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Fault classification is crucial in fault mitigation to maintain selectivity in tripping only the faulted phase or zone in power system networks. However, inverter-interfaced renewable energy sources' unique fault current profile poses challenges to classifiers designed for conventional systems, which are inadequate in the presence of renewable energy resources such as inverter-interfaced photovoltaic (PV) or wind turbine systems in the grid. The inverters have internal protection schemes that trip during unbalanced conditions; however, in grids with high penetration of renewable energy, the inverter must ride through the fault and let relays protect the system. Moreover, the different control strategies for inverters can make the fault current small enough to be unreliable to use as a parameter in fault classifications. This study proposes a reliable fault classification method that can accurately identify faults in power systems with high penetration of renewable energy sources. This paper discusses a machine learning (ML)-based classifier using phase current and voltage magnitude to classify faults. The performance of the proposed classifier is validated against different fault scenarios in power systems like the IEEE 9-bus system. The classifier discussed in this paper achieved a satisfactory accuracy of 99.78% with voltage measurements for test conditions within three-quarters of a cycle. The classifier can be used for any three-phase system to provide correct faulted phase information to other protection components. The same methodology is extended to identify evolving faults, achieving an accuracy of 99.6% in determining the evolving fault type. Thus, the proposed ML-based classifier provides a reliable and accurate method for fault classification in power systems with high penetration of renewable energy sources. [ABSTRACT FROM AUTHOR]

The use of multi-terminal high voltage direct current (HVDC) power transmission systems is being adopted in many new links between different generation and consumption areas due to their high efficiency. In these systems, cable energization must be performed at the rated voltage. Healthy energizations at the rated voltage result in large inrush currents, especially in long cables, primarily due to ground capacitance. State-of-the-art protection functions struggle to distinguish between transients caused by switching and those associated with ground faults, leading to potential unwanted tripping of the protection systems. To prevent this, tripping is usually blocked during the energization transient, which delays fault detection and clearing. This paper presents a novel method for prompt discrimination between healthy and faulty energizations. The proposed method outperforms conventional protection functions as this discrimination allows for earlier and more reliable tripping, thus avoiding extensive damage to the cable and the converter due to trip blocking. The method is based on the transient analysis of the current in the cable shields, therefore, another technical advantage is that high voltage-insulated measuring devices are not required. Two distinct tripping criteria are proposed: one attending to the change in current polarity, and the other to the change in current derivative sign. Extensive computer simulations and laboratory tests confirmed the correct operation in both cases. [ABSTRACT FROM AUTHOR]

This research paper presents a proposed system for fault identification, classification and location in transmission lines using a Support Vector Machine (SVM)-based technique in conjunction with a Positive Sequence Analyzer. The objective is to develop an accurate and reliable method for identifying, classifying and locating different fault types in transmission lines. The proposed system leverages the capabilities of SVMs in handling high-dimensional feature spaces and the fault signature extraction capabilities of the Positive Sequence Analyzer. Experimental evaluations are conducted to assess the performance and effectiveness of the proposed system, comparing it with existing fault identification and classification methods. The results demonstrate the superior performance and robustness of the SVM-based technique utilizing the Positive Sequence Analyzer, providing a valuable contribution to fault management and system reliability in transmission line networks. [ABSTRACT FROM AUTHOR]

Arc fault detection devices are mandatory worldwide for mitigating DC series arc faults in photovoltaic systems. However, they are prone to nuisance tripping. Artificial intelligence-based approaches can be a solution, but they are “black boxes” and challenging to modify. This paper proposes an explainability and attention-based method to investigate the intensive details of such an algorithm. The contributions of an arc feature to the proposed model can be visualized by the proposed interpretable methodology so that insensitive arc features can be removed to reduce the quantity of input data. Additionally, the structure of the proposed model can be optimized by cutting the redundant layers. Thus, an accuracy of 99.63% is achieved with only 48.48% of the parameters compared to the original model. Finally, the optimized model is implemented by a Cortex M7-based microprocessor with a runtime of only 7.8 ms, making it ready for industrial application.

This research paper presents a proposed system for fault identification, classification and location in transmission lines using a Support Vector Machine (SVM)-based technique in conjunction with a Positive Sequence Analyzer. The objective is to develop an accurate and reliable method for identifying, classifying and locating different fault types in transmission lines. The proposed system leverages the capabilities of SVMs in handling high-dimensional feature spaces and the fault signature extraction capabilities of the Positive Sequence Analyzer. Experimental evaluations are conducted to assess the performance and effectiveness of the proposed system, comparing it with existing fault identification and classification methods. The results demonstrate the superior performance and robustness of the SVM-based technique utilizing the Positive Sequence Analyzer, providing a valuable contribution to fault management and system reliability in transmission line networks.

Automatic fault detection is currently combined with deep networks owing to the possibility of dispensing signal processing, which significantly accelerates reactions to faults. Changing the type of defect or object forces repetition of the network learning process and its implementation. The design of universal systems for detecting different faults can be developed using transfer learning techniques. This paper presents the application of the transfer learning of a convolutional neural network to the fault diagnosis of permanent magnet synchronous motors. The crucial point of this research was to develop accurate diagnostic applications based on the data obtained from the motor field model and to use their functionality for a real object. This study compares the accuracy of diagnostic systems using three currently known techniques: neural network-based, instance-based, and mapping-based transfer learning. The experimental verification of the systems was carried out on an experimental bench with a 2.5 kW motor.

