A review for solar panel fire accident prevention in large-scale PV applications

Due to the wide applications of solar photovoltaic (PV) technology, safe operation and maintenance of the installed solar panels become more critical as there are potential menaces such as hot spot effects and DC arcs, which may cause fire accidents to the solar panels. In order to minimize the risks of fire accidents in large scale applications of solar panels, this review focuses on the latest techniques for reducing hot spot effects and DC arcs. The risk mitigation solutions mainly focus on two aspects: structure reconfiguration and faulty diagnosis algorithm. The first is to reduce the hot spot effect by adjusting the space between two PV modules in a PV array or relocate some PV modules. The second is to detect the DC arc fault before it causes fire. There are three types of arc detection techniques, including physical analysis, neural network analysis, and wavelet detection analysis. Through these detection methods, the faulty PV cells can be found in a timely manner thereby reducing the risk of PV fire. Based on the review, some precautions to prevent solar panel related fire accidents in large-scale solar PV plants that are located adjacent to residential and commercial areas

Arc fault protections for aeronautic applications: a review identifying the effects, detection methods, current progress, limitations, future challenges, and research needs

©2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Arc faults are serious discharges, damaging insulation systems and triggering electrical fires. This is a transversal topic, affecting from residential to aeronautic applications. Current commercial aircrafts are being progressively equipped with arc fault protections. With the development of more electric aircrafts (MEA), future airliners will require more electrical power to enhance fuel economy, save weight and reduce emissions. The ultimate goal of MEAs is electrical propulsion, where fault management devices will have a leading role, because aircraft safety is of utmost importance. Therefore, current fault management devices must evolve to fulfill the safety requirements of electrical propelled aircrafts. To deal with the increased electrical power generation, the distribution voltage must be raised, thus leading to new electrical fault types, in particular arc tracking and series arcing, which are further promoted by the harsh environments typical of aircraft systems, i.e., low pressure, extreme humidity and a wide range of temperatures. Therefore, the development of specific electrical protections which are able to protect against these fault types is a must. This paper reviews the state-of-the-art of electrical protections for aeronautic applications, identifying the current status and progress, their drawbacks and limitations, the future challenges and research needs to fulfill the future requirements of MEAs, with a special emphasis on series arc faults due to arc tracking, because of difficulty in detecting such low-energy faults in the early stage and the importance and harmful effects of tracking activity in cabling insulation systems. This technological and scientific review is based on a deep analysis of research and conference papers, official reports, white papers and international regulations.This research was partially funded by the Ministerio de Ciencia e Innovación de España, grant number PID2020-114240RB-I00 and by the Generalitat de Catalunya, grant number 2017 SGR 967.Peer ReviewedPostprint (author's final draft

A novel intelligent detection schema of series arc fault in photovoltaic (PV) system based convolutional neural network

Series Arc Fault (SAF) can be defined as the failure that occurs between any electrical contact and any electrical circuitry. However, it considered one of the common failures that affect the operation of the PV system and causes serious problems such as fires and electrical shock hazards. Several reasons increase the possibility of this type of failures such as incorrect installation, irregular maintenance, and some environmental effects. This paper presents a new intelligent and accurate detection method of SAF in the PV system. In this method, Convolutional neural networks (CNN) which is a discriminative (supervised) deep learning algorithm used for the process of fault detection. In normal cases, the signal consists of DC component, inverter component and noise of Network. In the case of SAF, a new component will add to the signal; therefore, CNN used to discriminate against the new component to accurately detect the SAF. PSCAD is used to generate the Arc fault model; Performance evaluation and the results of the proposed method implemented using Python. The achieved accuracy of the proposed detection method is 98.9%.

Technology transfer: Transportation

The application of NASA derived technology in solving problems related to highways, railroads, and other rapid systems is described. Additional areas/are identified where space technology may be utilized to meet requirements related to waterways, law enforcement agencies, and the trucking and recreational vehicle industries

Arc fault detection using artificial intelligence: Challenges and benefits

This systematic review aims to investigate recent developments in the area of arc fault detection. The rising demand for electricity and concomitant expansion of energy systems has resulted in a heightened risk of arc faults and the likelihood of related fires, presenting a matter of considerable concern. To address this challenge, this review focuses on the role of artificial intelligence (AI) in arc fault detection, with the objective of illuminating its advantages and identifying current limitations. Through a meticulous literature selection process, a total of 63 articles were included in the final analysis. The findings of this review suggest that AI plays a significant role in enhancing the accuracy and speed of detection and allowing for customization to specific types of faults in arc fault detection. Simultaneously, three major challenges were also identified, including missed and false detections, the restricted application of neural networks and the paucity of relevant data. In conclusion, AI has exhibited tremendous potential for transforming the field of arc fault detection and holds substantial promise for enhancing electrical safety

