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The low fault current contribution from inverter-interfaced distributed generators (IIDGs) and bidirectional flow of current in the power networks introduces protection challenges when islanded. The existing fault detection scheme based on the transient monitoring function (TMF) fails under such scenarios. A new decentralised machine learning (ML) based fault detection and classification scheme is proposed in this paper using terminal voltage information of IIDG (v o−abc). TMF of v o−abc , along with the zero sequence component of v o−abc , is utilised for training and testing of ensemble bagged trees ML algorithm. The proposed scheme is implemented inside each IIDG, and hence, it does not require communication. It is validated on a modified CIGRE test benchmark microgrid for all shunt faults at various locations with different loading, fault resistances, and inception angles. The accuracy of detection and classification of fault (DCF) during fault cases with unbalanced loads, noise, harmonic loads, and no-fault scenarios such as load and DG switching proves the effectiveness of the proposed method with varying scenarios. The performance of the proposed scheme is found to be superior when compared with the existing threshold based TMF and Hilbert–Huang transform (HHT) based techniques considering accuracy, dependability, security, and response time. • Queries by all the reviewer 3 are addressed in details. • Made suitable changes to the introduction to bring in more clarity. [ABSTRACT FROM AUTHOR]

The transmission line is an essential and long element that plays a vital role in the operation of an electric power system. Quick and accurate fault detection along with classification helps the system operator locate the fault as soon as possible in the transmission line. The method proposed in this paper works fast and accurately for fault detection and classification. A fault identification matrix (FIM) based on the change in the resultant current samples of α - β components and signs of the rate of change in α and β component currents (R o C C α and R o C C β ) at both ends of a transmission line for fault detection is proposed. A strategy using the second-order harmonic component of redefined Clarke-transformed currents of the transmission line for the fault classification is framed. The proposed method has been tested with all types of short circuit faults and validated under various abnormal conditions in PSCAD/EMTDC simulations. In all fault cases, the proposed method detects and classifies the faults within a cycle. The fault detection and classification results signify that the proposed method is fast, reliable and parameter-independent. It is concluded that the proposed method can effectively work on any configured transmission network. • |FIM| and RoCCs are proposed using Clarke's currents to detect faults. • A classification strategy proposed using SHCs of redefined Clarke's currents. • The method was validated on the standard two-bus and IEEE-9 bus test systems. • The proposed algorithm is simple and set free. • The reliability of the proposed method has been ensured in all abnormal conditions. [ABSTRACT FROM AUTHOR]

Currently, microgrids are becoming more prevalent. Therefore, it is crucial to develop robust and reliable microgrid protection schemes. Researchers have recently explored various approaches to microgrid protection, including adaptive protection and AC microgrid protection. The study offers insights into fault detection and localization in microgrid protection, utilizing precise measurements from the point of common coupling (PCC). It distinguishes between fault occurrences and overload cases, addressing various challenges. Additionally, the analysis explores the role of Linear Discriminant Analysis (LDA) within the framework of multi-machine learning techniques, shedding light on its application in microgrid protection. Ultimately, the aim is to bolster microgrid resilience and reliability for end-users and stakeholders. The system model being tested has been created using Matlab/Simulink software and is based on a real system. The training and testing of the algorithms are developed and evaluated using MATLAB tools. The accuracy of the proposed method is demonstrated in the paper, indicating that it can be used to modernize the current protection apparatus in preparation for the eventual implementation of an advanced microgrid station protection system. • High Accuracy in Fault Identification: DTE and ANN models achieve 100% validation accuracy. • Efficient Overload Differentiation: Superior performance in managing faults and overloads. • Enhanced Fault Localization: Leveraging LDA for improved accuracy in fault localization. • Innovative Voltage Signature Signal: Novel composition enhances signal representation. • Promising Role of LDA: Proficient in fault localization, offering valuable insights. [ABSTRACT FROM AUTHOR]

