Identifying Uncertainty Distributions and Confidence Regions of Power Plant Parameters

Abstract

Power system operators, when obtaining a model's parameter estimates; require additional information to guide their decision on a model's acceptance. This information has to establish a relationship between the estimates and the chosen model in the parameter space. For this purpose, this paper proposes to extend the usage of the particle filter (PF) as a method for the identification of power plant parameters; and the parameters' confidence intervals, using measurements. Taking into consideration that the PF is based on the Bayesian filtering concept, the results returned by the filter contain more information about the model and its parameters than usually considered by power system operators. In this paper the samples from the multi-modal posterior distribution of the estimate are used to identify the distribution shape and associated confidence intervals of estimated parameters. Three methods [rule of thumb, least-squares cross validation, plug-in method (HSJM)] for standard deviation (bandwidth) selection of the Gaussian mixture distribution are compared with the uni-modal Gaussian distribution of the parameter estimate. The applicability of the proposed method is demonstrated using field measurements and synthetic data from simulations of a Greek power plant model. The distributions are observed for different system operation conditions that consider different types of noise. The method's applicability for model validation is also discussed.

Keywords

• Confidence intervals
• measurements
• parameter identification
• particle filters
• power plant models
• power system identification