Bus load identification method and system

Abstract

The invention relates to a bus load identification method and system. The method comprises the steps of collecting load identification data of a to-be-identified bus; determining load power time sequence data corresponding to various electrical loads on the to-be-identified bus according to the load identification data of the to-be-identified bus. According to the technical scheme provided by theinvention, the identification of the load types of large-scale and diversified loads on the bus is realized through the learning capability of the LSTM neural network, and the identification precisionis improved.

Description

technical field [0001] The invention relates to the technical field of power system automation analysis, in particular to a bus load identification method and system. Background technique [0002] Some large power supply points (Bulk Supply Points, BSPs) in the power grid, such as the equivalent load nodes of 220kV substation transformers in provincial power grids, and 10kV feeder nodes in regional power grids, are usually connected to a large number of load resources of different types and characteristics. How to effectively predict these busbars The change of node load power and how to effectively utilize the aggregation characteristics of the flexible load response connected to these nodes are the focus of grid dispatchers. Determining the type and quantity of loads connected to large-scale power supply nodes is the basis and key to carry out the above research. However, in the actual power grid, due to problems such as measurement cost, imprecise network model, and syst...

AI Technical Summary

Problems solved by technology

However, in the actual power grid, due to problems such as measurement cost, imprecise network model and system ownership, the composition of BSP load is often difficult to obtain directly through measurement equipment

The existing load identification and extraction methods have the following problems: First, the existing load resource identification technology is mostly aimed at small-scale household users, and the research method is difficult to apply to the identification of large-scale and diversified load-side resources; The algorithm is sensitive to load classification, unit scale, load model, external environment and other factors, and the identification accuracy is limited

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