Search

This paper proposes a fast protection scheme of a hybrid AC/DC distribution network based on features extraction of the decomposed signals. The signal decomposition is achieved using discrete wavelets transform (DWT). The proposed scheme offers three main functions, fast fault detection for both network sides, AC fault classification with the ability to identify the faulted phase/ phases, and protection coordination between AC and DC sides. In addition, the scheme is based on local measurement so no need for extra-cost for communication infrastructure. The absence of communication reduces the overall cost and, as a most important feature, it reduces the fault clearing time. The system is thoroughly analyzed under many types of faults in both AC and DC sides. Also, different disturbance conditions are considered which used in threshold determination. The simulation results show that the proposed protection scheme was not only able to protect the system but also it offers a generalized fast acting communication-less approach. [ABSTRACT FROM AUTHOR]

The presented paper introduces an accurate approach for detecting and classifying parametric or soft faults that affect analog integrated circuits. This technique is based on the use of machine learning algorithm to improve the accuracy and the performance of fault classification process. To achieve this, the real and imaginary frequency responses of output voltage and supply current of the circuits under test (CUT) are used to extract features for both normal and faulty cases. These features are then exploited to train machine learning classifiers, from which the selected one among its equivalents is the quadratic discriminant classifier since it allowed the highest average accuracy score. The faults to be investigated are parametric ones affecting resistors and capacitors values. The proposed approach is validated using three filters circuits that are Sallen-Key band-pass filter, four op-amp biquad high-pass filter, and a leapfrog filter circuit. Obtained results indicate a high classification average accuracy for all circuits that are undergone testing. The proposed approach has provided a highest classification accuracy level comparing to other research works. [ABSTRACT FROM AUTHOR]

A wind turbine (WT) is an electromechanical system that often operates under a wide range of production conditions. These electrical systems are nowadays expanding rapidly, and they have considerable importance due to their efficiency as renewable energy sources. This led to proposing an innovative and efficient solution with intelligent systems to maintain and ensure the safe and stable operation of these dynamic systems. Maintenance tasks are based on the development of high-performance diagnostic tools, which consist in detecting and locating correctly and upstream the various failures affecting this wind machine. Where, the condition monitoring and supervision systems must rely on reliable fault diagnosis techniques in order to: avoid breakdowns and unscheduled shutdowns, improve their operation, and increase their energetic performances. In order to ensure adequate maintenance actions for the wind system, the purpose of this article is to propose and develop a robust and intelligent fault diagnosis structure. In this work, Kalman filters (KF) as state estimators are used to observe the output states of the sub-systems in order to generate the appropriate residuals evaluated by predetermined thresholds. Adaptive and hybrid network-based fuzzy inference systems (ANFIS) have been employed for the evaluation and classification stages of the detected faults to minimize the degradation of the wind turbine. All possible faults of wind turbine systems, sensors, and actuators are tested and investigated in all parts: pitch angle systems, drive, and generator with converter. The developed fault detection and identification structure are tested on a horizontal WT benchmark model using different scenarios and faults. The simulation results show the ability of the proposed and developed diagnostic methodology to detect the faults occurring efficiently and correctly in the machine. Thus, by using this robust diagnostic strategy, the condition monitoring system can maintain and ensure stable and safe operation to generate sufficient electrical power. [Display omitted] [ABSTRACT FROM AUTHOR]

