Fault Isolation Process Research Articles

Fault Diagnosis of Maritime Equipment Using an Intelligent Fuzzy Framework

The task of automatically and intelligently diagnosing faults in marine equipment is of great significance due to the numerous duties that shipboard professionals must handle. Incorporating automated and intelligent systems on ships allows for more efficient equipment monitoring and better decision-making. This approach has attracted considerable interest in both academia and industry because of its potential for economic savings and improved safety. Several fault diagnosis methods are documented in the literature, often involving mathematical and control theory models. However, due to the inherent complexity of some processes, not all characteristics are precisely known, making mathematical modeling highly challenging. As a result, fault diagnosis often depends on data or heuristic information. Fuzzy logic theory is particularly well suited for processing this type of information. Therefore, this paper employs fuzzy models to diagnose faults in a marine pneumatic servo-actuated valve. The fuzzy models used in fault diagnosis are obtained from the data. These fuzzy models are identified for the normal operation of the marine pneumatic servo-actuated valve, and for each fault, predicting the system’s outputs from the inputs and outputs of the process. The proposed fault diagnosis framework analyzes the discrepancy signals between the outputs of the fuzzy models and the actual process outputs. These discrepancies, known as residuals, help in detecting and isolating equipment faults. The fault isolation process uses an intelligent decision-making approach to determine the specific fault in the system. This method is applied to diagnose abrupt faults in a marine pneumatic servo-actuated valve. The approach presented was used to detect and diagnose three very important faults in the operation of a marine pneumatic servo-actuated valve. The three faults were correctly detected and isolated, and no errors were detected in this detection and isolation process.

Ground fault protection algorithm of active distribution network based on energy extremum direction

AbstractAfter large‐scale distributed power sources are connected to the distribution network, the fault current undergoes noticeable changes, affecting the accuracy of traditional single‐phase grounding fault algorithms. Therefore, the objective of this paper is to address the impact of distributed power integration on the grounding algorithms of distribution networks. The main contribution is: by establishing an electrical system structure and model for distribution network grounding faults that include distributed generation (DG), theoretically deriving and calculating the transient zero‐sequence current frequency changes at the moment of fault, analysing the directional characteristics of zero‐sequence currents under DG connection conditions, designing a local grounding fault judgment algorithm based on energy extremum direction, and providing a fault judgment and isolation process for grounding fault monitoring devices. The results show: through simulation and experimentation, the algorithm was tested, and the method can reliably judge grounding faults under various transition resistances, different numbers and capacities of connected distributed power sources, and different grounding switch‐on angles. The applicability of the algorithm covers both methods of neutral grounding through an arc suppression coil and ungrounded neutrals, adapting to scenarios with or without DG connections.

Synergistic frameworks for sensor fault isolation and accommodation in grid-side converters

The stable and reliable operation of grid-integrated renewable energy systems requires advanced control and coordination of grid-side converters (GSCs), utilizing the feedback measurements of voltage and current sensors from both the direct current (DC) and alternating current (AC) sides of the converter. However, the effective operation of the converter is susceptible to sensor failures or divergence from their proper operation. Although sensor fault detection algorithms are usually effective under abrupt faults, the fault propagation effect caused by the physical interconnection between the DC and AC sides of the converter may limit the performance of the sensor fault isolation process in revealing the exact location of a potential faulty sensor. Therefore, this work proposes a robust, model-based fault isolation and accommodation scheme. Specifically, a synergistic sensor fault isolation framework based on adaptive estimation schemes is proposed for both single and multiple faults in the DC voltage and AC current sensors, considering modeling uncertainty and measurement noise. The performance analysis in terms of stability, learning capability, and fault isolability is rigorously examined. An accommodation scheme based on a virtual sensor utilizing dynamic sensor fault estimation with real-time learning capabilities is applied to a GSC. Finally, the performance of the proposed fault isolation and accommodation scheme is evaluated through simulation analysis under several scenarios involving single and multiple sensor faults.

