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In this article, a long short-term memory based protection scheme for power transmission lines is presented. A fault detection framework is developed that uses voltage and current signals' RMS values as input. The proposed work is established for various shunt faults, both low-impedance and high-impedance faults which are tested on a standard IEEE 14 bus system and an existing real transmission network. Results confirm the detection and classification of faults with accuracy and precision higher than 99%. The impact of non-faulty events such as load switching, capacitor switching, noisy data, and load variation conditions is also studied to analyze the model's performance. The efficacy of the proposed method is confirmed by comparing it with different methods in the literature. Results indicate the aptness of the proposed scheme for the protection of power transmission lines. [ABSTRACT FROM AUTHOR]

The growing presence of monitoring systems based on phasor measurement units (PMU) in distribution systems has strengthened the development of data-based solutions. The correct identification of events in distribution networks is strictly benefited from large data availability, especially those challenging to detect, such as high-impedance faults (HIFs). In this sense, this work presents the usage of harmonic synchrophasors on HIF classification in active distribution networks. A total of 2800 simulated events from a microgrid are considered, and six classic machine learning classification models are evaluated. The performed analyses focus on establishing how the amount and quality of the PMU data can influence the distinction of common microgrid events. Results show that low-resolution PMU data, like one phasor estimated per cycle, is sufficient to discriminate the events accurately. Regarding synchrophasors, the classification corroborates that odd harmonic contents present better discriminant potential when compared to even, and the usage of angle information subtly enhances outcomes. When dealing with noisy data, the classification stays stable for mild scenarios of signal-to-noise ratio. By validating the classification task with real HIF data, 99% of the events are correctly classified. This harmonic synchrophasor-based strategy is a promising and original approach for commercial PMU data, and its robustness is adequate for distribution-level applications. [ABSTRACT FROM AUTHOR]

Hybrid HVDC transmission technology advancement has led to significant progress. A reliable and effective DC line protection scheme is crucial for DC transmission systems. The traditional protection schemes suffer from insufficient sensitivity and reliability during high-impedance faults on DC lines. To address the issue, this study introduces a hybrid HVDC line single-ended protection scheme based on the coordination of converter control objectives. In the fault-ride-through(FRT) stage, different control strategies and objectives are applied to the converter based on the fault pole and fault direction identification results. Internal and external faults are discerned by calculating measured voltage with the coordination of the converter control objective. This approach maintains heightened sensitivity to high-impedance DC line faults. Moreover, it operates independently of double-ended communication and demands a low sampling rate. Extensive simulation results robustly substantiate the efficacy of the proposed protection scheme.

The neutral effectively grounded system (NEGS), increasingly adopted in medium-voltage urban AC distribution networks, exhibits heightened susceptibility to fault-induced overcurrent. Despite the deployment of traditional three-zone and zero-sequence overcurrent protection relays, they continue to struggle with sensitivity and speed limitations. A further complication arises from the prevailing reliance on the zero-sequence component, given the practical difficulties associated with installing zero-sequence current transformers (CTs). To overcome these challenges, this paper introduces a protection relay that works independently of the sequence component, offering superior speed, sensitivity, and robustness. First, it is established that any given phase’s superimposed faulted phase current (SFPC) bears a consistent distribution pattern, irrespective of the fault type. This distinctive attribute is represented by introducing the concept of the postfault locus (PFL). Then, a novel protection relay is proposed, which computes the PFL based on the inner product of individual feeders and a reference phasor. Furthermore, integrating the phaselet algorithm augments the relay’s response speed while effectively mitigating the risk of CT saturation. Case studies demonstrate that the proposed relay boasts sufficient sensitivity, tolerating fault resistance up to 2000 O, and has sufficient robustness under noise contamination of 30 dB.

High-impedance fault detection poses significant challenges for distribution network maintenance and operation. We propose a dual-path neural network for high-impedance fault detection. To enhance feature extraction, we use a Gramian Angular Field algorithm to transform 1D zero-sequence voltage signals into 2D images. Our dual-branch network simultaneously processes both representations: the CNN extracts spatial features from the transformed images, while the GRU captures temporal features from the raw signals. To optimize model performance, we integrate the Crested Porcupine Optimizer (CPO) algorithm for the adaptive optimization of key network hyperparameters. The experimental results demonstrate that our method achieves a 99.70% recognition accuracy on a dataset comprising high-impedance faults, capacitor switching, and load connections. Furthermore, it maintains robust performance under various test conditions, including different noise levels and network topology changes.

The growth of Internet of Things (IoT)-enabled devices has increased the amount of data created by the distribution network's periphery nodes, requiring more data transfer capacity. Recent applications' real-time requirements have strained standard computing paradigms, and data processing has struggled to keep up. Edge computing is employed in this research to detect distribution network faults, allowing for instant sensing and real-time reaction to the control room for faster investigation of distribution problems and power outages, making the system more reliable. Moreover, to overcome the challenges of fault detection, advanced signal processing methods need to be integrated with the Adaboost classifier. An Adaboost-based edge device, suitable for installation on top of a power pole, is proposed in this research as a means of real-time fault detection. To increase throughput, decrease latency and offload network traffic, data collecting, feature extraction and Adaboost-based problem identification are all performed in an integrated edge node. Enhanced detection accuracy (98.67%) and decreased latency (115.2 ms) verify the effectiveness of the suggested approach. In this research, we enhance the classical chirplets transform to create the scaling-basis chirplet transform (SBCT) for time–frequency (TF) analysis. This approach modulates the TF basis around the relevant time function to modify the chirp rate with frequency and time. By carefully selecting the sampling frequency, it is possible to discriminate between short circuit fault and high-impedance fault (HIF) by calculating spectral entropy. The TF representation obtained with the SBCT provides considerably higher energy concentrations, even for signals with numerous components, closely spaced frequencies and heavy background noise. [ABSTRACT FROM AUTHOR]

