Abstract
With the wide deployment of advancing metering infrastructure (AMI) in power distribution systems, the quantity of power consumers’ electricity data is increasing rapidly and the data also become more and more accurate. To make full use of these power consumers’ AMI data, a data-driven abnormity assessment algorithm for low-voltage power consumers is proposed based on the CRITIC (CRiteria Importance Though Intercrieria Correlation) method and the improved radar chart method. First, the indexes that characterize the consumer’s abnormal features of power consumption and supplies are extracted from the original AMI data. Then, the abnormity assessment algorithm is used to determine power consumers’ abnormal features of power consumption and supplies by using the extracted indexes, in which the weights of indexes are determined by the CRITIC method and the assessment value of abnormal features is determined by the improved radar chart method. Next, the abnormity assessment algorithm is used again to assess power consumers’ power consumption and supplies abnormities. Finally, the effectiveness of proposed algorithm is demonstrated in case studies by employing AMI data collected from power utilities in Zhejiang Province, China, and the results show that the algorithm can be used in actual applications.
Highlights
With the rapid development of economy, the highest electricity load in Zhejiang Province has set a new record, and various of highly risk power consumption and supplies abnormities of power consumers have existed extensively
Some related researches have been done in the hardware design of the advancing metering infrastructure (AMI), such as openZmeter [6], [7], Open Power Quality (OPQ) [8], [9] and some other high-precision sensors, to achieve data collection capability with larger range, higher accuracy and faster speed. oZm is an advanced low-cost and open-source hardware device for high-precision energy and power quality measurement in low-voltage power systems [6], [7]
This paper aims to propose an effective data-driven algorithm to assess the abnormities of electricity consuming of lowvoltage power consumers in distribution systems based on the AMI data collected from the Electricity Information Acquisition System (EIAS)
Summary
With the rapid development of economy, the highest electricity load in Zhejiang Province has set a new record, and various of highly risk power consumption and supplies abnormities of power consumers have existed extensively. There is few research work on the abnormity assessment of power consumption and supplies for low-voltage consumers in power distribution systems, so it is necessary to propose an algorithm to deal with this problem appropriately. There are few studies on the abnormity assessment for low-voltage power consumers of distribution systems Given this background, this paper aims to propose an effective data-driven algorithm to assess the abnormities of electricity consuming of lowvoltage power consumers in distribution systems based on the AMI data collected from the Electricity Information Acquisition System (EIAS). The main contributions of the proposed algorithm are as follows: i) The abnormity assessment algorithm of low-voltage power consumers is presented considering statistical indexes and electrical indexes, which makes the features of abnormity assessment more comprehensive. Considering weights and values of the features, the improved radar chart method in this paper can be more comprehensive compared with traditional radar chart method which only displays the values of the features
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
