Electrical Faults Research Articles

Dynamic voltage restore based on switched-capacitor multilevel inverter with ability to compensate for voltage drop, harmonics, and unbalancing simultaneously

The voltage harmonics and voltage unbalance, as well as voltage drop due to the electrical faults, can damage the sensitive loads. Various voltage compensators, such as the series active power filter (SAPF) and dynamic voltage restore (DVR) have been developed to overcome these problems. In this paper, a new compensator is introduced, which can simultaneously operate as SAPF and DVR. To this end, an adaptive control method is utilized for generating the compensator reference signal to mitigate the voltage drop, harmonics, and unbalance. On the other hand, for reduction of complexity and cost, a switched-capacitor multilevel inverter has been employed in the proposed compensator. The effectiveness of the compensator based on the improved control method is studied in the presence of symmetrical and asymmetrical faults with distorted utility.

Fault-tolerant control via four-leg inverter of a full-electric propulsion system for lightweight fixed-wing UAVs

The work deals with the development and the performance characterization of a novel control strategy for the detection, isolation and accommodation of coil faults in a three-phase Permanent Magnet Synchronous Motor (PMSM), used to drive the propeller of a modern lightweight fixed-wing UAV. The health-monitoring algorithms on motor currents (used to detect the open-circuit fault and to activate the control reconfiguration) are based on a slope method, associated to the evaluation of the current phasor trajectory in the Clarke plane. Actually, when an open-circuit fault occurs in PMSM driven by a standard three-leg converter, the typical circular trajectory of the current phasor in the Clarke plane collapses into a linear track and relevant torque ripples are generated. On the other hand, if the PMSM is driven by a four-leg converter, a control reconfiguration can be applied: the fourth leg of the power bridge is in stand-by when the system operates without faults, but it is enabled to regulate the current flowing at the central point of the Y connection of the 3-phase PMSM. The performances of the fault-tolerant algorithms are assessed via detailed nonlinear simulation of the propulsion system (including propeller loads, electrical faults, mechanical transmission compliance, digital signal processing and sensors errors). The results demonstrate that the health-monitoring algorithms and the fault-tolerant control strategies permit to obtain extremely small detection and isolation latencies, and negligible performance degradation in terms PMSM torque.

A novel model-based metaheuristic method for prognostics of aerospace electromechanical actuators equipped with PMSM

The prior knowledge of incipient failures of primary flight command electromechanical actuators (EMAs) with prognostic algorithms can be very beneficial. Indeed, early and proper detection and interpretation of the deterioration pattern can warn for replacing the servomechanism before the actual manifestation of the abnormal behaviour. Furthermore, such algorithms often exploit a model-based approach established on the direct comparison between the actual (High Fidelity) and the monitor (Low Fidelity) systems to identify fault parameters through optimization processes. The monitor model allows the acquisition of accurate and precise results with a contained computational effort. The authors developed a new simplified monitor model capable of faithfully reproducing the dynamic response of a typical aerospace EMA equipped with a Permanent Magnet Sinusoidal Motor (PMSM). This digital twin senses mechanical and electrical faults: friction, backlash, coil short circuit, static rotor eccentricity, and proportional gain. Fault detection and identification task are performed by comparing the output signal of the reference system (real or simulated) with the one obtained from the monitor model. After that, the Genetic Algorithm is chosen as the optimization algorithm to match the two signals by iteratively changing the fault parameters to detect the global minimum of a quadratic error function. Once a suitable fit is obtained, the corresponding optimization parameters are correlated with the considered progressive failures to evaluate the system’s health status. The high-fidelity reference models analysed in this work have been previously conceived, developed, implemented in Matlab-Simulink, and validated experimentally by researchers of the ASTRA group of the DIMEAS of Politecnico di Torino.

