Fault Detection Scheme Research Articles

A reliable protection scheme for high resistance fault detection in wind generator-integrated HVDC transmission system using ensemble of kNN

A reliable protection scheme for high resistance fault detection in wind generator-integrated HVDC transmission system using ensemble of kNN

Fault Detection Strategy of Partial Least Squares Based on Temporal Neighborhood Difference

ABSTRACTAiming at the difficulty of detecting time‐lag faults in dynamic processes, a fault detection strategy based on time neighborhood difference (TND) is proposed, and it is introduced into the partial least squares (PLS) method to suggest the PLS‐TND fault detection method. The TND method takes the mean to the multibatch training set to obtain a baseline training set, and it constructs the mean squared Euclidean distance (MSED) statistic by calculating the average distance between the sample's first k‐moments neighborhood samples and samples at the same moment in the baseline training set. The TND method can help the PLS method to effectively detect time‐lag faults and significantly improve the fault detection capability of PLS by measuring the overall positional difference between the temporal neighborhood sample set of the sample and its temporal neighborhood sample set in the baseline training set. The PLS‐TND method is compared with some classical fault detection methods through a numerical simulation process and a Continuous Stirred Tank Reactor (CSTR) system design fault detection experiment. The PLS‐TND method gives the best performance of fault detection.

Quality-related fault detection for dynamic process based on quality-driven long short-term memory network and autoencoder

Fault detection consistently plays a crucial role in industrial dynamic processes as it enables timely prevention of production losses. However, since industrial dynamic processes become increasingly coupled and complex, they introduce uneven dynamics within the collected data, posing significant challenges in effectively extracting dynamic features. In addition, it is a tricky business to distinguish whether the fault that occurs is quality-related or not, resulting in unnecessary repairing and large losses. In order to deal with these issues, this paper comes up with a novel fault detection method based on quality-driven long short-term memory and autoencoder (QLSTM-AE). Specifically, an LSTM network is initially employed to extract dynamic features, while quality variables are simultaneously incorporated in parallel to capture quality-related features. Then, a fault detection strategy based on reconstruction error statistic squared prediction error (SPE) and the quality monitoring statistic Hotelling T2 (H2) is designed, which can distinguish various types of faults to realize accurate monitoring for dynamic processes. Finally, several experiments conducted on numerical simulations and the Tennessee Eastman (TE) benchmark process demonstrate the reliability and effectiveness of the proposed QLSTM-AE method, which indicates it has higher accuracy and can separate different faults efficiently compared to some state-of-the-art methods.

Time-Domain Fault Detection and Location Scheme for Flexible DC Distribution Networks

Accurately detecting and locating the fault point of the DC line is significant for eliminating the fault and restoring the power supply of the flexible DC distribution network as soon as possible. Firstly, a direction pilot protection scheme for a complex DC distribution network is proposed based on the integral of the current superposition to identify the fault direction. Then, an online time-domain fault location method based on the least square (LS) method to solve the overdetermined equations is proposed by analyzing the fault loop circuit after the DC line fault. The proposed location scheme utilizes fault data at both ends of the line to eliminate the theoretical impact of fault resistance, and the calculation of the current difference method is discussed to reduce the location error of whether the fault current-limiting reactor (CLR) exists. Finally, various simulations by PSCAD/EMTDC V4.5 demonstrate that the proposed scheme has high protection reliability and location results after different fault positions and resistances. The proposed scheme has low requirements for the sampling rate, fault data length, and implementation costs, which can meet practical application requirements.

Comprehensive Analysis of Faults and Diagnosis Techniques in Cascaded Multi-Level Inverters

Reliability is a crucial factor to consider for multi-level inverters (MLIs) used in industrial applications. With the increasing number of power semiconductor devices, the potential for defects to significantly degrade the overall system is heightened. A highly effective fault-detection technique is required to minimize the impact of faults. This paper provides a comprehensive overview of the fundamental principles of multi-level inverters and the various sorts of faults that can occur in multi-level inverters. This study provides a comprehensive analysis of five-level cascaded H-bridge multilevel inverters (MLIs) under both normal and defective conditions. The paper outlines a fault-detection method that utilizes total harmonic distortion and a normalized output voltage factor. In addition, the paper discusses a fault-isolation strategy that relies on reducing amplitude modulation. This method leads to the development of a fault-tolerant inverter. The utilization of level-shifted pulse-width modulation (LSPWM) technology is employed for the purpose of switching operations. LSPWM is the most appropriate technique for MLIs that require a low amount of computational resources. The fault-diagnosis approach given is suitable for MLI-based drives, grid-connected operations, and other applications. This paper presents a comprehensive examination of the 5L-CMLI (5-Level Cascaded Multi-Level Inverter) under various fault scenarios in CMLI. Subsequently, various fault diagnosis approaches will be examined, including their advantages and disadvantages. The paper discusses several defects that can occur in the Insulated Gate Bipolar Transistor (IGBT) of a Current Mode Logic Inverter (CMLI), and also presents a design for a reliable fault diagnosis system. Furthermore, this analysis examines several fault detection strategies in CMLI, categorized according to open-loop and closed-loop dynamic systems fault classifications.

