High Fault Resistance Research Articles

Enhancing HVDC transmission line fault detection using disjoint bagging and bayesian optimization with artificial neural networks and scientometric insights

DC grid fault protection techniques have previously faced challenges such as fixed thresholds, insensitivity to high-resistance faults, and dependency on specific threshold settings. These limitations can lead to elevated fault currents in the grid, particularly affecting multi-modular converters (MMCs) vulnerability to large fault current transients. This paper proposes a novel approach that combines the disjoint-based Bootstrap Aggregating (Bagging) technique and Bayesian optimization (BO) for fault detection in DC grids. Disjoint partitions reduce variance and enhance Ensemble Artificial Neural Network (EANN) performance, while BO optimizes EANN architecture. The proposed approach uses multiple transient periods instead of a fixed time to train the model. Transient periods are segmented into multiple 1 ms intervals, and each interval trains a separate neural network. In this way, a robust local relay is created that does not require high-speed communication systems. Additionally, a discrete wavelet transform (DWT) is applied to select detailed coefficients of the transient fault current, measured at the DC line’s sending terminal for fault protection. EANN is trained in comprehensive offline data that considers noise impact. Simulation results demonstrate the scheme’s ability to detect faults as high as 400 Ω accurately. This makes it a robust, reliable, and effective solution for fault detection on high-voltage direct current (HVDC) transmission lines. Lastly, this research provides the first-ever scientometric analysis of HVDC transmission line fault protection using neural network algorithms, highlighting major research themes and trends. The scientometric analysis was based on a dataset of 136 available research articles from the Scopus database from the last ten years. Therefore, this research provides valuable insights into the use of ANN for HVDC transmission line fault protection.

An enhanced algorithm for detection of HIAF in active distribution networks and real-time analysis

Identifying a downed conductor in distribution is challenging due to the insignificant variations in the current signal. Most of the distribution system faults are identified using overcurrent and earth fault protection. Fault path resistance decides the current magnitude while high resistance path may create timely issues in identifying a fault. Downed conductor and high resistance faults create arc in between the ground and conductor. Due to this the fault at the location is highly nonlinear with reduced magnitude. These faults are known as high impedance arcing fault (HIAF). Such faults can be identified effectively by conducting harmonic analysis of the signals. But in a modern distribution system apart from other renewable sources, the integration of electric vehicle (EV) may create HIAF detection challenges due to the inclusion of inverters and switching harmonics associated with EV. Considering the available advanced architecture of modern microgrid system, an effective solution is proposed in this work to securely detect HIAFs. The method is robust and highly reliable for HIAFs. The system stability and continuity can be unaffected with the help of the proposed method in case of various non-fault or transient/switching conditions. Comparison with other profound techniques is done to show the superiority of the proposed method.

2D-convolutional neural network based fault detection and classification of transmission lines using scalogram images

The reliable operation of power transmission systems is essential for maintaining the stability and efficiency of the electrical grid. Rapid and accurate detection of faults in transmission lines is crucial for minimizing downtime and preventing cascading failures. This research presents a novel approach to fault detection and classification in transmission lines employing 2D Convolutional Neural Networks (2D-CNN).The proposed methodology leverages the inherent spatial characteristics of fault signals, converting them as 2D scalogram images for input to the CNN model. By converting fault signals into scalogram representations, the network can capture both temporal and frequency domain features, enabling a more comprehensive analysis of fault patterns. The 2D-CNN architecture is designed to automatically learn hierarchical features, allowing for effective discrimination between different fault types. To evaluate the performance of the proposed approach, extensive simulations and experiments were conducted using MATLAB/SIMULINK modeled transmission line data. The results demonstrate the superior fault detection accuracy and classification capabilities of the 2D-CNN model. The performance of the proposed model is evaluated using 10-fold cross-validation, and its effectiveness is assessed by comparing it with current state-of-the-art techniques. Proposed 2D-CNN model has evidenced an accuracy of 99.9074 with ideal dataset for 12- class fault classification and performing consistently in presence of noise, having an accuracy of 99.629 %,99.72 % and 99.814 % in 20.30 and 40 dB noises respectively. The proposed model also verified in high resistance fault condition. The model exhibits robustness to noise and is capable of generalizing well to various fault scenarios. The proposed methodology offers a scalable and efficient solution for transmission line fault analysis, paving the way for the integration of advanced machine learning techniques into the operation and maintenance of power transmission infrastructure.

