Fault diagnosis for photovoltaic array based on convolutional neural network and electrical time series graph

Lu, Xiaoyang; Lin, Peijie; Cheng, Shuying; Lin, Yi; Chen, Zhicong; Wu, Lijun; Zheng, Qianying

doi:10.1016/j.enconman.2019.06.062

Cited by 130 publications

(57 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Each particle updates its velocity and position after an iteration step through Eq. (11) and Eq. (12) [9]:…”

Section: Theory Of Psomentioning

confidence: 99%

“…With the advantages of strong self-adaptability, robustness and great fault tolerance, artificial neural networks (ANNs) can store large amounts of precise nonlinear input-output mapping relationships through sufficient training without revealing the mathematical equation [9]. Consequently, ANNs have recently become applied in fault diagnoses [10,11]. Based on an electric current signal, investigations of fault diagnosis of HVCB by ANNS are popular recently [12][13][14], by which the card acerbity and stroke over the iron core idle in HVCB has been diagnosed successfully.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Particle Swarm Optimization-Support Vector Machine Model for Machinery Fault Diagnoses in High-Voltage Circuit Breakers

et al. 2020

Chin. J. Mech. Eng.

View full text Add to dashboard Cite

According to statistic data, machinery faults contribute to largest proportion of High-voltage circuit breaker failures, and traditional maintenance methods exist some disadvantages for that issue. Therefore, based on the wavelet packet decomposition approach and support vector machines, a new diagnosis model is proposed for such fault diagnoses in this study. The vibration eigenvalue extraction is analyzed through wavelet packet decomposition, and a four-layer support vector machine is constituted as a fault classifier. The Gaussian radial basis function is employed as the kernel function for the classifier. The penalty parameter c and kernel parameter δ of the support vector machine are vital for the diagnostic accuracy, and these parameters must be carefully predetermined. Thus, a particle swarm optimizationsupport vector machine model is developed in which the optimal parameters c and δ for the support vector machine in each layer are determined by the particle swarm algorithm. The validity of this fault diagnosis model is determined with a real dataset from the operation experiment. Moreover, comparative investigations of fault diagnosis experiments with a normal support vector machine and a particle swarm optimization back-propagation neural network are also implemented. The results indicate that the proposed fault diagnosis model yields better accuracy and efficiency than these other models.

show abstract

“…Each particle updates its velocity and position after an iteration step through Eq. (11) and Eq. (12) [9]:…”

Section: Theory Of Psomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization-Support Vector Machine Model for Machinery Fault Diagnoses in High-Voltage Circuit Breakers

et al. 2020

Chin. J. Mech. Eng.

View full text Add to dashboard Cite

show abstract

“…Finally, the AI algorithm is adopted to mine the difference of characteristics under different fault conditions for realizing the fault diagnoses. At present, more advanced AI algorithms have been developed to mine the differences between curves or images actively and recognize them, such as CNN [24], residual network (ResNet) [25], adversarial generative network [26], transfer learning [27] etc. Compared with traditional methods, these methods in [24]- [27] do not require the multi-step processing of the signal and realize the fast diagnosis.…”

Section: Introductionmentioning

confidence: 99%

“…At present, more advanced AI algorithms have been developed to mine the differences between curves or images actively and recognize them, such as CNN [24], residual network (ResNet) [25], adversarial generative network [26], transfer learning [27] etc. Compared with traditional methods, these methods in [24]- [27] do not require the multi-step processing of the signal and realize the fast diagnosis. To address the problem of the lack of measured arc signal samples, Lu et al [26] used the domain adaptive of deep convolutional generative adversarial network (DA-DCGAN) to realize the enhancement of arc samples from laboratory simulation to actual measurement environment.…”

Section: Introductionmentioning

confidence: 99%

A Novel Fault Identification Method for Photovoltaic Array via Convolutional Neural Network and Residual Gated Recurrent Unit

Gao

Wai

2020

IEEE Access

View full text Add to dashboard Cite

Under the background of the large-scale construction of photovoltaic (PV) power stations, it is crucial to discover and solve module failures in time for improving the service life and maintaining the normal operation efficiency of modules. Based on analyzing the difference of I-V curves of PV arrays under different fault states, the I-V curves, temperatures and irradiances are taken as input data, and a fusion model of convolutional neural network (CNN) and residual-gated recurrent unit (Res-GRU) is proposed to identify the PV array fault. This model consists of a 1-dimensional CNN module with a 4-layer structure and a Res-GRU module. It has the advantages of end-to-end fault diagnosis, no manual feature extraction, strong anti-interference ability, and usable in the absence of irradiances and temperatures. Moreover, it can not only identify a single fault (e.g., short circuit, partial shading, abnormal aging, etc.), but also can effectively identify hybrid faults. Experimental results show that the classification accuracy of the proposed method is 98.61%, which is better than the ones of the artificial neural network (ANN), the extreme learning machine with kernel function (KELM), the fuzzy C-mean (FCM) clustering, the residual neural network (ResNet), and the stage-wise additive modeling using multi-class exponential loss function based on the classification and regression tree (SAMME-CART). In addition, in the absence of temperatures and irradiances, the classification accuracy still reaches 95.23%, which has a broad application prospect in the online fault diagnoses of PV arrays. INDEX TERMS Photovoltaic, fault diagnosis, I-V curve, 1-dimensional convolutional neural network (1-D CNN), residual-gated recurrent unit (Res-GRU)

show abstract

“…One of the very popular and useful ways to research photovoltaic systems is to investigate their diagnostic systems. Intelligent algorithms allow for the quick and precise detection of irregularities in the operation of a photovoltaic system [21].…”

Section: Introductionmentioning

confidence: 99%

Charging electric cars as a way to increase the use of energy produced from RES

2020

View full text Add to dashboard Cite

AbstractThe article contains an analysis of charging electric cars as a way to increase the use of energy produced from a photovoltaic system. A photovoltaic system is described, consisting of two micro installations, with a power output of up to 40 kWp each, supplying two sectors of the building of the Lublin Science and Technology Park. An internet platform for monitoring the operation of a photovoltaic system is presented. Next, an innovative system for monitoring, consumption and production analysis of electric energy in individual building sectors is described. A surplus of energy produced in one of the sectors was found. It was proposed that this excess energy would be used to charge electric vehicles. An analysis of the surplus power generated by the photovoltaic system in excess of the energy consumed by the building sector was used to determine the power available to the wall charger. The surplus of energy produced was used for calculations related to the amount of charging the electric vehicle required and the cost of traveling 100 km. Charging an electric car not only provides a faster return on investment in the installation, but also presents drivers with a very ecological and economical transport solution.

show abstract

Fault diagnosis for photovoltaic array based on convolutional neural network and electrical time series graph

Cited by 130 publications

References 39 publications

Particle Swarm Optimization-Support Vector Machine Model for Machinery Fault Diagnoses in High-Voltage Circuit Breakers

Particle Swarm Optimization-Support Vector Machine Model for Machinery Fault Diagnoses in High-Voltage Circuit Breakers

A Novel Fault Identification Method for Photovoltaic Array via Convolutional Neural Network and Residual Gated Recurrent Unit

Charging electric cars as a way to increase the use of energy produced from RES

Contact Info

Product

Resources

About