Permanent Faults Research Articles

A novel variable neighborhood descent algorithm for service restoration in radial electrical distribution networks

Abstract This paper presents a variable neighborhood descent algorithm for the service restoration problem (SRP) in electrical distribution systems. The restoration problem appears when a permanent fault occurs in the system. The fault must be localized and isolated from the rest of the system. As a consequence of the isolation, the downstream area is de-energized and requires restoration. The variable neighborhood descent algorithm features its ability to solve the SRP for radial distribution systems and introduces a strategy that helps to deal with the system’s radiality. Tests are performed using a 53-node test system. The results show that the proposed algorithm can efficiently solve the SRP in a distribution system, and as a deterministic algorithm, the results can be used to help nondeterministic algorithms.

Assessment of reliability and availability of wireless sensor networks in industrial applications by considering permanent faults

Assessment of reliability and availability of wireless sensor networks in industrial applications by considering permanent faults

Anomaly detection of photovoltaic power generation based on quantile regression recurrent neural network

Distributed photovoltaic (PV) power generation systems are widely spread. Moreover, due to the randomness of meteorological conditions and the complexity of installation environments, it is difficult to eliminate the interference of factors such as meteorological fluctuations in the monitoring of abnormal states of PV equipment. Based on this, this paper proposes a PV power generation anomaly detection method based on Quantile Regression Recurrent Neural Network (QRRNN). First, the characteristics of solar irradiance on clear days are analyzed, and the clear day masking method is used to eliminate the interference of cloudy and rainy weather. Then, the output correlation of different power stations is analyzed to obtain PV stations with high output correlation as the horizontal reference, which is used to exclude interferences such as permanent faults at the power stations. At the same time, vertical comparison of the output curves of the station under test on different clear days is conducted to eliminate interference factors such as weather and environmental conditions. Subsequently, the metered active power output data, which is free from interference, is input into the QRRNN model to obtain the normal active power output range of the PV. The power threshold of the normal output range is utilized to identify anomalies in PV power generation. Finally, simulation analysis of actual PV system data is conducted, and the results show that the method can effectively identify PV power generation anomalies and has high accuracy in PV fault detection.

Power distribution system restoration based on soft open points and islanding by distributed generations

Abstract A powerful and efficient program for restoring the electrical distribution system by effectively utilizing the maximum capabilities within the system, including soft open points, distributed generation resources, and network configuration changes, can significantly reduce both the quantity and duration of lost loads caused by permanent faults. In this research study, we address the issue of distribution network restoration with a focus on soft open points (SOPs) and intentional islanding using distributed generations. This problem is formulated as a constrained optimization problem in order to determine the optimal distribution network configuration, quality of intentional islanding through DGs, control function of SOPs, and amount of load shedding. The objective is to minimize lost load while minimizing switching operations and preferably minimal islanding. Given that this problem involves multiple complexities such as combinatorial nature, mixed-integer variables, non-linearity, non-convexity along with numerous variables and constraints; we employ an evolutionary method known as simulated annealing (SA) algorithm to solve it without any simplifications or assumptions about convexity. To enhance efficiency during implementation of SA algorithm, Kruskal’s algorithm is utilized for generating radial solutions which restricts search space to feasible solutions resulting in quicker attainment of high-quality optimal solutions. Finally, the outcomes of implementing the suggested approach on the 69-bus IEEE distribution system are presented and examined. It is demonstrated that by leveraging the potential of soft open points in load transfer control and network voltage regulation, along with utilizing intentional islanding capability provided by distributed generation resources, the restoration capability of the distribution system can be greatly enhanced.

