A Fuzzy Logic based Electronic Load Controller for Three Phase ...

International Journal of Emerging Technology and Advanced Engineering

Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 5, Issue 3, March 2015)

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A Fuzzy Logic based Electronic Load Controller for Three

Phase Alternator Anurag Yadav

1, S.N. Singh

2, Appurva Appan

3

1,3Masters of Technology, Alternate Hydro Energy Centre, Indian Institute of Technology Roorkee, Roorkee, 247667,

Uttarakhand, India 2Senior Scientific Officer, Alternate Hydro Energy Centre, Indian Institute of Technology Roorkee, Roorkee, 247667,

Uttarakhand, India

Abstract— The present work deals with the use of Fuzzy

logic for the implementation of ELC (Electronic Load

Controller for three phase alternator). The proposed design of

the controller regulates both voltage and frequency of an

alternator working in isolated/stand-alone mode. The ELC

circuitry designed here is a combination of three phase diode

bridge rectifier, IGBT based chopper and a resistive

ballast/dump load for dump power consumption whenever

there is a mismatch between the generated power and

consumer demand. The fuzzy logic based controller is the

heart of ELC as it controls the amount of power dumped by

varying the duty cycle of the pulses generated based on PWM

scheme which are being fed to the IGBT (Insulated Gate

Bipolar Junction Transistor) based chopper switch. The

proposed ELC model for an alternator has been simulated in

MATLAB-SIMULINK environment and is being analysed for

both steady-state and transient performance.

Keywords—Alternator, Chopper, ELC, Fuzzy logic, PWM,

IGBT.

I. INTRODUCTION

In the present era, electricity has become inevitable for

the survival and for the reasons best known to all.

Dwindling fossil fuels have posed a formidable challenge

before the researchers to meet the growing energy

requirements. The major setbacks of the fossil fuels are

their rising prices, inability in meeting peak demands,

limited stocks, not being environment friendly etc. Fast

growing economy and expansion of the energy provisions

for the exploding population demands an increment in the

share of energy production from renewable energy sources

in the overall energy mix [1]. Simultaneously, there is a

need to export the power to the remote areas, rural

electrification etc. Micro-hydro generation system is quite

captivating alternative for remote, hilly areas where there is

facile availability of water resources. Power plant operation

in such areas demands less operation and maintenance

costs, robust construction, exemption from the requirement

of state of the art expertise etc. [2].

Alternators/Synchronous generators appear to be an apt

candidate due to its advantages viz. less maintenance,

robust construction, inherent short-circuit and overloading

protection, better frequency and voltage regulation, dual-

mode flow of power i.e. it can both import and export

power depending upon the type of excitation. In

isolated/stand-alone mode there are frequent and large

perturbations in the voltage and frequency which are

required to be controlled in order to prevent the damage to

the load as well as to the machine. Conventional hydraulic

governor system is quite big in size, sophisticated and is a

costly affair. Moreover, efficiency considerations also

make it impractical to be used in micro-hydro governing

system [3]-[4]. Aforesaid arguments in collusion demands

for an efficient, cheap and dynamic governing system for

control of voltage and frequency [5]. In the proposed

scheme, ELC is being paralleled with the consumer load

across the alternator terminals. ELC circuitry is composed

of three-phase diode bridge rectifier, IGBT based chopper

switch and a ballast load [1]. The fuzzy logic is

implemented for the PWM generation scheme for the

pulses that are fed to the chopper switch for controlling the

dump power. Pulses are of varying duty cycle in

accordance with the consumer load variations. The idea of

ELC is to maintain constant load on the alternator. Thus, in

the scheme alternator always runs at rated conditions in the

steady-state. The ELC is being analysed for three phase

balanced resistive load across the alternator terminals [6].