AC microgrids are a contemporary adaptation of traditional power distribution networks, propelled by the rapid integration of renewable energy resources. Yet, their dynamic operational nature poses distinct protection & control challenges. This paper introduces a protection approach for AC microgrids utilizing the Particle Filter Algorithm (PFA). The method involves initial measurement and state estimation of current and voltage signals at the designated bus using PFA. Then, the dual index is computed via PFA encompasses 1). per-phase particle residuals (PPPR), which are derived as an index for fault detection and classification, essentially capturing the difference among estimated & measured current. 2). also, a second index is generated named fifth and seventh root means square (RMS) harmonics distortion (F&SRHD) from the estimated current signals, computed by slight modification in conventional total harmonic distortion. Variances in any of the PPPRs and F&SRHD more than the threshold level indicate the fault within the AC microgrids. Fault localization is accomplished by analyzing the directional patterns of non-fundamental components of 3-phase active energy (3-pRE). Rigorous MATLAB/Simulink 2022b simulations authenticate the efficacy of the presented strategy. Both the high-impedance faults (HIF) and low-impedance solid faults (LISF) are successfully detected across radial and meshed scenarios, with 99% accuracy.

The rising demand for stable power supply in distribution systems has increased the importance of reliable supply. Thus, a networked distribution system (NDS) linked with individual lines is being adopted, gradually replacing the radial distribution system (RDS) currently applied to most distribution systems. Implementing the NDS can lead to various improvements in factors such as line utilization rate, acceptance rates of distributed power, and terminal voltages, while mitigating line losses. However, compared with the RDS, the NDS can experience bidirectional fault currents owing to its interconnected lines, thereby hindering protection coordination, which must be addressed before the NDS can be implemented in real-world power systems. Due to the characteristics of NDS, the reverse fault current is relatively small. However, this phenomenon becomes more severe when the high impedance fault (HIF) occurs. In this paper, the malfunction of protective devices during the HIF is directly verified and analyzed in the NDS. As a result, when the HIF occurs, the issue of the reverse protective device malfunctioning worsens because of a reduction in fault current and a failure in direction detection. To solve this issue, this work proposes a communication-based protection algorithm. Through the comparative verification of the proposed algorithm and the conventional protection method, protection coordination can be secured in the case of an HIF without new devices. It must be highlighted that the proposed method does not affect the settings of the protective device and provides a cost-effective and efficient solution since this algorithm is added independently to the existing relay. [ABSTRACT FROM AUTHOR]

High-impedance faults (HIFs) in distribution networks may result in fires or electric shocks. However, considerable difficulties exist in HIF detection due to low-resolution measurements and the considerably weaker time-frequency characteristics. This paper presents a novel HIF detection method using synchronized current information. The method consists of two stages. In the first stage, joint key characteristics of the system are extracted with the minimal system prior knowledge to identify the global optimal micro-phase measurement unit (µPMU) placement. In the second stage, the HIF is detected through a multivariate Jensen-Shannon divergence similarity measurement using high-resolution time-synchronized data in µPMUs in a high-noise environment. l2,1principal component analysis (PCA), i.e., PCA based on the l2,1 norm, is applied to an extracted system state and fault features derived from different resolution data in both stages. An economic observability index and HIF criteria are employed to evaluate the performance of placement method and to identify HIFs. Simulation results show that the method can reliably detect HIFs with reasonable detection accuracy in noisy environments.

• Using the waveform characteristics of arc current distortion offset, distortion degree, zero rest state. A HIF model based on mathematical equations can be constructed to characterize arc reignition and extinguishing fully. • By analyzing the phase-frequency characteristics of the feeder, the fitting curve relationship between the filtered arc current and voltage is obtained, which lays a good foundation for accurate fault detection. • Based on the least squares and quadratic polynomial fitting curve feature construction criteria, the reliability of fault detection can be effectively achieved. PSCAD simulation and on-site experiments have verified the algorithm's effectiveness. To solve the problem of weak transient fault currents and significant nonlinear characteristics caused by arcs in resonant grounding systems, proposing a high-impedance fault (HIF) detection method based on an improved Mayr arc model and fitting curve coefficients. Firstly, to address the issue of low accuracy in simulating fault waveforms in existing HIF models, construct an improved arc model based on Mayr and control the parameter values under different feature dimensions, achieving accurate simulation of arc waveforms. Secondly, based on the impedance characteristics of the feeder, analyze the phase frequency characteristics, determine the first capacitive frequency band, and then determine the cutoff frequency of the Chebyshev filter to improve the ability of waveform feature extraction and fault recognition. Finally, by fitting the voltage and current geometric characteristic curve within the characteristic frequency band, using the least squares method and the quadratic coefficient values of the fitting curve to construct detection criteria, accurate detection of HIF is possible. Research shows that the improved arc model can fully characterize the current arc waveform under various working conditions. Simulations and real-world measurements verify that HIF can be accurately detected using the quadratic coefficient value of the fitting curve. [ABSTRACT FROM AUTHOR]

