Power System Parameters Forecasting Using Hilbert-Huang Transform and Machine Learning

A novel hybrid data-driven approach is developed for forecasting power system parameters with the goal of increasing the efficiency of short-term forecasting studies for non-stationary time-series. The proposed approach is based on mode decomposition and a feature analysis of initial retrospective data using the Hilbert-Huang transform and machine learning algorithms. The random forests and gradient boosting trees learning techniques were examined. The decision tree techniques were used to rank the importance of variables employed in the forecasting models. The Mean Decrease Gini index is employed as an impurity function. The resulting hybrid forecasting models employ the radial basis function neural network and support vector regression. Apart from introduction and references the paper is organized as follows. The section 2 presents the background and the review of several approaches for short-term forecasting of power system parameters. In the third section a hybrid machine learning-based algorithm using Hilbert-Huang transform is developed for short-term forecasting of power system parameters. Fourth section describes the decision tree learning algorithms used for the issue of variables importance. Finally in section six the experimental results in the following electric power problems are presented: active power flow forecasting, electricity price forecasting and for the wind speed and direction forecasting

Application of a Volterra quadratic polynomial to modeling elements of heat engineering devices

This paper considers integral models built to describe dynamic processes in a 135 MW power unit condenser. For this purpose, we use a quadratic segment of the Volterra integral power series. The first set of models was built with a perturbation of the cooling water flow, and the second one with a perturbation of the steam flow. For all sets of models, changes in pressure and temperature in the condenser, as well as temperature changes in LHP-1, were considered as a response to perturbation. For models built with perturbation of the cooling water flow velocity, we considered an extreme problem of finding optimal amplitudes of the input perturbations. The results of calculations proved to be sufficiently accurate