In this paper, a hybrid system is proposed to predict and classifies the power system transmission line faults. The proposed technique is the consolidation of both the truncated singular value decomposition (TSVD) and Human urbanization algorithm (HUA) based Recurrent Perceptron Neural Network (RPNN), and hence it is named as TSVD-HUARPNN technique. TSVD is matrix decomposition, this technique qualify the outcome it as fast or not. In the proposed work, the qualification of the results from the TSVD is improved by a lemma theorem; it is a proven proposition which is used to obtain a larger and optimal result. For that reason, it is also known as a "helping theorem" or an "auxiliary theorem". Here, it has two modules for power system fault analysis: (i) fault detection, (ii) fault classification. The first process of the proposed system is the generation of the dataset of normal and abnormal conditions of transmission line parameters of power system using TSVD. The extracted dataset is assessed by HUA-based RPNN system to classify the fault analysis that occurs in transmission system. The TSVD-HUARPNN system is used to predict and classify the fault present in the transmission line. The proposed TSVD-HUARPNN system ensures the system with less complexity for the detection and classification of the fault, therefore the accuracy of the system is increased. By then, the proposed model is activated in MATLAB/Simulink, its performance is evaluated with the existing models. The performance with noise at 20 dB of the proposed technique is 99.77%. • Hybrid technique is proposed for predicts, classifies the power system transmission line faults. • The proposed technique is the consolidation of both TSVD and HUA based RPNN. • TSVD prepare dataset based on transmission line parameters normal and abnormal condition. • The extracted dataset is assessed by the HUA based RPNN technique. • The proposed technique guarantees the system with lessens complexity. [ABSTRACT FROM AUTHOR]

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]

[Display omitted] • A neural network is used to detect faults in photovoltaic modules. • According to the shape of the current vs voltage curve, the faults are classified. • The training uses synthetic curves and the test is performed also on experimental data. • The points are normalized and resampled radially to improve the results. • With two hidden layers of 100 neurons each, the hit rate reaches more than 98.5%. In this paper a model-based procedure for fault detection and diagnosis of photovoltaic modules is presented. A four-layered feedforward artificial neural network learns the correlation between the features of the current vs. voltage curve and the environmental variables, which are the irradiance and the temperature. This correlation describes the behavior of the module at normal conditions. Moreover, the effect of anomalous variation of some parameters is learnt and correlated to the shape of the same curve, thus associated to a specific failure mechanism and to some assigned ranges quantifying the fault severity. The neural network is trained by using synthetic curves simulated by employing the single diode model and some well assessed and validated translation formulae. The obtained results over the simulated set of curves with different failures allow to achieve a classification error lower than 1.5%. The proposed approach has been also validated for detecting anomalous increases of the series resistance in a large experimental set of curves; in this case, a classification error of 2.7% has been achieved. [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]

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]

• Detection and classification of faults including high impedance faults in microgrid. • Fault detection and classification from current signals at the substation. • Faulty feeder and sub-feeder identification within half cycle of post fault current signal. • Wavelet based power spectral density of current signals utilized to detect and classify fault and also identify faulty feeder. • Performance of the proposed algorithm verified under dynamic and challenging situations. The recent progress in the area of microgrid protection has led to the application of data-driven and signal processing techniques for fault detection in microgrids. The deployment of inverter interfaced distributed generators makes the traditional protection schemes ineffective. In this paper a wavelet based power spectral density (PSD) has been utilized to detect fault, identify faulty phases and locate fault in the microgrid. Wavelet based PSD decouples the frequency information from the time information and it is the optimal technique to reveal the change of distribution of harmonics in non-stationary faulty signals as for fault analysis it can consider the faulty frequency band instead of one particular harmonic. The proposed technique can detect fault and identify faulty phases by analyzing current signals from the substation only. The normalized value of PSD of three phase current signals has been considered for fault detection and probabilistic distribution of PSD has been considered for faulty phase identification. The normalized value of PSD of the current signals collected from both ends of the main feeders and one end of the non-DG sub-feeder have been used to locate the faulty feeder and sub-feeder. Appropriate threshold values have been considered to detect fault and to locate the faulty feeder. To locate faults, the technique does not require synchronized signals and thus provides a promising and economical solution for inverter interfaced microgrid. It does not require huge amount of data set for training and testing as it is essentially a threshold based technique and also does not require collection of data at high sampling rate. The proposed scheme has been extensively tested for high impedance fault (HIF), unbalanced loading, load switching, variation of DG parameters, measurement uncertainty and presence of noise in the signal with signal to noise ratio up to 10 dB. This technique provides accurate results for fault resistance from 0 Ω to 300 Ω and up to 10% measurement error. The performance of the proposed technique has been verified for feeder length variation up to 20%. Half cycle of current signal only after fault is required and three levels of wavelet decomposition has been considered. The simulation results and comparative assessment demonstrate the efficacy of the proposed scheme. [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]

