DISTRIBUTED SYSTEM OF POWER QUALITY IMPROVEMENT

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DISTRIBUTED SYSTEM OF POWER QUALITY IMPROVEMENT
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• In supply system are
• consumers who negatively influence the power
  quality
• controlled source of reactive power (static
  compensators, idle synchronous motors and
  generators with excitation current control),
• active power filters and distributed power
  sources (PV systems, wing generators etc.).
• There are also other systems dedicated for
  specific use
• voltage stabilizers and high harmonics parallel
  active filters,
• AC adjustable speed drives (ASDs) with VSI
  inverter
• etc.

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• Distinguishing features of Genetic Algorithms
  are
• AG do not transform directly parameters of a task
  but their coded forms,
• AG make a ransack, starting from a population of
  points, not from a single one,
• AG use only the goal function. It is no need to
  have derivatives or any other auxiliary
  information,
• AG employ probabilistic not deterministic rules
  of selection,
• The goal function can be freely formed
  including multicriterion optimization.

Block Diagram of Basic Genetic Algorithm
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Example 1
The spectrum of the voltage load RL2 In
practical industrial situations, the role of the
controlled current source (CCS) can be played by
an ASD with indirect frequency converter, which,
if not fully mechanically loaded or, if oversized
in design phase, can also play the role of
parallel active filter.
The example system with nonlinear load and
controlled current source (CCS) whose purpose is
the reduction of voltage distortion level at PCC
where another load, "sensitive" to high harmonics
(RL2), is connected.
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Spectrum of the RL2 load voltage after
optimisation of subsequent harmonics in CSS. (a)
3rd harmonic, (b) 3rd and 5th harmonic, (b) 3rd,
5th and 7th harmonic, (c) 3rd, 5th, 7th and 9th
harmonic, (d) 3rd, 5th, 7th, 9th, and 11th
harmonic.
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Amplitudes of the load (RL2) voltage harmonics
after optimisation of subsequent harmonics in CCS
(dark blue no filter, blue 3rd, light blue
3rd 5th, yellow - 3rd 5th 7th, red - 3rd 5th
7th 9th, brown 3rd 5th 7th9th 11th).
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Example 2
The example power system with nonlinear load and
three CCSs

• Active current sources are
• connected at different electrical distances from
  the considered "critical" load
• the transmission costs of generated electric
  power are different (depending on their distance
  from the protected load)
• their capability to participate in the
  compensation process may change with time (due to
  e.g. varying mechanical load of an ASD whose
  input rectifier is used as an active filter)
• their unit costs of generating electric power
  needed for compensation, etc. are different.
• All these factors should be taken into
  consideration as the constraints of the solved
  optimisation task (e.g. by means of introducing
  weighting coefficient), whose final effect should
  be determining the reference currents for all
  compensating devices participating in the
  compensation process.

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The spectrum of the load RL2 after harmonic
compensation
The spectrum of the load RL2 before compensation
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EXAMPLE MODEL OF POWER SYSTEM
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CONCLUSIONS In this case Genetic Algorithms were
employed in order to solve the formulated
multicriterial optimisation task. In general case
the objective can be e.g. minimization of energy
losses in the system and/or minimization of
harmonics generation cost (by different current
sources). These tasks can be approached
independently or, on their basis, can be
formulated the task of global optimization where
they will be used as constraints in solving the
optimization task, e.g. the distortion
minimization.
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