Research Of Low-power Diesel Generator Speed Governor Based On Intelligent PID

Diesel generators are widely used in social production, such as the field of backup power of organs and units, field operations, etc. The speed characteristics of diesel generators have great impact on the quality of power generated. In order to ensure the quality of the power supplied, diesel generator sets should be equipped with the governor to ensure that when the load changes, the engine speed maintains stable. Currently, in China, majority of small and medium-power diesel generators are still using analog electronic governor, rarely using digital electronic style. Besides, the control methods were mostly conventional PID, which was difficult to meet the requirements of speed regulation under complex conditions. In view of this, the paper launched a research and design of intelligent algorithms based digital electronic governor, in order to develop a reliable, universal strong governor.In this paper, the working principle, the structure and composition of diesel generators, and the operating characteristics of the diesel engine as the prime mover was analyzed. On this basis, the discussion on the diesel engine speed control system was launched, in which, several performance indicators to judge the merits of the system was proposed, such as steady volatility, instantaneous transfer rate and recovery time.In terms of control algorithms, the neural networks and fuzzy control theory was introduced on the basis of conventional digital PID to build a single neuron PID and fuzzy adaptive PID controller. Afterwards, simulating model was developed in MATLAB. In the simulation process, step response tests were carried out, as well as adaptive tests and immunity tests. The simulation results show that, PID control system based on intelligent control, gain more excellent dynamic and static quality, with small overshoot, fast and smooth speed response characteristics. Besides, when the parameters of control object changes, the system gets stronger adaptability and better robustness, which make it easier to meet the operating requirements of multiple conditions. Considering that, in the fuzzy adaptive PID, the fuzzy rules are primarily based on the experience of expertise, thus the system performance are greatly influenced by human factors. While in the single neuron PID controller, adjusting connection weights online to find the best PID parameters combination are mainly based on the conditions characteristic of the controlled object, therefore the outside influence is small. Including that the algorithm of single neuron PID is simple, which is easy to program, so the single neuron PID was ultimately selected as the core control algorithm in this article.On hardware design, the hardware circuits in this paper mainly include the smallest single-chip systems, power modules, speed signal shaping and isolation amplifier circuit, actuator driver circuit, voltage and current sampling circuit and other auxiliary circuits. Among them, the 16-bit microprocessor PIC24FJ64GA002 was selected as the MCU. Using the high-precision input capture module of MCU to acquire speed signal, and outputs the PWM with 16-bit resolution, which control the on and off of transistor in the actuator drive circuit. By changing the PWM duty cycle, the current in actuator was regulated, thereby the diesel fuel supply was changed.As to Software, the basic idea of software design was described in the paper, the core algorithm of which was single neuron PID. In the start-up phase, we designed a special oil control sinusoidal curve, in order to achieve good control of the amount of smoke. In addition, this article developed a experiment on the Cummins 220 KW diesel generator sets, whose results show that the single neuron PID based digital electronic governor had high sensitivity, high control accuracy and good speed performance, and meet the requirements of the generator.