Transient Faults Research Articles

An overview of machine learning applications in elastic power systems

Abstract. With the development of economy and the access of renewable energy, the power system is facing new challenges. Machine learning provides a new way for the intelligent management of power system through big data analysis, pattern recognition and predictive modeling. This paper reviews the application of machine learning technology in elastic power system, including load forecasting, transient stability assessment, intelligent scheduling and fault diagnosis. Machine learning will better improve the stability, reliability and economic benefit of the system. Future research will focus on algorithm optimization, model adaptability improvement and integration with traditional power system knowledge to promote the development of power system to a higher level of intelligence and automation.

Enhancing HVDC transmission line fault detection using disjoint bagging and bayesian optimization with artificial neural networks and scientometric insights

DC grid fault protection techniques have previously faced challenges such as fixed thresholds, insensitivity to high-resistance faults, and dependency on specific threshold settings. These limitations can lead to elevated fault currents in the grid, particularly affecting multi-modular converters (MMCs) vulnerability to large fault current transients. This paper proposes a novel approach that combines the disjoint-based Bootstrap Aggregating (Bagging) technique and Bayesian optimization (BO) for fault detection in DC grids. Disjoint partitions reduce variance and enhance Ensemble Artificial Neural Network (EANN) performance, while BO optimizes EANN architecture. The proposed approach uses multiple transient periods instead of a fixed time to train the model. Transient periods are segmented into multiple 1 ms intervals, and each interval trains a separate neural network. In this way, a robust local relay is created that does not require high-speed communication systems. Additionally, a discrete wavelet transform (DWT) is applied to select detailed coefficients of the transient fault current, measured at the DC line’s sending terminal for fault protection. EANN is trained in comprehensive offline data that considers noise impact. Simulation results demonstrate the scheme’s ability to detect faults as high as 400 Ω accurately. This makes it a robust, reliable, and effective solution for fault detection on high-voltage direct current (HVDC) transmission lines. Lastly, this research provides the first-ever scientometric analysis of HVDC transmission line fault protection using neural network algorithms, highlighting major research themes and trends. The scientometric analysis was based on a dataset of 136 available research articles from the Scopus database from the last ten years. Therefore, this research provides valuable insights into the use of ANN for HVDC transmission line fault protection.

Dynamic Analysis and Approximate Solution of Transient Stability Targeting Fault Process in Power Systems

Modern power systems are high-dimensional, strongly coupled nonlinear systems with complex and diverse dynamic characteristics. The polynomial model of the power system is a key focus in stability research. Therefore, this paper presents a study on the approximate transient stability solution targeting the fault process in power systems. Firstly, based on the inherent sinusoidal coupling characteristics of the power system swing equations, a generalized polynomial matrix description in perturbation form is presented using the Taylor expansion formula. Secondly, considering the staged characteristics of the stability process in power systems, the approximate solutions of the polynomial model during and after the fault are provided using coordinate transformation and regular perturbation techniques. Then, the structural characteristics of the approximate solutions are analyzed, revealing the mathematical basis of the stable motion patterns of the power grid, and a monotonicity rule of the system’s power angle oscillation amplitude is discovered. Finally, the effectiveness of the proposed methods and analyses is further validated by numerical examples of the IEEE 3-machine 9-bus system and IEEE 10-machine 39-bus system.

A self‐stabilizing distributed algorithm for the 1‐MIS problem under the distance‐3 model

SummaryFault‐tolerance and self‐organization are critical properties in modern distributed systems. Self‐stabilization is a class of fault‐tolerant distributed algorithms which has the ability to recover from any kind and any finite number of transient faults and topology changes. In this article, we propose a self‐stabilizing distributed algorithm for the 1‐MIS problem under the unfair central daemon assuming the distance‐3 model. Here, in the distance‐3 model, each process can refer to the values of local variables of processes within three hops. Intuitively speaking, the 1‐MIS problem is a variant of the maximal independent set (MIS) problem with improved local optimizations. The time complexity (convergence time) of our algorithm is steps and the space complexity is bits, where is the number of processes. Finally, we extend the notion of 1‐MIS to ‐MIS for each nonnegative integer , and compare the set sizes of ‐MIS () and the maximum independent set.

