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Go to Editorial ManagerIn electrical power plants, the excitation control system is an important part of controlling the output voltage of the synchronous generators. The purpose of this paper is to utilize various methods of excitation control, such as Proportional-Integral-Derivative (PID), Simulated Annealing (SA), and Neural Network (NN) controllers. Each method is examined in terms of its effectiveness in enhancing system stability, reliability, and adaptability to varying operational conditions. The study simulates and optimizes a 2 MVA/400 V synchronous generator driven by a three-phase diesel engine with mechanical coupling and an exciter system. MATLAB 2021 is used to implement the Simulink model. The dynamic responses of field voltage and field current to load changes were analyzed for each control technique. Additionally, the performance of three-phase voltage and current for synchronous generator were examined over a 10-second timeframe. Our findings indicate that the PID controller offers straightforward implementation and reliable performance under varying conditions. The NN controller implementation is more similar to the PID response, and the SA controller demonstrates superior adaptability. The research underscores the potential of integrating these advanced control techniques in synchronous generators, paving the way for enhanced stability and reliability in modern electric power systems, with further implications for renewable energy integration.
An accurate model for a permanent magnet syn- chronous generator (PMSG) is important for the design of a high-performance PMSG control system. The performance of such control systems is influenced by PMSG parameter variations under real operation conditions. In this paper, the electrical parameters of a PMSG (the phase resistance, the phase inductance and the rotor permanent magnet (PM) flux linkage) are identified by a particle swarm optimisation (PSO) algorithm based on experimental tests. The advantages of adopting the PSO algorithm in this research include easy implementation, a high computational efficiency and stable convergence characteristics. For PMSG parameter identification, the normalised root mean square error (NRMSE) between the measured and simulated data is calculated and minimised using PSO.