Fault Location Identification Research Articles

Fault location and type identification method for current and voltage sensors in traction rectifiers

Fault location and type identification method for current and voltage sensors in traction rectifiers

Hi‐Z fault location identification on low voltage distribution systems using IoT‐based digital twin model computation

AbstractDistribution feeders carry and supply power to industrial, commercial and residential loads from the point where sub‐transmission (33 kV level) ends after stepping down to suitable voltages, such as 11 kV and further down to 400/230 V. In recent times, high impedance (Hi‐Z) faults on distribution systems are creating unique challenges to utilities both from operational and safety perspectives. Most of these Hi‐Z faults occur at distribution voltages of 15 kV or below, with the problem being worse at lower voltages. Hi‐Z fault detection technologies emerged and were developed and incorporated on embedded platforms, such as relays, reclosers and sensors, which protect and monitor distribution systems. Although these technologies can detect Hi‐Z fault on feeders, most of them cannot identify the exact location of the fault. Specifically, there is no solution available in literature for detecting Hi‐Z fault location on low voltage (LV) circuits like 3‐phase 4‐wire 400 V distribution network. In this paper, we introduce a novel and a unique algorithm to identify the location of Hi‐Z faults using proposed smart metres IoT data‐based distribution system load flow and digital twin model representation of the network. Furthermore, a case study on the standard 33 bus LV system clearly depicts the functionality of the proposed algorithm.

Fault diagnosis method for rotating machinery based on SEDenseNet and Gramian Angular Field

The fault diagnosis in rotating machinery is crucial for ensuring the safe and dependable operation of intricate mechanical systems. Addressing the limitations inherent in traditional deep learning approaches concerning extended time sequence encoding and subpar generalization capability is paramount. The study utilizes the Gramian Angular Field (GAF) and Squeeze and Excitation (SE) attention mechanisms to alleviate these constraints. GAF enhances feature extraction by emphasizing the angular relationships among adjacent signal points to uncover latent fault characteristics. Simultaneously, through the integration of SE with DenseNet architecture, the network facilitates global information exchange and improves multi-scale fusion, thereby enhancing the precise identification of fault type and location within the signal. Experiments conducted on two datasets achieved accuracies of 100% and 99.85%, respectively, outperforming other methods and models, thereby validating the effectiveness of this study.

UHVDC transmission line diagnosis method for integrated community energy system based on wavelet analysis

With the large-scale development of renewable energy power, China has faced with the challenges of the reverse regional distribution of wind and solar resources and power load, as well as the intermittency and randomness of renewable energy power. Therefore, China is vigorously developing ultra-high voltage direct current (UHVDC) transmission technology to solve the problem of insufficient flexibility caused by the uncertainty of renewable energy and the fluctuation of multi-energy loads in integrated community energy systems. UHVDC plays an increasingly pivotal role in the west-east transmission system in China’s power system due to its high transmission capacity and long transmission distance. Once the fault occurs in the ultra-high voltage direct (UHVD) transmission line, quick and accurate fault location identification is of great significance. Hence, this paper proposes a UHVDC transmission line diagnosis method based on wavelet analysis for integrated community energy systems. Wavelet transform (WT) is used to decompose the transient signal on a multi-scale, and then power systems computer-aided design (PSCAD) software is utilized for simulation calculation to obtain the singular spectrum entropy of each layer and facilitate wavelet transformations for signal denoising with advanced tools such as MATLAB. The prediction results can distinguish outside the rectification side fault, within the rectification side fault, and outside the inverter fault with an accuracy of 100%. A large number of simulations demonstrate that combining singular spectrum entropy with support vector machines (SVM) has emerged as a robust technique for integrated community energy systems, suggesting its potential as a standard method in UHVDC transmission line diagnosis. This study is of significant reference for realizing the complementarity of multiple types of power supply and ensuring a reliable power supply.