• Existing PV fault detection techniques detect faults after occurrence. • Our proposed technique detect fault before occurrence. • Our proposed technique forecast fault based on solar cell parameters. • Deep learning-based transformer algorithm with U-Net classifier are used to predict faults. • Four faults with different severity levels are detected before occurrence. According to the US-based National Renewable Energy Lab (NREL), solar energy losses due to faults were 3.5 % in 2004, which increased to 17.5 % in 2018. Therefore, the fault prediction mechanism will enable PV practitioners to reduce losses effectively, enhancing the solar system's efficiency and power output. This paper proposes a deep learning-based Transformer model for robust fault prediction in photovoltaic. Transformer uses attention mechanism that considers data points as a language units "word" and learn dependencies among them to predict upcoming data points. Unlike other forecasting algorithms, our proposed approach does not rely on previous trends. In case of PV faults, trends do not exist. The proposed algorithm utilizes rate of change of solar cell parameters for establishing a trend to forecast faults, enabling proactive fault mitigation. It also classifies faults with different severity levels to identify the level of predictive maintenance required. The proposed approach is extensively evaluated using MATLAB on datasets of several faults with low, medium, and high severity levels. The proposed Transformer model achieves a forecasting mean average error (MAE) of 0.09377. Performance of the proposed forecasting and classification algorithm is compared with existing machine learning-based regression and classification techniques such as KNN, SVM, and NN, where proposed approach outperforms state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Due to the geographically widespread nature of the transmission lines, the chances of fault occurrence are more due to outdoor erection. This makes it inevitable to detect and isolate the faulty section of the system as quickly as possible. The existing fault detection and classification algorithms are limited to a specific configuration of transmission lines. This work has proposed a novel and simple methodology to recognize and classify faults on transmission lines by using single-end current data. A Harmonic Phasor Measurement Unit (HPMU) based on a non-recursive DFT algorithm is used to extract the fundamental and second-order harmonic components from current samples. The Equivalent Fundamental Component (EFC) and Δ EFC are estimated from the extracted data. Subsequently, an Index Matrix (P) is derived to recognize the fault. The non-zero value of the determinant of the index matrix detects the occurrence of a fault in the system. Second-order harmonic fault current coefficients are proposed for fault classification based on the second-order harmonic component extracted from HPMU. The proposed method accurately detects and classifies all types of conventional faults and cross-country faults (CCF) for any configuration of transmission network. The proposed method has also been validated under various abnormal conditions in PSCAD/EMTDC to ensure its efficacy. • | P | and SFCCs are proposed to detect and classify transmission line faults. • The proposed algorithm is simple and set free. • It can be implemented without changing the existing protection infrastructure. • The reliability of the proposed algorithm has been ensured in all abnormal conditions. [ABSTRACT FROM AUTHOR]

In this study, the post-fault synchronized phasors acquired from the local phasor measurement units (PMUs) are used to provide comprehensive fault reporting in Three terminal transmission line system (TTTL). The reporting data incorporates the classification, section identification, and localization of the fault based on the post-fault measurements of positive, negative, and zero sequence quantities. For accurate reporting, a proposed algorithm computes transmission line parameters, on hourly basis, to account for fluctuations in characteristics caused by temperature influences arising from either the loading factor or the climatic vagaries. Upon fault occurrence, the algorithm firstly determines fault classification and faulty section. Thereafter, localization of symmetrical faults is achieved via the positive sequence quantities to solve the positive sequence fault loop equation while that of asymmetrical faults is obtained through the negative sequence quantities to solve the negative sequence fault loop equation. The fault localization speed of the reporting proposed algorithm is attributed to the fact that synchronized data are obtained after one and half cycles from the fault onset. PSCAD/EMTDC platform is dedicated for time-domain investigations, while the proposed algorithm is carried out in MATLAB. The proposed algorithm was tested against 1537 fault characteristics at different locations along the TTTL, including varying fault resistances, loading conditions, and network configurations, in addition to assessing its sensitivity to changes in the TTTL parameters. These tests confirmed the algorithm's reliability to be used with confidence in a variety of scenarios as the only 13 tests were observed with an error of estimation greater than 1%. • This method is reluctant to the TTTL parameters variations, network configurations and loading or environmental influences. • It encloses only two equations to localize eleven types of faults, which reduces the requested computing steps. In addition, no further mathematical calculations are entailed as in several studies to exclude incorrect fault distances and determine the targeted distance. • Its fault classification approach is reliable for all types of faults with different locations and fault resistances along the TTTL owing to the introduced tolerance angle. • As the fault loop equations disregard the fault path resistance, the proposed algorithm can effectively pinpoints faults with high resistances up to 1500 Ω. • Being independent on the zero sequence network, the uncertainty about estimating the zero sequence impedance along the TTTL is avoided. [ABSTRACT FROM AUTHOR]

