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A Feature-Based Diagnosis Framework for Power Plant Model Validation

The importance of PMU-based power plant model validation (PPMV) is being recognized by researchers and practitioners. In North America, the PMU-based PPMV is typically performed manually by utilities and independent system operators (ISOs) for diagnosing and calibrating generator model problems. In order to guarantee the accuracy of the calibration results, engineers need to provide manual judgment to the mismatched PPMV cases, so that the type of the modeling problem (such as wrong machine parameters, missing governor models, etc.) can be determined before the model is sent for a detailed calibration. This manual judgment process has become the major bottleneck for automating the PMV-based PPMV application. To overcome this difficulty, this paper proposes a feature-based diagnosis approach to determining the types of the generator modeling problems. It mimics the human engineering judgment process via a semi-supervised learning engine. Instead of applying purely curve fitting or sensitivity analysis, this approach uses the engineering experience extracted from the labeled/unlabeled historical PPMV cases, establish the critical feature space, and then perform artificial learning to determine the type of a generator modeling error. The proposed approach could serve as a screening tool for the PPMV engineering judgment process, which could help automate the entire PPMV application.

Author(s):

Meng Wu    
Texas A&M University
United States

Le Xie    
Texas A&M University
United States

 

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