System Reliability Studies Research Articles

Modelos para la evaluación del error humano en estudios de fiabilidad de sistemas

Title: Models for assessment of human error in system reliability studies. Abstract: The probabilistic analysis of risks applied to complex industrial systems demonstrates that human error is a very important part of the to- tal risk, although its exact quantification is doubtful. On the other hand, the current techniques for modeling and calculating human errors presup- pose, in a way not always explicit, the election of a certain pattern of hu- man behavior; in such a way that many limitations revealed by the scien- tific literature seems due to the use of so named 'partial' behavior models, while the 'general' models, that are more ambitious, still need better meth- odological development. The present work describes the methods applied currently to human reliability studies and it points out the relationship that underlies between these and the models for human behavior representa- tion, showing the investigation lines ongoing for covering methodological lacks. We declare the protagonism of Psychology about defining the hu- man behavior models that underlie the reliability analysis techniques, and also about criticizing these techniques, especially on the simplifications that, to make them more governable, they have the perverse effect of hid- ing psychological mechanisms of errors.

Impact of WAMS Malfunction on Power System Reliability Assessment

Monitoring/control infrastructures are often assumed to be fully reliable in power system reliability studies. However, recent investigations on blackouts have revealed the crucial impacts of monitoring/control system malfunctions. This paper addresses the impact of situational awareness and controllability on power system reliability assessment. A methodology is proposed to simulate a situation in which a limitation of either or both monitoring and control functions could spread the consequence of power system events throughout the grid. It is assumed that the monitoring/control infrastructure is based on a wide-area measurement system (WAMS). While the proposed methodology is applicable to a variety of strategies for grid operations, certain assumptions are made for the simulation purposes. The Monte Carlo simulation is applied and a scenario reduction technique is considered for overcoming computational burdens. The performance of the proposed approach is simulated and analyzed on 9-bus and the IEEE 57-bus systems.

Research on Application of Power System Reliability Model Based on Principal-agent Mechanism

Power system reliability has been a widespread concern. With the continuous development of the electricity market, there has been a serious conflict, which between social obligations and necessary requirement. The so-called social obligation is that the power industry must ensure the power system operation reliability; the so-called necessary requirement is that the power industry must solve the power market operation economics. The power system operation reliability is serious challenge, so we must establish a new mechanism adapting to the electricity market, and ensure the power system operation reliability. In this paper, we have researched the model and method based on the principal-agent mechanism from three aspects, which are the reliability principal-agent model starting point, principal-agent model analysis and design, and information transparency level impact analysis. On this basis, we proposed the reliability management model based on the principal-agent mechanism, and carried out example analysis. The example analysis shows that through establishing the principal-agent relationship between power enterprises and the user, the entire power system reliability resources would be more efficient configuration to further enhance the entire society electricity reliability benefits. Further evidence that the principal-agent mechanism is scientific and applicability in the power system reliability study.

System Reliability Study Based on Bearing Capacity Update and Deformation Iteration

For the sensitive of distribution parameters of random variables, system reliability research should be focus on identifying the significant failure modes, while failure probability as a quantitative reference index, only combining both of them can better estimate system reliability. On the basis of load incremental step theory, a method for identifying the significant failure modes which could more accurately simulate the real failure process for considering the change of bearing capacity and spatial position is proposed in this paper. Then, the corresponding program is prepared with APDL on ANSYS platform. On the one hand, the efficiency and accuracy of identification is improved, on the other hand, the usefulness of this method is enhanced. Finally, the reliability of a space grid engineering project is analyzed and discussed by this method; moreover, the system reliability index β is calculated as well. These analysis results are helpful to provide scientific basis for structural optimization, structural testing, and structural reinforcement and so on.

Comparison of the parameter estimation methods of surge arresters using modified particle swarm optimization algorithm

SUMMARYThe measurements show that the residual voltage of the metal oxide surge arrester (MOSA) has a dynamic characteristic. The accurate modeling and simulation of this dynamic characteristic is very important for arrester allocation, systems reliability, and insulation coordination studies. Several models with acceptable accuracy have been suggested to describe this behavior. It should be noted that the determination of nonlinear elements of MOSAs is very important for all models. In this article, a comparison among the different methods of the determination of the surge arrester voltage–current characteristics is represented. A new method, which is the combination of particle swarm optimization (PSO) and ant colony optimization methods, is proposed to estimate the parameters of MOSA models. In the proposed algorithm, to overcome the drawback of the PSO algorithm (convergence to local optima), to improve the global search capability, and to prevent the convergence to local minima, ant colony optimization algorithm is combined to PSO algorithm. The proposed method is called modified PSO. The transient models of MOSA have been simulated by using ATP‐EMTP. The results of simulations have been applied to the program, which is based on the modified PSO method and can determine the fitness and parameters of different models. The validity and the accuracy of the estimated parameters are assessed by comparing the predicted residual voltage with the experimental results. Copyright © 2011 John Wiley & Sons, Ltd.

