Power distribution network risk identification system and method

Abstract

The invention provides a power distribution network risk identification system and method. The method comprises the following steps: obtaining multi-source information data; analyzing data needed by risks and extracting features of the risks; analyzing the features of the risks; calculating risk identification indexes and determining states of a power grid; analyzing and determining positions andreasons of the risks; simulating the risks and analyzing the sever degree of the risks; and comprehensively assessing the risks and publishing risk early-warning information. The system comprises thefollowing parts: a multi-source information system and an intelligent monitoring terminal are connected with a data center through a data interface module and a communication device; the data center is connected with a data analyzing and processing module, and the data analyzing and processing module and a risk visualization module are connected with a risk identification modeling and analyzing module, a risk source positioning module and a risk reason identification and analysis module; and a risk simulation module is connected with all the above modules except the multi-source information system and the intelligent monitoring terminal. The solution offers an accurate risk early-warning result and provides technical support for power grid operators to provide a practical and effective decision basis.

Description

technical field [0001] The invention relates to a risk identification method, in particular to a distribution network risk identification system and method. Background technique [0002] The structure of the distribution network composed of overhead lines, cables, towers, distribution transformers, isolating switches, reactive power compensation capacitors and some ancillary facilities is huge and complex, which plays an important role in distributing electric energy in the power network. Due to faults or load transfer operations The switching network structure of switches changes frequently. The safety voltage level can be divided into high-voltage distribution network (35-110KV), medium-voltage distribution network (6-10KV and low-voltage distribution network (220 / 380V). The distribution network is directly oriented to end users, with multiple points and wide areas and complex structure , has increasingly become an active network that includes distributed power sources an...

AI Technical Summary

Problems solved by technology

[0003] At present, domestic and foreign researches on distribution network risk warning mainly focus on three aspects: static security risk assessment based on probability, multi-index comprehensive analysis and evaluation based on hierarchical analysis, and dynamic security risk assessment based on online information. On the basis of research and development of the distribution network risk assessment system, although some useful research work has been carried out in the traditional power grid risk assessment based on probability statistics, there are still some deficiencies in practical applications: (1) The traditional power grid risk assessment theory and Technology focuses on establishing a risk assessment and early warning system at the macro level. The data source used is single and the amount of data is small. The relationship between the established risk assessment model and the data is not strong. The risk assessment indicators given are single and one-sided. Space and other dimensions comprehensively reflect the type and severity of risks faced by the power grid; (2) The traditional power grid risk assessment method based on probability statistics only gives the possibility and severity of power grid risks, although it can reflect the overall risk level of the power grid , but the obtained early warning results cannot provide a basis for operators to discover and find out the cause and source of risk, and have limitations in formulating risk prevention and control measures; (3) Risk mechanism analysis and risk source identification are highly dependent on data, and the current information Incomplete, small amount of data, barriers between data source systems, difficult to analyze risk mechanism

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