Ultra-short-time prediction technology of wind power station output based on variational mode decomposition and particle swarm optimization least squares vector machine

Abstract

Wind power is developing rapidly in the context of sustainable development, and a series of problems such as wind curtailment and power curtailment have gradually emerged. The forecast of power generation output has become one of the hotspots of current research. This paper proposes a wind power plant output ultra-short-time prediction technology based on variational modal decomposition and particle swarm optimization least squares vector machine. Variational Modal Decomposition (VMD) method decomposes the historical output data of wind power plants at multiple levels. At the same time, it explores the impact of various decomposition methods such as EMD decomposition on the prediction accuracy, and uses the least squares support vector machine based on particle swarm optimization algorithm. Predictive summation is performed on each level of data separately to obtain a more accurate prediction effect, which has a certain improvement in prediction accuracy compared with traditional prediction algorithms.

Research on Multi-Step Prediction of Short-Term Wind Power Based on Combination Model and Error Correction

“…However, using wind power is extremely challenging for current power systems. One reason is that the output power of wind farms has strong intermittency and fluctuation due to the characteristics of wind energy [3], and the large amount of wind power connected to the grid will lead to voltage fluctuations and frequency changes [4], which will significantly increase the instability of power system operation [5] and bring great safety risks to the power system. The other reason is that wind power cannot be dispatched [6] and the phenomenon of wind curtailment is serious, which causes huge energy waste and greatly hinders the further development of the wind power generation industry [7].…”