中国电机工程学报
中國電機工程學報
중국전궤공정학보
ZHONGGUO DIANJI GONGCHENG XUEBAO
2015年
3期
645-651
,共7页
周洪煜%杜学森%张振华%黄耀珍
週洪煜%杜學森%張振華%黃耀珍
주홍욱%두학삼%장진화%황요진
循环水余热%直接多步预测控制%混沌变异克隆选择%驱动蒸汽%径向基函数(RBF)神经网络
循環水餘熱%直接多步預測控製%混沌變異剋隆選擇%驅動蒸汽%徑嚮基函數(RBF)神經網絡
순배수여열%직접다보예측공제%혼돈변이극륭선택%구동증기%경향기함수(RBF)신경망락
circulating water waste heat%direct multi-step predictive control%chaotic particle clone selection (CPCS)%driven steam%radial basis function (RBF) neural network
循环水余热回收系统中,热泵热网水出口温度在跟踪供热负荷需求时,在受驱动蒸汽量的调节的同时,往往易受热网回水、循环水等工况变化的影响,传统 PID 控制方式超调量大、负荷跟踪能力差。提出一种混沌变异克隆选择?径向基函数(CPCS-RBF)直接多步预测控制策略,以热泵热网水出口温度预测值与设定值差值为目标函数,利用CPCS优化算法求取目标函数最小时的驱动蒸汽最佳值。预测模型由2个RBF神经网络结合热泵现场运行数据构建,以提高热泵系统适应工况变化的能力;实验结果表明,该控制策略能综合学习热网回水温度、循环水温度等参数的变化,使驱动蒸汽调门超前动作,及时跟踪供热负荷需求变化的同时,适应发电负荷变化下排气余热量的波动,具有更好的节能效果和变工况适应能力。
循環水餘熱迴收繫統中,熱泵熱網水齣口溫度在跟蹤供熱負荷需求時,在受驅動蒸汽量的調節的同時,往往易受熱網迴水、循環水等工況變化的影響,傳統 PID 控製方式超調量大、負荷跟蹤能力差。提齣一種混沌變異剋隆選擇?徑嚮基函數(CPCS-RBF)直接多步預測控製策略,以熱泵熱網水齣口溫度預測值與設定值差值為目標函數,利用CPCS優化算法求取目標函數最小時的驅動蒸汽最佳值。預測模型由2箇RBF神經網絡結閤熱泵現場運行數據構建,以提高熱泵繫統適應工況變化的能力;實驗結果錶明,該控製策略能綜閤學習熱網迴水溫度、循環水溫度等參數的變化,使驅動蒸汽調門超前動作,及時跟蹤供熱負荷需求變化的同時,適應髮電負荷變化下排氣餘熱量的波動,具有更好的節能效果和變工況適應能力。
순배수여열회수계통중,열빙열망수출구온도재근종공열부하수구시,재수구동증기량적조절적동시,왕왕역수열망회수、순배수등공황변화적영향,전통 PID 공제방식초조량대、부하근종능력차。제출일충혼돈변이극륭선택?경향기함수(CPCS-RBF)직접다보예측공제책략,이열빙열망수출구온도예측치여설정치차치위목표함수,이용CPCS우화산법구취목표함수최소시적구동증기최가치。예측모형유2개RBF신경망락결합열빙현장운행수거구건,이제고열빙계통괄응공황변화적능력;실험결과표명,해공제책략능종합학습열망회수온도、순배수온도등삼수적변화,사구동증기조문초전동작,급시근종공열부하수구변화적동시,괄응발전부하변화하배기여열량적파동,구유경호적절능효과화변공황괄응능력。
In the circulating water waste heat recovery system, when heat pump heating net water outlet temperature trace heating load demand, that’s not only adjusted by driven steam capacity, and is easily influenced by operating conditions variation of the heating net backwater and circulating water, the traditional PID control method has a large overshoot volume and a poor load tracking ability. So a chaotic particle clone selection (CPCS)-radial basis function (RBF) direct multi-step predictive control strategy was proposed, with difference between heat pump heat supply network water outlet temperature predicted value and the set values as the objective function, using CPCS optimization algorithm to calculate the optimal values of driven steam when the objective function is the minimum. The prediction model was constructed by two RBF neural networks according to the field operation data in order to improve the model variable condition adaptability. The experimental results show that the control strategy can comprehensively learn the change of the parameters such as the heating net backwater temperature and circulating water temperature, and make driven steam tone act in advance, trace heating load demand change in time, and adapt fluctuation of exhaust gas residual heat under power generation load change, so has better energy saving effect and variable condition adaptability.