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)
514
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
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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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
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[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
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4274, 2011.
[5] Ramirez J.M, et al, “An electronic load controller for self-excited
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[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
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5, 12-15 dec, 2006.
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[9] Garg Anjali, et al, “A fuzzy logic based ELC for self-excited
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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.
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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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.