Wire Fault Research Articles

Enhanced complex wire fault diagnosis via integration of time domain reflectometry and particle swarm optimization with least square support vector machine

AbstractUrban power systems rely on intricate wire networks, known as the power grid, which form the essential electric infrastructure in cities. While these networks transmit electricity from power plants to consumers, they are vulnerable to faults caused by manufacturing errors and improper installation, posing risks to system integrity. Thus, accurate identification and assessment of these faults are crucial to prevent damage and maintain system reliability. The objective of this research is to present an innovative and efficient methodology for diagnosing complex wire networks through the application of time domain reflectometry (TDR) combined with the particle swarm optimization (PSO) and least squares support vector machine (LSSVM) algorithm. This research addresses the imperative need to accurately locate and assess breakage faults within wire networks, emphasizing their role in both power transmission and communication infrastructure. To model the TDR answer of a specific complex wire network, a forward model is established utilizing resistance, inductance, capacitance and conductance (RLCG) parameters and the finite difference time domain (FDTD) method. Subsequently, the PSO‐LSSVM approach is used to solve the inverse problem of localizing faults in complex wire networks. The experimental results validate the practicality of this approach in real‐world systems.

Enhanced artificial intelligence technique for soft fault localization and identification in complex aircraft microgrids

- In recent years, the aviation industry has witnessed a substantial integration of power electronics technology within Aircraft Microgrids (AMs). Consequently, the extension of electrical wiring networks has expanded, resulting in heightened intricacies within these systems. Therefore, the identification and location of faults in wiring networks have become an important topic in AMs to guarantee the safety of the electrical power systems. Time Domain Reflectometry (TDR) is widely used to locate and recognize electric wire faults. However, soft fault location on complex electrical networks using TDR is perplexing due to its weak effect on the reflected signal. Moreover, the existence of noise in the environment can worsen the TDR's performance. In this paper, a new approach based on TDR, along with the Subtractive Correlation Method (SCM) and Neural Network (NN), is proposed. The TDR response of the complex wiring network is determined using a newly proposed model based on the Finite Difference Time Domain (FDTD) method. The validity of the proposed model is established through experimentation including two distinct cable types also the introduced model notably enhances computational efficiency, a fact substantiated by our experimental findings and an extensive benchmarking against recent publications. These evaluations collectively underscore a significant reduction in computational time. Then, the reflected signal undergoes processing through SCM, a technique employed to amplify the subtle influence of the soft fault in two scenarios: one accompanied by noise and the other noise-free. Furthermore, NN is used to handle the inverse problem of localizing and characterizing the soft faults by their exact resistance values. Even within noisy environments, the proposed methodology excels in accurately locating and characterizing soft faults with a high degree of precision, all in real-time diagnostic scenarios.

Real-world data analysis of distributed PV and battery energy storage system curtailment in low voltage networks

Curtailment of distributed photovoltaic (PV) and battery energy storage systems will have significant implications for power system transition around the world. Australia offers a unique case study as it has highest installation rate of distributed-PV and growing fleet of battery energy storage systems. Distributed-PV and battery inverters in Australia are required to exhibit voltage-responsive power-quality response modes to prevent excessive voltage rise caused by increasing day-time energy exports, but these modes can curtail power output and limit the value that can be gained from the renewable energy assets. For the first time in Australia, this paper studies all three inverter power-quality response modes and develops novel algorithms to assess curtailment. The algorithms are applied to a real-world dataset of 1-sec distributed-PV and battery power measurements from 1300 residential households; therefore, data resolution is higher and sample size is larger than previous related studies. Using these datasets, the study compares the experienced curtailment between distributed-PV only sites and distributed-PV coupled with battery sites for the first time. The study investigates and quantifies the energy loss due to curtailment within the South Australia region with over 40% distributed-PV penetration which has the highest uptake in the world. The results show that average curtailment loss was 1.5% for distributed-PV only sites and 0.2% for distributed-PV coupled with battery sites, however, some sites were heavily impacted losing up to 25% of generation. The reasons for the larger curtailment loss were identified as wiring and network connection faults and high voltage set-points at households' respective local substations. Other potential reason for the heavily impacted sites is being located towards the end of the low voltage feeder, however this couldn't be verified due to lack of spatial data. The large variation in curtailed generation across sites raises concerns for system owners and suggests further inquiry into the power-quality response mode design and regulations. The study also examines inverter conformance using real-world operational data for all three power quality response modes for the first time, and the results showed a low rate of conformance according to the Australian Standards and there were various discrepancies in the execution of these modes which raises concerns considering the increasing importance of distributed-PV and battery inverter fleet in supporting network operations and security. The results therefore warrant further investigation into the reasons behind these discrepancies and devising appropriate mechanisms to tighten inverter conformance checks.

