A new combined extreme learning machine variable steepest gradient ascent MPPT for PV system based on optimized PI-FOI cascade controller under uniform and partial shading conditions

Behera, Manoja Kumar; Saikia, Lalit Chandra

doi:10.1016/j.seta.2020.100859

Cited by 19 publications

(20 citation statements)

References 46 publications

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“…Several MPPT methods for solar PV system were reported in the literature utilizing several methods and aspects. Some of them are described here 36–42 …”

Section: Recent Research Work: a Brief Reviewmentioning

confidence: 99%

“…In real‐world applications, the detailed and statistical analysis was proved depends on quick variations of irradiance and field atmospheric data in terms of MPPT. Behera and Saikia 37 have presented a merged extreme learning machine variable steeper gradient ascent (ELMVSGA) maximal power point tracking (MPPT) for PV system. An effort was provided for initial time at MPPT to implement a novel fractional order integral proportional integral cascade controller.…”

Section: Recent Research Work: a Brief Reviewmentioning

confidence: 99%

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An elite hybrid strategy for solar photovoltaic system based optimized cascade controller under uniform and partial shading conditions

Kuppusamy¹,

Balaraman²

2022

Optim Control Appl Methods

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A hybrid technique for maximum power point tracking (MPPT) for a photovoltaic (PV) system is proposed in this paper. The proposed hybrid system is combination of Wing suit Flying Search (WFS) and modified Transient search optimization (MTSO), therefore it is called WFS-MTSO method. The proposed controller has three processes: (i) to identify the operating level of photovoltaic (uniform or in PSC), (ii) to estimate the maximal power point using WFS technique, and (iii) to ensure the photovoltaic system runs on the estimated maximum power point (MPP) by MTSO optimized cascade controller. This method begins with a sense of irradiance and temperature. The proposed photovoltaic system has two components. The first one is WFS maximal power point tracking algorithm attain maximal power point. The second one is MTSO optimized cascade controller to force the photovoltaic system to activate at maximal power point. Here, the proposed hybrid technique is utilized at MPPT to diminish tracking error and oscillation across MPP for optimizing power output. The proposed optimized cascade control improves the system efficiency by averting interruptions previously they propagate to the system. Finally, the performance of proposed hybrid system is executed on MAT-LAB/Simulink working platform and the performances are compared with various existing approaches. The statistical matrices, like mean, median, and standard deviation is analyzed the tracking efficiency of the proposed WFS-MTSO approach.

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“…Several MPPT methods for solar PV system were reported in the literature utilizing several methods and aspects. Some of them are described here 36–42 …”

Section: Recent Research Work: a Brief Reviewmentioning

confidence: 99%

Section: Recent Research Work: a Brief Reviewmentioning

confidence: 99%

An elite hybrid strategy for solar photovoltaic system based optimized cascade controller under uniform and partial shading conditions

Kuppusamy¹,

Balaraman²

2022

Optim Control Appl Methods

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“…Another MPPT algorithm of the PV system, which is based on irradiance estimation and the multi-Kernel extreme learning machine, was presented in [29] in order to reduce investment costs and improve PV system efficiency, while in [30], the deep reinforcement learning approach was used for MPPT control of partially shaded PV systems in smart grids. Two other literatures on the application of AI for MPPT control are provided in [31,32]. The authors of [31] presented a new combined ELM variable steepest gradient ascent MPPT for the PV system, while the authors of [32] presented a novel meta-heuristic optimization algorithm based MPPT control technique for partially shaded PV systems.…”

Section: Introductionmentioning

confidence: 99%

“…Two other literatures on the application of AI for MPPT control are provided in [31,32]. The authors of [31] presented a new combined ELM variable steepest gradient ascent MPPT for the PV system, while the authors of [32] presented a novel meta-heuristic optimization algorithm based MPPT control technique for partially shaded PV systems. As stated earlier, AI was also utilized for PV power prediction as in the case of [33], where the short-term PV power prediction was achieved using a hybrid improved Kmeans-GRA-Elman model based on multivariate meteorological factors and historical power datasets.…”

Section: Introductionmentioning

confidence: 99%

Review of Artificial Intelligence-Based Failure Detection and Diagnosis Methods for Solar Photovoltaic Systems

2021

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In recent years, the overwhelming growth of solar photovoltaics (PV) energy generation as an alternative to conventional fossil fuel generation has encouraged the search for efficient and more reliable operation and maintenance practices, since PV systems require constant maintenance for consistent generation efficiency. One option, explored recently, is artificial intelligence (AI) to replace conventional maintenance strategies. The growing importance of AI in various real-life applications, especially in solar PV applications, cannot be over-emphasized. This study presents an extensive review of AI-based methods for fault detection and diagnosis in PV systems. It explores various fault types that are common in PV systems and various AI-based fault detection and diagnosis techniques proposed in the literature. Of note, there are currently fewer literatures in this area of PV application as compared to the other areas. This is due to the fact that the topic has just recently been explored, as evident in the oldest paper we could obtain, which dates back to only about 15 years. Furthermore, the study outlines the role of AI in PV operation and maintenance, and the main contributions of the reviewed literatures.

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“…It is noted that the Bat-Beta command Int J Pow Elec & Dri Syst ISSN: 2088-8694  performs better under all test conditions. A new proportional integral (PI) fractional order incremental (FOI) technique optimized by salp swarm algorithm (SSA) is developed in order to operate the PV system at the estimated PPM [7]. As a result, the authors remarked that their algorithm offers a better tracking capability than the others.…”

Section: Introductionmentioning

confidence: 99%

A comparative study of meta-heuristic and conventional optimization techniques of grid connected photovoltaic system

Traoré

Ndiaye

Mbodji

2021

IJPEDS

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This paper presents the meta-heuristic and conventional optimizations techniques for the grid connected photovoltaic solar system. The perturb and observe (P&O) and particle swarm optimization (PSO) algorithms are proposed to track the maximum power point (MPP) of the photovoltaic solar system (PVSS). The regularization of the current supplied into the grid is ensured by the proportional integral (PI) corrector whose parameters are generated by the genetic algorithm (GA). The results of these two MPPT methods are compared and showed that the PSO is more efficient than the P&O. The use of GA algorithm to determine PI parameters allowed to obtain 0.89% of total distortion harmonic (THD).

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A new combined extreme learning machine variable steepest gradient ascent MPPT for PV system based on optimized PI-FOI cascade controller under uniform and partial shading conditions

Cited by 19 publications

References 46 publications

An elite hybrid strategy for solar photovoltaic system based optimized cascade controller under uniform and partial shading conditions

An elite hybrid strategy for solar photovoltaic system based optimized cascade controller under uniform and partial shading conditions

Review of Artificial Intelligence-Based Failure Detection and Diagnosis Methods for Solar Photovoltaic Systems

A comparative study of meta-heuristic and conventional optimization techniques of grid connected photovoltaic system

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