Non-intrusive household appliance load identification method based on bee colony algorithm

Abstract

The invention discloses a non-invasive household appliance load identification method based on a bee colony algorithm. According to the method, a non-intrusive load identification device is used for carrying out real-time load input removal event detection at a home-entry place; when a load input event is detected, electrical parameters on a bus are recorded; wherein the data include current effective values, active power, reactive power, current harmonics and the like, the device sends the data to the cloud after acquiring the data, and the cloud matches the data with data in a database through an artificial bee colony algorithm and sends an identification result back to the device, so that the purpose of household appliance load identification is achieved. The method is high in flexibility and high in reliability, the misjudgment rate and the missed judgment rate of the load can be effectively reduced, and powerful technical support is provided for load management of a power grid side and a user side.

Description

technical field [0001] The invention relates to the field of home appliance load identification, in particular to a non-invasive home appliance load identification method based on a bee colony algorithm. Background technique [0002] In recent years, with the deepening of research on artificial intelligence, the power grid has gradually become more intelligent. The so-called smart grid refers to the establishment of an integrated, high-speed two-way communication network, through the application of advanced sensing and measurement technology, advanced equipment technology, advanced control methods and advanced decision support system technology. Reliable, safe, economical and efficient operation. As an important part of the smart grid, load monitoring and identification is the first step to realize the intelligence of the grid. [0003] Through the continuous efforts of researchers, load monitoring and identification technology has been developed rapidly. At present, ther...

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Problems solved by technology

For example, in the literature (Zia, Tehseen, D.Bruckner, and A.Zaidi.2011."A Hidden Markov Model Based Procedure for Identifying Household Electric Loads."IEEE.doi:10.1109/IECON.2011.6119826.) using hidden Markov in unsupervised learning Although the Cove model for load identification simplifies the process of manual intervention, the overall identification accuracy is not high, and it is easy to fall into local optimum; literature (Li Ruyi, Wang Xiaohuan, Hu Meixuan, Zhou Hong, Hu Wenshan. RPROP neural network in non-invasive Application in load decomposition [J]. Power System Protection and Control, 2016,44(07):55-61.)

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