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ABSTRACT A fault isolation, estimation and fault‐tolerant control algorithm is proposed for non‐Gaussian stochastic distribution control systems with disturbance and multiple sensor faults. Sensor faults are represented as actuator faults virtually, and an observer is devised to detect the sensor fault occurrence time. Then two subsystems are separated by the expanded system through introducing the coordinate transformation matrices. One subsystem contains only sensor faults and does not contain disturbance and the other contains sensor faults and disturbance, which provides convenience for fault isolation. The faults are estimated respectively by the multiple fault isolation observers with the same number of sensors. A fault‐tolerant control scheme is proposed after getting the fault information to compensated sensor faults and track the desired probability density function. Finally, a MATLAB simulation example is used to verify the feasibility of the algorithm. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive bibliometricanalysis of solid-state circuit breakers, including technological developments and control methods in electric power distribution systems. By compiling and analyzing data from the Scopus database, the most cited papers in the field of protection system mechanisms can be identified. These papers, published in influential journals from various countries, covered different subject areas and case studies. Notably, 77.64 % of the articles focused on technological development and experimental control methods, while 22.35 % were review-based studies. This underscores the increasing interest among researchers in advancing the sustainability and reliability of protection devices in electric power distribution systems. The paper aims to identify and analyze the highly cited published articles on the respective field to provide future research direction on the technological development and control methods towards solid-state circuit breaker protection devices. The review also underlines numerous factors, issues, challenges, and difficulties that next-generation power semiconductor should overcome with in regards to system sustainability. Thus, this analysis will strengthen the scopes and provide context for the development of technology and control in the main protection devices in order to achieve an efficient, reliable, cost-effective, and sustainable power supply. [ABSTRACT FROM AUTHOR]

In industrial process monitoring, it is always a challenging and practical problem to analyze the causes of the system fault by isolating true fault variables from vast amounts of process data. However, the phenomenon of smearing effect occurs by using the traditional contribution analysis-based isolation methods since the defined isolation indices of different variables affect each other. In this paper, a new fault isolation method is proposed based on local outlier factor and improved k-nearest neighbor rule aiming to improve the isolation accuracy. Firstly, the nearest neighbors of each sample are obtained along the direction of a specific variable. Based on the nearest neighbors, the outlier-degree value of the variable is calculated and regarded as the contribution of the variable. Then, the contribution of the variable in all samples are obtained in the same way, among which the maximum one is selected as the isolation threshold value of this variable. During the online monitoring, the contribution of the variable in the newly collected sample is calculated in real time. Once the contribution is greater than the threshold, the variable is judged to be the dominant factor causing the system fault. Two cases on numerical example and Tennessee Eastman process are conducted to evaluate the effectiveness of the proposed method. • A fault isolation strategy is proposed where the contribution is defined by the distance between variables. • The variable contribution is obtained by calculating the outlier-degree relative to the k-nearest neighbors of variable. • Theoretical analysis of the isolability for the fault variables is given. [ABSTRACT FROM AUTHOR]

This review paper offers a comprehensive examination of the various types of faults that occur in inverters and the methods used for their identification. The introductory segment investigates the internal component failures of voltage-source inverters (VSIs), examining their failure rates and the consequent effects on the overall system performance. Subsequently, this paper classifies and clarifies the potential malfunctions in components and sensors, placing particular emphasis on their frequency of occurrence and the severity of their impact. The examination encompasses issues associated with transistors, including open circuits, short circuits, gate firing anomalies, as well as failures in capacitors, diodes, and sensors. Following this, the paper delivers a comparative assessment of fault diagnosis techniques pertinent to each type of component, appraised against specific criteria. The concluding section encapsulates the findings for each fault category, delineates the fault detection and diagnosis (FDD) methodologies, analyzes the outcomes, and provides recommendations for future scholarly investigation. [ABSTRACT FROM AUTHOR]

The integration of advanced information and communication technology in smart grids has exposed them to increased cyber attacks. Traditional model-based fault detection systems rely on mathematical models to identify malicious activities but struggle with the complexity of modern systems. This paper explores the application of artificial intelligence, specifically machine learning, to develop fault detection mechanisms that do not depend on these models. We focus on operational technology for fault detection, isolation, and identification (FDII) within smart grids, specifically examining a load frequency control (LFC) system. Our proposed approach uses sensor data to accurately identify threats, demonstrating promising results in simulated environments. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive bibliometricanalysis of solid-state circuit breakers, including technological developments and control methods in electric power distribution systems. By compiling and analyzing data from the Scopus database, the most cited papers in the field of protection system mechanisms can be identified. These papers, published in influential journals from various countries, covered different subject areas and case studies. Notably, 77.64 % of the articles focused on technological development and experimental control methods, while 22.35 % were review-based studies. This underscores the increasing interest among researchers in advancing the sustainability and reliability of protection devices in electric power distribution systems. The paper aims to identify and analyze the highly cited published articles on the respective field to provide future research direction on the technological development and control methods towards solid-state circuit breaker protection devices. The review also underlines numerous factors, issues, challenges, and difficulties that next-generation power semiconductor should overcome with in regards to system sustainability. Thus, this analysis will strengthen the scopes and provide context for the development of technology and control in the main protection devices in order to achieve an efficient, reliable, cost-effective, and sustainable power supply.

