Performance Analysis of Hybrid MPPT Controller for PV Boost Converter

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056

Volume: 12 Issue: 04 | Apr 2025 www.irjet.net p-ISSN: 2395-0072

Performance Analysis of Hybrid MPPT Controller for PV Boost Converter

Priya Manoria

Electrical engineering Department Government Polytechnic College Katni M. P.

Abstract - This study examines the Perturb and Observe (P&O) and IncrementalandConductance (I&C) MPPTs, both of which use neural network (NN)-based artificial intelligence techniques to improve their output. The purpose of the DC-DC converter is to control the PV module's output voltage in buck or boost mode as necessary. The DC-converter is designed using a single switch unidirectional architecture, with gate pulses regulated by an MPPT algorithm based on neural networks. The P&O and I&C algorithms with and without the NN approach are compared. To validate the suggested topology, the results are acquired using MATLAB programfor Simulink.

Key Words: Maximum Power Point Tracking (MPPT), incrementalandconductance(IC),constantvoltage,perturb andobserve(PO),neuralnetworks(NN),DC-DCconverter.

1.INTRODUCTION

During the past two decades, solar has astonishingly gotten deep into the main stream power system. Solar irradiation, which fluctuates on an hourly, daily, monthly, andannualbasis,isthesourceofsolarenergy.Therefore,it is not possible to generate power continuously from sunlight.However,bymonitoringthehighestgenerationat theinstantaneousirradiance,theoutputfromthesola-cell maybemaximizedatanygiventime.MaximumPowerPoint Tracking (MPPT) is used to accomplish this [1]. When PV production peaks at a location known as the Maximum PowerPoint(MPP),whichisconstantlyshiftinginrelationto temperatureandsolarradiation,MPPTaidsintrackingthe output.MPPisapointonthePV/VIcurvethatrepresentsa particular PV module's peak voltage, current, and power There are many different topologies for building MPPT controllers in the literature; the most widely used ones include hill climbing, incremental and conductance (IC), constantvoltage,perturbandobserve(PO),andothers[2–5].Allofthesetopologiesarewidelyusedandcustomary.By usinganyclevertechniques,theefficiencyofthetraditional topologiesmaybemultipliedbymany.Intelligentmethods, such as neural or fuzzy, aid in the quick and precise monitoringofMPP[6–8].

TheintelligentMPPThybridapproachbasedonneural networks(NN)ispresentedinthispaper.Withtheaidofan algorithmthatfacilitatesquick convergenceoftheMPP at the specified irradiance, NN is intended to calculate the

voltageandcurrentatMPP.TheDCloadispoweredbythe DC-DCconverter,specificallytheboost.Inordertoprecisely convalescetheMPPunderavarietyofoperatingsituations, the output from MPPT is used to create the converter's properdutycycle.WhenthehybridMPPTalgorithmisused, the duty cycles provide PWM for the converter's switches and achieve smooth fluctuations with regard to varied irradiations.AcompactDCsystemwith250Wofpowerand input-to-outputvoltagefluctuationsbetween30and80Vis built. In order to determine which strategies converge quicklytodeterminethemaximumvoltageandcurrentat the moment of irradiation variance, results are compared undervaryingirradianceandperformanceisexamined.

2. NEURAL NETWORK BASED HYBRID MPPT

A highly effective technique for improving PV system performanceandguaranteeingsmoothoperationinvarying weatherconditionsisMPPT.Inordertobuildtheduty-cycle oftheDC-DCconvertertofollowtheMPP,MPPTisusedto tracktheMPP[9].

UsingtheNN-algorithmtoadjustthefluctuationinMPP voltage and current may significantly improve the MPPT's convergencespeedandquickadaptation.Inordertosupply the gating of the switches of the PV-boost converter, this studydevelopsahybridNN-basedICandPOmethod[10]. ANN is used because of its high degree of convergence flexibilityanddependability.TheNNdeterminesthevoltage andcurrentatMPPforeachoperatingpointwithaspecified temperatureand irradiance. The PO algorithm determines theproperdutycyclefortheDC-converter'sswitchesbased on the estimated parameters. In a variety of climatic conditions,thiswillperfectlyrecoverthemaximumpower and be in line with the MPP. The conventional PO and IC approachesarethencomparedwiththeproposedapproach. Aproportional-integral(PI)controllerisusedtoensurethat thecapacitorvoltagebalanceismaintained.Figure1displays thefullschematicdesignofthesuggestedarchitecture.NNis often used for MPPT control since it doesn't require a physicalmodelorintricatemathematicalcomputations.Itcan also manage the large nonlinearities in the P/V characteristicsofthePVpanel[11-13].