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]

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]

• A new scheme is developed to identify the fault using reactive power measurements. • The reactive-power superiority compared with active measurements is investigated. • The optimal mother wavelet is selected based fault identification scheme. • The proposed scheme is immune to different fault parameters. • Testing the fault identification scheme at different operating conditions. The process of fault-identification in teed transmission lines (TTLs) has considerable attention in recent years. In this line, this paper proposes a novel scheme for fault detection, classification and faulted-phase/s identification on the basis of reactive-power components of TTLs. The current and voltage measurements are collected from a single-end measuring system of teed circuits. Based on the filtering of measured currents and voltages signals using improved recursive wavelet transform, the reactive-power measurements are computed. Thereafter, the Wavelet Multi-Resolution Analysis Technique (WMRAT) is employed for decomposition of reactive-power measurements. An improved recursive wavelet (IRW) transform that has good time-frequency characteristics is proceed to remove dc offsets and harmonics from the measured signals. The ATP/EMTP package and MATLAB program are used to simulate the faulted cases and verify of operating the proposed fault-identification scheme. Simulation outcomes show that the proposed scheme has the capability to detect and classify different fault types in TTLs. The presented scheme is designated to be insensitive to different fault resistances, different fault locations, and inception angles. In addition, the proposed scheme is also tested under different technical aspects such as different sampling rates, different locations of the single-end measuring system, loading change and source parameters variation. The superiority of reactive-power compared with active power measurements during faults occurrence is also investigated to show the notability of the proposed scheme. The obtained results corroborate that the proposed scheme can be correctly identified different fault types and locations along the line. [ABSTRACT FROM AUTHOR]

Highlights • Shaded PV cells degrade the performance of PV systems. • I-V curves contain useful information for health monitoring. • Data driven method for fault diagnosis can deal with non-controlled solar irradiance. • Principal component analysis has proven its efficacy under real working conditions. Abstract Health monitoring and diagnosis of photovoltaic (PV) systems is becoming crucial to maximise the power production, increase the reliability and life service of PV power plants. Operating under faulty conditions, in particular under shading, PV plants have remarkable shape of current-voltage (I-V) characteristics in comparison to reference condition (healthy operation). Based on real electrical measurements (I-V), the present work aims to provide a very simple, robust and low cost Fault Detection and Classification (FDC) method for PV shading faults. At first, we extract the features for different experimental tests under healthy and shading conditions to build the database. The features are then analysed using Principal Component Analysis (PCA). The accuracy of the data classification into the PCA space is evaluated using the confusion matrix as a metric of class separability. The results using experimental data of a 250 Wp PV module are very promising with a successful classification rate higher than 97% with four different configurations. The method is also cost effective as it uses only electrical measurements that are already available. No additional sensors are required. [ABSTRACT FROM AUTHOR]

This paper proposes a novel protection scheme for detection and classification of internal faults in bipolar HVDC transmission lines using the K -means data description (KMDD) method. In the proposed protection scheme, the inverter-side dc voltage and current signals are utilized. Relatively short-time windows are considered for these signals, and the sum values of data windows are calculated. In the preparation stage, for each dc fault type, the KMDD method is applied to some post-fault data generated in various conditions. Then, the obtained centroids and thresholds are used for detecting and classifying new internal dc faults even with unseen conditions. The performance of the proposed method is evaluated for 4320 internal and 2816 external fault cases in a 1000 km bipolar overhead HVDC transmission line under various conditions not seen in the preparation stage. Besides simplicity and low sampling frequency requirement of the proposed protection scheme, the obtained results show that it is fast and accurate enough for the internal dc faults, and also it is stable during the external ac faults and the pre-fault normal conditions. [ABSTRACT FROM AUTHOR]

