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DEVELOPMENT OF A FUZZY LOGIC BASED VOLTAGE REGULATOR FOR ELECTRIC GENERATING SET
LIST OF TABLES
Table 3.1 34
Figure 1.1 Conventional control design 5
Figure 1.2 Fuzzy control design 6
Figure2.1 Schematic diagram of LFC and AVR of a
synchronous generator 9
Figure 2.2 A simple diagram of an AVR 10
Figure 2.3 Block diagram of a simple AVR 12
Figure 2.4 Root locus plot for equation 2.13 15
Figure 2.5 Terminal voltage response 17
Figure 2.6 Simulink model for figure 2.5 18
Figure 2.7 Block diagram of a simple AVR
compensated with a stabilizer 19
Figure 2.8 Terminal voltage step response of CAVR
with stabilizer 20
Figure 2.9 Simulink model for figure 2.7 21
Figure 2.10 An AVR compensated with PID controller 22
Figure 2.11 Simulink model for figure 2.10 23
Figure 2.12 Terminal voltage step response with PID 24
Figure 3.1 Fuzzy controller architecture 27
Figure 3.2 Fuzzy control system 28
Figure 3.3a Membership functions for the error 32
Figure 3.3b Membership functions for ∫Ve 32
Figure 3.3c Membership function for the terminal voltage 33
Figure 3.4 FIS Editor 36
Figure 3.5 The Rule Editor 37
Figure 3.6 The Rule Viewer 38
Figure 3.7 Schematic diagram for fuzzy AVR operation 40
Figure 3.8 Fuzzy-AVR control loop 40
Figure 3.9 Terminal and desired voltage step response
with FPI-AVR 42
Figure 3.10 Fuzzy input voltage to the generator 42
Figure 3.11 A Simple machine infinite bus power system 44
Figure 3.12a Step response of FPI-AVR to three phase fault 47
Figure 3.12b Fuzzy input voltage to the generator 47
Figure 4.1 Terminal voltage response 49
Figure 4.2 Terminal voltage step response of CAVR
with stabilizer 50
Figure 4.3 Terminal voltage step response with PID 51
Figure 4.4a Terminal and desired voltage step response
with FPI-AVR 52
Figure 4.4b Fuzzy input voltage to the generator 52
Figure 4.5a Step response of FPI-AVR to three phase fault 53
Figure 4.5b Fuzzy input voltage to the generator 53
1.1 Background of the study
The main objectives of the control strategy in power system is to generate and deliver power in an interconnected system as economically and reliably as possible while maintaining the voltage and frequency in the steady-state within permissible limit .
A reliable, continuous supply of electric energy is essential for the functioning of today’s complex societies. Due to a combination of increasing energy consumption and impediments of various kinds concerning the extension of existing electric transmission networks, these power systems are operated closer and closer to their limits.
Deregulatory efforts will tighten the economical constraints under which utilities have to operate their own network or allow or prevent competitors from using it. This in turn will require more precise power flow control which is made possible by phase angle controllers being developed using new power electronic equipment. However, it is to be expected that these highly non-linear components will introduce harmonics and require non-linear control in order to prevent system destabilization.
This situation requires a significantly less conservative power system operation regime which, in turn, is possible only by monitoring and controlling the system state in much more detail than was necessary previously.
In electric power systems, , there are three different control levels: Generating Unit Controls which consist of prime mover control and excitation control with automatic voltage Regulator (AVR) and power system stabilization (PSS). The first controls generator speed deviation and energy supply system variable like boiler pressure or water flow. Excitation control aims at maintaining the generator terminal voltage and reactive power output within its machine-dependent limits.
