Anomaly detection method for intelligent power system

An anomaly detection and smart power technology, applied in the field of power equipment monitoring, can solve problems such as undetectable and classified, high resource occupancy, poor generalization ability, etc., to improve detection effect and classification accuracy, improve accuracy, and improve accuracy sexual effect

Active Publication Date: 2020-06-02
UNIV OF ELECTRONIC SCI & TECH OF CHINA
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Problems solved by technology

[0005] Aiming at the above-mentioned deficiencies in the prior art, the present invention provides an abnormality detection method for an intelligent power system to solve the problem that the existing machine learning technology cannot quickly and accurately detect a

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  • Anomaly detection method for intelligent power system
  • Anomaly detection method for intelligent power system
  • Anomaly detection method for intelligent power system

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[0043] The following describes the specific embodiments of the present invention to facilitate those skilled in the art to understand the present invention, but it should be clear that the present invention is not limited to the scope of the specific embodiments, for those of ordinary skill in the art, as long as various changes These changes are obvious within the spirit and scope of the present invention defined and determined by the appended claims, and all inventions and creations that utilize the concept of the present invention are protected.

[0044] Such as figure 1 As shown, in an embodiment of the present invention, a method for detecting abnormality of a smart power system includes the following steps:

[0045] S1. Obtain the training set from the big data platform of the power system;

[0046] S2. Determine whether the amount of data in the training set is greater than the threshold N th , If yes, skip to step S3, if otherwise, skip to step S4;

[0047] S3. According to ...

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Abstract

The invention discloses an anomaly detection method for an intelligent power system, and effectively improves the accuracy of anomaly detection and classification of the power system. When training data is insufficient, compared with a traditional supervised classification method, the method apparently improves the detection effect and the classification precision; when the training data is sufficient, the method employs an improved multi-granularity cascade forest for replacing traditional deep learning, so the problems that a deep learning algorithm model is complex and the training time istoo long are solved. Compared with a traditional detection classifier, the method has the advantages of less required training data, lower algorithm complexity, shorter training time, faster convergence speed and faster response time.

Description

technical field [0001] The invention relates to the field of electric equipment monitoring, in particular to an abnormal detection method of an intelligent substation. Background technique [0002] The power system is an electric energy production and consumption system composed of power plants, power transmission and transformation lines, power supply and distribution stations, and power consumption links. Its function is to convert the primary energy in nature into electric energy through the power generation device, and then supply the electric energy to various users through power transmission, transformation and distribution. [0003] The stability of the power system is mainly affected by unexpected events related to weather and equipment failures, which may cause interruptions in power transmission, causing abnormal power outages and interruptions at the user end. In addition to the impact of unexpected events such as equipment failures, cyber attacks against power s...

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Application Information

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IPC IPC(8): G06Q10/06G06Q50/06G06K9/62G06N3/12
CPCG06Q10/0639G06Q50/06G06N3/126G06F18/23213G06F18/2148G06F18/2155G06F18/24323
Inventor 廖丹黄润章苇杭孙健陈雪张明
Owner UNIV OF ELECTRONIC SCI & TECH OF CHINA
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