The reliability of the model significantly affects early detection and accurate classification of electrical faults. In this study, a Long Short Term Memory based fault classification model was developed for the Power System Machine Learning benchmark dataset, focusing on improving reliability by increasing interpretability. First, novel metrics are introduced to measure model interpretability. These interpretability metrics are uniquely defined based on the disentanglement of the fault classification factors. Subsequently, hyperparameter optimization was performed using multi-objective Bayesian Optimization to determine the optimal model architecture. The objective of optimization is to maximize interpretability and classification accuracy. The Pareto-optimal solution presents different model architectures with varying accuracy and interpretability trade-offs. Finally, the manifestation of interpretability in terms of subsequences is studied using Shapley Additive Explanations. The impact of class representation and architectural parameters on interpretability was also analyzed. Furthermore, the most accurate model in the Pareto front achieved highly competitive accuracy for the benchmark data.

Transmission lines are an imperative element of the modern power systems. Any faults in them can cause an undesirable interruption in power supply. Precise analysis of these faults is important in-order to ensure an incessant supply of power. For this purpose, fault detection and classification are needed to clear any such faults and re-establish the system to maintain its normal operation. In this paper, a novel integrated approach of protective relaying with enhanced support vector machine algorithm has been adopted for detecting faults and its location estimation in long transmission line. The proposed scheme is successfully able to detect and classify different symmetrical and unsymmetrical faults along with some peculiar cases related to High Impedance Faults (HIF) and evolving faults, current transformer (CT) saturation/ capacitive voltage transformer (CVT) transient, close-in faults, swing condition, source strength variation, etc. The comparative analysis with recent proposed techniques declared the potentiality and robustness of the scheme

Traditional electricity infrastructure inspections usually have high costs, risks and it takes a long time for specialized personnel to carry them out. Additionally, they also involve scaffolding risks, that lead to a high accident rate in most electricity companies. The recent emergence of Unmanned Aerial Vehicles (UAVs) is gradually leveraged to avoid such risks. However, UAVs usually face Global Positioning System instability issues especially in the distant or harsh infrastructure areas. This requires frequent manual UAV control and calibration by electricity operators. In this article, we propose a new method for automating the UAV infrastructure inspection procedure. The method uses Artificial Intelligence techniques to identify electricity infrastructures and the associated assets, as well for the real-time detection of infrastructure faults. Additionally, using 5G Network Function Virtualization technologies, such as end-to-end network slicing, combined with edge computing, significant latency and GPS accuracy improvements are realized during the inspection. We apply the method for the inspection of a Hydroelectric Power Plant of the Public Power Corporation. The experiments illustrate significant benefits in latency, GPS accuracy, fault discovery rate and accident reduction. Such benefits provide real-time response to infrastructure faults that supports business continuity and increase customer satisfaction.

The traditional manual periodic screening method of grading electrode sediments is prone to cause the equipment failure of high voltage direct current converter valves. Therefore, we propose to use ultrasonic time-domain reflection method to detect the sediments. However, the ultrasonic echo signals are characterized by nonlinearity and nonsmoothness, which makes it very difficult to extract effective features for sediment detection. To address this issue, we propose an intelligent detection method based on multiscale hybrid entropy characteristics in the time-frequency domain. First, a multiscale decomposition of the signal is performed. Second, the weighted form factor index is proposed to select the noise modes. Moreover, we propose to calculate the hybrid entropy in the time-frequency domain of each mode as the characteristic input bidirectional long and short-term memory network model, and verified that feature enhancement can be achieved by noise modes noise reduction. Finally, the experimental validation shows that the proposed method can achieve nondestructive testing and intelligent identification of graded electrode sediment with a correct identification rate of 94.25%.

An arc fault is an unintentional power discharge which may cause unexpected serious consequences such as fires. Under different voltage, current and loading conditions, along with unplanned locations, the occurrence times of arc faults are always random. Due to the randomness characteristic of arc fault, the measurement of the position and intensity of arc faults in real- time is a challenge to researchers. This brief proposes an analog- digital mixed-signal system applied to the arc fault detection in smart meters. The introduced digital system can automatically identify the load types and the filters of the reconfigurable analog circuit arranged according to the loads. The proposed digital system also implements the arc faults detecting threshold and its respective methods. A field-programmable analog array (FPAA) incorporates the involved analog functions, and an Altera Field Programmable Gate Array (FPGA) realizes the digital system. We test the performance of the proposed mixed-signal system under different load conditions with daily household appliances. The results show that the circuit is able to detect 99.6% of the arc faults during the experiment. The proposed mixed system provides a cost-effective package solution for accurately detecting the arc fault in modern smart meters. [ABSTRACT FROM AUTHOR]

This article proposes a fault detection scheme for modular multilevel converter-based multiterminal dc (MMC-MTdc) girds based upon a resistive-type superconducting fault current limiter (RSFCL). By analyzing the characteristics of the currents through the faulted and healthy lines in an MMC-MTdc system, the theoretical foundations of the scheme are established. This yields a fault detection scheme with fault identification, fault pole discrimination, and high-resistance tolerance abilities. Extensive simulations confirm that the proposed detection scheme can identify the metallic short circuit fault within 1 ms, with the ability to promptly detect faults with high resistance in less than 3 ms taking the communication delay into consideration. This method does not cause any mal-operation of dc circuit breakers as a result of faults on adjacent lines and power order changes. The proposed scheme has immunity to noise and provides full-length protection of the protected transmission line. Simulation results show significant suppression of both the amplitude and the rate-of-rise of the fault current during the fault detection process, which enhance the reliability and stability of the protected grid. [ABSTRACT FROM AUTHOR]