System Generation Control which determines active power output such that the overall system generation meets the system load. It further controls the frequency and the tie line flows between different power system areas. Transmission Control monitors power and voltage control devices like tapchanging transformers, synchronous condensers and static VAR compensators. From the view point of system automation, , Generating Unit Control is a complete closed-loop system and in the past a lot of effort has been dedicated to improve the performance of the controllers. The main problem for example with excitation control is that the control law is based on a linearized machine model and the control parameters are tuned to some nominal operating conditions. In case of a large disturbance, the system conditions will change in a highly non-linear manner and the controller parameters are no longer valid. In this case the controller may even add a destabilizing effect to the disturbance by for example adding negative damping.
These problems provide an important motivation to explore other modern control techniques like fuzzy logic control.
The system frequency is affected by changes in real power while changes in reactive power affect the voltage magnitude. While the Load Frequency Control (LFC) controls the real power and frequency, the Automatic Voltage Regulator (AVR) controls the reactive power and terminal voltage magnitude. Because the excitation time constant is much smaller than the prime mover time constant, its transient decays much faster. For this reason, the cross-coupling between the LFC loop and AVR loop is negligible and hence the load frequency and excitation voltage control can be analyzed independently.
In modern large power system interconnected networks, manual regulation is not feasible and therefore automatic generation and voltage regulation equipment are installed on each generator.
The increasing technological demands and performance requirements call for complex production and manufacturing systems that in turn requires sophisticated control systems. To satisfy the product quality requirement in a flexible production environment, an advanced control techniques that can deal with uncertainty and non-linear ties need to be introduced. Hence the need for fuzzy logic control which can handle both linear and non-linear system. Fuzzy logic is a paradigm for an alternative design methodology which can be applied in developing both linear and non-linear systems .
Ndubisi and Agu  puts it thus, Fuzzy logic concept incorporates an alternative way which allows one to design a controller using a higher level of abstraction without knowing the plant model.
Conventional modeling and control approaches based on differential equations are often insufficient, mainly due to the lack of precise formal knowledge about the process to be controlled. Unlike the conventional control, if the mathematical model of the process is unknown we can design fuzzy controllers in a manner that guarantees certain key performance criteria.
Lee et al  states that while the conventional control starts with a mathematical model of the process and controllers are designed based on the model, fuzzy logic control on the other hand starts with heuristics and human expertise knowledge (in terms of fuzzy IF-THEN rules) and controllers are designed by synthesizing these rules. An important source of information to the fuzzy design is the knowledge of the plant operators, control engineers and process designers. Fuzzy logic control (FLC) reduces the time and complexity in analyzing the differential equations involved in the conventional control and hence in the overall design development cycle as depicted in Figures 1.1 and 1.2 respectively.
Conventional Design Methodology
Using the conventional approach, our first step is to understand the physical system and its control requirements. Based on this understanding, our second step is to develop a model which includes the plant, sensors and actuators. The third step is to use linear control theory in order to determine a simplified version of the controller, such as the parameters of a PID, PI controllers. The fourth step is to develop an algorithm for the simplified controller. The last step is to simulate the design including the effects of non-linearity, noise, and parameter variations. If the performance is not satisfactory we need to modify our system modeling, re-design the controller, re-write the algorithm and re-try. With fuzzy logic the first step is to understand and characterize the system behavior by using our knowledge and experience. The second step is to directly design the control algorithm using fuzzy rules, which describe the principles of the controller’s regulation in terms of the relationship between its inputs and outputs. The last step is to simulate and debug the design. If the performance is not satisfactory we only need to modify some fuzzy rules and re-try. Although the two design methodologies are similar, the fuzzy based methodology substantially simplifies the design loop. This results in some significant benefits, such as reduced development time and simpler design. As reported in , seven fuzzy subsets were employed to develop software written in C++ in the design of a fuzzy AVR of a controller for a synchronous generator.
Fuzzy experts like Lofti Zadeh proved that the greater the number of fuzzy subsets, the better the performance of the controller. The cumbersome nature of the C++ language was reduced by the use of the MATLAB software. In this work, a rule based fuzzy logic controller is developed for controlling the terminal voltage and reactive power of a synchronous generator. Eleven fuzzy subsets were utilized to enhance the performance of the fuzzy controller. The work is arranged in this order; first the conventional control approach is discussed, followed by the fuzzy logic control approach. A comparison is made between the results of the two control approaches. All the simulations are carried out using MATLAB software package. The results show reduction in percent overshoot, rise time, peak time, settling time and overall responses when fuzzy logic approach is applied.
