基于在线减法聚类的变频水泵模糊建模方法
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摘要
水泵模型的建立是研究集中空调水系统节能优化控制的基础,现阶段水系统优化控制模型大多通过曲线拟合方式建立,这种方式存在精度有限、在线调整困难、通用性差等问题。引入了基于模糊思想的建模方法,并将其与在线减法聚类相结合,实现模糊模型的在线更新和修正。该方法将水泵的运行数据按不同运行工况归类,并利用这些数据样本逐渐扩充和完善模型中的模糊规则。通过实际水泵在多工况下产生的运行数据拟合出水泵模型,对本文所提算法进行了对比和验证。结果证明该算法具有较强的自学习、自调整能力,同时具有较好的通用性和适应性。
The establishment of pump model is the basis for studying the energy saving optimization control of the central air conditioning water system. At present, most of the water system optimization control models are established by curve fitting. This method has problems such as limited accuracy, difficulty in online adjustment, and poor versatility. Presents a fuzzy modeling method and combines with online subtractive clustering to realize online update and correction of the fuzzy model. The method classifies the running data of the pump according to different operating conditions, and gradually expands and improves the fuzzy rules in the model using these data samples. Fits the pump model by the actual operating data of the pump under multiple conditions, and compares and verifies the proposed algorithm. The results show that the algorithm has strong self-learning and self-adjusting ability, with good versatility and adaptability.
The establishment of pump model is the basis for studying the energy saving optimization control of the central air conditioning water system. At present, most of the water system optimization control models are established by curve fitting. This method has problems such as limited accuracy, difficulty in online adjustment, and poor versatility. Presents a fuzzy modeling method and combines with online subtractive clustering to realize online update and correction of the fuzzy model. The method classifies the running data of the pump according to different operating conditions, and gradually expands and improves the fuzzy rules in the model using these data samples. Fits the pump model by the actual operating data of the pump under multiple conditions, and compares and verifies the proposed algorithm. The results show that the algorithm has strong self-learning and self-adjusting ability, with good versatility and adaptability.
引文
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[16] QIAO J F, ZHANG Z Z, BO Y C. An online self-adaptive modular neural network for time-varying systems[J].Neurocomputing, 2014, 125(1):7- 16
[17] 颜永民.建筑冷热源系统自适应优化控制模型的研究[D]. 长沙: 湖南大学, 2007: 4- 20
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[2] WANG J Y, CHANG T P, CHEN J S. An enhanced genetic algorithm for bi-objective pump scheduling in water supply[J]. Expert System with Applications, 2009, 36 (7):10249- 10258
[3] TIAN Y Z, ZHANG J, LIANG D M. On-line optimization control method based on extreme value analysis for parallel variable-frequency hydraulic pumps in central air-conditioning systems[J]. Build Environ,2012,47:330- 338
[4] DAI Y C, JIANG Z Y, SHEN Q, et al. A decentralized algorithm for optimal distribution in HVAC systems[J]. Building and Environment, 2016,95: 21- 31
[5] KOORA M, VASSILJEVB A, KOPPEL T. Optimization of pump efficiencies with different pumps characteristics working in parallel mode[J]. Advances in Engineering Software, 2016, 101(11): 69- 76
[6] PAWEL O. Genetic optimization and experimental verifiation of complex parallel pumping station with centrifugal pumps[J]. Applied Energy, 2016,178: 527- 539
[7] 李苏泷, 朱孟标, 张国强. 水泵变频调速方案计算机辅助论证[J]. 暖通空调, 2005,35(3): 83- 85
[8] MOHAN B M, SINHA A. Analytical structure and stability analysis of a fuzzy PID controller[J]. Applied Soft Computing, 2008, 8(1): 749- 758
[9] MANJORO W S, DHAKAR M, CHAURASIA B K. Operational analysis of k-medoids and k-means algorithms on noisy data[C]//International Conference on Communication & Signal Processing. IEEE, 2016:1500- 1505
[10] KILI K, UNCU O, TüRKSEN I B. Comparison of different strategies of utilizing fuzzy clustering instructure identification[J].Information Sciences, 2007, 177 (23): 5153- 5162
[11] TSEKOURAS G, SARIMVEIS H, KAVAKLI E. A hierarchical fuzzy-clustering approach to fuzzy modeling[J].Fuzzy Sets and Systems, 2005, 150(2): 245- 266
[12] LUGHOFER E, KINDERMANN S, SPARSE F I S: data-driven learning of fuzzy systems with sparsity constraints[J]. Fuzzy Systems, 2010, 18(2): 396- 411
[13] CHIU S L. Fuzzy model identification based on clusterestimation[J]. Journal of Intelligent and Fuzzy Systems, 1994, 2(3): 267- 278
[14] ANGELOV P P, FILEV D P. An approach to online identification of Takagi-Sugeno fuzzy models[J]. IEEE Transactions on Systems, Man and Cybernetics-Part B: Cybernetics,2004,34(1):484- 498
[15] 潘天虹, 薛振框, 李少远.基于减法聚类的多模型在线辨识方法[J].自动化学报,2009, 35(2):220- 224
[16] QIAO J F, ZHANG Z Z, BO Y C. An online self-adaptive modular neural network for time-varying systems[J].Neurocomputing, 2014, 125(1):7- 16
[17] 颜永民.建筑冷热源系统自适应优化控制模型的研究[D]. 长沙: 湖南大学, 2007: 4- 20
[18] 严中俊.某酒店中央空调冷源系统节能运行优化研究[D]. 广州:华南理工大学,2013: 4