Fault Location Algorithm Research Articles

An ultra-fast MMC-HVDC fault location algorithm based on transient voltage features and regression neural network

An ultra-fast fault location algorithm based on the single-ended transient voltage features and regression neural network (RNN) is proposed, which utilizes 2.5 ms postfualt data window and is suitable for modular multilevel converter-based high-voltage DC (MMC-HVDC) grids equipped with quick-action protections and hybrid DC circuit breakers (HDCCBs). Firstly, the analyses based on the lumped RLC equivalent circuit demonstrate that the delay time, the first negative peak time and its value all have exact relationships with the fault location. Nevertheless, considering the actual parameters and topology of the MMC-HVDC grid, three features can only be approximately extracted. Thus, RNN is utilized to estimate fault locations. 2.02 × 104 distinct fault cases validate the algorithm’s high accuracy across all fault locations and transition resistances up to 1005 Ω. It can well tolerate reasonable deviations of line parameter and current limiting reactor value, as well as 40-dB white noise. Besides, it also has remarkable adaptability, and can be suitable for different systems.

High‐speed algorithm for fault detection and location in DC microgrids based on a novel time–frequency analysis

AbstractProtecting DC microgrids (DCMGs) from faults is critical due to the rapid current changes that occur in milliseconds. However, ensuring fast and accurate protection in DCMGs is more challenging than in AC systems. This study proposes a novel protection algorithm using traveling waves (TWs) for fault detection and localization. The high‐order synchrosqueezing transform (FSSTH) is applied to precisely identify TWs at the relay location. FSSTH offers a sharp time–frequency representation, enhancing the accuracy and speed of fault detection. This method can accurately detect transient phenomena like TWs in DCMGs, even with noise and variable fault resistance. By using the spectral envelope with FSSTH, ridges in time–frequency representations are extracted, improving fault diagnosis. The approach differentiates external from internal faults and recognizes fault direction by assessing TW polarity. Testing on two different DCMGs showed this algorithm's high efficiency and accuracy, with fault location errors ranging from 1 to 50 meters in low‐voltage and 13 to 64 meters in medium‐voltage DCMGs, even under challenging conditions like high resistance (up to 500 Ω) and low signal‐to‐noise ratio (5 dB). These results demonstrate the method's superior accuracy and robustness compared to existing techniques.

A fault detection and location algorithm for the LVDC interconnection network in rural area

AbstractLow voltage DC (LVDC) microgrids (MGs) can be linked together through an interconnection network to enhance the utilization of their energy resources in remote locations, particularly in rural low‐income areas. However, the identification of the fault is challenging due to the fast fault transients and equipment limitations, where there are no sensors and DC circuit breakers (DCCBs) in the lines. To solve this problem, this article proposes a fault detection and location algorithm without requiring extra sensors and DCCBs in lines. The proposed algorithm uses the sensors of the interface converters to detect the fault. Following this, a coordinated current injection method is used to identify the faulty element by coordinating converters with disconnectors. This process employs two strategies “weight check” and “scope check” to minimize the time and the number of actions. The algorithm is robust to various fault impedance, fault types and network topology modifications. The effectiveness of the algorithm is validated through a series of simulation case studies.

Research on single-phase grounding fault location technology in distribution networks based on impedance method

This article mainly introduces the impedance based single-phase grounding fault location method for distribution networks, including its theoretical basis, algorithm steps, and simulation verification. First, starting from the impedance analysis of the transmission line model, the method of accurately measuring the location of the fault point through phase domain analysis is explained. Next, the process of impedance analysis for single-phase grounding faults was described in detail, that is, how to solve the impedance of the grounding fault points by calculating the voltage and current signals. Then, the specific process of the impedance based grounding fault location algorithm was introduced, including the calculation of equivalent load impedance, the calculation of starting voltage and current, the calculation of grounding current, and the solution of fault point location. Finally, simulation verification was conducted using an IEEE 34 node distribution system example in a MATLAB/Simulink environment, and the results showed that the algorithm has high positioning accuracy, with a maximum error of within 3%.

