Categorizing Indian states based on operating condition of photovoltaic system

Electricity generation of a photovoltaic (PV) module is primarily affected by local weather conditions, which vary significantly across vast geographic areas. This work introduces an approach to categorize Indian states based on outdoor operating conditions of PV modules that influence performance a...

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Main Authors: Arti Pareek, Humaid Mohammed Niyaz, Manish Kumar, Rajesh Gupta
Format: Article
Language:English
Published: Elsevier 2024-01-01
Series:Solar Energy Advances
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2667113124000020
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author Arti Pareek
Humaid Mohammed Niyaz
Manish Kumar
Rajesh Gupta
author_facet Arti Pareek
Humaid Mohammed Niyaz
Manish Kumar
Rajesh Gupta
author_sort Arti Pareek
collection DOAJ
description Electricity generation of a photovoltaic (PV) module is primarily affected by local weather conditions, which vary significantly across vast geographic areas. This work introduces an approach to categorize Indian states based on outdoor operating conditions of PV modules that influence performance and reliability. Module temperature and irradiance are the two most important parameters which affect PV performance. Relative humidity (RH), module temperature, and global horizontal irradiance (GHI) are the three most important parameters that affect PV reliability. In this work, the PV module's most frequent operating condition (MFOC) of temperature and irradiance corresponding to maximum energy production has been analyzed for dominant PV technology (multi-crystalline silicon). Data from various sites across India were analyzed and subsequently grouped by state as PV installation decisions are generally based on state-level factors, such as state business policies, incentives, availability of local human resources, state power policies, etc. The MFOC method used in this work was supported by experimental results. Based on estimated states' MFOC, PV module output power has been obtained and compared with its rated power. Further in this work, major stressors affecting PV modules namely average RH, module temperature, and total annual GHI have been analyzed for different Indian states. Based on these specific stressors, states with similar stressor patterns have been grouped by the k-means clustering method. Results of MFOC estimation show potential for additional standardization methods to estimate PV system performance accurately. Statistical analysis of stressors highlights the importance of selecting PV technology modules carefully.
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spelling doaj.art-9f05e510470c451badf25f963ecf70632024-01-12T04:57:48ZengElsevierSolar Energy Advances2667-11312024-01-014100052Categorizing Indian states based on operating condition of photovoltaic systemArti Pareek0Humaid Mohammed Niyaz1Manish Kumar2Rajesh Gupta3Department of Energy Science and Engineering, Indian Institute of Technology, Mumbai, IndiaDepartment of Energy Science and Engineering, Indian Institute of Technology, Mumbai, IndiaDepartment of Electronics and Communication Engineering, University Institute of Technology, Himachal Pradesh University Shimla, IndiaDepartment of Energy Science and Engineering, Indian Institute of Technology, Mumbai, India; Corresponding author.Electricity generation of a photovoltaic (PV) module is primarily affected by local weather conditions, which vary significantly across vast geographic areas. This work introduces an approach to categorize Indian states based on outdoor operating conditions of PV modules that influence performance and reliability. Module temperature and irradiance are the two most important parameters which affect PV performance. Relative humidity (RH), module temperature, and global horizontal irradiance (GHI) are the three most important parameters that affect PV reliability. In this work, the PV module's most frequent operating condition (MFOC) of temperature and irradiance corresponding to maximum energy production has been analyzed for dominant PV technology (multi-crystalline silicon). Data from various sites across India were analyzed and subsequently grouped by state as PV installation decisions are generally based on state-level factors, such as state business policies, incentives, availability of local human resources, state power policies, etc. The MFOC method used in this work was supported by experimental results. Based on estimated states' MFOC, PV module output power has been obtained and compared with its rated power. Further in this work, major stressors affecting PV modules namely average RH, module temperature, and total annual GHI have been analyzed for different Indian states. Based on these specific stressors, states with similar stressor patterns have been grouped by the k-means clustering method. Results of MFOC estimation show potential for additional standardization methods to estimate PV system performance accurately. Statistical analysis of stressors highlights the importance of selecting PV technology modules carefully.http://www.sciencedirect.com/science/article/pii/S2667113124000020Pv moduleIndiaMFOCk-means
spellingShingle Arti Pareek
Humaid Mohammed Niyaz
Manish Kumar
Rajesh Gupta
Categorizing Indian states based on operating condition of photovoltaic system
Solar Energy Advances
Pv module
India
MFOC
k-means
title Categorizing Indian states based on operating condition of photovoltaic system
title_full Categorizing Indian states based on operating condition of photovoltaic system
title_fullStr Categorizing Indian states based on operating condition of photovoltaic system
title_full_unstemmed Categorizing Indian states based on operating condition of photovoltaic system
title_short Categorizing Indian states based on operating condition of photovoltaic system
title_sort categorizing indian states based on operating condition of photovoltaic system
topic Pv module
India
MFOC
k-means
url http://www.sciencedirect.com/science/article/pii/S2667113124000020
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AT rajeshgupta categorizingindianstatesbasedonoperatingconditionofphotovoltaicsystem