Associated feature selection method suitable for multiple regulation and control operation scenes of power grid

Abstract

The invention relates to a power system feature selection technology, and particularly relates to an associated feature selection method suitable for multiple regulation and control operation scenes of a power grid. Firstly, starting from the perspective of historical actual data of a power system; standardizing and pre-treating the sample; the time series data can be directly brought into analysis calculation and a classifier; researching which characteristic variables may indicate the occurrence of a power system scene; or the typical scene is accompanied by the change of which characteristic variables; constructing a feature alternative set by utilizing the information, then quantitatively calculating correlation degree values between features and a target typical scene by utilizing a grey correlation degree analysis algorithm, sorting the correlation degree values, then setting a reasonable threshold value to obtain a final feature subset, and evaluating the feature subset by utilizing the accuracy of a classifier. The method is beneficial for dispatchers to have better indication when performing related operations, and meanwhile, the data processing time of the power system isreduced, and the data analysis efficiency is improved.

Description

technical field [0001] The invention belongs to the technical field of power system feature selection, and in particular relates to an associated feature selection method adaptable to various control and operation scenarios of a power grid. Background technique [0002] In recent years, with the integration of large power grids and the close interconnection, the integration of sending and receiving ends has become prominent. The access of high-penetration renewable energy, the security, stability and economic operation of the power system are also facing great challenges. A high proportion of power electronic equipment is invested in the power grid, which makes the dynamic stability of the system more complicated. When these new energy sources and random loads are connected to the power grid on a large scale, their randomness and intermittency will greatly increase the difficulty of power grid planning and scheduling, and may also cause serious faults in the power grid, hea...

AI Technical Summary

Problems solved by technology

If the data dimension significantly exceeds the number of training samples, it is likely to lead to overfitting and other phenomena.

And the current dispatching is passively waiting for the arrival of power grid accidents, and there is still room for improvement in the early warning and prevention of predictable risks such as natural disasters; there is no effective monitoring means for unit power fluctuations, relying on power plant reports; there is no monitoring means for power grid sudden public opinion events, relying on Lower-level scheduling reports; technical support system information is numerous and scattered, lack of centralized monitoring functions, etc.

[0003] The existing dispatching mode lacks characteristic analysis of typical scenarios of the power system, and cannot find out the relationship between typical scenarios and their associated features. The actual analysis of power system data is often not well targeted. The type judgment has the disadvantages of low accuracy, high time consumption, and large amount of data to be processed.

If the operating scenario of the power system cannot be predicted and judged well, dispatchers will not be able to make accurate operation decisions, which will cause great harm to the safe and stable operation of the power system

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