Enhancing frequency response using artificial intelligence techniques in the power system with renewable energy resources

This article aims to improve the performance of the modern electric power system with renewable energy resources, which have fluctuating power and low inertia contribution, by designing a control system based on different artificial intelligent (AI) techniques. Because of this power fluctuation, there is a constant mismatch between generation and load, which causes the power system's frequency to vary. Low-inertia operation amplifies the frequency fluctuation at the same time. Due to the stochastic variation of load and renewable resources in the system, an effective load frequency control (LFC) technique is therefore required. When working conditions change, LFC based on a fixed controller may perform unsatisfactorily even though it may respond optimally at a specific operating point. With the constraints and nonlinearities of the system taken into account, the controllers are applied to the secondary loop LFC of a multi-source generating system. The power system’s mathematical model was obtained using a transfer function approach, and the AI controllers were optimized using a particle swarm optimization technique (PSO) algorithm. Utilizing the FOPID reduces the settling time by 50.5 %, 64.6 % while FFOPID reduces it by 74.0 %, 81.4 % compared to optimal PID and FPID, respectively. Also, they reduces the system nadir for excessive load conditions. The results demonstrated that the power system's LFC is combined with AI controllers, the fuzzy fractional order proportional integral derivative (FFOPID) controller performs better than the other AI controllers.