Fault Detection Scheme Research Articles

A new integrated fault detection and control scheme of islanded DC microgrids: A resilient approach

This paper presents a new resilient integrated fault detection and control module for a DC microgrid operating in islanded mode. The proposed design is unique in its ability to simultaneously improve microgrid reliability and mitigate variations through a multi-objective approach that considers both control and diagnosis objectives in the form of Linear Matrix Inequalities through a presented algorithm. The module is designed by setting out desired performance indices to ensure system stability, mitigate disturbances and uncertainties, and detect faults while controlling the DC-link voltage. Additionally, the module is robust to its own variations and capable of detecting non-compensable faults for further diagnosis. The efficacy of the proposed approach is demonstrated through a comparative study that highlights the vulnerability of robust design to parameter variations.

Synchronous Generator Abnormality and Fault Analysis

The trouble-free operation of alternators is unavoidable to meet the rising demand for electrical energy. Any alternator failure causes overloading of the remainder of the units in a grid, posing a serious threat to network stability. In a stand-alone generation, production is seriously affected. The repairs of large capacity alternators require a trained workforce, which is not always locally available. When an alternator operates under typical fault conditions, it reduces its efficiency and shortens its lifespan. The synchronous generator’s prolonged outage has an adverse impact on network reliability and impacts the economy. As a result, early detection of alternator anomalies significantly reduces fault extension, loss of supply, and maintenance costs, while also improving the alternator’s life. In many cases, the early fault detection scheme for higher capacity alternators cannot be readily applied to a lower capacity one. The article discusses some practical root cause analyses of the commonly occurring faults in turboalternators. The identification, characteristics, and analysis of multiple fault occurrences have been thoroughly addressed. This article would provide utility and protection engineers impetus in determining the urgency, selecting, and implementing the appropriate protective system for various abnormality events.

Fault detection of new and aged lithium-ion battery cells in electric vehicles

In this paper, a novel model-based fault detection in the battery management system of an electric vehicle is proposed. Two adaptive observers are designed to detect state-of-charge faults and voltage sensor faults, considering the impact of battery aging. Battery aging primarily affects capacity and resistance, becoming more pronounced in the later stages of a battery lifespan. By incorporating aging effects into our fault diagnosis scheme, our proposed approach prevents false or missed alarms for the aged battery cells. The aging effect of battery, capacity fading and resistance growth, are considered unknown parameters. An adaptive observer is employed to design a fault detector, considering unknown parameters in the battery model. The adaptive observers are designed for two different scenarios: In the first scenario, it is presumed that aging effects remain constant over time due to their slow rate of change. Then, it is assumed that aging effects are time-varying. Therefore, the fault detection scheme can detect faults of new battery cells as well as aged cells. Some simulations have been conducted on a Lithium-ion battery cell and extended to battery pack, to demonstrate the performance of the proposed approach in more real-world scenarios. The results showed that the designed observers can detect faults correctly in a seven years old battery as well as a new one.

ROCOV scheme for Fault Detection and Location in HVDC systems

A reliable DC fault protection system is essential for the development of HVDC grids. Therefore, this paper deals with the voltage derivative ROCOV scheme to locate and detect DC faults. The algorithm is able to differentiate internal and external faults considerably fast. The proposed algorithm is analyzed in a HVDC grid with different fault case scenarios. Finally, the ROCOV protection thresholds are discussed.

A data-driven subspace distributed fault detection strategy for linear heterogeneous multi-agent systems

This paper presents a new data-driven subspace distributed fault detection strategy specifically designed for linear heterogeneous multi-agent systems (MASs). The proposed approach leverages the characteristics of heterogeneous MASs, where agents exhibit diverse dynamics and parameters. By utilizing subspace construction techniques, the proposed method captures the normal behavior of each agent and enables the detection of deviations that indicate the presence of faults. Unlike existing methods, the approach is completely data-driven and eliminating the need for centralized information or communication among the agents. Simulation results demonstrate the effectiveness and efficiency of the proposed approach in detecting simultaneous faults in different agents. Overall, the proposed approach represents a significant departure from existing methods and offers a powerful new tool for fault detection in heterogeneous multi-agent systems.

Effective protection scheme for transmission lines connected to large scale photovoltaic power plants

The performance of a distance relay can be significantly affected by various uncontrolled factors such as fault resistance, fault type, fault location and noise. Moreover, when photovoltaic power plants are integrated into electrical grids, they exhibit distinct fault current characteristics compared to conventional power systems with synchronous generators. These factors introduce variations in the recorded apparent impedance, deviating from the predefined settings of the distance relay. This paper presents an effective scheme for fault detection using ITD methodology. The ITD method decomposes the target signal into proper rotation and monotonic signals, which are then used to calculate the Energy Index. The performance of this scheme has been extensively investigated under various scenarios, including fault resistance, fault location, fault type, CT saturation, GC compliance, noise, and sampling frequency. To validate the effectiveness of the proposed scheme, simulations are conducted using both the PSCAD/EMTDC and MATLAB software. The proposed scheme is assessed using both the 2-bus and IEEE 39-bus test systems. The results show the merit and effectiveness of the proposed scheme in the accurate diagnosis of fault detection with 400 Ω fault resistance, in the presence of 20 dB noise, with variable sampling frequency (0.5-16 kHz) and variable irradiance values (600-1000(W/m2)) in power systems.