Assessing Software Reliability Using SPC - An Order Statistics Approach

There are many software reliability models that are based on the times of occurrences of errors in the debugging of software. It is shown that it is possible to do asymptotic likelihood inference for software reliability models based on order statistics or Non-Homogeneous Poisson Processes (NHPP), with asymptotic confidence levels for interval estimates of parameters. In particular, interval estimates from these models are obtained for the conditional failure rate of the software, given the data from the debugging process. The data can be grouped or ungrouped. For someone making a decision about when to market software, the conditional failure rate is an important parameter. Order statistics are used in a wide variety of practical situations. Their use in characterization problems, detection of outliers, linear estimation, study of system reliability, life-testing, survival analysis, data compression and many other fields can be seen from the many books. Statistical Process Control (SPC) can monitor the forecasting of software failure and thereby contribute significantly to the improvement of software reliability. Control charts are widely used for software process control in the software industry. In this paper we proposed a control mechanism based on order statistics of cumulative quantity between observations of time domain failure data using mean value function of Half Logistics Distribution (HLD) based on NHPP.

Power System Reliability Considering Combined Cycle Plants

In the nineties in Argentina, the growth of thermal power generation was mainly based on combined cycle plants. Currently, one problem associated with the use of combined cycle plants is the fact that they need to share the use of natural gas with commercial, industrial and domestic uses. Hence, the natural gas availability is becoming a critical issue. Therefore, power system reliability studies must consider fuel availability into the models, and as a consequence, reliability models must take into account the joint operation of electrical generation and natural gas systems. The aim of this paper is to evaluate the influence of combined cycle plants on reliability studies applied to system generation capacity and their dependency on natural gas availability. Monte Carlo simulation method is used to determine reliability indices such as Loss of Load Expectation, Loss of Energy Expectation, Loss of Load Frequency, and Loss of Load Duration. The IEEE RTS 96 test system is used as the base system, with is modified in order to incorporate combined cycle plants.

Reliability of electric power systems: challenges in the deregulated environment — a research perspective

Electric power industry deregulation has brought about the unbundling of generation, transmission and distribution services and as such, new techniques for reliability assessment are being developed to account for the consequent structural changes in the restructured environment. The transition towards deregulation imposes several unique challenges for establishing reliability modeling paradigms. Addressing these concerns requires a comprehensive knowledge of the technical and economic issues involved and the identification of relevant research gaps. This paper presents a broader framework for the research perspectives to holistically address the significant issues that can profoundly impact the reliability studies in the liberalized environment. Starting from the improvisations in the reliability network equivalent techniques in vogue, this paper advocates the use of power flow tracing as a potential tool for reliability and well-being studies of power systems, and expands upon their practical implications.

Insulator condition analysis for overhead distribution lines using combined wavelet support vector machine (SVM)

Condition analysis of overhead power distribution system insulators using combined support vector machine (SVM) and wavelet multi-resolution analysis (MRA) seems to be promising for distribution system monitoring (DSM) automation to cope with the increasing system complexity. Though system well-being analysis for engineering applications has been used mostly for electric power system reliability studies, the same principle has been extended for assessing the condition of insulators in a distribution system based on the extent of their damage. Video surveillance with fixed cameras provide the required images of power lines along with insulators at regular intervals and same is sent to a control room using remote terminal units (RTUs) for analysis. Not only the health of the insulators, but also the sagging of the lines, breakage of both insulators and lines can be captured with such cameras. This paper mainly focuses on application of wavelet-transform based feature extraction for digital image processing and SVM for subsequent condition analysis of insulators. The most significant contribution of the paper is to compute the condition indices for overhead power distribution line insulators to overcome difficulties related to vehicular applications in video surveillance. The results contained in this paper validate the efficacy of the proposed methodology for wide-scale applications in overhead power distribution system monitoring (DSM) automation.