Non‐contact and anti‐interference diagnosis of SiC MOSFET module bond wire faults for EV wireless charging device

AbstractSiC MOSFET modules are widely used for bidirectional wireless charging of electric vehicles. However, prolonged use can result in bond wire faults, leading to reliability issues. To address this problem, a non‐contact bond wire monitoring method that can be carried out using the charging coil of the wireless charging device is proposed. Here, two monitoring loops are constructed based on the circuit topology, and their impedance magnitudes are measured on the charging coil to avoid the influence of battery voltage on monitoring results. To detect the bond wire condition, the impedance magnitude difference of the two monitoring loops is chosen as an indicator, which is very sensitive to the bond wire lift‐off. However, the monitoring results may be influenced by the aging of resonant capacitors and the offset of the charging coil. In particular, the influence of the coil offset is significant. To mitigate this effect, a prognostic parameter calibration method is proposed. Experimental results confirm the effectiveness of the proposed method, which provides a promising solution for the reliable and safe operation of SiC MOSFET modules in wireless charging devices.

Assessment of the Feasibility of Applying the Electromagnetic Time Reversal Theory to Locate Defects in Grounding Electrodes

In recent years, the electromagnetic time reversal (EMTR) theory has been successfully applied for locating various sources of disturbances, such as short-circuit faults, in power systems. In this paper, a theoretical analysis of the feasibility of applying EMTR to locate defects (corrosion and break points) in grounding systems is presented. An EMTR algorithm to locate faults in buried grounding wires is proposed and a comprehensive analysis with respect to some salient influencing parameters, for instance, the ground conductivity, medium losses, the defect location and type (soft or hard), is carried out. According to the obtained results, the proposed method appears to be very promising for real applications. The need for experimental validation to confirm the applicability of this method is emphasized.

Numerical simulation and experimental study on metallographic structure characteristics of melting trace caused by overcurrent fault of copper wire

The microscopic characteristics of melting trace caused by copper wire fault are an important basis for the physical evidence identification of electrical fires. Metallography is an important method in electrical fire research, and computer-aided quantitative analysis on metallographic structure is common in materials aspect. In this study, the metallographic structure and phase composition characteristics of melting trace caused by overcurrent fault were explored with the aid of a metallographic microscope and an X-ray diffractometer (XRD). The results show that the grains are mainly slender dendritic and columnar crystals when the current is 128 A, while they are coarse dendritic and cellular crystals when the current is greater than 192 A; the average grain diameter of melting trace of copper wire grows with the increase in current. The main phases of melting trace caused by overcurrent fault of copper wire are α-Cu base and Cu2O, and the new phases Cu4Si and Cu2Mg are formed when the current is higher than 160 A. In addition, the solidification process of metallographic structure of copper wire under different currents was simulated by the Procast software. It is found that the simulation results are basically in agreement with the experimental data, which suggests that this model can effectively predict the microstructure characteristics of melting trace of copper wire under different currents. The research results can provide a sound basis for the physical evidence identification of electrical fire and improve the accuracy and scientificity of electrical fire investigation.

Rethinking lithium-ion battery management: Eliminating routine cell balancing enhances hazardous fault detection

Current battery management systems for lithium-ion battery packs incorporate circuitry and software to carry out routine voltage balancing of cells in order to optimise battery performance by preventing the state of charge of individual cells drifting apart over time. Drawing on evidence from previous published research, this paper reports experimental results testing the hypothesis that routine balancing can be successfully eliminated over a battery's lifetime. The test method requires that specified steps are taken to prepare the cells and that a novel charging protocol is employed thereafter to ensure the pack is fully charged while accommodating cell-to-cell variations in capacity, aging and dynamic response. Extensive cycle testing on a range of different battery configurations found no evidence of loss of balance and hence of reduction in lifetime battery performance compared to conventional battery management systems incorporating routine dissipative balancing. Importantly, the experiments also found that elimination of routine balancing greatly enhances the scope for early detection of low-level self-discharge caused by developing internal short circuits. Internal short circuits are implicated in occasional highly destructive spontaneous battery thermal runaway events which have hitherto proved very difficult to predict when using conventional cell voltage balancing systems. The described method of battery management thus offers significant potential, especially for applications where the risk of sudden battery fires cannot be tolerated. In addition, the absence of high voltage balancing wires may bring other benefits, including simplified battery assembly, safer maintenance and reduced risk of hazardous wiring faults.

