Method for recognizing grid topology based on measurement spanning tree

Abstract

The invention relates to a method for recognizing grid topology based on a measurement spanning tree, belonging to the technical field of electric power system dispatching automation. The method comprises the steps of: firstly, generating a bus-branch model of a network and a tree structure, and calculating voltages and phase angles of nodes by layers; calculating active and passive powers of twoends of the branches according to impedance parameters of the branches; checking the calculating values and the measurement values, if having larger deviation, adding a bad mark on a corresponding network element, sequencing suspicious degrees of the bus and the branches according to the quantity of the bad marks; speculating switch brakes with telecommands possibly having errors and lines with operation states possibly having errors from the suspicious bus and the suspicious branches; and determining whether the deflection of the switch brakes is reasonable or not through judging whether thequantity of the bad marks in the network after the corresponding switch brake deflects is reduced or not. The method is used for recognizing topological errors without state estimation results and convergence problems, and has the advantages of higher calculating speed and wide application range compared with the algorithm using state estimation.

Description

technical field [0001] The invention relates to a power grid topology error identification method based on measurement spanning tree, and belongs to the technical field of electric power system scheduling automation. Background technique [0002] In the energy management system (EMS), the state estimation uses the real-time information collected by the data acquisition and supervisory control system (SCADA), establishes a network model according to the topology status, and estimates the voltage amplitude and phase angle on the bus and the power on the components, etc. , to get a more accurate network running status. The results of state estimation are affected by bad measurement data, topology errors, and parameter errors, among which topology errors are the most serious. However, in the current power grid dispatching automation system that is actually applied, the function of topology error detection is still very weak. In some cases, the error detection is invalid, and ev...

AI Technical Summary

Problems solved by technology

This requires that the state estimation has been able to converge, and when there are topological errors, this condition may not be met

Furthermore, state estimation results do not necessarily directly reflect topological errors due to the possibility of parameter errors and bad measurement data

[0004] 2. There may be multiple bad data and multiple topological errors

However, the correct telemetry value depends on the state estimation result. When the topology error is not ruled out, the identification of bad data cannot be guaranteed to be accurate, so the correctness of the telemetry value cannot be guaranteed.

At present, there is no method that can give a perfect criterion to identify which factors (bad measurement data, topology errors, parameter errors) lead to large deviations in state estimation

[0005] 3. The wiring of the actual system is more complicated, and the network analysis is very cumbersome

The disadvantage is that the scope of application of the rules is limited. Simple rules can only handle a few types of errors, while complex rules are prone to self-contradictory problems. In general applications, the rule method often only detects some very obvious errors.

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