Machine Learning based Automatic Voltage Regulation - PhD Assistance

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Research paper
NOV 4, 2019
DISSERTATION ALGORITHM DEVELOPMENT TIPS FOR
DEVELOPING MACHINE LEARNING BASED AUTOMATIC
VOLTAGE REGULATION (AVR)
Dissertation Algorithm Development Ideas from
Existing Recent Research Gaps in Electrical
Engineering Domain Trending Research
Methodologies In Power Safety, Engineering
Technology Tags PhD Assistance Electrical
Research Machine Learning Training Error
Electrical Dissertation Topics Topics In
Electrical Engineering Solarphotovoltaic Power
System Power Safety Quality Generator
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SHORT NOTES
Machine learning algorithms are suggested for
controlling the voltage.
In order to identify the future research topics,
we have reviewed the Engineering literature
(recent peer-reviewed studies) on the automatic
voltage regulation.
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You will find the best dissertation research
areas / topics for future researchers enrolled in
Engineering.
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Automatic Voltage regulation is used to maintain
the voltage automatically in a generator.
Parameters required for the decision and
different modes are discussed.
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Necessity of automatic voltage regulation is
discussed in this work.
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BACKGROUND
Voltage regulation is an important component of
the power system.
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Output voltage of a generator changes, the
excitation current must be adjusted to
compensate the power.
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It is necessary to maintain the voltage of the
line or an appliance for optimum performance and
minimum loss.
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Automatic Voltage Regulator is used and the
voltage is altered by changing the number of
flux lines in the coil of the generator which
changes the current.
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Automatic Voltage Regulator is a component in an
excitation system that monitors the output
voltage of a generator or alternator to decide
the level of current.
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GENERATOR
01 02 During the operation, the magnetic
components rotate around the stator creating
achange in the magnetic flux lines and hence a
current is induced.
Magnetic component is placed on the rotor, while
the conductor is placed on the statorcomponent.

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• Voltage in the coil depends on three main When
  the flux density increases, the current
• things such as rotor speed, flux density and
  voltage also increases .Similarly,the
  andconductor length. speed and conductor length
  are also directly
• proportional to the parameter.

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NO LOAD CONDITIONS
During no load, there is no current in the
stator. This means that there is voltage inthe
terminals, but no current since the terminals
are open.
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Field current is increased, the voltage in the
stator is also increased.
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This relationship takes place linearly up to a
certain points after which there issaturation.
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Stator voltage depends on the speed, excitation
current. The saturation modifies themagnetic
circuit reluctance which affects the resistance
in the circuit.
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When the field current increases which increases
the flux density and then getssaturated.
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This means that when the flux density reaches a
particular value, the linearitychanges.
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Voltage regulation is possible to a certain
extent for no load conditions.
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AUTOMATIC VOLTAGE REGULATOR
Controller must be able to take in these values
and decide how much excitation currentmust be
varied to maintain the voltage in the generator.
Continuous changing require automatic control of
the voltage and current using a conceptof
Automatic voltage regulation.
Unsupervised machine learning techniques can be
used for the control .
Keep the voltage constant, the field current
must be constantly varied to change the number
of flux lines cutting the air gap.
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Field current that is required at load depends
on the field current used to generatethe rated
voltage t no load conditions and an additional
voltage to cancel thearmature reaction.
Controller is required to continuously monitor
the parameters and control it.
Algorithm will be able to monitor all the
parameters in real time and give the outputthat
is most suitable for maintaining the voltage.
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FUTURE SCOPE
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Smaller sized equipment that has lesser heat
loss must be designed for improvedefficiency.
Machine learning algorithm that is most suitable
must be identified. Since, very fewfactors are
involved, light weight algorithms can be used
instead of highcomputation neural networks.
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pursuing the UK dissertation in computer science.
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