Fault diagnosis method of photovoltaic array based on support vector machine

Wang, Junjie; Gao, Dedong; Zhu, Shaokang; Wang, Shan; Hai-xiong, Liu

doi:10.1080/15567036.2019.1671557

Cited by 30 publications

(9 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similarly, the authors also obtained features from the fully connected (fc7) layer of a pre-trained AlexNet and then used this in conjunction with classical ML methods for classification. The works reported in [ 26 , 32 ] yielded the same 97% fault detection accuracy. In particular, the developed method in [ 26 ] uses the SVM framework to classify only the LL and the OC faults, whereas Reference [ 32 ] successfully classified the GF, the OC, the SC, and hotspot faults by utilizing the PNN framework to yield a similar accuracy.…”

Section: Simulation Resultsmentioning

confidence: 69%

“… The works reported in [ 26 , 32 ] yielded the same 97% fault detection accuracy. In particular, the developed method in [ 26 ] uses the SVM framework to classify only the LL and the OC faults, whereas Reference [ 32 ] successfully classified the GF, the OC, the SC, and hotspot faults by utilizing the PNN framework to yield a similar accuracy. Moreover, the authors of [ 29 ] also achieved a 92.64% fault detection accuracy for the multi-class faults, such as the OC, the SC, degradation, and shadowing.…”

Section: Simulation Resultsmentioning

confidence: 69%

“…In [ 26 ], the researchers developed an SVM-based method to classify the OC and the LL faults, whereas [ 27 , 28 ] investigated the classification of LL faults only. These models reported good accuracy; however, the SVM can be used for binary classification and on simple datasets only.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

A Machine-Learning-Based Robust Classification Method for PV Panel Faults

Memon

Javed

Kim

et al. 2022

Sensors

View full text Add to dashboard Cite

Renewable energy resources have gained considerable attention in recent years due to their efficiency and economic benefits. Their proportion of total energy use continues to grow over time. Photovoltaic (PV) cell and wind energy generation are the least-expensive new energy sources in most countries. Renewable energy technologies significantly contribute to climate mitigation and provide economic benefits. Apart from these advantages, renewable energy sources, particularly solar energy, have drawbacks, for instance restricted energy supply, reliance on weather conditions, and being affected by several kinds of faults, which cause a high power loss. Usually, the local PV plants are small in size, and it is easy to trace any fault and defect; however, there are many PV cells in the grid-connected PV system where it is difficult to find a fault. Keeping in view the aforedescribed facts, this paper presents an intelligent model to detect faults in the PV panels. The proposed model utilizes the Convolutional Neural Network (CNN), which is trained on historic data. The dataset was preprocessed before being fed to the CNN. The dataset contained different parameters, such as current, voltage, temperature, and irradiance, for five different classes. The simulation results showed that the proposed CNN model achieved a training accuracy of 97.64% and a testing accuracy of 95.20%, which are much better than the previous research performed on this dataset.

show abstract

Section: Simulation Resultsmentioning

confidence: 69%

Section: Simulation Resultsmentioning

confidence: 69%

See 1 more Smart Citation

A Machine-Learning-Based Robust Classification Method for PV Panel Faults

Memon

Javed

Kim

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…The proof process is similar. The dual problem of TSVR-HGN original problem (10) is shown in Equations ( 13): max…”

Section: A Twin Support Vector Machine For Regression Of Heteroscedas...mentioning

confidence: 99%

“…It has been applied in many fields. For example, face speech recognition [8], [9], fault diagnosis [10], [11], regression prediction [12], [13] and so on. The principle of SVM is to maximize the geometric interval, which is the biggest difference between SVM and other classification learning machines.…”

Section: Introductionmentioning

confidence: 99%

Twin Support Vector Regression Model Based on Heteroscedastic Gaussian Noise and Its Application

et al. 2022

View full text Add to dashboard Cite

The main purpose of twin support vector regression (TSVR) is to find linear or nonlinear relationships in sample data, and then predict future data. TSVR is the decomposition of a large convex quadratic programming problem into two small convex quadratic programming problems. Therefore, TSVR not only has the advantages of fast computation and low computational complexity, but also has better regression performance. Classic SVR, TSVR is assuming that accords with mean zero, variance of noise with the variance of the gaussian distribution or not to consider the effects of noise, but in some practical applications, such as wind speed forecasting, noise characteristic is more in line with mean zero, variance for heteroscedasticity of gaussian distribution, therefore, the return of the existing technology is not the best. In this paper, the characteristics of heteroscedasticity Gaussian noise are introduced into the model TSVR, and the twin support vector regression model based on heteroscedasticity Gaussian noise (TSVR-HGN) is constructed. The Lagrange multiplier method is used to solve the problem, and the optimization algorithm is used to find the global optimization. The artificial data set, UCI data set and wind speed data set were selected for experimental comparison. The experimental results show that TSVR-HGN has better prediction accuracy.INDEX TERMS Twin support vector regression machine, heteroscedastic gaussian noise, short-term wind speed forecasting, inequality constraint.

show abstract