Artificial neural network based DC-DC converter for grid connected transformerless PV system

Abstract

The transformerless photo voltaic (PV) inverter system connected to grid has created a new trend in the energy market due to its reduced space requirement, low cost and increased efficiency when compared to its counterpart i.e with transformer. Transformerless inverter system suffers from common mode leakage currents due to parasitic capacitances between PV panels and ground. However, different new inverter topologies and state of the art modulation strategies are proposed in the literature to counter it. A dc-dc converter is of more significant to maintain the constant PV output voltage at string level and extract maximum power from PV. This paper presents Artificial neural network (ANN) algorithm-based dc-dc converter to track maximum power from PV module connected to grid without transformer. It also compares the performance of ANN based algorithm with conventional perturb and Observe maximum power point tracking (MPPT) technique. MATLAB/Simulink environment is used to pursue the simulation of ANN based algorithm and analyses its performance for variety of irradiance levels.

Modernisation of DC-DC converter topologies for solar energy harvesting applications: A review

“…This component converts the PV-produced variable DC voltage into a fixed voltage that is proportionate to the load requirements. The MPPT controller simultaneously works to achieve MPP conversion from PV and provide a pulse width modulation (PWM) signal to drive the DC-DC converter's switching mechanism [22], [23], [25], [27], [28], [32], [38], [39], [42], [43], [52], [77], [81], [93], [94], [98], [108], [112], [121]- [146]. These two processes happen simultaneously.…”

“…Solar tracking is the traditional way that is utilized to get the most out of the energy that is collected [156] [157]. One other approach that may be taken to get maximum power is called MPPT [6]- [8], [11], [12], [15], [19], [27], [28], [35], [41], [42], [48], [51], [52], [57], [62], [64]- [66], [69]- [71], [74], [77], [80], [82], [83], [85], [89], [90], [93], [95], [96], [98], [106], [108], [112], [113], [158]- [245]. The MPP can be detected with the use of an MPPT algorithm that is placed on the microcontroller [246].…”

“…Numerous methods are utilized on a regular basis to accomplish the task of tracking maximum power point in PV systems [185]. These methods include hill-climbing, perturbation and observation (P&O), look-up table, incremental conductance (Inc-Cond), linearization-based, DC-link capacitor droop control, curve fitting, extremum seeking control, feedback voltage or current, feedback of power variation with voltage, feedback of power variation with current, firefly algorithm, fractional open-circuit voltage (FOCV), fractional short-circuit current (FSCI), artificial neural network (ANN), fuzzy logic, genetic algorithm, particle swarm optimization (PSO), ant colony based optimization, one cycle control (OCC), ripple correlation control (RCC), parasitic capacitance, and slidingmode [6]- [8], [11], [12], [15], [19], [27], [28], [35], [41], [42], [48], [51], [52], [57], [62], [64]- [66], [69]- [71], [74], [77], [80], [82], [83], [85], [89], [90], [93], [95], [96], [98], [106], [108], [112], [113], [158]- [245].…”

“…In recent years, a variety of methodologies have been developed to monitor MPP in PV systems. The combination of ANN and the P&O algorithm is one of the available approaches [52], [174], [239]. This approach can determine the MPP in a short amount of time under a variety of weather conditions since it does not require costly irradiance sensors [251].…”

Independent swarm grid vehicle charging by a logical DC-DC converter with hybrid boundary conduction mode

“…A further enhancement of the economic viability of installed photovoltaic (PV) systems has occurred because falling prices for PV modules and increased power conversion efficiency have helped drive down system costs. In 2014, almost 42 GW of new PV capacity was installed worldwide, bringing the global total to 180 GW [1]- [5].…”