The purpose of this research is to study reactive power control equipment in power systems to improve steady-state stability and security performance. Shunt Capacitor Bank (SCB) is chosen as the typical reactive power control equipment. The objective is to find analytical algorithms that can be used to determine the optimum sizes and locations for SCBs that enhance power system stability and security performance To reduce the amount of computation, this research proposes several sensitivity analysis algorithms based on the defined local sensitivity indices to identify the Most Sensitive Bus Group for installing the SCBs. Two global performance measures are defined: the system steady state stability margin and generation MVAR reserve. They are used in the proposed algorithm to determine the optimum locations and sizes of the SCBs that can maximize the global performance measures. A second algorithm is also proposed for the SCB optimum size and location by considering future load increases Utilization of different Artificial Neural Networks (ANN) methods to learn the proposed algorithms is explored. The ANNs are trained to learn the expert evaluation criteria for the acceptance levels of the designated SCB configurations which specify the number of SCBs installed, the WAR size of the total SCBs, the MVAR size for each SCB at each location, the steps of each SCB and the incremented size of each SCB The proposed algorithms and ANNs are successfully tested in the New England 39-Bus power system and are documented in the dissertation