Development of fault detection system in PV system

Shimakage, Toyonari; Nishioka, Kojiro; Yamane, Hiroshi; Nagura, Masashi; Kudo, Mitsuru

doi:10.1109/intlec.2011.6099727

Cited by 32 publications

(10 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The solar photovoltaic system fault diagnoses of different diagnostic methods are introduced below. In 2011, Shimakage et al proposed the artificial neural network control for solar photovoltaic array fault diagnosis [14]. The diagnostic effect of the artificial neural diagnostic method proposed in that study was better than the effect of the traditional neural network.…”

Section: Introductionmentioning

confidence: 94%

Chaos Synchronization Based Novel Real-Time Intelligent Fault Diagnosis for Photovoltaic Systems

Hsieh

Yau

Shiu

2014

International Journal of Photoenergy

View full text Add to dashboard Cite

The traditional solar photovoltaic fault diagnosis system needs two to three sets of sensing elements to capture fault signals as fault features and many fault diagnosis methods cannot be applied with real time. The fault diagnosis method proposed in this study needs only one set of sensing elements to intercept the fault features of the system, which can be real-time-diagnosed by creating the fault data of only one set of sensors. The aforesaid two points reduce the cost and fault diagnosis time. It can improve the construction of the huge database. This study used Matlab to simulate the faults in the solar photovoltaic system. The maximum power point tracker (MPPT) is used to keep a stable power supply to the system when the system has faults. The characteristic signal of system fault voltage is captured and recorded, and the dynamic error of the fault voltage signal is extracted by chaos synchronization. Then, the extension engineering is used to implement the fault diagnosis. Finally, the overall fault diagnosis system only needs to capture the voltage signal of the solar photovoltaic system, and the fault type can be diagnosed instantly.

show abstract

Section: Introductionmentioning

confidence: 94%

Chaos Synchronization Based Novel Real-Time Intelligent Fault Diagnosis for Photovoltaic Systems

Hsieh

Yau

Shiu

2014

International Journal of Photoenergy

View full text Add to dashboard Cite

show abstract

“…Although this approach has a merit of low cost and less implementational complexity, it suffers the following setbacks: (1) it does not reveal the identity of the fault type on the PV array which can be useful for isolation and (2) it does not offer fault localization. In [59], 8 International Journal of Photoenergy the authors developed a fault detection system using CBT. Three detection methods were proposed by the authors as follows: (1) comparison of measured output value and estimated value derived from measured irradiation, (2) comparison of present and past performance ratios (PRs), and 3comparison of present and past output differences in an intersystem.…”

Section: Review Of Advanced Pv Array Fdd Techniquesmentioning

confidence: 99%

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

Appiah

Zhang

Ayawli

et al. 2019

International Journal of Photoenergy

107

View full text Add to dashboard Cite

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.

show abstract

“…Another set of tools for fault detection are the comparisons between measured and simulation expected current, voltage and power values. Such comparisons were used in fault detection algorithms described in [4] and [5]. A site specific and low complexity detection system was build around a threesigma (3σ) rule for the standard deviation of the measured over modeled ac power ratio [6].…”

Section: Introductionmentioning

confidence: 99%

“…Besides their accuracy, some methods are very costly [10], [13] while others study only a limited number of faults [4], [5], [7], [9]- [11], [13], [16]. Special attention must be also paid on the use of learning methods [11], [12] where the risk of not identifying a fault in case its data are different from the training data set is significant.…”

Section: Introductionmentioning

confidence: 99%

A Grid Connected PV System Fault Diagnosis Method

Sapountzoglou

Raison

2019

2019 IEEE International Conference on Industrial Technology (ICIT)

View full text Add to dashboard Cite

In this paper, a fault diagnosis method for gridconnected photovoltaic (GCPV) systems is presented. The method is based on the monitoring of the ac electrical variables and especially on the measurements of the currents and voltages at the output of the inverter. After a short introduction on the topic, the necessary steps to build a GCPV system model are described; the photovoltaic (PV) array is connected to the grid through a boost converter and an inverter. Four categories of faults were studied: a) faults inside the PV array, b) faults between the dc bus and the ground, c) faults in the power electronics devices and d) faults in the grid. The response of the system to each fault case was then observed, leading to the generation of a set of symptoms and the creation of the fault signature table. Using this fault signature table, a fault diagnosis method was developed. This latter is able to detect all the studied faults and localize sixteen out of the total of twenty fault cases; two groups of faults and fourteen individual fault cases. Finally, it creates an alarm signal in less than 60 ms, assuming that only one fault is occurring at a time. The robustness of the method was validated for different irradiance levels by simulation means.

show abstract

Development of fault detection system in PV system

Cited by 32 publications

References 1 publication

Chaos Synchronization Based Novel Real-Time Intelligent Fault Diagnosis for Photovoltaic Systems

Chaos Synchronization Based Novel Real-Time Intelligent Fault Diagnosis for Photovoltaic Systems

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

A Grid Connected PV System Fault Diagnosis Method

Contact Info

Product

Resources

About