PSO‐based SMC variable step size P&amp;O MPPT controller for PV systems under fast changing atmospheric conditions

Harrag, Abdelghani; Messalti, Sabir

doi:10.1002/jnm.2603

Cited by 52 publications

(28 citation statements)

References 53 publications

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“…The traditional analysis found it difficult in the uncertain complex power system to operate/control for the future development of microgrid. Conventional optimization methods like a genetic algorithm (GA), 11,12 ant colony optimization (ACO), 13,14 and particle swarm optimization (PSO) 15,16 were categories under metaheuristic method and its convergence to attain an optimal solution to specified problems are less in robustness and its lack in computation with machine learning. AI-based models are trained with big data collected from the wide-area monitoring system (WAMS) to attain deep learning.…”

Section: Need Of Algorithm In Microgridmentioning

confidence: 99%

Deep learning and reinforcement learning approach on microgrid

Kumar¹,

Kandasamy

Ramanathan

2020

Int Trans Electr Energ Syst

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Microgrid is a new era in the power system and it has more scope of investigation on research. Due to an increase in demand and future expansion of the power system, analyzing the complexities of the network becomes a challenging task. Artificial intelligence plays a vital role in resolving such issues in a microgrid in various aspects. Owing to the rapid growth of periodical update in computational cost reduction, enhanced data analysis-based algorithm artificial intelligence enters into new epoch Artificial Intelligence AI 2.0. Based on such approach, machine learning has been evolved as AI 2.0 initially. Now, it develops branches like deep learning, reinforcement learning, and a combination of both deep reinforcement learning algorithms. These algorithms are precise to attain higher priority in decision-making under a complex network. This paper deals with numerous challenges of the above algorithm to state the significance of AI 2.0 and summarization of their application toward microgrid is useful to analyze the power system. K E Y W O R D S deep learning, deep reinforcement learning, microgrid, reinforcement learning 1 | INTRODUCTION Microgrid is a cluster of distributed generators (DG), stored energy system, local loads along with protecting and monitoring devices. Microgrid has its own specialty in operating under two modes of operation: autonomous and grid-connected mode, mode selection attained by the use of static transfer switch that bridges operation selection in microgrid. The presence of multiple DGs creates a distribution access problem in multipoint is transformed into a concentrated

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Section: Need Of Algorithm In Microgridmentioning

confidence: 99%

Deep learning and reinforcement learning approach on microgrid

Kumar¹,

Kandasamy

Ramanathan

2020

Int Trans Electr Energ Syst

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“…P&O method is mostly used in the PV system because of the simplicity. 2 However, the efficiency of this approach decreases when the radiation changes rapidly; so many algorithms have been developed to mitigate this problem. Artificial intelligence (AI) methods such as fuzzy logic controller (FLC), neural network (NN), and adaptive neuro-fuzzy inference system (ANFIS) have been developed to extract MPPT and to avoid related problems with the techniques of fixed and variable step size.…”

Section: Introductionmentioning

confidence: 99%

Maximum power point tracking based on adaptive neuro‐fuzzy inference systems for a photovoltaic system with fast varying load conditions

Mohammed

Buyamin

Shams

et al. 2021

Int Trans Electr Energ Syst

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“…20 PSO based tuned sliding mode controller (SMC) is introduced in Ref. 21 to drive the variable step of the conventional P&O algorithm. A Ziegler-Nicholas-based PID controller for tracking MPP is introduced in Ref.…”

Section: Introductionmentioning

confidence: 99%

“…An Unbalanced fuzzy logic control (FLC) for tracking MPPT is proposed in Ref 20 . PSO based tuned sliding mode controller (SMC) is introduced in Ref 21 . to drive the variable step of the conventional P&O algorithm.…”

Section: Introductionmentioning

confidence: 99%

Variable step size fractional incremental conductance for MPPT under changing atmospheric conditions

Khan

Gulzar

Sibtain

et al. 2020

Int J Numerical Modelling

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This paper presents a new modified variable step size Fractional Order Incremental Conductance (FOIC) with maximum power point tracking using Fractional Order PID controller tuned by bio-inspired Particle Swarm Optimization (PSO) to find optimal gain values of fractional integrator order (λ) and fractional derivative order (μ). The classical incremental conductance and FOIC show drawbacks under changing irradiance, oscillation around maximum power point (MPP) which decreases its convergence speed. To resolve these prone a variable step size FOIC is proposed to achieve an adaptive duty cycle via tuning of FOPID through PSO. The robustness of the proposed technique is judged by its steady-state and dynamic response with fast converges, less response time, overshoot, and ripples under changing environmental conditions. Furthermore, the performance of the proposed technique is evaluated by comparing it with a fixed step size conventional incremental conductance algorithm and FOIC.

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PSO‐based SMC variable step size P&O MPPT controller for PV systems under fast changing atmospheric conditions

Cited by 52 publications

References 53 publications

Deep learning and reinforcement learning approach on microgrid

Deep learning and reinforcement learning approach on microgrid

Maximum power point tracking based on adaptive neuro‐fuzzy inference systems for a photovoltaic system with fast varying load conditions

Variable step size fractional incremental conductance for MPPT under changing atmospheric conditions

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