Abstract This paper presents a new approach for faults classification in analog integrated circuits using a multiclass adaptive neuro fuzzy inference system classifier. This is carried out to assist analog circuit's faults diagnosis suffering from inaccurate faults classification on one hand, and to lessen computational burden on the other hand. This has been achieved from features number reduction. These features serving as input feature vector are extracted from the selected circuits (CUT) frequency and transient responses under both fault free and faulty conditions. The considered faults are resistors and capacitors values variations of about 50% low and high from their nominal ones. The method accuracy has been validated with three experiment circuits, the Sallen Key band-pass, the four opamp biquad high-pass and the leapfrog filters. The obtained results reveal a high level of efficiency with an accuracy average reach to 99.76%. Hence, the proposed method has shown a good performance in term of fault classification accuracy when compared with those of both the Artificial Neural Networks (ANN) approach and the fractional Fourier transform (FRFT) method based on a statistical property. Highlights • A neuro-fuzzy classifier for faults classification in analog circuits is proposed. • Monte-Carlo simulation is used for features extraction. • Features selection technique is used to reduce the computational burden. • Classifiers fusion and winner takes all rule for multiclass fault discrimination. [ABSTRACT FROM AUTHOR]

This paper presents the development of a novel data-driven fault detection and classification scheme for DC faults in multi-terminal HVDC transmission system which incorporates superconducting cables and modular multi-level converters. As the deployment of superconducting cables for bulk power transmission from remote renewable generation is progressively increasing in the future energy grids, many fault-related challenges have been raised (i.e., fault detection, protection sensitivity/stability). In this context, the applications of Artificial Intelligence techniques have started to be considered as a powerful tool for the development of robust fault management solutions. The proposed artificial intelligence-based method utilizes local current and voltage measurements to detect and classify all types of faults on the DC cables and DC buses, without the requirement of measurements exchange among different DC substations. The performance of the proposed scheme has been assessed through detailed transient simulation analysis and the results confirmed its effectiveness against a wide range of fault conditions (i.e., various fault types, fault locations and fault resistances). Furthermore, the feasibility of the developed scheme for real-time implementation has been validated using real-time software in the loop testing. The results revealed that the proposed algorithm can correctly, and within a very short period of time (i.e. less than 2 ms) detect and classify the faults within the protected zone and concurrently remain stable during external faults. Additionally, the generalization capability of the algorithm has been verified against influencing factors such as the addition of noise, highlighting the robustness of the presented scheme. • The deployment of DC Superconducting Cables in HVDC systems has started to be considered a promising solution for bulk power transmission from remote renewable generation units. • The inherent fault current limiting capability and the unique electro-thermal properties of the SCs have introduced many fault management issues in power systems. • For the protection of SCs in meshed HVDC systems, schemes with increased sensitivity, discrimination capability and high operational speed are required. • Artificial-Intelligence-based protection schemes overcome the limitations of existing protection methods in the data-rich power systems, providing fast, and accurate fault detection and classification. • XGBoost-based fault detection and classification algorithm performs effectively under various faults occurred on SCs and DC busbars, providing a reliable protection solution and accelerating the uptake of superconducting technology. [ABSTRACT FROM AUTHOR]

Conventional relays fail to detect the high impedance fault (HIF) in distribution lines, as the change in the current magnitude is very negligible compared to conventional relay settings. Moreover, the incorporation of distributed generators in the distribution lines changes the fault current level, which makes the HIF-detection more complex. In this paper, a fuzzy-based intelligent fault detection and classification scheme is developed for the distribution lines integrated with DGs. Two different fuzzy inference systems (FIS) are modelled in each phase to detect the fault. The first FIS identifies the high magnitude of fault current associated with normal shunt faults; and the second FIS identifies the small magnitude of current owing to occurrence of HIF. The proposed scheme uses the features extracted from the Teager energy operator. An extensive study is conducted and the response time is found to be around ¼-1 cycle. Results validate the efficacy of the proposed scheme. [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]