Convolutional neural network approach for fault detection and characterization in medium voltage distribution networks

Power outages significantly impact the power industry by disrupting social welfare and economic stability. Still, existing methods for fault detection face challenges due to load and network topology, conditions, and installed equipment. However, recent advances in artificial intelligence (AI) are enabling researchers to create alternative approaches for fault detection and location strategies. Therefore, this paper introduces a novel method for detecting, classifying, and locating faults in power systems through voltage waveform analysis using a convolutional neural network (CNN) integrated with the Piecewise Function Put Together (PFPT) algorithm for fault detection and fault zone localization in a power distribution network. Utilizing Park's transformation, noise reduction PFPT sine fitting, and CNNs, the proposed method distinguishes between 'healthy' and 'faulty' conditions. Simulation results reveal that while the voltage Park's vector time behavior of a healthy system remains stable, it exhibits circular or mixed patterns under faulty conditions. These patterns enable the identification of four types of short circuit faults—single-line-to-ground (LG), line-to-line (LL), line-to-line-to-ground (LLG), and three-line (3L) faults—by analyzing 3D voltage Park's waveforms at network buses. The study validates fault type identification through the observation of rotating Park vectors from sine fitting of time-based voltage waveforms. By converting 3D voltage waveforms into high-resolution images, the method utilizes a CNN for fault recognition, achieving an accuracy of 93.1%. This innovative approach underscores the robustness and precision of combining traditional electrical engineering techniques with modern AI.

Double-Layer Distributed and Integrated Fault Detection Strategy for Non-Gaussian Dynamic Industrial Systems.

Currently, with the increasing scale of industrial systems, multisensor monitoring data exhibit large-scale dynamic Gaussian and non-Gaussian concurrent complex characteristics. However, the traditional principal component analysis method is based on Gaussian distribution and uncorrelated assumptions, which are greatly limited in practice. Therefore, developing a new fault detection method for large-scale Gaussian and non-Gaussian concurrent dynamic systems is one of the urgent challenges to be addressed. To this end, a double-layer distributed and integrated data-driven strategy based on Laplacian score weighting and integrated Bayesian inference is proposed. Specifically, in the first layer of the distributed strategy, we design a Jarque-Bera test module to divide all multisensor monitoring variables into Gaussian and non-Gaussian blocks, successfully solving the problem of different data distributions. In the second layer of the distributed strategy, we design a dynamic augmentation module to solve dynamic problems, a K-means clustering module to mine local similarity information of variables, and a Laplace scoring module to quantitatively evaluate the structural retention ability of variables. Therefore, this double-layer distributed strategy can simultaneously combine the different distribution characteristics, dynamism, local similarity, and importance of variables, comprehensively mining the local information of the multisensor data. In addition, we develop an integrated Bayesian inference strategy based on detection performance weighting, which can emphasize the differential contribution of local models. Finally, the fault detection results for the Tennessee Eastman production system and a diesel engine working system validate the superiority of the proposed method.

Enhancing fault detection in multivariate industrial processes: Kolmogorov–Smirnov non-parametric statistical approach

The accurate detection of abnormal events in modern process plants is extremely important. The detection of faults of small magnitude and in a noisy process environment is still a challenge that many industries face. In this paper, a Kolmogorov–Smirnov (KS) based non-parametric statistical fault indicator is proposed to identify a variety of sensor faults in process plants. The data collected from most modern process plants are randomly varying and non-Gaussian, due to which the multivariate scheme based on Independent Component Analysis (ICA) is considered. The KS-based indicator is amalgamated with the ICA multivariate model, which yields a novel ICA-KS-based fault detection (FD) scheme. The KS statistical indicator compares any two distributions and checks if they are similar or dissimilar. The potential of the KS indicator is extended in the FD domain, where the residuals of training and testing data are compared in a sliding window. The ability of the proposed ICA-KS-based FD strategy is validated on a simulated Distillation column (DC) process and the benchmark Tennessee Eastman (TE) process to identify a variety of sensor faults. The simulation results demonstrate the effectiveness of the detection performance of the ICA-KS scheme, which outperforms conventional FD-based methods with a high detection rate.