II. ALTERNATOR MODEL WITH ELC

Fig. 1: Per phase equivalent circuit model of alternator



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The per phase equivalent circuit of the stator of a typical

three phase alternator/synchronous generator is shown in

Fig. 1. The various parameters of the equivalent circuit are

defined as:

Ea = Excitation Voltage

Xs = Synchronous Reactance per phase

Va = Terminal Voltage

Ia = Armature Current

The ELC senses the difference between the reference

voltage and the actual/operating voltage. The difference in

the two voltages is due to the load variations across the

alternator terminals. It transfers the difference between the

rated power and the consumer demand to the dump/ballast

load for dissipation due to which the load on the machine

always remains constant and thus the speed/frequency of

the generator remains constant[7]-[8]. The scheme of ELC

is depicted in Fig. 2.

Fig. 2: Schematic diagram of ELC

III. CONTROL STRATEGY OF ELC

Electronic Load Controller (ELC) helps in maintaining

rated load on the generator irrespective of the variations in

the consumer load. The heart of its working is the PWM

generation scheme shown in Fig. 3 which generates the

pulses by comparing the output of the fuzzy logic controller

with a sawtooth carrier wave [9]. Here carrier frequency is

taken to be 1000Hz. The pulses so generated are of variable

duty cycle in accordance with the magnitude of error signal

which acts as one of the inputs to the fuzzy logic controller,

other being its continuous time derivative. The duty cycle

of the pulses changes as the load changes, thus the scheme

is also known as the pulse width modulated scheme for

switching the IGBT based chopper switch which finally

controls the dump power consumption [10].

Fig. 3: PWM based pulse generation scheme for firing the IGBT

IV. FUZZY LOGIC BASED CONTROL

Fuzzy logic is a multivalued mathematical tool that deals

with approximate rather than exact reasoning. It follows

simple IF and/or THEN based logical rules that are easily

understandable by the humans. Unlike other conventional

controllers which use mathematical variables, it employs

the use of simple linguistic variables which are easily

understandable by the human. The degree of truth or

falsehood is determined by the membership functions. It

doesn‟t require expert knowledge about mathematical

equations and functions. Fuzzy variables‟ value range from

0 to 1. With the use of fuzzy logic, quite sophisticated

systems can be controlled without any involvement of

integro-differential, difference, algebraic equations which

would have been very difficult or impossible in some cases

with the use of conventional controllers. Fuzzy logic based

system comprises of crisp values, fuzzification, fuzzy

inference system, rule base, membership functions. Firstly,

the fuzzification process takes place where real-valued

variables are transformed into fuzzy set variables and then

the fuzzified values interact with the fuzzy inference

system consisting of rule base and membership functions

which map input space to the output space. IF-THEN rules

form the heart of the fuzzy logic based system. After that

defuzzification process takes place which converts output

variable into a crisp value. This process of defuzzification

takes place by the use of various methods like centroid,

middle of maximum etc. [11]. In the present work the fuzzy

logic toolbox in MATLAB-SIMULINK is used to model a

MAMDANI type fuzzy inference system. The fuzzy logic

controller consists of two inputs as variables viz. „error‟

and „derror‟ and one output variable „control‟. Each

variable is mapped using seven membership functions.

Rule base for the controller is tabulated in Table I.



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Table I

Rule base for Fuzzy Logic Controller

Here NL means „negative large‟, NM means „negative

medium, NS means „negative small, Z means „zero‟, PS

means „positive small‟, PM means „positive medium‟, PL

means „positive large‟, error is the difference between the

reference voltage and actual/operating voltage, derror is the

continuous-time derivative of error. Fig. 4-Fig. 6 show the

membership functions for the input and output variables.

Fig. 4: Membership functions for input variable ‘error’

Fig. 5: Membership functions for input variable ‘derror’

Fig. 6: Membership functions for output variable ‘control’

V. MATLAB-SIMULINK MODEL DESCRIPTION

Fig. 7: MATLAB-SIMULINK model of ELC for alternator

The modeling of ELC shown in Fig. 7 has been done in

MATLAB-SIMULINK environment. The input power is

held constant at 60kW by the hydraulic turbine. However,

the full load on the generator is the rated load i.e. 60kW.

