Renewable sources-based automatic load frequency control of interconnected systems using chaotic atom search optimization

This paper proposes an improved form of chaotic based atom search optimization (IASO) algorithm by adapting one-dimensional (1D) chaotic map (tent, sine and logistic) to improve the search ability by intensifying the exploration and exploitation phase. The IASO avoids premature convergence and trapping into local optima. Initially, the proposed IASO is validated using a classical benchmark function and its performance is compared with ASO algorithm. Test results indicate that the proposed algorithm outperforms in terms of mean, standard deviation, and best values. Further, the proposed technique is used to design the parameters of fractional-order proportional integral derivative controller for automatic load frequency control (ALFC) of multi-area, multi-source hybrid power system (HPS) by minimizing the integral time absolute error. The results obtained show that the proposed control scheme improves the frequency response of the system by 48 %, 70 %, 15 % and 69 % in terms of settling time, peak undershoot, steady state error value and control effort, respectively compared to ASO. Moreover, the sensitivity analysis is carried out by considering ±25 % variation in HPS parameters and the real-time applicability is tested with Malaysian meteorological data of solar radiation and wind speed variation. These analysis indicates that the transient oscillations are damped out with minimum settling time and the system regains to stable operating conditions. Further, the evaluation of transient and steady-state performance indices shows that the tent map-based IASO is found to be more efficient for obtaining the optimal solution in solving the ALFC problems. In addition, the stability of the system is analysed by approximating the fractional-order transfer function based on the oustaloup filter in frequency domain.

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