Concomitant Observer-Based Multi-Level Fault-Tolerant Control for Near-Space Vehicles with New Type Dissimilar Redundant Actuation System

This paper presents a concomitant observer-based multi-level fault-tolerant control (FTC) for near-space vehicles (NSVs) with a new type dissimilar redundant actuation system (NT-DRAS). When NSV flight control system faults occur in NT-DRAS and attitude-corresponding sensors, the NSV hybrid output states, including the concomitant observer usable states and the real system states, are applied to solve the FTC gain by using the linear quadratic regulator (LQR) technique. Furthermore, since NT-DRAS is used in NSVs, a multi-level (actuation system level and flight control level) FTC strategy integrating NT-DRAS channel switching and flight control LQR is proposed for complex and worsening fault cases. The most important finding is that though the proposed strategy is applicable for worsening fault cases in NSVs, systematic and accurate criteria for the process being performed are necessary and can improve the FTC efficiency with minimal FTC resources. Additionally, such criteria can improve the NSV’s responsiveness to comprehensive faults, provided that the real-time performance of the fault detection and diagnosis (FDD) scheme can be further optimized. The concomitant observer convergence and the multi-level FTC strategy have been verified by numerical simulations based on the Matlab/Simulink platform.

A randomized algorithm based approach to fault detection for uncertain systems subject to deception attack

This paper presents a fault detection strategy for discrete uncertain systems subject to deception attack based on the probabilistic framework. Probabilistic parameter models are utilized for characterizing model uncertainties, multiplicative faults and additive faults. An observer-based fault detection filter is constructed to generate fault information under deception attack. A randomized algorithm based approach is developed to determine the observer gain and threshold for attaining a trade-off between the false alarm rate (FAR) and fault detection rate (FDR). Simulation of an unmanned surface vehicle (USV) system and comparison against existing work are performed to illustrate the effectiveness of the proposed scheme.

Robust neuro-adaptive command-filtered back-stepping fault-tolerant control of satellite using composite learning

This study proposes an adaptive neural command-filtered backstepping control system for precise and fast attitude control of a satellite considering different uncertain dynamics. Mission success crucially depends on robust attitude control resilient to model uncertainties, unmodeled dynamics, external disturbances, and actuator faults. The proposed approach synergistically combines a neural network for handling model uncertainties, unmodeled dynamics, and actuator faults, with a disturbance observer for compensating external disturbances and neural network estimation errors. The command-filtered backstepping technique avoids the explosion of complexity inherent to traditional backstepping, while integrating integral action into the design results in the elimination of the steady-state tracking error. Besides, a composite learning method optimizes the update laws for the neural network and disturbance observer weights, enhancing control performance. Despite the presence of uncertainties, the closed-loop system stability is guaranteed by the Lyapunov stability theorem. Simulation results demonstrate the proposed controller’s ability to handle severe actuator faults, unmodeled dynamics, and measurement noise without requiring explicit fault detection and isolation schemes.

Preface

On behalf of the organizing committee, we are very pleased to introduce the proceedings of 2024 The 17th International Conference on Computer and Electrical Engineering (ICCEE 2024), which was held in Shenzhen, China (virtual) on June 28-30, 2024. Because more than two-thirds of the authors chose to attend the conference online, we had to switch to totally virtual conference to ensure the effectiveness of the conference. It is with great pleasure and anticipation that we convene once again to explore the latest advancements, challenges, and opportunities in the dynamic fields of Computer and Electrical Engineering. In today’s interconnected world, computer and electrical engineering play critical roles in shaping the technological landscape. ICCEE 2024 serves as a platform for fostering interdisciplinary collaboration, knowledge sharing, and networking among researchers, practitioners, and industry leaders. ICCEE 2024 lasted 3 days. There are online test sessions on ZOOM on the first day, and then followed with a wonderful array of 2 keynote speeches and 2 invited speeches, along with 4 technical sessions during June 29-30. The authors presentations cover topics on Power System Control Model and Simulation Analysis, Electrical Fault Detection and Maintenance Strategy, Digital Communication and Information Systems, Intelligent Electronic Systems and Status Monitoring, with 15 minutes for each presentation, including 2 minutes for the Q&A. Additionally, there are around 100 delegates attending online conference. Delegates had a wide range of sessions to choose from and had enough time in deciding which sessions to attend. List of Committee and Statement of Peer Review are available in this Pdf.