The high current carrying capacity of the power electronics based switches has gained much popularity to the VSC-based HVDC system. The challenge faced by the protection schemes is to identify and isolate the relevant fault within 5 to 6 ms. Moreover, the relay algorithm will not activate the circuit breaker to operate during other disturbances including external and non relevant internal faults. The Discrete Wavelet Transform (DWT) and Support Vector Machine (SVM) classifiers are used in this paper to formulate a protection method. Different types of faults are simulated on the using PSCAD/EMTDC software. Depending upon the relevant frequency components accompanied by the DC link voltage and current during fault, nine relevant features are judiciously selected. The acquired features are then used to train and test SVM for fault identification and classification. The proposed solution does not rely on communication as it only takes information from one end. Furthermore, the approach effectively distinguishes internal faults, external faults, and non-fault disturbances in a multi-terminal VSC-HVDC system. This method is capable of classifying faults in both fault-resistant and noisy situations. The extensive execution time study demonstrated that the proposed method is fast and computationally less expensive fault detection and classification technique. • The DC voltage and current are analysed to obtain the SVM-classifier features. • Method distinguishes internal faults, external faults, and non-fault disturbances. • Method is capable of classifying faults in both fault-resistant and noisy situations. • Execution time within 6ms proved the method is a computationally less expensive. [ABSTRACT FROM AUTHOR]

• A faulted phase selection method using locally measured instantaneous incremental currents. • Enhances the relay operating time and enable single-pole tripping schemes. • Does not need a communication channel or voltage transducers. • can be implemented in systems with low sampling rates and avoids the need to use transducers with high bandwidth. • Applicable to a wide range of line lengths, almost independent of fault resistance and not affected fault inception angle and require only three threshold settings. A novel, simple and fast method to identify the faulted phases using the maximum rate of change of incremental current (ROCOIC) is proposed. Six indices are calculated considering a pairwise comparison of ROCOIC. The decision-making process is based on selecting which two indices are the largest among all. The algorithm requires only three thresholds to detect fault inception and to identify the ground and three-phase faults. Simulation studies show that the method is robust and not affected by fault inception angle, fault location, and fault resistance. The proposed method only requires locally measured current signals. Since the method relies on the fundamental component of instantaneous incremental current, the proposed method can be implemented in systems with low sampling rates and avoids the need to use transducers with high bandwidth. [ABSTRACT FROM AUTHOR]

• 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]

A large amount of data is generated through monitoring, maintenance, repair and diagnostics of power transformer. However, all these data cannot preindicate the exact type and probability of failure. To overcome the problem this paper presents artificial intelligence based methodology for power transformers fault detection and classification. The possibility of presented monitoring methodology is to assist the operator’s engineers in decision making about urgency of intervention and type of maintenance of power transformer. The article analyzes the application of Mamdani-model and Sugeno-model in fuzzy expert system for fault diagnosis based on the current state of the power transformer. Paper presents two case studies with one unique and five separate controllers. In the first case inputs of controller are results of on-line and off-line transformer tests: age, the overheating temperature of the hot spot, frequency response analysis, temperature of insulation, dissolved gas-in-oil analysis, tgδ and polarization index. Second case study in addition to the existing inputs includes previous measurements. A fuzzy controller (FC) is designed to characterize the operating condition and to determine the urgency of intervention with possibility to indicate probability of specific type of failure. Cumulative probability of occurrence of the faults is also observed in second case study. FCs are tested based on real measurements from Serbian transmission system. The results show acceptable effectiveness in detecting different faults and might serve as a good orientation in the power transformer condition monitoring. [ABSTRACT FROM AUTHOR]

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]

• As suggested by majority of the reviewers, Abstract, Introduction and Novelty and contribution, Conclusion of the manuscript is revised. • Flow chart of the proposed scheme is modified and also, qualities of all the figures are improved by decreasing the font of label of figures in the revised manuscript. • Results of new fault cases are summarized in Table 1 Table 2 and Table 3 of the revised manuscript. • The proposed method is compared with one more recently published work [41] of the revised manuscript apart from the earlier comparison results. • As suggested, results for a high resistance single-line to ground fault is included in the revised manuscript. The conventionally used single-ended overcurrent relaying schemes find limitation in providing secured protection to large-scale wind farm medium voltage collector lines due to sensitivity and selectivity problems. In this paper, a two criteria-based improved relaying algorithm is proposed for effective protection of these collector lines. In the proposed method, the faulted line is identified first in a multi-feeder collector line of a doubley-fed induction generator (DFIG)-based wind farm using the transient energies derived from the voltage and current measurements near the collector substation bus. Once the faulted collector line is identified, then a time-time transform assisted fault classfication technique is proposed using only the current measurements of the faulted collector line. The efficacy of the relaying algorithm is validated on numerous fault and non-fault test cases simulated on different collector lines of DFIG-based grid-connected wind farm test system and modified IEEE 39-bus New England system through MATLAB/Simulink®. The algorithm is also implemented on OPAL-RT (OP4510) make real-time digital simulation platform. The simulation results justify the applicability of the proposed relaying scheme for effective detection, classification and isolation of faults in wind farm collector lines. [ABSTRACT FROM AUTHOR]

