Title: Research on grid-connected photovoltaic inverter based on quasi-PR controller adjusting by dynamic diagonal recurrent neural network

Authors: Zhenxiong Zhou; Bingshen Liu; Wenbao Wang; Hongxi Wang

Addresses: School of Electrical and Information Engineering, Beihua University, Jilin City, Jilin Province, China ' School of Electrical and Information Engineering, Beihua University, Jilin City, Jilin Province, China ' School of Electrical and Information Engineering, Beihua University, Jilin City, Jilin Province, China ' School of Electrical and Information Engineering, Beihua University, Jilin City, Jilin Province, China

Abstract: The single-phase grid-connected photovoltaic inverter system is studied in this paper. In view of the non-linear and time-varying characteristics of this system, the three-closed-loop control strategy consisting of DC voltage outer loop, grid-connected current inner loop and capacitive current inner loop based on quasi-PR control is proposed. Since the quasi-PR controller of fixed parameters is unable to adapt to changes of parameters in power network, a quasi-PR control method of dynamic self-tuning based on a dynamic Diagonal Recurrent Neural Network (DRNN) is presented. DRNN is based on the Recursive Prediction Error (RPE) algorithm with second-order gradient, which has a faster convergence rate than the BP algorithm. The simulation and experiment results prove that the grid-connected photovoltaic inverter with the above control algorithm has a good quality of the output current and fast performance in dynamic response.

Keywords: photovoltaic inverter; MPPT; three-closed loop control; solar energy; quasi-PR; DRNN; RPE; grid-connected inverter.

DOI: 10.1504/IJCAT.2019.102846

International Journal of Computer Applications in Technology, 2019 Vol.61 No.3, pp.220 - 228

Received: 08 Oct 2018
Accepted: 05 Jan 2019

Published online: 30 Sep 2019 *

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