Finite control-set model predictive control for leakage current suppression in grid interfaced solar PV system

Srinivas, Vedantham Lakshmi ORCID: https://orcid.org/0000-0002-6376-8602, Singh, Bhim and Mishra, Sukumar 2020. Finite control-set model predictive control for leakage current suppression in grid interfaced solar PV system. Presented at: IEEE 5th International Conference on Computing Communication and Automation (ICCCA 2020), Greater Noida, India, 30-31 October 2020. 2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA). IEEE, pp. 675-680. 10.1109/ICCCA49541.2020.9250840

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Abstract

This paper presents a single vector finite control-set model predictive controller for leakage current suppression in a grid interfaced transformer-less PV (Photovoltaic) system. The stray capacitance between PV array and the ground cause considerably high leakage currents in the transformer-less PV system. A control strategy is presented herein, to suppress these leakage currents in a practical solar PV system, by reducing the common mode voltage. The conventional control strategies for leakage current suppression employ multiple additional IGBT (Insulated-Gate Bipolar-Transistor) switches to reduce the leakage currents and their corresponding gating strategy. However, this algorithm eliminates the need for additional switches for reducing the leakage currents. It means the very same standard 3-leg PV-inverter topology can be used while ensuring reduced leakage currents and this strategy is possible to be appended in any voltage-controlled inverter. The controller uses a predictive optimal switching vector strategy for this purpose, which uses the system model to predict the output voltages and then a quadratic function decides the best switching vector at next sampling instant. An additional penalty is imposed on the quadratic function, to reduce the ripple content in common mode voltage of the system, thereby limits the excessive solar PV leakage currents. Simulation and test results validate the strategy.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Engineering
Publisher:	IEEE
ISBN:	9781728163246
ISSN:	2642-7354
Last Modified:	09 Nov 2022 10:39
URI:	https://orca.cardiff.ac.uk/id/eprint/140245

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