A pair of two-stage Kalman filters to detect and isolate failures in aircraft with redundant actuators

As critically-safe systems, aircraft must be able to adapt if a failure occurs. A fast failure detection may reduce the possibility of loss of control in-flight. Model-based Failure Detection and Isolation Systems (FDIS) have been proposed to identify actuation failures in aircraft, being most of them based on Multiple Models approaches. An additional challenge in FDIS strategies for aircraft is the high level of actuation redundancy. This article proposes to use only a pair of Two-Stage Kalman Filters (TSKF) for detecting and isolating failures. A Feed-Forward Differential (FFD) is introduced to facilitate the fault isolation process, providing further information with low perturbation during flight. The TSKF results are interpreted by a Supervisory Loop, which automatically diagnoses the aircraft for actuation failures. The proposed solution is validated using the Boeing 747 GARTEUR RECOVER Benchmark Simulator. The proposed solution has an overall better response when compared to a multiple-models solution of the literature with an 85% reduction in the computational time.

Sensor Set Decomposition for Enhanced Distributed Sensor Fault Isolability of Marine Propulsion Systems

This paper proposes a greedy stochastic optimization algorithm for the sensor set decomposition used in the sensor fault monitoring of marine propulsion systems, based on fault isolability criteria. These criteria are expressed mathematically in terms of the number of unique columns in the theoretical fault signature matrices (FSMs) used during the sensor fault isolation process. Due to the large scale and complexity of marine propulsion plants, the diagnostic layer follows a distributed architecture with a combinatorial logic used for fault isolation in two cyber levels; the local and global decision logic. As a result, the FSMs of both levels are formulated as an integrated optimization problem. Each solution regarding the sensor set decomposition is then used to generate the respective distributed monitoring architecture, using semantic (qualitative) knowledge for the propulsion plant. Thus, the need for an analytical model of the plant is removed. Moreover, based on the design of the distributed monitoring architecture, the respective theoretical FSMs (quantitative) are Automatically generated and used for the evaluation of the objective function. Finally, simulation results are used to illustrate the application of the greedy stochastic optimization algorithm and its efficiency.

Impact of cyber failures on operation and adequacy of Multi-Microgrid distribution systems

The primary objective of this paper is to create a suitable reliability assessment framework for Cyber–Physical Multi-MicroGrid (CPMMG) distribution systems, considering the device-level failures of control and protection systems. For this purpose, the typical structure of a CPMMG distribution system with a centralized protection system is first exemplified. Then, the interdependencies between cyber and power infrastructures are investigated in such systems. Regarding the protection system, a Markov model is presented to investigate hidden failures of fault location, isolation, and service restoration processes. Possible operation modes in a CPMMG distribution system – normal, islanding, joint, and shutdown modes – are then explained and modeled. Finally, a suitable Monte Carlo simulation-based reliability assessment framework is developed to quantify well-known reliability indices. In addition, two new adequacy indices are proposed: Interrupted but Gained Compensation (IbGC) and Supplied by Expensive Resources (SbER). A comprehensive case study is conducted to reveal the salient features of the proposed framework. The result showed that the impact of cyber failures in such systems is highly dependent on the design of the cyber system. Therefore, the adverse impact of cyber failures of control and protection systems can be effectively mitigated by proper design.

Application of Filter Capacitor Dynamics Based Short-Circuit Fault Detection Method in Ring-Type DC Microgrid

A healthy grid always needs precise and fast isolation because this makes the system reliable and improve the power quality. To achieve this, a short-circuit (SC) fault detection method is presented for low-voltage ring-type DC microgrid (LV-RDCMG). This method uses the current dynamics of filter capacitors to identify the faulty zone. Along with fault identification, a fault isolation process is also explained decently. In the considered ring- type architecture, active sources such as AC grid, battery, and solar photovoltaic, are connected to a common DC link via power converters. The proposed method considers an averaged capacitor current as a detection parameter. Additionally, a criterion for thresholds selection is also given in detail. The presented work is validated through both digital simulation and experimental studies. All the results assure that the proposed concept can detect and isolate the fault cable within 1.98 milli second.

Multi-port DC circuit breaker based on dual capacitor current limiting and energy sink

The coupling relationship between the removal of the fault current and the dissipation of the residual energy of the fault line during the fault isolation process of the multi-port capacitive current-limiting DC circuit breaker prolongs the breaking time of the fault to a certain extent. In order to solve the above problems, this paper proposes a multi-port DC circuit breaker based on dual-capacitor current limiting and energy sinking on the premise of decoupling the fault current attenuation and removal process and the fault line residual energy discharge process. Further reduce the fault breaking time and equipment withstand voltage requirements of the DC circuit breaker. Firstly, the topology and working principle of the proposed circuit breaker are analyzed, and then the parameters in the topology are designed. Then a simulation model is built in the PSCAD/EMTDC simulation platform to verify the fault breaking performance and breaking time. The effect of decoupling the fault current clearing process and the fault line residual energy discharge process on the effectiveness of shortening the fault breaking time is verified.