This paper introduces a new adaptive framework for fault detection and diagnosis using neural-based PCA. This framework addresses the limitations of traditional PCA in handling time-varying processes. The adaptive framework updates the correlation matrix recursively, allowing it to adapt to the natural time-varying behavior of processes. It also recursively determines the number of principal components and the confidence limits for three process monitoring statistics (T2, Q, and the combined index φ). To diagnose faults, four different types of contribution plots are used as follows: complete decomposition contributions (CDC), partial decomposition contributions (PDC), diagonal-based contributions (DBCs), and reconstruction-based contributions (RBCs). The evaluation through three simulation studies—including a numerical example, the continuous stirred tank reactor (CSTR) process, and water resource recovery facilities (WRRFs)—demonstrates that the combined statistics provided superior fault detection and diagnosis performance compared with individual statistics. Additionally, the study of the isolation method shows that no single method can definitively be claimed as superior. Overall, our study highlights the strength and versatility of neural-based PCA for detecting and diagnosing faults in dynamic processes. [ABSTRACT FROM AUTHOR]

Keeping electrical systems running is critical in industries especially in those that rely on static converters, for example in the case of inverters, continuous and safe of operation must be ensured even if one of the inverter’s static switch modules fails. This paper presents a method for fault detection in static converters. The study presents a 3-phase 3-level NPC (Neutral Point Clamped) inverter with IGBTs as static switches where open-circuit or closed-circuit faults are considered. In addition, leg failure and structural reconfiguration are also considered in this fault-tolerant inverter by adding a fourth 3-level leg.

In this paper, we propose a fault detection, fault isolation, and control reconfiguration (FDIR) for multiple automated guided vehicles (AGVs) to accomplish object transportation under actuator failures. Firstly, the observers are designed for the AGVs to detect any occurring faults in actuators. Secondly, banks of observers are employed to isolate the broken actuators. Finally, according to the isolation results, the controllers are reconfigured to accommodate the faults. The stability of the proposed methods is verified using the Lyapunov technique. It is worth noting that the proposed FDIR method considers the uncertainties in system dynamics, enhancing its practicality and convenience for implementation. The effectiveness of the proposed algorithm is demonstrated via both numerical and experimental examples involving multiple omnidirectional AGVs experiencing actuator faults. [ABSTRACT FROM AUTHOR]

In this paper, the fault isolation and fault diagnosis for the attitude control system of the quadrotor unmanned aerial vehicle (UAV) are shown, based on which the successive fault tolerant control (FTC) is also carried out. In most of modern literatures about the quadrotor UAV, the employed system dynamical models are established under ideal assumption conditions. However, system uncertainties of the quadrotor UAV system originating from modeling and other aspects, are seldom taken into account, which is deviated from the actual consequence. In this paper, a general dynamical model of the attitude control system of the quadrotor UAV is given, then for the aim to be more approximate to actual consequences, system uncertainties of state and input are considered. For the uncertain quadrotor UAV system, firstly, an observer method is employed to detect the fault location. Secondly, to diagnose the fault precisely, an adaptive observer with fault estimation term is employed. Lastly, on the basis of accurate fault estimation information, a sliding mode controller is employed to compensate the fault. Ultimately, the validity of the above mentioned methods is confirmed by numerical simulations. [ABSTRACT FROM AUTHOR]

The DC side fault of the high voltage DC transmission system based on modular multilevel converters high voltage DC (MMC-HVDC) is easily affected by the double influence of AC transient electrical quantity infeed and overvoltage and overcurrent, seriously threatening the safe operation of the power grid. Aiming at the problem above, a fault suppression strategy in dual mode of isolation and current limiting is proposed. Based on the half-bridge-full-bridge hybrid MMC, the fault transient mathematical model considering AC feed and sub-module switching feed is analyzed. AC isolation module with double conduction self-resistance structure and current-limiting control module is constructed, which can be adaptively adjusted to the system's direct current parameters. The ±500 kV double-ended hybrid MMC system model is established to verify the scheme's effectiveness. In addition, the influence of high AC modulation parameters on the suppression effect is investigated. PSCAD/EMTDC simulation results show that AC active and reactive power feed-in peaks are reduced by 388.69 MW and 119.82 Mvar, respectively, under dual-mode control. The peak value of DC overvoltage and overcurrent is reduced by 1211.53 kV and 22.5 kA, respectively, the fault self-clearing time is shortened by 43 ms, and the fault suppression effect is remarkable. In the high AC modulation ratio operating state, the modulation parameters are positively correlated with the AC power feed and negatively correlated with the overvoltage and overcurrent of the DC system. The research results provide support for the fault protection scheme of the hybrid MMC flexible and direct power grid. [ABSTRACT FROM AUTHOR]