The conventional distance relaying algorithms are unable to detect the inter-circuit faults, cross-country faults, high resistance faults which may occur in a double circuit line. This paper presents combined Discrete Wavelet Transform (DWT) and Support Vector Machine (SVM) based directional relaying and fault classification scheme including inter-circuit faults, cross-country faults and high resistance faults. SVM modules are designed for forward or reverse fault identification and fault classification using single terminal data. The 3rd level approximate discrete wavelet transform coefficients of three phase current signals only have been used. Proposed method is tested with variations in fault type, fault location, fault inception angle, fault resistance, inter-circuit faults, and cross-country faults. The proposed method based on SVM does not need any threshold to operate which is an exceptional attribute for a protective function. As SVMs are not based on comparing with some threshold, rather initially the SVMs are trained with the wide variety of fault patterns which is an offline process and then the trained SVMs are tested online to detect and classify the fault within short time. The test results show that all types of shunt faults can be identified within half cycle time. The proposed scheme offers both primary protection to 95% of the line section and also backup protection to 95% of the adjacent reverse and forward line section also. [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]

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]

Parallel transmission lines are difficult to protect due to mutual coupling between circuits. This paper proposes a Naïve Bayes classifier (NBC) based fault detection and classification technique for protection of parallel transmission line involving inter-circuit faults. NBC is a good classification tool for larger data sets as the training process takes less time with greater accuracy. Input given to the fault detection module is the fundamental components of three phase current signals of both circuits. Input given to the fault phase identification and fault classification module is the fundamental component of three phase current signals and zero sequence currents of both the circuits. Seven separate classifiers are designed for fault phase identification for A1, B1, C1, A2, B2, C2 and G. From fault phase identification module faults are classified. Accuracy of the proposed method is 100% for fault detection and 99.99% for classification of fault from all the tested fault cases. Response time of the proposed method is within 10 ms for all the fault cases studied. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Diagnosis aims at predicting the health status of components and systems. In photovoltaic systems, it is vital to guarantee energy production and extend the useful life of photovoltaic power plants. Multiple prediction and classification algorithms have been proposed for this purpose in the literature. The accuracy of these algorithms depends directly on the quality of the data and the features with which they are tuned or trained. In this paper, an innovative approach for predicting the health status of photovoltaic systems is proposed, which includes a feature selection stage. This approach first discriminates severely affected photovoltaic panels using basic electrical features. In a second step, it discriminates the other faulty panels using more elaborated time–frequency features and selecting the most relevant features through correlation and variance analysis. Finally, the approach predicts the health status of photovoltaic panels using a nonlinear regression method named partial least squares. This later is then combined with linear discriminant analysis and compared. The approach is validated with real current data from a photovoltaic plant composed of twelve photovoltaic panels with power between 205 and 240 Wp in three health states, namely broken glass, healthy, and big snail trails. The results obtained show that the proposed approach efficiently predicts the three health states. It determines the level of degradation of the panels, which indicates priorities to corrective and predictive maintenance actions. Furthermore, it is cost-effective since it uses only electrical measurements that are already available in standard photovoltaic data acquisition systems. Above all, the approach is generic and it can be easily extrapolated to other diagnosis problems in other domains. [ABSTRACT FROM AUTHOR]

This paper proposes fault detection and classification scheme for transmission line protection using WT and linear discriminant analysis (LDA). Current signals of each phase are used for the detection and identification of faulty phases and zero sequence currents are used for the detection of ground. Current signals are processed using discrete wavelet transform with DB-4 wavelet up to level 3. Approximate coefficients are reconstructed using wavelet reconstruction. Performance of the proposed based scheme is tested by variations of parameters such as fault type, location, fault resistance, fault inception angle and power flow angle. The scheme is applicable for both single circuit and double circuit transmission line. All shunt faults and multi-location faults which occur in different locations at the same time are also detected and classified by the proposed scheme within one cycle time. The simulation results show that the proposed scheme is not affected by non-linear high impedance fault and CT saturation. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes an intelligent method based on artificial neural networks (ANNs) to detect bearing defects of induction motors. In this method, the vibration signal passes through removing non-bearing fault component (RNFC) filter, designed by neural networks, in order to remove its non-bearing fault components, and then enters the second neural network that uses pattern recognition techniques for fault classification. Four different categories include; healthy, inner race defect, outer race defect, and double holes in outer race are investigated. Compared to the regular fault detection methods that use frequency-domain features, the proposed method is based on analyzing time-domain features which needs less computational effort. Moreover, machine and bearing parameters, and the vibration signal spectrum distribution are not required in this method. It is shown that better results are achieved when the filtered component of the vibration signal is used for fault classification rather than common methods that use directly vibration signal. Experimental results on three-phase induction motor verify the ability of the proposed method in fault diagnosis despite low quality (noisy) of measured vibration signal. [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]