Resilient bus-bar protection scheme for DC microgrids connected to AC electric grids

ABSTRACT In recent years, there has been a growing interest in incorporating microgrids into existing electrical power networks to reduce reliance on conventional grids. This is further due to the numerous benefits of microgrids, most notably the ability to operate in either autonomous or grid-connected mode, making microgrids a highly versatile structure to incorporate intermittent production and energy storage. However, despite the many benefits, it is difficult to present resilience protection for DC bus bar to ensure the efficient operation of a DC microgrid, as there is a non-zero crossing phenomenon of DC fault current, while zero crossing phenomenon of an AC fault. Additionally, the DC bus bar has both an AC side with rectified DC and a DC line current, creating protection concerns for microgrids. To address these issues, this article proposes a novel fast fault detection scheme for DC microgrids with multiple renewable energy sources, energy storage, and loads. The scheme incorporates current and voltage sensing through current and voltage transformers, along with advanced digital relays, to detect and isolate faults on the DC microgrid. By analyzing rectified AC and DC line data, the scheme employs a differential current-based protection strategy to safeguard the DC bus bar. Validated through comprehensive fault simulations in PSS/SINCAL, the method proves effective in maintaining DC bus voltage stability and enhancing microgrid flexibility.

An Unsupervised Framework Based on Dual-Domain Contrastive Learning and Tri-Indicator Joint Alarm Strategy for Early Fault Detection of Bearing

An Unsupervised Framework Based on Dual-Domain Contrastive Learning and Tri-Indicator Joint Alarm Strategy for Early Fault Detection of Bearing

Fault detection for saturated nonlinear system subject to redundant channel and additive time‐varying delays

AbstractUnder the influence of sensor saturation and packet dropout, this paper is concerned with the fault detection (FD) problem for a class of nonlinear systems with two additive time‐varying delay components. In the addressed measurement model, the sensor is assumed to have two communication channels, which can contribute to improving the probability of successfully delivering the measurements. A FD filter is designed to generate residual signals, and the sufficient criteria are established to ensure the stochastic finite‐time stability (SFTS) of the residual system with performance constraint by constructing new Lyapunov‐Krasovskii functionals. Based on the established criteria, the explicit expression of the desired FD filter is obtained via solving a set of linear matrix inequalities (LMIs). Finally, the usefulness of the proposed FD scheme is verified by a simulation example.

Early anomaly detection of wind turbine gearbox based on SLFormer neural network

Detecting faults in wind turbines at early stage is of great significance in improving the economic efficiency of wind farms. However, the widely used fault detection techniques are mostly based on traditional deep learning frameworks, which are limited in handling long-term dependencies, leading to constraints in global feature extraction. Additionally, current studies often build models based on the operational performance of an individual wind turbine, limiting the generalizability of the models. Therefore, this study introduces an improved model called SLFormer, which integrates Long Short-Term Memory into the Transformer encoder for early fault detection in the gearbox. Simultaneously, a novel fault detection strategy based on SCADA data analysis and transfer learning is proposed. Case studies from three wind farms indicate that the SLFormer model significantly outperforms six other popular prediction models in terms of stability and accuracy in modeling normal behavior and exhibits high robustness to random disturbances in SCADA data. The SLFormer model can predict gearbox anomalies twenty-one days in advance and effectively avoids false fault reports. The proposed strategy can help the model acquire fault knowledge from multiple wind farms, thus creating a framework with generalizability.

Incipient fault detection and isolation with Cauchy–Schwarz divergence: A probabilistic approach

To monitor the dynamics and non-stationarity inherent in industrial processes, we propose a novel incipient fault detection and isolation scheme grounded in a probabilistic perspective, using the Cauchy–Schwarz (CS) divergence. Our innovation lies in the utilization of marginal CS divergence for incipient fault detection and the conditional CS divergence for fault isolation. This approach neither require prior parametric assumptions about the underlying data distribution nor depend on historical fault data, while simultaneously providing explanatory diagnostics. Beyond this, we develop a change point detection-base diagnosis technique for practical engineering applications. This online process monitoring technique guarantees timely intervention to uphold process stability and safety. We demonstrate the compelling performance, higher detection rate and lower alarm rate, of the CS divergence over prevalent divergence-based approaches, such as Kullback–Leibler divergence, Wasserstein distance and Mahalanobis distance. We also illustrate the explanatory insights offered by conditional CS divergence in fault isolation on synthetic data, benchmarks of continuous stirred-tank reactor process and continuous stirred-tank heater process and even a real-world continuous catalytic reforming process. Code of this CS divergence based fault detection and isolation is available at https://github.com/Feiya-Lv/Incipient-Fault-Detection-and-Isolation-with-Cauchy--Schwarz-Divergence.