Estimation of polarity of slope of instantaneous current increment for power swing immune protection of series compensated line

Existing protection schemes for series-compensated transmission line (SCTL) lack either in reliability or speed during power swing (PS). A new ultra-fast reliable method is proposed in the present article using the slope of instantaneous current increment (SICI). The peaks of SICI (denoted by PKS) of a modal component of currents above a dynamic threshold are obtained to detect the sudden jump in SICI, which occurs after a transient disturbance. PKS of two terminal-currents are of same polarity for a fault within SCTL but they are of opposite polarity for any external event. This unique characteristic is mathematically verified and exploited to discern internal faults from external events during PS. Proposed method is found unaffected for high level of series compensation, position of compensation, close-in fault, fault inception point, high fault resistance, synchronization error and CT saturation. The results are finally compared to the incremental quantity-based methods to justify its merit.

Bayesian-optimized LSTM-DWT approach for reliable fault detection in MMC-based HVDC systems

As Europe integrates more renewable energy resources, notably offshore wind power, into its super meshed grid, the demand for reliable long-distance High Voltage Direct Current (HVDC) transmission systems has surged. This paper addresses the intricacies of HVDC systems built upon Modular Multi-Level Converters (MMCs), especially concerning the rapid rise of DC fault currents. We propose a novel fault identification and classification for DC transmission lines only by employing Long Short-Term Memory (LSTM) networks integrated with Discrete Wavelet Transform (DWT) for feature extraction. Our LSTM-based algorithm operates effectively under challenging environmental conditions, ensuring high fault resistance detection. A unique three-level relay system with multiple time windows (1 ms, 1.5 ms, and 2 ms) ensures accurate fault detection over large distances. Bayesian Optimization is employed for hyperparameter tuning, streamlining the model’s training process. The study shows that our proposed framework exhibits 100% resilience against external faults and disturbances, achieving an average recognition accuracy rate of 99.04% in diverse testing scenarios. Unlike traditional schemes that rely on multiple manual thresholds, our approach utilizes a single intelligently tuned model to detect faults up to 480 ohms, enhancing the efficiency and robustness of DC grid protection.

Enhancing transmission line protection with adaptive ANN-based relay for high resistance fault diagnosis

Enhancing transmission line protection with adaptive ANN-based relay for high resistance fault diagnosis

A nondestructive high-resistance fault diagnosis method of XLPE medium voltage cables based on Chirp-TDR

Diagnosing and locating the high resistance fault (HIF) of XLPE medium voltage cables without causing secondary damage has been a trouble for power maintainers. Among the previous methods proposed by researchers, the frequency domain reflection method has the strongest sensitivity, which still cannot meet the broad dynamic range requirement of HIF detection. This paper proposes a chirp-TDR high resistance fault detection method, which fully taps the signal processing gain of the chirp signal after pulse compression. The method increases the practical detection limit to hundreds of kΩ. A prototype is implemented, and the performance is simulated. The influence of incidence and terminal reflection signals on fault point reflection signal detection is reduced by common mode filtering and access matching impedances. The selected detection signal with 5 ms duration and 20 MHz bandwidth can produce a signal processing gain of 50 dB. A signal coaxial cable and an XLPE power cable with artificial simulated defects were installed as test specimens. The test results show that the method is feasible and has strong anti-noise ability. The technology is compared with FDR, which also uses the chirp signal. The proposed method has a performance improvement of about 12 dB, showing better fault point resolution ability.

Active-Control-Based Three-Phase Reclosing Scheme for Single Transmission Line With PMSGs

Electric devices may be damaged due to the strong secondary impact when the circuit breaker recloses on permanent faults, so it is significant to identify the fault nature to reduce the harm of grid faults. To achieve this point, a new control scheme is designed to make permanent magnet synchronous generator (PMSG) based wind turbines behave as a three-phase symmetrical voltage source, so the overcurrent can be detected for permanent faults when the circuit breaker on the PMSG side is reclosed, but only the tiny capacitance current is present for temporary faults. Therefore, the fault nature can be identified by detecting the current amplitude. To accurately extract the power-frequency phasors, a fast matrix pencil extraction method is adopted. This method solves the difficulty in fault nature detection caused by the rapid disappearance of current and voltage after a three-phase trip using the controllability of power electronics. Meanwhile, the severe secondary impact can be reduced since the injected current is small even for permanent faults. Additionally, the proposed method can ensure the fault current at the inverter terminal is within the limiting value and operate properly for remote high-resistance faults. Extensive simulations and experiments are performed to verify this method.

