کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7934906 | 1513046 | 2018 | 13 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Modeling of PV system based on experimental data for fault detection using kNN method
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کلمات کلیدی
ABCNOCTSTCMLTCMMRESEIMCSOFDCKNNTDRANFISPLAMPPTECMk-Nearest Neighbors - K نزدیک ترین همسایگانArtificial fish swarm algorithm - الگوریتم ماهیگیری مصنوعیTime-domain reflectometry - بازه اندازه گیری دامنه زمانPSO - بهینه سازی ازدحام ذراتParticle swarm optimization - بهینه سازی ازدحام ذراتCat swarm optimization - بهینه سازی گربه گربهFault detection and classification - تشخیص و طبقه بندی گسلMachine learning techniques - تکنیک های یادگیری ماشینAlternating current - جریان متناوبDirect Current - جریان مستقیمMaximum power point tracking - حداکثر ردیابی نقطه قدرتStandard test conditions - شرایط آزمون استانداردPhotovoltaic - فتوولتائیکRenewable energy sources - منابع انرژی تجدیدپذیر artificial intelligence - هوش مصنوعیpersonal computer - کامپیوتر شخصیArtificial bee colony - کلنی زنبور عسل مصنوعی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی انرژی
انرژی های تجدید پذیر، توسعه پایدار و محیط زیست
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
In this paper, a string level fault detection and diagnosis technique for photovoltaic (PV)systems based on k-nearest neighbors (kNN) rule is proposed. It detects and classifies open circuit faults, line-line (L-L) faults, partial shading with and with-out bypass diode faults and partial shading with inverted bypass diode faults in real time. A detailed modeling of the PV systems based on experimental data is presented that only requires available data from the manufacturer's datasheet reported under standard test conditions (STC) and normal operating cell temperature(NOCT). This model considers the temperature dependent variables such as junction thermal voltage Vt, diode quality factor (A) and series resistance(Rs). Simulations of the developed model have been carried out using Matlab/Simulink. A PV analyzer (Solar I-V) of HT instruments is used to measure the I(V)characteristics of PV module. The developed model precisely traces theI(V)characteristics of PV systems at different irradiance and temperature levels. The simulation results indicate that the error between the measured data and developed model is less than the models available in the literature. The absolute error is confined in the range 0.61 to 6.5%. Finally, the data generated from proposed model and experimental setup are used to validate and test the performance of the proposed fault detection and classificationFDC technique. It is observed from the results that the average of fault classification gives a high accuracy of 98.70%.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Solar Energy - Volume 173, October 2018, Pages 139-151
Journal: Solar Energy - Volume 173, October 2018, Pages 139-151
نویسندگان
Siva Ramakrishna Madeti, S.N. Singh,