Implementations of Radio frequency wave propagation models are necessary to determine propagation characteristics through a medium. Its study provides an estimation of signal characteristics and the effect of environment and the medium over which it travels. This paper performs some analysis on few empirical Propagation models the mechanisms, their path loss behavior suitable for path loss prediction techniques in broadcast communication. Experimental measurements of received signal strength indication for the 92.9 MHz Radio broadcasting Station were made in urban areas of Federal Capital Territory Abuja, Nigeria. Measured data were compared with those obtained by three prediction models: COST-231, ECC-33 and OKUMURA-HATA models, the results show that in general the ECC-33 Model over-predicted the path loss in all environments with Root Means Square error (RMSE) of 166.46, while the COST-231 model has 18.33 having the best results. Okumura-Hata predicted well in the near field with 16.50 and deviated from measured data at the far field. The prediction analysis also accessed the Received Signal Strength Indication of Kapital FM in twenty (20) locations in Abuja; hence, it identifies Route A to be less susceptible to signal attenuation as compared to Route B.

The Point of Common Coupling (PCC) where suppliers’ responsibility and customers demand meet is of great concern due to increase degree of voltage variation assessment; valuable indicator of system conditions (voltage profile). Unstable condition of the power system outside operational or statutory limit, an adverse effect of nonlinear loads usually generate harmonics as well as fundamental frequency voltage variations and increase rate of power losses. These loads need to be compensated for. The major concerns of utility operations is to mitigate adverse effect of this system conditions. This research work focuses on optimal siting and sizing of Distributed Generation (DG) in a 43 bus distribution system. Power losses coupled with voltage deviation, considering objective function that compute present percentage losses in 11kV Dikko feeder, Abuja Electricity Distribution Company (AEDC), Suleja Distribution Network, Nigeria. We identified buses with poor voltage profile without DG installation and determined optimal sizing and siting of DGs where losses can be mitigated and power quality improved. ETAP version 12.6 2014 was used for load flow analysis to establish a decisive based case. The total load of the system considered was (3490 + j2700) kVA. Active and Reactive power losses in the system before DG installation were 246.300 kW and 289.903 kVAR respectively. DGs installation in the case study, has a considerable effects on loss reduction in the network. It is observed that 8.10% and 7.20% active and reactive power loss reduction was achieved while bus voltage improved by 0.4%. Genetic Algorithm Optimization techniques programmed in MATLAB 2015 software was used for optimal placement and sizing of the DG in the system.

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The Point of Common Coupling (PCC) where suppliers’ responsibility and customers demand meet is of great concern due to increase degree of voltage variation assessment; valuable indicator of system conditions (voltage profile). Unstable condition of the power system outside operational or statutory limit, an adverse effect of nonlinear loads usually generate harmonics as well as fundamental frequency voltage variations and increase rate of power losses. These loads need to be compensated for. The major concerns of utility operations is to mitigate adverse effect of this system conditions. This research work focuses on optimal siting and sizing of Distributed Generation (DG) in a 43 bus distribution system. Power losses coupled with voltage deviation, considering objective function that compute present percentage losses in 11kV Dikko feeder, Abuja Electricity Distribution Company (AEDC), Suleja Distribution Network, Nigeria. We identified buses with poor voltage profile without DG installation and determined optimal sizing and siting of DGs where losses can be mitigated and power quality improved. ETAP version 12.6 2014 was used for load flow analysis to establish a decisive based case. The total load of the system considered was (3490 + j2700) kVA. Active and Reactive power losses in the system before DG installation were 246.300 kW and 289.903 kVAR respectively. DGs installation in the case study, has a considerable effects on loss reduction in the network. It is observed that 8.10% and 7.20% active and reactive power loss reduction was achieved while bus voltage improved by 0.4%. Genetic Algorithm Optimization techniques programmed in MATLAB 2015 software was used for optimal placement and sizing of the DG in the system.