Missile Fault Detection and Localization Based on HBOS and Hierarchical Signed Directed Graph

The rudder surfaces and lifting surfaces of a missile are utilized to acquire aerodynamic forces and moments, adjust the missile’s attitude, and achieve precise strike missions. However, the harsh flying conditions of missiles make the rudder surfaces and lifting surfaces susceptible to faults. In practical scenarios, there is often a scarcity of fault data, and sometimes, it is even difficult to obtain such data. Currently, data-driven fault detection and localization methods heavily rely on fault data, posing challenges for their applicability. To address this issue, this paper proposes an HBOS (Histogram-Based Outlier Score) online fault-detection method based on statistical distribution. This method generates a fault-detection model by fitting the probability distribution of normal data and incorporates an adaptive threshold to achieve real-time fault detection. Furthermore, this paper abstracts the interrelationships between the missile’s flight states and the propagation mechanism of faults into a hierarchical directed graph model. By utilizing bilateral adaptive thresholds, it captures the first fault features of each sub-node and determines the fault propagation effectiveness of each layer node based on the compatibility path principle, thus establishing a fault inference and localization model. The results of semi-physical simulation experiments demonstrate that the proposed algorithm is independent of fault data and exhibits high real-time performance. In multiple sets of simulated tests with randomly parameterized deviations, the fault-detection accuracy exceeds 98% with a false-alarm rate of no more than 0.31%. The fault-localization algorithm achieves an accuracy rate of no less than 97.91%.

Intelligent distribution network fault monitoring integrating differential evolution and chaos whale optimization algorithm

Faced with rapid development and increasingly complex power grid structure structure of the power grid, it is necessary to accurately locate faults in modern intelligent distribution networks in order to ensure power supply reliability and improve power supply service quality. Aiming at the low positioning accuracy, long time consumption, and limited coverage types in existing positioning algorithms, a new intelligent distribution network fault positioning algorithm is designed by combining two algorithms to complete the monitoring task. Firstly, intelligent distribution network fault location methods under different distributed power grid connection methods are analyzed. Then, considering the distributed power grid connection, a fault location algorithm is designed by combining differential evolution algorithm, Sine chaotic mapping, and whale algorithm. The research results indicated that the designed method had good benchmark performance, with accuracy and recall values as high as 0.98 and 0.97, respectively. During the training process, it only required 175 iterations to reach a stable state. In practical applications, the accuracy of this algorithm in testing three types of faulty power grids was 98.90%, 98.41%, and 98.25%, respectively. The method can effectively improve the fault location accuracy, providing better positioning technology for fault monitoring problems in power systems.

Combination of Matrix and Genetic Algorithms for Fault Location of Active Distribution Network

Abstract Timely and effective location of faults in distribution networks is important for restoring power supply and dealing with faults. In this paper, a method combining matrix algorithm and genetic algorithm (GA) is proposed to determine the fault section. Firstly, the matrix algorithm is applied to locate the fault section initially by combining the alarm information uploaded from the feeder terminal unit (FTU). Secondly, in order to deal with the problem of outputting wrong location results, the switching function of the active distribution network is constructed to check the location results calculated by the matrix, and the results that do not meet the checking will be treated as a suspicious fault set, and the relevant evaluation function is constructed and optimized using the genetic algorithm, so as to arrive at the correct fault zone location results. The results of the example simulation show that the method used in this paper can achieve fast and fault-tolerant fault location in active distribution networks.

