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A power system distribution substation load data abnormity detection and repair method

A load data and power system technology, which is applied in the field of abnormal detection and repair of load data in power distribution substations, can solve the problems of less research work on abnormal detection and repair and less use of machine learning tools, and achieves improved computing speed and reduced computing costs. , Improve the effect of fine-tuning accuracy

Pending Publication Date: 2019-04-09
江苏云上电力科技有限公司 +1
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AI Technical Summary

Problems solved by technology

At present, there are few researches on anomaly detection and repair of low-voltage distribution network load data, and emerging tools such as machine learning are less utilized

Method used

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  • A power system distribution substation load data abnormity detection and repair method
  • A power system distribution substation load data abnormity detection and repair method
  • A power system distribution substation load data abnormity detection and repair method

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Embodiment Construction

[0064] The method for abnormal detection and repair of load data of power system distribution substation proposed by the present invention, its flow chart is as follows figure 1 shown, including the following steps:

[0065] (1) Collect the load data L of the power system distribution substation to be detected and repaired for N days (N needs to be greater than 180) and the temperature data T of the area where the distribution substation is located. Let the length of the load data collected every day in N days be H, for example, every Collect one point per hour, then H=24, for example, collect one point every 15 minutes, then H=96, and so on, the lengths of load data L and temperature data T are respectively N×H;

[0066] (2) Rearrange the load data L into a load data matrix L with N rows and H columns 1 , load data matrix L 1 Each row in corresponds to the load data of H periods in each day, and each column corresponds to the load data at the same time in different days;

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Abstract

The invention relates to a power system distribution transformer load data abnormity detection and repair method, and belongs to the technical field of power system analysis and control. The method comprises a coarse detection stage and a micro detection stage of the abnormal condition of the distribution transformer load data: in the coarse detection stage, missing data is complemented by using alow-rank matrix technology, and an obvious abnormal value is preliminarily repaired; In the micro-detection stage, a random forest quantile regression model is used for constructing the influence relation of factors such as historical loads, weather and day types on the to-be-detected loads, and local abnormal data are finely adjusted. According to the method, diversified data and historical loaddata can be fully utilized to detect and repair abnormal data in the distribution transformer load in the power system, a reliable data basis is provided for power load prediction and power system operation, the power load prediction precision is effectively improved, and the operation cost of the power system is reduced.

Description

technical field [0001] The invention relates to a method for detecting and repairing abnormal load data of a distribution substation in a power system, and belongs to the technical field of power system analysis and control. Background technique [0002] Ensuring the authenticity and integrity of power system load data is of great significance to the accuracy of power load forecasting and the reliable operation of power systems. However, in the process of load data collection, the collection of load data may be incomplete or have different degrees of deviation due to collection equipment failure, communication failure, information attack and other reasons. Missing values, continuous zero values, continuous constant values, abnormal sudden increases, abnormal sudden decreases, etc. are more common types of abnormal data. The lower the voltage level, the more difficult it is to detect and repair abnormalities in the corresponding load data. There are two main reasons: 1) The...

Claims

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Application Information

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IPC IPC(8): G06F17/18G06Q10/04G06Q50/06
CPCG06F17/18G06Q10/04G06Q50/06Y04S10/50
Inventor 李明轩罗卓伟王毅贺大玮刘羽霄张宁
Owner 江苏云上电力科技有限公司
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