Bond Wire Damage Detection Method on Discrete MOSFETs Based on Two-Port Network Measurement.

Bond wire damage is one of the most common failure modes of metal-oxide semiconductor field-effect transistor (MOSFET) power devices in wire-welded packaging. This paper proposes a novel bond wire damage detection approach based on two-port network measurement by identifying the MOSFET source parasitic inductance (LS). Numerical calculation shows that the number of bond wire liftoffs will change the LS, which can be used as an effective bond wire damage precursor. Considering a power MOSFET as a two-port network, LS is accurately extracted from frequency domain impedance (Z−parameter) using a vector network analyzer under zero biasing conditions. Bond wire cutoff experiments are employed to validate the proposed approach for bond wire damage detection. The result shows that LS increases with the rising severity of bond wire faults, and even the slight fault shows a high sensitivity, which can be effectively used to quantify the number of bond wire liftoffs of discrete MOSFETs. Meanwhile, the source parasitic resistance (RS) extracted from the proposed two-port network measurement can be used for the bond wire damage detection of high switching frequency silicon carbide MOSFETs. This approach offers an effective quality screening technology for discrete MOSFETs without power on treatment.

Partial discharge detection of insulated conductors based on CNN-LSTM of attention mechanisms

Under the condition of a strong electric field, partial discharge often occurs when insulated wire is damaged. The recognition of partial discharge is an effective method for the fast and accurate detection of high voltage insulated wire faults. This paper proposes a PD recognition algorithm based on a convolutional neural network and long short-term memory (LSTM). In addition, attention mechanisms are introduced to give separate weights to LSTM hidden states through a mapping, weighting, and learning parameter matrix. This is done to reduce the loss of historical information and to strengthen the influence of important information. The complex relationship between the voltage signal change and the grid operation state response has been established. The proposed method is verified by the ENET data set published by VSB University. The recognition accuracy is 95.16% for no-PD and 94.44% for PD. Results from the proposed algorithm show that this method has a higher detection accuracy.

A Novel Bond Wire Fault Detection Method for IGBT Modules Based on Turn-on Gate Voltage Overshoot

Bond wire degradation is one of the most common failure modes for wire-welded packaging insulated-gate bipolar transistor (IGBT) modules. This article proposes a novel bond wire detection method based on IGBT turn-on gate voltage overshoot. The degree of bond wires lift-off will change the stray inductance of the gate charge loop circuit, and therefore has a strong influence on turn-on gate voltage overshoot before miller platform, which can be used as an effective bond wire fault indicator. A double pulse test platform is built to verify the resolution and sensitivity of the proposed precursor for bond wire degradation. The dependences on bus voltages, load currents, junction temperatures are discussed and tested. The experimental results show that the gate turn-on voltage overshoot of IGBT increases with the increasing severity of bond wire faults, which agrees with the theoretical analysis. The proposed turn-on gate voltage overshoot-based method has a high sensitivity for identifying bond wire fault at the incipient stage. It offers an effective technique for detecting bond wire degradation for practical applications.

Volatility Diagnosis in Phase-to-Phase Fault Detection for Branch Across Wire Faults

High Impedance Faults (HIFs) are disturbances that can lead to bush fires. This work presents physical and electrical phenomena associated with ph-to-ph faults for a range of tree species. Physical ignition phenomena of a tree branch under a ph-to-ph fault scenario are analysed to identify stages of ignition. A methodology, based on the rate change of the RMS current and zero-crossings in the averaged RMS current, has been applied with 96.12% accuracy to identify ignition stages. The key contribution is the proposed volatility detector for detecting RMS current volatility during a HIF event. The concept of relative volatility is applied to detect signatures of HIFs and branch charring in the RMS current data stream with respect to the stream itself.