Fault Location and Section Identification Algorithm on Multi-Terminal Transmission Lines Using DWT

Fault Location and Section Identification Algorithm on Multi-Terminal Transmission Lines Using DWT

Voltage and Current Sensor Fault Diagnosis Method for Traction Rectifier in High-Speed Trains

The traction rectifier plays a key role in high-speed trains. Unexpected failure often occurs in the sensors of the rectifier, which may affect the control performance of the electric traction rectifier and even cause serious deterioration to high-speed trains. A sensor fault diagnosis method is presented in this paper, considering three kinds of common fault types. It can not only locate the sensor fault, but also identify fault types. Based on the influences of the sensor faults, the fault diagnosis thresholds can be calculated quantitatively. No additional hardware is required. First, the model of the rectifier is established, and the estimator is built. The current residuals with different faults can be obtained. Next, residuals are analyzed and features are acquired. Then, diagnosis functions are constructed, which are used for fault location and fault type identification. Finally, the feasibility and effectiveness of the method have been confirmed by the experimental results.

Fault Location Identification in Power Islands With Inverter Interfaced Distributed Generators

Fault Location Identification in Power Islands With Inverter Interfaced Distributed Generators

Design of an intelligent decision model for power grid fault location and isolation based on topology analysis

Detecting and isolating power grid faults quickly and efficiently ensures a constant power flow. The lack of speed and accuracy in conventional approaches to fault management highlights the importance of creating a smart decision model. The suggested approach uses topology analysis to better localize and isolate faults, reducing downtime and boosting grid performance. Real-time fault detection, precise fault location identification, and choosing the best isolation schemes are all difficulties in power grid fault management. Furthermore, significant issues need innovative solutions, such as managing the grid's complexity, responding to changing load conditions and guaranteeing cyber security. The present research unveils a new approach combining the Intelligent Decision Model integrated with the Supervisory Control and Data Acquisition system (SC-DAS). Here, Real-time decision-making, control, and monitoring are made possible by the SC-DAS system, which mediates between the intelligent decision model and the physical grid. In SC-DAS, Phasor Measurement Units (PMUs) are used for the real-time monitoring of the grid's electrical properties, which are collected using SCADA (Supervisory Control and Data Acquisition) systems. Further, SC-DAS uses decision-making models for instantaneous modifications and remedial measures of the grid's functioning. The grid's reliability uses Topological DAS for fault detection, localization, and isolation. Topological DAS represents a revolutionary approach that could significantly improve power grid reliability, reduce interruption frequency, and boost overall system performance in microgrids and renewable energy sources. The grid layouts, load circumstances, and fault scenarios used in the simulations help to improve the decision-making strategies in a controlled environment.

Optimized Fault Location Identification in Power Distribution Systems With Inverter-Interfaced Distributed Generations

This article proposes a fault location identification method for radial power distribution systems with inverter-interfaced distributed generations (IIDGs). The proposed method is not restricted to any specific types of data. It can fully utilize all available data types in power distribution systems to enhance the accuracy of results. The dataset may include synchronized voltage and current measurements collected by micro-phasor measurement units (micro-PMUs) and/or unsynchronized measurements collected by legacy measuring devices, active and reactive power measurements, pseudo measurements of load values, and virtual measurements based on Kirchhoff's laws. The proposed method explicitly considers uncertainties in the dataset and is applicable to different load types. The article proposes a mathematical model for representing IIDGs in fault location identification applications that does not require any synchronized sampled data or during the fault period data collection. Simulation results on IEEE 34-node feeder demonstrate the accuracy of the proposed method.

