper, Hybrid Artificial Neural Network (ANN) with Proportional Integral (PI) control technique has been developed for Doubly Fed Induction Generator (DFIG) based wind energy generation system and the performance of the system is compared with NN and PI control techniques. With the increasing use of wind power generation, it is required to instigate the dynamic performance analysis of Doubly Fed Induction Generator under various operating conditions. In this paper, three control techniques have been proposed, the first one is using PI controller, the second one is ANN control, and the third one is based on combination of ANN and PI. The performance of the proposed control techniques is demonstrated through the results, determined by...
The performance of the Hybrid Wind Energy System/Battery storage/Diesel Generator can be improved th...
In this paper, a Direct Power Control (DPC) based on the switching table and Artificial Neural Netwo...
AbstractThis paper presents modified neural network for dynamic control and operation of a hybrid ge...
In this paper, Hybrid Artificial Neural Network (ANN) with Proportional Integral (PI) control techni...
Abstract. With the increasing size of wind power generation it is required to perform power system s...
This paper describes the models of wind power system, such as the turbine, the generator, the power ...
The objective of this paper is to study the dynamic response of the wind energy conversion system (W...
During the last decade, wind energy gained much importance as an energy source in power systems. DFI...
With the increasing size of wind power generation it is required to perform power system stability ...
A neural network controller is presented for the direct power control of a doubly fed induction gene...
This paper describes the models of a wind power system, such as the turbine, generator, power electr...
In this context, we are taking a close interest in the optimization of wind energy production. It co...
This study aims to propose a robust hybrid sliding mode artificial neural network control (SM-ANN) s...
Abstract—This paper focuses on developing a novel algorithm which dynamically optimizes the controll...
Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)This paper presents a direct pow...
The performance of the Hybrid Wind Energy System/Battery storage/Diesel Generator can be improved th...
In this paper, a Direct Power Control (DPC) based on the switching table and Artificial Neural Netwo...
AbstractThis paper presents modified neural network for dynamic control and operation of a hybrid ge...
In this paper, Hybrid Artificial Neural Network (ANN) with Proportional Integral (PI) control techni...
Abstract. With the increasing size of wind power generation it is required to perform power system s...
This paper describes the models of wind power system, such as the turbine, the generator, the power ...
The objective of this paper is to study the dynamic response of the wind energy conversion system (W...
During the last decade, wind energy gained much importance as an energy source in power systems. DFI...
With the increasing size of wind power generation it is required to perform power system stability ...
A neural network controller is presented for the direct power control of a doubly fed induction gene...
This paper describes the models of a wind power system, such as the turbine, generator, power electr...
In this context, we are taking a close interest in the optimization of wind energy production. It co...
This study aims to propose a robust hybrid sliding mode artificial neural network control (SM-ANN) s...
Abstract—This paper focuses on developing a novel algorithm which dynamically optimizes the controll...
Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)This paper presents a direct pow...
The performance of the Hybrid Wind Energy System/Battery storage/Diesel Generator can be improved th...
In this paper, a Direct Power Control (DPC) based on the switching table and Artificial Neural Netwo...
AbstractThis paper presents modified neural network for dynamic control and operation of a hybrid ge...