ISSN: 2320-8848 (Online) ISSN: 2321-0362 (Print ...€¦ · local community by diesel generators. However, diesel generators cause significant impacts on the environment. [2]. Due

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Volume 4; Issue 02

Manuscript- 2

“LOAD FREQUENCY CONTROL OF AN ISOLATED WIND DIESEL

HYBRID POWER SYSTEM BY USING FUZZY LOGIC CONTROL”

www.ijmst.com February, 2016

International Journal for Management Science

And Technology (IJMST)

ISSN: 2320-8848 (Online)

ISSN: 2321-0362 (Print)

Prathap Thanikonda

Electrical Electronics Engineering,

Ramachandra College Of Engineering, Eluru,

India

Pavan Adhivshnu



India

Phani Prasad Challa



India

http://www.ijmst.com/

International Journal for Management Science and Technology (IJMST) Vol. 4; Issue 02

ISSN: 2320-8848(O.)/2321-0362(P.) Page 2 February, 2016

Abstract

This paper presents an analysis of multi stage fuzzy logic control application for load

frequency control of isolated wind-diesel hybrid power system. Due to the sudden load

changes and intermittent wind power, large frequency fluctuation problem can occur. An

effective controller for stabilizing frequency oscillations and maintaining the system

frequency within acceptable range is significantly required. The load frequency control (LFC)

deviates the frequency deviation and maintains dynamic performance of the system. As fuzzy

logic control approach can be easily implemented in practical systems, the fuzzy logic control

has been applied to design LFC system. In this paper, multi stage Fuzzy logic PID controller

is proposed for Load Frequency Control (LFC) of an isolated wind-diesel hybrid power

system. Simulations are performed for this hybrid system with the proposed multi stage

Fuzzy Logic PID controller conventional PI controller and Fuzzy logic controller with

different load disturbances and wind input disturbances. The performance of the proposed

approach is verified from simulations and comparisons. Simulation results explicitly show

that the performance of the proposed multi stage Fuzzy Logic PID Controller is superior to

the conventional PI controller and Fuzzy logic controller in terms of overshoot, settling time

and steady state error against various load changes and variations of wind inputs.

Key Words: Load Frequency Control, Wind Diesel hybrid system, Multi stage Fuzzy

Logic PID controller, Proportional Integral controller and Fuzzy Logic controller

1. Introduction

Global warming is one of the most serious environmental problems facing the world

community today. In most remote and isolated areas, electric power is often supplied to the

local community by diesel generators. However, diesel generators cause significant impacts

on the environment. [2]. Due to the environmental and economic impacts of a diesel

generator, interest in alternative cost efficient and pollution-free energy generation has grown

enormously. Currently, wind is the fastest growing and most widely utilized renewable

energy technology in power systems.

Sustainable energy is the provision of energy that meets the needs of the present without

compromising the ability of future generations to meet their needs. Sustainable energy

sources include all renewable energy sources, such as hydro energy, solar energy, wind

energy, wave power, geothermal energy, bio energy, and tidal power. Therefore,



implementing smart and renewable energies such as wind power, photo voltaic etc is

expected to deeply reduce heat-trapping emissions.

Moreover, wind power is expected to be economically attractive when the wind speed of the

proposed site is considerable for electrical generation and electric energy is not easily

available from the grid [1]. This situation is usually found on islands and/or in remote

localities. Wind power is intermittent due to worst case weather conditions, so wind power

generation is variable and unpredictable. Wind power is not fully controllable and their

availability depends on daily and seasonal patterns [3]. As a result, conventional energy

sources such as diesel generators are used in conjunction with renewable energy for reliable

operation.

The hybrid wind power with diesel generation has been suggested by [2] and [3] to handle the

problem above. The goal of wind-diesel hybrid power system is to obtain maximum

contribution of the wind resource in local power generation. A hybrid wind diesel system is

very reliable because the diesel acts as a cushion to take care of variation in wind speed and

would always maintain an average power equal to the set point [2]. The unsteady nature of

wind and frequent change in load demands may cause large and severe oscillation of power.