Fault detection and isolation for a class of uncertain nonlinear systems: A dual-monitoring model-based approach

This paper presents a fault detection and isolation (FDI) scheme for a class of uncertain nonlinear systems with unknown faults, in which fault isolation process and control reconfiguration are integrated via a supervisory switching strategy. A group of adaptive estimators is performed together with the switching process. The proposed FDI approach, namely dual-monitoring model-based approach, can exploit information both from backstepping procedure and estimators, by which a set of monitoring functions is constructed to detect the occurrence of fault and also identify the specific fault type. In particular, based on some principles, the fault isolation process can be accelerated under a novel interacted isolation scheme. It can be seen that the tracking error can be always guaranteed within a prescribed performance in the existence of system faults.

Comparative results of applying the FDLISR method for rural and urban distribution networks with distributed generations

High penetration of distributed generations (DGs) into a distribution network (DN) could not only enhance the power-system reliability indexes but also reduce power losses. The use of DGs is effective for service restoration plans of the DN to minimize outage customers and power loss. However, the presence of DGs could lead to the protection miscoordination of overcurrent relays/reclosers due to fault isolation and service restoration processes. In light of this assumption, a fault detection, location, isolation, and service restoration (FDLISR) method is proposed to apply to both rural and urban distribution networks with DGs such that the comparative results of this FDLISR method are analyzed and discussed to demonstrate which type of the distribution network has a high risk of protection miscoordination of overcurrent relays/reclosers from service restoration scenarios. The faulted-section isolation and the distribution-network service restoration can be performed by solving a two-constraints-based objective function and seven appropriate performance indices (PI). By using the E-Terra Dispatching Operator Training Simulator (E-DOTS), simulation results of the FDLISR method applied for both the 22 kV rural-type and urban-type distribution networks are presented and compared to demonstrate its effectiveness by showing the possibility of protection miscoordination and the consideration of power losses for each service restoration plan. Consequently, the computation time of the FDLISR method proposed for the DN is within one-and-half minutes. Moreover, two performance indices of power loss and protection miscoordination, LOSS-PI and PRO-PI respectively, can highly impact the priority rank of possible service restoration plans for the rural DN while only the PRO-PI affects multiple service-restoration plans for the urban DG-contained DN.

Fault Detection of Aero-Engine Sensor Based on Inception-CNN

The aero-engine system is complex, and the working environment is harsh. As the fundamental component of the aero-engine control system, the sensor must monitor its health status. Traditional sensor fault detection algorithms often have many parameters, complex architecture, and low detection accuracy. Aiming at this problem, a convolutional neural network (CNN) whose basic unit is an inception block composed of convolution kernels of different sizes in parallel is proposed. The network fully extracts redundant analytical information between sensors through different size convolution kernels and uses it for aero-engine sensor fault detection. On the sensor failure dataset generated by the Monte Carlo simulation method, the detection accuracy of Inception-CNN is 95.41%, which improves the prediction accuracy by 17.27% and 12.69% compared with the best-performing non-neural network algorithm and simple BP neural networks tested in the paper, respectively. In addition, the method simplifies the traditional fault detection unit composed of multiple fusion algorithms into one detection algorithm, which reduces the complexity of the algorithm. Finally, the effectiveness and feasibility of the method are verified in two aspects of the typical sensor fault detection effect and fault detection and isolation process.

Optimal Service Restoration Scheme for Radial Distribution Network Using Teaching Learning Based Optimization

