Application of case based reasoning in voltage security assessment

Nandanwar, Sonali R.; Warkad, S. B.

doi:10.1109/cipech.2016.7918729

Cited by 2 publications

(1 citation statement)

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“…Case-based reasoning is a branch of artificial intelligence (AI) that provides answers to new questions based on experience in historical cases [2,3]. In the latest studies, CBR has been used in load forecasting, energy management, grid system safety assessment, and power equipment failure assessment [4][5][6][7]. In Reference [8], a case-based reasoning method is utilized to diagnose the incipient fault of power transformer.…”

Section: Introductionmentioning

confidence: 99%

Partial Discharge Data Matching Method for GIS Case-Based Reasoning

et al. 2019

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With the accumulation of partial discharge (PD) detection data from substation, case-based reasoning (CBR), which computes the match degree between detected PD data and historical case data provides new ideas for the interpretation and evaluation of partial discharge data. Aiming at the problem of partial discharge data matching, this paper proposes a data matching method based on a variational autoencoder (VAE). A VAE network model for partial discharge data is constructed to extract the deep eigenvalues. Cosine distance is then used to calculate the match degree between different partial discharge data. To verify the advantages of the proposed method, a partial discharge dataset was established through a partial discharge experiment and live detections on substation site. The proposed method was compared with other feature extraction methods and matching methods including statistical features, deep belief networks (DBN), deep convolutional neural networks (CNN), Euclidean distances, and correlation coefficients. The experimental results show that the cosine distance match degree based on the VAE feature vector can effectively detect similar partial discharge data compared with other data matching methods.

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