Duty Cycle Of DC-DC Converter Research Articles

Enhanced MPPT approach for grid-integrated solar PV system: Simulation and experimental study

The global solar energy utilization has significantly risen, mostly due to the technological, economic, and environmental advantages it offers, particularly through the implementation of photovoltaic (PV) systems. The energy output of PV systems is contingent upon climate conditions. A new hybrid two-stage maximum power point tracking (MPPT) system developed to optimize power extraction and increase efficiency in PV systems. The system consists of two stages: a perturb and observe (P&O) MPPT for regulating the reference voltage in the first stage, and an enhanced model reference adaptive controller (EMRAC) for adjusting the DC-DC converter duty cycle in second stage. This system is designed primarily for 100.7 kW three-phase grid-integrated PV systems operating in environments with rapidly changing solar irradiation and temperature levels. In order to demonstrate efficacy of the suggested approach, a probabilistic evaluation is conducted across three levels of uncertain scenarios, namely ropp, step irradiance profile, and rapid changing irradiation and temperature profile. The proposed approach attained maximum power point (MPP) in 0.11 s and had an average tracking efficiency of 98.28 %. Quantitative, statistical, comparative and experimental analyses utilizing the dSPACE 1202 reveal the effectiveness of the proposed control strategy and demonstrate the superior performance over P&O, INC and ANN based on the power output, response time, power loss, efficiency, and error rates.

Voltage stability of a photovoltaic DC microgrid using fuzzy logic controller

This article employs a fuzzy logic controller (FLC) to investigate voltage stability in a PV-based DC microgrid. Several photovoltaic (PV) modules, a DC-DC converter, and loads make up the microgrid. Due to the widespread use of intermittent PV power, voltage stability is a crucial problem for DC microgrids and is difficult to accomplish. This study proposes an FLC-based voltage control technique that leverages input factors including PV output power, DC-DC converter duty cycle, and load current to identify the best course of action for preserving the system's voltage stability. The FLC's performance is assessed by simulation, and it is meant to be resilient to parameter fluctuations and uncertainties. The simulation results demonstrate that the suggested FLC-based control strategy successfully maintains the microgrid's voltage stability under a variety of operational circumstances, including changing solar irradiance and load variations. Moreover, the FLC performs better than other control methods.

Study Comparison Between Enhanced Firefly and Differential Evolution to Solve the Maximum Power Point Tracking Problem

The penetration of photovoltaic (PV) in electric power generation is continually increasing. On the other side, the load will receive the actual power, which is a part of power supplied by the photovoltaic. Therefore, it is necessary to extract maximum power from PV. One of a defy problem, is the tracking maximum power point (MPPT) in photovoltaic frameworks and it is a significant task. It can be a reproducer of maximum power from a photovoltaic system, which it depends on the adjusting of duty cycle of DC-DC converter. In order to produce a maximum power transfer, the impedance between the source and the load should be coincide by using of a buck boost converter. In this work, the proposed methods; Firefly algorithm (FA), Enhanced Firefly (EFA), Differential Evolution Scheme1, Differential Evolution Scheme2, and Differential Evolution Scheme 3 were tested for their performances in different conditions. Finally, the simulation results confirm that the second scheme of DE outperforms the others. Visual Basic. Net has been used to simulate the results and proceed with algorithms.

FFNN BASED MPPT CONTROLLER FOR PEMFC POWERED NON-ISOLATED HIGH GAIN DC-DC CONVERTER WITH SPWM INVERTER FED BLDC MOTOR

A Non-Isolated High Gain DC-DC converter fed from 1.2kW PEMFC is designed to boost the fuel cell voltage. The Feed-forward Neural Network based MPPT controller algorithm is implemented to adjust the duty cycle of DC-DC converter in order make sure optimum power extraction from PEMFC whose result is compared with Fuzzy Logic Controller. The boosted DC voltage is inverteted to drive BLDC motor by three phase inverter. The inverter switches are triggered with switching pattern generated by SPWM technique according to the Hall sensor signals placed at stator of BLDC motor to find the position of rotor. The simulation anlysis has been carried out using MATLAB (R2017a)/Simulink platform and discussed the results.

