doi: 10.3934/dcdss.2020257

PID parameter optimization algorithm for pressure control of heating system of ground source heat pump

1. 

Scientific Research and Industry Department, Hebei University of Architecture, Zhangjiakou 075000, China

2. 

Technology Center, North Navigation Control Technology Co., Ltd., Beijing 100000, China

3. 

The Library, Hebei North University, Zhangjiakou 075000, China

* Corresponding author: Yugui Jia

Received  April 2019 Revised  May 2019 Published  January 2020

In order to improve the pressure control effect of the ground source heat pump heating system, a PID parameter optimization algorithm for the pressure control of the ground source heat pump heating system was designed. The ground source heat pump heating control system and pressure PID control model with pressure value control object are established. BP neural network and gradient method are combined to optimize the PID control parameters. The descending method is used to correct the weighting coefficient of BP neural network. The global minimum inertia term for fast convergence. The BP neural network can modify the weight of the output layer and the hidden layer to make the control error of the geothermal heat pump heating system pressure PID control model meet the standard requirements, so as to optimize the PID parameters of the ground source heat pump heating system pressure control and obtain the optimal. Pressure control. The experimental results show that the algorithm has a good control effect on the pressure of the ground source heat pump heating system, no overshoot and fast response. When adding 50dB of noise, it takes only 5 seconds to use the algorithm to achieve the specified pressure value and good anti-noise performance. In addition, the algorithm also solves the thermal imbalance problem of the heating system, effectively reducing the energy consumption of the ground source heat pump heating system.

Citation: Jing Qin, Mengmeng Cui, Yiping Lu, Chao Yang, Yanhua Huo, Yugui Jia. PID parameter optimization algorithm for pressure control of heating system of ground source heat pump. Discrete & Continuous Dynamical Systems - S, doi: 10.3934/dcdss.2020257
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show all references

References:
[1]

N. AoH. Li and D. Wang, Icp and 11-regression based fisheye calibration estimation, Journal of China Academy of Electronics and Information Technology, 12 (2017), 67-72.   Google Scholar

[2]

S. Azali and M. Sheikhan, Intelligent control of photovoltaic system using bpso-gsa-optimized neural network and fuzzy-based pid for maximum power point tracking, Applied Intelligence, 44 (2016), 88-110.  doi: 10.1007/s10489-015-0686-6.  Google Scholar

[3]

H. Cao and L. Xi, Thermal management-oriented multivariable robust control of a kw-scale solid oxide fuel cell stand-alone system, IEEE Transactions on Energy Conversion, 31 (2016), 596-605.  doi: 10.1109/TEC.2015.2510030.  Google Scholar

[4]

I. CroallD. Tozer and B. Moynihan, Effect of standard vs intensive blood pressure control on cerebral blood flow in small vessel disease, Jama Neurology, 75 (2018), 720-727.  doi: 10.1001/jamaneurol.2017.5153.  Google Scholar

[5]

K. DongH. Min and I. Kong, Parametric study on interaction of blower and back pressure control valve for an 80-kw class pem fuel cell vehicle, International Journal of Hydrogen Energy, 41 (2016), 17595-17615.   Google Scholar

[6]

M. Emekli and B. Guvenc, Explicit mimo model predictive boost pressure control of a two-stage turbocharged diesel engine, IEEE Transactions on Control Systems Technology, 25 (2017), 521-534.  doi: 10.1109/TCST.2016.2554558.  Google Scholar

[7]

H. JeonJ. Lee and S. Han, Pid control of an electromagnet-based rotary hts flux pump for maintaining constant field in hts synchronous motors, IEEE Transactions on Applied Superconductivity, 28 (2018), 1-5.  doi: 10.1109/TASC.2018.2822704.  Google Scholar

[8]

G. LiA. Wu and H. Wen, Optimal design research of refrigeration system npid controller, Computer Simulation, 33 (2016), 339-344.   Google Scholar

[9]

Y. LuJ. LiH. Jiang and Q. Fan, Optimal design of parameters in llc voltage-multiplying resonant converter considering parasitic parameters, Journal of Power Supply, 14 (2016), 69-74.   Google Scholar

[10]

J. PinskerJ. Lee and E. Dassau, Randomized crossover comparison of personalized mpc and pid control algorithms for the artificial pancreas, Diabetes Care, 39 (2016), 1135-1142.  doi: 10.2337/dc15-2344.  Google Scholar

[11]

D. Saffer, Optimization strategy for pid-controller design of amb rotor systems, IEEE Transactions on Control Systems Technology, 24 (2016), 788-803.   Google Scholar

[12]

X. Yang, Convergence of single axis optimal perfectly matched layer method for scattering problems in two-layered media, Journal of Jilin University (Science Edition), 54 (2016), 983-988.   Google Scholar

