Non-intrusive load decomposition method based on hidden Markov model

By applying the factor hidden Markov model and Viterbi algorithm in non-invasive load monitoring, combined with the universal approximation characteristics of deep networks, the problems of insufficient load decomposition accuracy and efficiency in the prior art are solved, and more efficient and economical load monitoring and decomposition are achieved.

CN119718824APending Publication Date: 2025-03-28MARKETING SERVICE CENT OF STATE GRID QINGHAI ELECTRIC POWER CO +1
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Patent Information

Application Number
CN202411787383.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-06
Publication Date
2025-03-28

AI Technical Summary

Technical Problem

Existing non-invasive load monitoring methods have challenges in accuracy and efficiency, making it difficult to achieve accurate load decomposition in power systems.

Method used

The non-invasive load decomposition method based on the hidden Markov model is used to expand into the factor hidden Markov model to clarify the corresponding parameters and state inference problems of the task, and load decomposition is achieved using the Viterbi algorithm and deep network.

Benefits of technology

It improves the accuracy and efficiency of non-invasive load monitoring, reduces decomposition errors, reduces monitoring costs and complexity, and promotes the optimization management of the power system.

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Abstract

The invention relates to a non-intrusive load decomposition method based on a hidden Markov model, and the non-intrusive load decomposition method based on the hidden Markov model is characterized in that the hidden Markov model is expanded into a factor hidden Markov model which is applied to non-intrusive load monitoring; wherein the task of non-intrusive load monitoring corresponds to a parameter learning problem and a state inference problem in the hidden Markov model; a public data set is used for experimenting a load decomposition method based on a hidden Markov model to confirm a decomposition effect, the hidden Markov model (HMM) is used for solving a non-intrusive load decomposition problem, a Viterbi algorithm is adopted for step-by-step solving, the Viterbi algorithm searches for an optimal state sequence by traversing all state transition conditions, and a non-intrusive load decomposition problem is solved. And applying dynamic programming to efficiently solve the state sequence with the maximum probability, and speculating the load state condition at the t + 1 moment according to the load condition at the t moment and the total power at the t + 1 moment in the step-by-step decomposition process.
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