Composite System Reliability Research Articles

Accelerated State Evaluation and Latin Hypercube Sequential Sampling for Composite System Reliability Assessment

This paper proposes improvements in the sequential Monte Carlo (MC) simulation based approaches for reliability evaluation of composite generation and transmission systems. The main idea is to compute a threshold load level to facilitate the process of state evaluation, which is very time-consuming mainly due to optimal power flow computations. Two main sampling techniques namely random sampling (RS) and Latin Hypercube sampling (LHS) are applied to the proposed sequential MC simulation technique. Their performances in terms of simulation time as well as solution accuracy are compared. Case studies using the IEEE Reliability Test System (IEEE RTS) are utilized to demonstrate the performance of the proposed approach. Reliability indices including loss of load probability, expected unserved energy, loss of load frequency and duration are estimated. It is shown that the proposed approach considerably reduces the computational requirements during state evaluation, and that the application of LHS can further decrease the simulation time and required sample size to reach convergence.

Adaptive sequential importance sampling technique for short‐term composite power system adequacy evaluation

The well-known sequential Monte Carlo simulation is a powerful tool to analyse short-term reliability of complicated composite power system. However, the corresponding computational burden is tremendous when applied to a highly reliable system. Aiming at improving the simulation efficiency, an adaptive importance sampling technology is proposed in this study. The proposed method, on the basis of component state duration sampling technique combined with cross-entropy method, is able to provide reliability evaluation of highly reliable non-homogeneous Markov system. The Weibull and lognormal distributions are considered to describe repair time of individual components comprising a system. Through the case studies conducted on a reinforced Roy Billinton reliability test system, it is validated that the proposed method is effective and of high efficiency and the efficiency gain against the crude sequential Monte Carlo simulation is robust to variation of load level and lead timescale. The proposed method is useful and efficient to timely monitor the system operating pressure under different lead timescales.

Cold Duplication and Survival Equivalence in the Case of Gamma - Weibull Distributed Composite Systems

The reliability of composite system (series, parallel) is improved by (i) reduction method, and by (ii) cold duplication, considering the system’s survival functions. Then these reliability improvement methods are compared mentioning that the hot duplication method was studied recently by Pogany et al. [8]. In this companion article to [8], related survival equivalence functions and pointwise survival equivalence factors are derived in all cases when the components lifetime distribution follow the gamma–Weibull distribution introduced recently by Leipnik and Pearce, and studied intensively by Nadarajah and Kotz, then by Pogany and Saxena.

Reliability and Sensitivity Analysis of Composite Power Systems Under Emission Constraints

In view of the increasing role of environmental considerations in power generation, there is an evolving body of reliability methods that are cognizant of these emerging factors. This paper presents a method for analyzing the reliability of composite power systems under the constraints of CO2, SO2, and NOx emissions. It also utilizes the concept of the shadow price to perform sensitivity analysis with respect to these constraints. The method presented here uses a linearized power flow representation in the form of a linear programming (LP) model similar to those frequently used in power system reliability studies. In addition to power balance and flow constraints normally used, this work introduces a suitably formulated set of emission constraints. The dual solution of the LP provides the shadow prices that are used for determining the sensitivities. This paper also develops relationships for the sensitivities of several reliability indices with respect to the emission allowances. The method is demonstrated on the IEEE-RTS and the results are presented.

A Cross-Entropy-Based Three-Stage Sequential Importance Sampling for Composite Power System Short-Term Reliability Evaluation

Regarding short-term reliability of composite power system, probability of critical event resulting in system failure within a short lead time is extremely low, which renders classical sequential Monte Carlo simulation method inefficient. In this paper, a cross-entropy-based three-stage sequential importance sampling (TSSIS) method is proposed to solve the low efficiency problem resulted from the low rate of component state transition during a fixed lead time. First, by assuming the system state transition process conforms to continuous time Markov chain, an analytical solution to optimal distorted component state transition rate to be used for sequential importance sampling is found by means of cross-entropy method. Second, TSSIS for a fixed lead time is constructed as follows: 1) acceleration of producing system state transitions; 2) enhanced learning to give optimal distorted transition rate; 3) compensation to the cost function. Case studies based on a reinforced Roy Billinton reliability test system and RTS-79 are carried out respectively for illustration of parameter settings of TSSIS as well as efficiency gain in comparison with the classical sequential Monte Carlo simulation method. The results demonstrate that given rational setting of parameters, TSSIS is of relatively high efficiency for sequential short-term reliability evaluation of composite power system.