The circuit breakers are programmed to connect/disconnect

the main load and the ballast/dump load. The capacitor

filter at the output terminals of the rectifier is connected so

as to filter out the ripples in the rectified voltage. For

steady-state analysis, rated load of 60kW is connected

across the alternator terminals and for the transient

analysis, a sudden load of 50kW is applied to the generator

at the time instant of 0.1 second. In the development of

Simulink model, the preset model of alternator is taken

whose specifications are: 3-phase, 50Hz, 400V, 60kVA,

1500rpm, and salient-pole type rotor, star connected stator

windings. The simulation is being carried out in discrete

power gui mode with a step size of 5e-05 using ode23tb

stiff solver. The design parameters of the control circuit are

given by the equations (1) to (5).



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Where,

Vdc = Rectifier output voltage

VLL = Line-line Voltage of alternator

Rd = Dump load resistance

Pd = Dump Power

C = Filter capacitor

f = Line frequency

R.F = Ripple factor

Rs = Snubber resistance

Cs = Snubber Capacitance

Ts = Sampling time

Pn = Nominal power of the converter

Vn = Nominal line-line AC voltage

The parameters of the control circuit play a vital role for

the dumping of power effectively and efficiently, so these

are designed taking into consideration their tolerance

levels.

VI. RESULTS AND DISCUSSION

A. Transient Analysis

The waveforms of various parameters under transient

conditions are shown in Fig. 8 to Fig. 11.

Fig. 8: Normal and enlarged view of per phase terminal voltage

Fig. 9: Normal and enlarged view of stator currents

Fig. 10: Speed variation during transient analysis



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Fig. 11: Variation in power dumped during transient analysis

As the sudden load of 50kW is switched on at 0.1

second, there is a sudden drop in the voltage as shown in

Fig. 8. However, there is a sudden rise in the stator

currents, depicted in Fig.9. The abrupt rise in the current is

likely to happen due to sudden application of such a large

load which accounts to almost 84% of the rated capacity of

the alternator. The speed also drops suddenly below 0.99pu

as shown in Fig. 10. There is also a transient at 0.1 second

in the power dumped which finally settles down to a

steady-state value of 10kW as shown in Fig. 11. However,

there is a slight departure of stator currents from purely

sinusoidal nature. The various operating parameters settle

down to normal values in about a second which prove the

efficient and effective action of fuzzy logic controller.

B. Steady-state Analysis

The variation of various parameters under steady-state is

depicted by the waveforms shown in Fig. 12 to Fig. 15. The

explanation of waveforms under steady-state has been done

in this section to appreciate the performance of fuzzy logic

based controller.

Fig. 12: Normal and enlarged view of per phase terminal voltage

Fig. 13: Normal and enlarged view of stator currents

Fig. 14: Speed variation during steady-state analysis

Fig. 15 Variation in power dumped during steady-state analysis

During steady-state analysis, rated load of 60kW is

applied across the alternator terminals. It is evident from

Fig. 12 that voltage rises initially beyond 1pu and in about

0.2 seconds it settles to 1pu. Stator current also shoots up

beyond 1pu initially but settles to 1pu in about 0.1 seconds

as shown in Fig. 13 which shows that the losses above the

rated conditions are for a very short duration. Fig. 14 shows

the variation in speed during steady-state.



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As the generator is loaded up to its rated capacity, there

is an initial transient in the power dumped which reduces to

zero after sometime as shown in the Fig. 15. The speed

contains lesser number of transients as compared with the

case of transient analysis. The settling time for transients is

also less for all the operating parameters as compared with

the case of transient analysis.

VII. CONCLUSION

The fuzzy logic based electronic load controller has been

developed in MATLAB-SIMULINK and is simulated for

three phase alternator. It is evident from the waveforms that

fuzzy logic based controller gives very good performance

under transient as well as steady-state conditions. The

perturbation in the operating parameters is within

permissible limits. There are less overshoots and transients

in the operating parameters and system gets restored to its

normal operating condition in a very short duration. In a

nutshell it can be said that fuzzy logic based electronic load

controller is a strong candidate for governing the

alternators. The future scope is directed towards the

analysis of the developed controller for inductive dump

load, controller design can be improved by using neuro-

fuzzy technique. STATCOM can also be integrated with

ELC for reduction in the harmonic content of the rectified

voltage for better regulation. The design can be practically

developed and validated for the results in the laboratory.

