Composite Power System Reliability Evaluation Research Articles

A convolutional neural network-based approach to composite power system reliability evaluation

This paper proposes a machine learning-based approach in conjunction with Monte Carlo simulation (MCS) to improve the computation efficiency of composite power system reliability evaluation. Traditional composite system reliability evaluation approaches are computationally demanding and may become inapplicable to large integrated power grids due to the requirements of repetitively solving optimal power flow (OPF) for a large number of system states. Machine learning-based approaches have been used to avoid solving OPF in composite system reliability evaluation except in the training stage. However, current approaches have been derived to classify system states into success and failure states (i.e., up or down). In other words, they can be used to evaluate power system probability and frequency reliability indices, but they cannot be used to evaluate power and energy reliability indices unless OPF is solved for each failure state to determine minimum load curtailments. In this paper, a convolutional neural network (CNN)-based regression approach is proposed to determine the minimum amount of load curtailments of sampled states without solving OPF, except in the training stage. Minimum load curtailments are then used to evaluate power and energy indices (e.g., expected demand not supplied) as well as to evaluate the probability and frequency indices. The proposed approach is applied on several systems including the IEEE Reliability Test Systems (The IEEE RTS and IEEE RTS-96) and Saskatchewan Power Corporation in Canada. Results show that the proposed approach is computationally efficient (fast and accurate) in calculating the most common composite system reliability indices. The developed source code of the proposed method is available to the community for future research and development.

Enhanced Cross Entropy Method for Composite Power System Reliability Evaluation

Cross entropy method (CEM) can effectively accelerate the reliability evaluation of power system. It is mainly utilized to conduct importance sampling (IS) on discrete component state variables and independent or fully correlated continuous variables. To carry out IS on continuous variables with complicated correlation, this paper proposes an enhanced CEM (ECEM) that addresses the following two problems: the first one is the probabilistic modeling of the joint probability density function (PDF) for the correlated continuous variables, and the second one is the IS on the modeled joint PDF. For the first one, a Gaussian mixture model (GMM) is utilized to estimate the original joint PDF of correlated continuous variables. For the second one, the parameters of the GMM based joint PDF are optimized directly by a direct-updating-rule based method (DURM) to obtain the optimal IS-PDF. Furthermore, as an alternative to DURM, a Nataf transformation based method (NTM) is also presented to distort the parameters of the GMM-based joint PDF indirectly. Compared with NTM, which is suitable for normal copula correlation, DURM has a much wider application scope without any restriction on the correlation type. Finally, an improved unbiased estimator for the reliability index is presented to further accelerate ECEM. Test studies on RTS79 and MRTS with wind farm verify the effectiveness of ECEM.

MPLP-Based Fast Power System Reliability Evaluation Using Transmission Line Status Dictionary

Composite power system reliability evaluation is computationally time-consuming because the optimal power flow (OPF) with the least load curtailment is calculated for a large number of samples. Most current studies of probabilistic simulation methods focus on sampling techniques to improve the sampling efficiency and decrease the calculations of the OPF. This paper proposes a fast reliability evaluation method that accelerates the calculation of the OPF using multi-parametric linear programming (MPLP). In this paper, the sampled transmission line statuses are considered by the transmission line status dictionary (TLSD). We match the line status of each sample with the scenarios in the TLSD. For matching samples, the generation status and the sampled load are treated as MPLP parameters of the DC-OPF evaluation model. A dynamic learning algorithm is applied to solve the MPLP problem. Case studies are conducted on both IEEE Reliability Test Systems and a provincial power system in China. The results show that the proposed method improves the evaluation efficiency by 23–30 times compared with the normal DC-OPF-based model.

Contingency selection and ranking for composite power system reliability evaluation

In this paper, a probabilistic performance index (PI) has been developed to perform contingency selection and ranking. The results show that considering up to the second level contingency is satisfactory for both accuracy and computation time requirements. The results obtained constitute an essential basis for the analysis of power systems in terms of adequacy assessment and reliability evaluation. To substantiate the study results, comparison with other similar studies was performed.

