2018International Journal of Modelling and SimulationRequires access

Performance analysis of moth flame optimization algorithm for AGC system

Banaja Mohanty

Open publisher page 44 citations

Abstract

In this paper, an attempt has been made for comprehensive study of proportional-integral-double-derivative(PIDD) controller to solve automatic generation control (AGC) problem by applying moth flame optimization algorithm (MFOA). At first two unequal areas of thermal system is considered and the gains of PID/IDD/PIDD controller are optimized using MFOA technique. Simulation study depicted that MFOA-optimized PIDD controller provides better system performances considering settling time, overshoot and undershoot of area frequency and deviations in tie-line power as compared to other optimization techniques considered in this paper. The generation rate constraint (GRC) is included for two-area thermal system and dynamic stability of the system is investigated and compared with recent competitive algorithms. Further, the study is extended to non-linear AGC system with diverse source of generation. Generating unit in each control area consists of hydro, thermal and nuclear generation. Sensitivity analysis reveals that the MFOA-optimized PIDD controller parameter obtained at nominal condition need not necessary to change for wide changes in system parameters and with variation in random step load perturbation.

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What this paper is about

In this paper, an attempt has been made for comprehensive study of proportional-integral-double-derivative(PIDD) controller to solve automatic generation control (AGC) problem by applying moth flame optimization algorithm (MFOA). At first two unequal areas of thermal system is considered and the gains of PID/IDD/PIDD controller are optimized using MFOA technique. Simulation study depicted that MFOA-optimized PIDD controller provides better system performances considering settling time, overshoot and undershoot of area frequency and deviations in tie-line power as compared to other optimization techniques considered in this paper. The generation rate constraint (GRC) is included for two-area thermal system and dynamic stability of the system is investigated and compared with recent competitive algorithms. Further, the study is extended to non-linear AGC system with diverse source of generation. Generating unit in each control area consists of hydro, thermal and nuclear generation. Sensitivity analysis reveals that the MFOA-optimized PIDD controller parameter obtained at nominal condition need not necessary to change for wide changes in system parameters and with variation in random step load perturbation.

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Available abstract

In this paper, an attempt has been made for comprehensive study of proportional-integral-double-derivative(PIDD) controller to solve automatic generation control (AGC) problem by applying moth flame optimization algorithm (MFOA). At first two unequal areas of thermal system is considered and the gains of PID/IDD/PIDD controller are optimized using MFOA technique. Simulation study depicted that MFOA-optimized PIDD controller provides better system performances considering settling time, overshoot and undershoot of area frequency and deviations in tie-line power as compared to other optimization techniques considered in this paper. The generation rate constraint (GRC) is included for two-area thermal system and dynamic stability of the system is investigated and compared with recent competitive algorithms. Further, the study is extended to non-linear AGC system with diverse source of generation. Generating unit in each control area consists of hydro, thermal and nuclear generation. Sensitivity analysis reveals that the MFOA-optimized PIDD controller parameter obtained at nominal condition need not necessary to change for wide changes in system parameters and with variation in random step load perturbation.

Key concepts: Automatic Generation Control, Settling time, Overshoot (microwave communication), Control theory (sociology), PID controller, Controller (irrigation), Computer science, Sensitivity (control systems)

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