Dual-layer long-short term memory network-based early-stage state identification method of 10kV single-core cable

Abstract

The invention discloses a dual-layer long-short term memory (LSTM) network-based early-stage state identification method of a 10kV single-core cable. The LSTM network-based early-stage state identification method is applicably used in the electrical field. The LSTM network-based early-stage state identification method comprises the steps of firstly, selecting from observable electrical quantity toobtain five types of current observable electrical quantity, and extracting a time sequence from the five types of current observable electrical quantity to construct cable early-stage state combination time sequence characteristic matrix; secondly, constructing a dual-layer LSTM network of an time sequence handling input according to the characteristic of identification matrix size; and thirdly,performing model training under supervised learning by a self-adaptive learning rate optimization algorithm to obtain a cable early-stage state identification model. By the LSTM network-based early-stage state identification method, big mass running by the cable can be fully utilized, the time sequence is extracted from five types of observable data to construct a combined time sequence characteristic matrix as an input of the dual-layer LSTM network under the condition that statistical characteristic is not used, a corresponding relation between an input and an output is determined by handling capability of the dual-layer LSTM on the time sequence input, and the cable early-stage state identification is further completed. By the LSTM network-based early-stage state identification method,the identification accuracy can reach 99.06%.

Description

technical field [0001] The invention relates to a method for identifying the early state of a 10kV single-core cable, and is particularly suitable for a method for identifying the early state of a 10kV single-core cable based on a double-layer long-short-term memory network used in the electrical field. Background technique [0002] Large-section 10kV single-core cables are increasingly used in distribution networks, but due to the particularity of the cable structure, when an early fault occurs, it has little impact on the system and is not easy to be detected. But in many cases, when the cable fault is fully manifested, it has basically developed into a serious complete electrical circuit fault, causing protection action or even a power outage. Therefore, doing a good job in monitoring the status of 10kV single-core cables, timely identifying early cable faults, and taking corresponding measures are particularly important to ensure system stability and property and persona...

AI Technical Summary

Problems solved by technology

There are the following problems in the identification of traditional electrical quantities: the identification of the cable state is realized only by one or a few features, and it is easily affected by disturbance, which may reduce the accuracy of identification

However, when the idea of ​​artificial intelligence is applied to cable status diagnosis, there are two problems as follows: (1) The artificial statistical feature selection workload is heavy, and the selection of statistical features is appropriate or not determines the level of identification accuracy. Certain contingency; (2) Time correlation of signals is not considered

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