Volume: 12 Issue: 04 | Apr 2025 www.irjet.net p-ISSN: 2395-0072

Table -1: SampleTableformat

3. SIMULATION MODEL

The user-defined PV module, whose parameters are shown in Table 1, and the simulation model, which is displayed in Figure 2, are used in MATLAB program to generate the simulation results. The findings are based on variableirradiancerangingfrom200to1000wb/m2,which ischangedinincrementsof200overaperiodof0.2seconds at each division. The temperature in this experiment is maintainedat25°.However,thesystemunderinvestigation can also be examined under different temperature circumstances.

TheMPPT-controller,whoseprimaryfunctionistocreatethe PWM control for the DC-converter's switches,receives the PV'soutput.Performancestudyisdoneinthisworkforboth static loading situations and dynamic environmental conditions.Threecasesareexamined:

(i) ConventionalPOtechnique;

(ii) NN-POtechnique;and

(iii) NN-ICtechnique.

4. RESULT DISCUSSION

In this section results for the proposed neural network based MPPT algorithm is discussed. Figure 3 displays the Vmp (max. volt. at any input radiance and temperature) as determined by NN under different irradiance. It is evident that under the various operating circumstances,theVmpvalueofthemodule,whichis30.7V, is maintained by the Vmp acquired from NN. Figure 4 displaysthedutycycleinthisscenario.Figure5displaysthe outputpoweratthePVmodule'sterminalunderthespecified operatingcircumstances.Figure 6comparesthevoltageat the PV terminal forconventional P&O MPPT, NN-P&O and NN-IC ThegraphshowsthateventhoughNNwastrainedfor Vmp,italsoinfluencestheoutputatthePVsidewhenitisfed intotheMPPTalgorithm.Figure7displaystheoutputvoltage thatwasacquiredfromtheDC-converterside.Itisevident fromthegraphicthattheDc-converterincreasesthevoltage bymorethandoubletheinputvoltage.Theoutputterminal voltageisaround80V,whichvariesbetween80Vand40V dependingontheinputirradiance,whereastheinputvoltage isapproximately 32 V. Similarly, DC-converter side output currentispresentedinfigure8.

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056

Volume: 12 Issue: 04 | Apr 2025 www.irjet.net p-ISSN: 2395-0072

-7:DC-ConverteroutputterminalVoltage comparison

Fig -8:DC-Converteroutputcurrentcomparisonwith NN-MPPT

NNbasedMPPTalgorithmshashighdegreeofaccuracy.It trackstheMPPandfeedtotheMPPTalgorithmtodesignthe duty-cycle for the switches of the DC-converter. The Duty cycledesignedusingNNbasedMPPTalgorithmsisshownin figure9andtheonewithouttheNNisshowninfigure10.It is evident that, duty cycle is stable with NN, while with conventionaltopologiesithasfluctuationswhichaffectsthe performanceofthesystem.

Fig -9:DC-ConverterswitchesdutycyclewithNNMPPT

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056

Volume: 12 Issue: 04 | Apr 2025 www.irjet.net p-ISSN: 2395-0072

-10:DC-ConverterswitchesdutycyclewithoutNNMPPT

5. CONCLUSIONS

ThesolarirradiancethatstrikesthePVplatedeterminesthe outputacrossthesun.Justastheirradiancesvaryinnature, so does the solar power's production. The PV array's greatestpointofvoltageandcurrentgenerationatanygiven timeistrackedusinggreatestPowerPointTracking(MPPT). Thisaidsingettingthemostpowerpossiblefromthesunat anyirradiancelevel.

Intelligent algorithms can be used to secure this tracking method. The purpose of this research is to use a neural network trainer to create an MPPT controller that is both accurateandprecise.TheMMPTalgorithm'sresultanalysis has been shown both with and without a NN trainer. In contrasttothetraditionaltopologies,thecomparativestudy demonstratesthattheoutputfromNN-basedMPPTismore accurateandexact.

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