Probabilistic Performance Index based Contingency Screening for Composite Power System Reliability Evaluation

Composite power system reliability involves assessing the adequacy of generation and transmission system to meet the demand at major system load points. Contingency selection was being the most tedious step in reliability evaluation of large electric systems. Contingency in power system might be a possible event in future which was not predicted with certainty in earlier research. Therefore, uncertainty may be inevitable in power system operation. Deterministic indices may not guarantee the randomness in reliability assessment. In order to account for volatility in contingencies, a new performance index proposed in the current research. Proposed method assimilates the uncertainty in computational procedure. Reliability test systems like Roy Billinton Test System-6 bus system and IEEE-24 bus reliability test systems were used to test the effectiveness of a proposed method.

Composite power system reliability evaluation using modified minimal cut set approach

The composite power system reliability analysis is generally based on minimal path or cut enumeration, tracing of power flow paths from which the related reliability indices are calculated. The minimal cut set is a popular method in the reliability analysis for simple and complex configurations. Average availability of power supply at the consumer end is one of the reliability assessment parameter. This paper is concerned about the evaluation of this reliability index. A step by step procedure for a modified minimal cut set method is explained in this paper using IEEE 6 bus, 14 bus and Single area IEEE RTS 96 system. The proposed algorithm is easy to program and can be applicable to any system. The proposed algorithm is validated with the Classical Node Elimination method, Step by Step algorithm using Conditional Probability and Monte Carlo Simulation method. The proposed technique is tested with a practical example taken from Roy Billinton paper (Reliability evaluation in distribution and transmission systems).

Non-Sequential Monte Carlo Simulation for Cyber-Induced Dependent Failures in Composite Power System Reliability Evaluation

Cyber-induced dependent failures are important to be considered in composite system reliability evaluation. Because of the complexity and dimensionality, Monte Carlo simulation is a preferred method for composite system reliability evaluation. The non-sequential Monte Carlo or sampling generally requires less computational and storage resources than sequential techniques and is generally preferred for large systems where components are independent or only a limited dependency exists. However, cyber-induced events involve dependent failures, making it difficult to use sampling methods. The difficulties of using sampling with dependent failures are discussed and a solution is proposed. The basic idea is to generate a representative state space from which states can be sampled. The probabilities of representative state space provide an approximation of the joint distribution and are generated by a sequential simulation in this paper but it may be possible to find alternative means of achieving this objective. The proposed method preserves the dependent features of cyber-induced events and also improves the efficiency. Although motivated by cyber-induced failures, the technique can be used for other types of dependent failures as well. A comparative study between a purely sequential methodology and the proposed method is presented on an extended Roy Billinton Test 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.

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.

Reliability-performance-index probability distribution analysis of bulk electricity systems

System reliability performance is usually based on average customer-interruption indices. The average values are valuable information, but provide only a single customer risk dimension without the underlying probability distributions. The average annual indices give no insight as to how reliability may vary from year to year as a result of the random behaviour of a bulk electric system. Reliability-index probability distributions, therefore, provide additional valuable information and a more complete understanding of composite power system behaviour. A significant advantage when utilizing sequential Monte Carlo simulation in bulk electric system reliability analysis is the ability to provide reliability-index probability distributions in addition to the expected values of their indices. This paper illustrates the development of probability distributions for bulk electric system reliability performance indices using sequential simulation. The results obtained using the developed software show that the system performance-index probability distributions have unique characteristics that are basically dependent on the system topology, operating philosophy and conditions. System conditions such as the peak load level and system reinforcement options have significant impacts on the performance-index probability distribution characteristics. The basic concepts and their application in composite power system reliability evaluation are illustrated by application to a small practical test system.

Composite system reliability assessment using fuzzy linear programming

Composite power system reliability evaluation refers to assessment that considers both, generation and transmission facilities. The conventional dc load flow-based crisp linear programming (CLP) model used in composite power system reliability evaluation is modified as a fuzzy linear programming (FLP) model including fuzzy constraints and objective functions. This fuzzy linear programming model can include uncertainties that exist in certain variables and overcome the limitations of minor constraint violations in crisp linear programming model. This fuzzy optimization model is employed to test the system outage contingencies and to determine the degree of difficulty due to these contingencies. Fuzzy-based adequacy indices are obtained after all the selected contingencies have been analyzed using contingency enumeration approach. The effectiveness of the developed model is tested on the IEEE-14 bus system.