Control of Grid-Tied Inverter During Unbalanced Transient Fault

With increasing use of renewable and clean form of energy, PV systems have become popular due to its versatility and easy implementation. The overall performance of grid-connected PV systems is directly influenced by the performance of grid-tied inverter. Hence, control of grid-tied PV inverter has become a temptation. This project explores the development of grid-connected PV system and optimization of control strategies for grid-tied inverter to ensure its seamless operation and grid stability during transient faults. The transient faults occurring at the inverter side disrupts the normal operation of inverter and even lead to shut down of inverter during faults.Therefore, a control strategy is suggested and developed to control the overvoltage of DC-link capacitor and deliberately functioning and output of grid-tied inverter is improved. This is simply possible by introducing ELC so that the inverter needs not be shut down.

Self-stabilizing multivalued consensus in asynchronous crash-prone systems

The multivalued consensus problem is a fundamental issue in fault-tolerant distributed computing. It encompasses a wide range of agreement problems where processes must unanimously decide on a specific value v∈V, with |V|≥2. Existing solutions that handle process crash failures simplify the multivalued consensus problem by reducing it to the binary consensus problem. Examples of such solutions include Mostéfaoui-Raynal-Tronel [IPL 2000] and Zhang-Chen [IPL 2009].In this work, we aim to design an even more reliable solution by leveraging the concept of self-stabilization, which provides a strong form of fault tolerance. Self-stabilizing algorithms can recover from transient faults, which represent any deviation from the system's intended behavior (as long as the algorithm code remains intact) in addition to process and communication failures.To the best of our knowledge, this work presents the first self-stabilizing algorithm for multivalued consensus in asynchronous message-passing systems susceptible to process failures and transient faults. Our solution uses, at most, n concurrent invocations of binary consensus. This is another way we advance state-of-the-art solutions compared to previous non-self-stabilizing ones. For example, Mostéfaoui-Raynal-Tronel's solution requires an unbounded number of sequential invocations of binary consensus.

Transient Fault Current Calculation Method for the PV Grid-Connected System Considering Dynamic Response of PLL

Transient Fault Current Calculation Method for the PV Grid-Connected System Considering Dynamic Response of PLL

High impedance fault detection method based on improved Mayr Arc model and fitting curve coefficients

To solve the problem of weak transient fault currents and significant nonlinear characteristics caused by arcs in resonant grounding systems, proposing a high-impedance fault (HIF) detection method based on an improved Mayr arc model and fitting curve coefficients. Firstly, to address the issue of low accuracy in simulating fault waveforms in existing HIF models, construct an improved arc model based on Mayr and control the parameter values under different feature dimensions, achieving accurate simulation of arc waveforms. Secondly, based on the impedance characteristics of the feeder, analyze the phase frequency characteristics, determine the first capacitive frequency band, and then determine the cutoff frequency of the Chebyshev filter to improve the ability of waveform feature extraction and fault recognition. Finally, by fitting the voltage and current geometric characteristic curve within the characteristic frequency band, using the least squares method and the quadratic coefficient values of the fitting curve to construct detection criteria, accurate detection of HIF is possible. Research shows that the improved arc model can fully characterize the current arc waveform under various working conditions. Simulations and real-world measurements verify that HIF can be accurately detected using the quadratic coefficient value of the fitting curve.

Unsupervised Transfer Learning Method via Cycle-Flow Adversarial Networks for Transient Fault Detection under Various Operation Conditions.

The efficient fault detection (FD) of traction control systems (TCSs) is crucial for ensuring the safe operation of high-speed trains. Transient faults (TFs) can arise due to prolonged operation and harsh environmental conditions, often being masked by background noise, particularly during dynamic operating conditions. Moreover, acquiring a sufficient number of samples across the entire scenario presents a challenging task, resulting in imbalanced data for FD. To address these limitations, an unsupervised transfer learning (TL) method via federated Cycle-Flow adversarial networks (CFANs) is proposed to effectively detect TFs under various operating conditions. Firstly, a CFAN is specifically designed for extracting latent features and reconstructing data in the source domain. Subsequently, a transfer learning framework employing federated CFANs collectively adjusts the modified knowledge resulting from domain alterations. Finally, the designed federated CFANs execute transient FD by constructing residuals in the target domain. The efficacy of the proposed methodology is demonstrated through comparative experiments.