A new differential protection scheme based on Synchro-squeezing transform applied to AC Micro-Grid

In recent years, the protection of a microgrid has been a substantial challenge. This paper performs a differential protection scheme for the micro-grid based on Synchro-Squeezing Transform (SST). The proposed scheme protects the micro-grid in different modes i.e. islanded or grid-connected mode. Also, it is investigated in the ring and radial configuration. In the proposed scheme, currents on two sides of the line are measured by current transformers (CTs), and then differential current (IDiff) is calculated. This current is applied to extract the absolute value of the summation of the square root (ASSR). In this stage, transient and external faults without CT saturation and different switching such as connected or disconnected load/capacitor/distributed generation are detected from different internal faults. In the next stage, the frequency components of SST (FCSST) are extracted. By this feature, external fault and cross-country fault with CT saturation are detected. The proposed scheme is evaluated in noisy conditions, fault in different section of line with high resistance fault (HRF), and high impedance faults (HIF) of line. The micro-grid is simulated in PSCAD/EMTDC by various distributed generation (DG) like wind generation and photovoltaic systems penetrated at different buses. The proposed protection scheme is coded in MATLAB. The simulations represent the proposed protection scheme is correctly discriminate external and internal faults. At the same time, the method is immune to CT saturation and cross-country faults.

Non-unit protection method for MMC-HVDC grids based on selective drop rate of voltage traveling waves

Fast, sensitive, and selective protection principles are one of the major challenges in the feasibility of modular multi-terminal (MMC) high voltage direct current (HVDC) grids. Rate of change of voltage (ROCOV) and transient-based solutions are the traditional and widely accepted protection principles. Despite the speed and practicality of these solutions, they generally suffer from sensitivity and selectivity issues, particularly when dealing with high-resistance faults and low-size current limiting inductors (CLIs). To improve upon these methods, this paper proposes a new primary protection method that utilizes a selective drop rate of fault-generated voltage traveling waves (TW) to detect internal DC line faults. This is achieved by a comprehensive analysis of the line-mode fault-generated voltage (LFGV) under various internal and external fault scenarios. As the key fault characteristics, the proposed method exploits the minimum points of initial LFGV and the corresponding time to form the basis of the proposed protection method. The effectiveness of this approach is evaluated using a four-terminal MMC-HVDC grid in PSCAD/EMTDC. Compared to ROCOV and transient-based solutions, the proposed method identifies internal faults up to 1250 Ω with fast response, while maintaining its practicality and independence to CLI size.

Fault Identification of UHVDC Transmission Based on DF-AD and Ensemble Learning

High-resistance ground faults are difficult to detect with existing ultrahigh voltage direct current (UHVDC) transmission fault detection systems because of their low sensitivity. To address this challenge, a straightforward mathematical method has been proposed for fault detection in UHVDC system based on the downsampling factor (DF) and approximation derivatives (AD). The signals at multiple sampling frequencies were analysed using the DF, and the AD approach was used to generate various levels of detail and approximation coefficients. Initially, the signals were processed with different DF values. The first, second, and third order derivatives of the generated signals were calculated by the AD method. Next, the entropy features of these signals were computed, and the Random Forest-Recursive feature elimination with cross-validation (RF-RFECV) algorithm was used to select a high-quality feature subset. Finally, an ensemble classifier consisting of Light Gradient Boosting Machine (LightGBM), K Nearest Neighbor (KNN), and Naive Bayes (NB) classifiers was utilized to identify UHVDC faults. The MATLAB/Simulink simulation software was used to develop a ±800 kV UHVDC transmission line model and perform simulation experiments with various fault locations and types. Based on the experiments, it has been established that the suggested approach is highly precise in detecting several faults on UHVDC transmission lines. The method is capable of accurately identifying low or high resistance faults, irrespective of their incidence, and is remarkably resistant to transitional resistance. Furthermore, it exhibits excellent performance in identifying faults using a small sample size and is highly reliable.

