Fault Location Scheme Research Articles

Convolutional neural network approach for fault detection and characterization in medium voltage distribution networks

Power outages significantly impact the power industry by disrupting social welfare and economic stability. Still, existing methods for fault detection face challenges due to load and network topology, conditions, and installed equipment. However, recent advances in artificial intelligence (AI) are enabling researchers to create alternative approaches for fault detection and location strategies. Therefore, this paper introduces a novel method for detecting, classifying, and locating faults in power systems through voltage waveform analysis using a convolutional neural network (CNN) integrated with the Piecewise Function Put Together (PFPT) algorithm for fault detection and fault zone localization in a power distribution network. Utilizing Park's transformation, noise reduction PFPT sine fitting, and CNNs, the proposed method distinguishes between 'healthy' and 'faulty' conditions. Simulation results reveal that while the voltage Park's vector time behavior of a healthy system remains stable, it exhibits circular or mixed patterns under faulty conditions. These patterns enable the identification of four types of short circuit faults—single-line-to-ground (LG), line-to-line (LL), line-to-line-to-ground (LLG), and three-line (3L) faults—by analyzing 3D voltage Park's waveforms at network buses. The study validates fault type identification through the observation of rotating Park vectors from sine fitting of time-based voltage waveforms. By converting 3D voltage waveforms into high-resolution images, the method utilizes a CNN for fault recognition, achieving an accuracy of 93.1%. This innovative approach underscores the robustness and precision of combining traditional electrical engineering techniques with modern AI.

Time-Domain Fault Detection and Location Scheme for Flexible DC Distribution Networks

Accurately detecting and locating the fault point of the DC line is significant for eliminating the fault and restoring the power supply of the flexible DC distribution network as soon as possible. Firstly, a direction pilot protection scheme for a complex DC distribution network is proposed based on the integral of the current superposition to identify the fault direction. Then, an online time-domain fault location method based on the least square (LS) method to solve the overdetermined equations is proposed by analyzing the fault loop circuit after the DC line fault. The proposed location scheme utilizes fault data at both ends of the line to eliminate the theoretical impact of fault resistance, and the calculation of the current difference method is discussed to reduce the location error of whether the fault current-limiting reactor (CLR) exists. Finally, various simulations by PSCAD/EMTDC V4.5 demonstrate that the proposed scheme has high protection reliability and location results after different fault positions and resistances. The proposed scheme has low requirements for the sampling rate, fault data length, and implementation costs, which can meet practical application requirements.

Two-terminal traveling wave fault location approach based on frequency dependent electrical parameters of HVAC cable transmission lines

With the expansion of high-voltage alternating current transmission networks, the widely used two-terminal traveling wave method faces several challenges in cable fault location. On one hand, the frequency dependent characteristics of longer cable transmission lines can significantly impact the accuracy of traveling wave propagation velocity. On the other hand, the processing of traveling wave signals obtained synchronously is also a crucial factor that requires attention. To enhance the performance of cable fault location, the principles of two-terminal traveling wave method need to be updated. Firstly, the cable model derived from the theory of wave propagation in multi-conductor systems is presented, which reflects the frequency dependent characteristics of the electrical parameters. Whereupon, an innovative cable traveling wave fault location scheme primarily composed of arrival time identification and instantaneous frequency calculation is proposed. Eventually, based on electromagnetic transient simulation software PSCAD/EMTDC, the robustness of the innovative cable fault location method against fault type, fault location, fault resistance, fault initial angle and earth resistivity issues is demonstrated.

Fault indicators allocation to maximize the performance of a fault locator based on artificial intelligence

Fault location (FL) is one of the main challenges in Advanced Distribution Automation (ADA) of Active Distribution Networks (ADN). One of the commonly used strategies by utilities to deal with this challenge is the use of Fault Indicators (FIs), which indicate to the operator the path taken by the fault current. However, a good performance of this scheme depends on the number of installed devices, a high number of them could cause a high cost for the utility investment planning. In this context, this paper presents an artificial intelligence-based fault location strategy that determines the number and location of FI into ADN to maximize performance in fault section estimation. To achieve this objective, the ADN is divided into sections, and the FL problem is modeled as a classification problem to train an Artificial Neural Network (ANN). To determine the number of FIs to be installed and their location, the strategy uses the three-phase current magnitudes measured by the FI as features for an ANN model. Also, the strategy uses a feature selection and tuning scheme based on a multiverse optimization algorithm (MOA) to identify the features that maximize the accuracy of the ANN model. The strategy was validated on the IEEE123-node test feeder. The results showed accuracy close to 99.4 % with a reduction of 40 % of the number of FIs when compared with other method. The strategy shows its simplicity and promising prospects to apply it in the utility's investment planning.