Implementation of Intelligent Controlling Mechanism to Improve Conversion Efficiency of Solar PV Module

The research article aims at an intelligent control method to improve conversion efficiency of solar photovoltaic module by tracking maximum operating point irrespective of variations in temperature and irradiance. A novel hybrid maximum power point based on learning automata is proposed. The algorithm works in two stages, first the learning automata detects the temperature and irradiation of light incidenting on PV module, second it identifies the zone of operation and adjusts the duty cycle of DC-DC converter. This implementation is done by designing the learning automata using artificial neural network and the adjustment of duty cycle is implemented with fuzzy logic controller. The design is verified using matlab/simulink environment.

TEG Maximum Power Point Tracking Using an Adaptive Duty Cycle Scaling Algorithm

Abstract The thermoelectric generator (TEG) is a clean and noiseless renewable electrical power source that requires no moving parts. Unfortunately, the practicality of TEGs is currently limited by its typical low conversion efficiencies. Subsequently, researchers have taken many approaches to improve the efficiency of the TEG. One of such approaches is the utilization of maximum power point tracking (MPPT) techniques. MPPT techniques are popularly used in literature for maximizing the power that is extracted from solar panels. Such techniques can be reused for the TEG scenario because TEGs also have I-V and P-V characteristics that follow the same principles as that of solar panels. This paper presents a “Lock-On Mechanism” MPPT algorithm and applies it specifically to the TEG application. In comparison to conventional fixed step based MPPT algorithms, the proposed algorithm improves the MPP tracking performance by adaptively scaling the DC-DC converter duty cycle whenever the MPP is located. In doing so, the steady state oscillations become negligibly small thus be consideredeliminated and a smooth steady state MPP response is achieved. Simulation and experimental results prove that the proposed algorithm is fast and stable in comparison to the conventional fixed step hill climbing algorithm.

The Lock-On Mechanism MPPT algorithm as applied to the hybrid photovoltaic cell and thermoelectric generator system

The thermoelectric generator (TEG) is a clean and noiseless renewable electrical power source that requires no moving parts. In the meantime, the hybrid photovoltaic cell and thermoelectric generator (PV/TEG) system is also popularly considered in research because of its potential improved power conversion efficiency over its monolithic counterparts. This paper continues the work on several previous publications from the authors where the focus here is to perform maximum power point tracking (MPPT) on the hybrid PV/TEG system. It reuses the Lock-On Mechanism (LOM) MPPT algorithm which was previously used by the authors in two separate publications on the solar panel and TEG alone. In comparison to conventional fixed step based MPPT algorithms, the LOM algorithm improves the MPP tracking performance by adaptively scaling the DC-DC converter duty cycle whenever the MPP is located. In doing so, the steady state oscillations become negligibly small thus be considered eliminated and a smooth steady state MPP response is achieved. The simulation and experiment in this paper are conducted using a double SEPIC converter where each input source is treated independently with the proposed algorithm. Results prove that the proposed algorithm is fast and stable in comparison to the conventional fixed step hill climbing algorithm.

A proposed maximum power point operating strategy for photovoltaic applications using monthly irradiance estimates

In this paper, we propose a new application of irradiance estimate for photovoltaic (PV) applications. Maximum power point (MPP) operating strategy for PV system is developed using monthly irradiance estimate based duty cycle control. Duty cycle of DC-DC converter is calculated using monthly average irradiance (Gav) and average of maximum irradiance (Gmax,av) received at Pilani during 2002–2011. Designed PV system operates at 60–95% of MPP throughout the year with these monthly irradiances based duty cycle operations. MPP of 71.6% and 66.81% is observed for January and July months respectively when the model is validated for weighted mean of maximum power point (wMPP) operation. Further, a % MPP of 98.21 is achieved when proposed strategy is tested at short interval irradiance estimate based duty cycle control. Extensive simulation using MATLAB/SIMULINK is carried out to observe the operating points of PV system under dynamic changing irradiances levels. Experiments using solar array simulator (SAS) are performed to validate the simulated results.