Fault tree analysis of fires on rooftops with photovoltaic systems

A fault tree analysis of fires related to photovoltaic (PV) systems was made with a focus of understanding the failure rate of the electric components. The failure rate of different components of these systems was calculated from data obtained from reports, research studies, and fire incident statistics of four countries. The results explain the significant causes of fire on the component level and various failure patterns resulting in PV-related fires. The qualitative analysis identified seven major events that led to incidents caused by a PV-related ignition source, with electrical arcing being the main cause of fires. This finding is highly related to the imprudent installation practices due to negligence and low awareness of the fire risk associated with PV systems by installers. The quantitative results show that 33% of the PV fire incidents are due to unknown or unrelated ignition sources, indicating that great focus should be given to mitigate the consequences caused by PV-related fires. The PV module, isolator, inverter, and connector are the major PV system components that are highly responsible for the ignition of PV-related fires, with the connector being the prime contributor in 17% of the PV-related fires. Finally, the quantitative analysis established an annual fire incident frequency of 0.0289 fires per MW. The results enable estimation of the number of fire incidents linked to the installed PV capacity, and the fault tree analyses highlight where improvements are most critical. Based on the results of the analyses, two questions are suggested for implementation in the post-incident reports of the national fire and rescue services

Doctor of Philosophy

dissertationThree major catastrophic failures in photovoltaic (PV) arrays are ground-faults, line-to-line faults, and arc faults. Although the number of such failures is few, recent fire events on April 5, 2009, in Bakersfield, California, and April 16, 2011, in Mount Holly, North Carolina suggest the need for improvements in present fault detection and mitigation techniques, as well as amendments to existing codes and standards to avoid such accidents. A fault prediction and detection technique for PV arrays based on spread spectrum time domain reflectometry (SSTDR) has been proposed and was successfully implemented. Unlike other conventional techniques, SSTDR does not depend on the amplitude of the fault-current. Therefore, SSTDR can be used in the absence of solar irradiation as well. However, wide variation in impedance throughout different materials and interconnections makes fault locating more challenging than prediction/detection of faults. Another application of SSTDR in PV systems is the measurement of characteristic impedance of power components for condition monitoring purposes. Any characteristic variations in one component will simultaneously alter the operating conditions of other components in a closed-loop system, resulting in a shift in overall reliability profile. This interdependence makes the reliability of a converter a complex function of time and operating conditions. Details of this failure mode, mechanism, and effect analysis (FMMEA) have been developed. By knowing the present state of health and the remaining useful life (RUL) of a power converter, it is possible to reduce the maintenance cost for expensive high-power converters by facilitating a reliability centered maintenance (RCM) scheme. This research is a step forward toward power converter reliability analysis since the cumulative effect of multiple degraded components has been considered here for the first time in order to estimate reliability of a power converter

Intelligent Low Voltage Series Arc Detection System

Protection from electric power hazardous has been used ever since applications of electricity were discovered. Hazards in the electric system can be in the form of over voltage or over current leading to catastrophic system and equipment failure, resulting in physical damage or even to human fatalities. Electrical protection is achieved by installing protection devices throughout distribution system to isolate faulty branches and mitigate fault development. Fire is a principal cause of buildings damages and related personal injuries. A major contributor to buildings’ fire originates from electrical arc faults caused by electric distribution equipment and appliances failures. To remedy this problem, regulatory bodies required electric arc faults protection. Over the years this requirement was enforced by different electric codes and expanded to cover most of residential building areas and all living spaces. Arc fault circuit interrupters (AFCI) are devised to complement existing protection methodologies and devices, focusing on electric arc detection and preventions of subsequent risks, mainly fire ignition. Circuit interruption occurs whenever characteristics of arc failure is detected, either from current, voltage or electromagnetic radiation. Detecting the arc faults, and hence increasing the reliability of interruption, is a challenge, given that some household appliances produce arc-like behaviors in normal operating conditions, like electronic light dimmers and solid state controlled variable speed drives. This research focuses on developing an intelligent low voltage series arcing detection scheme based on pattern recognition, with immunity to false tripping. This point is the main drawback of most published work and issued patents on arc detection to date, mainly due to the difficulty of modelling such a transient behavior, especially on low current arc cases. Real data is generated in lab simulating series arc conditions at different combinations of linear and non-linear loads. Appliances current are recorded as well. Two disjoint datasets are used for training and testing of the proposed system with no components shared between the two datasets to verify classifier generality. The proposed pattern recognition method proved to be highly immune to false tripping in line with benchmark regulatory standard, and can be adapted to similar hard to model non-stationary problems

Aspects and directions of internal arc protectio

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