Reliability Evaluation Process Research Articles

Feeder Capacity Planning and Reliability Evaluation Model of Distribution Network Based on Support Vector Machine

Capacity planning and reliability evaluation models of the traditional distribution network feeder mainly use multi-option balance constraints for multi-objective solutions, which are prone to capacity constraints, resulting in lower reliability indicators for planning and evaluation. Therefore, a brand-new distribution network feeder capacity planning and reliability evaluation model based on a support vector machine is designed. By using support vector machines to design the reliability evaluation process of distribution network feeder capacity planning, we establish an optimization model for reliability evaluation of distribution network feeder capacity planning. The experimental results show that the designed feeder capacity planning and reliability evaluation model based on a support vector machine has good evaluation results, high-reliability indicators for planning and evaluation, reliability, and certain application values.

Revaluation Method of Sensor Power Supply

This study proposes an approach for wireless sensor power supply voltage early warning using statistical models. Experimental and statistical analysis of electrical parameters and battery discharge characteristics were conducted to establish a reliable evaluation process. A linear regression model was employed to analyze and fit known temperature and voltage data, establishing a relationship model between voltage and temperature and using it to predict unknown voltage threshold data. This solves the problem of the inability to real-time predict the warning voltage threshold of wireless sensors as the actual working temperature changes and ensures their reliability. The results of experiments and statistical analysis demonstrate the effectiveness and accuracy of this method. Compared to the traditional method of using fixed voltage thresholds for warning, the proposed method offers a practical solution that can timely address the issue of low power supply voltage in wireless sensor networks.

An Efficient Method for the Reliability Evaluation of Power Systems Considering the Variable Photovoltaic Power Output

The operational reliability of power systems is threatened by the random failure of components and uncertain power output of renewable energies, such as photovoltaics. Under such circumstances, reliability evaluation is necessary for maintaining a continuous and stable energy supply. However, traditional reliability evaluation methods are usually extremely time-consuming, considering the numerous system states that need to be analysed. Hence, the reliability evaluation process cannot follow up the dynamic changes in PV output, which makes the timeline of the evaluation disappointing. This paper proposes an efficient reliability evaluation method for power systems with PV integration. The method reveals the analytical relationship between the reliability levels of the power system and the uncertainty factors that influence the reliability, such as the PV output. In this way, the dynamic reliability evaluation is achieved, and the evaluation results can be updated timely when the output of PV changes. First, a Gaussian mixture-hidden Markov model (GMM-HMM) is used to model the distribution characteristics of PV output. Then, the state enumeration and the hyperbolic truncated polynomial chaos expansion method are used to determine the analytical relationship between the reliability indices and PV output. Lastly, based on the analytical function, the operational reliability of the power systems is dynamically evaluated considering the real-time PV output. The effectiveness of the proposed method is verified using the modified IEEE 30 system as an example.

Sequential most probable point update combining Gaussian process and comprehensive learning PSO for structural reliability-based design optimization

This paper proposes an efficient reliability-based design optimization (RBDO) method that advantageously decouples comprehensive learning particle swarm optimization (CLPSO) algorithm with Gaussian process regression (GPR) model, termed as GPR-CLPSO. The method iteratively performs the CLPSO with deterministic parameters based on the most probable point (MPP) underpinning limit-state functions (LSFs) iteratively updated by the active learning reliability evaluation process. The GPR model approximates, from the design data given by CLPSO, the spectrum of LSFs under random parameters, and hence enables a significant computational reduction of Monte-Carlo simulations (MCSs) for failure probability approximation. The expected feasibility function is maximized using the CLPSO code to systematically refine the GPR model by adaptively adding new (intelligent) learning points in the region with high-reliability sensitivity leading to the more accurate prediction of failure probability. A novel inverse MCS constraint boundary method is developed to redefine the MPP assigned for the CLPSO algorithm in determining the new optimal design. The method efficiently leverages the decoupling approach, whilst significantly alleviating computing efforts, to quickly and accurately capture the optimal RBDO design. The resulting failure probability well satisfies the allowable limit. Four RBDO examples are provided to illustrate applications and robustness of the proposed decoupling GPR-CLPSO approach.

