Abstract
The energy use analysis of coal-fired power plant units is of significance for energy conservation and consumption reduction. One of the most serious problems attributed to Chinese coal-fired power plants is coal waste. Several units in one plant may experience a practical rated output situation at the same time, which may increase the coal consumption of the power plant. Here, we propose a new hybrid methodology for plant-level load optimization to minimize coal consumption for coal-fired power plants. The proposed methodology includes two parts. One part determines the reference value of the controllable operating parameters of net coal consumption under typical load conditions, based on an improved K-means algorithm and the Hadoop platform. The other part utilizes a support vector machine to determine the sensitivity coefficients of various operating parameters for the net coal consumption under different load conditions. Additionally, the fuzzy rough set attribute reduction method was employed to obtain the minimalist properties reduction method parameters to reduce the complexity of the dataset. This work is based on continuously-measured information system data from a 600 MW coal-fired power plant in China. The results show that the proposed strategy achieves high energy conservation performance. Taking the 600 MW load optimization value as an example, the optimized power supply coal consumption is 307.95 g/(kW·h) compared to the actual operating value of 313.45 g/(kW·h). It is important for coal-fired power plants to reduce their coal consumption.
Highlights
Non-renewable energy and coal comprise the majority of the resources utilized in Chinese energy use and production
An accurate and reasonable reference value for operational parameters is of great significance for improving unit performance and reducing energy use
The objective was to determine controllable operating parameter reference values and sensitivity coefficients that influence the degree of coal consumption within a thermal unit
Summary
Non-renewable energy and coal comprise the majority of the resources utilized in Chinese energy use and production. An attribute reduction method based on fuzzy rough sets is introduced in this paper Using this method, the attributes of power units are reduced before the use of the clustering K-means algorithm, to enhance the efficiency and accuracy of the algorithm. The K-means algorithm is improved by the Canopy algorithm, and a new parallel clustering algorithm called FMK-means is realized This algorithm is used to mine the reference value of controllable operating parameters that affect power supply coal consumption under optimal operational circumstances. The method analyzes the sensitivity of each parameter to coal consumption under different working loads based on vector technology This step provides guidance for the optimization and debugging of power units. Compared to traditional data mining, the new algorithm for massive data mining enhances the accuracy of clustering, eliminates redundant data sets, and promotes clustering efficiency
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