Probability distribution-based distribution network reliability judgment method

Abstract

The invention discloses a probability distribution-based distribution network reliability judgment method. On the basis of existing reliability judgment indexes, three distribution network reliability judgment indexes are proposed by the judgment method as follows: time to system failure (TTSF), time to system repair (TTSR) and system interruption frequency (SIF), and moreover, the three distribution network reliability judgment indexes are solved through an analytical expression. According to the probability distribution-based distribution network reliability judgment method disclosed by the invention, based on a distribution network partition thought, analytical characterization is performed on the distribution network reliability judgment indexes by adopting random functions respectively from a system level and a node level, and then, the probability distribution computation of a reliability index random function expression is realized by further combining the nonparametric kernel density estimation technology. The new indexes proposed by the method disclosed by the invention are beneficial supplements to a traditional distribution network reliability index system and are in favor of the rapid and visual judgment of the overall random characteristic of the reliability level of the system.

Description

technical field [0001] The invention relates to the improvement of a distribution network reliability judgment method, in particular to a distribution network reliability judgment method based on probability distribution, and belongs to the technical field of distribution network reliability judgment. [0002] Background technique [0003] The power distribution system is at the end of the power system and is directly connected to the users. The power supply capacity and quality of the power system to the users must be realized through the power distribution system. The reliability of the distribution network is a concentrated reflection of the safe operation of the entire power system. The traditional distribution network reliability judgment uses the expected value index to reveal the long-term stable change trend of system reliability, which can conveniently and quickly provide valuable system outage risk information for planning and operation personnel. However, the exp...

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Problems solved by technology

Among them, the literature (B. Retterath, S.S. Venkata, A.A. Chowdhury. Impact of time-varying failure rates on distribution reliability [J]. Electrical Power and Energy Systems, 2005 (27): 682-688.) Considering the age of components Research on the influence of time-varying failure rate on distribution network reliability index; literature (Ning Liaoyi, Wu Wenchuan, Zhang Boming. A probability distribution of component repair time suitable for operation risk assessment[J]. Chinese Journal of Electrical Engineering, 2009, 29( 16): 15-20.) Proposed the maintenance time whose probability distribution is a superimposed exponential distribution and the density curve is "bell-shaped", and analyzed the influence of the time-varying repair rate on the system's instantaneous state probability value in the system operation risk assessment; However, the above two literatures are only limited to the influence of time-varying transition rate on the expected value of reliability index, and the probability distribution of reliability index has not been obtained.

In terms of analytical calculation of the reliability index probability distribution, some scholars have made preliminary explorations, the literature (R. Billinton, R. Goel. An analytical approach to evaluate probability distributions associated with the reliability indices of electric distribution systems [J]. IEEE Trans . on Power Delivery, 1986, 3(1): 245-251.) Under the assumption that the number of component failures approximately obeys the Poisson distribution, the calculation of the first four moments of the reliability index is realized, and the distribution network is obtained by means of the Pearson frequency curve The percentile of the reliability index, but it is extremely difficult to select the most suitable probability distribution type from the Pearson distribution curve family through the first four moments; literature [Enrico C, Gianfranco C. Evaluation of the probability density functions of distribution system reliability indices with a characteristic functions-based approach[J]. IEEE Trans. on Power Systems, 2004, 19(2): 724-734. ] deduced the characteristic function of the distribution network reliability index, and obtained the first four order origin moment, central moment and probability density curve of the distribution network reliability index, but this method involves complex discrete Fourier transform DFT and inverse transform IDFT , and cannot be applied to non-linear random functions, and the random variables in random functions are strictly required to be independent of each other; literature [Zhao Yuan, Xie Kaigui. Analytical Calculation Model of Probability Density Distribution of Power Grid Reliability Index[J]. Chinese Journal of Electrical Engineering, 2011, 31(4): 31-38. ] It is the first time to make a useful attempt to analyze and calculate the probability density distribution of reliability indicators in large power systems. Difficult to directly apply to distribution network reliability assessment

[0006] The evaluation focus of existing distribution network reliability indicators is mainly on the load side. Existing system-level indicators, such as the system average power outage frequency indicator SAIFI, can be determined by each load The point reliability index is used to express and take into account the impact of the number of load point users. It is a measurement index that only examines the overall reliability of the distribution network from the perspective of users. It only reflects the impact of random faults in the distribution network on load point users from the perspective of users. The impact of the annual power outage duration and annual power outage frequency, etc., cannot grasp the change law of system operation risk from a macro perspective

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