基于改进麻雀搜索算法优化模糊PID控制主通风机的节能降耗研究

Energy Saving and Consumption Reduction of Main Ventilator by Optimized Fuzzy PID Control Based on Improved Sparrow Search Algorithm

为解决模糊PID控制主通风机转速的超调量大导致输出功率低,从而影响电动机工作效率,增加能源消耗等问题,以北辛窑矿为研究背景,根据矿井主通风机特性,对其进行理论分析并建立数学模型。结合主通风机系统数学模型,利用粒子群优化算法、鲸鱼优化算法以及麻雀搜索算法对模糊PID控制策略进行优化,并在Simulink仿真环境中构建相应模型,进行对比试验,通过分析比对得出麻雀搜索算法优化模糊PID控制的电动机效率相对较高,能源消耗相对较低,但该算法对转速的控制存在波动较大、响应速度较慢等问题。因此,提出一种利用透镜成像反向学习改进麻雀搜索算法优化模糊PID控制,研究结果表明:改进麻雀搜索算法优化模糊PID控制的转速超调量小,可以更加有效地控制电机转速,比北辛窑矿目前电能消耗降低约15%。研究结果可为煤矿行业节能降耗提供一定的参考依据。

Due to large overshooting of the main ventilator speed by fuzzy PID control, the output power is low, which affects the efficiency of the motor and increases the energy consumption. In order to solve these problems, taking Beixingyao Mine as the research background, the main ventilator of the mine was theoretically analyzed and a mathematical model was set up according to its characteristics. Combined with the mathematical model of the main fan system, the particle swarm optimization algorithm, the whale algorithm and the sparrow search algorithm were used to optimize the fuzzy PID control strategy, and the corresponding model was constructed in the Simulink simulation environment for comparative experiments. Through analysis and comparison, it is found that the fuzzy PID control optimized by sparrow search algorithm has a relatively high motor efficiency and a relatively low energy consumption. However, this algorithm has some problems such as large fluctuations and slow response speed in speed control. Therefore, a method of using lens imaging reverse learning to improve sparrow search algorithm for optimizing fuzzy PID control was proposed. The research results show that the improved sparrow search algorithm for optimizing fuzzy PID control has a smaller overshoot in speed and can more effectively control motor speed, reducing power consumption by 15% compared to the current power consumption of Beixinyao Mine. The research results can provide a certain reference basis for energy conservation and consumption reduction in the coal mining industry.