Improve System Reliability Research Articles

Optimal Allocation of Distributed Thyristor Controlled Series Compensators in Power System Considering Overload, Voltage, and Losses with Reliability Effect

In this paper, optimal allocation of a distributed thyristor-controlled series compensator (DTCSC) in a power system is presented to minimize overload, voltage deviations, and power losses while improving system reliability. The decision variable was defined as the optimal reactance of the DTCSC in the power system, which was determined using a new meta-heuristic algorithm named the improved equilibrium optimization algorithm (IEOA). A nonlinear inertia weight reduction strategy was used to improve the performance of traditional EOA in preventing premature convergence and facilitate a quick global optimum solution. The effect of system critical line outage was evaluated for each of the considered goals. To evaluate the effectiveness of the proposed methodology, IEOA capability was compared with particle swarm optimization (PSO) and manta ray foraging optimizer (MRFO) methods. Simulations were carried out considering different scenarios on 14- and 118-bus test systems. The results showed that, for all scenarios, optimal allocation of DTCSC could result in a significant reduction in overloading, voltage deviation of network buses, as well as power losses under the condition of line outage, due to the optimal injection of reactive power. In all investigated scenarios, our results attested to the superiority of the IEOA over the traditional EOA, PSO, and MRFO in achieving a better value for the objective function. In addition, the results showed that improving reliability in the objective function could eliminate overloading, and hence, introduce further improvement in each of the objectives.

Special Issue on Intelligent Diagnostic and Prognostic Methods for Electronic Systems and Mechanical Systems

Fault diagnoses and prognostics are important tools to improve system reliability [...]

Thermal-Aware Standby-Sparing Technique on Heterogeneous Real-Time Embedded Systems

Low power consumption, real-time computing, and high reliability are three key requirements/design objectives of real-time embedded systems. The standby-sparing technique can improve system reliability while it might increase the temperature of the system beyond safe limits. In this paper, we propose a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</u> hermal- <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</u> ware <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</u> tandby- <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</u> paring (TASS) technique that aims at maximizing the Quality of Service (QoS) of soft real-time tasks, which is defined as a function of the finishing time of running tasks. The proposed technique tolerates permanent and transient faults for multicore real-time embedded systems while meeting the Thermal Safe Power (TSP) as the core-level power constraint, which avoids thermal emergencies in on-chip systems. Executing the main and backup tasks on the cores at any power consumption below TSP guarantees that no thermal violation occurs. Our TASS proposed method provides an opportunity to remove the overlaps of the execution of main and backup tasks to prevent extra power consumption due to applying the fault-tolerant technique. Meanwhile, in order to maximize the QoS, we employ a heterogeneous platform to execute the main tasks as soon as possible on high-performance cores with more power budget. The backup tasks are executed on low power cores after finishing the main tasks. In this case, when the main task finishes successfully, the whole of its corresponding backup task can be dropped, resulting in a significant amount of power and temperature reduction. Therefore, in the fault-free scenarios, the spare cores can be powered down, and only the main tasks are scheduled and executed on the primary cores. Experiments show that our proposed method improves QoS up to 39.78% (on average by 18.40%) and reduces the peak power consumption and temperature by up to 40.21% and 15.47˚C (on average 28.31% and 13.60˚C), respectively, at runtime, while keeping the system reliability at the required level.

An MMC DC-Link Voltage Control Method Without Actual Voltage Measurement

DC-link voltage control is of great importance for modular multilevel converter (MMC)-based high-voltage direct-current (HVDC) systems. Existing methods need to measure the actual dc-link voltage, which are referred to as direct control methods in this article and require a reliable dc voltage measuring device. The measurement system is complicated. Also, once malfunction occurs, dc-link voltage control may fail. This article aims to develop a dc-link voltage control method without actual voltage measurement. The idea is based on the coupling effect between dc-link voltage and submodule (SM) capacitor voltage. Based on the analysis, an indirect dc-link voltage control method is proposed. In this method, the existing dc-link voltage control loop is first replaced by the total capacitor voltage regulation control loop. Then, a compensation signal is introduced to the switching functions to mitigate undesired voltage deviation. Two approaches are proposed to generate a proper compensation signal. The proposed method can be integrated into the existing MMC control system. Also, in measurement failure cases, it can come into service and continue maintaining MMC dc-link voltage, improving system reliability. Both simulation and experimental results confirm the validity and effectiveness of the proposed method.