This article proposes a model for obtaining the failure path current and torque waveforms in synchronous machines with saliency under interturn short circuits (ISC). The variations of the magnetomotive force (MMF) due to an ISC can lead to changes in the inductances, which lead to errors when modeling motors with ISC. A compact approach based on complex numbers is proposed to solve the dynamic ISC equation. The proposed method uses rotor reference frame variables and parameters as inputs. These rotor reference frame quantities are transformed to a stationary frame aligned with the faulted coil, thus extracting the real part of these quantities, allowing to solve the ISC dynamic equation quickly. Look-up tables are implemented to account for the MMF variations due to an ISC and saturation. The proposed model is validated with finite element analysis and experiments. [ABSTRACT FROM AUTHOR]

To further improve the detection ability of residual current in low-voltage distribution networks, an adaptive residual current detection method based on variational mode decomposition (VMD) and dynamic fuzzy neural network (DFNN) is proposed. First, using the general $K$ -value selection method of VMD proposed in this study, the residual current signal is decomposed into $K$ intrinsic mode functions (IMFs). By introducing the cross-correlation coefficient $R$ and the time-domain energy entropy ratio $E$ as two classification indexes, IMFs are divided into three categories: effective IMFs, noise IMFs and aliasing IMFs. Then, the aliasing IMFs are denoised by recursive least squares (RLS), and the denoised IMFs are superimposed with the effective IMFs to obtain the reconstructed signal. Finally, the dynamic fuzzy neural network (DFNN) is adjusted by the minimum output method to achieve the detection of the reconstructed residual current signal, and the network is used to predict the residual current according to the detection results. The detection results of the simulation and measured data show that the proposed algorithm has high detection accuracy and is superior to the wavelet neural network, empirical mode decomposition-thresholding, and wavelet entropy-auto encoder-back propagation neural network methods in terms of mean square error, goodness of fit and running time. This method provides a reference for further research on new adaptive residual current protection devices.

This paper proposes a non-contact method for fault location in transmission lines, which is based on magnetic fields produced by current signals measured using magnetoresistive sensors installed only at transmission line terminals, under the phase conductors of the first transmission tower at both terminals or at the substations portals. The proposed method uses the Extended Kalman filter to process these measurements and is based on a travelling wave approach in order to perform the fault localization. This paper also describes the implementation and testing of the method, firstly introducing its overview, followed by an analysis of the magnetic fields produced by the current signals, as well as considerations on their measurement; secondly, detailing the Extended Kalman filter and the travelling wave approach; and, finally, presenting the results of the method with regards to simulations built using EMTP/ATP to evaluate its robustness under different conditions varying the fault resistance, fault inception angle, phases involved and fault location. The results indicate that the proposed method is robust and accurate.

Power converters are essential for process optimization and electric drives control. However, the presence of both dc and ac currents in the same circuit makes the diagnosis and protection of this type of systems difficult. For this reason, a new ground fault location method for the controlled ac drive side of a dc–ac system is presented in this article. It is based on a grounding resistor placed at the midpoint of the dc bus, where its voltage is measured simultaneously with the ac drive side phase-neutral voltages.Applying a phasor operated equation, the faulty phase can be detected by angle comparison. Furthermore, an estimation of the fault location can be obtained. To verify the method, numerous simulations have been performed. The results show certain inaccuracies when the fault is close to the neutral of the system, which is inconvenient since the phasor equation requires precise values. In contrast, the ground arc resistance is not needed in the fault location process. The effectiveness of the method has been also corroborated through experimental tests on a 140 kW power converter, making the ground fault diagnosis in dc–ac systems practical and useful. [ABSTRACT FROM AUTHOR]

Volume logic diagnosis is typically applied in two phases. In the first phase, a fault detection test set is used for collecting fail data and performing logic diagnosis. If the fail data based on a fault detection test set is not sufficient for accurate logic diagnosis, a faulty unit is considered again in the second phase with a larger diagnostic test set. When diagnostic tests are generated, the defects present in the faulty units they target are unknown, and the tests are generated for target faults. This article suggests that the use of multicycle tests with the same input test data (scan-in states and primary input vectors) as the tests in a fault detection test set can address this issue. Under the approach suggested in this article, a multicycle test is considered useful for diagnosis when it causes the faulty unit to produce more fail data than tests with fewer cycles and the same input test data. The volume of fail data is obtained directly from the faulty unit and does not require the use of target faults. This article discusses the tester support needed for this approach, and its effect on the logic diagnosis procedure. It presents experimental results for benchmark circuits to demonstrate the extent to which it is effective. [ABSTRACT FROM AUTHOR]

