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Low-voltage area KFCM-SVR reasonable line loss prediction method

A technology of reasonable line loss and prediction method, applied in prediction, data processing applications, instruments, etc., can solve the problems of insufficient mining and analysis of monitoring data, low accuracy of theoretical line loss prediction, etc.

Inactive Publication Date: 2016-03-09
HOHAI UNIV
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Problems solved by technology

[0005] At present, the status quo of line loss management is that the prediction accuracy of theoretical line loss is not high, and a large amount of monitoring data related to line loss has not been fully excavated and analyzed

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  • Low-voltage area KFCM-SVR reasonable line loss prediction method

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[0038] The present invention will be further described below in conjunction with the accompanying drawings. The following examples are only used to illustrate the technical solution of the present invention more clearly, but not to limit the protection scope of the present invention.

[0039] Such as figure 1 As shown, the method includes the following steps:

[0040] Step 1, data preparation: extract relevant modeling data from the Oracle database of the electricity consumption information collection system on a monthly basis, including parameters: total number of users, number of residents, number of non-residents, capacity of residents, capacity of non-residents, sales Electricity, transformer capacity, proportion of residential capacity, average capacity per household and monthly average line loss rate.

[0041] Step 2, data screening: remove the stations with large data changes or abnormal data. Screening out platform data includes:

[0042] 1) Data collection is not ...

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Abstract

The present invention discloses a low-voltage area KFCM-SVR reasonable line loss prediction method. The method comprises the following steps: 1) presorting data samples twice after data screening; 2) clustering six types of data by using fuzzy kernel clustering to obtain a plurality of small types; 3 ) establishing a regression fitting model by virtue of a least squares support vector machine (LS-SVR); 4 ) calculating a residual error confidence interval limit value; 5) classifying to-be-predicted data into each small types according to a Euclidean distance principle; 6) after classification, inputting the to-be-predicted data into each respective corresponding LS-SVR model, and calculating to obtain a prediction residual error; and 7) comparing the prediction residual error with the residual error confidence interval limit value to provide a conclusion. In the low-voltage area KFCM-SVR reasonable line loss prediction method disclosed by the present invention, an algorithm has certain reasonableness under a big data condition, and a computed result of the algorithm can provide relatively reliable guidance for area line loss management of an electric power department, and provide a new idea for reasonable utilization and mining of big data in the current smart power network.

Description

technical field [0001] The invention relates to a reasonable line loss prediction method for KFCM-SVR in a low-voltage station area, and belongs to the technical field of electric power system automation. Background technique [0002] Line loss is the energy loss generated in the process of power transmission and distribution, including statistical line loss, theoretical line loss, management line loss and other types. In the actual production and application of the power sector, the difference between the power supply and the electricity sales is used as the statistical line loss, that is, the actual line loss, and the ratio of the statistical line loss to the power supply is used as the line loss rate. The line loss rate is an important comprehensive economic indicator of the power sector, reflecting the comprehensive level of power grid planning and construction, technical equipment, and management and operation. Many research results have analyzed the causes and influen...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06
CPCG06Q10/04G06Q50/06
Inventor 梅飞
Owner HOHAI UNIV
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