Worm gearboxes (WG) are frequently used in many areas of the industry. WG is different from other gearbox types and due to their working principles they are under high risk of wear and fault. Therefore, detection of faults that may occur in WG and taking measures accordingly are especially important for systems and facilities that require uninterrupted operation. This paper introduces an intelligent feature selection and classification method for fault diagnosis of WGs under different working conditions. The novelty of the study lies in the selection of feature sources and different loading and speed conditions for condition monitoring studies of WGs experimentally. Fault detection and classification were performed based on vibration, sound and thermal images data which were acquired and processed from the healthy and the faulty WG. Apart from classical studies, time and frequency domain features and thermal images features were extracted and evaluated singularly, dual or triple forms with ANN (Artificial Neural Network) and SVM (Support Vector Machines) classifiers. Reasonable classification performances for fault detection were observed when the features of all three sources used (99.2% with ANN and 98.7% with SVM). ANN and SVM classification performances are almost equal for fault classification (98.9% with SVM and 98.6% with ANN). The findings of the study would be a possible means for online condition monitoring of industrial plants, conveyors, elevators, etc. [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]

The Open Circuit (OC) faults occurring in switches of Multilevel Converters (MLC) may lead to undesirable operation of the converter. Therefore, fault detection and its localization in minimum time are necessary. This paper focuses on the fast fault detection algorithm based on the two samples technique and the fault localization algorithm using the Entropy of Wavelet Packets (EWP) as a feature. The EWP feature is used to classify and localize the OC faults in Insulated Gate Bipolar Transistors (IGBTs) of three-phase, three-level inverter using Support Vector Machine (SVM) based fault classification algorithm. The proposed technique can detect the fault in single IGBT and multiple IGBTs in a lesser time range of microseconds to 0.33 ms. It gives better performance and accuracy (99.70%) than previously proposed SVM algorithms, as the EWP-based feature extraction process used in this paper is simple and accurate with a less computational burden. [Display omitted] • An observer-based algorithm is proposed using two samples-based technique. • The Entropy of Wavelet Packets (EWP-SVM) technique is used for fault diagnosis. • The proposed algorithm can detect the faults in a single IGBT and multiple IGBTs. • The technique is faster and accurate than techniques available in the literature. • The comparison of detection time of different techniques is also discussed. [ABSTRACT FROM AUTHOR]

The Auxiliary Power Unit (APU) is an integral part of an aircraft, providing electrical and pneumatic power to various on-board sub-systems. APU failure results in delay or cancellation of a flight, accompanied by the imposition of hefty fines from the regional authorities. Such inadvertent situations can be avoided by continuously monitoring the health of the system and reporting any incipient fault to the MRO (Maintenance Repair and Overhaul) organization. Generally, enablers for such health monitoring techniques are embedded during a product's design. However, a situation may arise where only the critical components are regularly monitored, and their status presented to the operator. In such cases, efforts can be made during service to incorporate additional health monitoring features using the already installed sensing mechanisms supplemented by maintenance data or by instrumenting the system with appropriate sensors. Due to the inherently critical nature of aircraft systems, it is necessary that instrumentation does not interfere with a system's performance and does not pose any safety concerns. One such method is to install non-intrusive vibroacoustic sensors such that the system integrity is maintained while maximizing system fault diagnostic knowledge. To start such an approach, an in-depth literature survey is necessary as this has not been previously reported in a consolidated manner. Therefore, this paper concentrates on auxiliary power units, their failure modes, maintenance strategies, fault diagnostic methodologies, and their acoustic signature. The recent trend in APU design and requirements, and the need for innovative fault diagnostics techniques and acoustic measurements for future aircraft, have also been summarized. Finally, the paper will highlight the shortcomings found during the survey, the challenges, and prospects, of utilizing sound as a source of diagnostics for aircraft auxiliary power units. [ABSTRACT FROM AUTHOR]

