Improved model predictive current control strategy for overvoltage suppression in long-cable PWM-driven induction motor electrical submersible pump systems
- 1School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, China.
- 2School of Electrical Engineering, Liaoning University of Technology, Jinzhou, China.
- 3Daqing Oilfield Powerlift Industrial Pump Co. Ltd, Daqing, China.
Abstract
Long-cable PWM-driven induction motor electric submersible pump (ESP) systems suffer from significant voltage reflection issues caused by distributed parameter effects, leading to transient overvoltages at the motor terminals that can reach several times the DC bus voltage. This severely threatens system stability and motor insulation integrity, and traditional control methods prove inadequate for effective suppression. To address this challenge, the paper proposes an improved model predictive current control (MPCC) strategy for induction motor ESP systems, which integrates overvoltage suppression and equivalent switching frequency reduction for long-cable PWM-driven applications. The reflection overvoltage mechanism is thoroughly analyzed through mathematical modeling of the motor and long-cable transmission line, and the applicability of predictive current control in such systems is investigated. The core improvement lies in a circular boundary constrained MPCC combined with a dynamic transition voltage vector selection mechanism, which enables dual-mode optimization based on current error boundary conditions: the normal cost function is applied within the circular boundary to jointly optimize voltage tracking, switching frequency, and dv/dt; while the backup cost function is activated outside the boundary to prioritize current error minimization. This effectively optimizes the voltage switching strategy, reducing both switching frequency and dv/dt, thereby preventing overvoltage excitation caused by abrupt voltage vector steps in PWM-driven systems. Simulation models under different cable configurations (1 km with RLC filter and 110 m without RLC filter) and an experimental platform based on a neutral-point-clamped three-level inverter (NPC-TLI) topology are established, with the 110 m no-filter configuration serving as a direct cross-validation baseline. Comparative simulation and experimental results demonstrate that the proposed strategy significantly reduces the duration of motor terminal overvoltage while maintaining excellent system dynamic performance. Moreover, the improved MPCC effectively suppresses overvoltage and reduces equivalent switching frequency without requiring hardware filter redesign, while the RLC filter remains necessary for full insulation safety compliance under more demanding long-cable conditions, substantially mitigating the risk of motor insulation breakdown.
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BibTeX
@article{gong2026improved,
title = {{Improved model predictive current control strategy for overvoltage suppression in long-cable PWM-driven induction motor electrical submersible pump systems}},
author = {Xiaopeng Gong and Xin Qi and Ming Feng and Liancheng Zhu and Nianxing Wang},
journal = {PLOS One},
year = {2026},
month = oct,
doi = {10.1371/journal.pone.0360296},
url = {https://doi.org/10.1371/journal.pone.0360296},
note = {CC BY},
}
RIS
TY - JOUR
TI - Improved model predictive current control strategy for overvoltage suppression in long-cable PWM-driven induction motor electrical submersible pump systems
AU - Xiaopeng Gong
AU - Xin Qi
AU - Ming Feng
AU - Liancheng Zhu
AU - Nianxing Wang
PY - 2026
DA - 2026/10/29
JO - PLOS One
DO - 10.1371/journal.pone.0360296
UR - https://doi.org/10.1371/journal.pone.0360296
AB - Long-cable PWM-driven induction motor electric submersible pump (ESP) systems suffer from significant voltage reflection issues caused by distributed parameter effects, leading to transient overvoltages at the motor terminals that can reach several times the DC bus voltage. This severely threatens system stability and motor insulation integrity, and traditional control methods prove inadequate for effective suppression. To address this challenge, the paper proposes an improved model predictive current control (MPCC) strategy for induction motor ESP systems, which integrates overvoltage suppression and equivalent switching frequency reduction for long-cable PWM-driven applications. The reflection overvoltage mechanism is thoroughly analyzed through mathematical modeling of the motor and long-cable transmission line, and the applicability of predictive current control in such systems is investigated. The core improvement lies in a circular boundary constrained MPCC combined with a dynamic transition voltage vector selection mechanism, which enables dual-mode optimization based on current error boundary conditions: the normal cost function is applied within the circular boundary to jointly optimize voltage tracking, switching frequency, and dv/dt; while the backup cost function is activated outside the boundary to prioritize current error minimization. This effectively optimizes the voltage switching strategy, reducing both switching frequency and dv/dt, thereby preventing overvoltage excitation caused by abrupt voltage vector steps in PWM-driven systems. Simulation models under different cable configurations (1 km with RLC filter and 110 m without RLC filter) and an experimental platform based on a neutral-point-clamped three-level inverter (NPC-TLI) topology are established, with the 110 m no-filter configuration serving as a direct cross-validation baseline. Comparative simulation and experimental results demonstrate that the proposed strategy significantly reduces the duration of motor terminal overvoltage while maintaining excellent system dynamic performance. Moreover, the improved MPCC effectively suppresses overvoltage and reduces equivalent switching frequency without requiring hardware filter redesign, while the RLC filter remains necessary for full insulation safety compliance under more demanding long-cable conditions, substantially mitigating the risk of motor insulation breakdown.
SN - 1932-6203
ER -