This paper presents a fault detection method in three-phase induction motors using Wavelet Packet Transform (WPT). The proposed algorithm takes a frame of samples from the three-phase supply current of an induction motor. The three phase current samples are then combined to generate a single current signal by computing the Root Mean Square (RMS) value of the three phase current samples at each time stamp. The resulting current samples are then divided into windows of 64 samples. Each resulting window of samples is then processed separately. The proposed algorithm uses two methods to create window samples, which are called non-overlapping window samples and moving/overlapping window samples. Non-overlapping window samples are created by simply dividing the current samples into windows of 64 samples, while the moving window samples are generated by taking the first 64 current samples, and then the consequent moving window samples are generated by moving the window across the current samples by one sample each time. The new window of samples consists of the last 63 samples of the previous window and one new sample. The overlapping method reduces the fault detection time to a single sample accuracy. However, it is computationally more expensive than the non-overlapping method and requires more computer memory. The resulting window samples are separately processed as follows: The proposed algorithm performs two level WPT on each resulting window samples, dividing its coefficients into its four wavelet subbands. Information in wavelet high frequency subbands is then used for fault detection and activating the trip signal to disconnect the motor from the power supply. The proposed algorithm was first implemented in the MATLAB platform, and the Entropy power Energy (EE) of the high frequency WPT subbands' coefficients was used to determine the condition of the motor. If the induction motor is faulty, the algorithm proceeds to identify the type of the fault. An empirical setup of the proposed system was then implemented, and the proposed algorithm condition was tested under real, where different faults were practically induced to the induction motor. Experimental results confirmed the effectiveness of the proposed technique. To generalize the proposed method, the experiment was repeated on different types of induction motors with different working ages and with different power ratings. Experimental results show that the capability of the proposed method is independent of the types of motors used and their ages. [ABSTRACT FROM AUTHOR]

A nonlinear robust fault detection scheme for synchronous generators in power systems is presented. The higher-order model of a synchronous generator having nonlinearity in dynamics is considered. A nonlinear observer-based fault detection (FD) scheme is proposed so that the designed FD is robust against disturbances, uncertainties, and sensitive to faults. To this end, a mixed $\mathcal {L}_{-}/\mathcal {L}_\infty$ induced norm-based framework is proposed, and various faults that are encountered in synchronous generators are investigated. The success of the proposed scheme is demonstrated through an application to IEEE 9-bus and 3-machine systems. [ABSTRACT FROM AUTHOR]

This brief addresses the design issues of real-time asynchronized observer based fault detection in uncertain switched systems combining with a mode estimation unit. To this end, the so-called $\mathcal {L}_{\infty }/\mathcal {L}_{2}$ type of residual generators is investigated in the asynchronized switching manner with the aid of average dwell time approach for the first time. A mode estimation unit is developed based on the measurements collected from the residual generators and the process, and further embedded in a multi-level detection logic. As a result, the conservative threshold setting caused by the model mismatch in the asynchronized residual generation, and the fault detectability can be improved. [ABSTRACT FROM AUTHOR]

Frequency response analysis (FRA) is an extended technique for transformer condition assessment. In recent times, its proven sensitivity for detecting different type of faults have generated interest for rotating machine applications. However, at present, the interpretation of FRA results is mostly made by visual inspection of curves and is a hard task for nonexpert maintenance personnel. This article presents a mathematical model to simplify diagnostic tasks from FRA results. The developed model is called “Fault Diagram” and it is conceptualized to be applied to any coil, so it could be extensive to any type of machine. The fault diagram is based on a paradigm which states that a healthy machine is a machine with infinite impedance fault. The model uses a first-order frequency response for the purpose of fitting a linear function ∊ R3. The input of the model is the FRA curve of the machine, and the output is a point in a three-dimensional space. This approach can give an accurate diagnosis without ambiguous interpretations about if the machine is healthy or not and can provide an estimation of the fault location in case of damage. For validating the method, it was applied in a pole of a 40 MVA synchronous generator. [ABSTRACT FROM AUTHOR]

The pole drop test is the most widely applied off-line test for detecting shorted turns in the field winding of salient pole synchronous motors. It is a simple test where the voltage drop (or inductance) of the poles are compared to detect the presence of shorted turns. Although it does not require special test equipment, false indications due to sensitivity problems are common. In this letter, the voltage and inductance distribution between the poles are analyzed under shorted field turn conditions. Some practical insight for enhancing the sensitivity and reliability of the pole drop test are given based on the qualitative analysis and experimental testing on a 30 kVA synchronous motor with shorted field turns. It is shown that the sensitivity of the test is significantly higher when the test is performed with the rotor inserted, which is not reported elsewhere. It is also shown that the reliability of the test can be improved by observing the voltage or inductance in poles adjacent to the pole suspected with shorted turns. [ABSTRACT FROM AUTHOR]

High speed trains supply is, nowadays, performed by 2 × 25 kV power systems. If a ground fault occurs, its location and correction are crucial to minimize downtime. One of the methods for locating ground faults in power systems is the impedance method. This was a good solution for 1 × 25 kV railways power systems, as the ratio between reactance and fault distance is linear. However, the use of autotransformers between the catenary, feeder and rails makes this ratio non-linear in 2 × 25 kV configuration. This work presents a novel ground fault location method for 2 × 25 kV power systems. The new method is based on the ratio of the currents of the autotransformers and the substation transformer. The main contribution, compared to previous methods developed, is that this new method is insensitive to the fault current variations produced by the network voltage fluctuations or by the main transformer tap changer position, among others for 2 × 25 kV power systems. The novel approach has been corroborated by numerous experimental tests in the laboratory and computer simulations, with satisfactory results. [ABSTRACT FROM AUTHOR]

In recent years, DC fault arc detection has been an electrical engineering research hotspot. At present, most proposed detection methods do not analyze the effects of fault arc electrical characteristics on both line current and supply voltage. Therefore, this study extensively analyzes variations of the line current and supply voltage because of DC arc faults based on the volt-ampere characteristics of DC arc faults. Then, a DC series arc fault detection method is proposed that comprehensively uses information on line current and supply voltage. An experimental platform for DC fault arc generation and detection was established using a DC-DC converter and a photovoltaic power supply as DC power supplies, and the proposed method was confirmed by experiments using this platform. Experimental results demonstrate that the proposed method can effectively distinguish arc faults and has the characteristics of clear physical meaning while maintaining a low amount of calculation.

