Patent ID: 11942784
Assignee: SICHUAN UNIVERSITY
Field: Electrical machinery, apparatus, energy (Electrical engineering)
Classification: CPC G  H  Y | IPC H

Claim 3:
4. The method for formulating the coordinated action strategy of SSTS and DVR for voltage sag mitigation according to claim 1, wherein the step 2 specifically comprises:
step 2.1: discretizing conditional attribute data
for the classification of the voltage sag duration time, selecting four characteristics of amplitude, phase jump, date and weather from multi-dimensional attributes as conditional attributes; for two types of continuous attribute data of the amplitude and the phase jump, merging adjacent sections according to chi-square test by a ChiMerge discrete method until criterion conditions are met; when discretizing date data, converting the date data into a digital quantity having a value changed continuously between 1 and 365 by taking days as a unit and years as a cycle; for the weather which is a language description data, dividing the weather according to weather categories;
step 2.2: building a decision tree root node
1) calculating an information entropy E(T) of the voltage sag duration time Tsag<T1:

E(T)=−(p1 log2p1+p2 log2p2)  (13)

wherein T1 is the SSTS switching time; p1 is probabilities that a datum which is greater than or equal to T1 is randomly selected from a historical data set T of the voltage sag duration time, p2 is probabilities that a datum which is less than T1 is randomly selected from a historical data set T of the voltage sag duration time;
2) Calculating a conditional entropy E(T,X) between the above four conditional attributes and Tsag<T1:, E
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wherein X represents four conditional attributes; c represents a conditional attribute; P(c) is a joint probability that a conditional attribute and Tsag<T1 appear at the same time; and E(c) is a conditional probability of Tsag<T1 under a conditional attribute and with different values;
3) calculating mutual information Gain(T,X) between the above four conditional attributes and Tsag<T1:

Gain(T,X)=E(T)−E(T,X)  (15)

the larger the value of mutual information is, the higher the correlation with Tsag<T1 is; a conditional attribute with largest mutual information is selected as a decision tree root node;
step 2.3: building decision tree branch nodes and leaf nodes
a specific operation process is the same as three points in step 2.2: calculating the information entropy, the conditional entropy and the mutual information; gradually discretizing results with the largest mutual information with Tsag<T1 from results by using the conditional attributes, and attributes with largest mutual information with Tsag<T1 from other conditional attributes as branch nodes; and iterating the process repeatedly until a complete decision tree with “Yes, No” of Tsag<T1 as a leaf node is finally constructed based on historical data;
step 2.4: conducting classification by the decision tree
when a monitoring device monitors a voltage sag event, inputting four conditional attribute data, and classifying the duration time of the new voltage sag event by a generated decision tree logic; and when the input data is consistent with the decision tree, entering a next branch for judgment, until a classification result of yes or no is obtained finally through the leaf node.