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AC microgrids are becoming increasingly important for providing reliable and sustainable power to communities. However, the evolution of distribution systems into microgrids has changed the way they respond to faults and hence their protection requirements. Faults in microgrids could hinder operation stability and damage the system components. The types, locations, and resistances of faults, as well as microgrid operation modes, distributed generation penetration levels, load changes, and system topologies, all affect how the microgrid responds to faults. In order to offer quick restoration and to protect the microgrid components, fault detection and classification are therefore essential for microgrids. In this direction, unconventional methods such as artificial intelligence have been increasing in popularity over the last years. Pattern recognition is a methodology that machine learning as an approach to artificial intelligence is concerned with. The combination of protection with machine learning may be motivating in order to achieve the goal of intelligent operation in the smart grid. In this paper, fault detection, classification and location methods are reviewed for microgrid application. Different methods applied for both fault location and fault classification are being classified by the implemented technique. Such methods are explained and analyzed providing the main advantages and disadvantages of each category. Additionally, the research trends in both fields are analyzed and state–of–the–art methods from each category are thoroughly compared. Finally, the research gaps and future directions are identified.

The theme of the paper is to emphasize the detection and classification of faults and their site location in the transmission line using machine learning techniques which help to indemnify the foul-up of the humans in identifying the site and type of occurrence of fault. Moreover, the transient stability is a supreme one in power systems and so the disturbances like faults are required to be separated to preserve the transient stability. In general, the protection of the transmission line includes the installation of relays at both ends of the line that constantly monitor voltages and currents and operate unless a fault occurs on a line. Therefore, this paper intends to introduce a novel transmission line protection model by exploiting the hybrid optimization concept to train the Convolutional Neural Network (CNN). Here, the fault detection, classification and site location are diagnosed by using CNN which is trained and tested by making use of diverse synthetic field data derived from the simulation models of distinct types of transmission lines. Hence, the location and the type of faults will be predicted by the CNN depending on the fault signal characteristics which are optimally trained by a new hybrid algorithm named Chicken Swarm Insisted Spotted Hyena (CSI-SH) Algorithm that hybrids both the concept of Spotted Hyena Optimization (SHO) and Chicken Swarm Optimization (CSO). Finally, the proposed method based on CNN for fault classification and site location of transmission lines is implemented in MATLAB/Simulink and the performances are compared with various measures like classification accuracy, fault detection rate and so on. [ABSTRACT FROM AUTHOR]

Modern solutions for precise fault localisation in Low Voltage (LV) Distribution Networks (DNs) often rely on costly tools such as the micro-Phasor Measurement Unit (μ PMU), which is potentially impractical for the large number of nodes in LVDNs. This paper introduces a novel fault detection technique using a distribution network digital twin without the use of μ PMUs. The Digital Twin (DT) integrates data from Smart Meters (SMs) and network topology to create an accurate replica. In using SM voltage-magnitude readings, the pre-built twin compiles a database of fault scenarios and matches them with their unique voltage fingerprints. However, this SM-based voltage-only approach shows only a 70.7% accuracy in classifying fault type and location. Therefore, this research suggests using the cables' Currents Symmetrical Component (CSC). Since SMs do not provide direct current data, a Machine Learning (ML)-based regression method is proposed to estimate the cables' currents in the DT. Validation is performed on a 41-node LV distribution feeder in the Scottish network provided by the industry partner Scottish Power Energy Networks (SPEN). The results show that the current estimation regressor significantly improves fault localisation and identification accuracy to 95.77%. This validates the crucial role of a DT in distribution networks, thus enabling highly accurate fault detection when using SM voltage-only data, with further refinements being conducted through estimations of CSC. The proposed DT offers automated fault detection, thus enhancing customer connectivity and maintenance team dispatch efficiency without the need for additional expensive μ PMU on a densely-noded distribution network. [ABSTRACT FROM AUTHOR]

Today, the classification and location of faults in electrical networks remains a topic of great interest. Faults are a major issue, mainly due to the time spent to detect, locate, and repair the cause of the fault. To reduce time and associated costs, automatic fault classification and location is gaining great interest. State-of-the-art techniques to classify and locate faults are mainly based on line-impedance measurements or the detection of the traveling wave produced by the event caused by the fault itself. In contrast, this paper describes the methodology for creating a database and a model for a complex distribution network. Both objectives are covered under the paradigm of the time-domain pulse reflectometry (TDR) principle. By using this technique, large distances can be monitored on a line with a single device. Thus, in this way a database is shared and created from the results of simulations of a real and complex distribution network modeled in the PSCADTM software, which have been validated with measurements from an experimental test setup. Experimental validations have shown that the combination of the TDR technique with the modeling of a real network (including the real injector and the network coupling filter from the prototype) provides high-quality signals that are very similar and reliable to the real ones. In this sense, it is intended firstly that this model and its corresponding data will serve as a basis for further processing by any of the existing state-of-the-art techniques. And secondly, to become a valid alternative to the already well-known Test Feeders but adapted to work groups not used to the electrical world but to the environment of pure data processing.

