<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rezgui Wail</style></author><author><style face="normal" font="default" size="100%">Mouss Leila Hayet</style></author><author><style face="normal" font="default" size="100%">Kadri Ouahab</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Electrical faults detection for the intelligent diagnosis of a photovoltaic generator, March, ISSN/ISBN 1582-4594/1335-3632.</style></title><secondary-title><style face="normal" font="default" size="100%">JEE Journal of Electrical Engineering,</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://pdfs.semanticscholar.org/8d54/cc8ebdfc720b5d95cbb6d4238667874f8799.pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">Vol. 14</style></volume><pages><style face="normal" font="default" size="100%">pp. 77-84</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;span style=&quot;left:244.973px;top:502.546px;15.8082px;sans-serif;transform:scaleX(0.91482);&quot;&gt;the work presented in&lt;/span&gt;&lt;span style=&quot;left:409.007px;top:502.546px;15.8082px;sans-serif;transform:scaleX(0.938393);&quot;&gt;this paper is&lt;/span&gt;&lt;span style=&quot;left:132.761px;top:520.639px;15.8082px;sans-serif;transform:scaleX(0.927281);&quot;&gt;dedicated to improving&lt;/span&gt;&lt;span style=&quot;left:296.588px;top:520.639px;15.8082px;sans-serif;transform:scaleX(0.908424);&quot;&gt;the methods of&lt;/span&gt;&lt;span style=&quot;left:407.674px;top:520.639px;15.8082px;sans-serif;transform:scaleX(0.901188);&quot;&gt;detection and &lt;/span&gt;&lt;span style=&quot;left:132.761px;top:538.924px;15.8082px;sans-serif;transform:scaleX(0.896152);&quot;&gt;diagnosis&lt;/span&gt;&lt;span style=&quot;left:208.024px;top:538.924px;15.8082px;sans-serif;transform:scaleX(1.01458);&quot;&gt;of&lt;/span&gt;&lt;span style=&quot;left:236.593px;top:538.924px;15.8082px;sans-serif;transform:scaleX(0.929885);&quot;&gt;faults affecting&lt;/span&gt;&lt;span style=&quot;left:358.726px;top:538.924px;15.8082px;sans-serif;transform:scaleX(0.900228);&quot;&gt;production systems,&lt;/span&gt;&lt;span style=&quot;left:132.761px;top:557.208px;15.8082px;sans-serif;transform:scaleX(0.957595);&quot;&gt;particularly&lt;/span&gt;&lt;span style=&quot;left:213.357px;top:557.208px;15.8082px;sans-serif;transform:scaleX(0.907186);&quot;&gt;photovoltaic systems.&lt;/span&gt;&lt;span style=&quot;left:362.154px;top:557.208px;15.8082px;sans-serif;transform:scaleX(0.883517);&quot;&gt;We proposed&lt;/span&gt;&lt;span style=&quot;left:456.432px;top:557.208px;15.8082px;sans-serif;transform:scaleX(0.883265);&quot;&gt;a new&lt;/span&gt;&lt;span style=&quot;left:132.761px;top:575.492px;15.8082px;sans-serif;transform:scaleX(0.943428);&quot;&gt;intelligent algorithm&lt;/span&gt;&lt;span style=&quot;left:272.019px;top:575.492px;15.8082px;sans-serif;transform:scaleX(0.876658);&quot;&gt;for the de&lt;/span&gt;&lt;span style=&quot;left:338.299px;top:575.492px;15.8082px;sans-serif;transform:scaleX(0.926749);&quot;&gt;tection&lt;/span&gt;&lt;span style=&quot;left:388.056px;top:575.492px;15.8082px;sans-serif;transform:scaleX(0.881836);&quot;&gt;and diagnosis of&lt;/span&gt;&lt;span style=&quot;left:132.761px;top:593.776px;15.8082px;sans-serif;transform:scaleX(0.974366);&quot;&gt;PV&lt;/span&gt;&lt;span style=&quot;left:160.796px;top:593.776px;15.8082px;sans-serif;transform:scaleX(0.927617);&quot;&gt;installations&lt;/span&gt;&lt;span style=&quot;left:237.355px;top:593.776px;15.8082px;sans-serif;transform:scaleX(0.902952);&quot;&gt;, capable of detecting &lt;/span&gt;&lt;span