The fluctuation of output power of such renewable sources may cause a serious problem of

frequency and voltage fluctuation of the grid [2,5]. In the worst case, the system may lose

stability if the system frequency cannot be maintained in acceptable range. As a result, the

proper frequency controller is greatly expected.

An effective controller for stabilizing frequency oscillations and maintaining the system

frequency within acceptable range is significantly required [5]. In the hybrid system

considered, synchronous generator is connected on diesel-generator (DG) and induction

generators connected on wind turbine [10]. The configuration of isolated wind diesel hybrid

system is shown in fig-I. The Blade pitch controller is installed in the wind side while the

governor is equipped with the diesel side. An exact forecast of real power demand is

impossible due to random changes in the load and therefore an imbalance occurs between the

real power generation and the load demand (plus losses). This causes kinetic energy of

rotation to be either added to or taken from the generating units (generator shaft either speed

up or slow down) and the frequency of system varies as a result. Therefore, a control system

is required to detect the load changes and control the power and stabilize the shaft speed and



hence the system frequency [8, 9]. Whenever the real power demand changes, a frequency

change occurs. This frequency error is amplified, mixed and changed to a command signal

which is sent to turbine governor [7, 8]. The governor operates to restore the balance between

the input and output by changing the turbine output.

Figure l: Configuration of isolated wind diesel hybrid system

The supplementary controller of the diesel generating unit, called the load frequency

controller may satisfy the requirements [5]. The function of the controller is to generate raise

or lower command signals to the diesel engine [9]. In the proposed paper, multi stage fuzzy

logic PID controller has been applied to design LFC system. Among the various types of

load-frequency control, the PI controller is most widely applied to speed-governor systems

for LFC schemes [11]. One advantage of the PI controller is that it reduces the steady-state

error to zero. However, since the conventional PI controller with fixed gains has been

designed at nominal operating conditions, it fails to provide the best control performance over

a wide range of operating conditions and exhibits poor dynamic performance. To solve this

problem, Fuzzy Logic techniques have been proposed [6]. System operating conditions are

monitored and used as inputs to a fuzzy system whose output signal controls the inputs to

governor for increasing or decreasing the generation for maintaining the system frequency [6,

11 and 12]. The input to the fuzzy system is a newly defined control error and change in

error. The proposed multi stage Fuzzy Logic PID controller for a governor in diesel side and

a blade pitch control in wind side are designed individually for the Wind diesel hybrid

system. The first two Fuzzy logic controllers are used to tune the Kp and Ki of PI control, it

acts as a pre- compensator. Further to improve the performance, one more Fuzzy logic



controller is used to tune the parameter Kd in the next stage. System with conventional PI

controller and Fuzzy logic controller is designed and simulated individually for comparison.

Simulation results in a wind-diesel hybrid system show the superior performance of the

proposed multi stage fuzzy logic PID controller in comparison with the conventional PI

controller and Fuzzy logic controller in terms of the settling time, overshoot against various

load changes and variations of wind inputs.

2. System Model Demonstration

The input power to the wind-power generating unit is not controllable in the sense of

generation control, but a supplementary controller known as LFC can control the generation

of the diesel unit and thereby of the system [5]. The transfer function block diagram of a

hybrid wind-diesel power generation used in this study is shown in Fig-2. In the wind turbine

generating unit, the multi stage Fuzzy logic PID controller is designed as a supplementary

control for the pitch control. This controller detects the deviation of the wind power

generation (∆Pgw) as an input signal, so that the wind power generation can be maintained

constant. For the diesel generating unit, the multi stage fuzzy logic PID controller is designed

to improve the performance of governor. It uses a system frequency deviation (∆fs) of the

power system as a feedback input, so that it can offset the mismatch between generation and

load demand by adjusting the speed changer position. The continuous time dynamic behavior

of the load frequency control system is modeled by a set of state vector differential equations.

X= AX + BU + rP ----------------- (1)

Where X, u and p are the state, control and disturbance vectors, respectively. A, B and r are

real constant matrices, of the appropriate dimensions, associated with the above vectors.

In this proposed paper, the design of multi stage Fuzzy logic PID controller of blade pitch

control in wind side and governor in diesel side is carried out.



