Reliable Detection Method of Variable Series Arc Fault in Building Integrated Photovoltaic Systems Based on Nonstationary Time Series Analysis

Chen, Silei; Wu, Haowei; Meng, Yu; Wang, Yuanfeng; Li, Xingwen; Zhang, Chenjia

doi:10.1109/jsen.2023.3256009

Cited by 10 publications

(3 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…PAFs, when they occur, typically result in overcurrent, which can be protected by a circuit breaker. On the other hand, SAFs pose a more significant challenge as the current rise in the circuit is not obvious due to the introduction of arc impedance, making it difficult for circuit breakers to provide effective protection [9]. SAFs are recognized as more harmful to the system compared to PAFs, necessitating focused investigation into their detection methods.…”

Section: Introductionmentioning

confidence: 99%

DC Series Arc Fault Diagnosis Scheme Based on Hybrid Time and Frequency Features Using Artificial Learning Models

Dang,

Kwak,

Choi

2024

Machines

View full text Add to dashboard Cite

DC series arc faults pose a significant threat to the reliability of DC systems, particularly in DC generation units where aging components and high voltage levels contribute to their occurrence. Recognizing the severity of this issue, this study aimed to enhance DC arc fault detection by proposing an advanced recognition procedure. The methodology involves a sophisticated combination of current filtering using the Three-Sigma Rule in the time domain and the removal of switching noise in the frequency domain. To further enhance the diagnostic capabilities, the proposed method utilizes time and frequency signals generated from power supply-side signals as a reference input. The time–frequency features extracted from the filtered signals are then combined with artificial learning models. This fusion of advanced signal processing and machine learning techniques aims to capitalize on the strengths of both domains, providing a more comprehensive and effective means of detecting arc faults. The results of this detection process validate the effectiveness and consistency of the proposed DC arc failure identification schematic. This research contributes to the advancement of fault detection methodologies in DC systems, particularly by addressing the challenges associated with distinguishing arc-related distortions, ultimately enhancing the safety and dependability of DC electrical systems.

show abstract

Section: Introductionmentioning

confidence: 99%

DC Series Arc Fault Diagnosis Scheme Based on Hybrid Time and Frequency Features Using Artificial Learning Models

Dang,

Kwak,

Choi

2024

Machines

View full text Add to dashboard Cite

show abstract

“…In spite of that, these sensors can work in the high-frequency band (HF) (3 to 30 MHz) or in the very high-frequency band (VHF) (30 to 300 MHz). Different topologies of coils, including Rogowski coils [ 33 ], and HFCT (High Frequency Current Transformer) [ 34 , 35 ] are suitable for this application.…”

Section: Introductionmentioning

confidence: 99%

Modelling Inductive Sensors for Arc Fault Detection in Aviation

Barroso-de-María,

Robles,

Martínez-Tarifa

et al. 2024

Sensors

View full text Add to dashboard Cite

Modern aircraft are being equipped with high-voltage and direct current (HVDC) architectures to address the increase in electrical power. Unfortunately, the rise of voltage in low pressure environments brings about a problem with unexpected ionisation phenomena such as arcing. Series arcs in HVDC cannot be detected with conventional means, and finding methods to avoid the potentially catastrophic hazards of these events becomes critical to assure further development of more electric and all electric aviation. Inductive sensors are one of the most promising detectors in terms of sensitivity, cost, weight and adaptability to the circuit wiring in aircraft electric systems. In particular, the solutions based on the detection of the high-frequency (HF) pulses created by the arc have been found to be good candidates in practical applications. This paper proposes a method for designing series arc fault inductive sensors able to capture the aforementioned HF pulses. The methodology relies on modelling the parameters of the sensor based on the physics that intervenes in the HF pulses interaction with the sensor itself. To this end, a comparative analysis with different topologies is carried out. For every approach, the key parameters influencing the HF pulses detection are studied theoretically, modelled with a finite elements method and tested in the laboratory in terms of frequency response. The final validation tests were conducted using the prototypes in real cases of detection of DC series arcs.

show abstract

“…[16][17][18] Liu et al 19 introduce a novel clustering method based on dilation and erosion theory, with enhanced fault diagnosis in PV arrays without the need for predetermining fault types. Research efforts have often been fragmented, focusing on specific fault types through methodologies, like, fuzzy logic, 20 neural networks, [21][22][23][24][25] and machine learning 26 leaving a void for a comprehensive fault detection and localization solution. 27 While macroscale fault detection within PV arrays has achieved success, microscale fault localization at the module level remains challenging, often requiring costly wireless sensor networks for detailed module insights, thereby increasing deployment expenses.…”

Section: Introductionmentioning

confidence: 99%

Advanced impedance mismatch technique for detecting faults in photovoltaic systems

Lamdihine,

Ouassaid

2024

Energy Science & Engineering

View full text Add to dashboard Cite

This paper presents a comprehensive exploration of the Advanced Impedance Mismatch Technique (AIMT), a novel approach designed for the accurate detection of simultaneous and varied faults within photovoltaic (PV) systems. This investigation integrates a spectrum of fault detection strategies, pinpointing reflectometry as a notably effective tool. Despite its utility, conventional reflectometry applications face critical constraints, notably the limitation to identify only the primary fault location within a PV array and the inability to distinguish between different fault types. This work introduces an innovative mathematical model that estimates the impedance of PV modules, enhancing the reflectometry method to enable the precise identification and localization of multiple defective modules within a string. The proposed technique exhibits a remarkable sensitivity to detect slight impedance differences between a functional PV string and one with defective modules. The validity of the AIMT's mathematical model is corroborated through simulation experiments on a string of seven PV modules afflicted with multiple simultaneous faults. These experiments rigorously evaluate the technique's accuracy in pinpointing the locations of defective modules within a PV string. The outcomes of our proposed ‐times faster AIMT reveal a strong concordance between the simulated reflective signals and the impedance values forecasted by the model, highlighting the proposed method's proficiency in the detailed detection and diagnosis of progressive faults within PV systems.

show abstract

Reliable Detection Method of Variable Series Arc Fault in Building Integrated Photovoltaic Systems Based on Nonstationary Time Series Analysis

Cited by 10 publications

References 29 publications

DC Series Arc Fault Diagnosis Scheme Based on Hybrid Time and Frequency Features Using Artificial Learning Models

DC Series Arc Fault Diagnosis Scheme Based on Hybrid Time and Frequency Features Using Artificial Learning Models

Modelling Inductive Sensors for Arc Fault Detection in Aviation

Advanced impedance mismatch technique for detecting faults in photovoltaic systems

Contact Info

Product

Resources

About