Computationally Efficient Electromagnetic Transient Power System Studies using Bayesian Optimization (2310.14945v3)

Abstract: The power system of the future will be governed by complex interactions and non-linear phenomena at small time-scales, that should be studied more and more through computationally expensive software simulations. To solve the abovementioned problems, power system engineers face problems with following characteristics: (i) a computationally expensive simulator, (ii) non-linear functions to optimize and (iii) lack of abundance of data. Existing optimization settings involving EMT-type simulations have been developed, but mainly use a deterministic model and optimizer, which may be computationally inefficient and do not guarantee finding a global optimum. Furthermore, the main focus has been on optimization routines, and less attention has been paid to other tasks such as classification. In this paper, an automation framework based on Bayesian Optimization is introduced, and applied to two case studies involving optimization and classification. It is found that the framework has the potential to reduce computational effort, outperform deterministic optimizers and is applicable to a multitude of problems. Nevertheless, it was found that the output of the Bayesian Optimization depends on the number of samples used for initialization, and in addition, careful selection of surrogate models, which should be subject to future investigation.