Photovoltaic system failure diagnosis based on adaptive neuro fuzzy inference approach: South Algeria solar power plant

Kaid, Imad Eddine; Hafaifa, Ahmed; Guemana, Mouloud; Hadroug, Nadji; Kouzou, Abdellah; Mazouz, Lakhdar

doi:10.1016/j.jclepro.2018.09.023

Cited by 35 publications

(20 citation statements)

References 29 publications

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“…For the uncertain variation of solar radiation and temperature, hybrid neuro fuzzy model shows an average response time and oscillation of 0.1 and 2.1 W, respectively with an accuracy of 99% which is around 95% to 97% in case of P&O, IC and FLC techniques 142 . An adaptive neuro fuzzy model is proposed by Kaid et al to indicate, detect, locate and eliminate the faults in photovoltaic system with 120 120 solar panels, installed on 60 ha area of South Algeria to generate power output of 30 MW and efficiency of 15% to 20% 143 . Jeyaprabha et al have developed ANN and ANFIS models to decide the optimal design for hybrid system based on photovoltaic/battery/diesel generator and to decide the optimal tilt angle of photovoltaic solar panel.…”

Section: Application Of Ai Techniques In Solar Photovoltaic Systemsmentioning

confidence: 99%

A review on modeling of solar photovoltaic systems using artificial neural networks, fuzzy logic, genetic algorithm and hybrid models

2020

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The uncertainty associated with modeling and performance prediction of solar photovoltaic systems could be easily and efficiently solved by artificial intelligence techniques. During the past decade of 2009 to 2019, artificial neural network (ANN), fuzzy logic (FL), genetic algorithm (GA) and their hybrid models are found potential artificial intelligence tools for performance prediction and modeling of solar photovoltaic systems. In addition, during this decade there is no extensive review on applicability of ANN, FL, GA and their hybrid models for performance prediction and modeling of solar photovoltaic systems. Therefore, this article focuses on extensive review on design, modeling, maximum power point tracking, fault detection and output power/efficiency prediction of solar photovoltaic systems using artificial intelligence techniques of the ANN, FL, GA and their hybrid models. In addition, the selected articles on the solar radiation prediction using ANN, FL, GA and their hybrid models are also summarized. Total of 122 articles are reviewed and summarized in the present review for the period of 2009 to 2019 with 90 articles in the field of {ANN, FL, GA and their hybrid models} + solar photovoltaic systems and 32 articles in the field of {ANN, FL, GA and their hybrid models} + solar radiation. The review shows the suitability and reliability of ANN, FL, GA and hybrid models for accurate prediction of the solar radiation and the performance characteristics of solar photovoltaic systems. In addition, this review presents the guidance for the researchers and engineers in the field of solar photovoltaic systems to select the suitable prediction tool for enhancement of the performance characteristics of the solar photovoltaic systems and the utilization of the available solar radiation.

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Section: Application Of Ai Techniques In Solar Photovoltaic Systemsmentioning

confidence: 99%

A review on modeling of solar photovoltaic systems using artificial neural networks, fuzzy logic, genetic algorithm and hybrid models

2020

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“…Researchers have also used neural networks to predict and assess the behaviour of photovoltaic solar panels. For example , Dhimish, et al, (2018) have compared between fuzzy logic and RBF ANN network for photovoltaic fault detection while Kaid, et al (2018) have used adaptive neuro fuzzy inference approach to predict the failure diagnostic of in-situ photovoltaic cells. Deep learning has been implemented by Wu and Wang (2018) for real time energy management and control strategy of micro-grid.…”

Section: Neural Network Predicationmentioning

confidence: 99%

Rapid evaluation of micro-scale photovoltaic solar energy systems using empirical methods combined with deep learning neural networks to support systems’ manufacturers

Al-Meshaiei

Al‐Habaibeh

Shakmak

2020

Journal of Cleaner Production

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Solar energy is becoming one of the most attractive renewable sources. In many cases, due to a wide range of financial or installation limitations, off-grid small scale micro power panels are favoured as modular systems to power lighting in gardens or to be integrated together to power small devices such as mobile phone chargers and distributed smart city facilities and services. Manufacturers and systems' integrators have a wide range of options of micro-scale photo voltaic panels to choose from. This makes the selection of the right panel a challenging task and risky investment. To address this and to help manufacturers, this paper suggests and evaluates a novel approach based on integrating empirical lab-testing with short-term real data and neural networks to assess the performance of micro-scale photovoltaic panels and their suitability for a specific application in specific environment. The paper outlines the combination of lab testing power output under seasonal and hourly conditions during the year combined with environmental and operating conditions such as temperature, dust accumulation and tilt angle performance. Based on the lab results, a short in-situ experimental work is implemented and the performance over the year in the selected location in Kuwait is evaluated using deep learning neural networks. The findings of this approach are compared with simulation and long-term real data. The results show a maximum error of 23% of the neural network output when compared with the actual data, and a correlation values with previous work within 87.3% and 91.9% which indicate that the proposed approach could provide an experimental rapid and accurate assessment of the expected power output. Hence, supporting the rapid decision-making process for manufacturers and reducing investment risks.

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“…The aim of this paper is to present and analyze the measures which have been implemented, with a view to reaching the required energy mix, and achieving sustainable development in Algeria. Some previous research has focused on the energy situation in Algeria. Some of them refer to specific technologies, such as the studies by Kaid et al [9] and Sahouane et al [10] related to photovoltaic, Nacer et al [11] related to wind energy, and Akbi et al [12] referring to bioenergy. Some others analyze the Algerian RE development or its potential.…”

Section: Introductionmentioning

confidence: 99%

Measures to Promote Renewable Energy for Electricity Generation in Algeria

2020

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Algeria has enormous renewable energy potential. However, fossil fuels remain the main electricity generation source, and the country is the third largest CO 2 emitter in Africa. Algeria is also particularly vulnerable to climate change. Therefore, a set of actions related to energy, forests, industry and waste sectors have been programmed, over the period 2015-2030, and the government action program has given priority to promote renewable energy. In this sense, Algeria is committed to significantly promote investment in renewable energy, during the period 2020-2030. Thus by 2030, renewable electricity production capacity will achieve 22,000 MW, representing 27% of total electricity generation. This paper analyzes the electricity generation measures implemented in Algeria to reach the required energy mix, the legislative framework, financial aid, the feed-in tariff system, the tax incentives, and the tender and auctions undertaken. The analyses reveal that, although the electricity price premium policy has not been revoked, the newly enacted tender scheme is designed to become the standard procedure for launching renewable energy projects in Algeria in the coming years.

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Photovoltaic system failure diagnosis based on adaptive neuro fuzzy inference approach: South Algeria solar power plant

Cited by 35 publications

References 29 publications

A review on modeling of solar photovoltaic systems using artificial neural networks, fuzzy logic, genetic algorithm and hybrid models

A review on modeling of solar photovoltaic systems using artificial neural networks, fuzzy logic, genetic algorithm and hybrid models

Rapid evaluation of micro-scale photovoltaic solar energy systems using empirical methods combined with deep learning neural networks to support systems’ manufacturers

Measures to Promote Renewable Energy for Electricity Generation in Algeria

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