A Decentralized Model-Based Fault Detection and Isolation Scheme for MVDC Shipboard Power Systems

A Decentralized Model-Based Fault Detection and Isolation Scheme for MVDC Shipboard Power Systems

Enhanced Fault Detection and Localization Strategy for High-Speed Protection in Medium-Voltage DC Distribution Networks Using Extended Kalman Filtering Algorithm

Enhanced Fault Detection and Localization Strategy for High-Speed Protection in Medium-Voltage DC Distribution Networks Using Extended Kalman Filtering Algorithm

Novel Fuzzy-based Open-Switch Fault Detection Scheme of Voltage Source Inverter Induction Motor Drive

Novel Fuzzy-based Open-Switch Fault Detection Scheme of Voltage Source Inverter Induction Motor Drive

Machine learning-based fault detection scheme for IoT-enabled WSNs

Machine learning-based fault detection scheme for IoT-enabled WSNs

Machine Learning based Fault Detection Scheme for IoT-Enabled WSNs

Machine Learning based Fault Detection Scheme for IoT-Enabled WSNs

Intelligent fault detection strategy for knowledge entities in fault semantic networks of distribution network based on siamese networks.

The advent of smart grid technologies has brought about a paradigm shift in the management and operation of distribution networks, allowing for intricate system information to be encapsulated within semantic network models. These models, while robust, are not immune to faults within their knowledge entities, which can arise from a myriad of issues, potentially leading to verification failures and operational disruptions. Addressing this critical vulnerability, our research delves into the development of a novel fault detection methodology specifically tailored for the knowledge entity variables of semantic networks in distribution networks. In our approach, we first construct a state space equation that models the behavior of knowledge entity variables in the presence of faults. This foundational framework enables us to apply an unknown input observer strategy to effectively detect anomalies within the system. To bolster the fault identification process, we introduce the innovative use of a siamese network, a neural network architecture which is proficient in differentiating between similar datasets. Through simulation scenarios, we demonstrate the efficacy of our proposed fault detection method.

Full Decoupling High-Order Dynamic Mode Decomposition for Advanced Static and Dynamic Synergetic Fault Detection and Isolation

Real industrial processes often present coupled static and dynamic characteristics, leading to significant challenges for fault detection and isolation. However, traditional dynamic modeling methods may lead to the coupling problem of statics and dynamics, which provide ambiguous process status descriptions and incorrect fault isolation results. In this work, a novel full decoupling high-order dynamic mode decomposition (FDHODMD) method is developed for fault detection and isolation of dynamic processes. Different from the existing dynamic methods, the proposed FDHODMD can separate the high-order dynamic information from static information. First, the static characteristics are separated by discarding the decomposed features corresponding to smaller singular values. Then, a high-order dynamic model is established to present the temporal relationships between variables. In this way, the effect of static characteristics on dynamics analysis can be eliminated. Accordingly, from both static and dynamic perspectives, multiple statistics are designed to comprehensively detect the anomalies and provide an explicit status identification. In addition to the static fault isolation strategy, a dynamic fault isolation strategy is designed to recognize the fault variables after detecting the deviation, which divides the complex dynamic system into several individual dynamic modes to avoid the interaction of dynamic characteristics. Thereupon, the contributions of different variables to each dynamic mode can be clearly presented to recognize the fault variables. Finally, the validity of the proposed method is illustrated through both a numerical case and a real industrial process. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In industrial processes, the static and dynamic characteristics of data are often coupled. This work presents a FDHODMD method, which can be considered the high-order version of DMD, to achieve the decoupling of dynamics and statics while extracting high-order dynamic characteristics. Then the proposed fault detection strategy designs multiple statistics for monitoring the process from both dynamic and static perspectives, which ensures the safe operation of industrial processes. After detecting the anomaly, the proposed fault isolation strategy can recognize the fault variables separately dominating the dynamic and static deviation, which may help engineers locate the fault source.

Improved Fault Detection in Chemical Engineering Processes via Non-Parametric Kolmogorov–Smirnov-Based Monitoring Strategy

