The estimation, operation and control of electrical power systems have always contained a degree of uncertainty. It is expected that, with the... Show moreThe estimation, operation and control of electrical power systems have always contained a degree of uncertainty. It is expected that, with the introduction of technologies such as distributed generation and demand-side management, the ability of system operators to forecast the dynamic behavior of the system will deteriorate and as a result, the cost of keeping the system together will increase. Sequential Monte Carlo or Particle Filtering is a family of algorithms to efficiently perform inference in non-linear dynamic systems by exploiting their structure without assuming any linearity or normality structure. In this thesis we provide two novel ways of employing these algorithms for inference and control of power systems. First, we motivate the use Bayesian statistics in load modelling by introducing a novel statistical model to capture the aggregated response of a set of loads. We then use the model to characterize load with measurement data and prior information using the Sequential Monte Carlo algorithm. Second, we introduce the Model Predictive Control for power system stabilization. We present the use of the Sequential Monte Carlo algorithm as a way of solving the stochastic Model Predictive Control problem and we compare its performance to existing regulators. In addition, Model Predictive Control is applied to load shedding Finally, we test the performance of the algorithm in a large power system scenario. Ph.D. in Electrical Engineering, May 2017 Show less
Query
(-) mods_name_creator_namePart_mt:"Maldonado, Daniel Adrian"
