Improving the Power Quality of Island Microgrid With Voltage and Frequency Control Based on a Hybrid Genetic Algorithm and PSO

Abstract

An efficient power control technique for inverter-based distributed generation (DG) in an islanded microgrid is investigated in this work. The objective is to raise the caliber of the electricity pumped from network-connected DGs. The characteristics that are taken into consideration include voltage and frequency control, dynamic response, and steady-state response, particularly when the microgrid is operating in island mode or when there is a load change. The control method consists of an internal current control loop and an external power control loop based on a synchronous reference frame and a conventional PI controller. The power controller is designed based on voltage-frequency (VF) control. In addition, an intelligent search technique that combines Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) is utilized to automatically modify power controller parameters. The control technique in this research is that the DG modifies its control mode to modify the system voltage and frequency when the microgrid is islanded or load conditions change. The simulation results in MATLAB/SIMULINK software show that the proposed control system has been able to improve the power quality well.

Frequency control of the islanded microgrid including energy storage using soft computing

“…( 15 ) and considering that in this paper the goal is to improve the PI coefficients to control the microgrid frequency, two factors can be defined as the maximum overshoot (OS) rate and settling time (ST) of the frequency signal as Eq. ( 19 ) 30 . …”

Coordinated Q‐V/P‐f Control Strategy Using Virtual Power Factor for Autonomous AC Microgrid with PV Smart Inverters

“…Excitation control using automatic voltage regulator (AVR) controls reactive power output for voltage control while load frequency control (LFC) regulates active power output for frequency management [4,5]. such as: fuzzy logic, artificial neural network, genetic algorithm, and particle-swarm optimization have been applied in Refs [15][16][17][18], respectively, to achieve optimal reactive and active power dispatch. Moreover, some past papers employed different optimization techniques such as linear programming and nonlinear programming methods to determine reactive and active power within given constraints [19,20].…”

“…Various CMs such as sliding mode control, model predictive control, current & voltage mode control are discussed [14]. Furthermore, soft computing techniques with adequate control response speed such as: fuzzy logic, artificial neural network, genetic algorithm, and particle‐swarm optimization have been applied in Refs [15–18], respectively, to achieve optimal reactive and active power dispatch. Moreover, some past papers employed different optimization techniques such as linear programming and nonlinear programming methods to determine reactive and active power within given constraints [19,20].…”

Islanding Detection and Power Quality Diagnosis of Wind Power Integrated Microgrid with Reduced Feature Trained Novel Optimized Random Decision Forest

“…Many researchers have already suggested several FSTs where the feature selection process is considered as a combinatorial problem [16]. But the classical FSTs have some flaws which can be overcame by evolutionary optimization techniques [17][18][19][20] those can be of two types, namely, continuous and discrete (binary). As the decision variable of feature selection varies in binary space, a binary optimization algorithm can be chosen to deal with this [21][22][23].…”