Multi-deme parallel genetic algorithm in reliability analysis of composite power systems

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

The probabilistic reliability evaluation of composite power systems is a complicated and computation intensive task. Monte Carlo Simulation (MCS) is often used as the method of choice for tackling this difficult problem, though MCS may also suffer from issues regarding high dimensionality leading to an increased need for computational resources. In order to address this issue an algorithmic method known as state space pruning has been developed in two flavors: Analytical and Metaheuristic based. The state space pruning methodology reduces the size of a given state space by removing states where there is no loss-of-load. This allows the MCS algorithm to sample a state space with a higher density of failure states which, in turn, leads to faster convergence. This study applies the CLONALG algorithm to the metaheuristic based version of state space pruning, compares and contrasts the results with genetic algorithm (GA) and particle swarm optimization (PSO) implementations, and discusses its strengths and weaknesses as applied to test systems both with and without the consideration of transmission line outages. Simulations are completed using the IEEE reliability test system (RTS) and the modified RTS (MRTS).

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.

Power systems has been subjected to significant upgrades in terms of structure and capacity. Reliability evaluation of composite power systems has surfaced as an essential step in operation and planning stages of the modern power system. It is an effective tool to investigate the ability of power systems to supply customers with reliable power service. The purpose of this review is to enhance the knowledge of reliability studies conducted on composite power systems by providing a critical and systematic review. This work investigates peer-reviewed articles published between 2007 and 2017 in three reliable databases. The findings reveal that the reliability of composite power systems has received considerable attention over the last few years. Secondly, investigation studies demonstrated a crucial role in verifying the impact of adopting new technologies. Third, studies on this topic have been intensively conducted in Asia, which highlights the promising sectors in these regions. However, researchers have generally focused on developing several aspects (e.g., evaluation speed and wind power integration) at the expense of others (e.g., realistic studies and other renewable energy resources). The lack of practical applications is evident in the surveyed publications. These findings imply a potential incoordination between the needs of the real applications and researchers’ tendencies. Future reliability evaluation scholars are advised to consider the findings of this systematic review including concentrating on insufficiently covered topics and enhance the coordination among the efforts devoted in this area.

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.

Reliability of an electrical power system plays a vital role in providing continuous power supply to consumers with greater quality. The power demand is increasing day by day due to the increased population, modern society and are seeking highly reliable power. The assessment of reliability is very difficult due to the presence of large number of components and complex power system network con-figurations. This paper address a novel useful step by step algorithm for the assessment of average power availability at load buses us-ing the concept of modified Petri nets with conditional probability. The proposed algorithm is very efficient and can applicable to any number of the bus system. The proposed algorithm is tested with Roy Billiton practical example, IEEE 6 bus, IEEE 14 bus and IEEE RTS-96 bus system. The obtained results are validated by Monte Carlo simulation method, Classical Node elimination method and mod-ified minimal cut set method.

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 paper illustrates a procedure for adequacy evaluation of a composite generation and transmission system containing a direct current (DC) link. The transportation model option of the composite reliability (COMREL) program, developed at the University of Saskatchewan, is used to evaluate the composite system adequacy indices. The IEEE Reliability Test System (RTS) is augmented by adding a DC link and is used as the test system. The composite system load point and the overall system indices are calculated to quantify the adequacy of the composite system. The basic COMREL program was modified to accept a remote generation source connected to a composite system through a DC link. The analysis involves the construction of a generating capacity model at the remote bus and the movement of each generating capacity state through the DC line contingency state, without violating the carrying capability of the transmission line state or the generator state capability restriction. This type of analysis can be used to evaluate the benefits associated with design changes and capital expenditures in the DC facility. The paper presents the techniques employed for the composite system study and the results obtained for the modified RTS.

Reliability assessment techniques and programs handling is an important issue for both power systems planning and testing in existing power system configurations. The Assessment techniques are suitable for detecting weak points in the reliability assessment. The reliability study of bulk power systems indicates the ability of the composite generation and transmission system to satisfy the load demand at major load points. The major burden of the developed methods used is the computation time required to solve a large number of credible contingencies or outage states. This paper presents a novel approach capable of the reliability evaluation for real large-size networks using normal size computers. It calculates the reliability indices at individual load buses and reliability of the whole system. The effect of the loading factor between loads on reliability is also investigated. The proposed approach is expanded to calculate the reliability of composite generation and transmission system taking the following constraints into consideration: amount of reliability of system generation, amount of reliability of transmission lines, maximum system generation capacity, maximum transmission lines capacity and maximum connected load at each bus. The approach is based on the minimal path set and dual fault tree techniques. A new concept of constant reliability region, which is utilized in the field of optimum operation of power networks, is introduced. A special program is developed for the proposed technique. The comparison between the proposed and the previous techniques confirms that the proposed method is more accurate and precise.

Three-parameter interval number theory and its application in power system reliability evaluation

A fuzzy-based approach for generation system reliability evaluation

Sequence operation theory and its application in power system reliability evaluation

FACTS technologies can have major positive impacts on power system reliability performance and the actual benefits obtained can be assessed using suitable models and practice. Emerging techniques for composite power system reliability evaluation mainly focus on conventional generation and transmission facilities. In this paper, improvement of Reliability in composite electric power system is examined by incorporating Unified Power Flow Controller (UPFC). Two test systems, Reliability Test System (RTS) and Roy Billinton Test System (RBTS) are considered to show major improvement in reliability. A state space reliability model of multi-module UPFC has been developed and incorporated in the system. Load point & System indices performances are presented to examine the impact of UPFC on the composite electric power system reliability. Investigation results show a significant improvement in the Load point & system indices when utilizing UPFC.

The intermittency of wind generation and the potential need for adequate transmission expansion are the major concerns in wind generation integration to power system. One solution being considered is to build on-site energy storage with the wind farms. The idea of building such a composite system is not only to minimize the real-time variation of the composite system output, but also to optimize the transmission upgrades needed for delivery of the wind generation. A novel probabilistic reliability assessment method is proposed in this paper for determining the adequate size of on-site energy storage and the transmission upgrades needed in connecting wind generation with the power system. The practical applications of the proposed model are illustrated using the IEEE Reliability Test System (IEEE-RTS).

In this paper a new approach for evaluation of composite system reliability of power systems is described. In this method Monte Carlo Simulation (MCS) is combined with Multilabel Radial Basis Function (MLRBF) classifier to compute system reliability indices. MLRBF is a classification technique in which target vector of each instance is assigned into multiple classes. In this study MLRBF is used to characterize states of a complete power system (failure or success) without requiring optimal power flow (OPF) analysis. As a result, the computational burden of the reliability evaluation analysis to perform OPF is eliminated. For illustration, the proposed method is applied to the IEEE Modified Reliability Test System (IEEE-MRTS). The obtained results demonstrate that MLRBF algorithm in reliability evaluation provides good performance in classification accuracy while reducing computation time dramatically.