Optimal Fuzzy PIDF Load Frequency Controller for Hybrid Microgrid System Using Marine Predator Algorithm

Abstract

This paper presents a fully optimized fuzzy proportional-integral-derivative with filter (FPIDF) load frequency controller (LFC) for enhancing the performance of a hybrid microgrid system. The Marine Predator Algorithm (MPA), a recent optimization algorithm, is used to optimize the gains as well as the input scaling factors and membership functions of the proposed fuzzy PIDF controller. The controller performance is tested on a two-area hybrid microgrid system containing various renewable energy sources and energy storage devices. The effectiveness of the MPA based FPIDF controller is compared with conventional PIDF and FPIDF controllers based on other heuristic techniques presented in literatures. Moreover, different scenarios are implemented in this study to verify the robustness and sensitivity of the proposed controller to different step load perturbations, system parameters variations, wind speed fluctuation and solar irradiance variation. The dynamic response of the system is compared using different controllers in terms of settling time, maximum overshoot and undershoot values. The results are presented in the form of time domain simulations conducted via MATLAB/SIMULINK. INDEX TERMSFuzzy control, load frequency control, marine predator algorithm, microgrid system, PIDF controller, renewable energy, storage systems. NOMENCLATURE A Rotor swept area ACE Area control error B1, B2 Frequency bias coefficients BES Battery energy storage CE Change of error Predator step size control ratio Power coefficient COR Competition over resources E Error FF Fitness function FLC Fuzzy logic controller FPIDF Fuzzy proportional-integral-derivative with filter G Solar radiation ( ) Controller transfer function Solar irradiance under standard test conditions 1 , 2 Input scaling gains 12

Marine Predators Algorithm: A Review

“…[36] Modified MPA MPA is proposed to optimize fuzzy proportional-integral-derivative with filter (FPIDF) load frequency controller (LFC) for enhancing the performance of a hybrid microgrid system [162] Economic load dispatch Modified MPA introduced another modified version of MPA, called (MGMPA), for global optimization and economic load dispatch problems [125] Fuel Cell Original MPA The MPA is proposed to obtain a very precise of parameters semiempirical equation that defines the proton exchange membrane fuel cell (PEMFC) [160] Optimal power flow Original MPA MPA is proposed to solve the optimal power flow (OPF) problem of multi-regional systems by using parameters' tuning to enhance the algorithm performance [146] System's investment costs Original MPA MPA is proposed to investigate a method for reducing the system's investment costs [89] Wind Farm Energy Original MPA MPA is proposed to optimize control strategy for the grid side converter, and rotor side converter of a doubly-fed induction generator [117] Economic Load Dispatch Original MPA Optimal solutions obtained by running MPA on several systems are employed to compare with many previous optimization methods [83] Multiple PV systems Original MPA MPA is proposed to find optimal location and sizing of multiple photovoltaic (PV) systems in a distribution network [73] Reactive power modulation…”

Robust Control of Frequency Variations for a Multi-Area Power System in Smart Grid Using a Newly Wild Horse Optimized Combination of PIDD2 and PD Controllers

“…Several control methods have been used to solve the problem of load frequency control in power systems. These include model predictive control (MPC) [11], artificial intelligence control [12], robust control approaches [13], and fuzzy logic control [14,15]. Due to its simplicity and cheapness, academic researchers have concentrated their studies on the conventional PID controller.…”

Implementation of Harris Hawks optimization for load frequency control of hydropower plant

“…Where, 𝐿𝐹(𝑓) = 0.01 × Where the random values u, v in the range between {0, 1}, β=1.5. Hence, the final soft besiege phase position is modelled as (22). When |E| < 0.5, the prey has very low energy to escape and it is a hard besiege stage to catch and kill the prey.…”