Accurate and timely fault detection and isolation (FDI) improve the availability, safety, and reliability of target systems and enable cost-effective operations. In this study, a shared nearest neighbor (SNN)-based method is proposed to identify the fault variables of a circulating fluidized bed boiler. SNN is a derivative method of the k-nearest neighbor (kNN), which utilizes shared neighbor information. The distance information between these neighbors can be applied to FDI. In particular, the proposed method can effectively detect faults by weighing the distance values based on the number of neighbors they share, thereby readjusting the distance values based on the shared neighbors. Moreover, the data distribution is not constrained; therefore, it can be applied to various processes. Unlike principal component analysis and independent component analysis, which are widely used to identify fault variables, the main advantage of SNN is that it does not suffer from smearing effects, because it calculates the contributions from the original input space. The proposed method is applied to two case studies and to the failure case of a real circulating fluidized bed boiler to confirm its effectiveness. The results show that the proposed method can detect faults earlier (1 h 39 min 46 s) and identify fault variables more effectively than conventional methods. [ABSTRACT FROM AUTHOR]

Apart from the quick detection of abnormal changes in a process, it is also critical to pinpoint faulty variables after an out-of-control signal. The existing diagnostic procedures mainly focus on the diagnosis of changes in the process mean. This article investigates the joint diagnosis of high-dimensional process mean and covariance matrix based on Bayesian model selection with nonlocal priors. The proposed procedure enjoys two promising features. First, in addition to the isolation of shifted components, it can also provide a probability that the identified components are true, which is very useful for elimination of root causes of abnormal changes. Second, it possesses the model consistency property in the sense that the probability of identifying the true components with shifts approaches one as the sample size increases. The performance comparisons favor the proposed procedure. A real example based on the urban waste water treatment process is provided to illustrate the implementation of the proposed method. [ABSTRACT FROM AUTHOR]

There is a growing trend in renewable energies for wind harvesting and power generation. As new offers for the energy sector continue to rise up, the operation and maintenance team plays a vital role in increasing the availability and decreasing downtime of wind turbines subassemblies, based on conditional monitoring of their assets. This paper developed an approach for fault detection and isolation of the pitch control system on a direct drive permanent magnet synchronous generator using dynamic modeling and digital twins. The work presents two models of wind turbines, whose conditions were replicated in a multi-domain simulation software and the analyses were conducted under rotor constant speed. One concept represents a wind turbine model with a fault in pitch system, while the other is a healthy replica or a virtual model. Through the difference between three observable variables, namely: active power, angular speed and pitch angle, it was possible to isolate and classify each type of fault in the pitch system in wind turbines. [ABSTRACT FROM AUTHOR]

In the present work, the design and the implementation of a Fault Detection and Isolation (FDI) system for an industrial machinery is proposed. The case study is represented by a multishaft centrifugal compressor used for the syngas manufacturing. The system has been conceived for the monitoring of the faults which may damage the multishaft centrifugal compressor: instrument single and multiple faults have been considered as well as process faults like fouling of the compressor stages and break of the thrust bearing. A new approach that combines Principal Component Analysis (PCA), Cluster Analysis and Pattern Recognition is developed. A novel procedure based on the statistical test ANOVA (ANalysis Of VAriance) is applied to determine the most suitable number of Principal Components (PCs). A key design issue of the proposed fault isolation scheme is the data Cluster Analysis performed to solve the practical issue of the complexity growth experienced when analyzing process faults, which typically involve many variables. In addition, an automatic online Pattern Recognition procedure for finding the most probable faults is proposed. Clustering procedure and Pattern Recognition are implemented within a Fuzzy Faults Classifier module. Experimental results on real plant data illustrate the validity of the approach. The main benefits produced by the FDI system concern the improvement of the maintenance operations, the enhancement of the reliability and availability of the compressor, the increase in the plant safety while achieving reduction in plant functioning costs. [ABSTRACT FROM AUTHOR]