Inspection and maintenance optimization for heterogeneity units in redundant structure with Non-dominated Sorting Genetic Algorithm III

Redundant structure has been widely deployed to improve system reliability, as when one unit fails, the system can continue to function by using another one. Most existing studies rely on the similar assumption that the heterogeneous units are subject to periodic inspections and identical in terms of their aging situations and the numbers of resisted shocks. In practice, it is often adequate to trigger a unit individually in the event of a single shock, which intensifies the degradation of that unit, accordingly, requiring a sooner inspection to ensure its safety. In this study, the stochastic dependency among units is addressed firstly by introducing a novel activation sequence. Secondly, an adaptive system-level inspection policy is proposed by prioritizing the unit with a worse state. Finally, we take advantage of Monte Carlo methods to simulate the whole process and estimate two objectives, referring to the average system unavailability and maintenance cost, in a designed service time. It is found that the two objectives are contradictory through numerical examples. The Non-dominated Sorting Genetic Algorithm III (NSGA-III) algorithm, therefore, has been employed to find the optimal solutions in system unavailability and cost, which provide clues for practitioners in decision-making.

Improving System Reliability of Secondary Distribution Networks Through Smart Monitoring

This paper presents results of studies of the impacts of substation monitoring on reliability indices of distribution systems. It was detected that the fault detecting and repair time was the main cause of the prolonged outages experienced in the system which increases the poor reliability indices of the system. The intelligent monitoring device detect open circuit faults and relay to the base station in 30s compared with 3 to 24 hours it was taking to detect fault. The intelligent substation monitoring device reduced the fault detection time and hence improves the reliability of distribution systems. The system was simulated, and result showed that SAIDI was reduced from 244 hours of outage per annum to just 98 hours of outage per annum while CAIDI was improved from 10 hours of interruption per outage to 5 hours of interruption per outage. This paper demonstrated that the fault detection and reporting time can be greatly reduced by the use of intelligent monitoring system and reliability indices.

Reliability Assessment Based on Optimal DGs Planning in the Distribution Systems

Due to increased load demands, distribution systems suffer from high power losses, low voltage levels, high current, and low reliability. To solve these problems, integrate distributed generator units (DG) into the distribution system. DG units are among the most popular methods of improving distribution system reliability, power losses, and bus voltage improvement through the placement and selection of distributed generator units in an optimal location and size. This work proposed Enhanced Particle Swarm Optimization (EPSO) technology to find the optimum location and size of DG units to reduce power losses, improve bus voltage level, and employed the Transient Electricity Analyzer (ETAP) to evaluate the reliability of the distribution system network. ETAP is a programming tool for modeling, analysis, design, optimization, operation, and control of electrical power systems. These findings may be useful in conducting reliability assessments and correctly utilizing dispersed generation sources for future power system growth by power utilities and power producer companies. The proposed method was employed on the Iraqi distribution system (AL-Abasia distribution network (F10 feeder)). After adding three DG units to the distribution system, theer adding three DG units to the distribution system, the obtained simulation results showed a significant reduction in power losses, voltage levels, and reliability enhancement.

