Acceleration techniques for on-line transient and dynamic stability assessment

Abstract

This thesis presents several new acceleration techniques for on-line transient and dynamic stability assessment. In recent years, power systems have grown in both size and complexity owing to extensive interconnection, de-regulation, high growth rate of electric power demand. The dynamic characteristics of the power system could vary rapidly as the system conditions change. Online transient and dynamic stability assessment has now become a critical issue for secure and reliable operation of the power system. In order to realize on-line transient and dynamic stability simulation for large-scale networks, it is necessary to use neoteric technology of hardware and software to speed up the computation. As the computation speed of serial personal computer cannot match the demand of on-line dynamic stability simulation, parallel method is an attractive low cost substitution. Furthermore, not only the parallel computer is used, but also the novel algorithm to further speed up the computation is necessary. Early studies showed that for the transient stability, solving the linear algebraic sparse network equation is the most computational demanding process. Parallel method had been adopted included W-matrix and parallel piecewise solution method. In piecewise solution method, solve the cut-node block is required in the final step to obtain the overall solution. However, conventionally, it is solved sequentially. In this thesis, the serial cut-node block solution has been identified as the bottleneck of the parallel piecewise method. An innovative add and change scheme is proposed to solve the cut-node block in parallel. Results on the UK 811 bus power system showed that the new approach significantly improves the performance of parallel piece-wise solution method. For dynamic stability assessment, Prony analysis has been shown as an effective method for the modal analysis of power system oscillations using measured or simulated data. However, for on-line applications, the computation speed of Prony analysis has to be improved. One of the bottlenecks of Prony analysis is as the need for solving the linear predication matrix using the single value decomposition (SVD) technique. In this thesis, a novel solution method called GEAE is proposed to accelerate the solution of the linear predication matrix. Test results showed that a significant speed-up can be achieved with an acceptable accuracy when compared to the standard SVD method. The above two novel algorithms have been proved to be effective and could significantly speed up the transient stability computation and dynamic stability assessment. In addition, an AI based damping classifier has been developed to automate the process of contingency screening and ranking.