Examination of Rectifier AC Side Electric Circuit Ground Fault Detection Method in DC Electric Railway Substation

There are no ground fault protection devices in the AC circuit between the rectifier transformer and rectifier of DC electric railway substations. Therefore, the ground faults in the AC electric circuit are expected to be protected by overcurrent relays or DC ground fault overvoltage relays (64P) installed in the upper system. However, these protections are inadequate, so we examined methods to detect ground faults in the AC circuit between the rectifier transformer and rectifier. Thus, it was found that the ground fault current included a third harmonic current that was absent when there were no ground faults. Therefore, we created a prototype device to extract the third harmonic current, and confirmed that ground faults in the AC electric circuit between the rectifier transformer and rectifier could be detected by artificial ground fault tests.

Preface

Due to recent pandemic, 2021 International Conference on Detection Technology and Intelligence System (DTIS 2021) was held virtually online during December 3-5, 2021. The decision to hold the virtual conference was made in compliance with many restrictions and regulations that were imposed by countries around the globe. Such restrictions were made to minimize the risk of people contracting or spreading the COVID-19 through physical contact. There were 90 individuals who attended this on-line conference, represented many countries including Malaysia, Iran, and India.The 2021 International Conference on Detection Technology and Intelligence System (DTIS 2021) is to bring together innovative academics and industrial experts in the field of Detection Technology and Intelligence System to a common forum. The primary goal of the conference is to promote research and developmental activities in advanced Detection Technology and Intelligence System is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working all around the world.During the conference, the conference model was divided into three sessions, including oral presentations, keynote speeches, and online Q&A discussion. In the first part, some scholars, whose submissions were selected as the excellent papers, were given about 5-10 minutes to perform their oral presentations one by one. Then in the second part, keynote speakers were each allocated 30-45 minutes to hold their speeches. In the second part, we invited three professors as our keynote speakers. Prof. Dejie Yu, Hunan University, China. Prof. Dejie Yu's research areas are equipment condition monitoring and fault diagnosis; dynamic signal analysis and processing; automotive vibration and noise control (NVH); research on the properties and applications of phononic crystals/acoustic metamaterials; and research on the mechanical properties of acoustic metamaterials. And then we had Prof. Aihua Dong, Zhengzhou University of Economics and Business, China. Prof. Dong is mainly engaged in electrical automatic control, fault detection and other directions of scientific research. Lastly, we were glad to invite Assoc. Prof. Zhaojing Tong, Henan Polytechnic University, China. Assoc. Prof. Zhaojing Tong is mainly engaged in teaching and research in intelligent detection and equipment fault diagnosis, automatic control, intelligent reasoning, etc. Their insightful speeches had triggered heated discussion in the third session of the conference. Every participant praised this conference for disseminating useful and insightful knowledge.List of Committee are available in this pdf.

