摘要
锅炉作为火电厂的重要设备,提高其运行经济性直接影响电厂的生产效益,而锅炉运行参数优化目标值的合理确定是保障锅炉经济运行的关键。首先提出改进的高斯混合模型算法应用于工况划分,即通过划分数据的分散度作为依据并基于马氏距离来构建评价准则函数,以确定聚类数;其次通过构建具备长短时记忆功能的深度循环神经网络(deep recurrent neural network with long-short term memory, DRNN-LSTM)建立各工况区间下的经济模型;最后在经济模型构建的基础上,针对传统粒子群算法容易陷入局部极值问题,通过对惯性权重和加速因子进行调整得到改进的粒子群算法(improved particle swarm optimization, IPSO),可更加精准地在不同工况下进行区间范围内寻优,确定运行参数的优化目标值。实验结果表明,采用本文方法确定的优化目标值对应供电煤耗优于历史最优运行值,说明了该方法在挖掘锅炉优化运行潜力上具有一定的优势,按此方案调整锅炉运行可有效降低能耗水平,以达到锅炉经济运行的目的。
The boilers as key equipment in thermal power plants,improving its operating economy directly affects production efficiency of the plant,while the reasonable determination the target value of boiler operation optimization is the key.Firstly,an improved Gaussian mixture model applicable to working condition division was proposed.The evaluation criterion function was constructed by dividing the dispersion of data as basis and based on Mahalanobis distance to determine the number of clusters.Secondly,by building a deep recurrent neural network with long-short term memory function,the economic model under each working condition interval was established.Finally,on the basis of the economic model,an improved particle swarm algorithm was obtained by adjusting the inertia weight and acceleration factor to address the problem that the traditional particle swarm algorithm was prone to fall into local extremes,which could more accurately perform interval optimization under different working conditions and determine the optimized target values of the operating parameters were determined.The experimental results show that the optimization target values determined by this method are better than the historical optimal operating values,indicating that this method has certain advantages in exploiting the potential of boiler optimization,and that the boiler operation can be adjusted according to this scheme to effectively reduce the energy consumption level,so as to achieve the purpose of boiler economic operation.
作者
钱虹
王海心
徐邦智
QIAN Hong;WANG Hai-xin;XU Bang-zhi(College of Automation Engineering,Shanghai University of Electric Power,Shanghai 200090,China;Shanghai Key Laboratory of Power Station Automation Technology,Shanghai 200072,China)
出处
《科学技术与工程》
北大核心
2024年第7期2749-2758,共10页
Science Technology and Engineering
基金
国家自然科学基金青年科学基金(51906133)。