Fault Handling and Localization Strategy Based on Waveform Characteristics Recognition with Coordination of Peterson Coil and Resistance Grounding Method

To address challenges in locating high-impedance grounding faults (HIGFs) and isolating fault areas in resonant grounding systems, this paper proposes a novel fault identification method based on coordinating a Peterson coil and a resistance grounding system. This method ensures power supply reliability by extinguishing the fault arc during transient faults with the Peterson coil. When a fault is determined to be permanent, the neutral point switches to a resistance grounding mode, ensuring regular distribution of zero-sequence currents in the network, thereby addressing the challenges of HIGF localization and fault area isolation. Fault calibration and nature determination rely on recognizing neutral point displacement voltage waveforms and dynamic characteristics, eliminating interference from asymmetric phase voltage variations. Fault area identification involves assessing the polarity of zero-sequence current waveforms attenuation during grounding mode switching, preventing misjudgments in grounding protection due to random initial fault angles and Peterson coil compensation states. Field experiments validate the feasibility of this fault location method and its control strategy.

Virtualized Fault Injection Framework for ISO 26262-Compliant Digital Component Hardware Faults

Simulation-based Fault Injection (FI) is crucial for validating system behaviour in safety-critical applications, such as the automotive industry. The ISO 26262 standard’s Part 11 extension provides failure modes for digital components, driving the development of new fault models to assess software-implemented mechanisms against random hardware failures (RHF). This paper proposes a Fault Injection framework, QEFIRA, and shows its ability to achieve the failure modes proposed by Part 11 of the ISO 26262 standard and estimate relevant metrics for safety mechanisms. QEFIRA uses QEMU to inject permanent and transient faults during runtime, whilst logging the system state and providing automatic post-execution analysis. Complemented with a confusion matrix, it allows us to gather standard compliant metrics to characterise and evaluate different designs in the early stages of development. Comparatively to the native QEMU implementation, the tool only shows a slowdown of 1.4× for real-time microcontroller-based applications.

An efficient deep learning based scheme for adaptive auto-reclosing in power transmission lines

As a large percentage of faults in transmission lines are single-line-to-ground (SLG) and transient in nature, the use of adaptive single-phase auto-reclosing (ASPAR) schemes is essential. In this paper, a new scheme for ASPAR is proposed to improve the stability and reliability of power transmission lines. The proposed approach distinguishes between the transient and permanent faults by using the feature of Stationary Wavelet Transform (SWT) and Gate Recurrent Unit (GRU) deep artificial neural network. Also, by utilizing another GRU network, the proposed scheme predicts the extinction time of the secondary arc (SA) one power cycle before the complete extinction. This can increase the speed of the faulty phase reclosing in the case of a transient fault. Simulation results carried out on a 400 kV power transmission line in the EMTP-RV and MATLAB software environments illustrate that the F1 score of the proposed ASPAR in classifying the faults is 99.55 %, and its average error in predicting the extinction time of the SA based on the root mean square error (RMSE) criterion is 0.0557. Also, the superiority and high-quality performance of the proposed protection scheme are demonstrated by comparing the results with several state-of-the-art methods.

Energy Management for Fault-tolerant (m,k)-constrained Real-time Systems That Use Standby-Sparing

Fault tolerance, energy management, and quality of service (QoS) are essential aspects for the design of real-time embedded systems. In this work, we focus on exploring methods that can simultaneously address the above three critical issues under standby-sparing. The standby-sparing mechanism adopts a dual-processor architecture in which each processor plays the role of the backup for the other one dynamically. In this way, it can provide fault tolerance subject to both permanent and transient faults. Due to its duplicate executions of the real-time jobs/tasks, the energy consumption of a standby-sparing system could be quite high. With the purpose of reducing energy under standby-sparing, we proposed three novel scheduling schemes: The first one is for (1, 1)-constrained tasks, and the second one and the third one (which can be combined into an integrated approach to maximize the overall energy reduction) are for general ( m,k )-constrained tasks that require that among any k consecutive jobs of a task no more than ( k - m ) out of them could miss their deadlines. Through extensive evaluations and performance analysis, our results demonstrate that compared with the existing research, the proposed techniques can reduce energy by up to 11% for (1, 1)-constrained tasks and 25% for general ( m,k )-constrained tasks while assuring ( m,k )-constraints and fault tolerance as well as providing better user perceived QoS levels under standby-sparing.

