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The photovoltaic (PV) array is the most sensible element in PV plants, which is subject to different type of faults and defects. Thus, to keep these plants working efficiently they should be monitored and protected carefully. Some faults if they are not detected and isolated promptly they may lead to hazardous risks. The diagnosis of PV systems is widely addressed and recently machine learning (ML) and deep leaning (DL) methods drawn the attention of many researchers. Most applications of ML methods are based on the use of the I–V curves measurement, as enough information and features can be extracted from the curves, to detect and classify faults. These methods showed their capability to classify some faults, like line to line, degradation, disconnected PV modules, partial shading effect, and bypass diode faults. Another approach is based on the use of thermal or electroluminescence images of PV modules/arrays to detect and identify defects, such as hot spot, snails crack, and others. In this paper, different ML and ensemble learning (EL) methods are evaluated for fault diagnosis of PV arrays. The focus is mainly on the detection and classification of some complex faults that may affect the PV arrays, i.e., multiple faults, and faults with similar I–V curves, that are not evaluated before. The results showed the ability of the methods developed to detect faults with very good accuracy (classification rate = number of classified instances/total instances), within 99%, while the classification faults is done with an acceptable accuracy, within 81.73%. Through this study it is shown when really ML and EL methods should be used, and some recommendations, challenges and future directions in this topic are presented. [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]

In recent years, power quality has become the main concern in power system engineering. Classification of power system faults is the first stage for improving power quality and ensuring the system protection. For this purpose a robust classifier is necessary. In this paper, classification of power system faults using Empirical Mode Decomposition (EMD) and Support Vector Machines (SVMs) is proposed. EMD is used for decomposing voltages of transmission line into Intrinsic Mode Functions (IMFs). Hilbert Huang Transform (HHT) is used for extracting characteristic features from IMFs. A multiple SVM model is introduced for classifying the fault condition among ten power system faults. Algorithm is validated using MATLAB/SIMULINK environment. Results demonstrate that the combination of EMD and SVM can be an efficient classifier with acceptable levels of accuracy. [ABSTRACT FROM AUTHOR]

This paper proposes a new method of fault detection and classification in asymmetrical distribution systems with dispersed generation to detect islanding and perform protective action based on applying a combination of wavelet singular entropy and fuzzy logic. In this method, positive components of currents at common coupling points are decomposed to adjust detailed coefficients of wavelet transforms and singular value matrices, and expected entropy values are calculated via stochastic process. Indexes are defined based on the wavelet singular entropy in positive components and three phase currents to detect and classify the fault. This protection scheme is put forward for fault detection and is investigated in different types of faults such as single-phase to ground, double-phase to ground, three-phase to ground and line to line in distribution lines in the presence of distributed generations, and different locations of faults are verified when the distributed generation is connected to the utility. The major priority of the proposed protection scheme is its reduction in time (10 ms from the event inception) in distinguishing islanding and protection transmission lines in the presence of distributed generations. [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]

Faults in transmission lines can cause several problems to transmission utilities. An adequate diagnosis of such events can improve service restoration time and, consequently, asset availability. This work proposes a machine learning-based approach to diagnose transmission line faults. The oscillography files of the fault case are processed and applied to four different classifiers, which must identify: phases involved, electrical nature, impedance level, and, fault cause. The method, which is inherently robust regarding signal noise and lack of synchronization between relays, combines Decision Trees, feature engineering, and optimization to perform the fault diagnosis based only on relay measures. It was tested in two fault data sets, one containing 24,000 faults generated using a Real Time Digital Simulator and another one with 46 real faults. The results obtained were very promising, with accuracy values close to 100% for involved phase and fault nature, 90% for impedance level, and 80% for fault cause. [Display omitted] • A ML approach is proposed for comprehensive fault diagnosis in transmission lines. • Fault phases, nature, impedance, and cause are inferred from oscillographs. • The approach is very robust regarding noise and lack of synchronism between relays. • It relies on very few adjustable parameters. Most internal parameters are optimized. • Results on RTDS simulated and real fault cases are provided. [ABSTRACT FROM AUTHOR]

Highlights: [•] Rough membership neural network (RMNN) approach is proposed for fault classification in transmission lines. [•] Wavelet multi-resolution analysis (MRA) is utilized for selecting time domain features and time-frequency domain features. [•] Rough neurons are used as input layer neurons and fuzzy neurons in hidden and output layer of the RMNNs. [•] The effectiveness and accuracy of proposed approach is analyzed, compared with the back-propagation neural network (BPNN). [Copyright &y& Elsevier]

• 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]

