Online Monitoring of Load States and Types Based on Smart Electric Appliance Network

doi:10.12204/j.issn.1000-7229.2022.04.008

Abstract

Abstract:

Perceiving state and identity of real-time electrical load is the premise of refined energy management and demand response. To meet the automatic and precise control requirements of smart electric appliance network, the on-line identification technology of appliance-level load state and type is proposed. On the basis of real-time measurement and communication of multi-appliances, the state of load is extracted by improved CUSUM segmentation and hidden Markov model, and the single classification method SVDD is used to identify the electrical appliance type extensively. The accuracy, timeliness and extensive ability of the proposed methods are verified by constructing smart electric appliance network in residential and office buildings.

Key words: smart electric appliance network, load state, load identity, cumulative sum, one-class classification

CLC Number:

TM714

GUO Zhiyuan, LI Zhiyong, SHAO Jie, HUANG Ting, ZHOU Huan, FAN Shuai, HE Guangyu. Online Monitoring of Load States and Types Based on Smart Electric Appliance Network[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(4): 69-80.

Figures/Tables 17

Fig.1 Smart electric appliance network

Fig.2 Load monitoring method based on event-driven multi-agent measurement mechanism

Fig.A1 Real-time identification process and intermediate data of each step

Table A1 Data set for the experiment of algorithm independece and generalization

Fig.3 Patterns of appliance power data

Fig.4 Comparison of characteristic distribution of power data

Fig.5 Incremental support vector data description with extended radius algorithm

Fig.A3 Data collection process based on Smart Electric Appliance Network

Fig.6 The data set built in this paper

Table A2 Data set for the experiment of algorithm outomatic scalability

Fig.A2 Analysis results of some electrical appliances states

Table A3 State characteristic statistics of electrical appliances power

Fig.7 Comparison of segmentation results

Table 1 The result of segmentation

Table 2 Type classification result of train/test set

Table 3 Type classification result of train/extend test set

Fig.8 Result of incremental support vector data description with extended radius algorithm

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