Jiangjiang Wang

Study of Neural Network PID Control in Variable-frequency Air-conditioning System

This paper aims at the control of variable-frequency air-conditioning system that has characteristic as large inertia and pure lag. The neural network PID control in the variable-frequency air-conditioning system is introduced and simulated. In the learning algorithm of neural network PID controller, the output of system is needed to tune the weights of neural network while it is difficult to obtain. So the output of system is predicted through the algorithm of nonlinear that adopts the neural network configure. Through simulation and optimization, it is found that the neural network PID control has the capability of self-study and self-adaptation. However, the neural network PID control system sometimes has the static error. To eliminate the static error, the hybrid control of neural network PID and conventional PID is applied to the variable-frequency air-conditioning system. The hybrid control is simulated to compare the performance of changed parameters of model. The simulation finds that the hybrid control of neural network PID and PID has both the advantages of neural network and PID, such as self-studying and self-adapting and obtain faster response and better performance.

Hybrid CMAC-PID Controller in Heating Ventilating and Air-Conditioning System

The controlled objects are modeled in heating, ventilating and air-conditioning (HVAC) system, mainly including the heat exchanger and the air-conditioning space. The HVAC system has large inertia, pure lag and nonlinear characteristic. The uncertain disturbance factors affect the control performance. To obtain better performance in HVAC system, this study proposes a hybrid Cerebellar model articulation controller (CMAC)-PID control system, which combines the CMAC neural network and general PID control. This method realized the feedback control by using traditional PID controller to enhance the stability and reject the disturbance and realized the feed forward control by using CMAC neural network to increase the response speed and control precision in HVAC system. The simulation results show the hybrid CMA-PID control system possesses the advantages of high precision, realtime standard, strong ability of anti-interfere and robustness.

Robust cascade control system design for central airconditioning system

In the central air-conditioning system, the control system uses traditionally single loop PID controller. The control structure is simple, but the performance is usually not satisfactory. In this paper, the thermodynamic model of the heat exchanger and air-conditioning space in the central air-conditioning system are modeled in mathematic. Based on the models, we propose a robust cascade control strategy for temperature control of air-conditioning system. The robust cascade control system aims at eliminating the disadvantages of traditional design via complete decoupling of the design and performance of two cascaded control loops. Finally, the three controllers, the designed cascade control, traditional cascade PID control and single PID control are simulated in central air-conditioning system. It is found that the setpoint response, the robust and the performance of the designed robust cascade control system are better than the traditional cascade PID control system and single PID control system.

Adaptive PID control with BP neural network self-tuning in exhaust temperature of micro gas turbine

Mathematical model of exhaust temperature control in micro gas turbine is introduced. To obtain better performance, a self-adaptive PID control is applied to the exhaust temperature control. The parameters of PID control are tuned by back propagation (BP) neural networks. In the tuning process, the plantpsilas predictive output is used to modify the weights of neural networks. The plantpsilas output is also predicted by BP neural networks and it is nonlinear prediction which improves the predictive accuracy. The effectiveness and efficiency of the proposed control strategy is demonstrated by applying it to the exhaust temperature control. The simulations show that the dynamic responses of the exhaust control system can be effectively improved and the anti-disturbance of the proposed controller is better than that of the PID controller.

Fuzzy immune self-tuning PID control of HVAC system

Aiming at the non-linear links such as time lag, large inertia, in the heating, ventilating, and air conditioning (HVAC) system, a fuzzy immune self-tuning PID control system is designed. With ideas from the biological immune system, fuzzy immune PID control strategy is applied to central air-conditioning system, in which the proportional coefficient of PID is adaptively modulated by means of fuzzy immune algorithm, and integral coefficient and differential coefficient are dynamically regulated by fuzzy logic scheme. Based on the model of central air-conditioning room, the simulation investigation has been carried out and the result shows validity of the control scheme and improvement in dynamic performance and robustness of central air-conditioning control system.