Abstract: A health index, Mahalanobis distance (MD), is proposed to indicate the health condition of cooling fan and induction motor based on vibration signal. Anomaly detection and fault classification are accomplished by comparing MDs, which are calculated based on the feature data set extracted from the vibration signals under normal and abnormal conditions. Since MD is a non-negative and non-Gaussian distributed variable, Box–Cox transformation is used to convert the MDs into normal distributed variables, such that the properties of normal distribution can be employed to determine the ranges of MDs corresponding to different health conditions. Experimental data of cooling fan and induction motor are used to validate the proposed approach. The results show that the early stage failure of cooling fan caused by bearing generalized-roughness faults can be detected successfully, and the different unbalanced electrical faults of induction motor can be classified with a higher accuracy by Mahalanobis–Taguchi system. Such works could aid in the reliable operation of the machines, the reduction of the unexpected failures, and the improvement of the maintenance plan. [Copyright &y& Elsevier]

As support to fault location algorithms, this paper presents a new method for detecting and classifying faults in active distribution networks using the energy moving averages of the current signals measured at the feeder head. A proposal for updating the energy thresholds for fault detection is introduced, aiming to achieve higher sensitivity in fault detection, especially for scenarios with high penetration levels of distributed generation (DG). Since the power injected by DG can rapidly change, this proposal is essential for maintaining the fault detection method's reliability. Also, the fault classification is based on an analytical approach, making it possible to be easily applied to different operating scenarios of a distribution system. Thus, its potential for real-world implementation is higher when compared to classifiers based on supervised learning algorithms. Performance tests on the IEEE 34-node test feeder showed that this method is reliable for detecting and classifying different fault types, with diverse generation-load scenarios and noisy signals. • Detection thresholds are adaptive to increase detection sensitivity. • Fault classification is based on a simple analytical approach. • The method uses only the current measurements at the feeder head to diagnose faults. • The method is applicable for active distribution networks. [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: Fault classification is very important for power system operation because it is the premise of fault analysis process. In this paper, an ANFIS (Adaptive Neural Fuzzy Inference System) based fault classification scheme in neutral non-effectively grounded distribution system is proposed. The transient currents are obtained by wavelet transform after faults occur. According to the statistic characteristic of transient currents in different fault types, the fault identifiers are defined. The fault identifiers can characterize the traits of fault type and show different disciplinarian in different fault types. They are inputted into three ANFISs to obtain the fault type. The proposed approach only needs the voltages and currents measured at substation, and can identify ten types of short-circuit fault accurately. The simulation model is established in PSCAD/EMTDC environment, and the performance of the proposed approach is studied. The results show that it has high accuracy. Besides, the adaptability of proposed approach to the neutral compensated grounding system, different network configurations and so on are verified through simulation. Through simulation, the proposed approach exhibits good performance. [Copyright &y& Elsevier]

Abstract: This paper proposes a new approach based on combined Wavelet Transform-Extreme Learning Machine (WT-ELM) technique for fault section identification (whether the fault is before or after the series capacitor as observed from the relay point), classification and location in a series compensated transmission line. This method uses the samples of fault currents for half cycle duration from the inception of fault. The features of fault currents are extracted by first level decomposition of the current samples using discrete wavelet transform (DWT) and the extracted features are applied as inputs to ELMs for fault section identification, classification and location. The feasibility of the proposed method has been tested on a 400kV, 300km series compensated transmission line for all the ten types of faults using MATLAB simulink. On testing 28,800 fault cases with varying fault resistance, fault inception angle, fault distance, load angle, percentage compensation level and source impedance, the performance of the proposed method has been found to be quite promising. The results also indicate that the proposed method is robust to wide variation in system and operating conditions. [Copyright &y& Elsevier]

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: The ability to detect and classify the type of fault plays a great role in the protection of power system. This procedure is required to be precise with no time consumption. In this paper detection of fault type has been implemented using wavelet analysis together with wavelet entropy principle. The simulation of power system is carried out using PSCAD/EMTDC. Different types of faults were studied obtaining various current waveforms. These current waveforms were decomposed using wavelet analysis into different approximation and details. The wavelet entropies of such decompositions are analyzed reaching a successful methodology for fault classification. The suggested approach is tested using different fault types and proven successful identification for the type of fault. [Copyright &y& Elsevier]

Abstract: This paper presents a high speed, computationally efficient scheme for protection of transmission lines. The relay logic consists of three parts: directional protection, fault classification and fault location. Wavelet transform is used for extracting information from the fault transients and only the first level high frequency details of the voltages and currents are used. Proposed protection logic compares the directional signals from both terminals to discriminate between faults inside and outside the zone of interest. Fault classification is achieved using local terminal current information. An estimate of the location of the faults is obtained utilizing single faulted phase current information from both terminals. The logic is deterministic and can work reliably in the presence of fault resistance, load variation and CT saturation. The validity of the proposed logic was exhaustively tested by simulating various types of faults on a four bus meshed system modeled in EMTP/ATP. [Copyright &y& Elsevier]