High voltage direct current transmission based on a modular multilevel converter (MMC-HVDC) is an effective method to solve the grid connection of the new energy. A DC fault is an issue that must be solved for MMC-HVDC. This paper proposes a protection scheme for HVDC converters to quickly suppress DC fault current without increasing the operation loss. By employing a bypass arm in conjunction with a switch-type zero-loss current limiter (SZCL), most of the DC current in the bridge arm flows through the bypass arm so the fault current of the power devices is reduced. There are a number of main advantages of this scheme. The fault isolation time can be greatly shortened, the cost of the system is effectively reduced, the steady-state operation loss of the system does not increase, the operation of the AC-grid can be maintained stably when a DC fault occurs, and the overcurrent impulse to the AC-grid is reduced. The proposed scheme is applicable to two-terminal systems and the DC grid, especially for faults at the converter outlet of the DC grid. Simulation results using PSCAD/EMTDC show the superiority of the proposed scheme when compared with other schemes and a comparison of the costs shows the feasibility of the proposed scheme in practical applications. [ABSTRACT FROM AUTHOR]

A high-resolution frontside fault isolation methodology for the analysis of wafer-to-wafer (W2W) hybrid bonding interconnects in three-dimensional integration is reported. The approach utilizes the visible light optical beam induced resistance change (VL-OBIRCH) method and incorporates a localized substrate removal technique, eliminating the need for a costly backside approach that requires a solid immersion lens. The top silicon substrate is removed using laser lithography and selective etching techniques, enabling the utilization of 405 nm excitation light for the VL-OBIRCH analysis. The validity of the methodology is demonstrated on W2W interconnect test structures with varying interconnect pitch and pad dimensions. The effectiveness of the proposed approach is confirmed through cross-sectional analysis. [ABSTRACT FROM AUTHOR]

In this paper, a fault isolation, diagnosis and fault tolerant control algorithm is proposed for nonlinear multiple multiplicative faults stochastic distribution control systems employing Takagi–Sugeno fuzzy system. To obtain the detailed fault information, a fault detection algorithm is introduced to discover the fault occurrence time. Then a fault isolation observer is built to produce the residual, and the error system is separated to subsystems affected only by disturbance and multiplicative faults. Moreover, a fault estimation scheme is presented to obtain the fault magnitude information. When faults occur, the system output probability density function will deviate from the desired distribution. So the model predictive control fault tolerant control scheme is needed to minimize the impact of faults as much as possible to make sure that the post fault output probability density function track the desired probability density function. The validity of the designed algorithm is demonstrated through a simulation example, where the fault tolerant control algorithm ensures that the system output probability density function still track the given output probability density function despite the complex case of multiple multiplicative faults occurring simultaneously. [ABSTRACT FROM AUTHOR]

Power MOSFETs are ubiquitously present in the structure of static power converters. The high switching frequency, power MOSFETs are subject to, along with the different inductive and capacitive parasitic elements unavoidably present in the structure of the electric circuits leads to the emission of undesirable Electromagnetic Interferences (EMI). Despite being harmful, the emitted EMIs are rich in information regarding the state of the emitting agent. Recent works have relied on the electromagnetic signature for open-circuit fault detection. However efficient, these methods usually relied on computationally heavy signal processing tools for fault signature extraction. In this work, however, we propose a cross-correlation-based EMI analysis to extract open-circuit switch faults signatures in a three-level NPC inverter. This method is more efficient and it results in a faster diagnosis time. The cross-correlations between the different intervals of the conducted EMI where each MOSFET pair is solicited are modeled in the healthy case. A change in the similarities between intervals, measured by the cross-correlations, is indicative of an unusual situation. The efficiency of the proposed fault diagnosis strategy is validated through various simulation and experimental results. [ABSTRACT FROM AUTHOR]

Fault detection and isolation is a ubiquitous task in current complex systems even in the linear networked case when the complexity is mainly caused by the complex network structure. A simple yet practically important special case of networked linear process systems is considered in this paper with only a single conserved extensive quantity but with a network structure containing loops. These loops make fault detection and isolation challenging to perform because the effect of fault is propagated back to where it first occurred. As a dynamic model of network elements, a two input single output (2ISO) LTI state-space model is proposed for fault detection and isolation where the fault enters as an additive linear term into the equations. No simultaneously occurring faults are considered. A steady state analysis and superposition principle are used to analyse the effect of faults in a subsystem that propagates to the sensors' measurements at different positions. This analysis is the basis of our fault detection and isolation procedure that provides the position of the faulty element in a given loop of the network. A disturbance observer is also proposed to estimate the magnitude of the fault inspired by a proportional-integral (PI) observer. The proposed fault isolation and fault estimation methods have been verified and validated by using two simulation case studies in the MATLAB/Simulink environment. [ABSTRACT FROM AUTHOR]