Evaluating an Analysis-by-Synthesis Model for Jazz Improvisation

This paper pursues two goals. First, we present a generative model for (monophonic) jazz improvisation whose main purpose is testing hypotheses on creative processes during jazz improvisation. It uses a hierarchical Markov model based on mid-level units and the Weimar Bebop Alphabet, with statistics taken from the Weimar Jazz Database. A further ingredient is chord-scale theory to select pitches. Second, as there are several issues with Turing-like evaluation processes for generative models of jazz improvisation, we decided to conduct an exploratory online study to gain further insight while testing our algorithm in the context of a variety of human generated solos by eminent masters, jazz students, and non-professionals in various performance renditions. Results show that jazz experts (64.4% accuracy) but not non-experts (41.7% accuracy) are able to distinguish the computer-generated solos amongst a set of real solos, but with a large margin of error. The type of rendition is crucial when assessing artificial jazz solos because expressive and performative aspects (timbre, articulation, micro-timing and band-soloist interaction) seem to be equally if not more important than the syntactical (tone) content. Furthermore, the level of expertise of the solo performer does matter, as solos by non-professional humans were on average rated worse than the algorithmic ones. Accordingly, we found indications that assessments of origin of a solo are partly driven by aesthetic judgments. We propose three possible strategies to install a reliable evaluation process to mitigate some of the inherent problems.

Combined Subset Simulation and Comprehensive Learning Particle Swarm Optimization in Reliability-Based Structural Optimization

Reliability-based design optimization (RBDO) addresses the cost-effective integrity design of structures in the presence of inherent uncertain parameters. Processing this class of problem is challenging from the computational burden to determine the failure probability of structures violating the limit-state function. This paper proposes an efficient decoupling RBDO method that advantageously couples a comprehensive learning particle swarm optimization (CLPSO) algorithm with a subset simulation (SS), termed as SS-CLPSO approach. In essence, the proposed method iteratively performs the CLPSO assuming deterministic parameters based on the most probable point underpinning limit-state functions updated within the reliability evaluation process. Based on the CLPSO design data, the SS approximates the spectrum of limit-state functions under uncertain parameters, and hence enables the significant reduction of Monte-Carlo simulations for the failure probability prediction. The SS map outs the failure probability from the conditional samples constructed at each intermediate event. The proposed SS-CLPSO terminates the optimal solution to the RBDO problem as when the resulting failure probability converges to the permissible threshold. The applications of the present approach are illustrated through the steel truss design under probabilistic uncertain parameters and constraints.

Data-driven power system reliability evaluation based on stacked denoising auto-encoders

The increasing penetration of renewable energy resources in power systems inadvertently leads to a surge in the number of random states. The calculation of optimal load shedding for all the proliferated states is rather overwhelming. It has been considered a bottleneck in the reliability evaluation process. To address this issue, a deep-learning-based approach is proposed to evaluate system reliability more efficiently considering the fluctuation of generation and loads. In the proposed power flow model, a stacked denoising auto-encoder (SDAE) serves as a multilayer neural network for the deep-learning process. Its stacked structure and encoding-decoding recursion enable it to extract high-order features even from non-linear equations. The features of contingency states are crucial to directly acquire the minimum load curtailment without the time-consuming optimal power flow (OPF). The after-trained model is applied to states simulated with the Monte-Carlo method in the RTS-79 system. The numerical result reveals the great advantage of the SDAE-based method in computation time while ensuring high accuracy. As an alternative way to classic OPF algorithms, it can be integrated with the impact-increment method and other state selection approaches.

Perceived Efficacy of e-Proctoring Software for Emergency Remote Online Based Assessment: Perceptions of Proctored Examinations

The switch from traditional teaching methods to emergency remote online learning as a result of the outbreak of COVID-19 necessitated a change in approaches to measurement and evaluation in our educational system. A lot of methods were adopted yet the need for credible, properly monitored, valid and reliable assessment continues to be an issue of debate. E-proctoring as a popular strategy for monitoring and administering online-based examinations has witnessed tremendous success over the years yet ethical issues and many more are yet to be extensively examined. While a teacher may embrace the use of this software for monitoring examination, the perception of preservice teachers has not been explored adequately, much more the opinions of the instructors and preservice teachers on the level of efficacy with regards to the use of e-proctoring strategy for monitoring online-based examinations in a hybrid/emergency remote online educational model has not been investigated. It is in this light that this study intends to examine the perceptions of instructors and preservice teachers on the use of e-proctoring devices during online-based assessments, two research questions were investigated through a quantitative research design. A structured questionnaire was used to elicit information from 40 participants comprising of 36 students and 4 teachers on their perceptions of the effectiveness and efficacy of the e-proctored software while administering and monitoring examinations. Findings from the study will be beneficial in drawing valid conclusions on the use of e-proctored devices in monitoring emergency remote online-based assessment. The conclusions will prepare school administrators and equip them with the possible approaches to adopt in ensuring that a credible valid and reliable evaluation process is conducted during the post-COVID 19 semesters.