High-impedance fault (HIF) is always a threat and the biggest challenge in the power transmission and distribution system (PTDS). For a PTDS to operate effectively, HIF diagnosis is essential. However, given the HIF’s nature and the involved complexity, detection, identification, and fault location are difficult. This will be even more complicated in conventional PTDSs as they are inefficient and highly vulnerable. Given the importance and urgent need for HIF diagnosis in PTDS, this study reviews state-of-the-art HIF phenomenon and detection techniques and proposes the use of “various signal processing techniques for fault feature extraction” and “ different classifiers for identifying HIF.” First, HIF current/voltage signals are analyzed using signal processing techniques, which include the discrete wavelet transform (DWT), pattern recognition, Kalman filtering, TT transform, mathematical morphology (MM), S transform (ST), fast Fourier transform (FFT), principal component analysis (PCA), linear discriminant analysis (LDA), and wavelet transforms, such as dual-tree, maximum overlap discrete wavelet transform (MODWT), and lifting wavelet transform (LWT). Second, the various HIF and non-HIF faults are classified using intelligent classifiers. The intelligent classifiers include artificial neural networks (ANNs), probabilistic neural networks (PNNs), genetic algorithms (GAs), fuzzy logic, adaptive neuro-fuzzy interface system, support vector machine (SVM), extreme learning machine (ELM), adaptive resonance theory, random forests (RFs), decision trees (DTs), and convolution neural networks (CNNs). In addition to the comparative discussion of various classifier techniques, their evaluation criterion and performance are prioritized. Third, this review also studied different test systems, such as radial distribution network, mesh distribution network, IEEE 4 node, IEEE 13 node feeder, IEEE 34 node feeder, IEEE 39 node feeder, IEEE 123 node feeder, Palash feeder, and test microgrid systems, to assess the pertinence of various HIF detection schemes and the behavior along with methods to locate the HIF. Overall, we believe this review would serve as a comprehensive compendium of advanced techniques for HIF diagnosis in different test systems.

Recently, the concept of microgrids has emerged in the world due to the integration of distributed energy resources (DERs) at the distribution end. The design of a reliable protection strategy is one of the top-most challenges associated with microgrids. This is because of the transition of microgrids between grid-tied and autonomous modes of operation. This paper presents a state-of-the-art microgrid protection scheme based on the Kalman filter (KF). The proposed scheme uses the one-end current signal of a distribution line for the detection and classification of faults. Firstly, the KF is applied to each phase of a three-phase current signal individually to generate residuals and total harmonic distortion (THDs). Next, the variations in the residuals and THDs of each phase are compared with pre-specified threshold values to detect the faulty events in the microgrid. As each phase is processed through KF individually, therefore, the proposed scheme is inherently phase segregated. Afterward, the KF is applied to extract the third harmonic component from the three-phase current and voltage signals. Then, the KF-based reactive power (KFBRP) is obtained from the extracted third harmonic components. Finally, the directional properties of the three-phase KFBRP are used to locate the faulty section in the microgrids. Extensive simulations in MATLAB/ Simulink software are performed for the grid-tied as well as the autonomous modes of operation under radial and meshed topologies. The results show that the proposed scheme is highly robust in all testing scenarios without any false tripping and blinding issues.

The nonlinear operation of metal oxide varistor (MOV)-protected series compensator in transmission lines introduces complications into fault detection approaches. The accuracy of a conventional fault detection schemes is adversely affected by continuous change of the system impedance and load current at the point of a series compensation unit. Thus, this study suggests a method for detecting the faulted phase in MOV-protected series-compensated transmission lines. Primarily, the fault feature is identified using the covariance coefficients of the current samples during the fault period and the current samples during the pre-fault period. Furthermore, a convenience fault detection index is established by applying the cumulative sum technique. Extensive validation through different fault circumstances is accomplished, including different fault positions, resistances, and inception times. The experimental results show that the proposed method performs well with high resistance or impedance faults, faults in noisy conditions, and close-in and far-end faults. The proposed method is simple and efficient for faulty phase detection in MOV-protected series-compensated transmission lines.

High-impedance faults are a challenging problem in power distribution systems. They often do not trigger protection devices and can result in serious hazards such as igniting fires when in contact with vegetation. The current research field dedicated to studying these faults is extensive but suffers from a constraining bottleneck of a lack of real experimental data. Many works set to detect and localize such faults rely on high-impedance fault low-fidelity models, and the lack of public data sets makes it impractical to have objective performance benchmarks. This letter describes and proposes a format for a data set of more than 900 vegetation high-impedance faults funded by the Victorian Government in Australia recorded in high-sampling resolution. The original data set is public, but it was made available through an obscure format that limits its accessibility. The presented format in this letter uses the standard hierarchical data format (HDF5), which makes it easily accessible in many languages such as MATLAB, Python, C++, and more. The data set compiler and visualizer script are also provided in the work repository1. [ABSTRACT FROM AUTHOR]