Localization and Detection of Bond Wire Faults in Multichip IGBT Power Modules

Multichip insulated gate bipolar transistor (mIGBT) power modules (PMs) degrade over power cycling. Bond wire lift-off is one of the major failure modes. This article presents a technique to diagnose bond wire lift-off by analyzing the on-state voltages across collector and emitter terminals and the voltages across collector and Kelvin emitter terminals. The proposed method can indicate the first lift-off out of 37 bond wires in a mIGBT. The main novelty of the proposed technique is that it can locate the chip that has bond wire lift-off(s). In addition, the temperature dependence of the proposed approach is negligible. The article describes the proposed technique in detail and shows results and discussions based on practical tests which are carried out on two mIGBT PMs with different packages.

Nondestructive wire fault diagnosis using resistance spectroscopy analysis

Wires in field applications, such as aircraft, are often exposed to stress conditions, including overheating, moisture ingress, or inherent defects, which may result in electrical discontinuity, wire insulation breakdown, and system failure. This study proposed a nondestructive and real-time wire fault diagnosis method through resistance spectroscopy to diagnose the health of wires under stress conditions. Wire abrasion tests were conducted by applying cyclic mechanical stress to produce gradual damage to a wire under test. During abrasion testing, a lock-in amplifier generated high-frequency signals and monitored the signals transmitted through the wire to diagnose the wire failure under test. Differences in signal amplitude, and phase shift spectra during wire degradation exhibited specific and distinguishable behavior based on the physical damage progression of the tested wire. Test results implied that resistance spectroscopy analysis could serve as a nondestructive wire fault diagnosis method, providing damage progression in real time.

Wire Mismatch Detection Using a Convolutional Neural Network and Fault Localization Based on Time–Frequency-Domain Reflectometry

In addition to diagnosing a wiring of the vehicle in operation, it is also very important to find wire mismatches during the assembly process. In this paper, we propose a new method combining time–frequency-domain reflectometry and deep learning to verify that the wire is connected to the proper port of the underhood electrical center. Considering the frequency characteristics of each wire (black, blue, red, and yellow), we develop an optimization signal design algorithm. Using the time–frequency cross correlation (TFCC), the reflected signal generated at the impedance discontinuities is acquired and converted into the Wigner–Ville distribution image. Through the proposed algorithm, the existing images are converted into new images, which are easy to distinguish among groups. The new images are used as input of the convolutional neural network and trained to learn the feature of each group. The lengths, compensation filters, and the port information to be connected to each wire are stored in the filter bank. If the distance derived using the TFCC is different from the stored length, the wire is considered defective, and the acquired signal is restored by the compensation filter designed by the overcomplete wavelet transform method. Experimental results demonstrate the effectiveness of the proposed method for detecting the wire mismatch and fault location.

An intelligent wire fault diagnosis approach using time domain reflectometry and pattern recognition network

ABSTRACTThe aim of this paper is to develop a new approach for wiring networks diagnosis. This approach is based on time domain reflectometry (TDR) and pattern recognition network all combined with a simple localisation and characterisation algorithm. The developed approach has the advantage of being fast, reliable and accurate. The TDR responses are generated using the analytical transmission line method. Furthermore, the manipulated data are reduced since only the main peaks are exploited. The performance of the proposed approach has been assessed using four cases, two using a simple Y-shaped network and two using a more complicated YY-shaped network. The obtained results are very satisfactory, and they show the applicability of the proposed approach for real systems.

Enhancing the Spatial Resolution for Wire Fault Detection Systems Using Multi-Carrier Signals

Recently, the world is witnessing a vast daily development in technology accompanied with more and more complex electronic systems. They are hosting cumulated lengths of electrical cables that are subject one day or another to the occurrence of wiring faults. Accordingly, wire diagnosis became essentially important for ensuring safety, security, integrity, and optimal performance. On the other hand, the emergence of sensor networks and connected objects has created the need for embedded and non invasive fault diagnosis solutions. Notably, multi-carrier reflectometry methods have shown promising and efficient results in locating upcoming defects on wires in an online manner. Nevertheless, their precision stays within the physical limits of their components, especially the sampling frequency of their analog/digital transitions. In this paper, we will propose several approaches combining multi-carrier reflectometry with phase analysis techniques to overcome this limit. Accordingly, an improvement in the precision of the fault localization and a high spatial resolution is obtained. Based on an FPGA implementation, our novel methods and the resulting systems have proven a five-time better accuracy than the state-of-the-art methods on the same platform.