■ Fault detection, classification and location based on traveling wave. ■ Lightning and fault classification with current traveling wave. ■ Ultra high speed protection of double circuit transmission line using wavelet transform. ■ Very fast algorithm for fault classification of parallel transmission line with single phase tripping. ■ Traveling wave protection of transmission line. The injection or absorption of reactive power by the static synchronous compensator (STATCOM) may lead to the misoperation of protective relays and single-phase-tripping systems that operate based on impedance measurement. Also, high-frequency harmonic components caused by switching the internal circuitry of a STATCOM lead to unintended tripping of relays that work based on traveling waves. This paper proposes a single-terminal approach to detect, locate, and identify the faulty line(s) and phase(s) in parallel transmission lines equipped with STATCOMs using traveling waves. To that end, these processes were designed so that adding a STATCOM or different operating modes does not degrade their efficiency and accuracy. The faults and faulty phases were simultaneously detected using the first set of traveling waves received at the sending end of the parallel lines at a sampling frequency of 200 kHz. Furthermore, a sampling frequency of 1 MHz was used briefly to improve the fault location accuracy. The discrete wavelet transform (with BD4 mother wavelet) was adopted to extract the traveling waves. All simulations and analyses were conducted in the PSCAD environment. The results obtained from numerous simulations at different places and states of the networks verified the high accuracy and validity of the proposed methods. [ABSTRACT FROM AUTHOR]

• Review of fault location and classification methods in distribution grids. • The review includes methods developed for low-voltage grids. • The consideration of distributed generation in the available methods is studied. • The research trends of the fault diagnosis methods are presented. • Identification of research gaps and future challenges in the field of fault isolation. The evolution of the conventional power systems to smart grids has changed the way to conceive and operate them. The part of the grid evolving the most is the distribution grid where the installation of additional sensors and actuators has increased its observability and controllability. These have enabled the development of more accurate and automated processes including some critical ones such as the fault detection, isolation and restoration techniques. In this direction, unconventional methods, e.g. artificial intelligence, have been increasing in popularity over the last years. In this paper, fault location and fault classification methods are reviewed for both medium–voltage and the until recently unexplored case of low–voltage distribution grids. 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 are identified. [ABSTRACT FROM AUTHOR]

• Fault detection and classification based on traveling wave • Lightning and fault classification with current traveling wave • Ultra high speed protection of double circuit transmission line using wavelet transform • Very fast algorithm for fault classification of parallel transmission line with single phase tripping This paper presents a new and very fast fault detection (FD), fault classification (FC), and fault phase selection (FPS) method, which is just based on the first group of current traveling waves (cTW) in double circuit transmission lines (DCTLs). All internal, inter-circuit faults and lightning strikes on transmission line (TL) phases and shield wires are reviewed. By the proposed algorithm, FC and FPS are performed in the shortest possible time. In this study, wavelet transform (WT) is used to extract the traveling wave (TW) caused by the fault from the current signal of one DCTL terminal, and only the first group of these waves is used for FC and PFS to increase velocity. To classify various fault types, the zero components of TWs of the two lines, which can be calculated using the conventional modal transforms and another modal transform of 6*6 dimensions defined on all six phases of the DCTL, is used. All simulations are performed in PSCAD on a 230kV grid and a DCTL of 200km length. Also, the performance of the proposed algorithm is tested under various fault and lightning conditions in the presence of white noise with different incidence angles and various fault resistances. [Display omitted]. [ABSTRACT FROM AUTHOR]