Novel traveling wave fault location method for HVDC transmission line based on wavefront frequency

The key of traveling wave (TW) fault location (FL) method is the calculation of TW velocity and the calibration of TW wave head. In order to achieve high-precision FL, detailed transmission line (TL) parameters are needed to accurately calculate the TW velocity. In view of the problem that the existing high-precision TWFL methods are highly dependent on the calculation of TW velocity, the paper contributes to following aspects: (i) the mapping relationship between wavefront frequency and fault distance/fault pole is constructed; (ii) a neural network model suitable for FL and fault pole identification is designed, which is optimized by Particle Swarm Optimization (PSO) algorithm and Levenberg–Marquardt (L-M) algorithm; (iii) a complete FL scheme is proposed. In order to verify the FL accuracy of proposed method, an ±800 kV bipolar high-voltage direct-current (HVDC) transmission system model is constructed using PSCAD/EMTDC. According to the simulation results, the errors of FL is less than 0.2 km and the accuracy of fault pole identification is 100%. For the problems of fault type, fault resistance and noise, the proposed method shows high robustness.

A fast data-driven fault detection and location method for unknown distributed thermal processes

The distributed thermal processes are widely present in various industrial operations, but the presence of time delay effect and unknown model information make achieving fast and timely fault detection challenging. Furthermore, the involvement of spatiotemporal coupled data in measurement processes further exacerbates the situation. To solve these problems, this paper proposes a data-driven fault detection and location method for thermal processes described by distributed parameter systems (DPS). Firstly, a Time/Space separation is employed to decoupling the spatio-temporal data into spatial basis functions and temporal coefficients. Subsequently, dynamic partial least squares (DPLS) is employed to obtain the temporal information of the model with fault-free data. Monitoring statistics in both the temporal and spatial domains are then constructed, and their thresholds are estimated by kernel density function. Finally, an online strategy for fault detection and location is presented. The method was validated on the catalytic rod, and an experimental snap curing oven.

A fault location scheme based on composite traveling wave with transients in adjacent fault-free lines

As the speedy expansion of power gird, the traveling wave fault locator in substation may not always connect to newly added transmission lines in time, which reduces the effectiveness of fault locator. In this paper, an adjacent fault-free line based fault location scheme for newly additional line is proposed. Since the traveling wave from adjacent fault-free line results in severe difficulty for identification of traveling waves from faulty line, the time–frequency matrix is performed to screen traveling waves. Firstly, in order to remove the influence of waves from remote terminals of adjacent fault-free line, the current refraction coefficient is theoretically analyzed. Due to the negative refraction coefficient, traveling waves from remote terminals of adjacent fault-free line can be reduced by the proposed composite traveling wave. Secondly, the time–frequency matrices of composite traveling wave and original traveling wave is obtained by S-transform. Then, these matrices are applied to form ratio matrix which thoroughly eliminated waves from remote terminals of fault-free lines. Moreover, the robustness of the proposed fault location scheme is thoroughly validated by the simulation of PSCAD/EMTDC. Finally, the proposed fault location method has the great accuracy in simulation tests and field data test.

ROCOV scheme for Fault Detection and Location in HVDC systems

A reliable DC fault protection system is essential for the development of HVDC grids. Therefore, this paper deals with the voltage derivative ROCOV scheme to locate and detect DC faults. The algorithm is able to differentiate internal and external faults considerably fast. The proposed algorithm is analyzed in a HVDC grid with different fault case scenarios. Finally, the ROCOV protection thresholds are discussed.

A Petri Net Strategy for Fault Diagnosis and Location in Power Distribution Systems to Prevent Local Power Shortages

A Petri Net Strategy for Fault Diagnosis and Location in Power Distribution Systems to Prevent Local Power Shortages

Location of Faults in Six Phase Transmission Line using a Neuro Fuzzy System

Fault location plays an important role in power systems. It is frequently used in transmission lines to reduce the system damage for growing electrical energy demand. However, the location estimation method itself is easy to be disturbed by different parameters and gives estimation errors. To solve this problem, this paper introduces a scheme that uses neuro fuzzy system (NFS) to locate all types of faults such as shunt faults, transforming faults, series faults and simultaneous faults in six phase overhead line (SPOL). This scheme has been performed on a 68 km, 138 kV, 60Hz, SPOL for all fault types by employing MATLAB software Simulink. Here, Haar wavelet transforms (HWT) are used to extract the current signals in order to evaluate the behavior and characteristics of higher frequency components. Then, the obtained HWT currents data are employed to construct the fault location scheme in SPOL based on NFS. The obtained experimental results confirm that by using the NFS, the maximum fault location error (MFLE) can be reduced by more than 10%. It can further reduce the maximum response time (MRT) for the operator to address the faults and ensure the power system reliability in the future. The test result executed in this investigation indicates that the NFS is resilient to wide changes in fault conditions, and it shows good performance and huge potential for location of faults in SPOL.

