Optimal allocation and sizing of capacitor bank and distributed generation using particle swarm optimization
Power systems are complicated to be solved due to vast geographical location and are influenced by many unexpected weather events. The rapidly increasing population growth and the expansion of urban development are undoubtedly the main reasons for increasing electrical power demands that may affect...
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| Format: | Thesis |
| Language: | English English |
| Published: |
2021
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| Subjects: | |
| Online Access: | http://eprints.utem.edu.my/id/eprint/26009/1/Optimal%20allocation%20and%20sizing%20of%20capacitor%20bank%20and%20distributed%20generation%20using%20particle%20swarm%20optimization.pdf http://eprints.utem.edu.my/id/eprint/26009/2/Optimal%20allocation%20and%20sizing%20of%20capacitor%20bank%20and%20distributed%20generation%20using%20particle%20swarm%20optimization.pdf |
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| Summary: | Power systems are complicated to be solved due to vast geographical location and are influenced by many unexpected weather events. The rapidly increasing population growth and the expansion of urban development are undoubtedly the main reasons for increasing electrical power demands that may affect the system voltage stability and the energy loss. Accurate long-term load forecasting (LTLF) is essential for load demand requirements. It is particularly significant under the influence of various weather factors, such as relative humidity and temperature. The research work presented in this thesis had investigated the effect of two additional weather parameters, namely wind speed and rainfall, in addition to the temperature and relative humidity using artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) in predicting the values of load demands. Moreover, the optimal allocation and sizing of the capacitor bank (C) and distributed generation (DG) were studied with the particle swarm optimization (PSO) technique to maintain the profile of bus voltages while reducing the energy loss of the network. This technique was also applied to the load incremental of 5% annually up to 40% for system planning purposes. As for the LTLF, ANFIS produced better results than ANN; and with two additional parameters of wind speed and rainfall, it delivered a more accurate prediction. The PSO algorithm allocates and determines the size of the capacitor and distributed generation in the power system. The capacitors and distributed generation are compensators that helped the power system network improve the voltage profile and reduce power loss. The proposed PSO algorithm was used with the OpenDSS engine to solve the power flow through the MATLAB and has been successful implemented in finding an optimal allocation and suitable size of the capacitor and distributed generation. In order to validate the functionality of the proposed PSO algorithm, the IEEE 14–bus and 30–bus systems were used as test systems. The research evidently indicated the PSO algorithm can be applied to the power system planning analysis for the placement and sizing of the capacitor and distributed generation while maintaining the acceptable voltage profile and minimizing the power loss. |
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