• Cost-effective protection scheme and more reliable. Moreover, it doesn't depend on distributed generation type, location, and rating. • UKF novel application in AC microgrids protection scheme as a frequency and time domain analysis. • The proposed method is computationally low. In addition, its deals the noisy measurement conditions. • Offers backup protection during primary protection unit failure. • Capabilities to protect the microgrid in different operational modes and topological structures. • Fast, accurate, secure, and reliable protection indexes for fault detection/location and phase identification. • The proposed method can protect microgrids from solids as well as HI-faults. Traditional protection methods were inadequate in multiple distributed generations (MDG) microgrids due to the bidirectional power flow/high fault current in the grid-connected (GC) mode, and reduced fault current in the islanded (ID) mode. Therefore, a reliable and robust protection method has become of paramount importance for such MDG-based microgrids. This article suggests an Unscented Kalman filter (UKF) to detect, classify, and locate faults in such MDG-based microgrids. Initially, the harmonic content is generated from the UKF-estimated current signal to compute the total harmonic RMS factor (THRF). Then the changes in the THRF of each phase are cross-checked to a pre-specified threshold level to distinguish the fault conditions. Furthermore, the cumulative covariance-dependent reactive energy (CCDRE) is computed from both the estimated state and covariance matrix of voltage and current signals provided by UKF. Finally, the directional trends in the CCDRE are used to identify the fault zone in the microgrids. The effectiveness of the proposed method is validated through ample simulations on CIGRE and IEC 61,850–7–420 microgrid test system via MATLAB® Simulink. The results illustrate that the presented strategy is robust in both the GC mode and the ID mode of operation in meshed and radial topologies with 99.9% accuracy. [ABSTRACT FROM AUTHOR]

Cabled underwater information networks (CUINs) have evolved over the last decade to provide abundant power and broad bandwidth communication to enable marine science. To ensure reliable operation of the CUINs, the technology for high-impedance fault diagnosis and isolation with high reliability and accuracy is essential. In this paper, we review diagnosis and location methods as applied to a constant voltage ring and the tree topology network. A high-impedance fault diagnosis method based on the variation of the sampling voltage in the primary nodes (PNs) for a constant current remote power supply system is proposed. The methods for analyzing the fault voltage with using power monitoring and control system (PMACS) and communications monitoring, control system (CMACS), and hybrid detection with alternating current and direct current are used for research the high-impedance fault location based on the designed fault isolation circuit. In particular, a verification scheme for high-impedance fault location is designed for the CUINs based on the classical mesh topology. Furthermore, high-impedance faults of nodes and submarine cable sections in the trunk cable are simulated, and the variations of leakage voltage are analyzed. By researching the change of leakage voltage before and after the fault occurs, the feasibility and practicability of the diagnosis and location scheme are verified.

High voltage dc (HVDC) transmission, especially voltage source converter (VSC)‐based HVDC transmission requires high‐speed operation for relay protection. However, traditional derivative‐based non‐unit travelling wave‐based protection (TWP) methods are sensitive to the fault impedance, resulting in low protection sensitivity in case of the high‐impedance fault. First, the fault information containing in the fault travelling wavefront is analysed based on the frequency‐dependent parameters of the dc transmission line and the broadband characteristic of the fault‐point initial travelling wave. It can be concluded that the distortion of the fault travelling wavefront depends on the fault distance and the amplitude of the fault travelling wavefront depends on the fault impedance. Then, a principle of non‐unit TWP for dc transmission line based on waveform correlation calculation is proposed. The proposed method is verified in both thyristor‐based HVDC and VSC HVDC PSCAD test systems. The simulation results verify the high‐speed, high fault‐impedance tolerability and correctness of the proposed protection method. [ABSTRACT FROM AUTHOR]

Due to the characteristics of small, nonlinear, random, and unstable current, the high impedance faults (HIF) are not always effectively cleared by conventional over current relays. A new simple and effective algorithm of HIF detection in distribution network based on the wavelet time–frequency entropy is presented in this article. On the basis of analysis, the time–frequency distribution characteristics in the concerned frequency band of HIFs by wavelet transform. The feature sequence developed by the wavelet time–frequency entropy at the time sequence is employed to detect HIF at substation. This criterion is calculated from the sum of wavelet time–frequency entropy at the time sequence every half cycle. If the value of the criterion is more than the threshold during four cycles, the HIF event can be identified. The developed detection method has been tested with real‐world recorded signals from different HIF medium and PSCAD/EMTDC‐generated signals. The result shows that the proposed method is robust to transients generated during normal events such as capacitor bank switching, load switching, and harmonic load. It has good performance for antinoise ability by noise reduction coefficient improved appropriately. And the proposed method has better reliability than the traditional third‐harmonic‐based method. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

High-impedance faults (HIF) are difficult to detect because of their low current amplitude and highly diverse characteristics. In recent years, machine learning (ML) has been gaining popularity in HIF detection because ML techniques learn patterns from data and successfully detect HIFs. However, as these methods are based on supervised learning, they fail to reliably detect any scenario, fault or non-fault, not present in the training data. Consequently, this paper takes advantage of unsupervised learning and proposes a convolutional autoencoder framework for HIF detection (CAE-HIFD). Contrary to the conventional autoencoders that learn from normal behavior, the convolutional autoencoder (CAE) in CAE-HIFD learns only from the HIF signals eliminating the need for presence of diverse non-HIF scenarios in the CAE training. CAE distinguishes HIFs from non-HIF operating conditions by employing cross-correlation. To discriminate HIFs from transient disturbances such as capacitor or load switching, CAE-HIFD uses kurtosis, a statistical measure of the probability distribution shape. The performance evaluation studies conducted using the IEEE 13-node test feeder indicate that the CAE-HIFD reliably detects HIFs, outperforms the state-of-the-art HIF detection techniques, and is robust against noise.