The increase in energy demand due to rapid population growth, industrialization, depleting fossil energy resources, distribution network congestion, and environmental concerns of fossil fuels used to generate electricity are some of the reasons behind the integration of renewable Distributed Generation (DG) units into the existing power grid. Integrating DGs into the existing distribution network makes the system more flexible and increases power availability for the load demand increment. Although there are significant technical benefits of integrating DGs into the power grid, protection philosophy has been affected by these network topologies. This paper proposes a hybrid fault protection scheme in a power-integrated system. The proposed technique aims at minimizing the impact of the fault in the design and appropriately locating the position of the fault. The proposed approach comprises a signal processing and feature extraction segment using the Wavelet Packet Transform (WPT), a fault classification and detection segment using the Support Vector Machine (SVM), and a fault location segment using the Gaussian Process Regression (GPR). Subsequently, these techniques are combined to design a hybrid protection scheme. The scheme is tested by using a modified Eskom power system network and modeled in Power Factory Software. The classification and the regression functions are implemented by using the MATLAB platform. The proposed technique's main contribution is the scheme's ability to classify, detect, and locate fault using a ½ cycle of the fault signal window. The results show that the proposed system has achieved a 99.8% classification accuracy and a minimum fault estimation error. [ABSTRACT FROM AUTHOR]

Thanks to smart grids, more intelligent devices may now be integrated into the electric grid, which increases the robustness and resilience of the system. The integration of distributed energy resources is expected to require extensive use of communication systems as well as a variety of interconnected technologies for monitoring, protection, and control. The fault location and diagnosis are essential for the security and well-coordinated operation of these systems since there is also greater risk and different paths for a fault or contingency in the system. Considering smart distribution systems, microgrids, and smart automation substations, a full investigation of fault location in SGs over the distribution domain is still not enough, and this study proposes to analyze the fault location issues and common types of power failures in most of their physical components and communication infrastructure. In addition, we explore several fault location techniques in the smart grid's distribution sector as well as fault location methods recommended to improve resilience, which will aid readers in choosing methods for their own research. Finally, conclusions are given after discussing the trends in fault location and detection techniques. [ABSTRACT FROM AUTHOR]

Accurately evaluating the fault type and location is important for ensuring the reliability of the power distribution network. A mushrooming number of distributed generations (DGs) connected to the distribution system brings challenges to traditional fault classification and location methods. Novel AI-based methods are mostly based on wide area measurement with the assistance of intelligent devices, whose economic cost is somewhat high. This paper develops a super-resolution (SR) and graph neural network (GNN) based method for fault classification and location in the power distribution network. It can accurately evaluate the fault type and location only by obtaining the measurements of some key buses in the distribution network, which reduces the construction cost of the distribution system. The IEEE 37 Bus system is used for testing the proposed method and verifying its effectiveness. In addition, further experiments show that the proposed method has a certain anti-noise capability and is robust to fault resistance change, distribution network reconfiguration, and distributed power access. [ABSTRACT FROM AUTHOR]

Transmission lines are an important part of the power system, as they are the carriers of power from one end to another. In the event of a fault, the power transferring process is disturbed and can even damage the equipment, which is attached to the generation end as well as the user end. Most of the power systems are connected to the transmission lines, so it is very important to make the transmission lines secure. For protection purposes, relays are used, but relays only trip in the event of a fault and do not tell us about the location of the fault. The power system requires a speedy protection system. For a speedy protection system, quick and fast fault analysis and classification are required. An effective approach for the analysis of the transmission line with three sources is proposed. This method is quite effective and accurate for locating the fault and classifying its types. This technique needs power measurement from both ends simultaneously for fault diagnosis. Instantaneous power and sign power values are used for fault detection and classification. A voltage profile is used to identify the fault location. For three-phase transmission lines, voltage profiles are built up at different segment points to locate the fault. The IEEE-9 bus system is simulated for this technique. MATLAB is employed for simulation purposes. The test system is simulated with different types of faults at different locations. Relay operation has not affected the accuracy of the system. This technique has an accuracy of more than 97%. This method is quite effective for the analysis of power transmission lines. It can discriminate the fault type, identify the faulty phase of the line, and locate the point of the fault. Faults are located with errors not more than 0.45%. Moreover, the time difference between the actual fault and the calculated fault obtained from the estimated location is not more than 0.004 s. Simulations are claimed to be executed in less computational time, ensuring effective and rapid protection against faults. [ABSTRACT FROM AUTHOR]

Modern solutions for precise fault localisation in Low Voltage (LV) Distribution Networks (DNs) often rely on costly tools such as the micro-Phasor Measurement Unit (μPMU), which is potentially impractical for the large number of nodes in LVDNs. This paper introduces a novel fault detection technique using a distribution network digital twin without the use of μPMUs. The Digital Twin (DT) integrates data from Smart Meters (SMs) and network topology to create an accurate replica. In using SM voltage-magnitude readings, the pre-built twin compiles a database of fault scenarios and matches them with their unique voltage fingerprints. However, this SM-based voltage-only approach shows only a 70.7% accuracy in classifying fault type and location. Therefore, this research suggests using the cables’ Currents Symmetrical Component (CSC). Since SMs do not provide direct current data, a Machine Learning (ML)-based regression method is proposed to estimate the cables’ currents in the DT. Validation is performed on a 41-node LV distribution feeder in the Scottish network provided by the industry partner Scottish Power Energy Networks (SPEN). The results show that the current estimation regressor significantly improves fault localisation and identification accuracy to 95.77%. This validates the crucial role of a DT in distribution networks, thus enabling highly accurate fault detection when using SM voltage-only data, with further refinements being conducted through estimations of CSC. The proposed DT offers automated fault detection, thus enhancing customer connectivity and maintenance team dispatch efficiency without the need for additional expensive μPMU on a densely-noded distribution network.

