Power quality improvement for grid interconnected solar PV system using neural network control algorithm. (2018)

• Record Type: Journal Article
• Authors: Pragathi, Bellamkonda; Veramalla, Rajagopal; Noorbasha, Fazal; Jampana, Bangarraju
• Is Part Of: International journal of power and energy conversion. Volume 9:Number 2(2018)
• Page Start: 187
• Page End: 204
• Publication Date: 2018
• Subjects: neural network control algorithm -- solar PV system -- DC-DC converter -- power quality -- maximum power point tracking -- MPPT; Energy conversion -- Periodicals; Power resources -- Periodicals; Electric power production -- Periodicals; 621.31205
• Languages: English
• ISSNs: 1757-1154

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.