Reconfigurable Guidance Strategy for Compensating Actuator Faults in Spacecraft Formation Flying

The capacity to keep a desired topology with a requested accuracy plays a significant role in every spacecraft formation-flying operation. These missions can be terminated in case of an unexpected spacecraft fault, preventing the system from returning to its nominal configuration. This paper presents and tests a new recovery solution, called Reconfigurable Guidance Strategy (RGS), for the spacecraft formation-flying control problem subject to a look-in-place permanent thruster fault. The proposed method relies on autonomously and in real-time reconfiguring the guidance function to compensate for the loss of the spacecraft actuation system. The performance and cost of the RGS have been tested in a high-fidelity simulation scenario, the 42 spacecraft simulator developed by NASA Goddard Space Flight Center, taking into account orbital and rotational nonlinear coupled dynamics, high-order perturbation models, and actuator and sensor models. The numerical simulation results have demonstrated the proposed recovery strategy’s effectiveness, feasibility, and robustness.

Service restoration in distribution systems considering priority customers and microgrids

Service restoration (SR) consists of automatically generating and executing a plan to restore the service in healthy zones using the least number of maneuvers after detecting and isolating a permanent fault in the distribution system zone. This component is essential to self-healing functionality in smart grids and allows customers to reconnect quickly to the distribution grid after a power outage. Distributed generation (DG) supports the distribution network when there is insufficient capacity to restore all zones out of service or supply the loads locally through microgrids. The power supply must be restored to the highest priority customers in case of partial restoration. Also, most research works use simplified or linearized models to propose restoration algorithms. This paper proposes a complete AC formulation for the service restoration problem in distribution systems considering network reconfiguration (NR), the integration of distributed generation (DG), and priority customers (PCs) into the solution. The optimization problem is solved by a centralized algorithm based on combining the Differential Evolution (DE) and Continuous Population-Based Incremental Learning (PBILc) metaheuristics techniques. Simulation results are presented for three case studies in which the IEEE 33-bus distribution system is tested for different fault scenarios. The numerical results show the robustness and efficiency of the proposed algorithm.

Design and Implementation of a Reconfigurable Corrective Control System Subject to Permanent Faults in the Controller.

This article presents a reconfiguration strategy for the corrective controller achieving model matching control of an input/state asynchronous sequential machine (ASM). The considered controller is vulnerable to permanent faults that degenerate a subset of the controller's states. If the controller has a certain amount of redundancy in terms of its states, one can build a reconfiguration scheme in which the functionality of degenerated states is taken over by supplementary states. The proposed reconfiguration scheme is superior to conventional methods of fault tolerance with hardware redundancy since the required number of redundant states is much smaller. Hardware experiments on field-programmable gate array (FPGA) circuits are provided to validate the applicability of the proposed scheme. The present study serves as the first research report on the reconfigurable corrective controller.

A Self-Healing Based Smart Technique for Electric-supply Restoration Following a Permanent Fault in Smart Distribution Networks

A Self-Healing Based Smart Technique for Electric-supply Restoration Following a Permanent Fault in Smart Distribution Networks

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.

Intermittent Fault Diagnosis Of Product Network Based On PMC Model

Abstract Fault diagnosis of processors plays a critical role in assessing the reliability of multiprocessor systems. The interconnection network’s diagnosability is an important metric for measuring its self-diagnostic capability which has been extensively studied in many novel mutiprocessor systems. Permanent fault diagnosability for many mutiprocessor systems has been determined; however intermittent fault diagnosability is hard to obtain due to its crypticity. In this paper, we focus on the problem pertaining to the diagnosability in the intermittent fault situation. First, by learning the characteristics of intermittent fault diagnosis in PMC model, we propose some theorems and lemmas for intermittent fault diagnosability. Secondly, we give the range of intermittent fault diagnosability of product network. Lastly, by adopting the theorems, we propose an Auto-IFD algorithm to find the intermittent faults of the hypercube network and conduct experiments to verify the theorems.