An interpretative fault location and section identification algorithm for a three-terminal non-homogeneous transmission network

An interpretative fault location and section identification algorithm for a three-terminal non-homogeneous transmission network

Deep learning-based application for fault location identification and type classification in active distribution grids

The high penetration of distributed energy resources, especially weather-dependent sources, even at the edge of the distribution grids, has increased the power system uncertainties and drastically shifted the operational status quo for the system operators. For the operators to ensure the uninterrupted electricity supply of the end-consumers, the fast and accurate response to fault events is of critical importance. This paper proposes a data-driven fault location identification and types classification application based on the continuous wavelet transformation and convolutional neural networks optimally configured through Bayesian optimization. This application leverages the proliferation of high-resolution measurement devices in distribution networks. It can locate the exact position of the short-circuit faults and classify them into eleven different types. Its intrinsic models grasp the spatial characteristics and the converted in frequency domain temporal ones of the three-phase voltage and current timeseries measurements stemming from the field devices, thus increasing the operators’ visibility of their networks in real-time. We conduct simulations through synthetic data, which we provide in an open-source repository, that replicate a wide range of fault occurrence scenarios with eleven different types, with the resistance ranging from 50Ω to 2kΩ and with duration from 20ms to approximately 2s, under noise conditions injected by devices and load variability. The results showcase the efficacy of the proposed method reaching an accuracy of 91.4% for fault detection, 93.77% for correct branch identification, 94.93% for fault type classification, and RMSE value of 2.45% for location calculation.

Phasor measurement unit application-based fault allocation and fault classification

<span>This paper makes a contribution to the field of fault location finding in a new way that helps in the improvement of grid reliability. This paper proposes a study-based approach for fault allocation and fault type classification that uses the study of voltage and current frequency during the abnormal condition. Although, ideally frequency of voltage and current are the same in the abnormal condition they may differ from each other. This difference in frequency is separately measured by the phasor measurement unit (PMU) block at MATLAB/Simulink platform. The PMU (PLL-based, positive-sequence) block is inspired by the IEEE Std C37.118.1-2011. In this approach, we measure the line voltage and current frequency variation with the help of installed PMU after this we present this measurement in characteristics form with the help of the scoping tool in MATLAB/Simulink and study them one by one, and proposed a conclusion for fault location identification and fault type classification. The proposed approach is able to identify the source side and load side fault location and also able to classify faults into two categories namely symmetrical and asymmetrical. The proposed approach is tested on two MATLAB/Simulink models and observed satisfactory.</span>

Intelligent Fault Diagnosis Using Deep Learning for a Microgrid with High Penetration of Renewable Energy Sources

—Fault diagnosis is essential to attain the microgrid’s stable, reliable, and economical operation. However, a microgrid’s fault current varies drastically based on the connected operating mode and connected renewable energy sources. This paper develops an intelligent fault detection and identification scheme for microgrids with high renewable energy penetration by combining Fourier-based Continuous Wavelet Transform (FTCWT) and Deep Learning (DL) methods. Several time-domain case studies representing different microgrid operating conditions were obtained and pre-processed in MATLAB/SIMULINK. The time-series data at the relay location are processed using an FTCWT to get time-frequency representation, and the sequence components waveforms were computed. The time-frequency representation and sequence components waveform contain helpful information, including fault conditions and healthy operating conditions for grid-connected and islanded microgrids that act as input datasets to the DL models. The proposed DL models perform fault location identification, fault type identification, and fault phase-detection for different operating modes. The development of the DL model for the fault detection/classification unit and fault location identification unit is carried out in the PyTorch framework. The experimental results show that the proposed model is more accurate when compared to the state-of-the-art methods.

Power quality improvement in power electronics systems using machine learning

This study focuses on improving power electronic components and devices, which have gained popularity due to their compact size and precise control over the output voltage and current. They are widely used in renewable energy systems, such as converters and inverters, and in industrial drives as control devices. However, the switching process in power electronic components introduces nonlinear behavior, leading to the generation of harmonics and power quality problems. To address these issues, various methods and techniques recommended by industry standards have been proposed to mitigate or eliminate harmonics and ensure the power quality. These standards provide limits and guidelines for assisting customers and manufacturers in maintaining acceptable power quality levels. Machine-learning algorithms offer a promising approach for improving power quality by leveraging data from a system to make predictions or classifications. In the context of power electronics, machine-learning algorithms can be trained to classify fault types, identify the exact location of faults, predict the remaining life of power electronic components, and detect voltage disturbances. Different machine learning techniques can be employed, depending on the specific application. For example, fault classification and fault location identification can be achieved through supervised learning algorithms, whereas predicting component life and detecting voltage disturbances can utilize techniques such as regression or anomaly detection. By leveraging the power of machine learning, enhanced reliability, performance, and efficiency can be achieved in various applications, thereby contributing to the overall advancement of power electronics technology.