INVESTİGATİON OF CARBON GAS İN OİL TRANSFORMERS

By their structure and design, transformers are devices that are reliable in operation and easy to maintain. In the operation of transformers, the main factors are timely inspection, performance of current and major repairs, as well as the necessary preventive tests. One of the important conditions for the correct use of this device is the correct introduction of the temperature regime and observance to the allowed overloading. Monitoring the condition of the oil is fundamental to the correct operation of the transformer. During the period of use, the oil ages and it loses its original properties. In addition to the deterioration of the properties of the oil due to aging, there is also a change in its properties under abnormal operating conditions of the transformer. So with local heating, build-up of insulation between the steel sheets of the magnetic circuit, poor contact in the tap changer or at the winding terminals under the transformer cover, and with turn short circuits in the windings, oil decomposes. Transformer oil has at least three functions in transformers. Task of transformer oil in oil-filled transformers cooling function, extinguishing the arc, and providing insulation is the main [1]. The appearance of defects caused by both electrical and thermal faults is accompanied by the formation of gases, and their further dissolution in transformer oil. In addition to normal operation, during the operation of the transformer, local thermal overheating (of an insulated conductor, core, etc.) and electrical discharges (partial, spark and arc discharges) can occur. Such defects lead to the destruction of insulation, physical wear and tear, and irreversible damage to the active part of the transformer. To determine the state of oil-filled electrical equipment, gases are used: hydrogen (H2), methane (CH4), ethane (C2H6), ethylene (C2H4), acetylene (C2H2), carbon monoxide (CO), carbon dioxide (CO2). In addition to the above gases, oxygen (O2) and nitrogen (N) are also present, and their concentration varies depending on the tightness of the transformer tank and gases such as propane, butane, butene and others can be released. The study of these gases for diagnostic purposes is not widespread [2]. In this regard, the method of analysis of gases dissolved in oil (DGR) has proven itself all over the world. The main advantage of this method is the timely diagnosis of the malfunction, identification of the nature and control of the defect, which leads to lower economic costs and time savings. Fault diagnostics can be performed using various methods, such as: the ratio of pairs of gas concentrations using the Duval triangle, Japanese ETRA, monogram method, etc. The most important tool for diagnosing the condition of a transformer is observing the evolution of gases by comparing different analyzes of the amount of dissolved gases and understanding this information. The Duval Triangle Method is based on the existing IEC 60599 Gas Ratio Method. Within this triangle there are six potential damage zones. Zones include partial discharges, electrical and thermal damage. The use of this triangle method is based on three main gases (CH2, C2H4 and C2H2). Firstly the concentration levels of these gases are calculated. Then lines are drawn along three sides of an equilateral triangle: on each side, the relative value of a certain gas is laid, which can vary from 0 to 100% clockwise. The sum of the values of the concentrations of these gases is taken as 100. When drawing the boundaries of the zones of defects, the lines are drawn parallel to the lagging side of the triangle. The considered method not only clearly shows the "location" of the defect, but also allows you to observe the trajectory of its development [3]. The oil analysis results reflect the overall condition of the transformer. For this analysis, the oils are sent to a laboratory for testing. The most important indicators are the rate of formation of the total amount of combustible gases and individual gases. Carrying out timely sampling makes it possible to track the dynamics of malfunctions and take appropriate decisions in time. To obtain more reliable results, you must follow the sampling rules, store the samples and transport the samples to the analysis site correctly. Some gases dissolved in transformer oil (ethylene and acetylene) may indicate overheating in the contacts of switching devices, heating of the shield attachment points and bushing pins, short-circuiting of the winding conductors and other defects. The main goal of this article is to solve the problem associated with the extraction of hydrocarbon gases from oil, and to correct the interpretation based on the obtained data [4]. Keywords: DGR, Duval triangle, transformer oil, gas discharges.

Fault detection and diagnosis in electric motors using 1d convolutional neural networks with multi-channel vibration signals

Fault detection and diagnosis in time series data are becoming mainstream in most industrial applications since the increase of monitoring sensors in machinery. Traditional methods generally require pre-processing techniques before training; however, this task becomes very time-consuming with multiple sensors. Recently, deep learning methods have shown great results on time series data. This paper proposes a multi-head 1D Convolution Neural Network (1D CNN) to detect and diagnose six different types of faults in an electric motor using two accelerometers measuring in two different directions. This architecture was chosen due to each head can deal with each sensor individually, increasing feature extraction. The proposed method is verified through a series of experiments with seven different induced faults and operation conditions. The results show that the proposed architecture is very accurate for multi-sensor fault detection using vibration time series. Since the experiments are based on real electric motors and faults, these results are promising in real applications.

Feature Engineering and Artificial Intelligence-Supported Approaches Used for Electric Powertrain Fault Diagnosis: A Review

Electric powertrain is constituted by electric machine transmission unit, inverter and battery packs, etc., is a highly-integrated system. Its reliability and safety are not only related to industrial costs, but more importantly to the safety of human life. This review is the first contribution to comprehensively summarize both the feature engineering methods and artificial intelligence (AI) algorithms (including machine learning, neural networks and deep learning) in electric powertrain condition monitoring and fault diagnosis approaches. Specifically, this paper systematically divides the AI-supported method into two main steps: feature engineering and AI approach. On the one hand, it introduces the data and feature processing in AI-supported methods, and on the other hand it summarizes input signals, feature methods and AI algorithms included in the AI method in cases. Therefore, firstly this review is to guide how to choose the appropriate feature engineering method in further research. Secondly, the up-to-date AI algorithms adopted for powertrain health monitoring are presented in detail. Finally, such current approaches are discussed and future trends are proposed.