Comprehensive Review on Fault Ride-Through Requirements of Renewable Hybrid Microgrids

The world is interested in applying grid codes to increase the reliability of power systems through a micro-grid (MG). In a common practice, the MG comprises a wind farm, and/or photovoltaic (PV) arrays that are integrated with diesel generators and energy storage devices. Fault ride-through (FRT) capability is an important requirement of grid codes. FRT means that the MG is still connected to the grid during numerous disturbances such as faults. This is required to ensure that there is no loss of power generated due to grid faults. Reactive currents must be injected into the grid to increase the power system stability and restore voltage. To enhance FRT for doubly fed induction generator (DFIG) based WT installation, internal control modifications of rotor-side converters and grid-side converters are applied. The solutions that depend on these modifications are traditional and advanced control techniques. Advanced control techniques are needed due to the non-linear nature and less robustness of traditional ones. External hardware devices are also added to improve the FRT of DFIG which are classified into protection devices, reactive power injection devices, and energy storage devices. A comprehensive review of FRT enhancements of DFIG-based WTs, PV systems, and MGs using hardware and software methods is presented in this effort. A classification of FRT of PV systems is characterized plus various inverter control techniques are indicated. Several FRT methods for hybrid PV-WT are presented, with full comparisons. The overall operation and the schematic diagrams of the DFIG-WT with FRT methods are discussed and highlighted. Many Robust control methods for controlling grid connected AC, DC and hybrid AC/DC MGs in power systems are addressed. A total of 210 reported articles were review, including the most up-to-date papers published in the literature. This review may be used as the basis to improve system reliability for those interested in FRT methods. Various traditional and advanced control techniques to improve the FRT abilities are summarized and discussed, including protection devices, reactive power injection devices, and energy storage. In addition, the classifications of FRT hardware methods for DFIG are presented, including grid code requirements.

Efficient Matching-Based Parallel Task Offloading in IoT Networks.

Fog computing is one of the major components of future 6G networks. It can provide fast computing of different application-related tasks and improve system reliability due to better decision-making. Parallel offloading, in which a task is split into several sub-tasks and transmitted to different fog nodes for parallel computation, is a promising concept in task offloading. Parallel offloading suffers from challenges such as sub-task splitting and mapping of sub-tasks to the fog nodes. In this paper, we propose a novel many-to-one matching-based algorithm for the allocation of sub-tasks to fog nodes. We develop preference profiles for IoT nodes and fog nodes to reduce the task computation delay. We also propose a technique to address the externalities problem in the matching algorithm that is caused by the dynamic preference profiles. Furthermore, a detailed evaluation of the proposed technique is presented to show the benefits of each feature of the algorithm. Simulation results show that the proposed matching-based offloading technique outperforms other available techniques from the literature and improves task latency by 52% at high task loads.

Design and Application of the Digital-Oriented Interactive System for Teaching Preschool Art Education

In this paper, the design of a teaching interactive system for preschool art education is digitally studied and analyzed, and the teaching interactive system is designed to be applied to the teaching process of preschool art education. It is to design and implement a convenient and feasible art education study and research mutual aid system. The art teaching mutual study and research system is based on the three-layer architecture of NET as the main system structure. The main function of the representation layer is to process the data requested by users and realize the interaction between users. The data layer is mainly to realize the processing of data logic, through the call and operation of the database, to realize the interactive access function of the database. The business logic layer is a relationship in the middle and plays the function of carrying on the top and bottom, realizing the logical processing of the database, and then realizing the business objectives of the system. The design and implementation of the multitier mechanism are of great importance in terms of improving system reliability as well as system compatibility. In the framework of activity theory research, the concept of dynamic integration of digital learning resources is proposed in conjunction with relevant research on digital learning resource integration. The intermediary of the interaction between subjects and digital learning resources in the digital learning process establishes the connection between different subjects through the interaction between elements in the activity system, promotes the accumulation of the number of digital learning resources and the two-way interaction of effective information, and thus realizes the orderly organization and management of the digital learning resource system. The theoretical basis of dynamic integration of digital learning resources, including activity theory, knowledge organization theory, and feedback principle, is discussed in detail in the study to provide a basis for the construction of the theoretical model of dynamic integration of digital learning resources.