This paper deals with fault detection and isolation (FDI) in complex embedded wired communication networks. The considered faults are soft faults which do not prevent the communication, but may evolve into hard faults, i.e. short or open circuit. A novel FDI method based on power line communication (PLC) transmission systems is proposed. In these PLC systems, the transmission coefficients between the source and each receiver are estimated for communication purposes using orthogonal frequency division multiplexing (OFDM). Health indicators and residuals are computed by comparing the online estimated transmission coefficients with the reference coefficients. A methodology for dealing with complex networks, such as bus networks, is proposed. It is based on the decomposition of the network into several Y-shaped sub-networks. Each of these sub-networks is monitored to detect the presence of a fault. The FDI method is first validated using real data extracted from a Y-shaped network test bench. Then, the proposed approach is validated on a more complex network using realistic simulated data. • Power line communication systems estimate the network transmission coefficients. • Residuals are computed to compare the estimated and reference transmission coefficients. • The residuals are sensitive to cable soft faults i.e. degradations and may be used to detect these faults. • Complex networks s.t. bus networks are decomposed into multiple Y-shaped subnetworks. • The faulty branch of the network is located. [ABSTRACT FROM AUTHOR]

In this paper, a brushless motor with a three-phase inverter is investigated under healthy and multiple open-circuit faults. The occurrence of faults in an inverter will lead to atypical characteristics in the current measurements. This is why many usual entropies and multiscale entropies have been proposed to evaluate the complexity of the output currents by quantifying such dynamic changes. Among this multitude of entropies, only some are able to differentiate between healthy and faulty open-circuit conditions. In addition, another selection is made between these entropies in order to improve diagnostic speed. After the fault detection based on the mean values, the open-circuit faults are localized based on the fault diagnostic method. The simulation results ensure the ability of these entropies to detect and locate open-circuit faults. Moreover, they are able to achieve fault diagnostics for a single switch, double switches, three switches, and even four switches. The diagnostic time to detect and to isolate faults is between 10.85 ms and 13.67 ms. Then, in order to prove the ability of the fault diagnostic method, a load variation is performed under the rated speed conditions of the brushless motor. The validity of the method is analyzed under different speed values for a constant torque. Finally, the fault diagnostic method is independent from power levels. [ABSTRACT FROM AUTHOR]

Apart from quick detection of abnormal changes in a multivariate process, it is also critical to accurately identify the factors of abnormal changes after an out-of-control signal in multivariate statistical process control. Some diagnostic methods for fault identification were proposed by scholars on which one of the basic assumptions is that the process data are independent. The independent assumption is reasonable for many applications. However, with the development of industrial automation, the process data usually meet the phenomenon of autocorrelation. As it is well known, the autocorrelation affects detection ability of control chart. It is a natural question that is whether the autocorrelation affects the diagnostic performance of the diagnostic procedures. In the article, we propose the method adopted the analogy residual sequences and compare it with the performance of existing diagnostic methods for different shift sizes and various autocorrelation and cross-correlation structures. To limit the complexity, our discussion employs a first-order vector autoregressive process and focuses mainly on bivariate data in this article. The results of the simulation show the performances of diagnostic procedures were affected by autocorrelation and the impacts become greater as autocorrelation increases. On the other hand, the proposed method in the article can be free from the impacts of autocorrelation. Finally, a real example is also presented to demonstrate the implementation of the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes a new time‐varying process monitoring approach based on iterative‐updated semi‐supervised nonnegative matrix factorizations (ISNMFs). ISNMFs are a type of semi‐supervised model that constructs a semi‐nonnegative matrix factorization (SNMF) model of a process using both labelled and unlabelled samples. Compared with the existing nonnegative matrix factorizations (NMFs) where NMFs are referred to as matrix factorization algorithms that factorize a nonnegative matrix into two low‐rank nonnegative matrices whose product can well approximate the original nonnegative matrix, ISNMFs have advantages in terms of the model update and the use of labelled samples. The ISNMFs‐based process monitoring approach concerns fault detection and isolation and updates an SNMF model iteratively using the latest samples to capture the change of statistical property of time‐varying processes. Moreover, the proposed fault detection and isolation approach is supported by the k‐means algorithm in theory. At last, we demonstrate the superiority of ISNMFs over the existing NMFs in terms of fault detection and isolation through a case study on the penicillin fermentation process. [ABSTRACT FROM AUTHOR]