Fault detection is crucial in maintaining reliability, safety, and consistent product quality in chemical engineering processes. Accurate fault detection allows for identifying anomalies, signaling deviations from the system’s nominal behavior, ensuring the system operates within desired performance parameters, and minimizing potential losses. This paper presents a novel semi-supervised data-based monitoring technique for fault detection in multivariate processes. To this end, the proposed approach merges the capabilities of Principal Component Analysis (PCA) for dimensionality reduction and feature extraction with the Kolmogorov–Smirnov (KS)-based scheme for fault detection. The KS indicator is computed between the two distributions in a moving window of fixed length, allowing it to capture sensitive details that enhance the detection of faults. Moreover, no labeling is required when using this fault detection approach, making it flexible in practice. The performance of the proposed PCA–KS strategy is assessed for different sensor faults on benchmark processes, specifically the Plug Flow Reactor (PFR) process and the benchmark Tennessee Eastman (TE) process. Different sensor faults, including bias, intermittent, and aging faults, are considered in this study to evaluate the proposed fault detection scheme. The results demonstrate that the proposed approach surpasses traditional PCA-based methods. Specifically, when applied to PFR data, it achieves a high average detection rate of 98.31% and a low false alarm rate of 0.25%. Similarly, when applied to the TE process, it provides a good average detection rate of 97.27% and a false alarm rate of 6.32%. These results underscore the efficacy of the proposed PCA–KS approach in enhancing the fault detection of high-dimensional processes.

An alternative bearing fault detection strategy for vibrating screen bearings

The transformation of the mining machinery industry is oriented towards the adoption of highly sophisticated technologies, with the aim of enhancing operational efficiency. Specifically, there is a focus on improving the nature and quality of operations carried out, such as vibrating screen. Vibrating screens, being one of the pivotal machines, have gained extensive utilization in mining processing plants. However, the demanding working conditions can lead to potential failures in certain components of vibrating screens. Bearings are one of the components that are susceptible to faults. Moreover, owing to the unique working environment and structure of vibrating screens, the signals extracted from defective bearings in vibrating screens exhibit greater complexity and distinctiveness, including (i) vibration interference related to the unbalanced shafts, (ii) cyclic impulses produced by the damaged bearing, (iii) non-cyclic random impulsive noise owing to pieces of coal falling into the deck, and (iv) inevitable noise from environmental conditions. To address this issue, an alternative procedure for bearing fault detection from vibrating screens served in the coal industry is proposed in the work. The strategy for bearing fault signature extraction covers three main steps: (a) a new fast resampled iterative filtering (FRIF) method is utilized to remove the vibration interference produced by the unbalanced shafts and simultaneously eliminate large amounts of background noise; (b) a signal processing method based stable distribution is designed to cancel the random impulses (impulsive noise); and (c) an improved energy operator demodulation technique is proposed to identify the bearing fault characteristics from the purified signal. The proposed strategy is validated using simulated and real tests.

Improved fault detection based on kernel PCA for monitoring industrial applications

The conventional Kernel Principal Component Analysis (KPCA) -based fault detection technique requires more computation time and memory storage space to analyze large-sized datasets. In this context, two techniques, Spectral Clustering (SpC) and Random Sampling (RnS), are developed to reduce the dataset size by retaining the more relevant observations while preserving the main statistical characteristics of the original dataset. These two techniques and others use the training dataset from two different industrial processes, Tennessee Eastman (TEP) and Cement Plant (CP) to be reduced and provided to build the Reduced KPCA (RKPCA) model-based fault detection scheme. The obtained results show the effectiveness of the proposed techniques in terms of some fault detection performance indices and computation costs.

Formula omitted] fault detection observer design for nonlinear conformable fractional-order systems

This study is concerned with the design of fault detection observers for nonlinear conformable fractional-order systems. An H∞/H− fault detection scheme is applied to maximize the sensitivity of faults and minimize the effect of disturbances. Based on basic fractional calculus lemmas and fractional-order Lyapunov stability theory, sufficient conditions are obtained to design a fault detection observer. Finally, the advantages and effectiveness of the proposed fault detection method are illustrated through a numerical example.

A Novel Fault Identification and Localization Scheme for Bipolar DC Microgrid

Traditional fault detection and location schemes become ineffective for detecting and locating the fault in DC microgrid systems due to integrating various types of power electronic-based DC loads and generators. To resolve this issue, advanced, intelligent, specialized fault detection and location schemes are necessary. This article suggests a novel fault detection scheme based on the difference in the teager energy available in the DC current wave at sending and receiving ends of lines. After the detection of the fault, the location is calculated by estimating the resistance of the cable up to the fault point as well as the total resistance of the cable, with the help of least square technique. The proposed scheme decides fault is internal if the estimated fault location is less than 1 p.u. Otherwise, the proposed scheme decides fault is external. A DC microgrid with different types of generating units and loads is simulated using MATLAB/SIMULINK to evaluate the developed algorithm. Internal and external faults, pole-to-ground and pole-to-pole faults with changing fault resistance and fault location are some of the fault scenarios that have been simulated. The obtained simulation results prove that the suggested algorithm can discriminate between internal and external faults and locate the fault. The proposed technique is also examined on a DC microgrid hardware testbed and results show the efficiency of the suggested approach.

A Lightweight and Explainable Data-Driven Scheme for Fault Detection of Aerospace Sensors

A Lightweight and Explainable Data-Driven Scheme for Fault Detection of Aerospace Sensors

A Novel Turn Fault Detection Strategy Based on High-Frequency Neutral-to-Ground Voltages

A Novel Turn Fault Detection Strategy Based on High-Frequency Neutral-to-Ground Voltages