Active-Control-Based Three-Phase Reclosing Scheme for Single Transmission Line With PMSGs

Electric devices may be damaged due to the strong secondary impact when the circuit breaker recloses on permanent faults, so it is significant to identify the fault nature to reduce the harm of grid faults. To achieve this point, a new control scheme is designed to make permanent magnet synchronous generator (PMSG) based wind turbines behave as a three-phase symmetrical voltage source, so the overcurrent can be detected for permanent faults when the circuit breaker on the PMSG side is reclosed, but only the tiny capacitance current is present for temporary faults. Therefore, the fault nature can be identified by detecting the current amplitude. To accurately extract the power-frequency phasors, a fast matrix pencil extraction method is adopted. This method solves the difficulty in fault nature detection caused by the rapid disappearance of current and voltage after a three-phase trip using the controllability of power electronics. Meanwhile, the severe secondary impact can be reduced since the injected current is small even for permanent faults. Additionally, the proposed method can ensure the fault current at the inverter terminal is within the limiting value and operate properly for remote high-resistance faults. Extensive simulations and experiments are performed to verify this method.

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.

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.

Path Planning for UAVs Under GPS Permanent Faults

Unmanned aerial vehicles (UAVs) have various applications in different settings, including e.g., surveillance, packet delivery, emergency response, data collection in the Internet of Things (IoT), and connectivity in cellular networks. However, this technology comes with many risks and challenges such as vulnerabilities to malicious cyber-physical attacks. This paper studies the problem of path planning for UAVs under GPS sensor permanent faults in a cyber-physical system (CPS) perspective. Based on studying and analyzing the CPS architecture of the UAV, the cyber “attacks and threats” are differentiated from attacks on sensors and communication components. An efficient way to address this problem is to introduce a novel approach for UAV’s path planning resilience to GPS permanent faults artificial potential field algorithm (RCA-APF). The proposed algorithm completes the three stages in a coordinated manner. In the first stage, the permanent faults on the GPS sensor of the UAV are detected, and the UAV starts to divert from its initial path planning. In the second stage, we estimated the location of the UAV under GPS permanent fault using Received Signal Strength (RSS) trilateration localization approach. In the final stage of the algorithm, we implemented the path planning of the UAV using an open-source UAV simulator. Experimental and simulation results demonstrate the performance of the algorithm and its effectiveness, resulting in efficient path planning for the UAV.

An Adaptive Reclosing Scheme for Cross-Line Faults on Double-Circuit Wind Power Outgoing Lines with Shunt Reactors

Wind turbines are vulnerable to negative sequence current injection, the conventional automatic reclosing scheme for wind power outgoing lines, since we may inject negative sequence components into the system and reclose it without distinguishing the nature of the fault through rectification. Moreover, reclosing in permanent faults could induce a secondary impact on the system. To solve the above problems, an adaptive reclosing scheme for cross-line faults on double-circuit wind power outgoing lines with shunt reactors is proposed. Firstly, a new tripping strategy and a single-side partial-phase reclosing method are proposed for multiple types of outgoing line faults, while, simultaneously, phase-to-phase coupling loops are established. Secondly, the criteria of fault nature are established based on the fault phase shunt reactor current and fault phase voltage characteristics, and transient faults are rapidly and accurately distinguished from permanent ones according to the criteria. Finally, the theoretical derivation and simulation experiments are conducted on the PSCAD/EMTDC platform to demonstrate that the proposed adaptive reclosing method is applicable to avoid the injection of negative sequence currents into wind turbines. Meanwhile, the success rate of reclosing for wind power outgoing line is significantly improved and the continuity of power transmission on the wind farm(s) is ensured.

Comprehensive fault diagnosis in UAV-assisted sensor networks: A three-phase automated approach

Ensuring operational safety in an automated working environment is a crucial requirement. In this article, we propose a technique for automated fault diagnosis in unmanned aerial vehicle-based sensor networks (UAV-sensor networks) aimed explicitly at the composite nature of different faults. Our technique consists of 3-stages: (1) UAVs collecting data from sensors and running a modified Z-score statistics to diagnose faulty readings; (2) the UAV cluster head running a multivariate analysis of variance (MANOVA) to identify data faults in cluster members; and (3) utilizing a probabilistic neural network with correlation centroids at the ground control station for further diagnosis of collected UAV data. This approach is intended for progressive improvement in fault diagnosis at different stages of data collection in a UAV-sensor network for critical applications. We conduct an extensive evaluation of our 3-stage approach on the data collected in real-time from the sensors in a physically created UAV-sensor network. The performance of the proposed modified Z-score test improves fault detection accuracy of ∼20.8%, false alarm rate of ∼54.3%, and false positive rate of ∼67.2% with the traditional and existing sensor fault detection approach. The performance of MANOVA to identify the permanent UAV data fault shows fault detection accuracy of ∼10.9%, false alarm rate of ∼44.3%, and false positive rate of ∼48.7% superior to the intermittent and transient faults. Our preliminary results suggest that the proposed approach can handle multiple composite faults and outperforms existing research results.

