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Abstract- Power System Controllers are most commonly used for stabilized operation of power system under different operating conditions. Different conventional controller exists for fast excitation control and stable operation of power system. In this paper simulation of a typical Power System and the impact of fuzzy logic controller on system performance through excitation control of synchronous generator is observed. Also fuzzy logic results are compared with the conventional controller (PID).
Keywords-Stabilized Operation, Angle stability, Excitation control, Intuition.
THe main objective of excitation system in the synchronous generator is to control the field voltage (sometimes field current). The field voltage is controlled so as to regulate the terminal voltage and enhance system transient stability and small signal stability at different load levels .
Analysis of power systems proved that many different forms of power system stability have emerged and become problematic in recent years, transient system stability still remains a basic and important consideration in power system
design and operation and it is observed that the operation of many power systems are limited by phenomena such as voltage stability and small signal stability. A power system stabilizer (PSS) is sometimes also included in the excitation
system to modulate the AVR input in such a manner as to contribute damping to inter machine oscillations.
In the most modern systems for ensuring stable response, (AVR) is a controller that senses generator output voltage (and sometimes current), then initiates corrective action by changing the exciter control in the desired direction. In power system excitation control, the fuzzy logic controllers are used due to their characteristics. The preference of fuzzy controllers over classical controllers is that classical controllers require a deep understanding of a system, exact equation and precise numeric values while fuzzy controllers can be used when the system is complex one (like power system nonlinear behavior).
The aim of this paper is the development of the fuzzy
controller (software) to simulate the automatic voltage regulator and enhance small signal stability.
Transient stability analysis
Power system stability was recognized as a problem as far back as the 1920 s at which time the characteristic structure of system consisted of remote power plants feeding load centers over long distances.
The transient stability is the ability of a power system to remain in synchronism when subjected to large transient disturbances. These disturbances may include faults on transmission elements, loss of load, loss of generation and loss of system components such as transformer or transmission lines .
The instability of power system can take different forms and is influenced by a wide range of factors. Instability may also occur without the loss of synchronism. Classification of power system stability is as follows :
Fig. 1 Stability Classification
Automatic controllers control generator voltages, output and frequency in order to keep them constant according to pre-established values . The automatic controllers include:
In transient stability, quickness of response is one of the important features. In order to attain field excitation control, AVRs are used. AVRs not only control the terminal voltage of the system but also control the reactive power generated and the power factor of the machine; once these variables are related to the generator excitation level.
Fuzzy Logic And Controllers
Fuzzy logic was introduced by Lotfi Zadeh in 1965 to represent/manipulate data and information possessing non statistical uncertainties. Fuzzy logic technology enables the use of engineering experience and experimental results in designing embedded systems.
The design of an FLC depends upon the fuzzification of controller inputs, defining of rules, rule inference and defuzzification.
Fig. 2 Basic configuration of fuzzy controller
The process of making a crisp quantity fuzzy is called fuzzification. It means that it involves the transformation of crisp control variables to corresponding fuzzy linguistic variables. The fuzzified input and output signals of FLC are interpreted into a number of linguistic variables as shown in figure. Each linguistic variable has a label and membership function to distinguish it from others. Once the membership is found for each of the linguistic labels, an intelligent decision can be made as what the output should be.
Rule Definition and Interference Mechanism
In the development of fuzzy logic controller, core part is the defining of rules. For designing FLC, control strategy is learned through experience and a set of rules. These rules describe the behavior of the controller using linguistic terms. The controller then infers the proper control from this rule base which plays the role of human operator. A typical rule can be written as follows:
IF the voltage error is positive large, AND the rate of change of voltage error is positive large, THEN the field voltage is negative large. The inference mechanism consists of two processes called fuzzy implication and rule aggregation. In this design, the Mamdani implication (max-min) is used to infer the output of the rules defined.
Defuzzification plays an important role in a fuzzy logic based control system. The last process of FLC is defuzzification which is a mechanism used to convert a fuzzy value/set to a crisp value/set so that the controller system can interpret the controller s action accordingly. The widely used method of defuzzification is the centre of gravity, also known as centroid.
The FLC design procedure just explained is applicable to any application and not restricted to the design of excitation system.
The knowledge base comprises of database of the plant. It provides all the necessary definitions for the fuzzification process such as membership functions, fuzzy set representation of the input-output variables and the mapping functions between physical and fuzzy domain.