style=&quot;left:393.008px;top:593.776px;15.8082px;sans-serif;transform:scaleX(0.866326);&quot;&gt;and resonate&lt;/span&gt;&lt;span style=&quot;left:484.81px;top:593.776px;15.8082px;sans-serif;transform:scaleX(0.929575);&quot;&gt;to &lt;/span&gt;&lt;span style=&quot;left:132.761px;top:612.06px;15.8082px;sans-serif;transform:scaleX(0.897905);&quot;&gt;define the&lt;/span&gt;&lt;span style=&quot;left:206.691px;top:612.06px;15.8082px;sans-serif;transform:scaleX(0.899847);&quot;&gt;type of defects&lt;/span&gt;&lt;span style=&quot;left:314.491px;top:612.06px;15.8082px;sans-serif;transform:scaleX(0.881152);&quot;&gt;that can&lt;/span&gt;&lt;span style=&quot;left:375.296px;top:612.06px;15.8082px;sans-serif;transform:scaleX(0.933645);&quot;&gt;affect&lt;/span&gt;&lt;span style=&quot;left:418.721px;top:612.06px;15.8082px;sans-serif;transform:scaleX(0.911722);&quot;&gt;this type&lt;/span&gt;&lt;span style=&quot;left:483.858px;top:612.06px;15.8082px;sans-serif;transform:scaleX(1.01458);&quot;&gt;of&lt;/span&gt;&lt;span style=&quot;left:132.761px;top:630.154px;15.8082px;sans-serif;transform:scaleX(0.907682);&quot;&gt;system. This new algorithm&lt;/span&gt;&lt;span style=&quot;left:325.728px;top:630.154px;15.8082px;sans-serif;transform:scaleX(0.85952);&quot;&gt;is based on&lt;/span&gt;&lt;span style=&quot;left:409.959px;top:630.154px;15.8082px;sans-serif;transform:scaleX(0.912144);&quot;&gt;the notion of&lt;/span&gt;&lt;span style=&quot;left:132.761px;top:648.438px;15.8082px;sans-serif;transform:scaleX(0.921087);&quot;&gt;pattern recognition,&lt;/span&gt;&lt;span style=&quot;left:270.114px;top:648.438px;15.8082px;sans-serif;transform:scaleX(0.964582);&quot;&gt;for that it is&lt;/span&gt;&lt;span style=&quot;left:365.963px;top:648.438px;15.8082px;sans-serif;transform:scaleX(0.899424);&quot;&gt;able to prepare&lt;/span&gt;&lt;span style=&quot;left:478.144px;top:648.438px;15.8082px;sans-serif;transform:scaleX(0.873452);&quot;&gt;the &lt;/span&gt;&lt;span style=&quot;left:132.761px;top:666.722px;15.8082px;sans-serif;transform:scaleX(0.869351);&quot;&gt;representat&lt;/span&gt;&lt;span style=&quot;left:202.317px;top:666.722px;15.8082px;sans-serif;transform:scaleX(0.858692);&quot;&gt;ion space&lt;/span&gt;&lt;span style=&quot;left:270.495px;top:666.722px;15.8082px;sans-serif;transform:scaleX(0.863901);&quot;&gt;and&lt;/span&gt;&lt;span style=&quot;left:299.635px;top:666.722px;15.8082px;sans-serif;transform:scaleX(0.859938);&quot;&gt;the decision space&lt;/span&gt;&lt;span style=&quot;left:426.148px;top:666.722px;15.8082px;sans-serif;transform:scaleX(0.853228);&quot;&gt;on the one &lt;/span&gt;&lt;span style=&quot;left:132.761px;top:685.006px;15.8082px;sans-serif;transform:scaleX(0.875684);&quot;&gt;hand,&lt;/span&gt;&lt;span style=&quot;left:172.034px;top:685.006px;15.8082px;sans-serif;transform:scaleX(0.905609);&quot;&gt;and on the other&lt;/span&gt;&lt;span style=&quot;left:280.97px;top:685.006px;15.8082px;sans-serif;transform:scaleX(0.889435);&quot;&gt;hand, the&lt;/span&gt;&lt;span style=&quot;left:344.203px;top:685.006px;15.8082px;sans-serif;transform:scaleX(0.934018);&quot;&gt;classification&lt;/span&gt;&lt;span style=&quot;left:432.434px;top:685.006px;15.8082px;sans-serif;transform:scaleX(0.9691);&quot;&gt;of all new&lt;/span&gt;&lt;span style=&quot;left:132.761px;top:703.291px;15.8082px;sans-serif;transform:scaleX(0.893071);&quot;&gt;observations&lt;/span&gt;&lt;span style=&quot;left:222.88px;top:703.291px;15.8082px;sans-serif;transform:scaleX(0.910763);&quot;&gt;collected during&lt;/span&gt;&lt;span