Auxiliary power consumption feature mining method weighted fuzzy Cmeans clustering and subjective and objective weighting combined

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    Power consumption is an important indicator of the economic and energy efficiency of thermal power units, in this paper, an improved weighted fuzzy Cmeans clustering algorithm is proposed. First of all, according to the historical operation data of auxiliary equipment of thermal power units, the factor analysis method is used to obtain the strong and weak correlation between the operation indicators and the influence on the target value of plant power, and extract the important indicators that affect the energy consumption of the unit, the operating indexes are assigned to the corresponding weights, and the weighted fuzzy Cmeans clustering is performed. Secondly, to overcome the shortcomings of the single weighting method, the combination of the analytic hierarchy process and the entropy weight method is further used to modify the indicators in the unit energy consumption assessment weight value. Finally, a sixpoint load interval of 10 months of historical data of a 600 MW unit in a thermal power plant is verified to that the model and algorithm are correct and effective.

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  • Online: September 04,2020
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