Gaussian Process Regression-Based Predictive Fault Location Algorithm for Three-Terminal Lines Using Two-Terminal PMUs

Gaussian Process Regression-Based Predictive Fault Location Algorithm for Three-Terminal Lines Using Two-Terminal PMUs

Faults locating of power distribution systems based on successive PSO-GA algorithm

As the terminal of the power system, the distribution network is the main area where failures occur. In addition, with the integration of distributed generation, the traditional distribution network becomes more complex, rendering the conventional fault location algorithms based on a single power supply obsolete. Therefore, it is necessary to seek a new algorithm to locate the fault of the distributed power distribution network. In existing fault localization algorithms for distribution networks, since there are only two states of line faults, which can usually be represented by 0 and 1, most algorithms use discrete algorithms with this characteristic for iterative optimization. Therefore, this paper combines the advantages of the particle swarm algorithm and genetic algorithm and uses continuous real numbers for iteration to construct a successive particle swarm genetic algorithm (SPSO-GA) different from previous algorithms. The accuracy, speed, and fault tolerance of SPSO-GA, discrete particle swarm Genetic algorithm, and artificial fish swarm algorithm are compared in an IEEE33-node distribution network with the distributed power supply. The simulation results show that the SPSO-GA algorithm has high optimization accuracy and stability for single, double, or triple faults. Furthermore, SPSO-GA has a rapid convergence velocity, requires fewer particles, and can locate the fault segment accurately for the distribution network containing distorted information.

Fault monitoring method for misalignment replacement operation error of electricity acquisition system based on internet of things engineering evaluation

AbstractWith the rapid development of Internet of Things (IoT) technology, electricity collection systems have been widely used in various fields. It can connect various items to the internet and achieve remote monitoring and maintenance of devices. During the operation of the electricity collection system, there may be issues such as misalignment due to various reasons, which can lead to errors in data collection and affect the accuracy and stability of the system. How to timely monitor the operational errors of the system and replace and repair them has become an urgent problem to be solved. The fault location algorithm can accurately diagnose the cause of the fault and provide corresponding repair suggestions, thereby reducing maintenance costs and optimizing the efficiency of the electricity collection system. This article would analyze the monitoring method for the misalignment replacement operation error of the power acquisition system based on IoT engineering analysis, and used fault location algorithms to locate its misalignment. The research results indicated that, under the same other conditions, the total satisfaction score of the X system was 253 points, and the total satisfaction score of the Y system was 141 points. The score of the X system was much higher than that of the Y system. The results indicated that IoT engineering analysis could optimize the monitoring method for operational errors caused by inaccurate replacement of electricity acquisition systems, and there was a positive relationship between the two.

Advancements in Fault Location in Electrical Power Systems with Distributed Generation using Deep Learning

Distributed generation (DG) has changed the energy landscape by increasing system reliability and decreasing environmental impacts through its incorporation into electrical power systems. The additional complexity brought forth by DG, however, also calls for the creation of more precise and effective fault finding techniques. This abstract explores current developments in fault localization strategies for DG-integrated electrical power systems using deep learning (DL) methods. Due to the non-linear, dynamic, and ever-changing nature of DG-rich networks, traditional fault finding algorithms generally struggle to correctly locate faults in these networks. Deep learning's potential as a solution may be seen in its capacity to recognise complex patterns and adjust to new information. Specifically, this study looks into how DL models like CNNs and RNNs can be used to improve the precision with which faults can be detected and localised in DG-integrated power systems. The creation of DL-based fault location algorithms that use high-resolution data from different sensors like smart metres and phasor measurement units (PMUs) is a major advancement. These algorithms draw on the temporal and spatial information available in power system data to pinpoint the exact location of malfunctions. Additionally, the study looks into the stability and transferability of DL models using various DG technologies and setups. Pre-trained models are transferred learning approaches to guarantee adaptability and reliability across a wide range of DG applications. The findings show that in DG-rich contexts, fault location algorithms based on DL perform much better than their conventional counterparts. These developments have the potential to strengthen electrical grids, reduce the frequency and duration of outages, and improve the reliability of today's power systems. The incorporation of deep learning into fault location algorithms is an important step towards building a more dependable and robust power system, especially as DG integration continues to grow

A ground fault location algorithm in double‐circuit transmission lines with T‐off connection to an industrial microgrid by using current and voltage phasors information of a single terminal