The present paper proposes a genuine, simple to implement, robust and reliable method for current sensors fault detection and compensation used for 3-phase inverters. Its functionality is based on an algorithm programed into a Field Programmable Gate Array (FPGA) as general controller. Usually, 3-phase inverters are controlled using field oriented control (FOC) which is generally triggered to sample the measured currents on the established switching frequency. As the latter is much smaller than the base clock of the FPGA, this allows hundreds of free time samples to perform other calculations in-between two FOC samplings. In this paper, a method that uses these free and unused time samples is presented. This method performs calculations for fault detection and compensation ensuring that at each new FOC sampling, this will receive the correct current data to reach continuous operation despite faults. The interleaving principle of the FOC with the fault detection method, as it will be proven in the paper, ensures high reliability of the complete controller diminishing the possibility of undesired or faulted readings to disturb the FOC's calculations. The fault detection philosophy is based on continuous comparison of the instantaneous measured currents (sensor's response) against reference values. The experimental results presented in the paper prove reliable operational performances of the method in both steady state and transient conditions. The added value of the paper consists in its genuine approach to handle the fault detection and compensation in-between two PWM ticks, ensuring that no faulted measurements can reach the control unit. This added value is based on functionality divided on several functions triggered by an internal generated clock synchronizing and handshaking their operations towards one goal: fault detection and compensation.

At present, in the suspension system of maglev train, the self-diagnosis of most of the components can be realized employing direct sensor measurement, however, there are still some components that cannot be directly measured by sensors or model-based methods, such as the power components in the suspension control box, electromagnetics and related connectors. And although the detection method based on Stable Kernel Representation (SKR) can detect the fault, the length of the data used for SKR affects the speed and results of the detection. To obtain better detection results with the shortest possible data, this paper studies a component-level fault detection method based on autocorrelation length and SKR. This method uses the autocorrelation length to determine the length of the data used to identify the SKR, and then applies the SKR to build a residual generator and set the residual threshold. The experimental results show that the studied method can detect faults of the suspension control box in real-time and effectively, and the simulation results show that the studied method can detect faults of the electromagnet in real-time and effectively. In addition, compared with the traditional method, the method in this paper can obtain better results with less data. [ABSTRACT FROM AUTHOR]

The use of variable speed drives made essential the presence of power electronic in electrical systems. The ac to dc and dc to ac conversions, thanks to rectifiers and inverters cause difficulties in the ground fault detection. In this article, a new method to detect ground faults in variable speed drives is presented. It is based on the analysis of the terminal voltage signal of a grounding resistor connected between the neutral at the secondary of the main power transformer and ground. Attending to the waveform, the ground fault can be detected on the ac grid side, dc positive or negative terminals, or ac inverter side. To verify the method, numerous simulations and experimental tests in a 140-kW power converter were carried out achieving satisfactory results. [ABSTRACT FROM AUTHOR]

Impedance reach of numerical distance relay is severely affected by Fault Resistance (RF), Fault Inception Angle (FIA), Fault Type (FT), Fault Location (FL), Power Flow Angle (PFA) and series compensation in transmission line. This paper presents a novel standalone adaptive distance protection algorithm for detection, classification and location of fault in presence of variable fault resistance. It is based on adaptive slope tracking method to detect and classify the fault in combination with modified Fourier filter algorithm for locating the fault. To realize the effectiveness of the proposed technique, simulations are performed in PSCAD using multiple run facility & validation is carried out in MATLAB® considering wide variation in power system disturbances. Due to adaptive setting of quadrilateral characteristics in accordance with variation in fault impedance, the proposed technique is 100 % accurate for detection & classification of faults with error in fault location estimation to be within 1 %. Moreover, the proposed technique provides significant improvement in response time and estimation of fault location as compared to existing distance relaying algorithms, which are the key attributes of multi-functional numerical relay

A well-established theory of statistical inference enables the generation of statistical fault detection approaches. Previous works mainly focus on parametric tests that assume that probabilistic distributions of both healthy and faulty residuals can be parameterized. However, such assumptions may be quite limited for general nonlinear stochastic systems because those residuals are usually with unknown distribution. In this article, a new fault indicator is defined to replace the traditional residual, and a multistep fault detection method is developed via the standard Mann–Whitney (MW) test. Moreover, with weak assumptions on faults and systems, a one-step fault detection approach is proposed by means of the modified MW test. Finally, the effectiveness of the proposed fault detection method is verified by a simulation of three water tank system. [ABSTRACT FROM AUTHOR]

In this brief, the problem of the $L_{1}/L_{-}$ fault detection for positive systems is revisited. In the existing literature, the $L_{1}$ -gain and $L_{-}$ index for positive systems are often characterized separately, and thus their linear programming descriptions involve different Lyapunov vectors. This casts the fault detection filter design as a bilinear optimization problem. To circumvent this obstacle, we first show that, for an externally positive system, the $L_{1}$ -gain and $L_{-}$ index are determined, respectively, by the largest and smallest column sums of the static gain matrices. Based on this fact, an exact characterization is given for the $L_{1}/L_{-}$ index for positive systems in terms of a linear program with equality constraints. The new characterization only involves one single Lyapunov vector, and thus renders the fault detection filter design problem convex. In addition, we find that the maximum fault sensitivity (characterized by the $L_{-}$ index from the fault to the residual) that can be achieved by the filter design approach is proportional to the required upper bound on the $L_{1}$ -gain from the disturbance to the residual. Finally, an illustrative example of a positive electric circuit is presented to show the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

