Abstract
With renewable energy (RE) being increasingly connected to power grids, pumped storage plants (PSPs) play a very important role in restraining the fluctuation of power grids. However, conventional control strategy could not adapt well to the different control tasks. This paper proposes an intelligent nonlinear model predictive control (NMPC) strategy, in which hydraulic-mechanical and electrical subsystems are combined in a synchronous control framework. A newly proposed online sequential extreme learning machine algorithm with forgetting factor (named WOS-ELM) is introduced to learn the dynamic behaviors of the coupling system. Specifically, the initial learning parameters are optimized by prior-knowledge learning and a new self-adaptive adjustment strategy is also put forward. Subsequently, the stair-like control strategy and artificial sheep algorithm (ASA) are used in rolling the optimization mechanism to replace the existing complex differential geometric solutions. Comparative experiments are carried out under different working conditions based on a PSP in China. The results show that the influence from coupling factors can be considerable and the proposed MPC strategy indicates superiority in voltage and load adjustment as well as the frequency oscillation suppression.
Highlights
This paper aims to use model predictive control (MPC) to deal with complicated coupling control problems of Pumped storage plants (PSPs) involved with multivariable interactions and strongly nonlinear behavior while the conventional strategies have been designed for a certain working condition and simplified the coupling model. e key of MPC is the prediction model, which is required to predict and update the nonlinear behavior of the plant precisely and quickly
An nonlinear model predictive control (NMPC) strategy is proposed based on the WOS-extreme learning machine (ELM) prediction model and artificial sheep algorithm (ASA) rolling optimization for the hydraulic-mechanical-electrical coupling system of PSP
The initial weights and the bias of the hidden layer in ELM, which can fully reflect the internal relationship of the modelling data, is optimized by ASA, while the output weights are still calculated by the least squares method
Summary
The application of ELM in MPC remains stagnated due to the lack of appropriate model updating mechanism Inspired by these ideas, this paper aims to use MPC to deal with complicated coupling control problems of PSP involved with multivariable interactions and strongly nonlinear behavior while the conventional strategies have been designed for a certain working condition and simplified the coupling model. E main contributions and novelty of this paper are reflected in the following: (1) a detailed nonlinear model, which combines hydraulic-mechanical and electrical subsystems is established to achieve precise simulations of dynamic response in PSP; (2) based on the coupling system, a synchronous control framework has been designed with MPC for the first time to fulfil the coordination of hydraulicmechanical and electrical subsystems; (3) in the MPC control framework, a novel prediction model of ELM with self-adaptive forgetting mechanism is designed, which will promote the prediction accuracy and timeliness.
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