AbstractThe paper presents a ground fault location algorithm (GFLA) in double‐circuit transmission lines (DCTL) with a T‐off connection to an industrial microgrid (IMG) containing electric arc furnaces (EAF), renewable distributed generations (RDG), and static VAR compensator (SVC). The design presented utilizes the information on the current and voltage phasors of one side of the transmission lines (TL) during the fault period, and there is no need for the availability of information on the type of faulty phase(s) and even the faulty section. To find the exact location of the fault in this design, the voltage phasor equations governing the fault point in positive, negative, and zero‐sequence (PNZS) domains, the zero‐sequence current (ZSC) equation of the fault point, and the boundary condition equation of the fault point have been used. The simulation system is implemented in Simulink/MATLAB using accurate modelling of all network components. The algorithm has been tested and evaluated in different fault conditions, including various locations, phase types, phase‐to‐ground connection resistance, occurrence time, and microgrid (MG) operation modes. The successful results of various tests of the proposed design as well as the analysis of various sensitivities confirm the satisfying performance of the suggested algorithm.

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

Generalized Equivalent Circuit and Algorithm for Single-End Fault Location in Transmission Systems

Generalized Equivalent Circuit and Algorithm for Single-End Fault Location in Transmission Systems

Исследование точности определения места повреждения воздушных линий по данным от устройств синхронизированных векторных измерений различных классов и производителей

The technology of synchronized phasor measurements has been widely used in the Russian power system to analyze the parameters of steady-state electrical power modes and to record electro-mechanical transient processes. Issues on fault location based on synchronized phasor measurements have mainly been discussed in foreign publications. A significant drawback of most research papers on this issue is simplified modeling of both overhead lines and current and voltage measurement channel as well as digital filters of phasor measurement units (PMUs). The goal of this research is to develop a fault location algorithm based on synchrophasor measurements and to study its accuracy by using specialized equipment including a real-time digital simulator (RTDS), PONOVO current and voltage amplifiers and production-grade PMUs. A PMU-based double-ended fault location algorithm is developed using long-line equations and well-known electromagnetic transient theory concepts. Currents and voltage oscillograms of both overhead transmission line ends are modelled in the MATLAB/Simulink software package. These oscillograms are saved as COMTRADE files and played back using the RTDS hardware and all-in-one software package RSCAD. In addition, the study uses two production-grade PMUs, the first one is TPA-02, and the second one is a merging unit ENMU acting as a PMU, thus imitating PMUs at the line terminals. To time-align all the measurements and to aggregate PMU data frames, various auxiliary software such as PMU Connection Tester, and hardware is used. A double-ended fault location (FL) method utilizing synchrophasors under a fault has been developed. The method is based on an overdetermined system of nonlinear equations that describes physical processes in an overhead power line and can be applied under various fault types. An integrated study of the efficiency of the developed FL method has been conducted. FL errors have been computed using production-grade PMUs ENMU and TPA-02, along with the RTDS and other equipment. The authors have considered the case of configuring PMUs of different classes at the overhead line terminals, and different phasor reporting rates as well. The conducted experiments make it possible to reveal that the fault location errors do not exceed the thresholds imposed by the standard STO 56947007-29.120.70.241-2017 in 88 % of all the analyzed fault scenarios. The developed FL method makes it possible to achieve the accuracy required by regulatory guide in calculating the distance to the short circuit point in most of the cases examined. Based on the results of numerical experiments for various types of fault cases, it can be concluded that the PMU class and phasor reporting rate do not have a significant impact on the FL accuracy, provided that the fault duration is enough for the PMU filter to approach a steady output.

A Fault Location Algorithm for Multi-Section Combined Transmission Lines Considering Unsynchronized Sampling

This paper introduces a fault location technique for multi-section combined transmission lines, utilizing unsynchronized fault record data. The approach determines the intersection points of the voltage magnitudes computed using the fault current and voltage records of each terminal and the constant line parameters of each section. Finally, the real fault location is identified based on the calculated inverse angle of equivalent impedance. To evaluate the performance of the proposed algorithm, a three-section combined transmission line is constructed using the PSCAD/EMTDC software version 5.0.0, and various fault scenarios are simulated and tested employing the proposed algorithm. The results indicate that the proposed algorithm is applicable to various types of faults in a multi-section combined transmission line; the estimations are accurate.