Fuzzy Fault Detection Observer Design for Unmanned Marine Vehicles Based on Membership-Function-Dependent H∞/H_ Performance

This paper studies the design problem of fault detection (FD) observer for unmanned marine vehicles (UMVs) based on the T-S fuzzy model. Firstly, T-S fuzzy systems are used to approximate the nonlinear dynamics in UMVs. Secondly, to improve the FD performance of UMVs, a new H∞/H_ performance index, which depends on the membership functions, is defined. Then, based on the membership-function-dependent H∞/H_ performance index, a new fuzzy FD observer strategy, where the fuzzy submodels are not all required to be with the same H_ performance index, is developed to detect the sensor fault in UMVs; the corresponding synthesis conditions of the FD observer are derived based on the Lyapunov theory. Different from the conventional FD strategies, in the proposed membership-function-dependent FD method, the fuzzy submodels—which the system always works on—can have a larger H_ performance index, such that the performance of the FD can be improved. In the end, an example is given to show the effectiveness of the presented method.

A cloud–edge collaboration based quality-related hierarchical fault detection framework for large-scale manufacturing processes

Against the backdrop of the new-generation intelligent manufacturing and Industrial Internet of Things, manufacturing processes are evolving towards integration, large-scale operations, and complexity, and the requirements for process safety and product quality reliability are constantly improving. However, these processes are generally distinguished by multi-production sub-processes with cooperative coupling, and multi-hierarchical levels integrated automation systems, where any deviation or abnormality occurring within a sub-process or system level will lead to the extensive propagation and evolution of faults, making the comprehensive quality-related fault detection face increasing challenges in practice. In response, a novel cloud–edge collaboration-based quality-related hierarchical fault detection framework is constructed in this paper, which can facilitate interconnected communication between sub-processes and enhance collaborative interactions at different hierarchical levels to improve fault detection accuracy and reliability. First, two quality supervised training mechanisms based on minimal gated unit are introduced for capturing different scale dynamics features on the cloud side and edge side. These methods enable more adequate extraction of quality-related deep hidden features, particularly in the presence of nonlinearity and dynamics. Subsequently, a federated bi-directional knowledge distillation-based strategy is proposed, leveraging the concept of federated learning. Quality knowledge such as prediction and quality-related features from both the cloud side and edge side are integrated into the training steps of federated learning, thereby strengthening the interaction between multiple sub-processes and distinct levels. Furthermore, comprehensive quality-related fault detection strategies are formulated based on the quality-related features of the cloud side and each sub-process on the edge side, respectively. Finally, the proposed framework is deployed into a cloud–edge-device collaborative prototype system based on a real hot strip mill process to demonstrate its effectiveness and applicability.

Fault detection and synchronization control in hybrid DC/AC microgrids using grid-forming inverter DC-link controller

This paper introduces a DC-link fault detection and synchronization control strategy for grid-forming inverters in hybrid DC/AC microgrids, aiming to bolster system stability and reliability. The proposed control scheme seamlessly integrates DC-link fault detection with virtual synchronous generator (VSG) control, facilitating inertia emulation and synchronization without the need for a phase-locked loop (PLL). The DC bus controller is designed to swiftly identify faults triggered by power imbalances, such as load fluctuations or generator integration, and dynamically adjust the power reference for the VSG controller to ensure inertia emulation. The Ziegler-Nichols method is utilized for meticulous tuning of the DC-link controller gains, while the modified swing equation governs VSG inertia and synchronization. MATLAB/Simulink demonstrate the superior performance of the proposed method compared to traditional GFM control strategies. Specifically, the proposed controller improved the frequency nadir to 59.98 Hz compared to 59.75 Hz and showed an enhanced power step response of 7.9×10^5 W compared to 6.9×10^5 W for conventional control method. The proposed GFM control also eliminated the 10×10^6 W switching spike observed with conventional controllers. These findings highlight the potential of this fault-controller approach to advance the reliability and efficiency of hybrid DC/AC microgrids.