Abstract
After the failure of the power system, a large amount of alarm information will flood into the dispatching terminal instantly. At the same time, there are inevitable problems, such as the abnormal operation of the protection and the circuit breaker, the lack of alarm information, and so on. This kind of uncertainty problem brings great trouble to the fault diagnosis algorithm. As a data processing algorithm for an uncertain information set, Top-k Skyline query algorithm can eliminate the data points that do not meet the requirements in the information set, and then output the final K results in order. Based on this background, this paper proposes a power grid fault diagnosis method based on the Top-k Skyline query algorithm considering alarm information loss. Firstly, the fault area is determined by using the information of the electrical quantity and switching value. Then, backward reasoning Petri nets are established for the nodes in the fault area to form the data set of fault hypotheses. Then, the Top-k Skyline query algorithm is used to sort the hypotheses and choose the hypothesis with higher reliability. Finally, an IEEE 39-bus system example is given to verify the reliability of the proposed method.
Highlights
Based on the above research, this paper proposes a fault diagnosis method in the case of alarm information loss based on the Top-k Skyline query algorithm
In order to solve the problem of fault element misdiagnosis, this paper proposes an algorithm based on the backward reasoning Petri net to determine the suspected fault element
When the dispatching terminal receives the alarm information, it starts the fault diagnosis algorithm in the case of missing alarm information proposed in this paper; The fault area is determined by the combination of the switching value and electrical quantity; Form the backward reasoning Petri net of each element; Form the fault hypothesis of each element; The fault hypotheses of each element are preliminarily processed through the degreescore function, and each element retains at most K fault hypotheses into the total set of fault hypotheses; Reprocess the total set of fault hypotheses and rank the results; the data points obtained by the DFTS algorithm are the final results
Summary
Based on the above research, this paper proposes a fault diagnosis method in the case of alarm information loss based on the Top-k Skyline query algorithm. The Top-k Skyline query algorithm is selected to deal with the fault element hypotheses provided by the backward reasoning Petri net. For the big data set formed by the backward reasoning Petri net, the Top-k Skyline query algorithm can carry out efficient screening and quickly arrange the hypotheses that best fit the fault situation in order. Through a large number of simulation experiments, this paper verifies that the application of the Top-k Skyline query algorithm has important value for fault diagnosis in case of missing alarm information
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