• Propose a stacking based data-driven model, to identify the fault type and location in a distribution system, including PV units and their stochastic nature. • All types of faults including faulty phase or phases, along with the no fault conditions (NF) are classified in three-phase test distribution systems with superior performance. • Detect low and high impedance faults. • Develop a straightforward yet accurate approach which requires only voltage measurements in some buses to identify and determine the type and location of the fault (without the need to further preprocessing stage of the input data). • Enhance the accuracy of the predictions comparing to individual machine learning models. This paper proposes an ensemble learning-based method to determine the type and location of faults in a smart distribution network. The employed scheme is a stacking based method that consists of two main levels, each of which includes one or more independent classifiers. In the structure of the proposed method for first level, multiclass support vector machine, K-nearest neighbors and random forest models are exploited. Also, for the next (final) level, which is also called meta-classifier, a random forest based model has been used, which itself is a kind of ensemble learning method. In order to supply the data used to feed the model, the voltage and current measurements in some buses and lines have been used. The proposed model is tested using the data related to all different types and location of faults and operating conditions in the IEEE 123-bus network. Besides, the model is implemented on another distribution test system to consider wider range and specs for fault parameters and also to comprise with other methods applied to it; high impedance faults are also regarded in this part. The obtained results show the appropriate accuracy of the proposed model and its better performance than other considered methods. [ABSTRACT FROM AUTHOR]

Highlights: [•] This article reviews protection schemes for series compensated transmission lines. [•] In the second section, the impact of series compensation has been summarized for EHV transmission line protection. [•] The third section gives bibliographical survey and general backgrounds of research and development in the field of series compensated line protection. [•] A separate section compiles basic schemes for fault location techniques. [•] Recent implementation and experiences in field of series compensated transmission line protection are summarized. [ABSTRACT FROM AUTHOR]

• Use of a novel phase selector algorithm for fault classification on half wavelength lines. • The new algorithm was coded in a RTDS hardware, emulating a relay. It use the derivate of Clarke's components for detect the type fault. • Identification of misoperations from relay commercial, when used to protection on half wavelength lines. • Robustness of proposed algorithm to identify faults with high impedance, conditions of transients caused by energization and tree phase auto recloser of half wavelength lines. • Use of COMTRADE files for the analysis and evaluation of the phase selector by the different types of faults. This study presents a new phase selector algorithm and fault type classification for very long transmission lines, known as the half-wavelength lines (HWL). For this work, the Clarke components variation rate method applied to current magnitudes was used. The method was implemented in a real-time digital simulation hardware-in-the-loop (HIL) test environment. We applied different fault types at each 5% line length, obtaining Clarke component current phasors. The proposed algorithm is stable for the native behavior of the half-wavelength line, including the resonance zones and their mitigation procedures. In addition, the algorithm is immune to the fault insertion angle, fault resistance, and line loading level. Finally, the algorithm could be applied in commercial microprocessor-based relays or could be used as a post-fault analysis tool. [ABSTRACT FROM AUTHOR]

• In this paper combined DWT and Fuzzy inference system has been utilized for fault detection and classification in distribution system integrated with DGs. • The proposed scheme is suitable for detection of both HIF and LIF. • The proposed scheme based on DWT and Fuzzy inference system has been tested for IEEE 13 Bus system and IEEE 33 bus system. • It considers the effects of various DGs, switching events and impact of noise. • The scheme has been able to distinguish between different conditions such as transformer energization, capacitor bank switching, load switching, evolving faults. Most of the high impedance faults (HIF) remain un-detected by the conventional relays due to non-linear nature of the fault and low magnitude of current. In this work, a combination of discrete wavelet transforms (DWT) and fuzzy inference system (FIS) has been proposed for HIF detection and classification. Modified IEEE 13 node test feeder system has been to validate the proposed scheme. The proposed method uses current signals from one end that are pre-processed using discrete wavelet transform to obtain appropriate input features. The wavelet processed features are given to the FIS for fault detection and classification. Proposed method has been validated using both Mamdani and Sugeno type FIS. Different operating and fault conditions are tested to validate the proposed method such as varying DG parameters, noisy signals, HIFs, evolving faults, fault inception angle, fault resistance, fault location, and non-fault events (e.g. motor load switching, capacitor switching, DG switching, transformer energization, non-linear load switching). The accuracy in detecting and classifying the faults is 100% of all the tested fault cases. Results shows that the overall detection time required to detect the HIFs is minimum 4.25 cycles in most of the cases and maximum 8 cycles in few cases whereas for shunt faults is within 4.25 to 6 cycles only. Advantage of the proposed method is that it can detect adverse situation faults like evolving faults, in presence of noisy signals and remains intact against any switching events. The results of the proposed method are promising and the method is robust against various operating conditions. [ABSTRACT FROM AUTHOR]