Impact of a FACTS controller on reliability of composite power generation and transmission system

This paper proposes an optimization model to use in composite power system reliability evaluation method incorporating the impact of FACTS devices. The conventional dc flow-based linear programming model used in composite system reliability evaluation method is converted into a non-linear optimization model to include the impact of FACTS devices on reliability of power system. The proposed model is tested on 24-bus IEEE-reliability test system (RTS). Annualized reliability indices are calculated using the model and compared with the indices calculated without considering FACTS devices.

Voltage stability considerations in composite power system reliability evaluation

This paper presents an approach which includes voltage stability considerations in the adequacy assessment of a composite power system. A fast technique for the probabilistic assessment of voltage stability is presented which can be used in conjunction with detailed contingency evaluation in a composite system adequacy assessment. Voltage stability is quantified using an indicator that can be easily included in the framework of an existing composite power system adequacy program. The effect on the adequacy indices (load point and system indices) of incorporating voltage stability constraints is illustrated by application to the IEEE-Reliability Test System.

Modeling the base load generating units with derated states in composite power system reliability evaluation

A novel approach for the construction of the equivalent two-state models for base load generating units with derated states is suggested in order to reduce the computational burden in composite power system reliability (adequacy) evaluation. The proposed equivalent is conceived in such a way to preserve the invariance of the failure probability of the generation system taken alone, under assumption that the network effects upon the generating units models are slight and can be ignored. The equivalent model suggested has been successfully tested and compared with the EFOR based model for the IEEE RT System.

Enhanced adequacy equivalent for composite power system reliability evaluation

The paper presents a technique for studying the adequacy of a selected area in a power network in which the remaining network is modelled as an adequacy equivalent. This approach can be very useful for evaluation of large composite power systems as it is often necessary in practice to perform adequacy analysis of selected relatively small regions in the network. It is useful in conducting adequacy studies of interconnected areas where one area can be replaced by its equivalent, thus reducing the size of the overall system. The paper presents a new approach and shows that annual adequacy indices can be efficiently calculated using this technique, leading to greatly decreased computation times.

Loadability in composite generation/transmission power-system reliability evaluation

An approach to composite (generation and transmission) power-system reliability evaluation that is based on contingency enumeration and on a simulation process to assess the system state is introduced. Using an AC load-flow model, the analysis starts with a nonlinear optimization procedure to determine system loadability. System characteristics, constraints, and operator intervention are taken into account. The nonlinear optimization procedure uses the modified augmented Lagrangian technique. Reliability indexes evaluated for the system and load buses are: loss of load probability (LOLP), (statistically) expected demand not served (EDNS), loss of voltage probability (LOVP), and (statistically) expected voltage violation (EVV). Sample numerical results are presented for the IEEE Reliability Test System. >

A security based approach to composite power system reliability evaluation

An electric power network containing generation and transmission facilities can be divided into several states in terms of the degree to which adequacy and security constraints are satisfied in a reliability evaluation of the composite system. The composite system is classified into different system states for which probabilistic indices are calculated. Both annualized and annual indices using a seven-step load model are presented for two test systems. Selection methods are used to detect problem-creating contingencies. A linear programming model for generation rescheduling and minimization of the amount of load shed is presented. A linear programming model for correcting the voltage problem is presented. >

A technique for reducing computational effort in Monte-Carlo based composite reliability evaluation

The authors describe a novel technique for reducing the number of samplings in Monte-Carlo-based composite power system reliability evaluation. The proposed technique uses analytical information from simple models (such as a generation reliability model) as regression variables to reduce the variance of LOLP (loss-of-load probability) and EPNS (expected power not supplied) estimates with the complete (composite) model. Sample size reductions of up to two orders of magnitude were obtained in case studies with a 24 bus modified reliability test system and a 124 bus reduced Brazilian system. Speed-ups of 6.1 to more than 40 were obtained for the estimate of the EPNS and of 2.1 to 3.8 for the LOLP estimate. In all cases, the EPNS index required more samplings to converge to the required tolerance (5% relative uncertainty) than the respective LOLP index (for the same tolerance). As a consequence, the higher speedups prevail in the overall convergence if a uniform criterion is adopted.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>