Simulation Research on Speed ​​Control System of Diesel Generator Set

Theoretical Study on Simulation of Diesel Generator Set Speed ​​Control System He Wei, Chen Gang (Hubei Institute of Technology, Wuhan 430068, Hubei, China) The fuzzy control system. The methods of fuzzy controller design using Matlab are discussed in detail, and their control performance is simulated and compared with Ma-lab.

22: The speed of the diesel engine used in the diesel generator set directly affects the frequency of the power generation voltage, and should be kept as stable as possible. According to the relevant standards, the transient rate should be less than 5% and the steady state rate should be less than 2%. However, since the generator load is constantly changing, this will inevitably cause fluctuations in the engine speed. Therefore, it is very important to control its speed effectively. Several control systems are described below.

1 governing system motion equations 1.1 rigid feedback speed control system motion equations / / diesel engine motion equation / / centrifugal speed sensor motion equation / rigid feedback actuator motion equation 1.2 constant speed feedback indirect action speed system motion equations Ta*f+T, = 1.3 fuzzy control system electro-hydraulic servo actuator motion equations.

Tsf+BZ;//Constant speed feedback hydraulic servo motion equation (relative deviation of crankshaft angular velocity in the above equation of motion; ZBosh oil side plunger angle deviation; a-unit load relative deviation; Ta-unit acceleration time Constant; Tg-unit speed recovery constant; Ts-rigid feedback constant; B-server time constant; u-fuzzy controller output voltage deviation.) 2 Fuzzy control link design 21 uses a two-dimensional fuzzy controller. The input is the velocity deviation E and its derivative EC, and the output is the voltage u. The fuzzy word set and the domain are divided into *66 and a 3, 3. The genus function is shown as ~.

EC; At the same time, the output scale factor h is introduced, so that h=300 *E. Thus, when E is large, the input E and output u of the fuzzy controller are both large. The opposite is smaller. In this way, the system gains certain adaptive capabilities and improves the control performance of the system.

Simulation and results analysis were performed using Matkb. The load change input is a step function from 0 to a 1, that is, the load change is 100%. Simulation parameters: 102, P=1.13 Speed ​​control system block diagram and simulation waveform 4 Results analysis can be seen from the simulation results, using the fuzzy control system can be obtained Faster dynamic response and lower steady-state error. The transient rate is less than 4%, and the steady-state rate is less than 2%. The transient rate of the indirect speed control system is greater than 8%. The fuzzy control system has different system parameters. Strong adaptability (when Ta changes from 3 to 20, constant speed feedback indirectly affects the speed control system after 10s is still unstable, and the fuzzy control system is stable after 2s. 5 Practical application effect. We use the above fuzzy control strategy scheme. In Cummins 250kW diesel generator. Its rated speed is 15 (1) i / min. In the experiment, we use 8051 single-chip microcomputer, through the AD650 chip, using the frequency-frequency conversion method to collect the signal of the electromagnetic sensor mounted on the flywheel. The control data table is placed in the single-chip microcomputer, and then the input current of the actuator installed in the EFC cavity of the PT pump is controlled by look-up and comparison, instead of its original electronic governor. The experimental data below shows that the transient speed of the diesel engine is 1 528 (/min) and the transient speed is less than 2%; the maximum steady state speed is 1513 (r/min), and the minimum steady state speed is 1494 (/min). Steady-state rate is less than 1%. 6 Conclusion The diesel generator is actually a nonlinear system with delay link. The constant speed feedback indirect action speed control system is more complicated and the adaptability is not strong. The use of PLC or microcomputer for fuzzy control can make it intelligent. The control is integrated into one, simplifying the system structure and obtaining better control effects and certain adaptability.

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