style=&quot;left:341.727px;top:703.291px;15.8082px;sans-serif;transform:scaleX(0.898026);&quot;&gt;the functioning of the &lt;/span&gt;&lt;span style=&quot;left:132.761px;top:721.575px;15.8082px;sans-serif;transform:scaleX(0.881193);&quot;&gt;system&lt;/span&gt;&lt;span style=&quot;left:176.605px;top:721.575px;15.8082px;sans-serif;transform:scaleX(0.91247);&quot;&gt;. &lt;/span&gt;&lt;span style=&quot;left:184.794px;top:721.575px;15.8082px;sans-serif;transform:scaleX(0.948491);&quot;&gt;This algorithm&lt;/span&gt;&lt;span style=&quot;left:283.065px;top:721.575px;15.8082px;sans-serif;transform:scaleX(0.906989);&quot;&gt;mainly based on&lt;/span&gt;&lt;span style=&quot;left:391.675px;top:721.575px;15.8082px;sans-serif;transform:scaleX(0.915423);&quot;&gt;the method of&lt;/span&gt;&lt;span style=&quot;left:484.23876499999994px;top:721.5746832866665px;15.80818px;sans-serif;&quot;&gt;k&lt;/span&gt;&lt;span style=&quot;left:492.0476249999999px;top:721.5746832866665px;15.80818px;sans-serif;&quot;&gt;-&lt;/span&gt;&lt;span style=&quot;left:132.761px;top:739.668px;15.8082px;sans-serif;transform:scaleX(0.887924);&quot;&gt;nearest neighbor &lt;/span&gt;&lt;span style=&quot;left:242.116px;top:739.668px;15.8082px;sans-serif;transform:scaleX(0.920124);&quot;&gt;and two tools&lt;/span&gt;&lt;span style=&quot;left:331.823px;top:739.668px;15.8082px;sans-serif;transform:scaleX(0.989529);&quot;&gt;of artificial &lt;/span&gt;&lt;span style=&quot;left:406.722px;top:739.668px;15.8082px;sans-serif;transform:scaleX(0.935892);&quot;&gt;intelligence&lt;/span&gt;&lt;span style=&quot;left:484.81px;top:739.668px;15.8082px;sans-serif;transform:scaleX(0.929575);&quot;&gt;to &lt;/span&gt;&lt;span style=&quot;left:132.761px;top:757.953px;15.8082px;sans-serif;transform:scaleX(0.913823);&quot;&gt;improve this&lt;/span&gt;&lt;span style=&quot;left:229.165px;top:757.953px;15.8082px;sans-serif;transform:scaleX(0.872351);&quot;&gt;method and&lt;/span&gt;&lt;span style=&quot;left:320.015px;top:757.953px;15.8082px;sans-serif;transform:scaleX(0.874362);&quot;&gt;increasing the rate of&lt;/span&gt;&lt;span style=&quot;left:482.334px;top:757.953px;15.8082px;sans-serif;transform:scaleX(0.932043);&quot;&gt;its &lt;/span&gt;&lt;span style=&quot;left:132.761px;top:776.268px;15.8082px;sans-serif;transform:scaleX(0.938719);&quot;&gt;classification, which are&lt;/span&gt;&lt;span style=&quot;left:306.111px;top:776.268px;15.8082px;sans-serif;transform:scaleX(0.974399);&quot;&gt;fuzzy logic to&lt;/span&gt;&lt;span style=&quot;left:413.007px;top:776.268px;15.8082px;sans-serif;transform:scaleX(0.94043);&quot;&gt;optimize&lt;/span&gt;&lt;span style=&quot;left:477.763px;top:776.268px;15.8082px;sans-serif;transform:scaleX(0.887259);&quot;&gt;the &lt;/span&gt;&lt;span style=&quot;left:132.761px;top:794.553px;15.8082px;sans-serif;transform:scaleX(0.877199);&quot;&gt;location of the&lt;/span&gt;&lt;span style=&quot;left:232.213px;top:794.553px;15.8082px;sans-serif;transform:scaleX(0.873935);&quot;&gt;centers of gravity of&lt;/span&gt;&lt;span style=&quot;left:369.582px;top:794.553px;15.8082px;sans-serif;transform:scaleX(0.835968);&quot;&gt;classes&lt;/span&gt;&lt;span style=&quot;left:418.911px;top:794.553px;15.8082px;sans-serif;transform:scaleX(0.863901);&quot;&gt;and&lt;/span&gt;&lt;span style=&quot;left:447.099px;top:794.553px;15.8082px;sans-serif;transform:scaleX(0.878677);&quot;&gt;also&lt;/span&gt;&lt;span style=&quot;left:477.954px;top:794.553px;15.8082px;sans-serif;transform:scaleX(0.879455);&quot;&gt;the &lt;/span&gt;&lt;span style=&quot;left:132.761px;top:812.837px;15.8082px;sans-serif;transform:scaleX(0.88913);&quot;&gt;new