In the event of a fault isolation process, all loads located downstream from the faulted point become out of service, and as a consequence, the power interruption affects a greater portion of the radial distribution system. This paper proposes an optimal Service Restoration (SR) method that entails changing the network topology configuration via optimal tie-switch and section switch combinations. However, when the network topology configuration is performed, it results in increased load currents. As a result, some Protective Devices (PDs) can operate undesirably and some network branches may become unprotected. Therefore, it is essential to consider protection constraints in the SR problem to maintain service continuity during power interruptions. The proposed method aims at optimal SR with minimum out-of-service loads, minimum power loss, and improved voltage profiles and at the same time ensures PDs operate correctly during the normal and overloading conditions. The proposed method was carried out on the Debre Markos distribution networks, using the Teaching Learning Based Optimization (TLBO), Particle Swarm Optimization (PSO), and Differential Evolutionary (DEV) algorithms. The proposed SR was carried out considering and without considering protection constraints. The obtained SR topology was not feasible for SR without considering protection constraints, since some PDs fail to operate properly in normal loading conditions. After executing the proposed SR algorithms by considering protection constraints for a single fault case, the power loss reductions in TLBO, DEV, and PSO were 64.9073%, 45.9073%, and 55.358 %, respectively. The minimum voltage profiles obtained in each proposed TLBO, DEV, and PSO algorithm were 0.96%, 0.95%, and 0.96%, respectively. In each algorithm, except for the branch under fault, all healthy out-of-service branches were restored. When the protection constraints were considered in an optimal SR, load current did not exceed the rating of the fuses. The results show the importance of considering protection constraints during SR to prevent dysfunction of the PDs in the network. Comparative analyses were carried out on each algorithm and TLBO algorithms performed better than PSO and DEV for search functions.

Fault detection and isolation of gas turbine using series–parallel NARX model

This paper describes the design and implementation of intelligent dynamic models for fault detection and isolation of V94.2(5)/MGT-70(2) single-axis heavy-duty gas turbine system. The series–parallel structure of nonlinear autoregressive exogenous (NARX) models are used for fault detection, which initiate greater robustness and stability against uncertainties and perturbations. Moreover, to improve the fault detection robustness against uncertainties, the Monte Carlo technique is used in the proposed fault detection structure to select the best threshold. The analysis of fault detectability and fault detection sensitivity are accomplished to analyze the performance of the suggested technique. The fault isolation process is also achieved by using the residual classification approach. The results show the feasibly, robustness, and performance of the presented approach for fault diagnosis of nonlinear systems in the presence of uncertainties.

Optimal allocation of protection and control devices in smart distribution systems: Models, methods, and future research

The fundamental goal of the distribution system operator (DSO) is to serve its customers with reliable and low-cost electricity. Failures in power distribution systems are responsible for 80% of customer service interruptions. The emergence of smart distribution system (SDS) with advanced distribution automation (DA) and communication infrastructure offers a great opportunity to improve reliability, through the automation of fault location, isolation, and service restoration (FLISR) process. DA includes the installation of protection and control devices (PCD). The use of PCD makes fault management more efficient, reduces average outage duration per customer in case of faults, reduces costs due to unsupplied energy, and improves distribution system reliability. Although the use of PCD remarkably enhances distribution system reliability, it is neither economical nor affordable to install them in all potential locations. To obtain the optimal allocation of PCD (OAPCD), an optimisation problem has to be formulated and solved. Several models and methods have been suggested for the OAPCD in SDSs. Herein, an overview of the state-of-the-art models and methods applied to the OAPCD in SDSs are introduced, identifying the contributions of reviewed works, identifying advantages and disadvantages, classifying and analysing current and future research directions in this area.

A clamping type DC circuit breaker with short fault isolation time and low energy dissipation

The development of DC grids faces challenges from DC fault protection. The conventional DC circuit breaker (DCCB) employs metal-oxide varistor (MOV) to isolate the faulted line, in which the fault isolation process is coupled with the energy dissipation process. In this study, a clamping type DCCB (CTCB) uses internal capacitors to clamp the converter voltage is proposed. Thanks to the proposed configuration, fault isolation and energy dissipation are decoupled, resulting in a fast fault isolation and low energy dissipation compared to the conventional DCCB. The working principle of the proposed CTCB is presented and verified in a DC grid simulation model. A comparison is made with the traditional DCCB. The fault isolation time can be reduced by 34.5 %. The dissipated energy can be reduced by 17.4 %. The energy dissipation power can be reduced by 76.2 %. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>

Enhancing Fault Isolation for Health Monitoring of Electric Aircraft Propulsion by Embedding Failure Mode and Effect Analysis into Bayesian Networks

This paper describes a fault isolation approach for electric powertrains of unmanned aerial vehicles.The approach leverages the combination of failure mode and effect analysis (FMEA) and Bayesian networks,&#x0D; thus introducing dependability structures into a diagnostic framework. Faults and failure events from the FMEA are mapped within a Bayesian network, where network edges replicate the links embedded whitin FMEAs. This framework helps the fault isolation process by identifying the probability of occurrence of specific faults or root causes given evidence observed through sensor signals. The framework is applied to an electric powertrain system of a small, rotary-wing unmanned aerial vehicle, demonstrating how a Bayesian network enhanced by FMEA helps disambiguate between root causes of incipient failures, which would otherwise be considered as equally probable.