Dimension reduction is an essential method used in multivariate statistical process monitoring for fault detection and diagnosis. Principal component analysis (PCA) and independent component analysis (ICA) are the most frequently used linear dimensional reduction tools, and the contribution plot is the most popular fault isolation method in the absence of any prior information on the faults. These methods, however, come with their shortcomings. The fault detection capability of linear methods may not be sufficient for non‐linear processes, and smearing effect is known to deteriorate the diagnostics obtained from contribution plots. While the fault detection rate may be increased by kernelized methods or deep artificial neural network models, tuning data‐dependent hyperparameter(s) and network structure with limited historical data is not an easy task. Furthermore, the resulting non‐linear models often do not directly possess fault isolation capability. In the current study, we aim to devise a novel method named ICApIso‐PCA, which offers non‐linear fault detection and isolation in a rather straightforward manner. The rationale of ICApIso‐PCA mainly involves building a non‐linear scores matrix, composed of principal component scores and high‐order polynomial approximated isomap embeddings, followed by implementation of the ICA‐PCA algorithm on this matrix. Applications on a toy dataset and the Tennessee Eastman plant show that the I2 index from ICApIso‐PCA yields a high fault detection rate and offers accurate contribution plots with diminished smearing effects compared to those from traditional monitoring methods. Easy implementation and the potential for future research are further advantages of the proposed method. [ABSTRACT FROM AUTHOR]

This paper covers model-based fault detection and isolation for linear and nonlinear distributed parameter systems (DPS). The first part mainly deals with actuator, sensor and state fault detection and isolation for a class of DPS represented by a set of coupled linear partial differential equations (PDE). A filter based observer is designed based on the linear PDE representation using which a detection residual is generated. A fault is detected when the magnitude of the detection residual exceeds a detection threshold. Upon detection, several isolation estimators are designed using filters whose output residuals are compared with predefined isolation thresholds. A fault on a linear DPS is declared to be of certain type if the corresponding isolation estimator output residual is below its isolation threshold while the other fault isolation estimator output residual is above its threshold. Next, the fault location is determined when a state fault is identified. The second part of this paper focuses on fault detection and isolation of nonlinear DPS by using a Luenberger type observer. Here fault isolation framework is introduced to isolate actuator, sensor and state faults with isolability condition by using additional boundary measurements and without filters. Finally, the effectiveness of the proposed fault detection and isolation schemes for both linear and nonlinear DPS are demonstrated through simulation.

This paper presents two improved circuit breaker (CB) topologies based on resonant networks for medium voltage DC (MVDC) applications, such as shipboard power distribution and subsea oil and gas production. The topologies feature simple LC resonant networks that efficiently suppress fault current in just a few tens of microseconds, making them suitable for integration with existing solid-state or hybrid DC CB. Moreover, they also help to minimize the CB energy and reduce (if not eliminate) the requirement for protection devices like surge arresters during fault events. The proposed topologies are first tested using Typhoon Hardware-in-the-Loop (HiL) simulations at 1.2 MW (6 kV, 200 A) power levels and thyristor-based solid-state circuit breakers (SSCB). Then, scaled-down laboratory prototypes, also based on thyristor-based SSCB, are experimentally validated. The results show that both topologies quickly bring the current of the CB’s main switch to 0 A. The main circuit pathway requires only a small inductor and switch, resulting in a steady-state efficiency of $>$ 99.9%. The resonant network-based design allows for a fast response time of less than $50 \mu \text{s}$ using a simple and low-cost auxiliary circuit, eliminating the need for complex charging circuitry and protection devices typically used to suppress voltage surges during faults.

As crucial equipment for multi-voltage DC interconnection， high-capacity high-voltage DC/DC converters can be used for voltage conversion and DC fault isolation. The DC/DC autotransformer capable of blocking DC faults and reducing their reliance on DC circuit breakers can significantly reduce construction costs. To this end， a DC/DC autotransformer topology with half-bridge modular multilevel converters （HB-MMCs） connected in series is proposed. The fault response after a DC bipolar short-circuit fault on each side of the DC/DC autotransformer is analyzed under two operating conditions： forward power feeding and reverse power feeding. A corresponding fault isolation strategy is proposed. Simulations are performed on the PSCAD/EMTDC platform for the bipolar short-circuit faults under different operating conditions. The simulation results show that the proposed autotransformer can block DC faults in two directions. In contrast to other types of DC/DC autotransformers， the proposed autotransformer is economical.

The diagnosis of systems is one of the major steps in their control and its purpose is to determine the possible presence of dysfunctions, which affect the sensors and actuators associated with a system but also the internal components of the system itself. On the one hand, the diagnosis must therefore focus on the detection of a dysfunction and, on the other hand, on the physical localization of the dysfunction by specifying the component in a faulty situation, and then on its temporal localization. In this contribution, the emphasis is on the use of software redundancy applied to the detection of anomalies within the measurements collected in the system. The systems considered here are characterized by non-linear behaviours whose model is not known apriori. The proposed strategy therefore focuses on processing the data acquired on the system for which it is assumed that a healthy operating regime is known. Diagnostic procedures usually use this data corresponding to good operating regimes by comparing them with new situations that may contain faults. Our approach is fundamentally different in that the good functioning data allow us, by means of a non-linear prediction technique, to generate a lot of data that reflect all the faults under different excitation situations of the system. The database thus created characterizes the dysfunctions and then serves as a reference to be compared with real situations. This comparison, which then makes it possible to recognize the faulty situation, is based on a technique for evaluating the main angle between subspaces of system dysfunction situations. An important point of the discussion concerns the robustness and sensitivity of fault indicators. In particular, it is shown how, by non-linear combinations, it is possible to increase the size of these indicators in such a way as to facilitate the location of faults.

