Hybrid and parallel-computing methods for optimization of power systems with electromagnetic transient simulators
This thesis introduces new methods for using electromagnetic transient (EMT) simulators to efficiently optimize controllers of the power electronic converters in power systems with complicated dynamic behavior. This work is motivated by several challenges that must be overcome during the design process, including high computational burden of simulating large switching systems, repetitive nature of the design cycle, the large number of variables that need to be handled, etc. These challenges are addressed in this research by combining an EMT simulator with optimization algorithms and by developing novel approaches to reduce the entire simulation time. Two screening methods are introduced in this thesis that can identify non-influential parameters so that the number of parameters to be optimized can be reduced, thus decreasing the computational burden of the process. Moreover, multi-algorithm and parallel processing techniques are developed to achieve additional computational benefits by making the design process faster. In this research, new pathways are created to solve simulation-based design problems with a large number of parameters by amalgamating all the above approaches. Several power system examples are simulated using PSCAD/EMTDC, and the accuracy and efficiency of the proposed methods are assessed and confirmed. The results show significant reductions in the time to design optimal systems without compromising the quality of the optimal performance.
Electromagnetic transient simulation, optimization algorithms, parallel processing, large-scale power systems