Dynamic layer diagnostic device and method of smart power grid fault

Abstract

A dynamic layer diagnostic device for smart power grid fault comprises a data collection and monitoring unit, a data processing unit, a data base unit, a communication unit and a man-machine interaction unit. A dynamic layer diagnostic method includes: when a smart power grid breaks down, calculating fault diagnosis starting conditions to conform a diagnosis strategy, wherein the fault diagnosis strategy comprises switch layer diagnosis, feeder layer diagnosis and transformer substation layer diagnosis; starting the switch layer diagnosis when changes of switch motion information are remarkable before and after the fault; starting the transformer substation diagnosis when changes of electricity amount information are remarkable before and after the fault; stopping the diagnosis when a fault element is the only one element during the diagnosis of the switch layer; otherwise, retrieving the switch historical action recording, and starting the transformer substation diagnosis when recording matched with the current switch action exists; and otherwise, starting the feeder layer diagnosis. The dynamic layer diagnostic method performs layering analysis on the fault, fully utilizes various fault information and improves fault diagnosis accuracy according to different characteristics of multisource information after the power grid fault and difficulty layer in obtaining and processing of various information.

Description

Technical field [0001] The invention belongs to the technical field of power transmission and distribution, and particularly relates to a dynamic hierarchical diagnosis device and method for smart grid faults. Background technique [0002] With the vigorous development of smart grids, the requirements for efficient, reliable, and safe operation of the grid are becoming higher and higher. Therefore, the grid fault diagnosis and fault recovery are required to have higher speed and effectiveness. When the power grid fails, fast and accurate fault diagnosis can effectively shorten the power outage time, narrow the fault area, and reduce the adverse impact on users. Currently, there are many methods for power grid fault diagnosis, including expert systems, artificial neural networks, fuzzy theory, optimization techniques, petri networks, rough set theory, etc. However, these methods have certain shortcomings, such as complex formation of expert system knowledge base, slow diagnosis s...

AI Technical Summary

Problems solved by technology

The information measured by each system includes repeated data and independent information, and they are information complementary to each other. Therefore, all fault information cannot be obtained through a single information source, and the diagnosis strategy based on all fault information is too cumbersome and the diagnosis speed is slow. This requires A Dynamic Diagnosis Decision Based on Extracting Different Data Information Based on Decision Needs

In addition, a large number of applications of various monitoring and management equipment in the power grid lead to a large degree of uncertainty in fault information. These uncertainties come from the randomness of fault occurrence, the fuzziness of data collection, and the rough Uncertainty due to lack of sexual and cognitive information

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