3. Fuzzy Logic Controller

Recently, the fuzzy logic based control has extensively received attentions in various power

systems applications[7,3]. FLCs are knowledge-based controllers usually derived from a

knowledge acquisition process or automatically synthesized from self-organizing control

architectures. These controllers typically define a nonlinear mapping from the system's state

space to the control space [7,11]. A fuzzy system knowledge base consists of fuzzy IF-THEN

rules and membership functions characterizing the fuzzy sets. It can be

A. Fuzzification

Fuzzification is the process of transforming real-valued variable into a fuzzy set variable. The

natural language representation of a variable is called as linguistic variable. The linguistic

variables used here is NL(Negative Large), NM(Negative medium), NS(Negative small),

Z(Zero), PS(Positive small), PM(Positive medium), PL(Positive large).

B. Knowledge Base

The heart of the fuzzy system is a knowledge base consisting of fuzzy IF-THEN rules. The

data base contains the easily implemented in practical systems [7] subsets characterized by

Figure 2: Simulink model of multi stage fuzzy logic PID controller for Load frequency

control of wind diesel hybrid system



membership function. The heuristic rules of the knowledge base are used to determine the

fuzzy controller action.

C. De-Fuzzijication

The purpose of De-fuzzification is to convert the output Fuzzy variable to a crisp value, So

that it can be used for control purpose. It is employed because crisp control action is required

in practical applications. The membership functions, knowledge base and method of de-

fuzzification essentially determine the controller performance. The schematic block diagram

of Fuzzy logic controller is shown in the figure-3.

Figure 3: Fuzzy Logic Controller

The input signal (ilPgw) is in a wind side for blade pitch control or (ilfs) in a diesel side for

governor is used as error signal for fuzzy logic controller. Here, the fuzzy logic controller

consists of seven membership functions (two trapezoidal memberships and five triangular

memberships) for two-input and one-output as shown in Fig. 4. For the case of two-input and

one-output, the control rules can be shown in Table-I, where every cell shows the output

membership function of a control rule with two input membership functions. The control

rules are built from the statement: if input 1 and input 2 then output 1. For example, consider

the third row and forth column, that means: if E is NS and M is Z, then u is NS.

Table I

Rule Base (With 7 Membership Functions)



4. Multi Stage Fuzzy Logic Pid Controller

Structure of the proposed multi-stage Fuzzy logic PID controller is shown in fig-5. It consists

of a pre-compensator with two Fuzzy logic controllers, which tunes the parameters Kp and Ki

of PI control block. And still to improve the performance of the hybrid system, another Fuzzy

logic controller is included to tune the parameter Kd of derivative control block in the next

stage. The membership functions and Rule base of the proposed multi stage Fuzzy logic PID

controller is shown in fig-6 and Table-II, III, IV respectively. The linguistic variables used

here for output variable Kp, Ki, Kd are Z, VS, MS, M, ME, VB, VL.

Figure 4: Schematic Block diagram of Multi stage Fuzzy logic PID controller

Figure 5: Input-1 (Error) Membership Functions for multi stage Fuzzy logic PID controller



Figure 6: Input-2 (Change in Error) Membership Functions for multi stage Fuzzy logic PID

controller

Figure 7: Output Membership Functions for multi stage Fuzzy logic PID controller

Simulation Parameter:

Rd= 5.0; Kd=0.3333; TdI= 1.0; Td2= 2.0; Td3= 0.025;

Td4= 3.0; Tw= 4.0; Kig= 0.9969; Kp= 72.0;Tp= 14.4;

Ktp= 0.003333; Kpc= 0.08; Kp I = 1.25; Tp I = 0.6;

Kp2= 1.0; Tp2=0.041; Kp3= 1.4;Tp3= 1.0;Kgh=0.2;

Th=l;



Table III - Rule For Kp

Table IIIII - Rule For Ki

Table IVV - Rule For Kd

Table V - Simulation Parameter

Simulation Parameters

Rd= 5.0; Kd=0.3333; TdI= 1.0; Td2= 2.0; Td3= 0.025;

