Fault Location in Ungrounded Photovoltaic System Using Wavelets and ANN

Karmacharya, Indra Man; Gokaraju, Ramakrishna

doi:10.1109/tpwrd.2017.2721903

Cited by 136 publications

(52 citation statements)

References 28 publications

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“…The application of artificial intelligence to wavelet transformations has been recently developed to improve the accuracy of the algorithm. Wavelet transform and artificial neural network (ANN)-based algorithm performance has been tested and evaluated in the Ungrounded Photovoltaic System [32]. Another ANN-based methodology on fault detection algorithms was tested on an ultrafast transmission line [33].…”

Section: Literature Reviewmentioning

confidence: 99%

Fault Classifications in Distribution Systems Consisting of Wind Power as Distributed Generation Using Discrete Wavelet Transforms

Patcharoen

Ngaopitakkul

2019

Sustainability

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This paper proposed a fault type classification algorithm in a distribution system consisting of multiple distributed generations (DGs). The study also discussed the changing of signal characteristics in the distribution system with DGs during the occurrence of different fault types. Discrete Wavelet Transform (DWT)-based signal processing has been used to construct a classification algorithm and a decision tree to classify fault types. The input data for the algorithm is extracted from the three-phase current signal under normal conditions and during fault occurrence. These signals are recorded from the substation, load, and DG bus. The performance of the proposed classifying algorithm has been tested on a simulation system that was modeled after part of Thailand’s 22 kV distribution system, with a 2-MW wind power generation as the DG, connected to the distribution line by PSCAD software. The parameters that were taken into consideration consisted of the fault type, location of the fault, location of DG(s), and the number of DGs, to evaluate the performance of the proposed algorithm under various conditions. The result of the simulation indicated significant changes in current signal characteristics when installing DGs. In addition, the proposed algorithm has achieved a satisfactory accuracy in terms of identifying and classifying fault types when applied to a distribution system with multiple DGs.

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Section: Literature Reviewmentioning

confidence: 99%

Fault Classifications in Distribution Systems Consisting of Wind Power as Distributed Generation Using Discrete Wavelet Transforms

Patcharoen

Ngaopitakkul

2019

Sustainability

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“…Its location performance is reported to be effective in many research works. In reference [7,] [8], the fault is located with wavelet-based frequency characteristics as well as the topology of the distribution network. In reference [9], [10], the instants of sudden variations of post-fault transients are calculated with Hilbert-Huang transform, and the fault location is calculated with these instants and transients propagating velocities.…”

Section: Introductionmentioning

confidence: 99%

Stacked Auto-Encoder-Based Fault Location in Distribution Network

Luo

Tan

et al. 2020

IEEE Access

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Accurate fault location is crucial for finding and clearing faults in distribution networks. It can help to reduce the loss of power failure and ensure safe and stable operation. Without efficient observation points and the lack of phase angle information, traditional fault location methods of distribution networks usually have large errors. As the application of µPMUs in the distribution network becomes more and more common, voltage and current waveforms, as well as phase angle can be recorded and provide more information for accurate fault location. In this paper, an intelligent location method is proposed to pinpoint the fault location based on the information of µPMUs which are properly allocated. The fault section is firstly determined by comparing the zero-sequence current waveforms on both sides of the fault section. Then, a Stack Auto-Encoder (SAE) is modeled to provide an end-to-end solution to pinpoint fault point with the voltage and current phasors. Finally, the performance of the proposed method is tested by a simulated distribution network on the platform of PSCAD. The results show that the proposed method is effective in locating faults and can withstand the effects of transition resistance, fault type, and noise. Compared to another popular method, the proposed method shows better location accuracy. INDEX TERMS Fault location, stacked auto-encoder, distribution network. FIGURE 3. Comparisons between zero-sequence currents, (a) non-faulted zone, (b) Faulted zone.

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“…Despite the attractiveness of the PV technology, its usage, especially for large-scale power generation, comes with multiple technical challenges. Notable among them are (1) the need to track and maintain the operating point of the PV module at its maximum power point (MPP) [5,6] and (2) the need to detect and mitigate the almost unavoidable PV array faults [7,8]. Tracking the MPP requires the use of a device known as maximum power point tracker (MPPT).…”

Section: Introductionmentioning

confidence: 99%

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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Fault Location in Ungrounded Photovoltaic System Using Wavelets and ANN

Cited by 136 publications

References 28 publications

Fault Classifications in Distribution Systems Consisting of Wind Power as Distributed Generation Using Discrete Wavelet Transforms

Fault Classifications in Distribution Systems Consisting of Wind Power as Distributed Generation Using Discrete Wavelet Transforms

Stacked Auto-Encoder-Based Fault Location in Distribution Network

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

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