• A new fault classification approach is presented for series-compensated line. • One-end current-based fault location method is proposed. • Different series-capacitor compensation configurations are analyzed. • The scheme is economic, reliable, fast, and generalized. This paper proposes a new efficient method for fault detection, faulty phase selection, and fault point identification on series capacitor-compensated transmission line. The method is based on sensing only the three phase current at the relay site. A reverse synchronous reference frame (RSRF) approach is proposed for fast fault detection and classification. Then, an analytical algorithm based on symmetrical components theory is presented for accurate estimation of fault location. Three possible arrangements of the series capacitor bank (SCB) on the line are analyzed. The SCB and the associated protective metal oxide surge arrestor (MOV) are modeled thoroughly to characterize its behavior during fault. In addition to its simplicity and practicality, the proposed scheme is equipped with decaying DC offset removal technique that allows fast extraction of the current phasors within one fundamental cycle. The proposed method works well for all ground and non-ground faults; it does not need pre-fault information; does not require any communication facilities; avoids high voltage measurement; and works smoothly for a wide range of fault resistance values. The efficacy of the proposed method is proved by a large number of simulated cases under a variety of operating conditions. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a real-time wavelet-Fuzzy combined approach for digital relaying. The algorithm for fault classification employs wavelet multi resolution analysis (MRA) to overcome the difficulties associated with conventional voltage and current based measurements due to effect of factors such as fault inception angle, fault impedance and fault distance. The proposed algorithm for fault location, different from conventional algorithms that are based on deterministic computations on a well-defined model to be protected, employs wavelet transform together with fuzzy logic. The wavelet transform captures the dynamic characteristics of the non-stationary transient fault signals using wavelet MRA coefficients. The fuzzy logic is employed to incorporate expert evaluation through fuzzy inference system (FIS) so as to extract important features from wavelet MRA coefficients for obtaining coherent conclusions regarding fault location. Computer simulations using MATLAB have been conducted for a 300Km 400KV line. Simulation results indicate that both the classification and localization algorithms are immune from effects of faults inception angle, impedance and distance. The most significant contribution of this paper is that the proposed location algorithm has a maximum error of 6.5% with a computational time of about one cycle. Thus both classification and location algorithms can be used as effective tools for real-time digital relaying purpose. [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]

• 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]

• 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]

In this paper, empirical Wavelet transform (EWT), Hilbert transform (HT) and weighted random vector functional link network (WRVFLN) are integrated for fault detection, classification, and location estimation in a series capacitor compensated double circuit transmission line (SCCDCTL). The full cycle current signals from the point of fault inception are decomposed using EWT to extract three band-limited modes (BLMs). The four efficacious instantaneous features namely energy, Shannon entropy, the standard deviation of the magnitude, and crest factor are computed from the Hilbert transformed array of the BLMs to construct the feature vector. A diagonal matrix W is computed from the zero sequence current of original six current signals as a weighting factor to categorize the ground fault accurately. Numerous faults are generated with a wide variation of the system conditions such as fault resistance, fault inception angle, fault distance, percentage compensation level, source impedance, line parameters, load angle, and inter-circuit fault in MATLAB/Simulink environments. An efficient WRVFLN computational intelligence technique is proposed to recognize and estimate the location of the faults by taking the extracted suitable feature vector with weight factor as an input. The performances of WRVFLN are compared with the recently developed advanced classifiers such as least-square support vector machine (LSSVM) and extreme learning machine (ELM) in the MATLAB interface. The lesser computational complexity, faster learning speed, superior classification accuracy, accurate fault location estimation, and short event detection time prove that the proposed EWTHT–WRVFLN method can be implemented in the real power system for online fault diagnosis. Finally, the developed system architecture is implemented on the reconfigurable digital field programmable gate array (FPGA) in ISE design suite 14.5 environments to verify the cogency of the proposed method in real-time. The feasibility of the proposed method is tested and validated by using the fast FPGA digital circuitry in a loop. • Empirical Wavelet Transform is used for series compensated lines for fault studies. • A weighted Random Vector Functional Link Network is used for fault classification and location. • A comparison with LS-SVM is presented in this paper to validate the superiority of WRVFLN. • Implementation of the WRVFLN using FPGA hardware exhibits the real-time capability of the new approach. [ABSTRACT FROM AUTHOR]