This brief focuses on presenting a state-based matrix approach to the problem of fault detection for discrete event systems (DESs). Firstly, the given system is modeled as a finite state machine and converted into algebraic structure with the help of semi-tensor product of matrices. Assuming that the faults influence only the state transition and can’t recover, there are two possible constructions for the system: a normative and a faulty one. Considering the evolutionary process for the two systems, labeled correlative matrices between current state and initial state are established, respectively, which have an advantage in computer storage compared with existent methods. And then, a theorem is proposed as a criterion of detectable faults, followed with a corresponding algorithm to verify whether the faults are detectable for a given system. Finally, the approach in this brief is tested effectively for the exhaust gas recirculation system. [ABSTRACT FROM AUTHOR]

Power devices may become damaged or even fail completely due to over-current caused by external factors such as ac line transients, mechanical overload, misfiring, inverter shoot-through, etc. Some of these incidents can result in a very high current (few times higher than the system’s rated current) flow through the electrical drive system. Electrical machines have the capability to withstand very high current for relatively longer time duration (milliseconds to seconds depending on the size of the machine). On comparison, power devices, especially IGBTs, can withstand short-circuit current only for very short time durations in the order of microseconds and prolonged exposure can easily damage the power device. Industrial application requires proper short-circuit fault detection and protection circuits to protect the IGBTs from fault currents. Therefore, this article presents the fault detection circuit is based on monitoring the collector-emitter voltage using external collector capacitor, and the protection circuit is based on softly turning off the gate voltage using a current diode. The proposed method reduces power dissipation, temperature rise and prevents damage of the drive system. [ABSTRACT FROM AUTHOR]

Aging effects in digital circuits change the switching characteristics of their transistors, resulting in timing violations that can lead to functional errors at the system level. In particular, bias temperature instability (BTI) is a degradation effect that changes the threshold voltage of transistors. Its effect is more prevalent as the scaling of transistor dimensions progresses. In this work, we present a method to enable defect-centric long-term modeling of BTI degradation that takes into account the effects of concrete workloads at the processor data path level. Based on this study, we propose a novel design flow to link the impact of BTI degradation at the transistor (${\Delta {}V_{\text {th}}}$), processor data path (e.g., maximum frequency) and application-functionality levels. This flow may be used to improve system correctness over the entire device lifetime, avoiding unsafe working points, or to achieve a graceful degradation of system characteristics. Our design flow is applicable to all types of digital circuits, including high-performance processors. However, in this specific work we focus on the domain of biosignal processing applications for wireless body sensor networks (WBSNs), the pseudoperiodic nature of which interacts with the partially recoverable nature of BTI. Our results in this domain show, for a 32-nm implementation, a variation of up to 54.6 mV in the threshold voltage of the circuit transistors after one year of continuous operation, with an impact of 8.4% in the maximum safe operating frequency. Such effects are expected to strongly worsen for longer lifetimes and more scaled technology nodes. [ABSTRACT FROM AUTHOR]

The growing adoption of electric vehicles has recently attracted increasing attention of scientists and brought about pioneering research studies from various fields. Safety concerns particularly those regarding ground faults detection and protection, have extensively been addressed. Ground faults occur quite frequently in electric vehicles and they may be due to severe operation conditions, such as vibrations, twists or even crashes. Generally, the first ground fault is not dangerous, since the powertrain systems, namely, the dc bus where the batteries are connected, the power inverter and one or more ac machines, are generally ungrounded. The second ground fault, however, can produce malfunction in some systems, power loss or even serious damages. Locating the fault has often proved hard and time consuming. For this reason, the present study focuses on developing a ground-fault detection method for electric vehicles capable of determining on which side, the dc or the ac, the ground fault is located. The method is based on the analysis of the voltage in a grounding resistor connected between the midpoint of the battery pack and ground. Based on the polarity and harmonics, it is possible to locate the ground fault. This method has been verified excellent results have been achieved using computer simulations and experimental tests in a 140-kW electronic power inverter fed by a 480 Vdc battery. [ABSTRACT FROM AUTHOR]

In this article, we propose an efficient circuit reliability analysis infrastructure utilizing on-demand transistor-accurate fault injection based on workload-specific distributional properties. A novel two-phase approach is developed to achieve circuit-level accuracy, via careful transistor-level precharacterization, and gate-level efficiency, via fast runtime fault generation. A time-consuming circuit characterization is performed once, and the result of the precharacterization is used multiple times at runtime to inject faults. Also, novel fault probability estimation and fault injection methods are developed. Fault probabilities are computed based on workload-specific voltage/temperature distribution, and faults are injected efficiently by scaling the computed fault probabilities. We demonstrate the proposed methodology on an OpenSPARC core targeting an implementation on a 32-nm technology node. Analysis indicates that the injector computes the system failure rate with 0.1-ms simulation overhead per injection while having circuit-level accuracy. [ABSTRACT FROM AUTHOR]

