Fuzzy grey wolf optimization for controlled low-voltage ride-through conditions in grid-connected wind turbine with doubly fed induction generator

Abstract

With its enormous environmental and monetary benefits, the wind turbine has become an acceptable alternative to the generation of electricity by fossil fuel or nuclear power plants. Research remains focused on improving the performance of wind turbines with maximum flexibility and gains. The main objective of the paper is to simulate a low-voltage ridethrough (LVRT) control system that is convenient for the development of a controller that should have the ability to rectify fault signals. This paper proposes a novel method called grey wolf optimization with fuzzified error (GWFE) model to simulate the optimized control system. Further, it compares the GWFE-based LVRT system with the standard LVRT system, systems with minimum and maximum gain, and conventional methods like genetic algorithm (GA), differential evolution (DE), particle swarm optimization (PSO), ant bee colony (ABC), and grey wolf optimization (GWO) algorithms. Accordingly, it analyses the simulation results regarding qualitative analysis like active power, V dc comparison, gain, pitch degree, reactive power, rotor current, stator current, and V dc and V dqs measurements; and quantitative analysis like RMSE computation of V dc with varying speed. Hence, the proposed GWFE algorithm is beneficial for simulating the LVRT system compared to other conventional methods.

Power System Reactive Power Optimization Sensor Dispatching Digital Grid Based on Improved Cuckoo Algorithm

“…Yaln et al proposed the Open Grid Services Architecture (OGSA) algorithm, optimizing the initial group with the idea of reverse learning, to further improve the optimization performance; use OGSA in CS to solve three different objective functions; the simulation system includes three different test systems; the final experimental results are compared with other published literature results, which prove its effectiveness and robustness [12]. Madan and Kumar proposed that the cuckoo search (CS) algorithm is a novel heuristic search algorithm, by observing the life habits of the cuckoo, according to the characteristics of its Levi flight, compared with other intelligent algorithms, and its nest-seeking and egg-laying behavior of breeding in a boarding manner; the CS algorithm has few parameters, simple structure, and strong robustness; the ability to jump out of the local optima is strong, so it has been widely used as soon as it was proposed [13]. Saffari et al aimed at the multiobjective scheduling problem; a cuckoo search algorithm combined with fuzzy systems is proposed to solve the problem and got better experimental results [14].…”

Recent Trends in Wind Energy Conversion System with Grid Integration Based on Soft Computing Methods: Comprehensive Review, Comparisons and Insights

“…The results of simulation showed that the proposed method can capture the maximum wind energy and efficiently restore the system after occurring a fault at the electrical grid. In [146], Madan and Kumar introduced an optimized LVRT control by using the grey wolf optimization technique with fuzzified error algorithm. This paper compared the proposed optimized method with other traditional optimization algorithms.…”

Enhancement of low voltage ride-through ability of the photovoltaic array aided by the MPPT algorithm connected with wind turbine

“…Followed by that the SWFE algorithm has allowed the optimal constant gain selection in which the fault function gets minimized. Further, the proposed technique was compared with the conventional algorithms such as GA-LVRT (Vrionis et al , 2014), DE-LVRT (Zorlu, 2017), GWO-LVRT (Mirjalili et al , 2014), ABC-LVRT (Koçer, 2016), GWFE-LVRT (Madan and Kumar, 2019), WOA-LVRT (Mirjalili and Lewis, 2016) and PSO-LVRT (Zhang and Xia, 2017). The simulated design of the SWFE-dependent CS is revealed by Figure 11.…”