Intelligent fault diagnosis of photovoltaic arrays based on optimized kernel extreme learning machine and I-V characteristics

Chen, Zhicong; Wu, Lijun; Cheng, Shuying; Lin, Peijie; Wu, Yue; Lin, Wen-Yang

doi:10.1016/j.apenergy.2017.05.034

Cited by 280 publications

(145 citation statements)

References 42 publications

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“…Chen et al [21] used a principal component analysis and support vector machine to classify the faults in PV systems. Some scholars used the extreme learning machine [22] and fuzzy clustering method [23] to classify the obtained data and then identified the various faults of the PV array. Chen et al [24] used the random forest ensemble learning algorithm for fault detection of PV array.…”

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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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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show abstract

“…In addition, some researchers suggest that the random forest (RF) ensemble learning algorithm and the emerging kernel based extreme learning machine (KELM) are explored for the detection and diagnosis of PV arrays early faults (including line-line faults, degradation, open circuit and partial shading). They are also based on the accurate acquisition of current-voltage (I-V) curve [12,13].…”

Section: Introductionmentioning

confidence: 99%

Prediction of I–V Characteristic Curve for Photovoltaic Modules Based on Convolutional Neural Network

Jia

et al. 2020

Sensors

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Photovoltaic (PV) modules are exposed to the outside, which is affected by radiation, the temperature of the PV module back-surface, relative humidity, atmospheric pressure and other factors, which makes it difficult to test and analyze the performance of photovoltaic modules. Traditionally, the equivalent circuit method is used to analyze the performance of PV modules, but there are large errors. In this paper-based on machine learning methods and large amounts of photovoltaic test data-convolutional neural network (CNN) and multilayer perceptron (MLP) neural network models are established to predict the I-V curve of photovoltaic modules. Furthermore, the accuracy and the fitting degree of these methods for current-voltage (I-V) curve prediction are compared in detail. The results show that the prediction accuracy of the CNN and MLP neural network model is significantly better than that of the traditional equivalent circuit models. Compared with MLP models, the CNN model has better accuracy and fitting degree. In addition, the error distribution concentration of CNN has better robustness and the pre-test curve is smoother and has better nonlinear segment fitting effects. Thus, the CNN is superior to MLP model and the traditional equivalent circuit model in complex climate conditions. CNN is a high-confidence method to predict the performance of PV modules. from the measured current-voltage (I-V) characteristic curves is important for evaluation, modeling and diagnosis of the actual operating state of in situ PV arrays [14]. Nowadays, manufacturers of photovoltaic modules provide standard reporting condition (SRC) or standard test condition (STC) ratings of PV modules. The main test conditions of PV modules are mainly under laboratory environment, which include irradiation intensity of 1000 W/m 2 , spectral distribution in accordance with the AM1.5 spectrum and temperature of 25 ± 1 • C for the PV module. However, in the practical engineering environment (outdoor weather conditions), these conditions rarely appear at the same time. The adequacy and applicability of PV modules under STC is still a controversial issue [15]. Actually, the PV modules based on SRC are still unreasonable for the real-world weather conditions. Therefore, performance test of PV module based on STC condition needs to be improved in the outdoor weather conditions [16]. Although the outdoor characteristics of PV modules can be predicted by algebraic or numerical methods, these methods used in photovoltaic system engineering ignore some second-order effects, like wind speed, shunt resistance, parasitic capacitance, spectral effect and non-linearity under low illumination, thus the prediction error of these methods is large [17]. Based on this, many photovoltaic modeling methods have been proposed in recent years. Generally, these methods can be divided into two types: the white box model based on equivalent circuit and the black box model based on regression [18][19][20]. The equivalent circuit method is used for the prediction of photovolta...

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“…Due to the aforementioned considerations, fault detection and diagnosis of PV systems has seen a particular interest in the last few decades, where several research studies and investigation have addressed the issue [13]. These methods are mainly classified into two categories: conventional threshold approaches and machine learning based approaches [7]. In conventional threshold approaches, fault detection and diagnosis can be achieved by analyzing several electrical indicators, such as operating current and voltage, and the output generated power.…”

Section: Introduction : 1state Of the Artmentioning

confidence: 99%

“…This type of network suffers from its slow training steps, and it could fall in local minima instead of global minima. These weaknesses could affect its reliability, efficiency and even its realtime implementation [7]. Moreover, this kind of network requires a lot of high-quality labeled data that describes very well the process [7].…”

Section: Introduction : 1state Of the Artmentioning

confidence: 99%

An enhanced machine learning based approach for failures detection and diagnosis of PV systems

Garoudja

Chouder

Kara

et al. 2017

Energy Conversion and Management

163

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In this paper, a novel procedure for fault detection and diagnosis in the direct current (DC) side of PV system, based on probabilistic neural network (PNN) classifier, is proposed. The suggested procedure consists of four main stages: (i) PV module parameters extraction, (ii) PV array simulation and experimental validation (iii) elaboration of a relevant database of both healthy and faulty operations, and (iv) network construction, training and testing. In the first stage, the unknown electrical parameters of the one diode model (ODM) are accurately identified using the best-so-far ABC algorithm. Then, based on these parameters the PV array is simulated and experimentally validated by using a PSIM™/Matlab™ co-simulation. Finally, efficient fault detection and diagnosis procedure based on PNN classifier is implemented. Four operating cases were tested in a grid connected PV system of 9.54 kWp: Healthy system, three modules short-circuited in one string, ten modules short-circuited in one string, and a string disconnected from the array. Moreover, the PNN method was compared, under real operating conditions, with the feed forward back-propagation Artificial Neural Network (ANN) classifiers method, for noiseless and noisy data to evaluate the suggested method’s accuracy and test its aptitude to support noisy data. The obtained results have demonstrated the high efficiency of the proposed method to detect and diagnose DC side anomalies for both noiseless and noisy data cases.Peer ReviewedPostprint (author's final draft

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Intelligent fault diagnosis of photovoltaic arrays based on optimized kernel extreme learning machine and I-V characteristics

Cited by 280 publications

References 42 publications

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

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

Prediction of I–V Characteristic Curve for Photovoltaic Modules Based on Convolutional Neural Network

An enhanced machine learning based approach for failures detection and diagnosis of PV systems

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