Anomaly detection method for power time series data based on long short-term memory network

A long-short-term memory and time-series technology, applied in data processing applications, neural learning methods, biological neural network models, etc., can solve problems such as high collection frequency, large data scale, and complex data types, and achieve the effect of reducing training time

Active Publication Date: 2022-04-12
FUDAN UNIV +2
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

Generally speaking, the real-time operation data of the power system has the characteristics of many data acquisition devices, high acquisition frequency, large data scale, complex data types, etc.

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  • Anomaly detection method for power time series data based on long short-term memory network
  • Anomaly detection method for power time series data based on long short-term memory network
  • Anomaly detection method for power time series data based on long short-term memory network

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Embodiment 1

[0015] Although the illustrative specific embodiments of the present invention have been described above, so that those skilled in the art can understand the present invention, 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 are within the spirit and scope of the present invention defined and determined by the appended claims, these changes are obvious, and all inventions and creations using the concept of the present invention are included in the protection list.

[0016] Such as figure 1 and image 3 As shown, an anomaly detection method of power time series data based on long short-term memory network, the method includes two parts: offline training model and anomaly detection based on the model of power time series data analysis method based on long short-term memory network model, including The following steps:

[0017] S1: Preprocessing of power tim...

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Abstract

The invention discloses an anomaly detection method of power time series data based on a long-short-term memory network. The method includes the following steps: (1) preprocessing of electric power time series data; (2) neural network model pre-training, adopting an encoder-decoder structure to calculate hierarchical dynamic attention; (3) abnormal data detection, in the neural network model After the training is completed, the model weight W is saved locally, and the model is directly loaded when the new power time series data x is detected, and the distance to the representative point c is calculated to obtain its abnormal score to judge whether it is abnormal. The method of the invention is used for anomaly detection of power time series data, and the method is simple and the detection accuracy is high.

Description

technical field [0001] The invention belongs to the technical field of data analysis and anomaly detection, in particular, it relates to an anomaly detection method of power time series data based on long short-term memory network. Background technique [0002] The real-time operation data of the power system has the potential ability to reflect the current operation status and future development trend of the power system. With the rapid development of power system intelligence, the scale of various sensors embedded in the power system continues to expand, making the types of data collected by the perception layer more refined, and the data to be processed increases sharply. According to incomplete statistics, the grid business data collected by a single city every day can reach PB level. Generally speaking, the real-time operation data of the power system has the characteristics of many data acquisition devices, high acquisition frequency, large data scale, and complex dat...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/06G06Q10/00G06Q50/06G06N3/04G06N3/08
CPCG06Q10/0639G06Q10/20G06Q50/06G06N3/08G06N3/048G06N3/044G06N3/045
Inventor 沙朝锋耿同欣郑伟杰
Owner FUDAN UNIV
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