Abstract
In this paper, Genetic Algorithm (GA) and Bacterial Foraging Optimization (BFO) are two optimization algorithms used for tuning of Proportional–Integral–Derivative (PID) controller parameters in designing a multi-machine power system network. These are popular evolutionary algorithms which are generally used for tuning of PID controllers. The proposed approach is easy for implementation as well as it has superior features. The computational techniques enhance the performance of the system, and the convergence characteristics obtained are also stable. The system scheduling BFO-PID and GA-PID controller is modeled using MATLAB platform. When a comparison in the performance of optimal PIDs with conventional PID controller is carried out, the performance of BFO-PID and GA-PID controller is better as it improves the speed, loop response stability, and steady-state error; the rise time is also minimized. The results after simulation show that the controller developed using the BFO algorithm achieves a faster response as compared to GA.
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Singh, M., Agrawal, A., Ralhan, S., Jhapte, R. (2019). Comparative Performance Analysis of Adaptive Tuned PID Controller for Multi-machine Power System Network. In: Behera, H., Nayak, J., Naik, B., Abraham, A. (eds) Computational Intelligence in Data Mining. Advances in Intelligent Systems and Computing, vol 711. Springer, Singapore. https://doi.org/10.1007/978-981-10-8055-5_72
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DOI: https://doi.org/10.1007/978-981-10-8055-5_72
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