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本文从信息融合的角度出发,利用解决不确定性问题的有力方法D-S证据理论建立燃烧发热量异常判断模型,并结合现场的热工测点,选取负荷、主蒸汽流量、总风量、磨煤机电流、空气预热器入口烟温作为D-S证据理论的证据体,利用基于典型样本的获取方法,给出了各个证据的基本概率赋值。通过现场的运行数据对评判模型进行测评,测试结果表明,该模型能够较准确的对燃烧发热量是否异常进行判断。
In this paper, from the perspective of information fusion, a powerful method for solving uncertainties DS evidence theory to establish an abnormal combustion calorie judging model, combined with on-site thermal measuring point, select the load, the main steam flow, the total air volume, coal mill Current and air preheater inlet smoke temperature are used as evidence of DS evidence theory. Based on the typical sample acquisition method, the basic probability assignment of each evidence is given. The evaluation model was evaluated by field operation data. The test results show that the model can judge whether the combustion heat is abnormal or not more accurately.