Model Predictive Phase Control for Single-Phase Electric Springs

Qingsong Wang (), Hao Ding, Shuo Yan and Giuseppe Buja
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Qingsong Wang: School of Electrical Engineering, Southeast University, Nanjing 210096, China
Hao Ding: School of Software Engineering, Southeast University, Suzhou 215000, China
Shuo Yan: School of Engineering, RMIT University, Melbourne, VIC 3000, Australia
Giuseppe Buja: Department of Industrial Engineering, University of Padova, 35131 Padova, Italy

Energies, 2022, vol. 15, issue 18, 1-14

Abstract: In this paper, model predictive control (MPC) is proposed for single-phase electric springs (ESs) with the help of the existing δ control, which is realized by controlling the instantaneous phase angle of the predefined sinusoidal reference of a certain controller. System modeling is analyzed first to get differential forms of state variables. The discrete-time state space model is obtained through first-order approximation. Critical load (CL) voltage can be predicted by the prediction of ES voltage and line current. The operating modes of ESs can be determined and the reference signal for CL voltage can be provided by δ control. As a result, cost function is obtained as the absolute value of the error between predicted CL voltage and its predefined reference. Two typical operating functions such as pure reactive power compensation mode and power factor correction (PFC) mode are selected and simulated to validate the proposed control and analysis. It is revealed that both control objectives can be achieved with the proposed MPC and δ control. Additionally, the total harmonic distortion on the critical load is limited to about 0.5%, which is better than other existing methods.

Keywords: electric spring; model predictive control; phase control; reactive power compensation; microgrids; distributed generation; grid connected (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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