A high‐impedance fault (HIF) is mostly associated with the arcing phenomenon, and its main features are low current, randomness, non‐linearity, and asymmetry. The low fault current of an HIF is a major challenge for overcurrent‐based protection system of distribution networks. Power loss, potential fire hazard, and electric shock are consequences of an undetected HIF. This study presents a time‐domain HIF detection algorithm based on monitoring the substation current waveform. Using the Kullback–Leibler divergence similarity measure, the non‐linearity, and asymmetry features of two subsequent half cycles of the current waveform are quantified as the HIF detection criterion. A time duration‐based criterion is also used to distinguish HIFs from the load, capacitor, feeder, and distributed energy resource switchings and the voltage sag and swell events. The proposed scheme satisfactorily works in the presence of non‐linear loads. Also, it does not require the training data set, transformation, and calculation of harmonic/sequence components. Extensive time‐domain simulation case studies using the IEEE 13 and 34 node test feeders demonstrate the merits of the proposed HIF detection algorithm. [ABSTRACT FROM AUTHOR]

This paper presents the high-impedance fault (HIF) detection and identification in medium-voltage distribution network of 13.8 kV using discrete wavelet transform (DWT) and intelligence classifiers such as adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM). The three-phase feeder network is modelled in MATLAB/Simulink to obtain the fault current signal of the feeder. The acquired fault current signal for various types of faults such as three-phase fault, line to line, line to ground, double line to ground and HIF is sampled using 1st, 2nd, 3rd, 4th and 5th level of detailed coefficients and approximated by DWT analysis to extract the feature, namely standard deviation (SD) values, considering the time-varying fault impedance. The SD values drawn by DWT technique have been used to train the computational intelligence-based classifiers such as fuzzy, Bayes, multi-layer perceptron neural network, ANFIS and SVM. The performance indices such as mean absolute error, root mean square error, kappa statistic, success rate and discrimination rate are compared for various classifiers presented. The results showed that the proffered ANFIS and SVM classifiers are more effective and their performance is substantially superior than other classifiers. [ABSTRACT FROM AUTHOR]

Highlights • Spectral domain measurement model considering one terminal data. • A system identification approach for fault coefficient estimation. • A weighted least squares estimator with parametric error processing. • Parametric error processing based on the normalized composed measurement error. Abstract High impedance faults create specific technical challenges in the context of power system protection. The initial challenge is to detect these faults. If the fault is detected successfully, maintenance actions must be taken to avoid further risk to the population. The following technical challenge after detection is the location of these faults, which contributes significantly to the fast and safe restoration of the system. Although high impedance faults location is a topic of great concern, few studies are specifically devoted to this subject. This work presents an analytical formulation for the high impedance faults location in distribution systems. Initially, a system model which incorporates specific characteristics of this fault type is developed in the spectral domain. Using this model, the fault distance is estimated through a weighted least squares estimator associated with a parametric error processing algorithm. This algorithm detects the presence of parameter errors based on the composed measurement error in normalized form and further corrects the model. The validation of the presented technique is evaluated through comparative case studies considering the IEEE 13 node test feeder modeled in the Alternative Transient Program. Test results are encouraging indicating the formulation's potential for real-life applications. [ABSTRACT FROM AUTHOR]

• A sensitive fault feature library based on high frequency components is constructed. • A feature selection method based on XGBoost can remove redundant features. • A reliable HIF identification scheme based on KELM can identify fault phase selection accurately. In distribution networks, high-impedance faults (HIFs) occur frequently and have a harmful impact on the distribution network. However, fault detection and fault phase selection of HIFs are challenging due to weak fault characteristics. Therefore, this paper proposes an improved HIF identification scheme based on a kernel extreme learning machine (KELM) that can sensitively identify HIFs and select the fault phase by adaptively extracting the weak fault characteristics. First, a fault feature extraction strategy based on discrete wavelet decomposition (DWT) and the Hilbert–Huang transform (HHT) is proposed to obtain multiple features that describe the weak fault characteristics of HIFs. Second, an XGBoost-based fault feature selection scheme is proposed for screening with sensitive characterization of HIFs. Next, a sensitive and accurate HIF identification scheme based on the improved learning algorithm (KELM) is proposed to enable accurate and sensitive HIF detection and phase selection. Finally, numerical simulations based on PSCAD/EMTDC and MATLAB were carried out, which reveals the effectiveness and accuracy of the proposed HIF identification scheme. Compared with the traditional HIF identification scheme, the proposed method exhibits conciseness and correctness. [ABSTRACT FROM AUTHOR]

High-impedance faults (HIFs) represent one of the biggest challenges in power distribution networks. An HIF occurs when an electrical conductor unintentionally comes into contact with a highly resistive medium, resulting in a fault current lower than 75 amperes in medium-voltage circuits. Under such condition, the fault current is relatively close in value to the normal drawn ampere from the load, resulting in a condition of blindness towards HIFs by conventional overcurrent relays. This paper intends to review the literature related to the HIF phenomenon including models and characteristics. In this work, detection, classification, and location methodologies are reviewed. In addition, diagnosis techniques are categorized, evaluated, and compared with one another. Finally, disadvantages of current approaches and a look ahead to the future of fault diagnosis are discussed.