• A fault detection scheme is proposed for MTDC by using local transient information. • Classify internal and external fault by tracking pulse count in a specific window. • The concept of protection unit is employed for rapid isolation of faulty part. • Results provide evidence for protection scheme's key role in handling MTDC faults. DC faults render hindrance in the flourishment of multi-terminal flexible high voltage DC (MTDC). The fault current rises rapidly, making a sharp peak, however, under a specific fault instance, isolation of the whole network is an impractical solution. For effective reliability of the whole network, rapid and accurate fault detection and selection of faulty pole is mandatory. A fault detection scheme is proposed in this paper to draw transient current harmonics from initial fault current by utilizing Discrete Fourier transform (DFT). Current harmonics employed the fault detection promptly at the converter stations without communication among the MTDC networks. Additionally, the proposed scheme can discriminate internal from external fault by tracking the number of pulse count in a specific interval of the harmonics of the voltage. In this way, the reliability of the protection scheme enhances and escape from the mal-operation of the circuit breaker. For four terminal MTDC transmission network, the simulations are carried out in PSCAD to authenticate the implementation of the proposed scheme and protection units (PU) are utilized for the isolation of faulty part with the combination of hybrid circuit breaker (HCB). A keen analysis of test results authenticated the ability of the proposed scheme to rapidly detect the faults with high precision, along with efficient classification of the nature of faults. Furthermore, consequential results provide a strong evidence for its key role in handling a multitude of MTDC faults. [ABSTRACT FROM AUTHOR]

The rapid development of photovoltaic (PV) technology and the growing number and size of PV power plants require increasingly efficient and intelligent health monitoring strategies to ensure reliable operation and high energy availability. Among the various techniques, Artificial Neural Network (ANN) has exhibited the functional capacity to perform the identification and classification of PV faults. In the present review, a systematic study on the application of ANN and hybridized ANN models for PV fault detection and diagnosis (FDD) is conducted. For each application, the targeted PV faults, the detectable faults, the type and amount of data used, the model configuration and the FDD performance are extracted, and analyzed. The main trends, challenges and prospects for the application of ANN for PV FDD are extracted and presented. • Applications of ANN techniques and hybrid application for PV FDD are summarized. • Detectable PV faults with ANN technique and diagnosis performance are investigated. • Reported public PV database of 1D data and 2D images are summarized. • Common trends for the selection of model for different PV faults are identified. • Challenges and prospects for PV FDD using ANN techniques are presented. [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]

• Enhancing the resilience of a compensated parallel line using a differential scheme. • Estimating the spectral energy of the currents by applying a tool known as FDOST. • Evaluating the scheme against various faults and dynamic power system conditions. This paper presents a resilience-oriented protection scheme for TCSC-compensated Line. A cross-differential protection scheme is proposed to enhance the resilience of a parallel transmission line (PTL) in the presence of a thyristor controlled series capacitor (TCSC). The faulty phase(s) is (are) identified by estimating the spectral energy of the three-phase currents and computed by applying an efficient powerful signal processing tool known as fast discrete orthonormal S-transform (FDOST). The proposed scheme is widely evaluated against various faults and dynamic power system conditions. The obtained results indicate the proposed resilience-oriented protection scheme is adaptable to different power system scenarios. The scheme precisely classifies the fault with effective discrimination between internal and external faults on a transmission line and a response time of a maximum one cycle from the fault inception. [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]