Td4= 3.0; Tw= 4.0; Kig= 0.9969; Kp= 72.0;Tp= 14.4;

Ktp= 0.003333; Kpc= 0.08; Kp I = 1.25; Tp I = 0.6;

Kp2= 1.0; Tp2=0.041; Kp3= 1.4;Tp3= 1.0;Kgh=0.2;

Th=l;



Where every cell shows the output membership function of a control rule with two input

membership functions. For example, consider the third row and forth column, that means:

For the PID parameter Kp- if e is NS and i1e is Z , then Kp is ME , for the parameter Ki- if e

is NS and i1e is Z, then Ki is MS and for the parameter Kd - if e is NS and i1e is Z ,then Kd

is VS.

5. Simulation And Results

Simulations were performed using the parameters given in Table-V, with Fuzzy Logic

controller (FLC), conventional PI controller and the proposed multi stage Fuzzy logic PID

controller to the Simulink model of wind diesel hybrid power system.

Simulations are carried out for the step load changes of 1 %, 2%,3%,4%,5% (i1PL

=0.01,0.02,0.03,0.4 and 0.05 p.u.) to the hybrid system at t = 0 s. The change in frequency of

the system, change in wind power generation and change in diesel power generation for 0.01

p.u. step load change is shown in Fig-8(a), 8(b) and 8(c) respectively for the proposed multi

stage Fuzzy logic PID controller and conventional PI controller . Again the same responses

for 0.01 p.u. step load change is shown in Fig-10(a), 10(b) and 10(c) respectively for the

proposed multi stage Fuzzy logic PID controller and Fuzzy Logic controller. The settling

time to attain steady state value are observed and tabulated in Table-IV for the proposed

multi stage Fuzzy logic PID controller, Fuzzy logic controller and conventional PI controller

for Change in Frequency response and Change in wind power generation. The performance

criteria utilized for the comparison are settling time, overshoot and steady state error values.

In the second case, the responses of change in frequency and change in wind power

generation are simulated against change in wind input power (step change 0.01p.u.) and

shown in fig-9(a), 9(b). Mat lab 7.3-Simulink software is used for simulation. The overshoot

and setting time of proposed multi stage Fuzzy logic PID controller are lower than those of

conventional PI controller and Fuzzy logic controller.



Figure 8(a): Frequency deviation of the hybrid system for the step load change of 1%

(0.01pu)

Figure 8(b): Change in wind power generation for the step load change of 1000 (0.0Ipu)

Figure 8(c): Change in diesel power generation for the step load change of 1 % (O.Olpu)



Figure 9(a): Frequency deviation of the hybrid system for the step wind input disturbance of

l% (O.Olpu)

Figure 9(b): Change in wind power generation for the step wind input disturbance of 10/0

(0.01 pu)

Figure 10(a): Frequency deviation of the hybrid system for the step load change of 1%

(O.Olpu)



Figure 10(b): Change in wind power generation for the step load change of 1000 (0.0Ipu)

Figure 10(c): Change in diesel power generation for the step load change of 1 % (O.Olpu)

Table-VI

Settling time in seconds for deviations in frequency and wind power generation for various

step load disturbances.



5. Conclusion

The Intelligent controller designed using fuzzy logic for LFC to the wind diesel hybrid

system provides a satisfactory performance compared to the conventional controller. In this

paper, a multi stage Fuzzy Logic PID controller has been investigated for automatic load

frequency control of an isolated wind-diesel hybrid power system and compared with

conventional controller. Performance comparison of the proposed paper indicates that the

system response of the Load Frequency Control with multi stage Fuzzy Logic PID controller

has a quite shorter settling time and less overshoots than conventional PI controller and fuzzy

logic controller. It has been shown that the proposed controller is effective and provides

significant improvement in system performance by automatically tuning the parameters Kp,

Ki and Kd of PID controller using multi stage Fuzzy logic technique. The proposed controller

maintains the system reliable for sudden load changes and proves its superiority.



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Documents

ISSN: 2320-8848 (Online) ISSN: 2321-0362 (Print ...€¦ · local community by diesel generators. However, diesel generators cause significant impacts on the environment. [2]. Due