Despite the growing deployment of photovoltaic (PV) systems, they are still facing challenges in developing the proper control and protection schemes. One of the main protection challenges of PV arrays is their low fault currents under low‐irradiance, low‐mismatch, and high‐impedance faults. In addition to these conditions, the operation of the maximum power point tracking algorithm may lead to the faults within the PV array remain undetected, resulting in potential fire hazards and power loss. This study presents a fault detection scheme based on monitoring the output power of the PV array. Using the sample entropy‐based complexity, the irregularity of the time series of the normalised fault‐imposed component of PV power is quantified as the fault detection criterion. The proposed protection scheme is capable of distinguishing the line‐to‐line, line‐to‐ground, and open‐circuit faults from the weather disturbances and partial shadings. Also, it does not require the training dataset and the prior information about the PV array and is effective for both grid‐connected and islanded PV systems. Extensive time‐domain simulation results demonstrate high accuracy of the proposed fault detection scheme. [ABSTRACT FROM AUTHOR]

High-impedance faults (HIFs) are linked to enduring unaddressed knowledge gaps due to their diverse and complex behavior, despite being extensively researched disturbances. Vegetation HIFs, for instance, are a particular type of fault that can lead to great fire hazards and life risks. They have unique fault signatures and should receive special attention if fire risk mitigation is desired. This paper focuses on the detection of these distinct, very small current faults. As the main correlational features, the proposed methodology uses the vegetation fault signatures’ high-frequency content. Different from many previous works that rely on HIF models, the approach validation is performed using a real dataset comprising a large number of experiments, sampled in a functioning network in the presence of noise. The classification is performed by boosted decision trees, which showed high dependability and security in the classification of small phase-to-earth and phase-to-phase HIFs. [ABSTRACT FROM AUTHOR]

In this study, an adaptive continuous wavelet transform (CWT)‐based overcurrent protection for smart grids is proposed to enhance the overcurrent protection performance encountering high‐impedance fault (HIF) and current transformer (CT) saturation, which are extremely complex phenomena and their impacts often cause mis‐coordination or mal‐operation. The proposed algorithm samples three phase current waveforms and imports them to CWT to extract high frequency coefficients. Afterwards, the sum of absolute values of the coefficients, Scoef, is calculated for each sample during the last cycle. Meanwhile, several simulations related to HIFs with different impedances and CT saturations with different severities are executed and the fault currents and the sum of absolute values of the coefficients are achieved and saved in the relay memory as (X, Y) coordinates of points (IL‐fault, SL‐coef), named 'learning data'. Thereafter, each Scoef is imported to the X −Y plane and, consequently, the occurrence of HIF or CT saturation is detected and the real‐fault current is estimated by both non‐linear interpolation and extreme learning machine approaches. Subsequently, new time dial setting and Ipickup of the relays are computed and reloaded. Security, dependability, and sensitivity of the proposed adaptive protection method are confirmed by numerous simulation studies. [ABSTRACT FROM AUTHOR]

• High impedance faults are difficult to get detected in active distribution networks. • An adaptive signal processing algorithm; ICEEMDAN is used to detect successive faults occurrence at different locations. • Proposed protection scheme is capable to detect both low impedance faults and high impedance faults, without involving separate strategy for each type. The natural disaster though may have less probability, but their impact can be extreme. For instance, windstorms can result in not only single instantaneous impact, but on multiple locations in the power grid. In such situations, detection of faults in active distribution networks (ADNs), having integration of different types of distributed generation (DG) resources, further escalates the challenging task for existing conventional techniques. The reliability of existing detection schemes is further compromised in case of high impedance faults (HIFs). This paper presents a unified approach for reliable detection of both low-impedance and high-impedance faults, while maintaining the discrimination against non-fault events. In the proposed detection algorithm, the fault detection index is calculated using a newer version of empirical mode decomposition (EMD), i.e., improved complete ensemble EMD with adaptive noise (ICEEMDAN). The performance is demonstrated for detection of multiple faults occurrence at different locations in the ADN. It is shown that with selection of uniform threshold value for all the relays in the network, only the dedicated primary and back-up relays operate reliably at the time of fault conditions. The scheme exhibits accurate, fast and reliable operation as compared to available works in the literature. [ABSTRACT FROM AUTHOR]