Grid-Impedance-Based Transient Current Control for Offshore Wind Turbines under Low-Voltage Fault

In order to enhance the transient stability of offshore wind turbines (OWTs) in marine energy systems, the grid codes stipulate that OWTs should possess the low-voltage ride-through (LVRT) ability of being grid-tied and injecting reactive current during grid fault. However, the grid-side converter (GSC) of OWTs may lose stability under weak grid or severe fault conditions due to inaccurate current references. To address this issue, a novel transient current control method is proposed to improve the transient stability of permanent-magnet-synchronous-generator (PMSG)-based OWTs. The feature of DC-link overvoltage is investigated and is alleviated by utilizing the GSC’s overcurrent capacity and chopper. Additionally, the equivalent circuit of the PMSG-based OWT connected to the onshore grid is derived based on Thevenin’s theorem. The feasible current region (FCR) is then determined, taking into account the GSC capacity, pre-fault power ability, LVRT requirement, and synchronization stability. Furthermore, a grid-impedance-based transient current control method is designed to enhance the fault ride-through performance and mitigate power oscillation of the OWT under various transient grid impedance and fault conditions. Finally, a simulation model is conducted using PSCAD v4.6.3 software to validate the effectiveness of the proposed method.

Self-stabilizing synchronous unison in directed networks

Self-stabilization is a general paradigm that characterizes the ability of a distributed system to recover from transient faults. Since its introduction by Dijkstra in 1974, self-stabilization has been successfully applied to efficiently solve many networking tasks. However, most of the literature focuses on bidirectional networks. Now, in today's networks such as WSNs, some communication channels may be one-way only. Considering such network topologies, a.k.a. directed graphs, makes self-stabilization more complicated, and sometimes even impossible. In this paper, we investigate the gap in terms of requirements and efficiency when considering a directed graph instead of an undirected one as network topology for a self-stabilizing algorithm. Our case study is a variant of a synchronous unison algorithm proposed by Arora et al.; the synchronous unison being a clock synchronization problem.

Multi-stage voltage sag frequency evaluation based on process immunity in the distribution network

Abstract The reclosing action of the distribution network may cause multi-stage voltage sag in the grid nodes, which leads to more serious losses for sensitive users. In order to more reasonably characterize the disturbance degree of multi-stage voltage sag on sensitive equipment, a frequency evaluation method based on process immune time (PIT) and improved analytic hierarchy process is proposed. Firstly, multi-stage voltage sag waveforms caused by the reclosing corresponding action under transient fault and permanent fault are analyzed, and the PIT characteristic curve of sensitive equipment under multi-stage voltage sag is characterized. Secondly, the weight function method is used to calculate the influence degree of each stage sag. The analytic hierarchy process is improved by utilizing the correlation and influence degree of each stage sag. The weight of each stage sag is determined, and then the amplitude of the multi-stage voltage sag is calculated equivalently. The multi-stage voltage sag frequency evaluation method is proposed in the distribution network. Finally, the IEEE 30-bus system is used for simulation verification. The results show that the proposed method is more accurate and reasonable for the evaluation of multi-stage voltage sag frequency. This method provides a basis for the frequency evaluation and governance of multi-stage voltage sag.