Nowadays, the applications of machine learning techniques have widely been implemented in various domains of the power system and especially to predict three-phase transmission line faults. This paper compares the results obtained from two powerful machine learning techniques such as Concurrent Neuro-Fuzzy and Decision Tree applied to predict faults classification and their location on a nine-phase transmission line system. The results obtained demonstrated that the utilization of these artificial intelligence techniques which have been well applied on various and diversified systems could also been applied on a future nine-phase transmission line system that carry 750 kV over 200 km to classify and locate various fault types and then, to increase the yield of the line.

In this paper, a fault protection diagnostic scheme for a power distribution system is proposed. The scheme comprises a wavelet packet decomposition (WPD) for signal processing and analysis and a support vector machine (SMV) for fault classification and location. The scheme is tested on a reduced Eskom 132 kV power line. The WPD is used to extract fault signatures of interest and the SVM is subsequently used for fault classification and locating various fault conditions. Furthermore, we investigate the effectiveness of the SVM scheme using different samples of the cycles for fault classification and location. The results show that the fault classification and location on a distribution line can be determined rapidly and efficiently irrespective of the fault impedance and incipient angle with minimum estimation error. Lastly, the proposed scheme is tested on a grid-integrated system with renewable energy sources. [ABSTRACT FROM AUTHOR]

Overhead transmission lines are the most vulnerable components in electrical power systems. Therefore, to improve the electrical system availability and reliability, fault location should be performed as quickly and accurately as possible. This procedure consists of several steps, including fault detection and classification, which are essential to successfully locate the fault. Due to the importance of these two steps in protection schemes, different approaches have been proposed recently, such as algorithms based on Fourier Transform, Wavelet Transform, Statistical and Artificial Intelligence Techniques. Although they are all promising, they require complex formulations or high computational efforts. This paper proposes a method based on the Euclidean distance measure to perform the fault detection and classification functions, only by using voltage signals from the transmission line ends. The proposed method is simple and robust, not requiring complex formulation or significant computational effort. A large number of tests were performed, considering the power system under different operational conditions and fault characteristics such as fault resistance and fault inception angle, among others. The observed results showed that the proposed method is capable of accurately detecting and classifying faults, regardless of the power system condition or fault characteristic. The comprehensiveness and applicability of the proposed method was confirmed by means of two different transmission lines (line parameters, voltage level, and AC equivalent systems). [ABSTRACT FROM AUTHOR]

This study develops a hybrid method to identify, classify, and locate electrical faults on transmission lines based on Machine Learning (ML) methods. Firstly, Wavelet Transform (WT) technique is applied to extract features from the current or voltage signals. The extracted signals are decomposed into eleven coefficients. These coefficients are calculated to the energy level, and the data of teen fault types are converted to the RGB image. Secondly, GoogLeNet model is applied to classify the fault, and Convolutional Neural Network (CNN) method is proposed to locate the fault. The proposed method is tested on the four-bus power system with the 220 kV transmission line via time-domain simulation using Matlab software. The conditions of the fault resistance random values and the pre-fault load changes are considered. The simulation results show that the proposed method has high accuracy and fast processing time, and is a useful tool for analyzing the system stability in the field of electricity. [ABSTRACT FROM AUTHOR]

Fault analysis (detection, classification and location) of transmission network is of great importance in power system. A Wavelet-Alienation-Neural (WAN) technique has been developed for the fault analysis of Unified Power Flow Controller (UPFC) compensated transmission network. The detection and classification of various outages are accomplished by alienation of wavelet based approximate coefficients computed from current signals. The precise location of faults is carried out by an Artificial Neural Network fed from estimated approximate coefficients computed from voltage and current signals of the same quarter cycle. The robustness of the algorithm is proved with the case studies of varying fault locations, sampling frequency, system parameters, effects of noise, fault incipient angle, different control strategies and fault path impedances.

In this paper, a novel relaying scheme is proposed for bipolar line commutated converter (LCC) high voltage direct current (HVDC) transmission lines that detects the fault, identifies the pole of fault and estimates the location of the fault. The scheme uses features extracted from rectifier end DC current and voltage signals. Long short term memory (LSTM), a deep learning method has been designed as classifier as well as predictor for carrying out different relaying tasks. Three modules have been designed namely LSTM-FD (LSTM module for fault detection (FD)), LSTM-FI (LSTM module for fault pole identification (FI)) and LSTM-FL (LSTM module for fault location estimation (FL)). The voltage and current signals are obtained from measuring units. Then with a moving window of one cycle, the RMS of signals is calculated. The current and voltage features are obtained in the time domain. The voltage and current features obtained are used as input to fault detection and fault pole identification module. For fault location estimation, half cycle samples of RMS current and voltage after the fault have been taken. From half cycle sample, maximum value of current and minimum value of voltage has been obtained. This single value of current and voltage are used as input feature to the fault location module. All the relaying modules have been tested varying fault types, locations, resistances, smoothing reactors, noisy signals, etc. Sensitivity and reliability of the proposed relaying scheme is 100% with all the tested fault cases. Error in location estimation is within 1% for all the tested fault cases. Advantage of the method is that it does not require communication link. Another advantage of the proposed method is that it can work with low sampling frequency. Additionally, reliability and sensitivity of proposed method is very high, hence can be used as an alternative to travelling wave based relaying schemes.

