Robust PCA-Deep Belief Network Surrogate Model for Distribution System Topology Identification with DERs: Article No. 106441

Liang Zhao, Youbo Liu, Junbo Zhao, Yingchen Zhang, Lixiong Xu, Yue Xiang, Junyong Liu

Research output: Contribution to journalArticlepeer-review

23 Scopus Citations

Abstract

With the expansion of distribution networks and increased penetration of distributed energy resources (DERs), it is becoming increasingly important to obtain accurate distribution network topology in real-time. In this paper, a robust principal component analysis coupled deep belief network (PCA-DBN) surrogate model is proposed for distribution system topology identification. It integrates the benefits of robust feature extraction from PCA to deal with data quality issues and filter out noise, and the strength of DBN in capturing the nonlinear relationship between voltage amplitudes and the binary states of switchable connections. This also significantly reduces the DBN training complexity without loss of accuracy. It is shown that the widely used standard deviation of voltage drop and the voltage covariance matrix features yield less accuracy as compared to that of the voltage amplitudes in presence of high penetration of DERs and ZIP loads. Comparison results with other alternatives, such as the random forest (RF), multi-output regression (MOR) and the traditional DBN methods demonstrate that the proposed method can achieve a much higher topology identification accuracy while maintaining robustness to missing data and measurement noise under various penetration levels of DERs.
Original languageAmerican English
Number of pages11
JournalInternational Journal of Electrical Power and Energy Systems
Volume125
DOIs
StatePublished - 2021

NREL Publication Number

  • NREL/JA-5D00-77827

Keywords

  • deep belief network
  • distribution network
  • principal component analysis
  • topology identification

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