Due to the nonlinear dynamics of direct current (DC) microgrids, the existence of input constraints, and their multi-input multi-output (MIMO) nature, classical linear controllers cannot provide an appropriate performance in a wide range of operations. In this paper, to address these issues, nonlinear suboptimal controllers are systematically developed in the primary layer of DC microgrids by employing a state-dependent Riccati equation (SDRE) methodology. To this end, the whole complexities of the nonlinear dynamics and input constraints are considered in the design procedure of the proposed SDRE controllers. After designing the controllers, and for a fast yet effective fault detection/isolation, an artificial neural network (ANN) is trained to identify the closed-loop microgrid at its nominal condition. Then, the trained ANN is employed to design a fault detection/isolation mechanism. Simulation results of the developed SDRE control scheme augmented by the ANN-based fault detection/isolation mechanism demonstrate the merits of the proposed scheme.

The series–parallel DC wind farm composed of a large number of DC wind turbine (DCWT) is a potential scheme for offshore wind power collection with lower construction costs and power losses. However, enhanced coupling issues, such as energy curtailment and fault isolation, exist in the DC wind farm. To overcome the difficulties, a series–parallel DC collection system is proposed, where DCWTs based on the single active bridge (SAB) are connected in series and then are connected in parallel via DC/DC converters. The coupling mechanism among series–parallel strings is analyzed first, and a coordinate control strategy for the whole system is proposed based on the characteristics of SAB. DC fault located at different strings is analyzed and strategies are proposed to protect the whole system. Simulation results in PSCAD/EMTDC verifies the proposed control strategy with the less coupling among series–parallel DC wind farms under unequal wind speeds and fault.

Deploying feeder remote terminal units (FRTUs) in the distribution network and then integrating them into existing manual switches (MSs) can facilitate automatic locating of faulted sections and rapid isolation from faults to improve the power supply reliability. However, the fault management process, including fault location, fault isolation, and service restoration, is usually simplified in traditional FRTUs deployment methods. In particular, the fault isolation and service restoration operations are usually modeled individually without considering the FRTUs operation during the whole fault management process in these traditional methods. In this paper, the FRTUs in the potential service restoration paths for the customers are ruled out to avoid unnecessary power outage, and the effect of FRTUs operation on the fault management process is analyzed and discussed in branched distribution networks. Secondly, a quantification analysis model considering FRTUs operation is built to obtain the accurate interruption time of the distribution network. Thirdly, the quantification model is used to obtain the revenue generated by interruption cost reduction for the deployed FRTUs. Finally, an optimal deployment model of FRTUs in mixed-integer nonlinear format is constructed and solved efficiently by the software LocalSolver, which combines heuristic techniques and stochastic search algorithms. The new method is compared with the traditional FRTUs deployment method in the CSG 53-node test system, and the results show that the proposed method can achieve greater return on investment and higher power supply reliability.

The automobile industry's rapid growth enables developments in all branches of the sector. This also includes electronics, where the latest technological nodes are used today. New challenges to providing safety and reliability for such circuits are thus to be faced. One of these is the diagnostics of analog and mixed-signal circuits. Performing failure analysis has become very difficult indeed, due to the lack of auto-diagnosis methods. This paper describes a solution to improve the diagnosis of such circuits. We can insert elementary devices that return circuit states via light emission by utilizing the basic principles of light emission from silicon electronic components. These elementary devices are used to check every fundamental signal of the circuit, with significant benefits for diagnostics and failure analysis. All actions leading from concept to real silicon are explained. The reported results for a real silicon circuit and a failure analysis case demonstrate the effectiveness of this method. [ABSTRACT FROM AUTHOR]

This article deals with the cyber security of industrial control systems. Methods for detecting and isolating process faults and cyber-attacks, consisting of elementary actions named "cybernetic faults" that penetrate the control system and destructively affect its operation, are analysed. FDI fault detection and isolation methods and the assessment of control loop performance methods developed in the automation community are used to diagnose these anomalies. An integration of both approaches is proposed, which consists of checking the correct functioning of the control algorithm based on its model and tracking changes in the values of selected control loop performance indicators to supervise the control circuit. A binary diagnostic matrix was used to isolate anomalies. The presented approach requires only standard operating data (process variable ( P V ), setpoint ( S P ), and control signal ( C V ). The proposed concept was tested using the example of a control system for superheaters in a steam line of a power unit boiler. Cyber-attacks targeting other parts of the process were also included in the study to test the proposed approach's applicability, effectiveness, and limitations and identify further research directions. [ABSTRACT FROM AUTHOR]