[13]

J. YaoX. ZhangZ. Zhou and Z. Pang, Optimization of tio2 photocatalytic oxidation coupled ceramic ultrafiltration membrane process parameters by matrix analysis, Automation and Instrumentation, 1 (2018), 107-110.   Google Scholar

[14]

L. ZhangS. Xu and Y. Wan, Multi-objective and multi-parameter optimization for centrifugal compressor used in pem fuel cells, Chinese Journal of Power Sources, 40 (2016), 81-83.   Google Scholar

[15]

Z. ZhangY. Song and H. Wu, Experimental study on vacuum control method for paschen tests of the superconducting magnet mockup, IEEE Transactions on Applied Superconductivity, 27 (2017), 1-4.  doi: 10.1109/TASC.2017.2732280.  Google Scholar

Figure 1.  Pressure differential flow control frame diagram
Figure 2.  Box diagram of BP neural network PID differential control system
Figure 3.  Principle diagram of neural network PID controller
Figure 4.  BP neural network structure
Figure 5.  Optimal algorithms of PID control parameters for BP neural networks
Figure 6.  PID comparison of three algorithms
Figure 7.  Comparison of 25 s sudden disturbance response curve
Figure 8.  Comparison of 40 s sudden disturbance response curve
Figure 9.  System change curve when pressure becomes 0.85 MPa
Figure 10.  System change curve when pressure becomes 1.65 MPa
Figure 11.  Add 30 dB white noise pressure change curve
Figure 12.  Add 50 dB white noise pressure change curve
Figure 13.  Comparison of energy consumption of PID heating system with different algorithms
Table 1.  Parameter values before and after adjustment
Building No. unit Area ($m^{2}$) Design Flow (Kg/h) Actual flow (Kg/h) Front and rear pipe network pressure difference (mm) Move head before adjustment (mm) Move the head after adjustment (mm) Prior to adjustment (Kg/h) Adjusted traffic (Kg/h)
1$\sharp$ 1 983 2.11 5.51 161.92 24.17 7.85 4.86 2.65
2 983 2.11 4.85 133.16 21.82 8.35 4.61 2.68
3 821 1.85 3.55 81.62 15.86 6.75 3.91 2.01
4 821 1.85 3.71 64.77 13.274 5.84 3.57 2.05
5 821 1.85 3.31 43.55 11.52 6.26 3.33 2.08
2$\sharp$ 1 1215 2.72 3.76 34.93 13.48 8.81 3.56 2.98
2 1131 2.52 4.15 68.71 16.27 8.53 3.96 2.98
3 1131 2.52 5.35 182.32 26.77 8.48 5.12 2.75
4 1131 2.52 5.26 77.12 17.45 8.71 4.11 2.85
4# 2 1131 2.52 1.12 34.15 1.15 5.62 1.12 2.94
5$\sharp$ 1 953 2.14 2.38 43.25 14.35 9.04 3.71 2.47
3 979 2.19 3.16 31.24 10.24 7.25 3.11 2.35
10$\sharp$ 1 947 2.12 5.51 63.58 17.48 10.25 4.11 2.65
15$\sharp$ 3 981 2.11 3.71 101.25 19.57 9.65 4.36 2.33
Building No. unit Area ($m^{2}$) Design Flow (Kg/h) Actual flow (Kg/h) Front and rear pipe network pressure difference (mm) Move head before adjustment (mm) Move the head after adjustment (mm) Prior to adjustment (Kg/h) Adjusted traffic (Kg/h)
1$\sharp$ 1 983 2.11 5.51 161.92 24.17 7.85 4.86 2.65
2 983 2.11 4.85 133.16 21.82 8.35 4.61 2.68
3 821 1.85 3.55 81.62 15.86 6.75 3.91 2.01
4 821 1.85 3.71 64.77 13.274 5.84 3.57 2.05
5 821 1.85 3.31 43.55 11.52 6.26 3.33 2.08
2$\sharp$ 1 1215 2.72 3.76 34.93 13.48 8.81 3.56 2.98
2 1131 2.52 4.15 68.71 16.27 8.53 3.96 2.98
3 1131 2.52 5.35 182.32 26.77 8.48 5.12 2.75
4 1131 2.52 5.26 77.12 17.45 8.71 4.11 2.85
4# 2 1131 2.52 1.12 34.15 1.15 5.62 1.12 2.94
5$\sharp$ 1 953 2.14 2.38 43.25 14.35 9.04 3.71 2.47
3 979 2.19 3.16 31.24 10.24 7.25 3.11 2.35
10$\sharp$ 1 947 2.12 5.51 63.58 17.48 10.25 4.11 2.65
15$\sharp$ 3 981 2.11 3.71 101.25 19.57 9.65 4.36 2.33
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