Reliability Analysis Of Electrical System Research Articles

ИССЛЕДОВАНИЕ НАДЁЖНОСТИ ЭЛЕКТРООБОРУДОВАНИЯ СИСТЕМЫ ЭЛЕКТРОСНАБЖЕНИЯ, ИМЕЮЩЕГО БОЛЬШОЙ СРОК НАРАБОТКИ

The article deals with the questions of reliability analysis of electrical systems with
 long term developments close to the assigned resource or it exceeding

Reliability analysis of electrical system of computer numerical control machine tool based on bayesian networks

The core of computer numerical control (CNC) machine tool is the electrical system which controls and coordinates every part of CNC machine tool to complete processing tasks, so it is of great significance to strengthen the reliability of the electrical system. However, the electrical system is very complex due to many uncertain factors and dynamic stochastic characteristics when failure occurs. Therefore, the traditional fault tree analysis (FTA) method is not applicable. Bayesian network (BN) not only has a unique advantage to analyze nodes with multiply states in reliability analysis for complex systems, but also can solve the state explosion problem properly caused by Markov model when dealing with dynamic fault tree (DFT). In addition, the forward causal reasoning of BN can get the conditional probability distribution of the system under considering the uncertainty; the backward diagnosis reasoning of BN can recognize the weak links in system, so it is valuable for improving the system reliability.

BULK ELECTRIC SYSTEM RELIABILITY EVALUATION WITH WIND TURBINE AND ENERGY STORAGE

This paper describes a new methodology for grid expansion planning considering the probabilistic reliability of a Bulk Electric Systems (BES) including Wind Turbine Generators (WTG) and Energy Storage System (ESS).The proposed model includes the main parameters used to create an operational history for each individual unit and transmission line unavailability. Bulk electric system reliability analysis associated with wind energy provides an opportunity to investigate the reliability benefits when large-scale wind power is injected in a BES. High wind power penetration can lead to high risk levels in the overall system reliability. The impacts of different wind turbine penetration levels on the reliability benefits from ESS are analyzed when the WTG capacity is utilized to replace the conventional generators with same total rated power capacity. The WTG and ESS capacity are installed to meet the annual growth of load demand and maintain the BES adequacy levels. The proposed method is applied to the IEEE Reliability Test System (IEEE– RTS ).

Reliability assessment of bulk electric systems containing large wind farms

Wind power is an intermittent energy source that behaves quite differently than conventional energy sources. Bulk electric system reliability analysis associated with wind energy conversion systems (WECS) provides an opportunity to investigate the reliability benefits when large-scale wind power is injected at specified locations in a bulk electric system. Connecting the WECS to different locations in a bulk system can have different impacts on the overall system reliability depending on the system topology and conditions. Connecting a large-scale WECS to an area which has weak transmission could create system operating constraints and provide less system benefit than connecting it to an area with stronger transmission. This paper investigates bulk electric system transmission constraints associated with large-scale wind farms. The analyses presented in this paper can be used to determine the maximum WECS installed capacity that can be injected at specified locations in a bulk electric system, and assist system planners to create potential transmission reinforcement schemes to facilitate large-scale WECS additions to the bulk system. A sequential Monte Carlo simulation approach is used as this methodology can facilitate a time series modeling of wind speeds, and also provides accurate frequency and duration assessments. An auto-regressive moving average (ARMA) time series model is used to simulate hourly wind speeds.

Predicting Bulk Electricity System Reliability Performance Indices Using Sequential Monte Carlo Simulation

System reliability performance is usually based on average customer interruption information. These average values are valuable information, but provide only a single customer risk dimension without underlying probability distributions. The average annual indices give no insight on how reliability may vary from year to year as a result of the random behavior of bulk electric systems (BES). 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. Parameter distribution analysis and its potential utilization are relatively new concepts in composite power system reliability analysis and decision making. This paper presents a technique to predict future reliability performance indices of the BES using a sequential simulation approach. The results obtained using the sequential software show that the system performance index probability distributions have unique characteristics that are basically dependent on the system topology and the operating philosophy. Operating policies involving load shedding procedures have a considerable impact on the system performance indices and their associated distributions. Two test systems are used to illustrate the concepts associated with the determination of system performance indices and their associated probability distributions. The results obtained using two different load shedding policies are presented and compared.

Delivery Point Reliability Indices of a Bulk Electric System Using Sequential Monte Carlo Simulation

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. Reliability index probability distributions provide a pictorial representation of the annual variability of these parameters around their mean values. Parameter distribution analysis and its potential utilization are relatively new concepts in composite power system reliability analysis and decision making. Computer software has been developed to produce reliability index probability distributions for the individual delivery points (DPs) in a bulk electric system. The results obtained using the sequential software show that the DP reliability index probability distributions have unique characteristics that are basically dependent on the system topology and the system operating philosophy. Operating policies such as load shedding procedures have a significant impact on the DP reliability indices and their associated distributions. The IEEE Reliability Test System is used to illustrate the concepts associated with the determination of DP and system indices and their associated probability distributions. The results obtained using two different load shedding policies are also presented and compared.

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.