Simple and efficient approach to detect and diagnose electrical faults and partial shading in photovoltaic systems

Chaibi, Y.; Malvoni, Maria; Chouder, Aissa; Boussetta, Mohammed; Salhi, Mohamed Salah

doi:10.1016/j.enconman.2019.05.086

Cited by 62 publications

(24 citation statements)

References 45 publications

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“…Whether it is a numerical, analytical, or metaheuristic based method [19][20][21], most of these approaches use I-V experimental data or datasheet information to simplify the estimation of the parameters . For this reason, the photo-generated current is generally calculated using Equation (4) [22]:…”

Section: Double-diode Modelmentioning

confidence: 99%

Machine Learning Based Approaches for Modeling the Output Power of Photovoltaic Array in Real Outdoor Conditions

Malvoni

Chaibi

2020

Electronics

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It is important to investigate the long-term performances of an accurate modeling of photovoltaic (PV) systems, especially in the prediction of output power, with single and double diode models as the configurations mainly applied for this purpose. However, the use of one configuration to model PV panel limits the accuracy of its predicted performances. This paper proposes a new hybrid approach based on classification algorithms in the machine learning framework that combines both single and double models in accordance with the climatic condition in order to predict the output PV power with higher accuracy. Classification trees, k-nearest neighbor, discriminant analysis, Naïve Bayes, support vector machines (SVMs), and classification ensembles algorithms are investigated to estimate the PV power under different conditions of the Mediterranean climate. The examined classification algorithms demonstrate that the double diode model seems more relevant for low and medium levels of solar irradiance and temperature. Accuracy between 86% and 87.5% demonstrates the high potential of the classification techniques in the PV power predicting. The normalized mean absolute error up to 1.5% ensures errors less than those obtained from both single-diode and double-diode equivalent-circuit models with a reduction up to 0.15%. The proposed hybrid approach using machine learning (ML) algorithms could be a key solution for photovoltaic and industrial software to predict more accurate performances.

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Section: Double-diode Modelmentioning

confidence: 99%

Machine Learning Based Approaches for Modeling the Output Power of Photovoltaic Array in Real Outdoor Conditions

Malvoni

Chaibi

2020

Electronics

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“…Solar energy is an eco-friendly, unlimited, and free energy source and it is one of most preferred renewable energy types. [1][2][3][4][5][6][7][8][9][10] For all mankind, because of these unique features, solar cells have a great potential to meet energy demand that will increase in future. 1,11 Solar cells convert radiation from sun into electrical energy.…”

Section: Introductionmentioning

confidence: 99%

“…Because efficiency of PVs is closely related to season, location, climatic conditions, and temperature change during day, which directly affect parameters such as the amount of radiation, panel temperature and angle of incidence. 8,12,14,[22][23][24][25][26] The effect of environmental factors such as temperature, solar radiation, dust, ambient temperature, humidity, clouding, shading effects, wind speed, and direction must investigated in order to make a more realistic evaluation of the performance of PV in outdoor conditions. Longterm outdoor testing does not only provide real information about efficiency but also allows manufacturers to revise panel models and costs.…”

Section: Introductionmentioning

confidence: 99%

Investigation effects of environmental and operating factors on PV panel efficiency using by multivariate linear regression

2020

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The aim of this study is to show the investigation effects of environmental and operating factors on photovoltaic (PV) panel efficiency using by multivariate linear regression. Also in this study, the relationship between PV panel efficiency and some environmental and operating factors (solar radiation, opencircuit voltage, short circuit current (I sc), power, fill factor, outside temperature, humidity, wind speed, and voltage) were investigated using multivariate linear regression. The effect of binary parameter interactions on panel performance was investigated using the backward elimination (or deletion) method. The data used in the analysis were obtained from monocrystal and polycrystal panels in December in the winter season. In all analyzes, the regression values are close to 1, which indicates that the compatibility between binary parameters and performance characteristics is perfect. In addition, the three most effective parameters on panel efficiency were found to be solar radiation, maximum power (P max), and I sc , respectively.

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“…Fouzi et al [12] and Yang et al [13] developed the reference model of the PV module from the historical data and used the deviation between the actual and theoretical output for fault diagnosis. Chaibi et al [14] used an artificial colony optimization algorithm to build the PV model, based on the deviation of measured and reference value for fault diagnosis. Through the PV model, Fu et al [15] and Liu et al [16] introduced the indicator of the array current dispersion rate of a combiner box.…”

Section: Introductionmentioning

confidence: 99%

A Photovoltaic Array Fault Diagnosis Method Considering the Photovoltaic Output Deviation Characteristics

Zhao¹,

Sun²,

Zhou³

et al. 2020

International Journal of Photoenergy

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There are a large number of photovoltaic (PV) arrays in large-scale PV power plants or regional distributed PV power plants, and the output of different arrays fluctuates with the external conditions. The deviation and evolution information of the array output are easily covered by the random fluctuations of the PV output, which makes the fault diagnosis of PV arrays difficult. In this paper, a fault diagnosis method based on the deviation characteristics of the PV array output is proposed. Based on the current of the PV array on the DC (direct current) side, the deviation characteristics of the PV array output under different arrays and time series are analyzed. Then, the deviation function is constructed to evaluate the output deviation of the PV array. Finally, the fault diagnosis of a PV array is realized by using the probabilistic neural network (PNN), and the effectiveness of the proposed method is verified. The main contributions of this paper are to propose the deviation function that can extract the fault characteristics of PV array and the fault diagnosis method just using the array current which can be easily applied in the PV plant.

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Simple and efficient approach to detect and diagnose electrical faults and partial shading in photovoltaic systems

Cited by 62 publications

References 45 publications

Machine Learning Based Approaches for Modeling the Output Power of Photovoltaic Array in Real Outdoor Conditions

Machine Learning Based Approaches for Modeling the Output Power of Photovoltaic Array in Real Outdoor Conditions

Investigation effects of environmental and operating factors on PV panel efficiency using by multivariate linear regression

A Photovoltaic Array Fault Diagnosis Method Considering the Photovoltaic Output Deviation Characteristics

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