Towards a Systematic Description of Fault Tree Analysis Studies Using Informetric Mapping

Fault tree analysis (FTA) is one of the important analysis methods of safety system engineering commonly utilized in various industries to evaluate and improve the reliability and safety of complex systems. To grasp the current situation and development trend of FTA research and to further point out FTA’s future development directions, 1469 FTA-related articles from the literature were retrieved from the SCIE and SSCI databases. Informetric methods, including co-authorship analysis, co-citation analysis and co-occurrence analysis, were adopted for analyzing the cooperation relationship, research knowledge base, research hotspots and frontier in the FTA research field. The results show that China has the highest number of publications, and the Loughborough University of England has the highest number of publications of relevant institutions. Dynamic fault tree analysis, fuzzy fault tree analysis and FTA based on binary decision diagrams are recognized as the knowledge bases in FTA studies. “Reliability Engineering and System Safety”, “Safety Science” and “Fuzzy Sets and Systems” are the core journals in this field. Fuzzy fault tree analysis, dynamic fault tree analysis based on Bayesian networks and FTA combined with management factors may be both the main research hotspots and the frontiers. Then, by deriving the above results, this study can help scholars better master the current research status and frontiers of FTA to improve system reliability and safety.

Cross-Subject Emotion Recognition Using Fused Entropy Features of EEG

Emotion recognition based on electroencephalography (EEG) has attracted high interest in fields such as health care, user experience evaluation, and human–computer interaction (HCI), as it plays an important role in human daily life. Although various approaches have been proposed to detect emotion states in previous studies, there is still a need to further study the dynamic changes of EEG in different emotions to detect emotion states accurately. Entropy-based features have been proved to be effective in mining the complexity information in EEG in many areas. However, different entropy features vary in revealing the implicit information of EEG. To improve system reliability, in this paper, we propose a framework for EEG-based cross-subject emotion recognition using fused entropy features and a Bidirectional Long Short-term Memory (BiLSTM) network. Features including approximate entropy (AE), fuzzy entropy (FE), Rényi entropy (RE), differential entropy (DE), and multi-scale entropy (MSE) are first calculated to study dynamic emotional information. Then, we train a BiLSTM classifier with the inputs of entropy features to identify different emotions. Our results show that MSE of EEG is more efficient than other single-entropy features in recognizing emotions. The performance of BiLSTM is further improved with an accuracy of 70.05% using fused entropy features compared with that of single-type feature.

Predictive control of uncertain systems over networks with redundant link

Networked systems using redundant channels to transmit data can effectively reduce the probability of data loss and improve system reliability and control margin. However, the structural complexity and economic cost of the system are also increased. To balance the redundancy and feasibility, the ratio of attraction domain to packet loss rate is defined as a balanced feasibility index. In this paper, single-channel packet loss is considered as Bernoulli distribution and a bounded packet loss network system control model is constructed as the arbitrary bounded packet loss control problem for redundant channel transmission network system. Therefore, the robust conditions of the closed-loop system and the constraints of the input and state are established under the framework of robust predictive control to construct the linear matrix inequality (LMI) optimization problem. Finally, to verify the effectiveness of the design method proposed in this paper, the discrete time-varying linear system and the main steam control system with redundant channels are used as study cases.