Faulty signals from global navigation satellite systems (GNSSs) often lead to erroneous position estimates. A variety of fault detection and exclusion (FDE) methods have been proposed in prior research to both detect and exclude faulty measurements. This paper introduces a new technique for the FDE of GNSS measurements using Euclidean distance matrices. After a brief introduction to Euclidean distance matrices, both the detection and exclusion strategy is explained in detail. Euclidean distance matrix-based FDE is verified in two separate real-world data sets and proven to accurately detect and exclude GNSS faults on an average of 1.4-times faster than residual-based FDE and 70-times faster than solution separation FDE. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this work, a novel approach on active fault detection and isolation for linear time‐invariant systems, named forced diagnosability, is proposed. This approach computes a continuous state feedback law to render a fault diagnosable, even when it cannot be diagnosed by using passive diagnosis methods. To do that, this work derives novel geometric relationships between unobservability and (A,B)$$ \left(A,B\right) $$‐invariant subspaces that, under certain conditions, guarantee the existence of such state feedback law. The objective of the state feedback law is to force all the faults, except the one required to be diagnosed, named Ld$$ {L}_d $$, to reside in an unobservability subspace. This effectively decouples the effect of Ld$$ {L}_d $$ on the system output, from the effect of the other faults, allowing the design of a residual generator to detect and isolate the desired fault. The proposed state feedback law continuously forces diagnosability, and it can be computed in polynomial time. This avoids testing faults only at fixed time intervals and solving complex optimization problems required in other active diagnosis approaches. A numerical example is presented to illustrate the efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

The multiport dc/dc converter with input independent output series structure is simple and efficient for photovoltaic (PV) applications. Through the cascade of sub-modules (SM), the converter has a high step-up ratio to connect MVdc bus. However, it has many ports which increase the risk of short-circuit faults. Therefore, this article proposes a fault ride-through (FRT) topology for multiport converter. When low-voltage ports are short-circuited, they can be isolated by blocking the switches of their own. The FRT topology can support the output voltage of faulty SMs to avoid the disconnection from MVdc bus. Other healthy ports can still generate power to MVdc bus. When the medium-voltage port is short-circuited, the FRT topology can quickly clear the fault current to prevent devices from being damaged. Due to the zero voltage switching, the proposed topology has higher efficiency. For medium-voltage fault, the FRT topology has faster fault clearing speed because more capacitors are reused. Finally, the proposed topology is verified by simulation and experiment. [ABSTRACT FROM AUTHOR]

Copyright of Journal of National University of Defense Technology / Guofang Keji Daxue Xuebao is the property of NUDT Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The current-limiting inductors are usually installed in the multi-terminal dc grid to limit the fault current rising speed. However, they will increase the energy dissipation pressure and fault isolation time of the dc circuit breaker, which puts forward higher requirements on the capacity of arresters in hybrid dc circuit breaker and the heat dissipation of grid equipment. Therefore, an improved integrated hybrid dc circuit breaker is proposed in this brief for applications in the multi-terminal dc grid. Compared with the fault isolation solution based on existing integrated hybrid dc circuit breakers, the proposed breaker has slightly higher cost of semiconductor switches but significantly reduced arrester capacity requirement and faster fault isolation speed by bypassing the inductance components. Extensive electromagnetic simulations and physical experiments verify the effectiveness of the proposed device. [ABSTRACT FROM AUTHOR]

Due to the presence of actuator disturbances and sensor noise, increased false alarm rate and decreased fault detection rate in fault diagnosis systems have become major concerns. Various performance indexes are proposed to deal with such problems with certain limitations. This paper proposes a robust performance-index based fault diagnosis methodology using input–output data. That data is used to construct robust parity space using the subspace identification method and proposed performance index. Generated residual shows enhanced sensitivity towards faults and robustness against unknown disturbances simultaneously. The threshold for residual is designed using the Gaussian likelihood ratio, and the wavelet transformation is used for post-processing. The proposed performance index is further used to develop a fault isolation procedure. To specify the location of the fault, a modified fault isolation scheme based on perfect unknown input decoupling is proposed that makes actuator and sensor residuals robust against disturbances and noise. The proposed detection and isolation scheme is implemented on the induction motor in the experimental setup. The results have shown the percentage fault detection of 98.88%, which is superior among recent research. [ABSTRACT FROM AUTHOR]