Fault Diagnosis Algorithm of Chemical Process Based on Information Entropy

In modern chemical industries, significant economic losses and unnecessary energy consumption are constantly resulted from process failures. In order to eliminate them in time, the early identification of the root cause of abnormal process deviation is crucial. Generally, causal analysis based on process knowledge plays an important role in process fault isolation. But with the increasing process complexity, the root cause is difficult to obtain using knowledge-based method alone. As a result of the wide application of distributed control systems, a large number of process data have been collected, which makes data-driven methods for fault diagnosis an active field in recent years. In previous research, contribution plots are widely used in practice to find the variables that are major contributors to the fault. However, the propagation of contribution among variables makes the results fluctuate at different sample points. In this study, a novel fault diagnosis method based on information entropy and signed directed graph (SDG) is proposed. Information entropy is first applied to select significant variables to specific faults according to the distribution feature of variables. Then, the propagation path of selected nodes is identified by SDG model to diagnosis the root cause. Due to the exclusion of less correlated variables, the results at each sample are relatively consistent compared with contribution plots. In order to verify its effectiveness, the proposed method is applied to the benchmark Tennessee Eastman process. The propagation path of most process faults is identified, which is well matched with the fault description, indicating that the proposed method has a good performance on diagnosing process faults.

Disturbance-Observer-Based Fault Tolerant Control of High-Speed Trains: A Markovian Jump System Model Approach

This paper addresses the fault tolerant control problem for high-speed trains in case of multiple possible failures. A new multiple point-mass model with system faults is built based on a stochastic jump system model approach. A novel active fault tolerant composite hierarchical anti-disturbance control strategy based on the disturbance observer is proposed such that the resulting composite system is stochastically stable with position and velocity tracking performance. According to whether the transition probabilities (TPs) of the failure and fault detection and isolation process can be accessed completely, three different cases (TPs are completely known, partially known, and completely unknown) are analyzed. For each case, based on the Lyapunov functional approach, a composite hierarchical controller is synthesized via a convex optimization problem. Finally, the simulations are given to illustrate the performance of the proposed methodologies.

The Voltage Clamping Based DC Circuit Breaker With Decoupled Fault Isolation and Energy Dissipation Processes

This paper proposes a DC Circuit Breaker (DCCB) topology for the DC fault clearance of the Modular Multilevel Converter based High Voltage Direct Current (MMC-HVDC) grid. By utilizing the voltage clamping and current limiting functions of the pre-charged capacitors in the DCCB, the DC-side short-circuit fault is isolated. First, the topological mechanism and operational principle of the unidirectional topology are analyzed, and the bidirectional topology is provided for backup protection. Then, the residual energy dissipation branch of the DC transmission line is introduced and the reclosing strategy after the deionization process is provided. Further, a step-insert method of the clamping branch is designed to relieve the sudden change of the electrical stresses during the voltage clamping based fault clearance process. Compared to the conventional DCCB schemes, the main advantage of the proposed topology is the decoupling of fast fault isolation and slow energy dissipation processes, which will accelerate the power recovery speed of the healthy parts of the HVDC grid. The proposed approaches are verified first by fully detailed Electro-Magnetic Transient (EMT) simulations on PSCAD/EMTDC then by a down-scaled hardware experiment.

Elaborate Reliability Evaluation of Cyber Physical Distribution Systems Considering Fault Location, Isolation and Supply Restoration Process

The existing reliability models of the cyber physical distribution systems, which are mostly based on some simplified assumptions, cannot accurately evaluate the reliability of complex cases in practical engineering applications. To solve this problem, an elaborate reliability evaluation method considering the whole process of fault location, isolation and supply restoration is proposed. This paper establishes reliability models of components and the two systems, summarizes the mapping relationship between various cyber system failures and the physical fault handling into several laws, proposes the reliability evaluation procedure in the framework of Monte Carlo method, and verifies the feasibility and effectiveness of the method in an actual distribution network cyber physical system. In the proposed method, the multiple component characteristics, complex topological structure, coupling relationship of the cyber-physical distribution systems, and the actual human-computer joint participation are considered in the analysis of fault location, isolation and supply restoration process, which provides a effective and accurate theory for the application of reliability evaluation in the actual distribution network and prosumer energy management system.