Reliability Evaluation of Bulk Power Systems Incorporating UPFC

Unified power flow controller (UPFC) is one of the most advanced flexible AC transmission system (FACTS) devices that can simultaneously and independently control both the real and reactive power flow in a transmission line. The utilization of UPFC can result in significant reliability benefits in modern power systems. This paper proposes a novel reliability network model for a UPFC, which incorporates the logical structure and the distinct operating modes of a UPFC. Two-state or three-state models have been used for UPFC by previous researchers. The proposed model divides the UPFC operating modes into four states, namely the UPFC up state, STATCOM state, SSSC state and UPFC down state, in order to improve the accuracy of the model by recognizing the practical operating states of a UPFC. The new model also incorporates an AC flow-based optimal load shedding approach to assess the impact of bus voltages and reactive power flow on UPFC in order to decide appropriate load curtailment in the reliability evaluation process. The performance of the proposed model is verified using a test system, and compared with different reliability models of UPFC. Various operating schemes, such as different placement locations of UPFC, and different capacities of UPFC are used to illustrate the advantages of the developed models, and to examine the impacts of UPFC on the system reliability.

Multi-criteria decision analysis towards robust service quality measurement

ImportanceThe role of airports is critical for a region in which it is viewed as an engine for the economic development. Facilities, infrastructure, information and in general the services offered by an airport represent the fuel for this engine. Evidently, customers and travelers expect standard-quality services that need to be framed and measured. Therefore, services in airports should be quantified and maintained, accordingly. ObjectivesThis article reports a case study for evaluating quality of services offered by five main airports located in Spain. Quality of service was modelled based on a number of factors such as convenience, comfort, courtesy of staffs, information visibility, prices, security, and transportation facilities. The grey based multi-criteria decision analysis (MCDA) was employed towards a reliable evaluation process by airport experts and to accommodate the several qualitative and conflicting evaluation factors with distinct definitions. To this end, Grey Step-wise Weight Assessment Ratio Analysis (SWARA-G) and grey Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS-G) methods were applied for quantifying relative weights of decision factors and rating airports, respectively. Several sensitivity analysis, simulations, and comparisons were conducted for verifying the preciseness of the revealed results. FindingsResearch findings demonstrate that the proposed SWARA-G-MARCOS-G-based methodology (i) enables decision makers to express their preferences clearly; and (ii) attenuates the embedded subjectivity and uncertainty within the decision-making process. In addition, they revealed that access to the parking and Wi-Fi connection are amongst the critical factors in evaluating service quality of airport. ContributionThis paper contributes to related literature in presenting a novel decision-making approach for measuring service quality of airports, validated via a real-case study. The employed interval and linguistic grey variables allow experts, in airport operations, to express their opinions with higher flexibility and comfortability. The presented model could be re-applied for other studies or practical cases as a user-friendly decision support system.

자동차 구동축 소음 저감을 위한 신뢰성 개선프로세스의 개발

Purpose: Securing reliability of the components in automobiles is a key issue. The purpose of this research is to develop a step-by step process for reliability improvement using accelerated tests to reduce the noises from the drive shafts in automobiles.BRMethods: The step-by-step reliability improvement process includes problem definition, reliability test planning, executing an accelerated test, reliability analysis, deriving an improvement scheme, and making a reliability qualification standard.BRResults: A real case study shows that the proposed process can reduce the noises from the drive shafts by identifying the failure causes and mechanisms through material tests and bench tests. Finally, intended reliability target is proved to be satisfied by quantitative reliability analysis.BRConclusion: Reliability improvement can be realized by establishing the systematic reliability evaluation process. We expected that the proposed reliability improvement process can be widely applied to other components in automobiles or other industries to establish the reliability management policy.

