Optimal Voltage and Frequency Control of an Islanded Microgrid Using Grasshopper Optimization Algorithm

Abstract

Due to the lack of inertia and uncertainty in the selection of optimal Proportional Integral (PI) controller gains, the voltage and frequency variations are higher in the islanded mode of the operation of a Microgrid (MG) compared to the grid-connected mode. This study, as such, develops an optimal control strategy for the voltage and frequency regulation of Photovoltaic (PV) based MG systems operating in islanding mode using Grasshopper Optimization Algorithm (GOA). The intelligence of the GOA is utilized to optimize the PI controller parameters. This ensures an enhanced dynamic response and power quality of the studied MG system during Distributed Generators (DG) insertion and load change conditions. A droop control is also employed within the control architecture, alongside the voltage and current control loops, as a power-sharing controller. In order to validate the performance of the proposed control architecture, its effectiveness in regulating MG voltage, frequency, and power quality is compared with the precedent Artificial Intelligence (AI) based control architectures for the same control objectives. The effectiveness of the proposed GOA based parameter selection method is also validated by analyzing its performance with respect to the improved transient response and power quality of the studied MG system in comparison with that of the Particle Swarm Optimization (PSO) and Whales Optimization Algorithm (WOA) based parameter selection methods. The simulation results establish that the GOA provides a faster and better solution than PSO and WOA which resulted in a minimum voltage and frequency overshoot with minimum output current and Total Harmonic Distortion (THD).

Dynamic response enhancement of grid‐tied ac microgrid using salp swarm optimization algorithm

“…Therefore, it is very hard to obtain the optimal parameters under continuously varying operating conditions in real‐time online optimization. Hence, an offline optimization has been chosen for this research work instead of an online optimization method where it is not feasible to achieve optimal parameters under continuously varying and unpredictable MG operating conditions in real‐time . Furthermore, unlike the online optimization methods, in offline optimization, the optimized parameters obtained by the intelligent metaheuristic algorithms are suitable for all the MG operating conditions.…”

Salp Swarm Optimization Algorithm-Based Fractional Order PID Controller for Dynamic Response and Stability Enhancement of an Automatic Voltage Regulator System

“…Therefore, the proposed algorithm uses ITAE as the fitness function to optimize the studied system's response. A mathematical representation of ITAE is expressed in Equation (12).…”

“…Despite their superior performance over the traditional methods of FOPID tuning, all the mentioned optimization algorithms are bound with major drawbacks. For example, the localized solutions by GA restrict its usage to static data sets [11,12]. The PSO suffers from high uncertainty in its parameter selection and suffers local optimum stagnation for high dimensional search spaces [13].…”

Salp Swarm Optimization Algorithm-Based Controller for Dynamic Response and Power Quality Enhancement of an Islanded Microgrid

“…However, it may be noted that the voltage and frequency are two interrelated parameters [12], and therefore, it is not practically possible to independently optimize these two inter-dependent parameters. Most recently, in November 2018, the authors in reference [14] explored the grasshopper optimization algorithm (GOA) in order to regulate voltage and frequency along with the optimal transient response of an islanded MG. The authors achieved a better dynamic response as compared to previous quoted literature; however, as compared to WOA and PSO, no significant improvement was observed in overshoot and settling time during load change conditions.…”