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Power System security assessment and the associated planning s tudies are becoming more and more complex with ever increasing uncertainties in all time horizons. An effective means of performing operational and investment planning studies of ne twork limitations associated with static or dynamic post-disturbance performance pr oblems has been to take a Monte Carlo simulation based approach. The approach harnesses computi ng ower to develop a database of post-contingency response over a wide range of diff rent operating conditions, and then apply statistical or machine learning methods to extract useful planning and operational information from the database. Key to the machine learning based planning approach is the manner in w hich the different operating conditions are sampled to generate a trai ning database. This work develops an efficient sampling procedure that maximizes information content in the training database while minimizing computing requirements to generate it, b y finding the most influential region in the sampling state space and sampling opera ting conditions from it according to their relative likelihood. The Monte-Carlo variance-re duction methods are used to construct the proposed sampling approach, which is envisioned to allow ma rket-oriented industries to operate the system according to economic rule. The dissertation also develops a comprehensive methodology to perform decis ion tree based security assessment for multiple contingencies. The syst m ecurity limits and associated operating rules depend on the set of contingencies cons idered for planning. Considering the probabilistic nature of the power system, this work deve lops a risk based contingency ranking method that helps in screening the most critic al contingencies from a contingency list. The developed contingency risk estimation method gi ves realistic risk

Efficient processing of system scenarios in statistical and machine learning studies for power system operational and investment planning