An adaptive scale factor based MPPT algorithm for changing solar irradiation levels in outer space

Maximum power point tracking (MPPT) techniques are popularly used for maximizing the output of solar panels by continuously tracking the maximum power point (MPP) of their P-V curves, which depend both on the panel temperature and the input insolation. Various MPPT algorithms have been studied in literature, including perturb and observe (P&O), hill climbing, incremental conductance, fuzzy logic control and neural networks. This paper presents an algorithm which improves the MPP tracking performance by adaptively scaling the DC-DC converter duty cycle. The principle of the proposed algorithm is to detect the oscillation by checking the sign (ie. direction) of the duty cycle perturbation between the current and previous time steps. If there is a difference in the signs then it is clear an oscillation is present and the DC-DC converter duty cycle perturbation is subsequently scaled down by a constant factor. By repeating this process, the steady state oscillations become negligibly small which subsequently allows for a smooth steady state MPP response. To verify the proposed MPPT algorithm, a simulation involving irradiances levels that are typically encountered in outer space is conducted. Simulation and experimental results prove that the proposed algorithm is fast and stable in comparison to not only the conventional fixed step counterparts, but also to previous variable step size algorithms.

Embedded Control of n-Level DC–DC–AC Inverter

A generalized multilevel inverter (MLI) with frontend dc-dc conversion stage followed by a synchronized H-bridge is presented. By using this configuration along with the proposed embedded control, any desired number of levels (n) in the output voltage can be produced. The dc-dc conversion stage employs an asynchronous buck converter. The duty cycle of dc-dc converter is varied in the form of m-level piecewise constant (PWC) unidirectional sine wave to produce a similar output voltage across the dc-link capacitor. The unidirectional PWC voltage is made into n-level ac voltage, where n = (2m - 1), by the synchronized H-bridge. Hence, it is named as dc-dc-ac MLI. An 8-bit Xilinx SPARTAN 3AN field programmable gate array (FPGA)-based digital controller is utilized for the simultaneous generation of high-frequency switching pulses for dc-dc converter and synchronized fundamental frequency switching pulses for H-bridge. The desired number of levels in ac output voltage and its frequency are the essential inputs to the pulse generation algorithm implemented in FPGA. The proposed MLI is simulated in MATLAB/Simulink environment; its functioning is verified with resistive (R) and resistive-inductive (R-L) loads. The hardware prototype of MLI is built in the laboratory and its performance is validated with R, R-L loads, and few home appliances.

A Review: Implementation of Maximum Power Point Tracking Algorithm for PV System on FPGA

Photovoltaic power generation has two major problems: the conversion efficiency of existing PV modules is less and amount of power generated by PV system changes with weather conditions. Also, the PV cell I-V characteristics are non-linear due to complex relationship of voltage & current and varies with change in temperature or insolation. There is only one point on P-V or I-V curve called Maximum Power Point at which PV system operates at maximum efficiency and produces maximum output power. Failure to track MPP causes significant power loss. So, Maximum Power Point Tracking (MPPT) is required to operate PV system at MPP. The P&O algorithm and INC algorithm are commonly used methods to track MPP by adjusting duty cycle of DC-DC converter. The existing methods use microcontroller or DSP controller to implement MPPT algorithm. FPGA provides number of advantages over sequential machine microcontroller, as FPGA does concurrent operation i.e. instructions executed continuously and simultaneously. However DSP does signal processing related calculation only. Using FPGA number of components required is less and FPGA is faster than DSP. Thus, the size of components required for power converter decreases. The MPPT algorithm is implemented on FPGA which continuously track the maximum point under rapid environmental changes.