Soft Computing Techniques for Optimal Capacitor Placement - Complex System Modelling and Control Through Intelligent Soft Computations - page 612

results, it can be concluded that with NM-PSO, better solutions are obtained for the
OCP resulting into improved system performance as well as the economic bene ts.

Also, it depicts capability of the NM-PSO algorithm for handling more complex,
nonlinear problems such as CPP, with superior performance.

7 Conclusion

With advancement in the computation power of the computing machines, several
new algorithms have evolved for ef cient and quick calculations. Soft-computing

techniques are algorithms, which allow the user to solve the real-life non-linear
problems. These algorithms, inspired from natural experiences, have been found to
perform well in comparison to other analytical methods, for solving the problems.
Capacitor placement is one such problem which has been a topic of research for a
very long time. Being a mixed-integer type problem, i.e., involving both continuous
and discrete variables the previously developed analytical and numerical algorithms
were not able to provide an optimum solution to the problem. But, the soft com-
puting techniques such as GA, PSO, NM-PSO, etc. are able to provide an optimum
solution to the OCP problem.
Here, a study performed on IEEE-69 bus radial distribution system using the
Static Cost Based Planning Method, shows that PSO provides better solution than
GA for OCP. For the same operating conditions, with solutions using PSO, results
obtained using P-2 are found better than that obtained using P-1. Thus, better
formulation of objective function leads to superior results. Also, with P-2, both,
PSO and NM-PSO algorithms give the same size of the capacitors, in spite of the
fact that the NM-PSO provides improved results. This is due to better local
searching capability of NM-PSO, outreaching to the global optimum solution to the
problem. Thus, for better results better modeling and choice of algorithms is
important. To achieve this, it is important to include factors such as, space avail-
ability, existing infrastructure, and dynamic network models, etc.

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