Design of fault location algorithm based on online distributed travelling wave for HV power cable.

To tackle the challenge of localization failure due to traveling wave dispersion during a mid-line fault in a long high-voltage cable, this study conducts an in-depth analysis of the refraction characteristics of the cumulative three-phase sheath currents at cross commutation points and direct grounding points, facilitated by detailed theoretical derivation. We introduce the novel concept of a 'first traveling wave attenuation index,' which quantifies the peak of the initial faulted traveling wave. We strategically place distributed traveling wave detection devices solely in the first direct grounding box of each cross-interconnected main section, effectively segmenting the line into five distinct zones. These zones are identified based on the unique transient polarities exhibited by the first traveling wave of fault current at each impedance mismatch point along the cable. To overcome the issue of weak traveling wave signals collected at the first end measurement point, we propose an innovative peak detection method for the first wave. This method harnesses the power of empirical wavelet transform (EWT) and multi-resolution singular value decomposition (MRSVD), providing a significant boost in ranging accuracy compared to traditional wavelet methods. Simulation results validate the efficacy of our proposed fault location method, which accurately pinpoints the fault section by contrasting the polarity of the first wave peak of the sheath current detected at each measurement point. Notably, our method ensures safety and convenience as the equipment does not require direct contact with high voltage.

Self-Healing Photovoltaic Microconverter With Zero Redundancy and Accurate Low-Cost Fault Detection

This paper studies a zero-redundancy fault-tolerant dc-dc series resonant converter (ZR-SRC) for residential photovoltaic (PV) applications. This converter can handle faulty conditions on-the-fly using topology morphing control without increasing the number of components. Furthermore, it can withstand a single fault in both input and output bridge semiconductors, i. e., up to two semiconductor faults. The proposed dc-dc converter could increase the availability of residential photovoltaic-powered dc microgrids. Comprehensive fault diagnosis and localization algorithms are proposed for MOSFETs on both sides of the ZR-SRC. The paper proposes а short-circuit fault (SCF) detection in the input-side MOSFETs using the existing PV voltage measurements, decreasing the converter cost and size. The open-circuit fault (OCF) in the output-side MOSFETs is detected by sensing the voltage of the bottom two switches sharing the same ground with the dc bus. In addition, the post-fault operation of the converter is thoroughly analyzed. Recommendations for the PV power clipping are given to ensure the safe operation of the converter after a fault. A 300 W experimental setup is built to demonstrate the feasibility and performance of the proposed fault-tolerant converter.

Accurate Fault Location Algorithm for Untransposed Transmission Lines Based on Network Phasor Equations in Positive-, Negative-, and Zero-Sequences Domain During Fault

Accurate Fault Location Algorithm for Untransposed Transmission Lines Based on Network Phasor Equations in Positive-, Negative-, and Zero-Sequences Domain During Fault

Research on visualization monitoring technology of vulnerable high-voltage electrical equipment in substation based on BP artificial neural network

Abstract This study develops a visualization monitoring system for substation equipment operational status, utilizing mobile monitoring technologies. The system architecture integrates a core functional module aligned with comprehensive system requirements, enhanced by a BP neural network to optimize the server’s data mining capabilities. The research focuses on the analysis of typical faults in crucial substation electrical equipment, applying a Fourier algorithm for preprocessing the fault data. Employing the diagnostic principles of the BP neural network, the study designs a thermal fault diagnosis process for the substation apparatus. Experimental scenarios were established to evaluate the BP neural network’s performance by comparing three linear regression sample values. The practical application of the BP neural network model was assessed through integration with substation field data. Cross-validation of the field data indicates that the fault location algorithm accurately identifies 11 types of faults from 85 alarm signals in the secondary condition monitoring of substations, achieving a reliability of 98% or higher, which underscores its high applicability and operational feasibility.