Исследование практических аспектов ОМП на ЛЭП сверхвысокого напряжения по векторным измерениям на базе RTDS

Application of Phasor Measurement Unit (PMU) is a modern technology in electric power systems with quite a few possible applications. Remote identification of fault location (FL) at extra-high voltage transmission lines is one of the possible applications of PMU. The number of research works dedicated to this issue is rather limited. Also, there is no in-depth analysis of the PMU digital filter performance in case of short circuit; some factors that affect the FL accuracy are not covered; and the number of methods to identify FL remotely is insufficient. The current research goal is to study all the above-mentioned issues. The research has been conducted using the methods of mathematical modeling of fault-initiated transients represented with a real-time digital simulator RTDS (including the RSCAD software). A total of 16 FL expressions both in Russian and foreign scientific papers have been analyzed. Monte-Carlo method has been used to assess the impact of various factors on the FL accuracy. The PMU functionality presented in the RTDS simulator has been used to obtain required synchrophasors. A typical timespan of faults at extra-high voltage transmission lines has been deduced (it turns out to be at least three cycles of the fundamental frequency component). Dynamic characteristics of both M and P class PMUs have been thoroughly examined. The authors have made a conclusion that PMU-based FL is as accurate as that provided by conventional FL units. The influence of some major factors on the FL results has been investigated as well. PMU-based FL is considered to be a promising application. At the same time, P filter class PMUs are primarily used. One is to avoid getting synchrophasor data of different class PMUs at the line terminals. The obtained results are meant to be introduced to improve the PMU-based FL accuracy, especially in the context of processing real PMU data.

Ensemble voting‐based fault classification and location identification for a distribution system with microgrids using smart meter measurements

AbstractThis study presents an ensemble learning approach for fault classification and location identification in a smart distribution network containing photovoltaics (PV)‐based microgrid. Lack of available data points and the unbalanced nature of the distribution system make fault handling a challenging task for utilities. The proposed method uses event‐driven voltage data from smart meters to classify and locate faults. The ensemble voting classifier is composed of three base learners; random forest, k‐nearest neighbours, and artificial neural network. The fault location (FL) task has been formulated as a classification problem where the fault type is classified in the first step and based on the fault type, the faulty bus is identified. The method is tested on IEEE‐123 bus system modified with added PV‐based microgrid along with dynamic loading conditions and varying fault resistances from 0 to 20 Ω for both unbalanced and balanced fault types. A further sensitivity analysis has been done to test the robustness of the proposed method under various noise levels and data loss errors in the smart meter measurements. The ensemble method shows improved performance and robustness compared to some previously proposed FL methods. Finally, the proposed method has been experimentally validated on a real‐time simulation‐based testbed using a state‐of‐the‐art digital real‐time simulator, industry standard DNP3 communication protocol and a cpu‐based control centre running the FL algorithm.

