**7. Conclusion**

76 An Update on Power Quality

**Table 7.** The voltage profile of Case 2

**Table 8.** The voltage profile of Case 3

**Table 9.** Summary results of the approach

Voltages in harmonic order

Voltages in harmonic order

 1 5 7 11 13 17 19 23 25 Vrms HDF Bus x1 x10-2 x10-2 x10-3 x10-3 x10-4 x10-4 x10-4 x10-4 x1 % 1 0.998 1.05 5.08 1.64 1.03 6.41 5.45 3.95 2.98 0.998 1.20 2 1.000 1.06 5.11 1.65 1.04 6.46 5.50 3.98 3.00 1.000 1.19 3 0.991 0.99 4.54 1.33 0.80 4.42 3.53 2.38 1.69 0.991 1.11 4 0.983 0.95 4.20 1.14 0.66 3.25 2.36 1.47 0.91 0.983 1.07 5 0.963 0.81 3.08 0.75 0.52 2.35 1.36 1.05 0.28 0.963 0.90 6 0.955 0.79 2.96 0.74 0.50 2.18 1.26 1.20 0.49 0.955 0.89 7 0.944 0.76 2.81 0.68 0.45 1.95 1.14 1.04 0.44 0.940 0.86 8 0.917 0.69 2.48 0.57 0.35 1.49 0.90 0.73 0.34 0.917 0.80 9 0.902 0.63 2.18 0.45 0.25 1.05 0.69 0.40 0.25 0.902 0.74

Maximum voltage (p.u.) 0.999 0.999 1.000 Minimum voltage (p.u.) 0.837 0.901 0.902 Total power loss (p.u.) 0.007812 0.007065 0.007036 Qc(4) (p.u.) 0.024 0.036 Qc(5) (p.u.) 0.024 0.018 Qc(9) (p.u.) 0.009 0.009 Cost (\$ / year) 131238 128494 129334

Average CPU Time (sec.) 0.8 1.20 3.39 Maximum HDF (%) 6.15 1.40 1.20

Case 1 Case 2 Case 3

 1 5 7 11 13 17 19 23 25 Vrms HDF Bus x1 x10-2 x10-2 x10-3 x10-3 x10-4 x10-4 x10-4 x10-4 x1 % 1 0.997 1.190 5.86 1.93 1.22 7.45 6.27 4.49 3.33 0.999 1.40 2 0.999 1.190 5.90 1.94 1.23 7.51 6.32 4.53 3.36 0.988 1.40 3 0.988 1.130 5.34 1.62 0.99 5.51 4.37 2.94 2.05 0.980 1.32 4 0.980 1.100 5.02 1.44 0.85 4.36 3.23 2.05 1.29 0.980 1.26 5 0.962 0.887 3.42 0.81 0.52 2.29 1.33 0.96 0.29 0.962 1.02 6 0.954 0.861 3.28 0.79 0.51 2.12 1.24 1.12 0.49 0.954 0.99 7 0.939 0.827 3.10 0.73 0.46 1.90 1.12 0.97 0.44 0.939 0.95 8 0.915 0.751 2.72 0.60 0.36 1.45 0.89 0.68 0.34 0.915 0.89 9 0.900 0.682 2.37 0.47 0.25 1.04 0.69 0.39 0.25 0.901 0.82

This chapter presents a Particle Swarm Optimisation (PSO) approach to searching for optimal shunt SVC location and size with harmonic consideration. The cost or fitness function is constrained by voltage and Harmonic Distortion Factor (HDF). Since PSO is a stochastic approach, performances should be evaluated using statistical value. The performance will be affected by initial condition but PSO can give the optimal solution by increasing the population size. PSO offers robustness by searching for the best solution from a population point of view and avoiding derivatives and using payoff information (objective function). The result shows that PSO method is suitable for discrete value optimization problem such as SVC allocation and the consideration of harmonic distortion limit may be included with an integrated approach in the PSO.