Enhanced fault localization in multi-terminal transmission lines using novel machine learning

Accurate fault location on transmission lines is paramount for ensuring the reliable and efficient operation of the electricity grid, which underpins every aspect of modern society. Existing fault localization methods for transmission lines often face shortcomings, particularly in scenarios involving multi-terminal transmission lines, where complexities arise due to dispersed generations and intricate network configurations. Traditional approaches may struggle to provide accurate fault localization, impacting the reliability and efficiency of the electricity grid. Research provide a unique fault localization technique in this article depends on Phasor Measurement Units (PMUs) and Bidirectional Gradient Boost Random Forest (BDGB-RF) machine learning technique to address these challenges. The proposed method offers several advantages over traditional methods, including enhanced accuracy and efficiency. By leveraging PMU data and BDGB-RF, the method provides a two-phase fault localization strategy, incorporating fault line selection based on nodal current imbalance and subsequent fault distance determination. Simulation results demonstrate the effectiveness of the proposed approach, achieving up to 97% accuracy in the majority of studied scenarios, even under different tapping configurations. The adoption of this approach could significantly impact the practices of experts in the field, facilitating more reliable fault detection and localization in complex transmission line networks. This, in turn, can contribute to the resilience and stability of power systems, ultimately improving grid reliability and minimizing downtime.

Capacitor voltage change rate based fault localization strategy for modular multilevel converters

Capacitor voltage change rate based fault localization strategy for modular multilevel converters

A new fault location scheme based on local measurements for transmission lines connected to inverter-based resources

This paper aims to reveal conventional impedance-based fault location methods’ shortcomings in the presence of inverter-based resources (IBRs) through an analytical framework established for the analysis of the IBR sequence impedances. Thereafter, a single-ended method is put forward in order to accurately specify the fault location in the lines emanating from the IBRs. The proposed fault location formulation utilizes the negative and zero sequence currents as alternative polarizing currents, therefore it is independent of the pre-fault data, and it can be applied for all kinds of asymmetrical short circuit fault. Furthermore, the established formulation is further amended to make it applicable to long transmission lines. The suggested method also provides acceptable accuracy in various conditions, such as different fault resistances, different host grid codes, and dynamic behavior of the fault resistance. The performance of the proposed scheme is scrutinized using the PSCAD/EMTDC software with an extensive number of simulations. The obtained results reveal the effectiveness and efficacy of the proposed fault location method in the presence of IBRs.

Fault nature identification and location scheme for distribution network based on active injection from IIDG

In view of the difficulty in passively identifying the nature of faults in distribution networks and the high equipment cost of existing active detection schemes, a permanent fault identification and location scheme using an inverter-interfaced distributed generator (IIDG) connected to feeders for signal injection is proposed. After the faulty feeder is isolated, the power electronics in the IIDG are switched on for a short time and a voltage signal is injected into the feeder through the modified LCL filter and grid-connected transformer. The analysis shows that the aerial mode voltage of the feeder will not remain 0 under permanent faults. A highly sensitive criterion with fixed thresholds is constructed using approximate entropy (ApEn) to identify fault nature. Then the fault distance is further calculated based on the detected mutation point of the aerial mode voltage under permanent faults. The correctness of the theory and the effectiveness of the scheme are verified in PSCAD/EMTDC simulations.© 2017 Elsevier Inc. All rights reserved.

A Novel Fault Identification and Localization Scheme for Bipolar DC Microgrid

Traditional fault detection and location schemes become ineffective for detecting and locating the fault in DC microgrid systems due to integrating various types of power electronic-based DC loads and generators. To resolve this issue, advanced, intelligent, specialized fault detection and location schemes are necessary. This article suggests a novel fault detection scheme based on the difference in the teager energy available in the DC current wave at sending and receiving ends of lines. After the detection of the fault, the location is calculated by estimating the resistance of the cable up to the fault point as well as the total resistance of the cable, with the help of least square technique. The proposed scheme decides fault is internal if the estimated fault location is less than 1 p.u. Otherwise, the proposed scheme decides fault is external. A DC microgrid with different types of generating units and loads is simulated using MATLAB/SIMULINK to evaluate the developed algorithm. Internal and external faults, pole-to-ground and pole-to-pole faults with changing fault resistance and fault location are some of the fault scenarios that have been simulated. The obtained simulation results prove that the suggested algorithm can discriminate between internal and external faults and locate the fault. The proposed technique is also examined on a DC microgrid hardware testbed and results show the efficiency of the suggested approach.