Thanks to smart grids, more intelligent devices may now be integrated into the electric grid, which increases the robustness and resilience of the system. The integration of distributed energy resources is expected to require extensive use of communication systems as well as a variety of interconnected technologies for monitoring, protection, and control. The fault location and diagnosis are essential for the security and well-coordinated operation of these systems since there is also greater risk and different paths for a fault or contingency in the system. Considering smart distribution systems, microgrids, and smart automation substations, a full investigation of fault location in SGs over the distribution domain is still not enough, and this study proposes to analyze the fault location issues and common types of power failures in most of their physical components and communication infrastructure. In addition, we explore several fault location techniques in the smart grid’s distribution sector as well as fault location methods recommended to improve resilience, which will aid readers in choosing methods for their own research. Finally, conclusions are given after discussing the trends in fault location and detection techniques.

Accurately evaluating the fault type and location is important for ensuring the reliability of the power distribution network. A mushrooming number of distributed generations (DGs) connected to the distribution system brings challenges to traditional fault classification and location methods. Novel AI-based methods are mostly based on wide area measurement with the assistance of intelligent devices, whose economic cost is somewhat high. This paper develops a super-resolution (SR) and graph neural network (GNN) based method for fault classification and location in the power distribution network. It can accurately evaluate the fault type and location only by obtaining the measurements of some key buses in the distribution network, which reduces the construction cost of the distribution system. The IEEE 37 Bus system is used for testing the proposed method and verifying its effectiveness. In addition, further experiments show that the proposed method has a certain anti-noise capability and is robust to fault resistance change, distribution network reconfiguration, and distributed power access.

In this paper, a fault protection diagnostic scheme for a power distribution system is proposed. The scheme comprises a wavelet packet decomposition (WPD) for signal processing and analysis and a support vector machine (SMV) for fault classification and location. The scheme is tested on a reduced Eskom 132 kV power line. The WPD is used to extract fault signatures of interest and the SVM is subsequently used for fault classification and locating various fault conditions. Furthermore, we investigate the effectiveness of the SVM scheme using different samples of the cycles for fault classification and location. The results show that the fault classification and location on a distribution line can be determined rapidly and efficiently irrespective of the fault impedance and incipient angle with minimum estimation error. Lastly, the proposed scheme is tested on a grid-integrated system with renewable energy sources.

In this paper, a protection scheme of 500 kV, 50 Hz, and 220 km double end transmission system based on wavelet transform (WT), high-speed decision tree (DT), and artificial neural network (ANN) are proposed. The scheme is implemented using a microcontroller co-simulated with MATLAB/Simulink and it is validated in real-time. The proposed scheme performs fault classification, location estimation, and fault zone identification using both DT and ANN in a comparative study. The scheme calculates the wavelet transform approximation coefficients of the current and voltage signals and then sends its energy to a specific decision tree/neural network to recognize the fault type, its zone, and its location. It was found that the accuracy of the decision tree classifier outperforms the ANN classifier, in addition, achieves a significantly higher classification speed of about 0.4 msec inference time and it is easier to be applied in real-time. In contrast, the neural network is more accurate in calculating fault zone and locations. DT and ANN are trained and tested using different sets of data obtained from simulated faults that differ in type, resistance, and location. Modeling fault scenarios and data processing are carried out using the MATLAB/Simulink software package. The hardware implementation uses an 8-bit ATmega microcontroller that is interfaced with the simulated model using MATLAB support package for Arduino. Simulation results confirm that the proposed scheme using DT for fault classification and ANN for zone and location estimation is accurate and reliable even with varying fault resistance, type, and distance, and is therefore applicable in practice on a digital platform. [ABSTRACT FROM AUTHOR]

• Detecting the high resistance internal fault and identifying the correct faulted phase in shunt-compensated lines using phase currents only. • No symmetrical component and frequency-domain analysis are required which reduces the computational burden. • Validation of the proposed algorithm with filed data obtained from a 400 kV line. • Independent of compensation level, fault location, line length and its parameters. The proposed method performs correctly for time synchronization errors, variation in fault location, CT saturation, source impedance variations, different line lengths and power swing, etc. Static synchronous compensators (STATCOMs) are used in the power transmission line to improve the voltage profile and stability. For a fault on the line, additional uneven reactive current from the compensator causes the maloperation of local data-based relaying schemes. In this work, using the current of both ends of the line, internal faults and faulty phases are identified by the ratio of both end's current difference to the local one. Both the magnitude and angle of the ratio are utilized to enhance the dependability and security of the relay operation. The performance of the proposed technique is validated in Western System Coordinating Council (WSCC), a 9-bus system using data obtained from PSCAD. The authenticity of the proposed method is further validated using field data collected from a 400 kV shunt compensated line in the Indian power grid. The suggested strategy is resilient to changes in fault resistance, source impedance, fault location, current transformer saturation, various compensation levels, and high resistance faults (HRF). A comparison of the proposed Algorithm with existing methodologies demonstrates its superiority. [ABSTRACT FROM AUTHOR]

In this paper, a fault diagnosis scheme is proposed in the transmission line, by which the faults can be classified and located correctly and rapidly using voltage amplitudes and support vector machine. A high voltage power system transmission line is simulated by MATLAB to produce a fault data set. The three-phase fault voltages are obtained at one terminal point of the line. The amplitudes of the three-phase fault voltages will be applied as the fault features to train the support vector classification (SVC) and realize fault classification after the three-phase fault voltages pass through a low-pass filter to remove the noise. After knowing the fault type, the voltage amplitude of the phase where the fault occurs will be used as the fault feature to train the support vector regression (SVR) and realize the fault location. Compared with other fault classification and fault location schemes, the proposed fault diagnosis scheme needs less information to provide one hundred percent classification accuracy and high accurate estimations of fault location. Simulations and comparisons show the proposed scheme is better than other schemes. [ABSTRACT FROM AUTHOR]