The rule base is essentially the control strategy of the system. It is usually obtained from expert knowledge or heuristics and expressed as a set of IF THEN rules.
Results are compared by using fuzzy logic control and conventional excitation system. Two different solutions are analyzed here, one situation is considered when the fuzzy controller is used and the other result is compared when conventional control is applied.
Fig. 3 AVR and PSS are fuzzy logic based
DEVELOPMENT OF FUZZY LOGIC CONTROLLER
In the development of fuzzy logic controller, the main theme is to relate the numeric values to linguistic variables. In the AVR system of synchronous machine, two fuzzy state variables and one fuzzy control variable is applied. The two fuzzy state variables are:
1) Error: Error is the difference between the predetermined set voltage and the actual voltage. This error can be either positive or negative.
2) Rate of Change of Error: Another input parameter is the rate of change of voltage. Here the rate of change of voltage is taken to enhance the controller action.
The other fuzzy control variable is the output voltage. Intuitively, the fuzzy control variable out-volt will be positive if the actual voltage drop below the set voltage and negative if the actual voltage rises above the set voltage.
In the development of controller, fuzzy variables are divided into three broad divisions namely positive, zero and negative. These divisions can be further quantized into small, medium and large. This quantization yields the following subdivisions:
These divisions can be further quantitized into small, medium and large.This quantitization yields the following subdivisions:
NL: negative large
NM: negative medium
NS: negative small
PS: positive small
PM: positive medium
PL: positive large
Fig. 4 Rules of fuzzy controller
In the building of fuzzy controller, triangular membership functions are applied by using intuition -. It is simply derived from capacity of humans to develop membership functions through own innate intelligence and understanding. It should be remembered that in choosing the fuzzy set for the input variables, the domain of each input variable is completely covered. The voltage control FAM bank is a 7-by-7 matrix with linguistic fuzzy set entries.
Fig. 5 Output variable
Fuzzy Logic vs PID Controller (A Case Study)
In order to compare the two controllers i.e. fuzzy and PID, a simple power system is considered. A fault is generated at the load end and the system development of fuzzy logic controller, the main theme is to relate the numeric values to linguistic variables. In the AVR system of synchronous machine, two fuzzy state variables and one fuzzy control variable is applied. The two fuzzy state variables are:
Fig. 9(a) Fault Time (0.1sec)
Comparison of Load Angle, Active Power, Electromagnetic Torque.
Fig. 6 Comparison of Load Angle
Fig. 7 Comparison of Active Power
Fig. 8 Comparison of Electromagnetic Torque
Terminal Voltage (RMS) Response to Three Phase Line to ground Fault
Fig. 9(a) Fault Time (0.1sec)
Fig. 9(b) Fault Time (0.15sec)
Fig. 9(c) Fault Time (1.8 sec)
Simulation results and discussions
Satisfactory experimental results come from the simulation of synchronous machine. Load angle, electromagnetic torque and active power are well controlled by using fuzzy logic technique. For different fault times the terminal voltage is observed and it give better results over PID. It is evidently seen that the fuzzy logic is a powerful tool to control the power system stability.
In classical control, a lot of time is used to determine the transfer functions of the motor, generator and other circuits of the system. In many cases, transfer function that is obtained as an approximation of the actual one. Also transfer function may change while the system is operating. In comparison a fuzzy controller does not need any transfer function and can be developed and tested in a fairly short amount of time. Another significant advantage of the fuzzy controller is the tendency of the system to have minimal overshoot, if any, this tends to smooth out operations and improve overall efficiency.
Even though fuzzy logic is a powerful tool that can be used to replace classical controller, it is not to be used carelessly. The stability of fuzzy systems on the other hand cannot be predicted, therefore, it is necessary to simulate fuzzy systems as well as perform trial runs on them so as to obtain the system response. One major drawback of this approach is that one can never simulate all cases. Fuzzy control systems and classical control systems both have their pitfalls, but in situations where classical models are hard or impossible to obtain, fuzzy systems have a distinct advantage. In many cases, fuzzy controllers provide a powerful computation alternative to classical controller.
The fuzzy system was successfully designed and simulated in stability and control of 200MVA synchronous machine. FAM rules were generated using human experience and intuition. The simulation results for the testing of faulty conditions. It shows a very sharp reduction in settling time,
overshoot, rise time and zero steady state error. Hence, it is observed that excellent performance of fuzzy control over the conventional one for the excitation control of synchronous machine can be achieved.