RBM-MCDM framework for optimization of maintenance and inspection intervals of small unmanned aircrafts

PurposeUnmanned aircraft applications are quickly expanded in different fields. These systems are complex that include several subsystems with different types of technologies. Maintenance and inspection planning is necessary to obtain optimal performance and effectiveness. The failure rate in these systems is more than commercial and manned aircraft since they are usually cheaper. But maintenance and operation planning are difficult because we deal with a system that has multi-components, multi-failure models, and different dependencies between subsystems without any advanced health monitoring system. In this paper, this matter is considered and a framework to determine optimal maintenance and inspection plan for this type of system is proposed to improve system reliability and availability. The new criteria according to this field are proposed.Design/methodology/approachMaintenance of unmanned systems influences their readiness; also, according to the complexity of the system and different types of components, maintenance programming is a vital requirement. The plan should consider several criteria and disciplines; thus, multicriteria decision approaches may be useful. On another side, the reliability and safety of unmanned aircraft are the most important requirements in the design and operation phases. The authors consider these parameters and develop a framework based on risk-based maintenance to overcome the problems for unmanned systems. This framework consists of two stages: at the first stage, the critical components and failure modes are determined by FMEA, and in the second stage, the priority of maintenance tasks is determined by a fuzzy multicriteria weighted decision system. In this study, fourteen criteria with different levels of importance are developed and proposed to find the best plan for maintenance and inspection intervals. These criteria have been extracted from the literature review, the author's experience, and expert opinions.FindingsA novel framework for risk-based maintenance has been proposed. Risk determination and risk criteria are the most important factors in this framework. Risks are determined by FMEA, and new criteria are proposed that are used for decision-making. These criteria are proposed based on practical experience and experts' opinions for the maintenance process in the aeronautic industry. These are evaluated by industrial cases, and this framework capability has been demonstrated.Research limitations/implicationsThe proposed framework and criteria for small unmanned aircraft have been developed based on a practical point of view and expert opinion. Thus for implementation in other aeronautic industries, the framework may need a minor modification.Practical implicationsTwo important subsystems of an unmanned aircraft have been studied, and the capabilities of this method have been presented.Originality/valueThis research is original work to determine a maintenance program for unmanned aircraft that their application has rapidly grown up. Practical and design parameters have been considered in this work.

Real-Time Implementation of qZSC for MVDC to Microgrids Link

Nowadays, power systems require new solutions to integrate renewable energies. In this paper, microgrids linked to MVDC are proposed through quasi-impedance-source converters to improve system reliability. Several prototypes are implemented using real-time platforms to analyze the system behavior, but the real-time implementation of the shoot-through state of the qZSC requires a very low time-step and sample time, which is not easy to achieve. The results obtained with these prototypes are included. Finally, a satisfactory solution is presented, implementing the power system in Typhoon HIL-402, the qZSC control in dSPACE MicroLabBox, and generating the gate signals in the FPGA included in the MicroLabBox platform.

On-Orbit Implementation of Discrete Isolation Schemes for Improved Reliability of Serial Communication Buses

Serial communication buses are used in electronic systems to interconnect sensors and other devices, but two of the most widely used protocols, i.e., interintegrated circuit (I2C) and serial peripheral interface (SPI), are vulnerable to bus-wide failure if even one device on the bus malfunctions. For aerospace applications demanding increasingly more distributed processing and sensing capability, the compounding risk to system reliability as device count scales becomes a limiting factor in mission scope, performance, and lifetime. We propose a simple external circuit to be added to each node on a communication bus that automatically isolates the node in the event of device failure. By automatically isolating failed devices, the integrity of the bus is preserved without requiring additional signals or processing overhead from the host controller. In this article, I2C and SPI isolation circuits are simulated, fabricated, and experimentally verified to be effective at preserving bus integrity in the event of peripheral device failure. The isolation circuits were integrated into three spacecraft for the successful NASA V-R3x mission and found to significantly improve system reliability by eliminating single-point failure modes of the I2C and SPI buses. The developed protection schemes are a valuable tool for decoupling system reliability from serial bus device count and can readily be integrated into the existing aerospace systems.

