Phase identification is a process to determine which of the three phases a particular house is connected to. The state-of-the-art identification methods usually exploit smart metering data. However, the data sets are not always available and the major challenge is hence to identify phases with incomplete data set. This paper proposes a novel spectral and saliency analysis identification method to overcome this hurdle. Spectral analysis is first performed to extract the high-frequency features from the incomplete data. Saliency analysis is then adopted to extract salient features from the variations of high-frequency loads in the time domain. Correlation analysis between customer features and the phase features is used to determine customers' phase connectivity. The method is executed iteratively until all customers with smart meters have been allocated to a specific phase or no salient features can be found. It is validated against real data from over 6000 smart meters in Ireland and achieves an accuracy of over 93% with only 10% smart meter penetration ratio in a 100-household network.
- LV distribution network
- Phase identification
- incomplete data set
- smart metering data
- spectral analysis
ASJC Scopus subject areas
- Computer Science(all)
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- Department of Electronic & Electrical Engineering - Professor
- Centre for Doctoral Training in Decarbonisation of the Built Environment (dCarb)
- Centre for Sustainable Energy Systems (SES)
- EPSRC Centre for Doctoral Training in Advanced Automotive Propulsion Systems (AAPS CDT)
- Institute for Advanced Automotive Propulsion Systems (IAAPS)
Person: Research & Teaching, Core staff, Affiliate staff