observations,&lt;/span&gt;&lt;span style=&quot;left:264.591px;top:812.837px;15.8082px;sans-serif;transform:scaleX(0.882317);&quot;&gt;and the neural network that&lt;/span&gt;&lt;span style=&quot;left:475.287px;top:812.837px;15.8082px;sans-serif;transform:scaleX(0.865875);&quot;&gt;can &lt;/span&gt;&lt;span style=&quot;left:132.761px;top:831.121px;15.8082px;sans-serif;transform:scaleX(0.908451);&quot;&gt;classify the&lt;/span&gt;&lt;span style=&quot;left:215.262px;top:831.121px;15.8082px;sans-serif;transform:scaleX(0.881312);&quot;&gt;case of disch&lt;/span&gt;&lt;span style=&quot;left:304.206px;top:831.121px;15.8082px;sans-serif;transform:scaleX(0.83888);&quot;&gt;arges&lt;/span&gt;&lt;span style=&quot;left:344.774px;top:831.121px;15.8082px;sans-serif;transform:scaleX(0.952231);&quot;&gt;ambiguity&lt;/span&gt;&lt;span style=&quot;left:416.435px;top:831.121px;15.8082px;sans-serif;transform:scaleX(0.8705);&quot;&gt;and&lt;/span&gt;&lt;span style=&quot;left:446.909px;top:831.121px;15.8082px;sans-serif;transform:scaleX(0.838632);&quot;&gt;releases&lt;/span&gt;&lt;span style=&quot;left:132.761px;top:849.215px;15.8082px;sans-serif;transform:scaleX(0.879436);&quot;&gt;distance&lt;/span&gt;&lt;span style=&quot;left:189.937px;top:849.215px;15.8082px;sans-serif;transform:scaleX(0.907232);&quot;&gt;which presents&lt;/span&gt;&lt;span style=&quot;left:290.684px;top:849.215px;15.8082px;sans-serif;transform:scaleX(0.910714);&quot;&gt;the limitations of the method&lt;/span&gt;&lt;span style=&quot;left:484.048px;top:849.215px;15.8082px;sans-serif;transform:scaleX(0.972811);&quot;&gt;of &lt;/span&gt;&lt;span style=&quot;left:132.761px;top:867.499px;15.8082px;sans-serif;transform:scaleX(0.875031);&quot;&gt;the&lt;/span&gt;&lt;span style=&quot;left:156.60629866666667px;top:867.4987866199999px;15.80818px;sans-serif;&quot;&gt;k&lt;/span&gt;&lt;span style=&quot;left:164.60561866666663px;top:867.4987866199999px;15.80818px;sans-serif;&quot;&gt;-&lt;/span&gt;&lt;span style=&quot;left:169.748px;top:867.499px;15.8082px;sans-serif;transform:scaleX(0.891851);&quot;&gt;nearest neighbor. &lt;/span&gt;&lt;span style=&quot;left:283.256px;top:867.499px;15.8082px;sans-serif;transform:scaleX(0.833719);&quot;&gt;We&lt;/span&gt;&lt;span style=&quot;left:308.587px;top:867.499px;15.8082px;sans-serif;transform:scaleX(0.892525);&quot;&gt;tested the performance&lt;/span&gt;&lt;span style=&quot;left:458.336px;top:867.499px;15.8082px;sans-serif;transform:scaleX(0.954494);&quot;&gt;of our &lt;/span&gt;&lt;span style=&quot;left:132.761px;top:885.783px;15.8082px;sans-serif;transform:scaleX(0.909367);&quot;&gt;algorithm on&lt;/span&gt;&lt;span style=&quot;left:221.547px;top:885.783px;15.8082px;sans-serif;transform:scaleX(0.81633);&quot;&gt;a database&lt;/span&gt;&lt;span style=&quot;left:294.683px;top:885.783px;15.8082px;sans-serif;transform:scaleX(1.01458);&quot;&gt;of&lt;/span&gt;&lt;span style=&quot;left:313.539px;top:885.783px;15.8082px;sans-serif;transform:scaleX(0.859778);&quot;&gt;a photovoltaic system at the&lt;/span&gt;&lt;span style=&quot;left:132.761px;top:904.067px;15.8082px;sans-serif;transform:scaleX(0.904784);&quot;&gt;research unit of&lt;/span&gt;&lt;span style=&quot;left:235.26px;top:904.067px;15.8082px;sans-serif;transform:scaleX(1.02627);&quot;&gt;GHARDAIA&lt;/span&gt;&lt;span style=&quot;left:322.3px;top:904.067px;15.8082px;sans-serif;transform:scaleX(0.95823);&quot;&gt;Algeria.&lt;/span&gt;</style></abstract><issue><style face="normal" font="default" size="100%">Issue. 1</style></issue></record></records></xml>