Concise convolutional neural network model for fault detection

Fault detection is an urgent need for maintenance to obtain the optimal scheduling of production activities, improve system reliability, and reduce operation and maintenance costs. Many studies published in recent years focus on machine learning models to detect any system anomalies in line with the era of big data and the fourth industrial revolution (Industry 4.0). Say, a working condition of bearing can be monitored and then any fault can be detected using the vibration analysis of bearing acceleration data. Most of the published works are presented based upon the knowledge of signal processing in which the result depends heavily on feature extraction. It becomes a challenge then to apply a machine learning algorithm directly to the raw acceleration data as it has been successfully applied to raw data in other science and engineering domains. In this article, a concise Convolutional Neural Networks-based deep learning model is proposed for bearing fault detection. The proposed model was concise with 98% less number of parameters compared to other well-known models. It produced 21.21% and 7.03% better accuracy and fault detection rate, respectively. The model was also tested in different operating parameter environments and still gave an excellent result. Since the proposed concise architecture of the model needed short training time, it is deemed suitable for application on manufacturing floor where the pace of production moves fast and the change of the production machine configuration likely occurs.

Optimal Scheduling of Reconfigurable Microgrids in Both Grid-Connected and Isolated Modes Considering the Uncertainty of DERs

In this study, an operation strategy is introduced for distributed energy resources (DERs) in reconfigurable microgrids (MGs) to improve voltage profiles, minimize power losses, and boost the system performance. The proposed methodology aims to minimize power loss and energy not supplied (ENS) in MGs with an intelligent share of DERs and network reconfiguration in grid-connected and islanded modes. Due to the inherent uncertain nature of renewable DERs, these sources’ power output is considered as an uncertain parameter, and its effect on the system characteristics is analyzed. The state-of-the-art information gap decision theory (IGDT) approach is utilized to explore different decision-making strategies in the energy scheduling of reconfigurable MGs to deal with such uncertainty. To validate the effectiveness of the proposed method, the IEEE 33-bus radial system is utilized as the test MG. The simulation results show the importance of energy storage systems and reconfiguration in dealing with uncertainty and improving system reliability.

Digital Regulation of Wireless Power Transfer Systems Using an Embedded Lock-In Amplifier

High-performance operation of inductive wireless power transfer (WPT) systems requires judicious tuning of the inverter connected to the transmission hardware for high-performance operation and reliability. In many applications, disturbances to both the coupling condition and the secondary-side load reshape the system dynamics, motivating the design of closed-loop regulation and impedance detection techniques. Although several analog methods have been presented, they require multiple additional components and are dependent upon fixed component values and operating frequency. In this article, a novel digital approach is presented for use in series–series compensated WPT systems. The proposed system extracts both the phase and the magnitude of the primary current in real time. Through closed-loop regulation, zero-voltage switching is sustained without additional compensation components, while the output current is controlled. By utilizing a digital framework, no additional analog components are required, improving system reliability and cost. The state identification method is self-tuning, providing robust suppression of switching noise even with a variable switching frequency. A computationally efficient construction ensures a fast converging measurement and enables high-frequency operation. Analysis, simulations, and experimental results are included to comprehensively verify these characteristics.

Soft Actor–Critic Algorithm Featured Residential Demand Response Strategic Bidding for Load Aggregators

Load aggregators (LAs) gather regional demand response (DR) resources as a representative of regional users to transact with the grid. They achieve this by facilitating residential customers to flexibly participate in DR programs. This article proposes a residential DR strategic bidding approach for LAs to improve system reliability while maximizing their financial advantages. It is based on the deep reinforcement learning (DRL) algorithm and is capable of overcoming the computational limitations of conventional model-based strategy design. A probabilistic model considering the electricity usage habits and response willingness was developed to reliably quantify the uncertainty responsive behaviors. A DR bidding model considering bilateral interaction with declaration risk was developed to balance the contradictory relationship between subsidy prices with uncertain DR quantities. In addition, the soft actor-critic algorithm was implemented to optimize the decision-making process, and thereby, optimize the continuous-action-space problem of maximizing the profits of LAs by DR scheduling. The proposed approach demonstrated its effectiveness and superiority in diverse DR situations, compared with a conventional method and another DRL approach.