A pilot protection based on low frequency component model for HVDC transmission lines

AbstractReliable and fast identification of high‐voltage direct current (HVDC) transmission line faults is important to the safety of power system. In traditional DC transmission line protection, differential protection is invulnerable to high fault resistance, but it needs long time delay for the influence of distributed capacitance current. In order to solve this problem, a novel pilot protection is proposed. The characteristics of the voltage and current processed by low‐pass filter under different fault conditions are analysed, and based on it the protection criteria is proposed. When external fault occurs, it reflects the line capacitance where the first forward fault traveling wave passes, which is increasing until equal to the whole line capacitance, then remains constant. However, when internal fault occurs, its value is negative in the initial stage, and after that its value is very different from the whole line capacitance. The analysis proves that this pilot protection method has fast operation speed, and can be applied for a relatively long period of time. What is more, the applicable time of superposition principle is considered in the analysis, which is not considered in many literatures. An UHVDC system is modelled for simulation in PSCAD/EMTDC, and the correctness and effectiveness of the proposed scheme is verified.

Universal Adaptive Differential Wavelet Energy-Based Protection Scheme for an AC Microgrid

A protection engineer’s top priorities are the detection of faults and the recognition of faulty phases. Modern distribution networks are interconnected; thus, any breakdown poses a hazard to the entire system. One of the challenges with microgrids is developing an effective protection scheme. In this paper, a new scheme has been proposed that effectively detects faults in the grid-connected, as well as off-grid modes also radial and looped topology of the microgrid. The scheme can adapt to the system changes and even detects the high resistance faults in a low-voltage distributed feeder. Moreover, various non-fault events such as load switching, capacitor switching, DG outage, section cutoff, and external faults have also been considered for evaluation. The performance of the scheme has also been tested for changes in fault inception angles, fault types, fault locations, simultaneous, evolving, and composite faults. The suggested method has also been tested using real-time data from the OPAL-RT simulator. The results demonstrate that the suggested scheme is suitable for real-time practical applications for the protection of microgrid feeders. Further, a comparative study has been done to prove that the proposed scheme is better than several existing protection schemes considered in this paper.

Transient Stability of Synchronous Condenser Co-located with Renewable Power Plants under High-Resistance Faults and Risk Mitigation

Transient Stability of Synchronous Condenser Co-located with Renewable Power Plants under High-Resistance Faults and Risk Mitigation

Frequency Domain Localization Method for High Resistance Faults in Railroad Signal Cables based on Active Noise Reduction

Frequency Domain Localization Method for High Resistance Faults in Railroad Signal Cables based on Active Noise Reduction

Research on Fault Detection and Localization Techniques for Distribution Networks Based on Edge Clustering

Abstract With the intelligent transformation of the power grid, the number and type of various terminals, sensors, and new types of loads in the distribution network increase, and the huge amount of information and noise information accessing the power grid brings great challenges for fault detection and localization. In this paper, we propose to combine wavelet transform and LSTM unit to form a novel neural network basic unit TFM and examine the fault detection and localization capability of TFM by combining them with a typical example system, IEEE33. Spectral clustering and the K-Means algorithm are utilized to cluster the edge nodes, and the number of nodes in the three partitions after partition correction is 11, 10, and 12 nodes in order. The data shows that the TFM system performs the fault diagnosis task with better test accuracy than LSTM in every dimension, and its accuracy improvement for fault localization is the largest, at 5.39%. Introducing two reconfiguration scenarios for three edge node partitioning models and retrograde fault localization detection, compared with the no reconfiguration scenario, their localization accuracies all produce different degrees of decline, but always not less than 80%. The localization accuracy of the MⅡ model is still not less than 98% in the fault resistance range of 500~1000Ω, which proves that the TFM system can effectively extract high-resistance fault features.

Optimal single settings based relay coordination in DC microgrids for line faults

A novel time–current-rate-based inverse characteristic curve for relays in a DC microgrid is proposed in this paper. Line current rise rate is used as actuating quantity, ensuring quick line fault clearing (relay operating time is in order of a few μs). The advantage of using line current rise rate as actuating quantity is that for a line short-circuit fault, it does not vary significantly for grid-connected and islanded modes of operation and varying network topologies of DC microgrid. Consequently, using the proposed characteristic, a single set of optimal relay settings is obtained using an optimization solver in MATLAB. The obtained settings ensure reliable and selective coordination between primary and backup relays for various pole-to-ground and pole-to-pole line faults under different operating conditions with multiple sources in two different 4-bus 400V low voltage DC microgrids. The maximum relay operating time for a high resistance fault with the proposed curve is 394.9μs for the first considered low voltage DC microgrid. Simulations in the Real Time Digital Simulator and comparisons with previous schemes (one comparison on the second considered DC microgrid), conventional standard inverse, and extremely inverse curves for DC microgrid protection indicate the effectiveness of the proposed scheme.

