A modified MPPT algorithm using incremental conductance for constant power generation of photovoltaic systems

Prasetyono, Eka; Anggriawan, Dimas Okky; Firmansyah, Ahmad Zakky; Windarko, Novie Ayub

doi:10.1109/elecsym.2017.8240362

Cited by 17 publications

(10 citation statements)

References 9 publications

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“…A dual mode distributed economic control for a fuel cell-photovoltaic-battery hybrid power generation system (HPGS) was proposed by Yang et al [99]. In order to solve the problem that the maximum power of PV will cause the overvoltage of the grid, Prasetyono et al [104] proposed a modified MPPT algorithm using incremental conductance for constant power generation of PV systems. Nguyen et al [105] introduced a new MPPT algorithm for photovoltaic power generation systems.…”

Section: Statistical Approaches and Hybrid Approaches For Energy Fore...mentioning

confidence: 99%

A Survey of the Researches on Grid-Connected Solar Power Generation Systems and Power Forecasting Methods Based on Ground-Based Cloud Atlas

Deng¹,

Da²,

Shao³

et al. 2023

Energy Engineering

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Photovoltaic power generating is one of the primary methods of utilizing solar energy resources, with large-scale photovoltaic grid-connected power generation being the most efficient way to fully utilize solar energy. In order to provide reference strategies for pertinent researchers as well as potential implementation, this paper tries to provide a survey investigation and technical analysis of machine learning-related approaches, statistical approaches and optimization techniques for solar power generation and forecasting. Deep learning-related methods, in particular, can theoretically handle arbitrary nonlinear transformations through proper model structural design, such as hidden layer topology optimization and objective function analysis to save information that can increase forecasting accuracy while filtering out irrelevant or less affected data for forecasting. The research's results indicate that RBFNN-AG performed the best when applying the predetermined number of days, with an NRMSE value of 4.65%. RBFNN-AG performs better than sophisticated models like DenseNet (5.69%), SLFN-ELM (5.95%), and ANN-k-means-linear regression correction (6.11%). Additionally, scenario application and PV system investment techniques are provided to evaluate the current condition of new energy development and market trends both domestically and internationally.

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Section: Statistical Approaches and Hybrid Approaches For Energy Fore...mentioning

confidence: 99%

A Survey of the Researches on Grid-Connected Solar Power Generation Systems and Power Forecasting Methods Based on Ground-Based Cloud Atlas

Deng¹,

Da²,

Shao³

et al. 2023

Energy Engineering

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“…If the voltage (V) falls in the area to the left of the maximum voltage (Vmpp) or (V) < (Vmpp), then (V) will be increased until it reaches (Vmpp), and vice versa if (V) is to the right of the maximum voltage (Vmpp) or (V) > (Vmpp), then (V) will be lowered until it reaches (Vmpp). After reaching (Vmpp), automatically the output power will also be maximum (Pmpp), whose job is to increase and decrease the voltage is the DC-DC converter [11]. The working graph of MPPT is shown in Fig.…”

Section: Maximum Power Point Tracking (Mppt)mentioning

confidence: 99%

“…CPG is a method used to limit the power generated by solar panels. With this method, the use of solar panels with MPPT control can avoid disturbances such as overvoltage [11]. One of the control strategies of the CPG method that can be implemented is to modify the MPPT control by adding a CPG mode (MPPT-CPG).…”

Section: E Constant Power Generation (Cpg)mentioning

confidence: 99%

Design and Implementation MPPT-CPG for Constant Power Battery Charger

Fajrianingrum

Rakhmawati

Prasetyono

2022

JAREE

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The power produced by photovoltaic is very dependent on irradiation conditions and temperature so that the power can be low or high. The load cannot work if the power is low, but it harms the load if the power is too high. Two modes are used for this system, they are MPPT and CPG mode. When PV power is less than limit power (Plimit), then it works on MPPT mode. The MPPT finds maximum power, then if PV power reaches Plimit or more, it works on CPG mode. During CPG mode, SEPIC converter output power is maintained constant at Plimit so the battery can be charged using the Constant Power Method. The Algorithm used for each mode is the variable Step Size Climbing. The Variable Step Size Hill-Climbing on MPPT performs maximum power according to irradiation conditions. Variables Step Size Hill-Climbing on CPG stabilizes the output power value of SEPIC being constant to charge the battery using the power constant method by keeping the power as constant as its limit power, voltage and current electricity bring into line with charge conditions. The results of the hardware integrated test for MPPT-CPG, the Variable Step Size Hill Climbing algorithm can search for the maximum power point (MPP) generated by PV and can produce an output power of 27.97 W in average.

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“…To perform precisely power flow on fluctuated condition precise PWM is needed, the way to get precise PWM researcher susgedted by applying intelligent controller. The Fuzzy Logic Controller is the one of the recomended controller by researcher [15][16][17][18], because it simple, can be done through computer simulation and can also be embedded in a microcontroller. The fuzzy logic controller for DC-DC converter application does not require too much computational process.…”

Section: Fb-bdc Sharing Modementioning

confidence: 99%

A Full-Bridge Bidirectional DC-DC Converter with Fuzzy Logic Voltage Control for Battery Energy Storage System

Prasetyono

Sunarno

Fuad

et al. 2019

emitter

Self Cite

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Renewable energy sources require an energy storage system because its are fluctuating and electricity producing at certain times, even sometimes not in accordance with the needs of the load. To maintain continuity of electricity, smart battery energy storage system is needed. Therefore, this paper of a full-bridge bidirectional DC-DC Converter (FB-BDC) with Fuzzy Logic Control (FLC) is designed and implemented for battery energy storage application. The FLC has error and delta error of voltage level as input and duty cycle of FB-BDC as output. The FB-BDC is controlled by a microcontroller ARM Cortex-M4F STM32F407VG for voltage mode control. The FB-BDC topology is selected becuase battery storage system needed isolated and need high voltage ratio both for step-up and step-down. The main purpose of FB-BDC to perform bidirectional energy transfer both of DC-Bus and battery. Moreover, FB-BDC controls the DC-Bus voltage according to referenced value. The power flow and voltage on DC-Bus is controlled by FLC with voltage mode control. The experiment result shows the ability of FLCÂ voltage mode control to control FB-BDC on regulate charging voltage with an error 1% and sharing voltage 1.5% form referenced value.

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A modified MPPT algorithm using incremental conductance for constant power generation of photovoltaic systems

Cited by 17 publications

References 9 publications

A Survey of the Researches on Grid-Connected Solar Power Generation Systems and Power Forecasting Methods Based on Ground-Based Cloud Atlas

A Survey of the Researches on Grid-Connected Solar Power Generation Systems and Power Forecasting Methods Based on Ground-Based Cloud Atlas

Design and Implementation MPPT-CPG for Constant Power Battery Charger

A Full-Bridge Bidirectional DC-DC Converter with Fuzzy Logic Voltage Control for Battery Energy Storage System

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