An Irradiance-Independent, Robust Ground-Fault Detection Scheme for PV Arrays Based on Spread Spectrum Time-Domain Reflectometry (SSTDR)

A healthy photovoltaic (PV) array has a specific impedance between node pairs, and any ground fault changes the impedance values. Reflectometry is a well-known technique in electromagnetics, and it could be exploited to detect fault and aging-related impedance variations in a PV system. A fault detection algorithm using the spread spectrum time-domain reflectometry (SSTDR) method has been introduced in this paper. SSTDR has been successfully used for detecting and locating aircraft wiring faults. However, the wide variation in impedance throughout the entire PV system, which is caused by the use of different materials and interconnections makes PV fault detection more challenging while using reflectometry. Unlike other conventional ground-fault detection techniques specifically developed for PV arrays, SSTDR does not depend on fault-current magnitudes. Therefore, SSTDR can be used even in the absence of the solar irradiation, which makes it a very powerful fault-detection tool. The proposed PV ground-fault detection technique has been tested in a real-world PV system, and it can confidently detect PV ground faults for different configurations of PV arrays (single and double strings) and fault resistances (0.5, 5, and 10- $\Omega$ ). Moreover, it has been experimentally verified that our proposed algorithm works at low irradiance and can detect specific ground faults that may remain undetected using the conventional ground-fault detection and interrupter (GFDI) fuses.

PHM-based wiring system damage estimation for near zero downtime in manufacturing facilities

Wiring system is usually installed in manufacturing facilities to construct a data network. During the operation of the manufacturing facility, environmental and operational stress conditions may degrade material properties of the wires, and cause wire faults and failures, which deteriorate the electrical connection within and out of the wiring system. Conventional health monitoring approaches, such as time domain reflectometry, have been actively developed to detect wire faults with improved accuracy. However, these approaches often require external devices connected with the wiring system, which may interfere system operation, and lead to inevitable system downtime of the facility for maintenance. In order to eliminate unnecessary system downtime, alternative means for wiring health monitoring is required.This paper discusses a prognostics and health management(PHM)-based means required for wiring system maintenance planning using digital signal characteristics. Degradation of wire material property causes impedance discontinuities, which in turn deteriorate the integrity of the transmitted signal. The extent of wire damage can be detected by monitoring the integrity of digital signal. In order to demonstrate the ability of digital signal for wire health monitoring, wire abrasion tests were performed with continuous monitoring of digital signal characteristics. The test results showed the changes in digital signal characteristics and the extent of wire damage were highly correlated, and could detect multiple failure modes of wiring system without requiring additional sensing or monitoring devices. Thus, digital signal based wiring health monitoring should enable improved system maintenance planning towards near zero downtime.

Implementation of Antiestablishment Wire Fault Sensing Using CC3200

Underground cables are inclined to faults because of underground conditions. Detecting particular fault area in the underground cable is a tedious one for that the whole line is to be dug check and settle issues. The proposed method using IoT identifies the correct fault position that makes repairing work simple. The professional domain workers identify the fault area sometimes they also are not able to predict the fault location. Determining fault in the cable itself take lots of time, cash and endeavours and furthermore permit to service underground links speedier. An excellent Wi-Fi featured ARM Cortex-M4 microcontroller is utilised to implement the system. This device offers a total, ease, practical and easy to use method for continuous checking. The fault is acknowledged with the help of sensors placed over the cable. The cell phone application furnishes with a GUI to screen the information from a remote area. The cloud server act as the centre man between the hardware device and the cell phone application dealing with the data movement.

Performance improvements of wire fault diagnosis approach based on time‐domain reflectometry

The aim of this study is to improve the performance of a wire fault diagnosis approach based on time-domain reflectometry (TDR). In this approach, the TDR response collected from measurements for a given (faulty) coaxial cable network is used as a reference for the cable network model response computed by an accurate analytical transmission line (ATL) method. The ATL model is iteratively tuned according to the outcome of the optimisation algorithm proposed. The advantage of the proposed ATL method over the more classic finite difference time-domain method is the extremely faster computational time which represents a significant factor for the diagnosis of faults in wiring networks. The proposed ATL method accurately provides the features of the resistance-inductance-conductance-capacitance-based model of a RG-58 coaxial cable employed as a device under test. The ATL model has been validated by comparison with measurement based and circuit simulations. Furthermore, seven experiments have been investigated in order to evaluate the performance of the ATL method for the diagnosis of wiring networks. The obtained results reveal that the ATL method together with the efficient optimisation algorithm is a reliable, efficient and fast method for the diagnosis of wiring networks.