1.2 Statement of the problem
Fuzzy logic control is a non-mathematical decision algorithm that is based on an operator’s experience. This type of control strategy is suited well for nonlinear systems such as the synchronous generator, which exhibits non-linearity between the field current in and the armature voltage out.
1.3 Aims and Objectives
To solve a this nagging problem with limited functionality of generating sets in Nigeria owing to poor supply of electricity in Nigeria. This research is aimed at boosting power supply.
1.5 Scope of the study
This work and research is acarried on in Enugu south lga of Enugu state.
1.6 Limitation of the study
Time constraint for this research, limited access to certain useful data for this research are factors
1.7 Definition of terms
|Rise time:||The time for a system to respond to a step input and attains a response equal to the magnitude of the input.|
|Peak time:||The time for a system to respond to a step input and rise to a peak response.|
|Overshoot:||The amount the system output response proceeds beyond the desired response.|
|Settling time:||The time required for the system output to settle within a certain percentage of the input amplitude.|
|PID controller:||A controller with three terms in which the output of the|
controller is the sum of a proportional term, an integrating term, and a differentiating term, with an adjustable gain for each term.
Steady-state error: The error when the time period is large and when the transient response has decayed, leaving the continuous response.
1.8 List of symbols and abbreviations
KR Sensor gain
KA Amplifier gain KE Exciter gain KG Generator gain τR Sensor time constant τA Amplifier time constant τE Exciter time constant τG Generator time constant
Vtss Steady-state response of the AVR Vs(s) Output voltage of the sensor
Vref Reference voltage
Vt Generator terminal voltage
VR Input voltage to the exciter
VF Field voltage
PID Proportional Integral Derivative
PI Proportional Integral
Vref Reference/specified voltage to generator
Vt Terminal voltage of the generator
V1 Voltage error (Vref – Vt ) ∫Ve Integral of the error, ∫ Ve e(t) Error
Int. e Integral of the error e
U Output of the fuzzy rules (input to the plant) g1 Scaling gain for tuning of the membership functions for e (t) g2 Scaling gain for tuning of the membership functions for “int. e” g 0 Scaling gain for tuning of the membership functions for u
d(t) Power angle of the generator in radian w(t) Rotor speed of the generator in rad/sec. D Damping coefficient of the generator H Inertia coefficient of the generator
w0 Speed of the generator at the operating point
Pm Motor mechanical power in p.u.
Pe (t) Active electrical power delivered by the generator in p.u.
E f (t) Equivalent EMF in the excitation coil in p.u.
E, f (t) Transient EMF in the generator coil in p.u.
E,d (t) Transient direct axis EMF of the generator
Eq ,(t) Transient EMF of the generator in the quadrature axis Xd Direct axis reactance of the generator
X ,d Transient direct axis reactance of the generator
X q, (t) Transient reactance in the quadrature axis in the generator in p.u.
Id Direct axis current of the generator
Iq Quadrature axis current of the generator I f Excitation field current
Vs Infinite bus voltage in p.u.
U f Output of the PI fuzzy controller
X ds Stator reactance in the direct axis
SMIB Single Machine Infinite Bus
LFC Load frequency control
AVR Automatic Voltage Regulator
MATLAB Matrix Laboratory
PSS Power system stabilization
VAR Volt Ampere Reactive FLC Fuzzy Logic Control
|FIS||Fuzzy inference System|
|FLPSS||Fuzzy Logic Power System Stabilizer|
|FPI-AVR||Fuzzy Proportional Integral-Automatic Voltage Regulator|
|CAVR||Conventional Automatic Voltage Regulator|
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