Composite Systems Reliability Evaluation Based on Monte Carlo Simulation and Cross-Entropy Methods

This paper proposes a new approach to evaluate loss of load indices in composite generation and transmission systems. The main idea is to combine a Cross-Entropy (CE)-based optimization process and nonsequential Monte Carlo Simulation (MCS) to obtain an auxiliary sampling distribution, which can minimize the variance of the reliability index estimators. This auxiliary sampling distribution will properly modify the original unavailabilities of both generation and transmission equipment, so that important failure events are sampled more often. As a result, the MCS algorithm can reach convergence faster and with fewer samples, leading to significant gains in computational performance, especially when dealing with very reliable system configurations. The proposed method is tested using several composite power systems, including the IEEE RTS 79, IEEE RTS 96, and a configuration of the Brazilian system.

Composite Power System Vulnerability Evaluation to Cascading Failures Using Importance Sampling and Antithetic Variates

Large-scale blackouts typically result from cascading failure in power systems operation. Their mitigation in power system planning calls for the development of methods and algorithms that assess the risk of cascading failure due to relay overtripping, short-circuits induced by overgrown vegetation, voltage sags, line and transformer overloading, transient instabilities, voltage collapse, to cite a few. This paper describes such a method based on composite power system reliability evaluation via sequential Monte Carlo simulation. One of the impediments of the study of these phenomena is the prohibitively large computational burden involved by the simulations. To overcome this difficulty, importance sampling technique utilizing the Weibull distribution is applied to power generator outages. Another method combing importance sampling and antithetic variates together is implemented as well. It is shown that both methods noticeably reduce the number of samples that need to be investigated while maintaining the accuracy at a given level. It is found that the combined method outperforms importance sampling to certain extent. To illustrate the developed techniques, two case studies are conducted and analyzed on the IEEE one-area and three-area reliability test system.

Eliminating Redundant Line Flow Constraints in Composite System Reliability Evaluation

Reliability evaluation of composite systems involves extensive calculations. Current solutions to this computational burden have mainly focused on extracting failure states from the state space. Instead, the evaluation of failure states is accelerated by methods presented in this paper. The scale of optimizations required for generation redispatching and/or load shedding in failure states is reduced by eliminating redundant line flow constraints. First, a sufficient and necessary condition for a line flow constraint to be redundant is established in the form of a linear programming problem, based on the concept of steady-state security region (SSR). Then, two redundancy elimination methods are proposed-a conservative one based on a heuristic, and a radical one based on an analytical condition. Numerical tests are conducted on IEEE-RTS79 and a real-life system. More than half of the line flow constraints are eliminated by the conservative method and nearly 90% by the radical method. The proposed methods can be used in conjunction with most of the existing acceleration techniques to further improve efficiency.

Reliability Modeling of Dynamic Thermal Rating

In this paper, a Markov model for reliability analysis of transmission lines equipped with a dynamic thermal rating (DTR) system is proposed. Moreover, a fuzzy procedure is proposed to determine an annual equivalent fuzzy DTR number to represent hourly variations of the line DTR. To implement the fuzzy annual DTR in composite system reliability analysis, an interactive method resolution technique for solving fuzzy optimization problems is developed. This method is chosen to address the fuzzy constraints associated with the lines equipped with the DTR system. Numerical results are presented using the 24-bus IEEE RTS, and demonstrate the validity of the proposed approaches.

Incorporating multiple correlations among wind speeds, photovoltaic powers and bus loads in composite system reliability evaluation

This paper presents a composite generation and transmission system reliability evaluation method incorporating multiple correlations among wind speeds, insolations and bus/regional load curves. The proposed method can accurately model any probability distribution and all the correlations between wind speeds, between solar insolations, between load curves, between wind speeds and solar insolations, between wind speeds and load curves, and between solar insolations and load curves. The IEEE-Reliability Test System (RTS) with additional two wind farms and two PV power stations is used to demonstrate the application of the presented method in composite system reliability evaluation.

