Load Monitoring System using Unsupervised Methods
Date Issued
2013
Date
2013
Author(s)
Chou, Po-An
Abstract
Efficient energy use is an important research topic of the smart grid. Load monitoring is an integral part of energy management, convenient information, communication technology, and sensor applications. So far, many monitoring techniques have been developed, and non-intrusive load monitoring is one of them. However, there are many technical difficulties, which are still unable to achieve non-intrusive completely. The traditional methods by supervised learning required to obtain training sample. But if the unknown loads exist in the circuit, it will be causing much more difficult to monitor. In order to achieve the complete non-intrusive concept and to adapt to the changes in environment, this research proposes the unsupervised method that applied in the monitoring system, taking low frequency acquisition and steady-state feature extraction for reducing its setup costs. It uses the K-means clustering algorithm, building a Gaussian mixture model (GMM) to represent load status; the expectation-maximization (EM) algorithm is for estimating the model parameters. Additionally, the adaptive system is proposed. By getting real-time/online data, it is continuing to update the database and feedback the monitoring information of loads in the circuit. And it provides the identification interval and detection unknown loads methods by proposed failure detection index (FDI), which endows system with flexibility and scalability. The experiment shows that this method is effective to trace the changes in environment and to detect the unknown loads, making a complete solution in non-intrusive load monitoring system.
Subjects
非侵入式負載監測
智慧電網
高斯混合模型
非監督式
自適應系統
負載狀態辨識
SDGs
Type
thesis
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