Power System Voltage Security Studies

Power industry is one of the pillar industries in national economy, which exerts great influence on social production and people's livelihood. Since power system is a large-scale nonlinear dynamic system, its stability has always been an important academic issue of power systems researchers. As an essential aspect of power systems stability, voltage stability has attracted much attention during the recent decades.This thesis conducts some studies on static voltage security analysis which is based on power flow equations. In order to match up the needs of power industry development, the Risk Theory was applied to power systems analysis and some risk assessment methods has been established. The advantages of these methods based on Risk Theory, are that they could consider both the possibility and severity of a failure quantitatively and set up some Risk Index Systems to reflect the systems risk level quantitatively, which may be helpful for operators to accurately grasp the system security situation.After thorough study on the Possibility Assessment Method (PAM) and the Certainty Evaluation Approach (CEA), this thesis has discussed the Risk Theory's application to voltage security assessment, established the possibility model and result model of failures, provided some voltage risk indices and conducted graded warning for systems voltage security based on the value of the indices. In addition, some control measurements will be taken if necessary in order to guarantee the voltage stability.Because of the accumulation characteristic of risk indices and the time-consuming characteristic of risk calculation, this thesis brings the application of parallel-computing to risk analysis into discussion. Parallel-computing could be helpful to decrease the time cost, increase the accuracy of security analysis, which will improve the performance of Risk Assessment. This thesis employs the parallel-computing algorithm based on PC clusters and message function mechanism, which successfully accelerated the calculation of power systems risk assessment, reduced the equipment investment and improved the maintenance efficiency.