With the growing integration of distributed generation, distribution networks have evolved toward the concept of microgrids. Microgrids can be operated in either the grid-connected mode to achieve peak shaving and power loss reduction or the islanded mode to increase the reliability and continuity of supply. These two modes of operation cause a challenge in microgrid protection, because the magnitude of fault current decreases significantly during the transition of a microgrid from the grid-connected mode to the islanded mode. This paper proposes a protection scheme for the microgrid based on superimposed reactive energy. The proposed scheme uses the Hilbert transform to calculate the superimposed reactive energy (SRE). The sequence components of superimposed current are adopted to detect fault incidents in the microgrid. The faulty phase and section are recognised by using the directional characteristics of SRE along with a threshold value. Moreover, a relay structure, which enables the proposed protection scheme, is designed. The significant feature of the proposed protection scheme is that it has the ability to protect the looped and radial microgrids against solid and high-impedance faults. To verify the efficacy of the proposed approach, extensive simulations have been carried out using the MATLAB/SIMULINK software package. The results show that the proposed scheme successfully identifies and isolates various types of fault in a microgrid and performs well with different fault resistances and fault locations. [ABSTRACT FROM AUTHOR]

• A simple and efficient line fault detection. • Suitable for high impedance faults. • Fast protection method for WAP. • Power swing detection. • This method doesn't need any threshold for tuning. Due to multiple transmitted datasets, complexity, and failure to detect high-impedance faults, many researches have been conducted to overcome protection problems. These studies have addressed some challenges such as the need for a threshold value for detection, or in some cases, prediction of power swings. This paper is an attempt to overcome the problems of previous researches using a new and accurate wide area protection method. The proposed method determines the closest bus to the fault, considering the highest variations in positive-sequence voltage according to the information received from PMUs. To detect the faulted line, all lines connected to the selected bus are examined. Then, the line with the same polarity of current changes on both sides of the line is selected as the faulted line. The performance of the proposed method is evaluated considering various faults in the IEEE 39-bus test system, simulated by DIgSILENT software. The results indicate that the proposed method can correctly detect the faulted line. It is also shown that the proposed method can detect high-impedance and unsymmetrical faults and distinguish power swing from fault conditions. [ABSTRACT FROM AUTHOR]

This study presents a method for high-impedance fault (HIF) detection in a low-voltage DC (LVDC) distribution system via mathematical morphology (MM), which is composed of two elementary transformations, namely, dilation and erosion. Various MM-based filters are used to detect abnormal signals of current waveform. The LVDC distribution system, including power conversion devices, such as AC/DC and DC/DC converters, is modelled with electromagnetic transient program (EMTP) software to verify the proposed method. The HIF arc model in the DC system is also implemented with EMTP/MODELS, which is a symbolic language interpreter for EMTP. Simulation results show that the proposed method can be applied to detect HIF effectively in the LVDC distribution system. [ABSTRACT FROM AUTHOR]

High-impedance fault location has always been a challenge for protection engineering. On the other hand, if this task is successfully realized, maintenance action could be performed in order to avoid potential injuries. For an effective protection scheme, high-impedance fault location should be performed, but a lack of research on this area is noted. This paper proposes a new analytical formulation for high-impedance fault location in power systems. The approach is developed in the time domain, considering a high-impedance fault model consisting of two antiparallel diodes. Using this model, the fault distance and parameters are estimated as a minimization problem. First, a linear least square-based estimator is applied without consideration of line capacitance. Second, a steepest descent-based estimator is proposed in order to consider the line capacitance. Studies were carried out with the IEEE 13 bus modeled in the Alternate Transients Program. Encouraging test results are found indicating the method's potential for real-life applications. [ABSTRACT FROM PUBLISHER]

The fault identification process in transmission systems involves three functions: discrimination, classification and phase selection. The current study classifies the methods that applied for each function. Moreover, this study introduces criticism and assessment study that helps the power system protection engineer to choose the best fault identification scheme at responsible indices. Investigated solutions for the drawbacks appeared with the previous methods are suggested. This study also proposes sensitive and automated fault identification scheme to solve the existing challenges such as high‐impedance faults (HIFs), non‐linear modelling of arcing etc. Several simulation studies are employed using alternative transients program/electromagnetic transient program (ATP/EMTP) package on a sample 500 kV test system to ensure the performances of the proposed scheme compared with the previous methods. Simulation results concluded that: the proposed identification scheme has the ability to discriminate correctly between HIF and low‐impedance faults using current signal captured from one end only. Moreover, the proposed scheme alleviates perfectly the problems associated with load variations by adaptive threshold settings and reduces the impacts on the environmental and external phenomena. [ABSTRACT FROM AUTHOR]

The study presents a differential scheme for microgrid protection using time‐frequency transform such as S‐transform. Initially, the current at the respective buses are retrieved and processed through S‐transform to generate time‐frequency contours. Spectral energy content of the time‐frequency contours of the fault current signals are calculated and differential energy is computed to register the fault patterns in the microgrid at grid‐connected and islanded mode. The proposed scheme is tested for different shunt faults (symmetrical and unsymmetrical) and high‐impedance faults in the microgrid with radial and loop structure. It is observed that a set threshold on the differential energy can issue the tripping signal for effective protection measure within four cycles from the fault inception. The results based on extensive study indicate that the differential energy‐based protection scheme can reliably protect the microgrid against different fault situations and thus, is a potential candidate for wide area protection. [ABSTRACT FROM AUTHOR]

