Leakage diagnosis of district heating-network based on system simulation and PCA_BP neural network

To increase leakage diagnosis (LD) efficiency of heating-networks and overcome the lack of actual leakage data, a LD method is proposed based on system simulation and principal component analysis (PCA)_back propagation (BP) neural network that treats the LD problem as a pattern recognition one. In this method, a hydraulic working condition (HWC) simulation model is constructed to obtain model datasets (MD) of heating-network operation and HWC experiments are conducted to obtain experimental datasets (ED), then cross datasets (CD) is constructed by mixing MD and ED with different ratio. These three datasets are handled through the PCA to unify data feature distribution and realize feature transfer learning in neural network. Then two BP neural networks are trained by ED and CD respectively and both tested with ED. Finally, four kinds of experimental heating networks, including a branch network with single heat source (B-SHS), a branch network with double heat sources (B-DHS), a single-ring network with single heat source (SR-SHS) and a double-ring network with double heat sources (DR-DHS), are researched. The results indicate that the LD prediction accuracy of BP neural network trained by CD is higher than that one trained by ED. The LD prediction accuracy varies with the cross-data ratio: from 97.52% to 92.21% for B-SHS, from 97.23% to 92.02% for B-DHS, from 97.85% to 89.74% for SR-SHS, and from 95.89% to 92.50% for DR-DHS. Furthermore, the proposed LD method based on BP neural network has better transfer learning performance than that based on random forest or support vector machine.