Abstract: This paper exploits the Wavelet Transform (WT) analysis along with Artificial Neural Networks (ANN) for the diagnosis of electrical machines winding faults. A novel application is presented exploring the problem of automatically identifying short circuits of windings, which often appear during machine manufacturing and operation. Such faults are usually resulting from electrodynamics forces generated during the flow of large short circuit currents, as well as forces occurring when the machines are transported. The early detection and classification of winding failures is of particular importance, as these kinds of defects can lead to winding damage due to overheating, imbalance, etc. Application results and investigations of windmill generator winding turn-to-turn faults between adjacent winding wires are presented. The ANN approach is proven effective in detecting and classifying faults based on WT features extracted from high frequency measurements of the admittance, current, or voltage responses. [Copyright &y& Elsevier]

Abstract: This paper describes a new algorithm to identify the reflective waves for fault location in noisy environment. The new algorithm is based on the correlation of detail components at adjacent levels of stationary wavelet transform of current signal from one end of the cable. The algorithm is simple and straightforward. Simulation results based on a real power transmission system proved it can detect and locate the fault in very difficult situations. [Copyright &y& Elsevier]

Abstract: An approach for classification of transmission line faults is presented. The approach is based on fuzzy logic and requires the consideration of the samples of three phase currents at one end of transmission line. To illustrate the effectiveness of the proposed approach extensive simulation studies, using EMTP and MATLAB, have been carried out for different types of fault considering wide variations in the operating conditions. [Copyright &y& Elsevier]

Abstract: This paper presents a new method for identification and classification of faults based on wavelet multiresolution analysis (MRA). Daubechies eight (D-8) wavelet transforms of the three phase currents on a transmission line fed from both ends are used. The peak absolute value, the mean of the peak absolute values and summation of the 3rd level output of MRA detail signals of current in each phase extracted from the original signals are used as the criterion for the analysis. The effects of fault distance, fault inception angle and fault impedance are also examined. Extensive simulations are carried out to generate time domain input signal using EMTP (Microtran) on a 230kV, 200km long line fed from both ends and simulation results show that the proposed method is a simple, effective and robust method suitable for high impedance faults also. [Copyright &y& Elsevier]

A simple and fast method to identify the faulted conductors in a double circuit line with the help of a single circuit transient based fault type classifier is presented in this paper. In addition to a transient based single circuit fault type classifier, only currents locally measured through each conductor are required. Having known the fault type, the magnitudes of the rate of change of currents (ROCOC) through two circuits of faulted phases are compared by calculating three indices to find the conductors involved in the fault. The method can identify faulted conductors during both inter-circuit faults as well as intra-circuit faults. The method can be easily implemented on a simple hardware platform capable of performing filtering and simple mathematical functions. As only the current measurements in the frequency components between 0.5 kHz to 1 kHz are suggested, the need for high bandwidth current transducer is avoided. [ABSTRACT FROM AUTHOR]

• Proposed a deep learning based CNN model for fault classification in Power System distribution network integrated with distributed generators (DGs). • In this proposed work, fault classification is done without any pre-processing or feature engineering steps. • All types of fault in 3-phase power system network including no-fault is classified using proposed model with superior performance. • Proposed model is compared with conventional machine learning methods for fault classification in terms of both performance and computational cost. • The performance of proposed model is also tested on a mixed transmission line and distribution network with PV as DG. Fault detection is the critical stage of the relaying system and their successful completion in minimum time is expected for fault clearance. With the increasing usage of distributed generators (DGs) in a distribution network, the conventional relaying methods are becoming inappropriate due to changing fault current levels. This paper presents a deep learning algorithm i.e. Convolutional Neural Network (CNN) customized for fault classification in the distributed networks integrated with DGs. This is first time that CNN has been used for fault detection using raw and sampled-data of three-phase voltage and current signals of various fault classes and no-fault class. The 10-fold cross-validation is used to demonstrate the performance of the proposed model in terms of different metrics such as accuracy, sensitivity, specificity, precision, and F1 score. The proposed model has attended an average 10-fold cross-validation accuracy of 99.52% for all the tested fault cases. This featureless proposed method has been compared with conventional approaches from literature and has shown better performance in terms of accuracy and computation burden. Further, a similar fault study is conducted on a mixed transmission line and distribution network with PV as DG using the proposed method and found performance accuracy of 99.92% and 99.97%, respectively. [ABSTRACT FROM AUTHOR]

