Online dynamic perception method of energy routing equipment based on deep autoencoder network
A self-encoding network and state-aware technology, applied in the field of online dynamic perception of energy routing equipment, can solve the problems of many types of monitoring data, large quantities, and low efficiency of state monitoring
Active Publication Date: 2021-07-06
南方电网人工智能科技有限公司
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[0004] The present invention provides an online dynamic perception method for energy routing equipment based on a deep self-encoding network, which aims to solve the problems of the existing monitoring data types, large quantities, and low state monitoring efficiency of energy routing equipment in the existing energy Internet of Things, and the analysis of these problems. Data technical issues that put a lot of pressure on Energy IoT hub servers
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[0044] In order to illustrate the technical solution of the present invention more clearly, the specific implementation manners of the present invention will be described in detail below in conjunction with the accompanying drawings.
[0045] Such as figure 1 and 2 As shown, the present invention discloses an online dynamic perception method for energy routing equipment based on a deep self-encoding network, including two parts: offline model training and online state perception;
[0046] In the process of offline model training, multivariate data fusion is performed on various historical monitoring data of the same type of energy routing equipment from the beginning of use to complete failure, and the fused data is used to train the deep self-encoder network to calculate the energy routing equipment. State characteristic index Y， And store the trained deep self-encoder network model;
[0047] The implementation steps of the presence awareness process are as follows:
[00...
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The invention discloses an online dynamic perception method for energy routing equipment based on a deep self-encoding network, which includes two parts: offline model training and online state perception; the offline model training uses the historical monitoring data of the energy routing equipment to train the deep self-encoding network, Calculate the state feature index of the energy routing device; the online state perception process includes: Step 1: Collect the index data of the energy router during operation; Step 2: Process the data collected in Step 1 to obtain the state feature vector of the energy routing device; Step 3 : Use the deep self-encoding network model to calculate the status monitoring indicators of energy routing equipment and judge the status of the equipment; Step 4: If the equipment fails, add the operating data of the equipment to the historical database and recalculate the status characteristic indicators; if no failure occurs , continue to perceive. The invention can be used for online monitoring of the running state of the energy routing equipment, reducing the frequency of failures and reducing the maintenance cost.
Description
technical field [0001] The invention relates to the technical fields of artificial intelligence and energy Internet of Things, in particular to an online dynamic perception method for energy routing equipment based on a deep self-encoding network. Background technique [0002] The Internet of Things realizes the ubiquitous connection of different entities through the architecture of the Internet through massive and multi-type high-precision sensors. The energy Internet of Things is a special case of the ubiquitous Internet of Things. It realizes the mutual transformation of multiple energy sources, intelligent monitoring and control functions through key technologies such as intelligent device state perception technology, intelligent human-computer interaction configuration technology, and intelligent power-saving control gateway. [0003] Energy routing equipment is the core device of the energy Internet of Things. It is responsible for the input, output, conversion, and st...
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IPC IPC(8): H04L12/26H04L12/24G06N3/04G06N3/08
Inventor 李鹏于力张斌姜臻姚森敬郭志诚陈煜敏陈浩敏敖榜
Owner 南方电网人工智能科技有限公司