Research on the Electric Life Prediction of Relay Contact Based on Harris Hawk Optimized

The failure of ac relay contacts will cause electrical control system fault, which will bring certain economic losses. Therefore, accurate prediction and evaluation of the electrical life of the relay have certain research value and practical significance. The measurement of the contact resistance of the relay was performed using a self-designed experimental platform. An electrical life prediction method of ac relay was proposed. First, the measured contact resistance signal was decomposed using empirical mode decomposition (EMD), and several sub-sequence components and residual components were obtained. Second, prediction of these components was conducted using a long short-term memory neural network (LSTM), and the prediction value was reconstructed. Third, the group method of data handling (GMDH) algorithm and Harris Hawk optimization (HHO) algorithm were used to optimize LSTM. Finally, contact pressure was calculated using the predicted contact resistance. The electrical life of the relay was predicted according to a contact-pressure-based electrical life criterion. Compared with other models, the prediction accuracy of the contact resistance is higher. The electrical life of the relay can be predicted effectively using the prediction results of both contact resistance and contact pressure. In practical application, a pressure sensor can be installed in the relay, and the electrical life of the contact can be predicted by the measured contact pressure.

Image-Based Incipient Fault Classification of Electrical Substation Equipment by Transfer Learning of Deep Convolutional Neural Network

A transfer learning-based deep convolutional neural network (DCNN) is employed in this article to identify several typical electrical substation equipment incipient faults. Image dataset contains 11 000 captured electrical substation equipment images, and each image inside the dataset was labeled as normal condition or typical incipient faults (insulation oil leakage, insulator contamination, rusting, and paint off). High-dimension features of electrical substation equipment images were first extracted by classical pre-trained DCNN architectures through the transfer learning method, and different incipient faults were then classified by the fully-connected (FC) neural network using the SoftMax activation function. Remarkable fault classification accuracy was obtained on the validation image, which verifies the effectiveness of the purposed method. Performances of various pre-trained classic DCNN architectures were explored and evaluated by t-distributed stochastic neighbor embedding (t-SNE) feature cluster maps, learning curves, and confusion matrixes. Results show that a model consists of MobileNetV2 DCNN architecture and two FC neural network layers could finalize fault classification tasks on 1000 images in 25 s with an accuracy of 98% which achieves better performance than traditional image classify methods. The proposed method could be useful for designing an electrical substation image monitoring system thus providing early prediction of incipient equipment faults.

Activity Detection and Recognition With Passive Electric Field Sensors

Use of far-field electric field sensors in an outdoor event detection is described. Electric field variations accompany broad variety of physical events, and field signature signals can be used in determination of activities in proximity of the sensor. Perimeter monitoring, moving objects recognition, electric power faults detection are only a few examples of such applications. This work presents development and signal processing for electric field sensor uses in human and animal motion detection and characterization.

Reliable Estimation for Health Index of Transformer Oil Based on Novel Combined Predictive Maintenance Techniques

Transformer oil insulation condition may be deteriorated due to electrical and thermal faults, which may lead to transformer failure and system outage. In this regard, the first part of this paper presents comprehensive maintenance for power transformers aiming to diagnose transformer faults more accurately. Specifically, it aims to identify incipient faults in power transformers using what is known as dissolved gas analysis (DGA) with a new proposed integrated method. This proposed method for DGA is implemented based on the integration among the results of five different DGA techniques; 1) conditional probability, 2) clustering, 3) Duval triangle, 4) Roger’s four ratios refined, and 5) artificial neural network. Accordingly, this proposed integrated DGA method could improve the overall accuracy by 93.6% compared to the existing DGA techniques. In addition, the second part used for predictive maintenance is based on determining the health index for five new transformers and an aged power transformer (66/11 kV, 40 MVA) filled with NYTRO 10XN oil by evaluating the breakdown voltage, DGA, moisture content, and acidity for the oil. In the breakdown voltage test, two practical types of transformer oil; NYTRO 10XN and HyVolt III alongside their mixtures are estimated and compared. In addition, aged oil samples extracted from a real case study in-service transformer during operation with different aged durations; 9, 10, 11, 12, and 13 years, are tested for breakdown voltage, and then compared with fresh oil samples. For DGA, a temperature rise test is performed on the five new transformers with a comparison between dissolved gases before and after the temperature rise. In addition, winding resistance is measured after the temperature rise. Also, acidity and moisture are measured for oils extracted from the new five transformers and from the 13-year in-service transformer for studying their health index. The health index of the transformer insulation system is examined using only DGA and DGA plus breakdown voltage (BDV), moisture, and acidity. The results show that by using DGA plus BDV, moisture, and acidity, the health index provides reliable estimation results compared to using only DGA.