Jamming and fault injection attacks on memory arrays can be efficiently detected by quadratic-sum-based robust codes. When the number of levels in a memory cell is not a power of two, each memory cell corresponds to a fractional number of bits and a conversion circuit must be implemented to convert the binary input into a $q$ -ary word. This conversion expurgates the code and can degrade its error detection capability. In some cases, this unwanted degradation can be minimized by careful design of the converter but in other cases this is impossible. This brief presents bounds that help determine whether for a $q$ -ary memory width and required security level, a security enhancing converter design exists. [ABSTRACT FROM AUTHOR]

A series ac arc fault detection method based only on the voltage waveform measured at the power source is implemented. A relay de-energizes the arc fault after receiving an arc fault detected signal. Traditional arc fault detection methods use the current waveform, which may have false alarms with loads, such as electric hand drills (which intentionally generate small electric arcs) and dimmer switches because they show similar energy characteristics to an arc fault. However, in the proposed voltage-based method, a symmetric energy profile is observed, unique to the arc fault, which allows improved detection. This symmetry in the voltage waveform is exploited to derive threshold values from the sample energy that are eventually used to detect the ac arc fault. The approach avoids non-stationary signal analysis, machine learning, and advanced filtering techniques. The experimental results validate that the algorithm can successfully detect an arc fault and avoid common unwanted tripping events. Finally, devices, such as surge protectors will be able to integrate this detection algorithm without the addition of current sensors. [ABSTRACT FROM AUTHOR]

This paper reports an experimental feasibility study of the potential for using Fibre Bragg Grating (FBG) thermal sensing technology for electrical fault detection in random wound coils. The study is performed on prototype test coils housed in a purpose built steel core section. The FBG thermal sensors are embedded between copper conductors in multiple positions within the coil structure and in close proximity of the known hot spots of interest. The examined coils are designed to enable emulation of hot spots representative of those that would be expected at the outset of winding fault events. A series of experiments were separately conducted in order to characterise the potential of coil internal thermal stress monitoring to provide recognition of a developing winding fault. To this end, winding fault scenarios with different severity levels of inter-turn fault are experimentally examined. The reported findings demonstrate the potential of the proposed in-situ thermal sensing scheme to enable monitoring and recognition of coil internal thermal stress induced at various stages of emulated electrical fault.

This two-part paper presents a method to mitigate half-cycle saturation of transformers caused by monopolar operation of neighboring HVDC transmission lines while keeping the ability to detect the zero-sequence fault currents (ZSFC) when ground faults occur. Part I of this paper has presented the mitigation principles and device design of the proposed neutral current blocking switch. In Part II, the performance of the proposed method to permit the circulation of zero-sequence current is investigated. An operation strategy is proposed that simultaneously allows the mitigation of half-cycle saturation and the detection of ZSFC. Simulations on a widely-used 500 kV system show that the proposed mitigation technique, using a sub-synchronous switching frequency (no higher than 30 Hz), can effectively mitigate the half-cycle saturation while allowing the circulation of ZSFC. The novel mitigation method exploits the characteristic differences between half-cycle saturation (dc) and asymmetric faults (ac). The method provides an implementable solution to the dc-bias phenomenon because it delivers concurrently dc-bias mitigation, minimal impact on ground fault detection, and no switching stresses on the power electronic switches. The proposed technique can also be applied to the mitigation of geomagnetically induced currents. [ABSTRACT FROM AUTHOR]

This brief considers the problem of fault detection and isolation using the geometric approach for discrete-time systems. The description of unobservable subspace is presented. The residuals are generated by the filter via employing the geometric technique so that each residual is influenced by a specific fault and uncoupled from others. Sufficient conditions are established such that the $ {\mathcal {H}}_{\infty }$ norm of the transfer function is less than a pre-given value. In order to reduce computational cost, an event-triggered mechanism is presented to determine whether the current data should be released or not. Finally, numerical demonstration is given to verify the usefulness of the proposed new design technique. [ABSTRACT FROM AUTHOR]

This paper proposes a Multi-task Logistic Low-Ranked Dirty Model (MT-LLRDM) for fault detection in power distribution networks by using the distribution Phasor Measurement Unit (PMU) data. The MT-LLRDM improves the fault detection accuracy by utilizing the similarities in the fault data streams among multiple locations across a power distribution network. The captured similarities supplement the information to the task of fault detection at a location of interest, creating a multi-task learning framework and thereby improving the learning accuracy. The algorithm is validated with real-time PMU streams from a hardware-in-the-loop testbed that emulates real field communication and monitoring conditions in distribution networks. The results showed that the MT-LLRDM outperforms other state-of-the-art classification methods using actual synchrophasor data achieved from a power hardware-in-the-loop testbed. [ABSTRACT FROM AUTHOR]

This paper reports an experimental feasibility study of the potential for using Fibre Bragg Grating (FBG) thermal sensing technology for electrical fault detection in random wound coils. The study is performed on prototype test coils housed in a purpose built steel core section. The FBG thermal sensors are embedded between copper conductors in multiple positions within the coil structure and in close proximity of the known hot spots of interest. The examined coils are designed to enable emulation of hot spots representative of those that would be expected at the outset of winding fault events. A series of experiments were separately conducted in order to characterise the potential of coil internal thermal stress monitoring to provide recognition of a developing winding fault. To this end, winding fault scenarios with different severity levels of inter-turn fault are experimentally examined. The reported findings demonstrate the potential of the proposed in-situ thermal sensing scheme to enable monitoring and recognition of coil internal thermal stress induced at various stages of emulated electrical fault. [ABSTRACT FROM AUTHOR]

