Load Frequency Control in a Hybrid Power System Considering Renewable Energy Sources and Electric Vehicles Using Fractional Order PID Controller Based on Wavelet Neural Network

Restructuring of power systems and integration of different renewable energy sources with complex dynamic behaviors and high structural uncertainties has made the issue of load frequency control more important. For a hybrid power system that includes a thermal power plant taking into account nonlinear limitations such as the governor dead band and generator rate constraints and renewable energy sources including a wind turbine, solar-thermal power plant, electrolyzer, fuel cell, and plug-in electric vehicle, this paper proposes an adaptive wavelet neural network fractional order PID controller (AWNNFOPID) based on self-recursive wavelet neural networks and fractional order PID controller. To compare the performance of the proposed AWNNFOPID controller, four different scenarios are considered and the simulation results are compared with traditional I, PI, and PID controllers as well as with the optimized FOPID controller. The simulation results show that the proposed AWNNFOPID controller has better performances than the other control strategies used for the studied hybrid power system based on performance indicators such as settling time, rise time, maximum overshoot, maximum undershoot, integral time absolute error (ITAE), and integral absolute error (IAE).