Nonlinear characteristics of solar cells and changes in environmental conditions, such as temperature, and in particular, the intensity of daytime irradiation, make it difficult to identify faults by the conventional means of protection. Therefore, a variety of machine learning techniques are proposed for fault detection in photovoltaic (PV) arrays. In this regard, classifying and identifying the location of a fault event is essential. In addition to fault recognition, selecting the method of fault correction is another issue to be addressed. However, there are scarce investigations in this field. In this paper, a comprehensive method for identifying, classifying, locating, and correcting faults is introduced. The proposed method is assessed with the expansion of the diagnostic space of the graph-based semisupervised learning algorithm and an increased number of class labels. After identifying the type and location of a fault, the system temporarily isolates the fault to function without interruption until it is fully corrected. The problem of overlapping cell data in normal and fault-prone modes is resolved by applying different methods of normalization. The results show that all faults including unlearned and learned in a wide range of environmental conditions, where possible PV arrays are experienced, are properly identified and corrected. Moreover, our studies demonstrate that the proposed system mitigates the output voltage variations over a fault-prone mode. [ABSTRACT FROM AUTHOR]

Fault location with the highest possible accuracy has a significant role in expediting the restoration process, after being exposed to any kind of fault in power distribution grids. This paper provides fault detection, classification, and location methods using machine learning tools and advanced signal processing for a radial distribution grid. The three-phase current signals, one cycle before and one cycle after the inception of the fault are measured at the sending end of the grid. A discrete wavelet transform (DWT) is employed to extract useful features from the three-phase current signal. Standard statistical techniques are then applied onto DWT coefficients to extract the useful features. Among many features, mean, standard deviation (SD), energy, skewness, kurtosis, and entropy are evaluated and fed into the artificial neural network (ANN), Multilayer perceptron (MLP), and extreme learning machine (ELM), to identify the fault type and its location. During the training process, all types of faults with variations in the loading and fault resistance are considered. The performance of the proposed fault locating methods is evaluated in terms of root mean absolute percentage error (MAPE), root mean squared error (RMSE), Willmott's index of agreement (WIA), coefficient of determination ( R 2 ), and Nash-Sutcliffe model efficiency coefficient (NSEC). The time it takes for training and testing are also considered. The proposed method that discrete wavelet transforms with machine learning is a very accurate and reliable method for fault classifying and locating in both a balanced and unbalanced radial system. 100% fault detection accuracy is achieved for all types of faults. Except for the slight confusion of three line to ground (3LG) and three line (3L) faults, 100% classification accuracy is also achieved. The performance measures show that both MLP and ELM are very accurate and comparative in locating faults. The method can be further applied for meshed networks with multiple distributed generators. Renewable generations in the form of distributed generation units can also be studied. [ABSTRACT FROM AUTHOR]

The ever-increasing load demand motivates the adoption of the six-phase transmission system as a viable alternative to the conventional three-phase double-circuit line because of its enhanced power transfer capability. However, the acceptance of six-phase transmission is highly dependent on its protection scheme, which is a challenging task due to the large number of possible faults. In this regard, this paper describes a novel concept based on the hybridization of wavelet transform, adaptive particle swarm optimization (APSO) and ANN for fault classification and location in the six-phase transmission line. The proposed scheme overcomes the difficulties associated with existing ANN-based protection schemes related to the selection of architecture and convergence to the non-global optimal set of weight vectors and biases post-training. From the features/attributes extracted using wavelet transform, the hybrid algorithm utilizes the strong exploration and mapping capability of APSO and ANN, respectively, for input–output mapping. It significantly increases the probability of global convergence for ANN training, thereby improving the performance of the protection scheme in terms of fault detection, classification and location estimation accuracy. The proposed scheme has been extensively tested for wide variation in different fault attributes. In addition to the offline simulations, the effectiveness of the proposed scheme has been validated in real-time environment by carrying out hardware in loop simulations using OPAL-RT digital simulator. [ABSTRACT FROM AUTHOR]

Abstract This paper proposes a pattern recognition based differential spectral energy protection scheme for ac microgrids using a Fourier kernel based fast sparse time-frequency representation (SST or simply the sparse S-Transform). The average and differential current components are passed through a change detection filter, which senses the instant of fault inception and registers a change detection point (CDP). Subsequently, if CDP is registered for one or more phases, then half cycle data samples of the average and differential currents on either side of the CDP are passed through the proposed SST technique, which generates their respective spectral energies and a simple comparison between them detects the occurrence and type of the fault. The SST technique is also used to provide voltage and current phasors and the frequency during faults which is further utilized to estimate the fault location. The proposed technique as compared to conventional differential current protection scheme is quicker in fault detection and classification, which is least effected from bias setting, has a faster relay trip response (less than one cycle from fault incipient) and a better accuracy in fault location. The significance and accuracy of the proposed scheme have been verified extensively for faults in a standard microgrid system, subjected to a large number of operating conditions and the outputs vindicate it to be a potential candidate for real time applications.