Online Diagnostic Method for the Detection of High-Resistance Connections and Open-Phase Faults in Six-Phase PMSM Drives

Even though multiphase machines have inherent fault-tolerant capabilities due to their additional degrees of freedom, fault diagnosis still plays an important role to continuously monitor these machines and, thus, prevent critical failures. High-resistance connection (HRC) is one of the most common electric faults in electrical machines and is caused by damaged power connections. These faults create a current unbalance, increasing both the machine losses and torque pulsations, and can eventually lead to open-phase faults (OPFs). This article proposes an online fault diagnostic method to detect HRCs and OPFs in six-phase permanent magnet machine drives. The proposed method is intended for application in electric drives controlled by a predictive current control strategy and does not require the installation of extra hardware. Two variants of the fault diagnostic algorithm are presented in this work: one based on the analysis of the secondary components of the reference current errors and the other variant based on the prediction current errors. Experimental results demonstrate the effectiveness and sensitivity of the proposed diagnostic method for the detection of these types of fault.

Intelligent Electrical Fault Detection and Recognition Based on Gray Wolf Optimization and Support Vector Machine

With the increase of electrical equipment, the hidden danger of its failure also rises. Aiming at the problem that the classification accuracy of the basic traditional classification algorithm is not high when judging whether the electrical failure occurs, this paper proposes a Gray Wolf Optimization Support Vector Machine model to improve the recognition rate of electrical diagnosis, referred to as GWO-SVM. We first collected the most common linear (incandescent lamps) and non-linear (microwave ovens) household electrical appliances in normal working and arc fault waveform signals in real life, and then carried out frequency domain feature extraction. Finally, compared with no-optimized SVM and BP neural network, we employed the gray wolf optimization algorithm to optimize support vector machine, and the accuracy of GWO-SVM reaches 90%.

Managing Wildfire Risks: Protection System Technical Developments Combined With Operational Advances to Improve Public Safety

Utilities around the world are facing unprecedented levels of fire risk from routine electrical faults and failures in transmission and distribution lines and equipment. The risk is elevated by changing weather patterns that produce extreme drought conditions and violent storms. These utilities are confronting aging power apparatus and difficult-to-detect failure and arcing fault scenarios. Major fires in recent years have increased public awareness of the risk.

Real-Time Resilience Optimization Combining an AI Agent With Online Hard Optimization

In the highly interdependent environment of a large city, failures in the Electrical Distribution System (EDS) can cause direct or indirect consequences to other critical infrastructures and the well-being of the citizens. To increase the resilience of the supply of electricity to the city, this work combines the pre-training of an AI agent and very fast calculation of the optimum recovery path after the number and location of the electrical faults are known. In the introduced Soft-Hard Optimal Convergence (SHOC) method, machine learning techniques are used to train an AI agent with thousands of off-line scenarios for optimum system restoration. In real-time, after the actual fault information is known, the agent will provide a subset of solutions (soft solution) to be considered for hard optimization algorithms. The Infrastructure Interdependencies Simulator (i2SIM) is used to assist the prioritization of the sequence of fault recovery and topological reconfiguration to minimize the black-out time of the most critical loads. A 70-node distribution system case is used to demonstrate the proposed methodology, with solution times in the order of seconds to find the optimum repair sequence and switches topological reconfiguration to optimize the city's resilience index.