A new differential protection scheme based on Synchro-squeezing transform applied to AC Micro-Grid

In recent years, the protection of a microgrid has been a substantial challenge. This paper performs a differential protection scheme for the micro-grid based on Synchro-Squeezing Transform (SST). The proposed scheme protects the micro-grid in different modes i.e. islanded or grid-connected mode. Also, it is investigated in the ring and radial configuration. In the proposed scheme, currents on two sides of the line are measured by current transformers (CTs), and then differential current (IDiff) is calculated. This current is applied to extract the absolute value of the summation of the square root (ASSR). In this stage, transient and external faults without CT saturation and different switching such as connected or disconnected load/capacitor/distributed generation are detected from different internal faults. In the next stage, the frequency components of SST (FCSST) are extracted. By this feature, external fault and cross-country fault with CT saturation are detected. The proposed scheme is evaluated in noisy conditions, fault in different section of line with high resistance fault (HRF), and high impedance faults (HIF) of line. The micro-grid is simulated in PSCAD/EMTDC by various distributed generation (DG) like wind generation and photovoltaic systems penetrated at different buses. The proposed protection scheme is coded in MATLAB. The simulations represent the proposed protection scheme is correctly discriminate external and internal faults. At the same time, the method is immune to CT saturation and cross-country faults.

Safety hysteresis comparator design for transient overvoltage detection

This research introduces a safety hysteresis comparator that detects transient overvoltage in the track circuit relay interlocking of railway signaling system. This overvoltage is caused by voltage faults transmitted through the electric conductor on the track feed unit to the receiver equipped with the track relay, which acts as the occupied track circuit controller. The circuit was designed using safety system design principles and concepts. Findings illustrated that the transient overvoltage detection of the safety hysteresis comparator in the track circuit activated when the input voltage (Vin) was higher than the high hysteresis signal level (Vhyst_H). When Vin was less than low hysteresis signal level (VVhyst_L), the output voltage (Vo) state was low. Otherwise, it was high. The hysteresis voltage was 4.4 V. The installation of the transient overvoltage detector in the track circuit was to monitor the transient overvoltage fault in the track circuit and to confirm that the hysteresis comparator was in the safety failure mode, which was the safety function for the track circuit to be compliant with the IEC 61800-5-2 standard to ensure the system stability and reliability. It would maximize the performance of the controlling function and commanding of train arrangement of State Railway of Thailand (SRT).

Method for lightning flashover types identification on transmission lines utilizing OPGW optical polarization state signal characteristics

The Optical Fiber Composite Overhead Ground Wire (OPGW) has undergone rapid development and has been extensively applied in the realm of intelligent power system status monitoring. This article conducts research on lightning signal waveform in optical fibers within the context of lightning fault location using the OPGW optical polarization state method. A transmission line model is simulated employing ATP-EMTP to capture electrical characteristics when lightning strikes the OPGW. Subsequently, by employing the Faraday magneto-optical effect principle, electrical signals are transformed into optical polarization states within the internal fibers of OPGW. Using such data, the optical signal propagation characteristics when lightning strikes OPGW are deduced. Finally, a method for discerning between induced lightning and direct lightning strikes through the analysis of time-frequency domain characteristics in OPGW. This method addresses the limitation of traditional OPGW lightning monitoring, which is unable to deduce electrical signals, thus achieving precise identification of lightning-induced transient faults in transmission lines.

Novel second zone acceleration technique for distance relay utilizing sequence voltages

An algorithm for second zone acceleration of the distance relay utilising the sequence voltages along with healthy phases impedances is presented in this paper. Specifically, the algorithm employs variation in sequence voltage ratio indices to sense the single-pole remote circuit breaker operation (RCBO), while utilising both magnitude and angle of the healthy phase impedance for sensing three-pole RCBO operation. Additionally, the developed algorithm can also identify the faulty phases based on variations in impedance magnitude. To avoid false-trip during transient faults, impedance of the faulted phase before RCBO is compared with its value after RCBO. Performance of the proposed approach is validated for various internal and external faults covering addition of parallel link, presence of noise, different load angle, fault location, fault inception angle, fault impedance, source impedance and errors in measurement/transmission line parameters on an existing 230 kV transmission line modelled in PSCAD/EMTDC software. The results, obtained from both simulation as well as Hardware in Loop (HIL) simulation through laboratory prototype, designate that the proposed approach performs appropriately regardless of power flow direction, fault resistance magnitude, and light load conditions. The results also reveal that the suggested approach is able to accelerate trip within a cycle after RCBO event, which is comparable to or faster than many existing methods. Compared to several existing approaches in the literature, it also outperforms in terms of sensitivity, reliability and selectivity.