Ground fault identification and location of autotransformer traction network for high‐speed railway based on multi‐terminals synchronous measure information

AbstractWith the rapid development of high‐speed railway, the auto‐transformer traction network (ATN) is also expanding. However, due to the improved performance of electric multiple units (EMUs) and the non‐linear relationship between impedance and ground fault distance, distance protection has limited coverage and cannot accurately locate the fault distance by measuring impedance. This paper presents a model of the ATN considering the presence of EMUs, which effectively demonstrates the characteristics of current distribution under the ground faults. Furthermore, a ground fault identification method based on multi‐terminal currents is proposed to enhance the accuracy of fault identification and differentiate faults and EMUs. The mapping relationship between current distribution and fault location is derived, and an accurate fault location method is proposed. Compared to existing methods, this method is not affected by EMUs and AT parameters. The fault identification and location scheme for ATN employs multi‐terminal synchronous information. Finally, through simulation and experimental setups, case studies have been conducted to verify the accuracy and robustness of the proposed method in various fault scenarios. Certainly, this research has significant application prospects in fault analysis and detection for HSR.

A Flexible DC Ranging Scheme Based on Variational Mode Decomposition and Teager Energy Operator

In this paper, a new Cable fault location scheme is proposed for the existing flexible DC Cable fault location methods, in which the fault signal is mixed with noise and the traveling wave speed is affected by line parameters. This scheme is based on the propagation characteristics of fault line mode voltage traveling waves in flexible DC transmission. Firstly, the fault voltage is effectively decomposed using Variational Mode Decomposition (VMD). Then, the decomposed intrinsic mode functions (IMF) are reconstructed to obtain the high-frequency components of the recorded fault information. Finally, the traveling wave head is calibrated using the Teager Energy Operator (TEO). A dual terminal ranging algorithm that is not affected by wave velocity has been proposed, which can effectively avoid the impact of fault traveling wave velocity uncertainty on the ranging results. A dual ended flexible DC power grid model was built in PSCAD/EMTDC, and a large number of simulation results show that the algorithm proposed in this paper can accurately calibrate the fault traveling wave head, has little interference from transition resistance, strong noise resistance, and can obtain high-precision fault location results in different fault locations.

Automated fault location scheme for low voltage smart distribution systems

AbstractIntegrating the self‐healing capability realizes the automated protection of smart distribution systems. This article presents a fault location scheme for low voltage (LV) distribution systems based on the information collected from the smart meters. First, a fault condition is detected and classified by processing the current signal at the secondary side of the distribution transformer by an intelligent electronic device. Then, the faulty feeder and section are determined by calculating the fault‐imposed component of nodal voltages. The reliable performance of the proposed scheme is verified through several case studies using a real semi‐rural distribution system.

Fault Location Scheme for Multi-Terminal Transmission Line Based on Frequency-Dependent Traveling Wave Velocity and Distance Matrix

Fault Location Scheme for Multi-Terminal Transmission Line Based on Frequency-Dependent Traveling Wave Velocity and Distance Matrix

A Hybrid Technique for Fault Classification and Location in a Jointed Overhead-Underground Distribution Line

The electrical distribution system is crucial for the utility grid to transmit power from generators to consumers. Considering the intricate structure of distribution systems and their significant role in power networks, establishing a robust fault classification and location scheme is vital. Due to ageing, distribution systems are often prone to faults from factors like poor operational conditions and wear and tear. The line faulting rate and the pertinent restoration epochs influence the frequency and duration of power disruptions. Thus, precisely locating the fault section is essential to minimize power restoration timeframes. This paper presents a hybrid fault classification and location technique in a combined continuous overhead and underground distribution line. A simulation of the hybrid model was designed in Simulink for an 11 kV combined continuous overhead and underground electrical distribution line, considering short circuit faults as they are the most predominant and cause massive damage in distributed systems. The proposed technique first classifies the fault using Discrete Wavelet Transforms (DWT) and Multi-layer Perceptron-Artificial Neural Networks (MLP-ANN). Next, the impedance and Adaptive Neuro-Fuzzy System (ANFIS) based technique is employed for fault location. At a sample rate of 50 kHz, the DWT was applied to current signals and the coefficients used for ANN training, while phase impedance values were used as input to the ANFIS for training. The simulation results showed accuracy of 96.6% for fault classification and 99.17% for fault location. The developed models can significantly enhance fault location for speedier outage resolution by promptly repairing the affected distribution lines.