Intelligent state space pruning for Monte Carlo simulation with applications in composite power system reliability

The probabilistic reliability evaluation of composite power systems is a complicated, computation intensive, and combinatorial task. As such evaluation may suffer from issues regarding high dimensionality that lead to an increased need for computational resources, MCS is often used to evaluate the reliability of power systems. In order to alleviate this burden, an analytical method known as state space decomposition has previously been used to prune the state space that is sampled using MCS.This paper extends the state-of-the-art by proposing a novel algorithm known as intelligent state space pruning (ISSP). This algorithm leverages the intelligence of highly modified population based metaheuristic (PBM) algorithms including genetic algorithms (GA), particle swarm optimization (PSO), ant colony optimization (ACO), and artificial immune systems (AIS) to quickly, efficiently, and intelligently prune the state space that is used during MCS. The presented PBMs are modified using domain-specific knowledge to improve their performance and fine tune their intelligence. This new algorithm leads to reductions of up to 90% in total computation time and iterations required for convergence when compared to non-sequential MCS. Results are reported using the IEEE Reliability Test Systems (RTS79/MRTS).

Hot duplication versus survivor equivalence in Gamma-Weibull distribution

The reliability of composite system (series, parallel) is improved by (i) reduction method, and by (ii) hot duplication, considering the systems survivor function. Related survivor equivalence functions and pointwise survivor equivalence factors are derived in all cases when the components lifetime distribution follow the gamma-Weibull distribution introduced recently by Leipnik and Pearce.

Particle Swarm Optimization based Control Setting of TCSC for Improving Reliability of Composite Power System

the introduction of competition in the existing electrical market, it became necessary to increase the capability of transmission of power through the existing transmission lines. This paper describes a method of improving reliability of composite power system by increasing transmission system capability using FACTS devices. The utilization of FACTS (1) technologies will have a significant positive impact on power system performance in terms of reliability. Considerable efforts have been made in the past for developing techniques to evaluate composite power system reliability. The proposed method is based on the probabilistic approach to determine the load point reliability indices in a practical system. Composite system reliability can be increased by adjusting the settings of the FACTS device (such as reactance, phase angles, reactive power injection) with respect to the system parameters. In this paper, the settings of TCSC are obtained using Particle Swarm Optimization technique. The settings thus obtained are used for enhancing the transmission power capacity while improving the composite power system reliability. The proposed scheme is tested on Roy Billinton Test System (RBTS).

Techniques for improving precision and construction efficiency of a pattern classifier in composite system reliability assessment

Pattern classifiers have been widely utilized to improve computational efficiency in composite power system reliability assessment using Monte Carlo simulation. Construction of a classifier for reliability assessment demands sufficient amount of training vectors such that success and failure states can be effectively differentiated. Typically, the training vector is obtained by sampling states according to their original distributions. Failure states are therefore hardly sampled. This raises two issues. First, a large number of samples are needed to extract sufficient amount of failure states. Second, a set of training vectors becomes highly imbalanced, leading to undesirable level of precision of a classifier. This paper proposes two techniques to address aforementioned issues as well as to enhance precision of a classifier. The first technique is based on worsening system reliability to obtain balanced amount of success and failure states for training vectors. This technique enhances construction efficiency of a classifier in general and solves imbalance issue. The second is based on relaxed decision boundary which is used to improve precision of general classifiers. Various case studies are conducted on IEEE-RTS79 in order to justify the proposed techniques. Results show that the proposed techniques improve both precision and construction efficiency of a classifier.

Modeling of IPFC for Power Flow control in 3-Phase line –Further Aspects And its Limitations

advanced FACTS controller, which can be used for dynamic compensation and effective power-flow management among transmission corridors. This paper investigates the impact of IPFC on the reliability of composite generation and transmission power systems and is arranged in the following order. Explains the structure and the principle function of the IPFC. A state-space Markov model associated with different operation modes of IPFC is developed. The application of IPFC in the IEEE-RTS is examined. Two types of performance indices designated as delivery-point and system indices are introduced to illustrate the impacts of employing IPFC in power systems. The study results and the effectiveness of the proposed technique and the developed model are illustrated. The conclusions drawn from the analysis are provided. p p se se

