Optimal PID Tuning of PLL for PV Inverter Based on Aquila Optimizer

Abstract

Phase-locked loop (PLL) is a fundamental and crucial component of a photovoltaic (PV) connected inverter, which plays a significant role in high-quality grid connection by fast and precise phase detection and lock. Several novel critical structure improvements and proportional-integral (PI) parameter optimization techniques of PLL were proposed to reduce shock current and promote the quality of grid connection at present. However, the present techniques ignored the differential element of PLL and did not acquire ideal results. Thus, this paper adopts Aquila optimizer algorithm to regulate the proportional-integral-differential (PID) parameters of PLL for smoothing power fluctuation and improving grid connection quality. Three regulation strategies (i.e., PLL regulation, global regulation, and step regulation) are carefully designed to systematically and comprehensively evaluate the performance of the proposed method based on a simulation model in MATLAB/Simulink, namely, “250-kW Grid-Connected PV Array”. Simulation results indicate that PLL regulation strategy can effectively decrease power fluctuation and overshoot with a short response time, low complexity, and time cost. Particularly, the Error(P) and the maximum deviation of output power under optimal parameters obtained by PLL strategy are decreased by 418 W and 12.5 kW compared with those under initial parameters, respectively.

A Comprehensive Survey on Aquila Optimizer

“…To significantly guarantee a high-quality connection grid of a PV system, Guo et al [ 38 ] suggested a unique PID parameter tuning technique of PLL with AO. The AO was utilized to decrease power fluctuations and enhance grid connection quality.…”

Accurate Nonlinearity and Temperature Compensation Method for Piezoresistive Pressure Sensors Based on Data Generation

“…By observing and imitating the predation process of the aquila, Abualigah et al [29] proposed the AO algorithm in 2021. Compared with other meta-heuristic algorithms, the AO exhibits considerable superiority in practical engineering problems [29][30][31][32]. The modeling process for the parameter optimization of MPKELM using the AO algorithm is as follows.…”

Disturbance rejecting PID-FF controller design of a non-ideal buck converter using an innovative snake optimizer with pattern search algorithm

“…Extensive investigations have been carried out in the realm of power electronics and power conversion systems to optimize controller parameters. Diverse metaheuristic algorithms, such as atom search optimization (ASO) [ 36 ], weIghted meaN oF vectOrs optimizer (INFO) [ 37 ], hunger games search (HGS) optimizer [ 38 ], Aquila optimizer (AO) [ 39 ], particle swarm optimization [ 40 ], manta-ray foraging optimizer (MRFO) [ 41 ], chimp optimization algorithm (ChOA) [ [42] , [43] , [44] ], marine predators algorithm (MPA) [ 45 ], fuzzy whale optimization algorithm (FWOA) [ 46 ], grey wolf optimizer (GWO) [ 47 ], snow ablation optimizer (SAO) [ 48 ], and gorilla troops optimizer (GTO) [ 49 ] have been employed for this purpose. Snake optimizer (SO) [ 50 ] is a relatively new algorithm with limited utilization in power electronic converters and control in the literature at the time of writing this article.…”