A method for identifying and correcting bad data of reactive power curve
A bad data and data technology, applied in AC network circuits, electrical components, load forecasting in AC networks, etc., can solve the problems of DC near-area fluctuation reactive power throughput, no data correction method, difficulty in meeting the day-ahead power generation plan, etc. Achieve the effect of precise control, significant economic and social benefits, and high prediction accuracy
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Embodiment 2
[0104] In this embodiment, further, the first bad data, the second bad data and the third bad data can also be corrected to obtain the corrected corrected data, such as Figure 4 As shown, the correction method includes:
[0105] S1. Get the z corresponding to the bad data i , the z iPerform a linear transformation x' for the dataset to be processed i =ax i After +b, the deviation value obtained by linear fitting with the standard unitized reference data set.
[0106] S2. Obtain the correction data of the bad data.
[0107] Correct the z corresponding to bad data i , so that z i Equal to the z corresponding to the two nearest correct data i average of. For each bad data, find the nearest two correct data to its two ends, and compare the z corresponding to these two correct data i Perform a linear difference so that these bad data correspond to z i Equal to the result of the linear interpolation. z corrected according to the following formula i Calculate the correct...
Embodiment 3
[0120] A reactive power curve bad data identification device, such as Figure 5 shown, including:
[0121] The data set to be processed acquisition module 301 is used to obtain the data set to be processed; the data set to be processed includes reactive daily curve data;
[0122] A reference acquisition module 302, configured to acquire reference data and deviation limits;
[0123] A rough identification module 303, configured to perform rough identification on the data set to be processed according to the reference data to obtain first bad data;
[0124] A first preprocessing module 304, configured to remove the first bad data to obtain a first preprocessing data set;
[0125] A fine identification module 305, configured to perform fine identification on the first preprocessing data set according to the reference data and the deviation limit to obtain second bad data;
[0126] The second preprocessing module 306 is configured to remove the second bad data to obtain a secon...
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