Dynamic Operational Stress Measurement of MEMS Using Time-Resolved Raman Spectroscopy

A dynamic-stress analysis method, based on time-resolved micro Raman spectroscopy, has been developed for reliability studies of microelectromechanical systems. This novel technique is illustrated by measuring temporally and spatially resolved stress maps of a piezoelectrically actuated silicon microcantilever when driven at its first- (6.094 kHz) and second-order (37.89 kHz) resonant frequencies. Stress amplitudes of up to 180 plusmn 10 MPa were measured at the maximum stress locations. The time-resolved Raman stress measurements are compared to the results of finite-element analysis and laser Doppler vibrometry.[2008-0003].

Cost of Reliability Analysis Based on Stochastic Unit Commitment

This paper presents a model for calculating the cost of power system reliability based on the stochastic optimization of long-term security-constrained unit commitment. Random outages of generating units and transmission lines as well as load forecasting inaccuracy are modeled as scenario trees in the Monte Carlo simulation. Unlike previous reliability analyses methods in the literature which considered the solution of an economic dispatch problem, this model solves an hourly unit commitment problem, which incorporates spatial constraints of generating units and transmission lines, random component outages, and load forecast uncertainty into the reliability problem. The classical methods considered predefined reserve constraints in the deterministic solution of unit commitment. However, this study considers possible uncertainties when calculating the optimal reserve in the unit commitment solution as a tradeoff between minimizing operating costs and satisfying power system reliability requirements. Loss-of-load-expectation (LOLE) is included as a constraint in the stochastic unit commitment for calculating the cost of supplying the reserve. The proposed model can be used by a vertically integrated utility or an ISO. In the first case, the utility considers the impact of long-term fuel and emission scheduling on power system reliability studies. In the second case, fuel and emission constraints of individual generating companies are submitted as energy constraints when solving the ISO's reliability problem. Numerical simulations indicate the effectiveness of the proposed approach for minimizing the cost of reliability in stochastic power systems.

Effects of Load Forecast Uncertainty on Bulk Electric System Reliability Evaluation

Load forecast uncertainty is an important factor in long-range system planning and has been shown to have a significant impact on the calculated reliability indices in generating capacity studies. In general, a higher capacity reserve is required to satisfy a future uncertainty load than to serve a known load, at a specified level of reliability. Load forecast considerations are also important in composite generation and transmission system reliability studies, and their associated probability distributions. This paper examines the effects of load forecast uncertainty in bulk system reliability assessment incorporating changes in system composition, topology, load curtailment policies and bus load correlation levels.

Integration of wind-generating units into the Corsican electrical grid: determination of the maximal integration rate from a reliability analysis based on a stochastic process

This paper investigates the potentiality of Wind Electric Conversion Systems (WECS) integrated into the islander electrical grid of Corsica. Probabilistic models for WECS and their use in the reliability studies of combined wind electric and conventional generation systems are discussed. The method, based on the evaluation of the Loss of Load Probability (LOLP), uses a Markov model applied to the output power of 'classical' units and WECS, and to the hourly electrical energy demand. Then the margin state (the difference between grid total capacity state and load state) and its associated probability are computed to allow determination of the LOLP. So, the limit of the WECS power that can be supported by the grid is estimated. This study has been realised for the total thermal capacity installed in the grid and various ranges of WECS. Furthermore, we study the energetic balance on the grid with this wind farm and the benefit regarding gas emissions induced by this system.

Incorporating multi‐state unit models in composite system adequacy assessment

AbstractComponents are usually represented by a two state model in conventional generating capacity and composite generation and transmission system reliability studies. Multi‐state generating unit models create a significant increase in the number of generation contingency states and can result in a considerable increase in the overall solution time. In order to avoid this problem, the derated states are usually amalgamated with the totally forced out state to create the derating‐adjusted forced outage rate (DAFOR). This statistic is also known as the equivalent forced outage rate (EFOR). Studies have shown that modeling large generating units in generating capacity adequacy assessments using DAFOR can provide pessimistic appraisals. Many utilities therefore use multi‐state generating unit representations to assess generating capacity adequacy, in order to obtain more accurate appraisals. There is relatively little published material dealing with the effects of using multi‐state generating unit representations in composite system adequacy assessment. This paper illustrates these effects by application to the IEEE‐Reliability Test System (RTS). Load point and system indices for the test system are presented to illustrate the impact of incorporating multi‐state representations in composite system adequacy assessment. Attention is focused on the effects of model variations including how many derated states should be used in a multi‐state model to obtain a reasonably accurate appraisal. Copyright © 2007 John Wiley & Sons, Ltd.