Highlights • A new protection scheme is proposed for digital relaying in MGs. • Extensive evaluations of the proposed method in noisy conditions are carried out. • A threshold is used for fault detection and classification using the UT to accommodate low SNRs. • The superior performance of the proposed method over existing methods is shown. Abstract This paper presents a new scheme for microgrid protection using the Time-Time (TT) transform. Initially, the bus currents are processed through the TT-transform to compute the TT-matrixes. To determine the fault patterns, an appropriate index based on the TT-matrix z-score vector is introduced. A threshold is used for fault detection and classification within a few cycles from the fault inception for both grid-connected and islanded modes. A threshold selection approach using the unscented transformation (UT) is used to accommodate low signal-to-noise ratios (SNRs). The protection scheme is evaluated through extensive simulations using PSCAD/EMTDC software for two different microgrids (MGs). Various test scenarios consisting of all shunt and high impedance faults are investigated in MGs with radial and loop topologies for grid-connected and autonomous modes of operation. The results verify that the proposed scheme can reliably detect and classify faults. Furthermore, the proposed method is robust to changes in fault parameters, and performs well under highly noisy conditions. [ABSTRACT FROM AUTHOR]

Accurate pin-pointing of faults on transmission lines minimizes outage time, labor, and costs. This letter proposes a simple yet effective ensemble Kalman filter (EnKF) approach to accurately locating the faults on transmission lines within half-cycle time. The approach is easy to implement and does not require foreknowledge of either the fault type or an approximate guess of the fault location. Extensive results validate the effectiveness and accuracy of the EnKF-based fault location approach. [ABSTRACT FROM AUTHOR]

High voltage direct current (HVDC) transmission systems are suitable for power transfer to meet the increasing demands of bulk energy and encourage interconnected power systems to incorporate renewable energy sources without any fear of loss of synchronism, reliability, and efficiency. The main challenge associated with DC grid protection is the timely diagnosis of DC faults because of its rapid built up, resulting in failures of power electronic circuitries. Therefore, the demolition of HVDC systems is evaded by identification, classification, and location of DC faults within milliseconds (ms). In this research, the support vector machine (SVM)-based protection algorithm is developed so that DC faults could be identified, classified, and located in multi-terminal high voltage direct current (MT-HVDC) systems. A four-terminal HVDC system is developed in Matlab/Simulink for the analysis of DC voltages and currents. Pole to ground and pole to pole faults are applied at different locations and times. Principal component analysis (PCA) is used to extract reduced dimensional features. These features are employed for the training and testing of SVM. It is found from simulations that DC faults are identified, classified, and located within 0.15 ms, ensuring speedy DC grid protection. The realization and practicality of the proposed machine learning algorithm are demonstrated by analyzing more straightforward computations of standard deviation and normalization.

• The maximum fault detection time is found to be less than half-cycle, which helps in faster maintenance and classification accuracy is 100%. • The algorithm proposed for fault location is based on k-Nearest neighbor regression method. The achieved accuracy of fault location is found to be better than the methods proposed in the literature. • Numerous case studies have been done to check the robustness of the algorithm. This paper presents a novel protection algorithm based on K-medoids clustering and weighted k-Nearest neighbor regression. K-medoids clustering is used for fault detection and classification, while weighted k-Nearest neighbor regression is used to locate the fault. The three phase current signals are sampled at substation with frequency of 3.84 kHz and then decomposed with a db1 mother wavelet. Using the moving window of one cycle, k-medoids clustering is applied to wavelet approximate coefficients over a cycle to obtain two medoids. The difference of medoids is defined as fault index, which is compared with threshold to detect and classify the faults. Various statistical features are computed from post fault approximate wavelet coefficients obtained are fed to k-Nearest Neighbor classifier detect the two nearest buses. Followed by this detection the fault location between these two busses is estimated using weighted k-NN regression. The process of detection, classification and location of fault is accomplished within half a cycle. The various case studies used for established the robustness of the algorithm include type of fault, fault impedance and fault incidence angle and fault location. The proposed algorithm is proved to be not affected by DG trip, islanding and noise. [ABSTRACT FROM AUTHOR]

This paper investigates support vector machine based fault type and distance estimation scheme in a long transmission line. The planned technique uses post fault single cycle current waveform and pre-processing of the samples is done by wavelet packet transform. Energy and entropy are obtained from the decomposed coefficients and feature matrix is prepared. Then the redundant features from the matrix are taken out by the forward feature selection method and normalized. Test and train data are developed by taking into consideration variables of a simulation situation like fault type, resistance path, inception angle, and distance. In this paper 10 different types of short circuit fault are analyzed. The test data are examined by support vector machine whose parameters are optimized by particle swarm optimization method. The anticipated method is checked on a 400 kV, 300 km long transmission line with voltage source at both the ends. Two cases were examined with the proposed method. The first one is fault very near to both the source end (front and rear) and the second one is support vector machine with and without optimized parameter. Simulation result indicates that the anticipated method for fault classification gives high accuracy (99.21%) and least fault distance estimation error (0.29%.

In this paper, time domain relaying schemes for complete protection of parallel transmission lines using wavelet and artificial neural network (ANN) are presented. Four different ANN networks are designed for detection of the fault, fault section identification, classification of fault and location of fault in time domain. The 3rd level approximate discrete wavelet transform (DWT) coefficients of signals of one end are used as input to ANN network. Proposed method is tested with varying fault location, inception angle, fault type and fault resistance. The test results show that the fault is detected and located within 5 ms time accurately. This scheme offers primary protection as well as backup protection to the lines.

