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With the development of the national economy, the demand of electric power market has become higher than before. The stable and reliable power system is one of the important national economic securities. Reliability of generator excitation system is one of the important elements to determine the stability of the power system. Traditional PID cannot meet the requirements of the increasingly complex power system due to some defects. This essay introduces FLC control, combining with the traditional PID control. Using Matlab software, we analyze the curve and FLC is better than that by comparing with the traditional PID.

Generator’s excitation control is a typical non-linear time-variant control system. Due to the complexity of the electric power system, the demands towards the generator’s excitation control system have been constantly enhancing. With the development of the control theory, it is a trend of development that the integration of the intelligent control theory and generator’s excitation control can achieve a better effect of control. The traditional PID control structure is simple, has a certain robustness, and can achieve better control effect, but because the traditional PID controller needs to manually adjust the parameters, so that the generator excitation adjustment process is more complicated and inconvenient. FLC is the fuzzy logic control, which is characterized by the non dependent control object’s mathematical model, and the design method is simple and has strong adaptability, and is easy to implement [

In the simulation system, the process of the control method is to be measured parameters, such as temperature, pressure, flow, composition, liquid level, current, etc., by the sensor into a unified standard signal into the regulator. In the regulator and the given value, and then compare the difference between the PID after the operation to the executive body, to change the amount of feed, in order to achieve the purpose of automatic adjustment. Simulation PID control system schematic diagram shown in

PID is a linear controller; it constitutes a control program according to a given value R and the actual value Y:

And the control rule is:

The role of the correction link is:

Proportional links: the proportion of the control system to reflect the deviation of E(T), once produced, the controller immediately to produce control to reduce the deviation.

Integral link: mainly used to eliminate static error, improve the system of no difference. The integral function of the strength is the integral time constant T, T is more, the integration of the more weak, the smaller the T, the more integral role.

Differential link: the change trend of the deviation signal, and can be introduced into the system in order to get a valid early correction signal, so as to speed up the system’s movement speed and reduce the adjustment time [

Computer cannot be the same as people thinking, reasoning and judgment, only when the given accurate information, the computer can make the wrong judgments, but the human brain even in only part, even not fully to the situation, can be judged, the computer to simulate human thinking and judgment process, it is necessary to have the language of the ambiguous, uncertain information quantitative representation, fuzzy concept [

Fuzzy control is a complex system which is difficult to describe. The computer control technology based on natural language and fuzzy reasoning is not dependent on the traditional mathematical model, but is dependent on the fuzzy rules by the operation experience and the expression of knowledge. The basic flow chart of the fuzzy controller is shown in

This framework contains 5 important parts, that is, the definition of variable, fuzzy, knowledge base, logical reasoning and anti-mode.

Defining variables: that is, the state of the program is observed and the action to consider. For example, the input variable is CE and the output error is u, while the control variable is the next state. Where e, CE, u are collectively referred to as fuzzy variables.

Fuzzy: the input values are converted to the numerical value of the domain of the input values. The process of measuring the physical quantity is expressed by using the colloquial variables. The relative membership degree is obtained according to the appropriate language value.

Knowledge base: including two parts, including database and rule base, where the database is to provide the relevant definitions of processing fuzzy data, and the rule base is a group of language control rules to describe the purpose and strategy.

Logical reasoning: the fuzzy concept of imitating human judgment, fuzzy logic and fuzzy inference, fuzzy control signal.

Anti-mode: the fuzzy transformation from the inference to the explicit control signal as the input value of the system.

Fuzzy control depends on the “fuzzy rules”, which is a scientific and reasonable way to combine the fuzzy control with the excitation control of generator.

Synchronous generator excitation control is mainly composed of excitation control, power module, synchronous generator and measurement module.

The classical synchronous generator voltage regulator excitation model is shown in

In the system, the output winding is synchronous, and the transfer function is a part of the generator when the saturation characteristics of the magnetic circuit of the generator are not considered.

T_{d} time constant, K is the amplification factor of the generator. Power unit refers to the output of the excitation controller Upwm, the output voltage of the excitation output voltage U, power conversion. The unit can be considered as a first-order inertia link, and the transfer function is shown in the Formula (5):

Formula (5), TA for the amplification of the time constant, usually very small, usually take. Voltage measurement unit is the output voltage of the excitation synchronous generator, the input to the digital controller, the change of the input signal, because the rectifier filter circuit has a slight delay, so with a first-order inertia link to describe, expressed as transfer function, such as Formula (6):

In the formula: K_{C} is the ratio of the input and output of the voltage sensor, and the T_{R} is the time constant of the filter circuit.

The basic structure of the excitation controller for synchronous generator based on FLC is shown in

As shown in

In the 2 section, the influence of the traditional PID excitation controller, the three parameter PID on the control factors, and the expert experience, the fuzzy rules are formulated.

When e < 0, ec > 0, we should eliminate the deviation, increase the weight of the deviation, close to the steady state, increase the weight of the deviation, and reduce the integral effect.

When e < 0, ec < 0, we should try our best to reduce the overshoot, increase the weight of the deviation.

When e > 0, ec < 0, the system basically stable, should reduce the control effect.

When e > 0, ec > 0, the differential parameters, make the system fast and stable [

When e = 0, ec = 0, the system move into stable state.

After the system is stable, the PID parameter is recovered. Based on the above rules, a fuzzy control table is established, and the fuzzy excitation control of generator is realized.

In this paper, we use Simulink Matlab to build the model, input fuzzy logic control rules, simulation analysis. The model of the synchronous generator excitation control system based on FLC is shown in

In this paper, traditional PID control chart comparing simulation analysis was shown in

With Using Matlab fuzzy module, made the fuzzy rule input fuzzy logic controller [

Saved as fis files, put into fuzzy control module to achieve the fuzzy control generator excitation control [

After the establishment of the model and putting the fuzzy control rules in, the model simulation results are as

shown below:

Control debugging process compared with traditional PID excitation control has better results [

This paper analyzes the generator excitation control based on FLC; adding FLC into generator excitation control system is mainly aimed at the feature of generator excitation control system which is nonlinear, time varying, and complex. After adding fuzzy control system, the generator excitation control system can do more reasonable measures according to the experience which make the generator excitation control system more reliable and effective. The controller based on FLC is designed with advantages of fuzzy control with that of PID control com-

bined, which is characterized by simpleness, high accuracy of PID control, good adaptability and speed ability of fuzzy control.

Sichuan University of Science & Engineering Cultivation Project 2012py18, and Artificial Intelligence of Key Laboratory of Sichuan Province Project 2014RYY05, 2015RYY01.

Zhiting Guo,Hong Song,Penggao Wen,Zhizheng Fan, (2015) Study on Synchronous Generator Excitation Control Based on FLC. World Journal of Engineering and Technology,03,232-239. doi: 10.4236/wjet.2015.34024