An Innovative Circuit Breaker for Renewable Power Grids

A key step towards a carbon-neutral future could be reached through dispersed power grids, featuring networks of local-scale renewable energy and battery storage plants. To prevent these power grids from damaging themselves and their surroundings when electrical faults arise, they must be integrated with ‘circuit breakers’, which temporarily interrupt the current flowing through them. However, currently available circuit breakers cannot handle the medium-voltage direct currents best suited for these grids. Through an innovative new circuit breaker design, Steve Schmalz and his colleagues at Eaton Corporation, the Illinois Institute of Technology (IIT), Virginia Tech and the National Energy Technology Laboratory (NETL), hope that this challenge will soon be overcome.

Conditional Probability Approach for Fault Detection in Photovoltaic Energy Farms

Detection of electric faults in photovoltaic (PV) farms enhances a sustainable service continuity of farm energy generation. In this paper, a probabilistic function is introduced to detect the faults in the PV farms. The conditional probability functions are adopted to detect different fault conditions such as internal string faults, string-to-string faults, and string-to-negative terminal faults. As the diodes are important to make the PV farms in-service safely during the faults, the distribution currents of these faults are evaluated with different concepts of diode consideration as well as without considering any diode installation. This part of the study enhances the diode utilization in the PV farms concerning the protection point of view. The PV string currents are used as inputs to the conditional probability detection algorithms. However, the setting of the fault detection technique is not portable for the other PV systems due to broad ranges of PV system ratings. To accordingly generalize the proposed fault detection algorithm, the PV string currents are first normalized to the total array current for universally applying the detection function at different PV string ratings. The limiting fault resistances are evaluated to show the sensitivity of the proposed fault detector. The results ensure the application of the proposed probabilistic detection function for PV farms.

Blockchain-Enabled Electrical Fault Inspection and Secure Transmission in 5G Smart Grids

The maturity of the 5th-Generation (5G) communication technology promotes a new round of industrial revolution and supports the high-quality development of economic society. However, owing to the scarce communication resources, costly labor and complex geographic environment, inspecting and maintaining electrical faults accurately and timely in a remote grid is rather challenging. To solve this problem, we comprehensively consider secure and efficient signal transmissions to construct an automatic grid fault inspection system and formulate a multi-objective optimization problem. Due to its complexity, we decompose it into two sub-problems, propose a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</u> lockchain- <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</u> nabled <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</u> ecure <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</u> ransmission scheme (BEST) and an <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</u> mproved <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</u> arket <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</u> atching (IMM) algorithm, correspondingly. Considering the latency magnitude difference between blockchain verification and intra-domain transmission, the BEST scheme integrates <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</u> eep <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</u> einforcement learning-based <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</u> mproved <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</u> roximal policy optimization training algorithm (DRIP) and <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u> *-based <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</u> i- <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</u> bjective multi-destination <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</u> ptimization algorithm (ABOO) to achieve the intra-domain secure transmission. Based on the real city topology and the YouTube video service data statistics, our algorithms can optimize the network performance while guaranteeing the security of signal transmissions.

Contribution Mechanism and Impact Analysis of AC System at the Diode Natural Commutation and Conduction Stage During Bipolar Short-Circuit Fault for Single-Terminal VSC-Based DC Distribution Networks

For single-terminal voltage source converter (VSC)-based DC distribution networks, the characteristics of bipolar short-circuit faults are a key factor for the design of protection schemes and the selection of power electronic devices. However, the contribution of AC systems is usually ignored at the diode natural commutation and conduction stage. There is no precise mathematical analysis model for this stage, and no influence mechanism of AC system has been demonstrated from theoretical perspective. This article presents a fault analysis model of the diode natural commutation and conduction stage, focusing on the transient feeding process of VSC converter during the bipolar short-circuit fault for single-terminal VSC-based DC distribution networks. According to equivalent circuits of different transient substages, accurate mathematical state equations are established. Expressions of each electrical fault variation during different substages are provided. A precise mathematical model is constructed, which considers the contribution mechanism of AC system at this stage. Then, based on this mathematical model, the influence of the AC system on electrical fault variations is analyzed from a theoretical perspective. The analysis results show that the influence is always small under different conditions and that ignoring the contribution of AC system can still meet actual engineering needs. A simulation model is constructed using MATLAB/Simulink software. The simulation results validate the effectiveness and superiority of the analysis model.