Fault location with the highest possible accuracy has a significant role in expediting the restoration process, after being exposed to any kind of fault in power distribution grids. This paper provides fault detection, classification, and location methods using machine learning tools and advanced signal processing for a radial distribution grid. The three-phase current signals, one cycle before and one cycle after the inception of the fault are measured at the sending end of the grid. A discrete wavelet transform (DWT) is employed to extract useful features from the three-phase current signal. Standard statistical techniques are then applied onto DWT coefficients to extract the useful features. Among many features, mean, standard deviation (SD), energy, skewness, kurtosis, and entropy are evaluated and fed into the artificial neural network (ANN), Multilayer perceptron (MLP), and extreme learning machine (ELM), to identify the fault type and its location. During the training process, all types of faults with variations in the loading and fault resistance are considered. The performance of the proposed fault locating methods is evaluated in terms of root mean absolute percentage error (MAPE), root mean squared error (RMSE), Willmott’s index of agreement (WIA), coefficient of determination ( R 2 ), and Nash-Sutcliffe model efficiency coefficient (NSEC). The time it takes for training and testing are also considered. The proposed method that discrete wavelet transforms with machine learning is a very accurate and reliable method for fault classifying and locating in both a balanced and unbalanced radial system. 100% fault detection accuracy is achieved for all types of faults. Except for the slight confusion of three line to ground (3LG) and three line (3L) faults, 100% classification accuracy is also achieved. The performance measures show that both MLP and ELM are very accurate and comparative in locating faults. The method can be further applied for meshed networks with multiple distributed generators. Renewable generations in the form of distributed generation units can also be studied.

This work investigates an improved protection solution based on the use of artificial neural network on the 330 kV Nigerian network modeled using MATLAB R2014a. Measured fault voltages and currents signals decomposed using the discrete Fourier transform implemented via fast Fourier transform are fed as inputs to the neural network. The output plots of the neural network shows its successful application to fault diagnosis (fault detection, fault classification and fault location). Unlike conventional protection schemes, the neural network can be adapted to distances which can cover the entire length of the protected line. Numerical assessment carried out on the neural network fault locator shows a reduced time of operation of 5.15 ms as compared to the 0.350 s with the use of ordinary numerical relays. This work also investigates the adaptive auto-reclosure scheme implemented using artificial neural network. The adaptive reclosure scheme has been adapted for use in the Nigerian network successfully to distinguish transient and permanent faults. Simulation results prove that the adaptive reclosure scheme was able to detect a line-to-ground transient fault and clear this fault in 0.1 s while the line-to-ground permanent fault is cleared after 0.14 s. The fault diagnostic algorithm designed using artificial neural network (ANN) for the 330 kV network was tested on a 132 kV network. Results show and prove that the algorithm is flexible and can be adopted to other networks. [ABSTRACT FROM AUTHOR]

A novel method to locate the zone of transient faults and to classify the fault type in Power Distribution Systems using wavelet transforms and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) has been developed. It draws on advanced techniques of signal processing based on wavelet transforms, using data sampled from the main feeder current to extract important characteristics and dynamic features of the fault signal. In this method, algorithms designed for fault detection and classification based on features extracted from wavelet transforms were implemented. One of four different algorithms based on ANFIS, according to the type of fault, was then used to locate the fault zone. Studies and simulations in an EMTP-RV environment for the 25 kV power distribution system of Canada were carried out by considering ten types of faults with different fault inception, fault resistance and fault locations. The simulation results showed high accuracy in classifying the type of fault and determining the fault area, so that the maximum observed error was less than 2%. [ABSTRACT FROM AUTHOR]

The need for enhancing grid resilience has led to wider acceptance of photo voltaic (PV) integrated microgrid. The variation in fault current during grid connected and islanding operation makes the microgrid protection task challenging. A protection scheme based on ensemble of classifiers has been devised by exploiting the effectiveness of a classifier set coupled with voting strategy. Unlike the existing classifier‐based approaches involving single classifier, the ensemble‐based approach is insensitive to the biasness of individual classifier and dimension/size of dataset. The proposed scheme is formulated to simultaneously perform the tasks of mode detection, fault detection/classification, section identification and location. The scheme is able to discriminate between faults and power quality disturbances, which avoids unintended false tripping. Along with accuracy, the reliability assessment of proposed scheme has been carried out using two indices, i.e. dependability and security for different faults, operating mode and contingencies. For evaluating the reliability of the fault locator, a stochastic approach (Monte‐Carlo simulation) has been adopted. The simulation results justify the applicability of the proposed ensemble classifier‐based scheme for complete protection and reliable operation of microgrids with PV penetration. The scheme also has been validated for real‐time settings using hardware in loop simulations. [ABSTRACT FROM AUTHOR]

In this paper a radial distribution feeder protection scheme against short circuit faults is introduced. It is based on utilizing the substation measured current signals in detecting faults and obtaining useful information about their types and locations. In order to facilitate important measurement signals features extraction such that better diagnosis of faults can be achieved, the discrete wavelet transform is exploited. The captured features are then utilized in detecting, identifying the faulted phases (fault type), and fault location. In case of a fault occurrence, the detection scheme will make a decision to trip out a circuit breaker residing at the feeder mains. This decision is made based on a criteria that is set to distinguish between the various system states in a reliable and accurate manner. After that, the fault type and location are predicted making use of the cascade forward neural networks learning and generalization capabilities. Useful information about the fault location can be obtained provided that the fault distance from source, as well as whether it resides on the main feeder or on one of the laterals can be predicted. By testing the functionality of the proposed scheme, it is found that the detection of faults is done fastly and reliably from the view point of power system protection relaying requirements. It also proves to overcome the complexities provided by the feeder structure to the accuracy of the identification process of fault types and locations. All the simulations and analysis are performed utilizing MATLAB R2016b version software package. [ABSTRACT FROM AUTHOR]

