To date, the potential of on-line Dynamic Security Assessment (DSA)-to monitor, alert, and enhance system security-is constrained by the longer computational cycle time. Traditional techniques requiring extensive numerical computations makes it challenging to complete the assessment within an acceptable time. Longer computational cycles produce obsolete security assessment results as the system operating point evolves continuously. This thesis presents a DSA algorithm, based on Transient Energy Function (TEF) method and machine learning, to enable frequent computational cycles in on-line DSA of power systems.The use of selected terms of the TEF as pre-processed input features for machine learning demonstrated the ability to successfully train a contingency-independent classifier that is capable of classifying stable and unstable operating points. The network is trained for current system topology and loading conditions. The classifier can be trained using a small dataset when the TEF terms are used as input features. The prediction accuracy of the proposed scheme was tested under the balanced and unbalanced faults with the presence of voltage sensitive and dynamic loads for different operating points. The test results demonstrate the potential of using the proposed technique for power system on-line DSA. Power system devices such as HVDC and FACTS can be included in the algorithm by incorporating the effective terms of a corresponding TEF.An on-line DSA system requires the integration of several functional components.Some of these components already exist and the others are newly introduced to the i power system. The practicality of the proposed technique in terms of a) critical data communications aspects b) computational hardware requirements; and c) capabilities and limitations of the tools in use was tested using an implementation of an on-line DSA system. The test power system model was simulated using a real-time digital simulator. The other functional units were distributed over the Local Area Network (LAN). The implementation indicated that an acceptable computational cycle time can be achieved using the proposed method.In addition, the work carried out during this thesis has produced two tools that can be used for a) web-based automated data generation for power system studies; and b) testing of on-line DSA algorithms.ii

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With the Nelson River Bipole 1 mercury arc valve group replacement project and planning for the expansion of the Nelson River HVDC system with a third bipole underway, it was decided to pursue a program to develop and validate detailed models of the existing HVDC transmission facilities and their associated ac systems for use in system studies. The first phase of the program concentrated on the development of detailed controls models associated with the Bipole 1 transmission facility. Based on previous experience at Manitoba Hydro with the Electromagnetic Transient DC simulation program (EMTDC), it was decided that model development and validation would use this program.This paper presents the reasons behind the development of detailed models, the methods used in developing models related to Bipole 1, results of validation tests, difficulties encountered during the process, and the overall benefits resulting from the project. An example of applying the models to investigate a low frequency oscillation which has occurred on the dc system in the past is also presented.

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