Power Transformer Top Oil Temperature Estimation with GA and PSO Methods

Power transformer outages have a considerable economic impact on the operation of an electrical network. Obtaining appropriate model for power transformer top oil temperature (TOT) prediction is an important topic for dynamic and steady state loading of power transformers. There are many mathematical models which predict TOT. These mathematical models have many undefined coefficients which should be obtained from heat run test or fitting methods. In this paper, genetic algorithm (GA) and particle swarm optimization (PSO) are used to obtain these coefficients. Therefore, a code has been provided under MATLAB software. The effects of mentioned optimization methods will be studied on improvement of adequacy, consistency and accuracy of the model. In addition these methods will be compared with the Multiple-Linear Regression (M-L R) to illustrate the improvement of the model.

热风润叶工序工艺参数优化设计

为优化热风润叶工序工艺参数,采用正交试验,对直接影响热风润叶后烟叶含水率、温度的加温加湿方式、均匀性影响规律进行分析,不断提高工艺技术水平和改善设备工艺性能。结果表明:(1)热风温度由90℃升高至100℃、风门开度由50%调至100%状态、滚筒转速由40 Hz提高至45 Hz,对润后烟叶含水率均匀性的提高有明显作用;(2)确立了润叶工序最佳参数的组合,1次、2次润叶环节加水比例为10∶0的分配条件下,1次润叶热风温度95℃、2次润叶热风温度125℃,1次、2次润叶环节蒸汽喷射比例5∶5出口烟叶含水率均匀性较好。润后烟叶含水率更加趋于稳定,能够满足叶梗分离工序质量指标的最佳条件。