A new spectral energy differential protection scheme using sparse Fourier kernel fast time-frequency transform is proposed for the detection, classification, and location of faults either on the grid-connected or islanded AC microgrid. Initially, the three-phase average and differential components of the current samples measured on either side of the distribution line are processed through an alteration detection filter, which identifies the fault incipient and consequently registers an alteration index for the particular phase, which identifies the fault. The spectral energy of the differential current components are than computed to classify the type of the fault under a number of intrinsic operating conditions like the meshed and radial architectures and grid-connected or islanded mode of operation and varying fault distance ratios. Extensive numerical experimentation illustrates satisfactory results for all the cases investigated in this paper, which include detection, classification, and location of faults on the microgrid. [ABSTRACT FROM AUTHOR]

The HVDC transmission system is winning hearts of researchers and electrical engineers because of its notable merits as compared to the HVAC transmission system in the case of long-distance bulk power transmission. The HVDC transmission system is known for its low losses, effective control ability, efficiency and reliability. However, because of the sudden build-up of fault current in the HVDC transmission system, conventional relays and circuit breakers are required to be modified. Detection of fault location is an important parameter of protection of the HVDC transmission system. In this research paper, fault location methods based on traveling waves are reviewed for the HVDC transmission system. Arrival time and natural frequency are the two parameters of measurement in traveling waves. Advantages and disadvantages of methods of traveling waves with respect to their quantities of measurements are analyzed critically. Further, a two-terminal HVDC test grid is simulated over Matlab/Simulink. Different types of AC and DC faults and at different locations are analyzed on a test grid. A traveling wave-based technique of fault estimation is developed and is evaluated for identification, classification and finding location of faults to validate its performance. Moreover, this technique is supported with analysis of fast Fourier transform to accelerate its practicality and realization.

This paper presents a non-unit protection scheme for series capacitor compensated transmission lines (SCCTL) using discrete wavelet transform and k-nearest neighbor (k-NN) algorithm. All the protective relaying functions such as fault detection, fault classification, faulty phase identification and fault location estimation have been considered in this work. Such a comprehensive work providing all protective relaying functions for protection of double circuit SCCTL utilizing k-NN has not been reported so far. The signal processing and feature extraction are done using discrete wavelet transform due to its capability to differentiate between high and low frequency transient components. For fault detection and classification, only approximate wavelet coefficient of current signal up to level 1 has been used; while for k-NN location estimation, both voltage and current signals of the two circuits are decomposed up to level 3 have been used. Finally, the standard deviation of one cycle pre-fault and one cycle post-fault samples of the approximate wavelet coefficients are calculated to form the feature vector for the k-NN-based algorithm. The performance of the proposed technique is evaluated for large number of fault events with variation in fault type including inter-circuit faults, fault inception angle, fault location and fault resistance. The change in position of series capacitor and different degree of compensation has been discussed. The accuracy of the proposed k-NN-based fault detection and classification module is 100% for all the tested fault cases with a decision period of less than half cycle. The k-NN-based fault location scheme estimates the location of fault with ≤1% error for most of the tested fault cases, which is an exceptional attribute of the proposed scheme as compared with 10–15% error of conventional distance relaying scheme. [ABSTRACT FROM AUTHOR]

Several methods for the location and classification of faults in power transmission lines using computational intelligence and digital signal processing techniques have been described in literature. Artificial neural networks (ANNs) and wavelet transform (WT) have drawn significant attention lately, but they present some drawbacks when dealing with power systems faults where data are often contaminated by noise. This paper proposes an approach by combining independent component analysis (ICA) with travelling wave (TW) theory and support vector machine (SVM). The approach is adequate to locate and recognize faults in high-voltage (HV) transmission lines, while the acquired signals are noisy. Experiments performed for distinct types and locations of faults in a real transmission line model have shown that the proposed combined methods are able to provide excellent performance in fault location. The obtained errors are lower than 1% and accuracy is 100% for the classification of fault signals with noise. It can be stated that this method presents better performance than those regarding the main conventional techniques such as wavelets and neural networks in the presence of noise. [ABSTRACT FROM AUTHOR]

This paper presents a complete protection scheme for detecting, classifying and locating the fault in HVDC transmission lines using support vector machines (SVM). SVM has been used for protective relaying application in HVAC transmission line, however very limited works have been reported for HVDC transmission line. In this work, a ±500 kV HVDC transmission system is developed in PSCAD/EMTDC and the measurement signals obtained are analyzed in MATLAB. The rectifier side AC RMS voltage, DC voltage and current on both the poles are continuously monitored, and given as input to the SVM binary classifier in order to detect the presence of fault in the line. Once a fault is detected, the SVM multi‐class classification module predicts the type of fault and the SVM regression algorithm predicts the location of fault. The feature vector used in the classification and location modules is the standard deviation of the signals over half cycle before and after the occurrence of fault. The method proposed is simple as it requires single‐end data and a direct standard deviation of one cycle data gives very accurate results. The detection and classification modules are 100% accurate whereas the fault location module has a mean error of 0.03%. [ABSTRACT FROM AUTHOR]