Three-phase dual active bridge (DAB) topology is a potential alternative for high-power applications when a compact and efficient converter with a bidirectional power transfer capability is desired. In a constructed prototype, high-power SiC modules with dedicated drivers are utilized to achieve high-efficiency and compact size. Each module has two interconnected switches with anti-parallel diodes resembling a converter leg. It is observed that the driver halts the module operation as a result of protective actions such as overcurrent, gate undervoltage, or gate overvoltage. In this frozen leg mode, the module operates as a leg with two diodes until an external hardware signal resets the driver. The converter continues operation but with a reduced performance. Analysis, simulation, and verification of a three-phase DAB converter under a frozen leg operation are considered in this paper. The converter with a frozen leg has two different behaviors at light loads and heavy loads. Consequently, two different analysis methods are developed to solve converter operation in different load conditions. Results show that the power transfer capability is reduced, but this fault mode is nondestructive. [ABSTRACT FROM AUTHOR]

The difficulties and uncertainties related to earth fault loop impedance measurement are addressed in this paper. Based on the presentation of the measurement procedure implemented in the test equipment (diagrams and measured quantities, respectively, interpretation of results), the shortcomings and errors that accompany it are highlighted. The position in the power system, the influence of power transformers, and the use of effective quantities instead of phasors are important sources of errors, but, as will be seen, the switching of loads at the consumer sides and/or the occurrence of fault regimes during measurements can lead to the most serious impairment of the accuracy in the impedance assessment. The clarification of these aspects is achieved, both starting from the equivalent diagrams of the measurement circuits and the analytical interpretation of the phenomena associated with the measurements, as well as based on the modeling and simulation of TN low-voltage electrical distribution networks, in a specialized program, Eaton xSpider, which allows the complete and complex analysis of a large number of scenarios. Thus, essential conclusions were drawn regarding the level of errors and their causes, obviously, with the research coming with solutions to be implemented at the level of the measurement protocols of the devices used.

This paper proposes an approach based on acoustic signals for detecting faults appearing in synchronous motors. Acoustic signals of a machine were used for fault detection. These faults contained: broken coils and shorted stator coils. Acoustic signals were used to assess the usefulness of early fault diagnostic of synchronous motors. The acoustic signal recognition system was based on methods of data processing: normalization of the amplitude, Fast Fourier Transform (FFT), method of frequency selection (MoFS), backpropagation neural network, classifier based on words coding, and Nearest Neighbor classifier. A plan of study of acoustic signals of synchronous motors was proposed. Software of acoustic signal recognition of synchronous motors was implemented. Four states of a synchronous motor were used in analysis. A pattern creation process was carried out for 28 training samples of noise. An identification process was carried out for 60 test samples. This system can be used to diagnose synchronous motors and other electrical machines.

Thermography is a technology that enables recognition of objects in the specific area. The goal of using thermographic techniques for ironworks is to diagnose electrical equipment. These techniques can be also use to increase safety and quality control in ironworks. Faulty equipment can be dangerous for engineers. Article describes the method of the recognition of imminent failure states of synchronous motor. Thermal images of the stator are used for an analysis of electrical machine. Researches of image processing techniques have been carried out for three states of motor. Proposed approach uses patterns recognition. Using of medial axis transformation and classifier based on words gave good results. In the future electrical machines and metallurgical equipment will use diagnostic systems based on recognition of thermal images.

With the unprecedented advancement of sensing technology, smart city applications are now enriched with massive measurement data related to system states, patterns, and the behavior of its users. However, classic data analysis or machine learning tools ignore some unique characteristics of the multistream measurement data, in particular, the coexistence of strong temporal correlation and interstream relatedness. To this end, in this paper we discuss the problem of novelty detection with multiple coevolving time series data. To capture both the temporal dependence and the interseries relatedness, a multitask nonparametric model is proposed, which can be extended to family of data distributions by adopting the notion of Bregman divergence. Albeit convex, the learning problem can be hard as the time series accumulate. In this regard, an efficient randomized block coordinate descent algorithm is proposed. The model and the algorithm is tested with a real-world application, involving novelty detection and event analysis in smart city power distribution networks with high-resolution multistream measurements. It is shown that the incorporation of interseries relatedness enables the detection of system-level events, which would otherwise be unobservable with traditional methods. The experimental results not only justify the benefits of incorporating information from different sources, but also demonstrate the potential of the proposed multistream analysis tool as one of the core computational components to improve smart city observability, security, and reliability. [ABSTRACT FROM AUTHOR]

A diagnostic test generation procedure targets fault pairs in a set of target faults with the goal of distinguishing all the fault pairs. When a fault pair cannot be distinguished, it prevents the diagnostic test set from providing information about the faults, and consequently, about defects whose diagnosis would have benefited from a diagnostic test for the indistinguishable fault pair. This is referred to in this paper as a diagnostic hole. This paper observes that it is possible to address diagnostic holes by targeting different but related fault pairs, possibly from a different fault model. As an example, this paper considers the case where diagnostic test generation is carried out for single stuck-at faults, and related bridging faults are used for addressing diagnostic holes. Considering fault detection, an undetectable single stuck-at fault implies that certain related bridging faults are undetectable. This paper observes that, even if a pair of single stuck-at faults is indistinguishable, a related pair of bridging faults may be distinguishable. Based on this observation, diagnostic tests for pairs of bridging faults are added to a diagnostic test set when the related single stuck-at faults are indistinguishable. Experimental results of defect diagnosis for defects that do not involve bridging faults demonstrate the importance of eliminating diagnostic holes. [ABSTRACT FROM AUTHOR]