Summary Optimal PID‐fuzzy‐PID hybrid controller (PID‐FPID) has been presented here for load frequency control (LFC) analysis in a two‐area interconnected system. The optimum parameters of this suggested PID‐FPID hybrid controller are achieved using modified grey wolf optimization (MGWO) algorithm. Initially, the investigation is performed on a reheat turbine‐based two‐area unified system. The effectiveness of the recommended controller is proved (a) by altering the size and location of step load perturbation (SLP), (b) by modifying the system constraints, and (c) by inserting a random loading in the system. The dominance of the employed controller is recognized by relating the results with pre‐published outcomes such as Differential evolution‐Particle swarm optimization (DEPSO) tuned fuzzy‐PID controller and artificial bee colony optimization (ABC) tuned PID controller. Lastly, the analysis is prolonged by implementing the recommended control method in a hybrid‐source power system to exhibit the flexibility of the suggested method in a hybrid power system.

The operation and control of the modern power system has become complex and difficult due to the incessant penetration of nonconventional energy sources integrated to the power grid and the structural variation of power system with continuing escalation of power demand in recent years. This entails the implementation of intelligent control strategy for satisfactory operation of the power system. Hence, a fractional order fuzzy proportional integral derivative (FOFPID) controller is suggested in this article for automatic generation control of two unequal area interconnected power system with diverse generating units such as thermal, hydro, diesel and wind power plants. The dynamic performance of the system is investigated by using proportional integral derivative (PID), fractional order PID (FOPID), fuzzy PID (FPID) and fractional order fuzzy PID (FOFPID) controllers separately. The parameters of these controllers

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To facilitate the frequency regulation, here an adaptive artificial neural network (ANN) tuned proportional‐integral‐derivative (PID) controller is suggested for load frequency control (LFC) investigation in a system with distributed generation (DG) resources. The various DG resources include wind turbine generators (WTG), battery energy storage system (BESS), aqua electrolyzer (AE), diesel engine generators (DEG), and fuel cell (FC). Initially, an isolated thermal generating system is considered with DG. Then an interconnected two‐area thermal power system with DG is considered for LFC analysis. The implemented PID controller parameters are achieved using two methodologies. In the first case, the PID controller parameters are tuned by a recent optimization technique known as grasshopper optimization algorithm (GOA). In the second case, the PID controller parameters are tuned by an ANN. The dynamic behavior of the two categories of the system is inspected with GOA tuned PID controller and ANN tuned PID controller and it is established that ANN tuned PID controller exhibits superior performance as compared to GOA tuned PID controller in terms of time‐based performance evaluative factors such as minimum undershoots, settling time and maximum overshoots. Also, the robustness of the recommended ANN tuned PID controller is verified by applying random loading in the system.

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