Online Fault Detection and Diagnosis in Photovoltaic Systems Using Wavelet Packets

Kumar, B. Pradeep; Ilango, G. Saravana; Reddy, M. Jaya Bharata; Nagamani, C.

doi:10.1109/jphotov.2017.2770159

Cited by 123 publications

(58 citation statements)

References 18 publications

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“…The fuse melts when the LLF current exceeds a threshold value for a certain duration. The fuse rating is usually fixed at 2.1 times the shortcircuit current of the string [37]. Although OCPD benefits from less implementation complexity and cost, it has the following setbacks:…”

Section: Llf Protection Techniques and Theirmentioning

confidence: 99%

“…Although this study benefits from simplicity, it can easily suffer from inappropriate threshold setting. CBT-based FDD techniques can suffer the following setbacks [3,37]:…”

Section: Review Of Advanced Pv Array Fdd Techniquesmentioning

confidence: 99%

“…However, instead of PV array faults, the study concentrated on faults in the power conditioning systems of the PV array. In [37], the authors had a preference for a wavelet packet transform (WPT) over DWT and accordingly proposed a WPT-based online PV array fault detection. The WPT was utilized to extract input features for the fault detection algorithm from PV array voltage and current data.…”

Section: Review Of Advanced Pv Array Fdd Techniquesmentioning

confidence: 99%

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Review and Performance Evaluation of Photovoltaic Array Fault Detection and Diagnosis Techniques

Appiah

Zhang

Ayawli

et al. 2019

International Journal of Photoenergy

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The environmentally clean nature of solar photovoltaic (PV) technology causes PV power generation to be embraced by all countries across the globe. Consequently, installation and utilization of PV power systems have seen much growth in recent years. Although PV arrays of such systems are robust, they are not immune to faults. To guarantee reliable power supply, economic returns, and safety of both humans and equipment, highly accurate fault detection, diagnosis, and interruption devices are required. In this paper, an overview of four major PV array faults and their causes are presented. Specifically, ground fault, line-line fault, arc fault, and hot spot fault have been covered. Next, conventional and advanced fault detection and diagnosis (FDD) techniques for managing these faults are reviewed. Moreover, a single evaluation metric has been proposed and utilized to evaluate the performances of the advanced FDD techniques. Finally, based on the papers reviewed, PV array fault management future trends and possible recommendations have been outlined.

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Section: Llf Protection Techniques and Theirmentioning

confidence: 99%

“…Although this study benefits from simplicity, it can easily suffer from inappropriate threshold setting. CBT-based FDD techniques can suffer the following setbacks [3,37]:…”

Section: Review Of Advanced Pv Array Fdd Techniquesmentioning

confidence: 99%

Section: Review Of Advanced Pv Array Fdd Techniquesmentioning

confidence: 99%

See 1 more Smart Citation

Review and Performance Evaluation of Photovoltaic Array Fault Detection and Diagnosis Techniques

Appiah

Zhang

Ayawli

et al. 2019

International Journal of Photoenergy

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“…Chen, L. et al [22] employed an Auto Regressive (AR) model to measure time correlation in the output signals of PV system to describe the faulty signal. Kumar, B. P. et al [23] proposed a method to identify faults based on the observed current and voltage data points of PV array using Wavelet Packet Transforms (WPTs) technique. The WPT technique requires expensive software/hardware platforms and also it is difficult to integrate with the existing systems.…”

Section: Introductionmentioning

confidence: 99%

A simplified method for fault detection and identification of mismatch modules and strings in a grid-tied solar photovoltaic system

Rakesh

Banerjee

Subramaniam

et al. 2020

International Journal of Emerging Electric Power Systems

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The foremost problem facing by the photovoltaic (PV) system is to identify the faults and partial shade conditions. Further, the power loss can be avoided by knowing the number of faulty modules and strings. Hence, to attend these problems, a new method is proposed to differentiate the faults and partially shaded conditions along with the number of mismatch modules and strings for a dynamic change in irradiation. The proposed method has developed in two main steps based on a simple observation from the Current versus Voltage (I-V) characteristic curve of PV array at Line-Line (LL) fault. First, the type of fault is detected using defined variables, which are continuously updated from PV array voltage, current, and irradiation. Second, it gives the number of mismatch modules (or short-circuited bypass diodes) and mismatch strings (or open-circuited blocking diodes) by comparing with the theoretical predictions from the I-V characteristic curve of PV array. The proposed algorithm has been validated both on experimentation using small scale grid-connected PV array developed in the laboratory as well as MATLAB/Simulink simulations. Further, the comparative assessment with existing methods is presented with various performance indices to show the effectiveness of the proposed algorithm.

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“…In [19], a methodology based on probabilistic neural network and wavelet packet transform was presented to predict and classify grid faults in a PV system. Various intelligent fault detection methods in PV systems have been proposed based on computational intelligent methods and modern signal processing techniques to detect grid faults correctly and to improve the performance of PV systems [20][21][22][23][24][25][26]. The cumulative sum algorithm (CUSUM) [27] and permanent magnet synchronous motor [28] have been used to improve the performances of fault detection and diagnosis.…”

Section: Introductionmentioning

confidence: 99%

A Fast Fault Identification in a Grid-Connected Photovoltaic System Using Wavelet Multi-Resolution Singular Spectrum Entropy and Support Vector Machine

et al. 2019

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A new protection scheme based on applying a combination of wavelet multi-resolution singular spectrum entropy and support vector machine is proposed to identify different types of grid faults in a three-phase grid-tied photovoltaic system. In this technique, discrete wavelet transform with multi-resolution singular spectrum entropy is utilized to extract the unique features of three-phase voltage signals at the point of common coupling. The three-phase voltage signals are decomposed to provide detail and approximation coefficients of wavelet transform. Then, various features between different types of grid faults can be extracted by a combination of multi resolution analysis and spectrum analysis with entropy as the output. The constructed features vector is utilized as input data of a support vector machine classifier to identify and classify various types of faults. The results illustrate that the proposed intelligent technique not only recognizes different types of grid faults correctly, but also performs quickly in identifying grid faults in a grid-connected photovoltaic system. Apart from this, a graphical investigation is executed to observe the effects of different types of grid faults in photovoltaic (PV) operation which highlight the necessity of intelligent protection methods to protect PV systems.

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Online Fault Detection and Diagnosis in Photovoltaic Systems Using Wavelet Packets

Cited by 123 publications

References 18 publications

Review and Performance Evaluation of Photovoltaic Array Fault Detection and Diagnosis Techniques

Review and Performance Evaluation of Photovoltaic Array Fault Detection and Diagnosis Techniques

A simplified method for fault detection and identification of mismatch modules and strings in a grid-tied solar photovoltaic system

A Fast Fault Identification in a Grid-Connected Photovoltaic System Using Wavelet Multi-Resolution Singular Spectrum Entropy and Support Vector Machine

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