• Data-driven approach proposed to bridge power system protection gaps. • Deep learning applied to complement traditional protection technologies. • Transfer learning adopted to address the data limitation problem in practice. Protection is a critical function in power systems to avoid equipment damage, maintain personnel safety, and support system reliability. The existing protection technology that mainly relies on protective relays is not capable of providing 100% protection for all system events. Whenever protective relays fail to respond or respond incorrectly, it leads to the protection gap. In this paper, a new data-driven approach is proposed to complement the traditional protection technology so that the faulty conditions can be adequately distinguished from the transients resulted from normal operations. The proposed approach combines convolutional neural network and long short-term memory (CNN-LSTM) to develop a deep neural network that achieves the invariance in data translation and captures the temporal correlation of input data in time series. It can accurately detect the system faults with the variations and noises in the input data. The use of CNN-LSTM eliminates the complicated and often manual feature extraction procedure that is commonly required by conventional data-driven approaches. In order to address the issue of insufficient training data in practice, a transfer learning method is also applied to facilitate the future practical applications. The efficacy of the proposed data-driven approach is tested for the protection gaps with respect to the transmission line high-impedance fault and transformer inter-turn fault, respectively. The extensive study results demonstrate that the proposed approach can effectively bridge the protection gaps in power system operations. [ABSTRACT FROM AUTHOR]

A high-impedance fault is generated when an overhead power line physically breaks and falls to the ground. Such faults are difficult to detect and locate in electric power systems because of the small currents and voltage drops involved, which cannot be detected by conventional protection. Furthermore, arcing accompanies high-impedance faults, resulting in fire hazard, damage to electrical equipment, and risk to human life. This article presents an analytical description of the interaction between the electric arc associated with high-impedance faults and a transmission line. A joint analytical solution to the wave equation for a transmission line and a non-linear equation of the arc model is found for the case of an arbitrary reflection coefficient at the substation end, and a methodology for high-impedance fault detection and localization is proposed. The developed model is validated by means of a comparison with measurements. The comparison demonstrates the accuracy and effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Many high-impedance ground faults (HIGFs) that happen in low-voltage (LV) systems often cause customer supply loss, fire, and human safety hazards. Traditional ground-fault protection is provided by the residual current circuit breaker (RCCB). The RCCB is usually employed in an analogue measuring circuit and often causes nuisance tripping due to capacitive leakage current and load-switching operations. It offers only ground fault protection to a certain extent and has difficulty in detecting HIGFs associated with a dielectric material defect. In this paper active power variation-based protection for HIGFs is developed, and a dissipation factor (DF)-based criterion for identifying load-switching operation is proposed. They are implemented by cross-correlation analysis between phase voltage and residual current in single-phase networks. A digital protection scheme is also designed. EMTP simulation results show that the new protection can remove the influences of capacitive leakage current and load-switching operations and is able to detect HIGFs and prevent electric shock with high sensitivity and robustness. [ABSTRACT FROM AUTHOR]

• Low-cost method for detection and area location of impedance faults to ground. • The method is based on non-synchronized measurements. • The economic feasibility of the method is based on the use of conventional RMS measurements. • Tested in the IEEE 123 Node Test Feeder and a practical simulation with RTDS. This paper presents a low-cost method for detection and area location of impedance faults to ground in medium voltage of unbalanced Distribution Systems (DS). The method is called Zero-Sequence non-synchronized Protection (ZSNP) and its economic feasibility is based on the use of conventional measurements (non-synchronized RMS), which are already installed on DS. The name "non-synchronized" derives from the fact that ZSNP is based on non-synchronized RMS measurements (current angle is not necessary) and fault location relies on RMS differential zone, defined by two points of measurement. In the ZSNP method, the data is sent to a control center, and faults are detected by analyzing the phasor magnitude of zero-sequence currents on distribution feeders. The method has special application for DS with medium voltage grounding reference only at the substation. The method may also be applied to multi-grounded systems, but the efficiency is usually reduced. A practical simulation with RTDS and a real automation equipment is also presented for validation. ZSNP was tested in the IEEE 37 and IEEE 123 Node Test Feeders and results demonstrated fault detection rates greater than 95%. The method did not present any false positive result, that is, it does not erroneously indicate the existence of fault. Economic analysis and comparisons with other methods were performed. The high success rates demonstrate that the method can be applied in real DS to identify faults that traditional protection may not detect. [ABSTRACT FROM AUTHOR]

High-impedance faults (HIF) are difficult to detect because of their low current amplitude and highly diverse characteristics. In recent years, machine learning (ML) has been gaining popularity in HIF detection because ML techniques learn patterns from data and successfully detect HIFs. However, as these methods are based on supervised learning, they fail to reliably detect any scenario, fault or non-fault, not present in the training data. Consequently, this paper takes advantage of unsupervised learning and proposes a convolutional autoencoder framework for HIF detection (CAE-HIFD). Contrary to the conventional autoencoders that learn from normal behavior, the convolutional autoencoder (CAE) in CAE-HIFD learns only from the HIF signals eliminating the need for presence of diverse non-HIF scenarios in the CAE training. CAE distinguishes HIFs from non-HIF operating conditions by employing cross-correlation. To discriminate HIFs from transient disturbances such as capacitor or load switching, CAE-HIFD uses kurtosis, a statistical measure of the probability distribution shape. The performance evaluation studies conducted using the IEEE 13-node test feeder indicate that the CAE-HIFD reliably detects HIFs, outperforms the state-of-the-art HIF detection techniques, and is robust against noise. [ABSTRACT FROM AUTHOR]