Use of statistical analysis in power plant performance monitoring

Abstract

A technique of implementing performance monitoring in a power plant is appropriate to control operating parameters and factors connected with the efficiency of the energy production process in an energy marketplace that is more complex than in the past, and that takes into account more than just the cost of fuel. In particular, this method works well when the real costs of production are dependent on other variable costs besides the cost of fuel, such as environmental credits, equipment degradation and repair costs, as well as electrical energy trade market factors like ramp rate, LMP factors, and the ability to deliver contracted power levels and spot transactions. The power plant performance monitoring technique applies a statistical analysis to collected power plant data to determine the factors that are best controlled or changed to affect (increase) the efficiency or other primary performance indication of the plant, in whatever state or operating level the plant is run. Because heat rate calculation applications are typically performed on-line, it is possible to analyze collected plant data in detail and to apply for example, principal component analysis (PCA) and linear and nonlinear regression analysis to the data, which enables the performance method to obtain a more accurate detection of the influence of the principal process parameters that affect heat rate deviation (efficiency), as well as to establish baseline or best-possible operational constraints to be used to control the plant in the future. This performance based control methodology will allow for near optimum performance of power plants by constantly allowing for refinement and best practices and control to be realized.

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