Reliability modeling and assessment of grid-connected PEM fuel cell power plants

Fuel cell (FC) power plants are subject to a number of possible outage and derated states due to partial or full failure of auxiliaries. Furthermore, most of the power system reliability studies reported in the literature assume mean values of the particular measure of reliability. However, the reliability indices for grids such as failure rate, outage duration, etc. vary for different time period due to weather conditions, variety of power demands and random faults. It is essential to obtain the estimation of reliability under all environmental, operational and loading conditions. This paper considers above mentioned seasonal variation of grid reliability indices as well as partial or full failure of fuel cell auxiliaries for grid-connected PEM fuel cell power plants (FCPPs). In the paper, a detailed state–space model of the grid-connected FCPP is presented which is a combination of proton exchange membrane fuel cell (PEMFC) power plant generation model and grid outage model. The state–space generation model of a PEMFC power plant is formed based on the failure modes of system auxiliary components. As for the grid outage state–space modeling, the effects of weather conditions such as normal and adverse weather are taken into consideration in modeling the failure and repair rates. The functional relationship between weather conditions and transition rates, namely failure and repair rates are developed based on the fuzzy set theory and embedded into Markov model (MM). Simulation results are obtained for a 5 kW grid-connected PEMFC that supplies a typical residential house using the MATLAB software package.

Power system probabilistic security assessment using Bayes classifier

This paper presents a method for power system security assessment based on the Bayes classifier. This method can be applied to calculate probabilistic security indices as well as on-line security assessment. In general, the determination of security breach is a cumbersome and time-consuming process due to dynamic and steady-state effects. These effects can be incorporated by considering transient stability, satisfaction of system load without violation of constraints, and voltage stability studies. The variation of the system load, as well as contingencies, may cause system transition to a different operating state. It is impractical if not impossible to evaluate all these situations, such as contingencies resulting from load variation. The straightforward Monte Carlo simulation, one of the possible methods in power system reliability analysis, requires the evaluation of a system operating state for each sampled state and is computation-intensive. In the proposed method, first the joint probability density of feature vectors has been obtained by using some training data. Once this joint distribution is obtained, the Bayes classifier provides the assessment of system security without complicated contingency analyses and can reduce the computational burden. Security status of a given feature vector can be determined by a posteriori probability rule called Bayes rule, which can be implemented in on-line security assessment or power system reliability studies.

Reliability assessment of polysilicon MEMS structures under mechanical fatigue loading

This paper examines the current status and methodologies of study of material and system reliability in Microelectromechanical Systems (MEMS). This includes: a review of the current literature in the area of MEMS regarding failure analysis experimental investigations; testing methods and philosophies for material characterization and possible mechanistic analytical solutions for estimating material properties. The paper proposes a reliability framework that encompasses all the available information. This statistical platform will enable the MEMS design engineer to distill all the available information in the literature into a stand-alone semi-empirical material reliability model, and a holistic system-level model for a complete system.

Composite system reliability evaluation incorporating a six-phase transmission line

Composite system reliability studies incorporating a six-phase transmission line are presented. Both load-point and system indices are used to examine the impact on the individual bus and overall system reliability of incorporating a six-phase transmission line. Comparative studies are conducted in order to investigate the impact of the location of a six-phase transmission line on the load-point and system reliability indices. The worth of employing the six-phase transmission line is expressed in terms of the reduction in the customer interruption cost. The results are illustrated using a small test system.

Optimization of replacement stocks using a maintenance programme derived from reliability studies of production systems

This paper is the outcome of a study carried out in a nine‐cell flexible manufacturing unit which produces components for Diesel engine injection pumps. The study involved the monitoring and recording of the various types of failure which led to interruptions in production. The objective of the study was to design a maintenance system for the optimal management of replacement stocks, both from the technical and economic point of view. The design of this programme was based on matching a given statistical distribution (Weibull’s distribution, exponential, normal, log‐normal, or gamma) to the time between failures. By means of this fit, the basic system parameters were determined: average lifetime, failure rate, mean time between failures, mean time between inspections. Knowledge of these variables permits us to determine the frequency of inspections in relation to the level of reliability we require of the system. This information also enables us to establish a programme to determine which elements require inspection, the frequency of these inspections and the operations that need to be carried out.

Effects of adverse weather and aging on power system reliability

Most power system reliability studies assume a constant failure rate and an exponentially distributed repair time. For redundant (parallel) components, using the average component failure rates gives erroneous results. The effect of time-varying failure rates is discussed for adverse weather and for component aging. A primary-selective supply is used to introduce the phenomena. In particular, adverse weather is shown to significantly affect the interruption frequency. Both adverse weather and component aging require additional data collection efforts. Including component aging also requires a serious modeling effort.