Fuzzy multi-criteria evaluation model of HVAC schemes in optimal combination weighting method

Six heating, ventilation and air conditioning (HVAC) schemes, constant air volume, variable air volume, fan-coil, induction unit, variable refrigerant volume and distributed HVAC system are evaluated and compared in multi-criteria. Because of the uncertainty of qualitative criteria, this paper presents a fuzzy multi-criteria model to select the optimal HVAC system. The methods of fuzzy set theory, linguistic value, analytic hierarchy process, entropy weighting and optimal weighting are used to obtain the criteria weights. Fuzzy technique for order preference by similarity to ideal solution (TOPSIS) method is employed to get the ranking of HVAC schemes. Finally, the application of the fuzzy multi-criteria evaluation model demonstrates detailedly the computation and evaluation procedure in the selection of HVAC schemes. Practical applications: The fuzzy multi-criteria evaluation model combined with the optimal weighting method in this paper is believed by the authors to contribute to the selection and comparison of HVAC systems or schemes in the building. It is proposed that both qualitative and quantitative criteria existing in reality are used to evaluate HVAC schemes and the decision-making method combines both subjectivity and objectivity. In addition, simple and practical computation and evaluation procedure is easily applied by HVAC designers or decision-makers. Such an approach could become a powerful tool to make an optimal selection for HVAC schemes.

Distributed Combined Cooling Heating and Power System and Its Development Situation in China

Distributed natural gas-driven combined cooling, heating and power (CCHP) systems, including various technologies, provide an alternative for the world to meet and solve energy-related problems, such as energy shortages, energy supply security, emission control, the economy and conservation of energy, etc. This paper analyzes the energy consumption structure in China at first; then the characteristics of natural gas-driven CCHP technologies, especially technical performances, are presented, as well as the status of utilization and development. The status of distributed CCHP development in China is briefly introduced by dividing China into four main sections: Beijing, Shanghai, Guangdong and other areas. Several problems regarding further distribution of natural gas-driven CCHP applications for buildings in China are discussed. It is concluded that, within decades, promising CCHP technologies can flourish with the cooperative efforts of government, energy-related enterprises and professional associations.Copyright

Self-adaptive RBF neural network PID control in exhaust temperature of micro gas turbine

Mathematical model of exhaust temperature control in micro gas turbine is introduced. To obtain better performance, a self-adaptive PID control is applied to the exhaust temperature control. The parameters of PID control are tuned by radial basis function (RBF) neural network. In this paper, the RBF neural network is given which has been used extensively in the areas of pattern recognition, systems modeling and identification. The effectiveness and efficiency of the proposed control strategy is demonstrated by applying it to the exhaust temperature control. The simulations show that the dynamic responses of the exhaust control system can be effectively improved and the anti-disturbance of the proposed controller is better than that of the PID controller. However, the learning rate of RBF neural network and PID parameters is not too large due to the great gain of micro gas turbine. Otherwise the output will surge acutely.

Research of Cascade Control with an Application to Central Air-Conditioning System

The heat exchanger and air-conditioning space in the central air-conditioning system are modeled in mathematic. Based on the models, we propose a cascade control strategy for temperature control of air-conditioning system. The setpoint response controller in the cascade control system is designed in terms of the robust control H2 optimal performance specification. According to the system operation requirement for disturbance rejection, a closed-loop for rejecting disturbance signals is configured and the controller in internal loop is figured out by proposing the desired closed-loop complementary function. Finally, the three controllers, the designed cascade control, traditional cascade PID control and single PID control are simulated in central air-conditioning system. It is found that the setpoint response, the disturbance rejection, the robust and the performance of the designed cascade control system are better.

Economic analysis and optimization design of a solar combined cooling heating and power system in different operation strategies

The economic benefits of building cooling heating and power (BCHP) system are closely dependent upon its design and operation strategy. In this paper, a solar BCHP system which integrates solar facilities with traditional BCHP system is proposed to reduce the operation energy cost. The energy flow and operation strategy of the novel system are analyzed. Relative payback period (RPD) is employed to evaluate the cost saving potentials of BCHP systems in FEL and FTL operation modes with respect to SP system. A case study of the solar BCHP system for a commercial office building in Beijing in China is applied to evaluate the economical feasibility of the solar BCHP system. Then the capacities of energy equipments are optimized to obtain the optimal system economical performances during its life span. The results show that when the solar BCHP system operates in FEL operation strategy, the optimal economic benefits can be achieved.