PV/WT Integrated System Using the Gray Wolf Optimization Technique for Power Quality Improvement

Goud, B. Srikanth; Reddy, Ch. Rami; Kalyan, Ch. Naga Sai; Udumula, Ramanjaneya Reddy; Bajaj, Mohit; Samad, Bdereddin Abdul; Shouran, Mokhtar; Kamel, Salah

doi:10.3389/fenrg.2022.957971

Cited by 14 publications

(9 citation statements)

References 28 publications

(38 reference statements)

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“…Among these approaches 9 , intelligent control methods utilizing fuzzy logic control (FLC) 10 , 11 and artificial neural networks (ANN) 12 demand substantial input data. Optimization-based control strategies including particle swarm optimization (PSO) 13 , genetic algorithm (GA) 14 , firefly algorithm (FA) 15 , seagull optimization algorithm (SOA) 16 , artificial bee colony (ABC) algorithm 17 , and gray wolf optimization (GWO) algorithm 18 , model predictive control (MPC) 19 and sliding mode control (SMC) 20 approach have been harnessed for GMPP tracking. Due to their simplicity in design and implementation, MPC and its variations have been extensively explored for GMPP tracking in PV systems.…”

Section: Related Workmentioning

confidence: 99%

An adapted model predictive control MPPT for validation of optimum GMPP tracking under partial shading conditions

Siddique,

Zhao,

Rehman

et al. 2024

Sci Rep

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The energy generation efficiency of photovoltaic (PV) systems is compromised by partial shading conditions (PSCs) of solar irradiance with many maximum power points (MPPs) while tracking output power. Addressing this challenge in the PV system, this article proposes an adapted hybrid control algorithm that tracks the global maximum power point (GMPP) by preventing it from settling at different local maximum power points (LMPPs). The proposed scheme involves the deployment of a 3 × 3 multi-string PV array with a single modified boost converter model and an adapted perturb and observe-based model predictive control (APO-MPC) algorithm. In contrast to traditional strategies, this technique effectively extracts and stabilizes the output power by predicting upcoming future states through the computation of reference current. The boost converter regulates voltage and current levels of the whole PV array, while the proposed algorithm dynamically adjusts the converter's operation to track the GMPP by minimizing the cost function of MPC. Additionally, it reduces hardware costs by eliminating the need for an output current sensor, all while ensuring effective tracking across a variety of climatic profiles. The research illustrates the efficient validation of the proposed method with accurate and stable convergence towards the GMPP with minimal sensors, consequently reducing overall hardware expenses. Simulation and hardware-based outcomes reveal that this approach outperforms classical techniques in terms of both cost-effectiveness and power extraction efficiency, even under PSCs of constant, rapidly changing, and linearly changing irradiances.

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Section: Related Workmentioning

confidence: 99%

An adapted model predictive control MPPT for validation of optimum GMPP tracking under partial shading conditions

Siddique,

Zhao,

Rehman

et al. 2024

Sci Rep

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“…For the optimization of the system's controllers, two advanced methods were used: Particle Swarm Optimization Artificial Neural Network (PSOANN) (22) and Fractional Order Proportional-Integral (FOPI) enhanced with Grey Wolf Optimization (23). The PSOANN is pivotal in fine-tuning the system's operational parameters, leveraging the power of artificial neural networks and the global search capability of particle swarm optimization (24).…”

Section: Figure 3 Voltage Control For Emsmentioning

confidence: 99%

Optimizing DC Microgrid Systems for Efficient Electric Vehicle Battery Charging in Ain El Ibel, Algeria

Younes,

Ait Abbas,

Laroussi

et al. 2024

IJE

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“…( 3) represents the FOPID controller modelling. Figure .4 represent FOPID controller structure [12][13].…”

Section: Fopid Controllermentioning

confidence: 99%

Dynamic Voltage Restorer to Mitigate Voltage Sag/Swell using Black Widow Optimization Technique with FOPID Controller

Srikanth Goud,

Kiran Kumar,

Ashok Kumar

et al. 2023

EAI Endorsed Trans Energy Web

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The efficiency with which electrical equipment use electricity is essential for several reasons. First, superior power quality (PQ) improves efficiency and facilitates peak performance in electronic equipment. This article's goal is to make the advantages of installing a Dynamic Voltage Restorer (DVR) to enhance PQ for energy users clearer. To improve DVR dependability and user friendliness, the suggested technique uses a hysteresis voltage control system that works with variable switching frequency. Simulation findings show that voltage compensation is successful under disturbances when a black widow optimization (BWO) based Factional order proportional derivative (FOPID) controller is used. This paper proposed is to improve PQ using a BWO- FOPID controller and compare the outcomes to those achieved from a previously developed PI controller in a distribution power system using MATLAB.

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PV/WT Integrated System Using the Gray Wolf Optimization Technique for Power Quality Improvement

Cited by 14 publications

References 28 publications

An adapted model predictive control MPPT for validation of optimum GMPP tracking under partial shading conditions

An adapted model predictive control MPPT for validation of optimum GMPP tracking under partial shading conditions

Optimizing DC Microgrid Systems for Efficient Electric Vehicle Battery Charging in Ain El Ibel, Algeria

Dynamic Voltage Restorer to Mitigate Voltage Sag/Swell using Black Widow Optimization Technique with FOPID Controller

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