Temperature Control of a SOFC and MGT Hybrid System

2011 ◽  
Vol 8 (5) ◽  
Author(s):  
Xiao-Juan Wu ◽  
Qi Huang ◽  
Xin-Jian Zhu ◽  
Chang-Hua Zhang
Keyword(s):  
Neural Network ◽  
Hybrid System ◽  
Temperature Control ◽  
Rbf Neural Network ◽  
Learning Ability ◽  
Inlet Temperature ◽  
Micro Gas Turbine ◽  
Proportional Integral ◽  
Internal Reforming ◽  
On Line

Transients in a load have a significant impact on the performance and durability of a solid oxide fuel cell (SOFC) integrated into a micro gas turbine (MGT) hybrid power system. One of the main reasons is that the SOFC operating temperature and turbine inlet temperature change drastically due to the load change. Therefore, in order to guarantee the temperature to operate within a specified range, an adaptive proportional-integral-derivative (PID) decoupling control strategy based on a dynamic radial basis function (RBF) neural network is presented to control the temperature of a natural gas fueled, tubular SOFC/MGT hybrid with internal reforming in this paper. Using the self-learning ability of the dynamic RBF neural network, the proportional, integral, and differential factor of the PID controller are tuned on-line. The simulation results show that it is feasible to build the adaptive PID decoupling controller for temperature control of the SOFC/MGT hybrid system.

The Optimization of Adaptive PID Control Algorithm Based on RBF Neural Network

2014 ◽  
Vol 998-999 ◽  
pp. 943-946
Author(s):  
Jing Liu ◽  
Guo Xin Wang
Keyword(s):  
Neural Network ◽  
Pid Controller ◽  
Rbf Neural Network ◽  
High Reliability ◽  
Simple Algorithm ◽  
Poor Performance ◽  
Learning Ability ◽  
The Neural Network ◽  
Pid Controller Tuning ◽  
Deterministic Control

As the earliest practical controller, PID controller has more than 50 years of history, and it is still the most widely used and most common industrial controllers. PID controller is simple to understand and use, without a prerequisite for an accurate model of the physical system, thus become the most popular, the most common controller. The reason why PID controller is the first developed one is that its simple algorithm, robustness and high reliability. It is widely used in process control and motion control, especially for accurate mathematical model that can be established deterministic control system. But the conventional PID controller tuning parameters are often poor performance, poor adaptability to the operating environment. The neural network has a strong nonlinear mapping ability, competence, self-learning ability of associative memory, and has a viable quantities of information processing methods and good fault tolerance.

scholarly journals Single-Phase Photovoltaic Grid-Connected Inverter Based on Fuzzy Neural Network

2021 ◽  
Vol 25 (3) ◽  
pp. 310-316
Author(s):  
Shenping Xiao ◽  
Zhouquan Ou ◽  
Junming Peng ◽  
Yang Zhang ◽  
Xiaohu Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Pid Controller ◽  
Fuzzy Neural Network ◽  
Single Phase ◽  
Fuzzy Inference ◽  
Learning Ability ◽  
Proportional Integral ◽  
Fuzzy Neural ◽  
Grid Connected Inverter ◽  
Self Learning

Based on a single-phase photovoltaic grid-connected inverter, a control strategy combining traditional proportional–integral–derivative (PID) control and a dynamic optimal control algorithm with a fuzzy neural network was proposed to improve the dynamic characteristics of grid-connected inverter systems effectively. A fuzzy inference rule was established after analyzing the proportional, integral, and differential coefficients of the PID controller. A fuzzy neural network was applied to adjust the parameters of the PID controller automatically. Accordingly, the proposed dynamic optimization algorithm was deduced in theory. The simulation and experimental results showed that the method was effective in making the system more robust to external disruption owing to its excellent steady-state adaptivity and self-learning ability.

On-Line Thickness Measurement of Thin Film Based on Neural Network

2014 ◽  
Vol 484-485 ◽  
pp. 307-310
Author(s):  
Li Cai ◽  
Yue Gang Tan ◽  
Qin Wei
Keyword(s):  
Neural Network ◽  
Thin Film ◽  
Film Thickness ◽  
Production Cost ◽  
Rbf Neural Network ◽  
Thickness Measurement ◽  
Monitoring Method ◽  
Line Thickness ◽  
On Line ◽  
Thickness Deviation

This paper proposes a on-line thickness measurement scheme of thin film based on the capacitance thickness sensor and introduces the composition and principle of thickness measurement system. Then it further states the principle and simulation of the RBF neural network, which can effectively predict the thickness deviation of thin film by setting the appropriate parameters. The monitoring method based on the RBF neural network will reduce production cost and make the film thickness uniformity better, combining the traditional film production line with an new idea of controlling the opening degree of wind ring.

scholarly journals Research on temperature control of heating furnace with intelligent proportional integral derivate control algorithm

2020 ◽  
Vol 24 (5 Part B) ◽  
pp. 3069-3077
Author(s):  
Feilong Zheng ◽  
Yundan Lu ◽  
Shuguang Fu
Keyword(s):  
Neural Network ◽  
Control Theory ◽  
Temperature Control ◽  
Intelligent Control ◽  
Pid Control ◽  
Control Algorithm ◽  
Network Control ◽  
Furnace Temperature ◽  
Proportional Integral ◽  
Intelligent Pid

In view of the problems of large overshoot and large oscillation frequency in cur?rent furnace temperature control, based on the development of intelligent control theory, expert control, fuzzy control, and neural network control in intelligent control theory are combined with proportional integral derivative (PID) control. The intelligent PID control algorithm is used to carry out numerical simulation and experimental research on these several control algorithms. The results show that the adjustment effect of the intelligent PID control algorithm is significantly better than the traditional PID control algorithm. Among them, the fuzzy self-tuning PID control algorithm and the fuzzy immune PID control algorithm are feasible in the application of furnace temperature control. The neural network PID control algorithm It also has good development and application potential.

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