Load Frequency Control of a Hydrothermal Hybrid Power System Using Evolutionary Optimization Algorithm

In a networked distributed power system, even unexpected changes in load can result in significant tie-line frequency and power swings. To prevent this, load frequency management (LFM) is used to keep the frequency and energy output necessary to control tie line power variations between various places, which is the main objective of this work. An essential aspect of LFM is automatic generation control, which governs speed control, so LFM's purpose is fulfilled. In this work, a linked power system is considered, with each location having a thermal and a hydroelectric plant with reheat. Governor Dead Band (GDB) has been applied to each plant to provide a more realistic approach to the system. The proposed controller is the fuzzy PID, fractional-order PID (FOPID) and proportional–integral–derivative (PID). However, it is noticed that the conventional use of PID controller, and FOPID fails to operate correctly, leading to high instability in the power network. Then, FPID with triangular membership is considered, which performed better than PID and FOPID. Here, two robust heuristic algorithms are taken, giving the controller the best gains via teaching learning-based optimization (TLBO) and Differential evolution (DE) with Integral absolute Time-Multiplied Error (IATE) as the primary objective function. Further, performance evaluation of TLBO and DE optimized PID, FOPID and FPID controllers is done. The simulation findings show that, compared to DE-optimized PID and FOPID controllers, DE optimized Fuzzy PID controlling device lessens the frequency shift of the control regions and strengthens the line power. Also, DE optimized controllers give a better output as compared to TLBO tuned ones. MATLAB/SIMULINK has been used to carry out modelling and simulations.