• This article presents a relaying algorithm that works equally well for an EHV transmission line and when the line is subjected to fixed series compensation. • This is also without any functional modification in methodology or adaption in the proposed algorithm. • The algorithm proved to work with various levels of fixed series compensation with same level of accuracy. • This eliminates need of relay setting changes required after introduction of series compensation to any existing transmission line. Moreover, it also eliminate requirement of re-setting of relays in case of bypass operation of the series compensator. • The algorithm has been developed with help of wavelet transform and chebyshev expanded neural network. • The fault detection algorithm converges within half-cycle after fault inception. Therefore, it is proved to be fast also. This article presents a new and fast approach for detection and classification of the fault on a transmission line. The proposed method can effectively detect and classify a fault on a regular uncompensated transmission line. Moreover, the same can detect and classify faults when these lines are subjected to fixed series compensation, with the same accuracy and without any functional customization of the algorithm. Therefore, this method gets rid of the new setting requirement for the inclusion of series compensation. Vice versa can provide protection to a series compensated line during maintenance as well as in the eventuality of bypass of the compensator. This makes the developed algorithm versatile. The proposed algorithm normally converges for fault detection within half cycle from the fault inception, and requires no data after generating fault detection signal for fault classification; which enables the application of faster circuit breaking devices. The two-stage approach uses Wavelet Transform (WT) with Chebyshev Neural Network (ChNN) and uses only measured three-phase current signals at relaying end. The accuracy, speed, and effectiveness of the scheme have been verified with a fault data generation system developed on PSCAD/EMTP with different system parameter variations like fault resistance, load angle, fault inception angle, and types of faults. The results obtained show that the proposed scheme is accurate and fast as well. [ABSTRACT FROM AUTHOR]

• A systematic review for Smart Grid systems faults. • A classification framework on relevant grid requirements. • Future developments related to fault detection and classification. • Advance Metering Infrastructure under the perspective of power faults. Smart Grid (SG) is a multidisciplinary concept related to the power system update and improvement. SG implies real-time information with specific communication requirements. System reliability relies on the best capabilities for monitoring and controlling the grid. Among other aspects, SG applications involve three main challenges, sufficient real-time capable measurement units, managing large data sets, and two-way low-latency communications. Considering fault detection and classification a key factor to SG reliability, this work provides a systematic review of SG faults from the most significant research databases and state-of-the-art research papers aiming at creating a comprehensive classification framework on the relevant requirements. This paper includes in detail the classification of different fault scenarios in a comprehensive framework that involves system-level of application, e.g. , transmission, distribution, commercial, DG, and EV. To this end, We analyze and indicate relevant topics for future developments related to the monitoring and fault detection and classification in SG systems. [ABSTRACT FROM AUTHOR]

• Identification of the fault section and fault phase selection are considered the main tasks. • The suggested technique uses the wavelet packet transform. • A fast mother wavelet is used with a three-coefficient high pass filter to analyze signals. • Several fault cases are simulated and analyzed in the ATP/EMTP environment. • The proposed scheme shows high speed and accuracy of fault detection. Identification of the fault section and fault phase selection are considered the main tasks of a multi-terminal transmission line protection scheme. The current study classifies the methods applied to multi-terminal transmission lines. Moreover, investigated solutions for the drawbacks that appeared with the previous methods are suggested. Based on wavelet packet transform, a scheme for a multi-terminal transmission line is presented in this article. The suggested technique uses the wavelet packet transform (WPT) to get the approximate and detail coefficients of the differential current and model voltage for all buses in a system, detects and classifies the faults (FDC) within a half-cycle window, and identifies the faulted section (FSI). A fast mother wavelet is used with a three-coefficient high pass filter to analyze signals of the model voltage and differential. In this regard, two frequency levels were chosen, in which the first level of the differential current signal was chosen to detect and classify a fault, while the first level of the model voltage signal was chosen to identify the faulted section. Several fault cases are simulated and analyzed in the ATP/EMTP environment for an N-terminal system followed by successful testing on three- and five-terminal systems to evaluate the performance of the proposed scheme. The simulation results obtained show that the proposed scheme is efficient and shows high speed and accuracy of fault detection compared to other methods in the literature. [ABSTRACT FROM AUTHOR]