Reliability and power supply capability evaluation of active distribution networks with four‐terminal soft open points

Soft open points (SOPs) have the ability to regulate the power flow among their terminals in a continuous manner, which can solve the problems brought by the synergy of distribution networks and distributed generation, and improve the system reliability and power supply capability. To address the current lack of quantitative measurement on the effects of SOPs, the power supply capability evaluation method for active distribution networks (ADNs) with four-terminal SOPs considering reliability is proposed. Firstly, the topology and configuration modes of four-terminal SOPs are studied, and their control modes are investigated in normal operation and supply restoration conditions. Then, using the feeder partition method, the effects of four-terminal SOPs on the power outage duration of different load areas after a fault are studied, and the reliability evaluation process for ADNs with four-terminal SOPs is developed based on the quasi-Monte Carlo method. Later, with reliability as the main constraint, the power supply capability evaluation model for ADNs with four-terminal SOPs is established, and the solution algorithm is proposed. Finally, the effectiveness and applicability of the method proposed are verified through a case study.

Automated system for work preparation of parts production by sheet stamping

The procedure of automated process reliability evaluation is developed in order to prevent recurrent defects in parts manufactured by die stamping. The procedure is based on the analysis of such factors as part design, material, its mechanical and physical properties; equipment parameters, tool performance, etc. The list of reliability factors may vary according to type of operation as deformation process is different for each group of operations. The adjustment of stamping process reliability performance prevents any defects emerging during production of critical parts as early as the work preparation stage.

Impacts of load modeling on generalized analytical reliability assessment of smart grid under various penetration levels of wind/solar/non-renewable distributed generations

Abstract There is a knowledge gap about how the load modeling of smart grid affects the accuracy of the analytical reliability evaluation process and its calculation time. This paper tries to fill such a research gap by contributing to developing a methodology which is used to quantify the time reduction of the smart grid reliability assessment provided by various load state reduction strategies. In this paper, four novel load state reduction strategies (the aggregation of identical load states and time/magnitude/time of use pricing-dependent ones) are developed. It is useful to determine the appropriate number of load states according to the trade-off between the computing time and calculation accuracy. The sensitivity analyses are performed to get insight into how the load modeling of smart grids is affected due to various DG technology scenarios and their different penetration levels. To illustrate the advantages of the proposed method, it is applied to IEEE 69-bus test system. The test results imply that a significant time reduction of the reliability assessment process has been achieved while the desired accuracy criteria have been satisfied.

An approximate approach for reliability evaluation of a multistate flow network in terms of minimal cuts

An approximate approach, based on exact methods, is proposed to assess reliability of the multistate flow networks in terms of minimal cuts (MCs). The method selects some MCs to remove from the reliability evaluation process, reducing the computing cost and resulting in a very good approximation. Several benchmarks have been employed to show the efficiency of the proposed technique. The numerical experiments show that at times our approach finds even the exact solution more than 3 or 4 times faster, and in other cases it obtains a very good approximation more than 114 times faster than the available exact algorithms in the literature.

CC-SPRA: Correlation Coefficients Approach for Signal Probability-Based Reliability Analysis

In nanometric integrated circuits, reliability has become one of the main design measures. This makes the reliability evaluation process an inevitable part in the circuit design flow. In this paper, a probabilistic method has been developed for reliability evaluation of combinational logic circuits. At the core of the proposed method, correct and incorrect probabilities for binary logic values (0 and 1) of a circuit’s gate are derived using probability transfer matrix and the correct and incorrect probabilities of gate’s inputs. An efficient approach has been proposed to handle the reconvergent fanouts problem using correlation coefficients concept. Both accuracy and scalability of the proposed method have been illustrated by various simulations on ISCAS 85 and LGSynth91 benchmark circuits. The results have shown a less than 2% average error for reliability estimation comparing Monte Carlo simulation. Comparing to state-of-the-art methods the proposed approach has a better performance in reliability estimation and algorithm runtime.

