Abstract
The traditional power load identification is greatly restricted in application because of its high cost and low efficiency. In this paper, the similarity model is established to realize the noninvasive load identification of power by determining the feature database for the equipment. Firstly, the wavelet decomposition method and the wavelet threshold processing method are used to remove abnormal points and reduce noise of the original data, respectively. Secondly, the transient and steady-state characteristics of electrical equipment (active power and reactive power, harmonic current, and voltage-current trajectory) are extracted, and the feature database for the equipment is established. Thirdly, the feature similarity is defined to describe the similarity degree of any two devices under a certain feature, and the similarity model of automatic recognition of a single device is established. Finally, the device identification and calculation of power consumption are carried out for the part of data in annex 2 of question A in the 6th “teddy cup” data mining challenge competition.
Highlights
With the emergence of various new types of power load components in an endless stream, users put forward higher requirements on the reliability, safety, economy, and stability of power system
Li and Yu [2] further carried out research on noninvasive load monitoring and determined characteristic parameters based on fuzzy clustering results of steady-state load characteristics of electrical appliances, so as to realize noninvasive load monitoring based on differential evolution algorithm
Cai et al [5] calculated the similarity between the transient waveform and the fixed characteristic template in the electrical load characteristic database, established the electrical load characteristic membership matrix based on similarity, and determined the characteristic type of electrical load
Summary
With the emergence of various new types of power load components in an endless stream, users put forward higher requirements on the reliability, safety, economy, and stability of power system. It is worth noting that Hart [1] established the first noninvasive appliance load monitoring system (NIALM) to develop a monitoring tool that does not affect the target or affect the target as little as possible. It can provide power companies with specific power consumption data of different electrical equipment. The research on nonintrusive power load monitoring and decomposition mainly focuses on the optimization and improvement of electrical load feature extraction and load identification algorithm. E data used to support the findings of this study are available at the teddy cup data mining challenge website (http:// www.5iai.com/bdrace/tzjingsai/20170921/1253.html#sHref )
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