Title: Power quality improvement for grid interconnected solar PV system using neural network control algorithm

Authors: Bellamkonda Pragathi; Rajagopal Veramalla; Fazal Noorbasha; Bangarraju Jampana

Addresses: Electronics and Communication Engineering Department, K L University, Greenfields, Vaddeswaram, Guntur District, 522502, India ' Electrical and Electronics Engineering Department, Stanley College of Engineering and Technology for Women, Abids, Hyderabad, Telangana, India ' Electronics and Communication Engineering Department, Koneru Lakshmaiah University, Vaddeswaram, Guntur District, Andhra Pradesh, India ' Electrical and Electronics Engineering Department, B V Raju Institute of Technology, Narsapur, Medak, Telangana, India

Abstract: This paper deals with neural network control algorithm-based grid connected to solar photo voltaic (PV) system consisting of DC-DC converter, solar PV with maximum power point tracking (MPPT) controller, three-leg voltage source converter (VSC), ripple filter at PCC, interfacing inductor and three phase grid connected to three phase linear/nonlinear loads. The reference solar-grid current for three-leg VSC are estimated using neural network control algorithm. The neural network based on least mean square (LMS) control algorithm is also known as adaptive linear element to estimate reference fundamental grid currents. A three phase non-isolated zigzag transformer is connected to solar grid PCC for neutral current compensation. The proposed solar PV grid connected system maintains UPF at the grid, reactive power compensation for ZVR operation along with load balancing, neutral current compensation and harmonic compensation. In the proposed solar PV system, MPPT is obtained using DC-DC boost converter and DC bus voltage is controlled by using DC bus proportional integral (PI). The neural network control algorithm based solar PV system is modelled in MATLAB R2013a along with SIMULINK.

Keywords: neural network control algorithm; solar PV system; DC-DC converter; power quality; maximum power point tracking; MPPT.

DOI: 10.1504/IJPEC.2018.090684

International Journal of Power and Energy Conversion, 2018 Vol.9 No.2, pp.187 - 204

Received: 05 Feb 2016
Accepted: 29 Jun 2016
Published online: 27 Mar 2018 *

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