A machine-learning approach to estimating the performance and stability of the electric frequency containment reserves

Abstract: For a number of years, the frequency quality has been decreasing in the Nordic synchronous area. The Revision of the Nordic Frequency Containment Process project has introduced a proposed set of pre-qualification requirements to ensure the stability and performance of frequency containment reserves. The purpose of this thesis has been to examine the potential of complementing the evaluation of the requirements through the use of machine learning methods applied to signals sampled during normal operation of a power plant providing frequency containment. Several simulation models have been developed to generate such signals with the results fed into five machine learning algorithms for classification: decision tree, adaboost of decision tree, random forest, support vector machine, and a deep neural network. The results show that on all of the simulation models it is possible to extract information regarding the stability and performance while with high accuracy preserving the distribution of physical parameters of the approved samples. The conclusion is that machine learning methods can be used to extract information from operation signals and that further research is recommended to determine how this could be put to practice and what precision are needed.