Abstract
This paper presents dynamic performance comparison of a fuzzy logic-based proportional, integral, and derivative controller (FPID) with different membership functions such as triangular, trapezoidal, and Gaussian for load frequency control (LFC) in an interconnected two-area thermal power system. The parameters of controller are optimized by using spider monkey optimization (SMO) algorithm. The superiority of the proposed algorithm is established by comparing the results with popularly used algorithms like particle swarm optimization (PSO) and teaching–learning-based optimization (TLBO). Initially, the linearized model of the system is considered with reheat turbine; then, the study is extended by imposing nonlinearity such as generation rate constraints (GRC) and governor dead band (GDB). The result comparison is analyzed using various time domain specifications like peak undershoot, peak overshoot, and settling time of different area frequencies and tie-line power deviation between them applying a step load perturbation (SLP) of 1%.
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Appendices
Appendix [8, 12]
f = 60 Hz; R1 = R2 = 2.4 Hz/pu.; TG1 = TG2 = 0.08 s; TT1 = TT2 = 0.3 s; B1 = B2 = 0.425 p.u. MW/Hz; Tr1 = Tr2 = 10 s; Kr1 = Kr2 = 0.5 TP1 = TP2 = 20 s; KP1 = KP2 = 120 Hz/p.u. MW T12 = 0.545, a12 = −1.
Appendix
Population size (n) = 50, number of iteration = 100, and number of runs = 20.
Control parameters for SMO: LLlim = (n/2) = 25, GLlim = ((n * d)/2) = 250. MG. = 10, pr = 0.1–0.9 increasing linearly.
Control parameters for PSO: Inertia weight (w), decreases linearly from 0.9–0.1; acceleration coefficients (c1 = 2, c2 = 2).
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Tripathy, D., Barik, A.K., Choudhury, N.B.D., Sahu, B.K. (2019). Performance Comparison of SMO-Based Fuzzy PID Controller for Load Frequency Control. In: Bansal, J., Das, K., Nagar, A., Deep, K., Ojha, A. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 817. Springer, Singapore. https://doi.org/10.1007/978-981-13-1595-4_70
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DOI: https://doi.org/10.1007/978-981-13-1595-4_70
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