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Applying Artificial Neural Networks
to Energy Quality Measurement
Fernando Soares dos Reis
Fernando César Comparsi de Castro
Maria Cristina Felippetto de Castro
Luciano Chedid Lorenzoni
Uiraçaba Abaetê Solano Sarmanho
Pontifical Catholic University of the Rio Grande do Sul
Brazil
Table of Contents
 INTRODUCTION
 OBJECTIVES
 TERMS AND DEFINITIONS
 GENERATION OF THE INPUT VECTOR
 PARAMETERS OF THE NEURAL NETWORK
 SIMULATION ANALYSIS
 CONCLUSIONS
INTRODUCTION
Market-optimized solution for electric
power distribution involves energy
quality control.
In recent years the consumer market
has
demanded
higher
quality
standards,
aiming
efficiency
improvement in the domestic as well
in the industrial environment.
INTRODUCTION
Electric power quality can be assessed by
a set of parameters :
 Total Harmonic Distortion (THD);
 Displacement Factor;
 Power Factor;
These parameters are
obtained by ...
INTRODUCTION
Measuring the
voltage and
current in the
electric mains.
 Most measurement systems
employs some filtering in order to
improve the measured parameters.
 It is crucial for the measurement
performance that the filter does not
introduce any phase lag in the
measured voltage or current.
OBJECTIVES
In this work, a linear Artificial Neural
Network (ANN) trained by the Generalized
Hebbian Algorithm (GHA) is used as an
eigenfilter, so that a measured noisy
sinusoidal signal is cleaned, improving
the measurement precision.
TERMS AND
DEFINITIONS
An artificial neural network is a
mathematical model that emulate some
of the observed properties of biological
neural systems.
TERMS AND
DEFINITIONS
The key element of the ANN paradigm is
the structure of the information
processing system. It is composed of a
large number of highly interconnected
processing elements that are analogous
to neurons and are tied together with
weighted connections that are
analogous to synapses.
TERMS AND
DEFINITIONS
A linear Artificial Neural Network (ANN)
trained by the Generalized Hebbian
Algorithm (GHA) is used as an eigenfilter,
so that a measured noisy sinusoidal
signal is cleaned, improving the
measurement precision.
TERMS AND
DEFINITIONS
A linear ANN which uses the GHA as
learning rule performs the Subspace
Decomposition of the training vector
set ;
Each subspace into which the training
set is decomposed, contains highly
correlated information;
Therefore, since the auto-correlation of
the noise component is nearly zero,
upon reconstructing the original vector
set from its subspaces, the noise
component is implicitly filtered out.
TERMS AND
DEFINITIONS
The adopted learning rule is the
postulate of Hebb´s learning.
If neurons on both sides of a
synapse are activated synchronous
and repeatedly, the strenght of the
synapse is increased selectively.
This simplifies in a significant way
the complexity of the learning
system.
167
GENERATION OF THE INPUT VECTOR
The input vector set comprises ten vectors (ten
positive semicycles with different harmonic noises),
each vector of size R167. This is due to the fact that
the sinusoidal signals were sampled with 167
samples.
PARAMETERS OF THE
ARTIFICIAL NEURAL
NETWORK (ANN)
 The net was parameterized
considering only three sub-spaces of
the initially presented one hundred
sixty seven.
The core of the problem was that the
eigenvalues were adjusted in the
direction of the eigenvectors in order
to be considered just the fundamental
components of the sinusoidal waves,
disregarding the other noise signals.
PARAMETERS OF THE ARTIFICIAL
NEURAL NETWORK (ANN)
These are the parameters of the net:
 Sub-spaces: The number of
considered sub-spaces was three,
because in this application the goal
was to extract the fundamental
sinusoidal wave.
PARAMETERS OF THE ARTIFICIAL
NEURAL NETWORK (ANN)
Initial Learning Rate: The learning
rate (the speed in which the neural
network learns) used was of 1x 10-20,
which is considered to be a slow rate,
due to the dimension of the input
vector.
PARAMETERS OF THE ARTIFICIAL
NEURAL NETWORK (ANN)
 All synapses are randomly initialized
with values in the interval [–7,5; 7,5].
SIMULATION ANALYSIS
 Next we show some of the obtained
results. In the graphs are indicated
the Input Signal (E), the Output Signal
(S) and the Difference Signal (D). The
Difference Signal consists of the
Harmonic Noise (D = E-S). For the
best visualization the Input Signal (E)
curves were offset, so, there is no DC
gain involved in the process.
SIMULATION ANALYSIS
192.017
200
150
100
Ei
Si
Di
50
0
 22.608 50
0
0
20
40
60
80
100
i
120
140
160
180
166
SIMULATION ANALYSIS
181.624
200
150
100
Ei
Si
Di
50
0
 20.795 50
0
0
20
40
60
80
100
i
120
140
160
180
166
SIMULATION ANALYSIS
185.558
200
150
100
Ei
Si
Di
50
0
 20.582 50
0
0
20
40
60
80
100
i
120
140
160
180
166
CONCLUSIONS
The results obtained in this work
demonstrate the potential ability of
linear Artificial Neural Networks
trained by the Hebbian Learning
Algorithm in the filtering of the
harmonic noise in the power bus.
Although in some cases the filtering
was not perfectly effective, the output
waveform presented lesser harmonic
content than the originally one
presented to the Neural Network.
CONCLUSIONS
In all cases, no phase lag was
observed, which is a quite desired
feature. The obtained results suggest
that further Neural Network
architectures should be assessed.
OBRIGADO!
Gracias!
Thank You!