Differential Reactor Voltage Based Fault Detection and Classification for Smart DC Microgrid

The primary challenges in the implementation of differential protection techniques are Time Synchronization Error (TSE) due to communication delay and low sensitivity towards High Resistance Faults (HRFs). In this paper, a new technique utilizing the difference in voltage measured across the external reactors connected at opposite ends of the cable is proposed for fault detection and classification of HRFs in islanded and grid connected mode of operation of DC microgrid. Further, to overcome the challenges associated with TSE, which causes differential schemes to mal-operate, the proposed technique determines reactor voltage polarity at opposite ends of the cable to distinguish between internal and external faults. The proposed technique is tested for different types of internal faults and system transients such as external faults, load switching, generation uncertainties etc. to demonstrate sensitivity and accuracy of the proposed technique. Finally, a comparative analysis of the proposed technique is performed with the schemes reported in the literature, which confirms the fast fault detection, high sensitivity for internal HRFs, selectivity and stability of the proposed technique for different operating conditions.

DC line fault discrimination based on maximum information coefficient

In DC networks, conventional line differential protection has the disadvantages of low reliability, weak resistance to transition resistance, and vulnerable to harmonic interference. In this paper, a fault identification method based on maximal information coefficient (MIC) is proposed for non-high resistance faults in the DC side of a flexible DC distribution network containing a modular multilevel converter (MMC) with single pole grounding DC protection inside and outside segments. The failure prediction is performed based on the reciprocal information at two sides of the line current. At the same time, for single-pole grounded high-resistance faults, the MIC is compared using the zero moduli of the current at two sides of the line for the recognition of high-resistance faults. The MMC-based flexible DC transmission network model is built and validated in the PSCAD/EMTDC platform. The results show that the method can identify faults protection inside and outside segments on the DC line area quickly and reliably. It also has the ability to resist harmonic interference and has an endurance of resistance for single-pole faults on the DC side; meanwhile, for high-resistance faults, it can also be detected accurately.

Protection Scheme based on Artificial Neural Network for Fault Detection and Classification in Low Voltage PV-Based DC Microgrid

With the expansion of the DC distribution market, protection, and operational concerns for Direct Current (DC) Microgrids have increased. Different systems have been investigated for detecting, finding, and isolating defects utilising a variety of protective mechanisms. It might be difficult to locate high-resistance faults and shorted DC faults on low-voltage DC (LVDC) microgrids. Therefore, in this study, a Field Transform Technique like Short-Time Fourier Transform (STFT) is proposed for detecting the Fault Current (FC). This method detects the faults Pole-ground (PG), pole-pole (PP), and Arc fault are the major fault types in the DC network with PG fault as the most common and less severe. One of the difficulties the DC system faces in the incidence of a malfunction is the protection of essential converters. During this fault, the diodes, being the most vulnerable component of the system, may encounter a substantial surge in current, which can potentially cause damage if the current surpasses double their specified capacity for withstanding. After the Fault detection (FD), a Taguchi-based ANN is presented to classify the detected faults. This method effectively classifies PV-based faults. Then, to safeguard the FC, the Improved Self-Adaptive Solid State Circuit Breaker (I-SSCB) is introduced. It safeguards the FC in the low-voltage PV-based DC microgrid (DCMG) and restricts FC in the DCMG. The suggested approach is evaluated using the Matlab software and the proposed method produces 400A current and 100 KW power during the PV temperature of 25°C. The output current of the ANN is then 1A for a duration of 0.3 to 0.4 seconds. The fault voltage and FC produced in this proposed work are 1900V and 1950A. Therefore, the proposed work's current and voltage values are 21 KV and 0.35 I. Therefore, the proposed method produces more power and limits the FC in the LV-DCMG. In future studies, the improved or modified neural network or machine learning (ML)-based techniques can be utilized which may improve the protection scheme of the work.