An Active Injected Location Method for DC Fault of Wind Power Based on Parallel Energy Absorption Module

In recent years, the widespread adoption of wind power has been facilitated by the rapid advancements in new energy generation technology. The precise identification and efficient management of DC faults in onshore wind farms has gained increasing significance. To address issues such as limited functionality, low device utilization, and the inefficient use of fault energy prevalent in current fault location methods applied in wind power flexible grid-connected systems, this study explores the storage and reuse technology of fault energy. The article proposes a topology structure designed to absorb and reuse fault energy under multiple DC faults, aiming to overcome the challenges posed by single-function methods. The structure and control strategy of this topology are clearly delineated, and the design methodology for topology electrical parameters is thoroughly investigated. Simulation studies were conducted to validate the electrical characteristics of the employed devices and assess the operational effectiveness of the proposed topology within wind power grid-connected systems. In comparison with conventional fault location methods, the proposed scheme exhibits multiple energy absorption functions for DC faults and fault location capabilities, especially in the presence of permanent DC faults. As a result, this approach maximizes the utilization of DC fault energy, ensuring higher device efficiency and a broader spectrum of functionalities.

Fault prognosis for linear stochastic systems with intermittent fault and strong noise via health indicator extraction approach

AbstractIn this paper, the problem of fault prognosis is investigated for linear stochastic systems with intermittent faults (IFs) and strong noise. Different from permanent faults, the degradation state of IFs are related to the fault duration time, leading to difficulties in the first step of fault prognosis, that is, the health indicator (HI) extraction. Generally, HI extraction of IFs is achieved by fault detection techniques. Therefore, the proposed fault prognosis approach mainly covers the following steps. First, the HI and degradation model are introduced for linear stochastic systems with IFs. Then, considering dynamic characteristic of systems and influence of strong noise, an IF detection algorithm under strong noise is proposed using weighted moving average methods to extract the HI. Moreover, an alternative HI extraction approach is given by an HI estimation way, which avoids using fault detection techniques. Subsequently, the degradation model is fitted by virtue of HIs and the least square method, and the remaining useful life is predicted with the aid of the obtained model. Finally, an experiment concerning the rotary steerable drilling tool system is provided to verify the effectiveness of the derived results.

Solar and Wind Data Recognition: Fourier Regression for Robust Recovery

Accurate prediction of renewable energy output is essential for integrating sustainable energy sources into the grid, facilitating a transition towards a more resilient energy infrastructure. Novel applications of machine learning and artificial intelligence are being leveraged to enhance forecasting methodologies, enabling more accurate predictions and optimized decision-making capabilities. Integrating these novel paradigms improves forecasting accuracy, fostering a more efficient and reliable energy grid. These advancements allow better demand management, optimize resource allocation, and improve robustness to potential disruptions. The data collected from solar intensity and wind speed is often recorded through sensor-equipped instruments, which may encounter intermittent or permanent faults. Hence, this paper proposes a novel Fourier network regression model to process solar irradiance and wind speed data. The proposed approach enables accurate prediction of the underlying smooth components, facilitating effective reconstruction of missing data and enhancing the overall forecasting performance. The present study focuses on Midland, Texas, as a case study to assess direct normal irradiance (DNI), diffuse horizontal irradiance (DHI), and wind speed. Remarkably, the model exhibits a correlation of 1 with a minimal RMSE (root mean square error) of 0.0007555. This study leverages Fourier analysis for renewable energy applications, with the aim of establishing a methodology that can be applied to a novel geographic context.