Machine learning tools for active distribution grid fault diagnosis

Faults in power distribution networks cause customer minute and economic losses. A crucial part of the protection system of such grids is effective fault diagnosis for the acceleration of the restoration process after being subjected to any faults. This article presents the fault diagnosis approach for the active distribution grid, and the method consists of machine learning tools and signal processing techniques. The Hilbert-Huang transform (HHT) and discrete wavelet transform (DWT) are considered as the signal processing tools whereas the feedforward neural networks (FFNN) are considered as the machine learning tools. The extracted features using the signal processing tools are fetched into the neural network models for the development of fault detection, fault classification, and either fault location or faulty section identification models. The proposed approach is tested on two different distribution feeders. The first one is a simplified four-node test feeder modeled in MATLAB/SIMULINK environment. In contrast, the second one is the IEEE 13-node distribution network with the incorporation of three renewable energy resources (RER) modeled in the Real-Time Digital Simulator (RTDS) machine. The uncertainty, e.g., RER generation, load demand, and fault information, associated with the test feeders are modeled using different probability density functions. Obtained results demonstrate the efficacy of the proposed models for both noise-free and noisy data. Finally, the developed models show their independence in the variation of the pre-fault loading conditions, fault inception angle, and fault resistance.

Approach for Identification of Inter-Turn Fault Location in Transformer Windings Using Sweep Frequency Response Analysis

As inter-turn faults in transformer windings are the most severe, their delayed detection and localization may create extensive damage to the winding. Therefore, a new inter-turn fault localization methodology based on sweep frequency response analysis (SFRA) through calculation of Pre-fault Inter-Turn Fault Factor ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ITFF_pre</i> ) for all possible fault locations by estimating the values of series and shunt capacitance of the windings during healthy conditions is presented. For a faulty transformer, Post-fault Inter-Turn Fault Factor ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ITFF_post</i> ) is calculated by determining the value of equivalent capacitance using SFRA. The exact fault location in the faulty transformer is obtained by comparing the single calculated value of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ITFF_post</i> of the faulty transformer with the values stored in the lookup table. The efficacy of the suggested approach is verified by performing faults at various locations on the transformer model developed in the laboratory. Its authenticity is also verified on an existing 400 kVA, 11 kV/440 V distribution transformer installed in the real field. The results indicate that the suggested method is capable to identify inter-turn fault location with the minimum ladder network parameters. Finally, a relative assessment of the suggested approach with numerous other methods confirms the superiority in terms of inter-turn fault localization.

Fast Low Frequency Fault Location and Section Identification Scheme for VSC-Based Multi-Terminal HVDC Systems

Penetration of DC networks is rapidly increasing in modern power systems. As transient behavior of DC grids is different from that of AC ones, AC grid protection schemes might not be applicable to protect DC networks. Exact fault location is vital for protection of DC networks to reduce repair cost and achieve fast power recovery. This paper proposes a novel topology independent multi-terminal DC scheme to identify fault section and location for DC transmission systems. Voltage and current signals measured at the terminals are analyzed by the least squares error (LSE) algorithm. Then, the proposed scheme uses low frequency components of the signals to locate DC faults offline. Unlike some other methods, it does not require sophisticated sensors and hardware to capture very high frequency content of the signals. Hence, the proposed scheme can be implemented in practice without additional measuring infrastructure. The performance of the proposed scheme is evaluated under various fault conditions for different topologies of DC networks in both overhead lines and cables. Numerous simulations carried out demonstrate that the proposed scheme is quite accurate and provides excellent performance under different fault resistances, sections, and locations.

A Fault Location Identification Technique for Active Distribution System

Impedance based fault location schemes using substation measurements are becoming absolute due to the introduction of distributed generation and islanding-mode operating capability of distribution system (DS). In this article, a new fault location algorithm is presented for active distribution systems using microphasor measurement units (μPMU) and smart meter (SM) data. The line current data obtained from the μPMUs in the DS are exploited to divide the system into different zones. To identify the faulted zone, a fault zone identification parameter is devised based on the pre and during fault positive sequence current injection data recorded in the μPMUs. Next, fault location analysis is limited to the identified zone to reduce the computational complexity. To locate the fault within the identified zone, the work proposes two parameters for the identification of bus nearest to fault (BNF) and faulted line section connected to the identified bus. The scheme does not require the information about the type of ground faults and is applicable to balanced, unbalanced, grid connected and as well as islanded distribution systems.