Improved Nonprobabilistic Global Optimal Solution Method and Its Application in Bridge Reliability Assessment

Utilizing the improved one‐dimensional optimization algorithm conveniently solved the nonprobabilistic reliability index, however, only searching the part of probable failure points. Utilizing the global optimal solution method produced heavy computation, although it was capable of searching all probable failure points. This paper, based on these two methods, proposed an improved nonprobabilistic global optimal solution method. The presented method possessed the advantages of searching all probable failure points and generating less computation, by means of which the values of interval variables were determined based on the monotonicity of performance function to the corresponding variables. Without losing any probable failure points, this method was contributive to reducing root value point equations, lowering the computational complexity, and improving the computational efficiency. The effectiveness and feasibility of the presented method were verified by two examples. The proposed method was also introduced to build the nonprobabilistic reliability evaluation process of existing bridges. Taking the Longganhu bridge for example, the nonprobabilistic reliability index of it was calculated using the presented nonprobabilistic reliability evaluation process. The computed nonprobabilistic reliability index η = 0.737 < 1 indicated that the Longganhu bridge was unreliable and needed to be reinforced. Reinforcement measures were carried out for a hollow slab and bridge deck of the Longganhu bridge, respectively. The reinforced bridge was reevaluated as η = 2.159 > 1. The results showed that the bridge was reliable after reinforcement. The study illustrated the application and feasibility of the improved nonprobabilistic global optimal solution method and nonprobabilistic reliability evaluation process in reliability assessment and reinforcement of existing bridges.

Reliability Evaluation of Composite Power Systems Considering Wind Farms Based on Cross Entropy and Dynamic Orthogonal List

With large-scale wind farms integrated into power systems, the computational requirements for reliability evaluation of composite power systems are increasing. This paper introduces a new concept of dynamic orthogonal list, which is a form of multiple failure retrievals to avoid repeatedly calling the optimal power flow (OPF) program in the stage of state evaluation. An existing algorithm of cross-entropy (CE) method was developed to deal with the rare events in sampling. The major contribution of CE is to achieve convergence faster and reduce the number of samples, thus shortening the consuming time in the stage of sampling. This paper proposes a fast reliability evaluation process which combines the CE method with dynamic orthogonal list (CE-DOL), comprehensively improving the computational efficiency of Monte Carlo simulation (MCS). The proposed method is tested on a modified IEEE-RTS 79 system. In addition, for comparison purposes, the efficiencies of MCS, CE, and CE-DOL are estimated in the cases of different system adequacies. It is shown that, in range of certain reliability level, the proposed method is much more efficient compared with the CE method. Finally, the applicability of different methods is analyzed under different system reliability levels.

Research on Short-Term Reliability of Master Slave Control Based on Running Microgrid

A short-term reliability evaluation method based on time-varying dynamic fault tree is proposed for the time-varying and redundancy dynamic characteristics of the short term reliability of the microgrid operating in the main and Slave Island. First, a time varying dynamic fault tree is defined, and the solution process of dynamic subtree and static subtree is given. Then the top event is divided into direct and indirect events based on the power supply interruption of the load point, and the probability calculation is carried out. On this basis, the load probability and the expected power outage of the load point of the microgrid with master-slave control are calculated, and the reliability evaluation process is given. The validity and effectiveness of the proposed method are verified by an example analysis. The results can provide a basis for the operation and operation of the microgrid operating in the isolated island.

The impact of PHEVs charging and network topology optimization on bulk power system reliability

Exhausting fossil fuels and increasing environmental pollutions call for the wider deployment of plug-in hybrid electric vehicles (PHEVs), which enable the interactions of transportation and electric sectors. The extra loads for charging massive PHEVs could compromise the power system reliability and impose considerable stress on the power system. Transmission line congestions and generation capacity inadequacy could be caused under this circumstance. However, there is little literature which comprehensively studies the impact of large-scale PHEVs on the bulk power system reliability when smart grid technologies are incorporated including smart charging and flexible transmission topology control technologies. This work studies the impacts of different charging strategies and transmission network operation strategies on the bulk power system reliability with high PHEVs integration. An evolution strategy particle swarm optimization (ESPSO) algorithm is applied to optimize the charging load of PHEVs and shave the peak load. A network topology optimization (NTO) operation strategy is employed to lessen the transmission congestions and reduce the load curtailment. Based on the sequential Monte Carlo simulation method, these technologies are integrated into the reliability evaluation process. Numerical studies and sensitivity analysis are conducted on modified IEEE RTS-79 systems. The results verify the effectiveness of the technologies to realize the potential of existing power system infrastructures so as to relieve the stress caused by the PHEVs load, increase the available penetration of PHEVs and promote the bulk power system reliability which is meaningful for power system planning and risk management.