Improvement In Reliability Of Composite Power System Using Tcsc, Upfc Of 6 Bus Rbts – A Comparison

Emerging techniques for composite power system reliability evaluation mainly focus on conventional generation and transmission facilities. Reliability analysis of composite power system is determined by applying the FACTS devices like UPFC & TCSC in IEEE 6 Bus RBTS system at different buses & transmission lines. In this paper, a comparison is carried out between the systems using UPFC, TCSC in order to describe which FACTS device is best suitable for the system. The comparison is made in different parameters via. availability & unavailability, System Indices, Probability of Failure & Expected Energy Not Supplied (EENS), by considering different modules & bus numbers of the composite power system. Keywords - UPFC, TCSC, Reliability, EENS, Probability of Failure, System Indices.

Composite Reliability Evaluation Using Monte Carlo Simulation and Least Squares Support Vector Classifier

This paper presents a fast and efficient method which combines the Monte Carlo simulation (MCS) and the least squares support vector machine (LSSVM) classifier, for reliability evaluation of composite power system. LSSVM is used to accurately pre-classify the power system operating states as either success or failure states during the Monte Carlo sampling. These pre-classified failure states are then evaluated for adequacy analysis using DC power flow to calculate reliability indices. As a result, the computing time to perform power flow analysis of the system success states is eliminated. The proposed hybrid method is applied to the IEEE Reliability Test System (IEEE-RTS-79) and simulation results obtained using LSSVM with linear and nonlinear kernels are compared with that of nonsequential MCS. These promising results demonstrate the efficacy of the proposed MCS-LSSVM based hybrid method in terms of both classification accuracy and computational time in evaluating the composite power system reliability.

The Ply Stacking Optimization for Composite Laminated Structure: Application of Tabu Search Algorithm

In this paper, tabu search algorithm, the laminated overlay has been optimized. The total thickness of the laminated structure under certain circumstances, to composite laminates maximum system reliability indices as the objective function, the composite fiber orientation angle and relative thickness of the optimized design. It is through the local neighborhood search mechanism and the corresponding tabus to avoid circuitous search criteria and standards to cut through the broken tabu excellent release from the state, thereby ensuring a variety of effective exploration in order to ultimately achieve global optimization. This article is laminated overlay design provide a new and efficient framework, the framework of the algorithm relative to the previously used method has obvious advantages: After a limited number of cycles that can converge to satisfactory results, the optimization process shows good robustness of the algorithm, and the method to solve the structural optimization of composite materials and other combinatorial optimization problems provide a new way of thinking. This paper describes the basic idea of tabu search algorithm, composition, processes, principles, and so on.

A Methodology for Evaluation of Hurricane Impact on Composite Power System Reliability

Adverse weather such as hurricanes can have significant impact on power system reliability. One of the challenges of incorporating weather effects in power system reliability evaluation is to assess how adverse weather affects the reliability parameters of system components. In this paper, a fuzzy inference system (FIS) built by using fuzzy clustering method is combined with the regional weather model to solve the preceding problem. The composite power system is assumed to be partitioned into different regions and the FIS maps the nonlinear functional relationship between hurricane parameters and the increment multipliers of the failure rates (IMFR) of the transmission lines in different regions. The possible case that transmission lines traverse bordering regions is investigated by using the weighted average method. Since hurricanes last only a limited time period, the short-term reliability indices over the duration of hurricane instead of the steady-state ones are calculated by using the minimal cut-set method (MCSM). The proposed method is applied to the modified IEEE Reliability Test System (RTS). The implementation demonstrates that the proposed method is effective and efficient and is flexible in applications.

Integration of large-scale wind farm projects including system reliability analysis

This study intends to develop a comprehensive procedure for evaluating locational value of a wind farm project incorporating reduction in transmission system losses, load delivery point interruption cost and operating cost of generating units. The energy extracted from the wind farm in normal operation condition is considered to replace the energy from fossil-fueled conventional units. In addition, composite system reliability analysis in the presence of wind power is carried out to evaluate total costs associated with curtailed energy at different load points as well as generation of generating units in contingency conditions. System reliability analysis related to large-scale wind farms, along with operating cost and transmission losses analysis, can assist policy makers to prioritise wind farm projects based on the total benefits of wind power including reliability benefits and savings in fossil-fueled energy sources.