Abstract
An artificial neural network (ANN)-based supplementary frequency controller is designed for a doubly fed induction generator (DFIG) wind farm in a local power system. Since the optimal controller gain that gives highest the frequency nadir or lowest peak frequency is a complicated nonlinear function of load disturbance and system variables, it is not easy to use analytical methods to derive the optimal gain. The optimal gain can be reached through an exhaustive search method. However, the exhaustive search method is not suitable for online applications, since it takes a long time to perform a great number of simulations. In this work, an ANN that uses load disturbance, wind penetration, and wind speed as the inputs and the desired controller gain as the output is proposed. Once trained by a proper set of training patterns, the ANN can be employed to yield the desired gain in a very efficient manner, even when the operating condition is not included in the training set. Therefore, the proposed ANN-based controller can be used for real-time frequency control. Results from MATLAB/SIMULINK simulations performed on a local power system in Taiwan reveal that the proposed ANN can yield a better frequency response than the fixed-gain controller.
Highlights
To increase the percentage of green energy, wind farms are being built in Taiwan
The artificial neural network (ANN) can yield controller gains that are very close to the optimal gains, even when the input variables such as wind speed, wind penetration, and load disturbance are not included in the training patterns of the ANN
Controller based on the present load disturbance (∆PLoad ), wind penetration, and wind speed (VW ), which are based on the present load disturbance (∆PLoad ), wind penetration, and wind speed (VW ), which are provided as the inputs to the ANN
Summary
To increase the percentage of green energy, wind farms are being built in Taiwan. It is expected that the installed capacities of these wind farms will reach 4.2 GW by 2025 [1]. Numerous works have been devoted to the design of a supplementary frequency controller for a doubly fed induction generator (DFIG) in order to improve the system frequency under disturbance conditions [2,3,4,5,6,7,8,9,10,11,12,13,14]. Computer simulations are first conducted to obtain the optimal droop gains that give the highest frequency nadir (FN) (in the case of load increase) or lowest peak frequency (in the case of load decrease) for the system under different values of load disturbances, wind power penetrations, and wind speeds. The designed ANN-based frequency controller can yield the desired droop gain in a very efficient manner The ANN can yield controller gains that are very close to the optimal gains, even when the input variables such as wind speed, wind penetration, and load disturbance are not included in the training patterns of the ANN
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