DEVELOPMENT OF A FUZZY LOGIC BASED VOLTAGE REGULATOR FOR ELECTRIC GENERATING SET

Category: Electronics Engineering Tag: DEVELOPMENT OF A FUZZY LOGIC BASED VOLTAGE REGULATOR FOR ELECTRIC GENERATING SET

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DEVELOPMENT OF A FUZZY LOGIC BASED VOLTAGE REGULATOR FOR ELECTRIC GENERATING SET

LIST OF TABLES

Table 3.1 34

LIST OF FIGURES AND DIAGRAMS

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 ∫V_e 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

CHAPTER ONE

INTRODUCTION

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 [1].

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, [2], 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, [3], 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 [4].

Ndubisi and Agu [5] 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 [6] 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

Fig 1.1: Conventional control design

Fig.1.2: Fuzzy control design

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 [7], 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 understand the methods of developing a fuzzy logic based voltage regulator for electric generating set
The understand the relationship between fuzzy logic based voltage regulator and functionality of electric generating set.

1.4 Motivation

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

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

K_R Sensor gain

K_A Amplifier gain K_E Exciter gain K_G Generator gain τ_R Sensor time constant τ_A Amplifier time constant τ_E Exciter time constant τ_G Generator time constant

V_tss Steady-state response of the AVR V_s(s) Output voltage of the sensor

V_ref Reference voltage

V_t Generator terminal voltage

V_R Input voltage to the exciter

V_F 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

P_m Motor mechanical power in p.u.

P_e(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

PT	Potential Transformer
NV	Negative Very
NL	Negative Large
NB	Negative Big
NM	Negative Medium
NS	Negative Small
Z	Zero
PS	Positive Small
PM	Positive Medium
PB	Positive Big
PL	Positive Large
PV	Positive Very
FIS	Fuzzy inference System
D/A	Digital/Analogue
FLPSS	Fuzzy Logic Power System Stabilizer
FPI-AVR	Fuzzy Proportional Integral-Automatic Voltage Regulator
CAVR	Conventional Automatic Voltage Regulator
G	Generator
CB	Circuit Breaker