Biomedical Engineering Reference
In-Depth Information
1. By varying real and reactive power loads simultaneously at all load buses of
RDS.
2. By varying both the real and reactive power loads simultaneously at a single
load bus of the RDS.
3. By varying only the real power load at a single load bus of the RDS.
4. By only varying the reactive power load at single load bus of the RDS.
The kth such load so generated is referred to by the vector X
k
. Similarly, the
corresponding output vector for kth input referred to O
k
. The output vector refers
to the bus voltages magnitude and the corresponding angle. Summarizing, several
of these sets of input and output vectors are generated using the above method and
are stored. After the successful training of the ANN-BPN it should be able to
produce the bus voltage magnitude and phase angle for any of the input load
pattern with maximum accuracy and minimum time.
In the next section, the application of the analysis and the results of the pro-
posed technique of determining load flow solution is discussed.
Results of the System Study
The proposed approach has been tested for 33-bus radial distribution system
(Fig.
5
) using MATLAB environment. By using the
Ladder Iterative Technique
as
explained in section two, the power flow equations were solved. Approximately
150 input and output vector pairs were generated for considering 33-bus system, in
order to achieve a broad representation of the power system in the ANN-BPN. The
BPN was trained and the results are as shown in Fig.
6
and the output voltages of
BPN can be seen in Fig.
7
.
And hence, the ANN-BPN is ready to use. The results from the actual load flow
solution and from the training ANN for a particular pattern is shown in Table
1
.
The method seems to be fast and found to be very efficient. It works well and
smooth.
Conclusion
The novel approach in this paper presents a well-defined technique to determine
the load flow solution of a radial distribution system, which is simple to implement
and efficient in computation. Several load pairs were considered and their solution
was assessed using the conventional method of Ladder Iterative technique. Then
using these pairs of input and target vector sets, the ANN-Back propagation
Network is trained. Thereafter, the BPN is ready for use wherein, given a load, it
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