REFERENCES

[1] Das Dibyendu, M.Tech dissertation Work On, “Steady-State analysis of Electronic Load Controller for Three Phase Alternator” , Alternate

Hydro Energy Centre, Indian Institute of Technology Roorkee, 2011.

[2] Appurva Appan, et al, “A Novel MATLAB GUI Comparitive

Technique to Evaluate Generated Frequency and Saturated

Magnetizing Reactance of a Three phase SEIG”, Vol. 5, Issue: 2, pp 233-239, Feb. 2015.

[3] Singh B., et al, “Analysis and design of ELC for SEIG”, IEEE

Transactions on energy conversion, Vol. 21, No. 21, pp 285-293, March 2006.

[4] Singh B., et al, “Neural-Network based integrated ELC for isolated

asynchronous generator in small hydropower generation”, IEEE

Transactions on Industrial Electronics, Vol. 58, Issue: 9, pp 4264-

4274, 2011.

[5] Ramirez J.M, et al, “An electronic load controller for self-excited

induction generator”, IEEE Transactions on energy conversion, Vol. 22, No. 2, pp 1-8, 2007.

[6] Yellaiah. Ponnam, et al, “Electronic load controller for SEIG using

fuzzy logic”, IOSR Journal of Electrical and Electronics Engineering, Vol. 5, Issue 3, pp 49-54, 2013.

[7] Murthy S.S., et al, “A novel digital control technique for ELC for SEIG based micro hydel power generation”, IEEE International

Conference on Power Electronics, drives and energy systems, pp 1-

5, 12-15 dec, 2006.

[8] Rajagopal V., et al, “Electronic load controller for isolated

asynchronous generator in pico hydropower generation”, Conference

paper, Department of Electrical Engineering, Indian Institute of Technology Roorkee, 2010.

[9] Garg Anjali, et al, “A fuzzy logic based ELC for self-excited

induction generator”, Journal of Alternate Energy Sources and Technologies, Vol. 2, No.2, 2011.

[10] Kathirvel C., et al, “Fuzzy logic based voltage and frequency of a SEIG for micro-hydro turbines for rural applications”, Journal of

Theoretical and Applied Information Technology, Vol.54, No. 1,

2013.

[11] Dhanalaxmi R, et al, “ANFIS based neuro-fuzzy controller in LFC

of wind-micro hydro-diesel hybrid power system, International

Journal of Computer Applications, Vol. 42-No. 6, March 2012.

BIOGRAPHIES

Anurag Yadav was born in Meerut,

Uttar Pradesh, India, in 1989. He

received his B.Tech degree in

Electrical and Electronics Engineering

from Gautam Buddh Technical

University, Lucknow, India, in 2012.

His areas of interest include Electrical

Machines, Power Electronics, Power

Systems, Distributed Generation and

Renewable Energy Development

Technology. He is currently pursuing Masters of

Technology in Alternate Hydro Energy Systems at Indian

Institute of Technology (IIT) Roorkee.

S.N. Singh was born in Gorakhpur,

Uttar Pradesh, India, in 1956. He

received his B.E. in Electrical

Engineering from MMM

Engineering College, Gorakhpur,

India, in 1978 and M.E. in

Electrical Engineering from

University of Roorkee, Roorkee,

India in 1982. He is currently

working as a Senior Scientific Officer at Alternate Hydro

Energy Centre, Indian Institute of Technology Roorkee.

His areas of interest include Electrical Machines,

Transmission and Distribution of Small Hydro Power

(SHP). He is a Life Member of Indian Institute of

Technology Roorkee, Indian Water Resource Society,

Indian Society for Technical Education and is an individual

corporate member of the Indian National Hydropower

Association.



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Appurva Appan was born in Patna,

Bihar, India, in 1991. He received

his B.Tech degree in Electrical

Engineering from North Eastern

Regional Institute of Science and

Technology, Arunachal Pradesh,

India, in 2013. His areas of interest

include Electrical Machines,

Renewable Energy, Distributed

Generation. He is currently pursuing Masters of

Technology in Alternate Hydro Energy Systems at Indian

Institute of Technology (IIT) Roorkee.

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