The　word’s　demand　for　renewable　energy　has　be　rinsing　incrementally.　One　of　the　solutions　for　the　energy　crisis　is　photovoltaic.　However,　the　design　and　development　of　better　performing　photovoltaic　cells　and　modules　requires　accurate　extraction　of　their　intrinsic　parameters.　Metaheuristic　algorithms　have　been　reported　to　be　the　best　methods　for　obtaining　accurate　values　of　these　intrinsic　parameters.　However,　local　convergence　goes　against　the　recently　devised　heuristic　methods　and　inhibits　them　from　producing　optimal　result.　This　paper　proposes　a　hybrid　method　that　is　based　on　the　Newton　Raphson　method　and　a　self-adaptive　algorithm　called　the　Drone　Squadron　Optimisation.　The　latter　is　an　artifact　technique　inspired　by　the　simulation　of　a　drone　squadron　from　a　command　centre.　It　is　proposed　that　this　hybrid　method　can　help　extract　the　best　intrinsic　parameters　of　photovoltaic　cell　and　module.　This　study　also　provides　insights　and　clarification　on　the　reported　approaches　that　have　been　recently　proposed　to　formulate　the　objective　function.　Further,　this　study　also　computes　and　compares　the　ten　best　recently　published　heuristics　algorithms　in　the　domain　of　photovoltaic　estimation.　The　study’s　results　obtain　point　to　the　difference　between　the　two　formulations　and　the　accuracy　of　the　best　formulation.　The　results　obtained　from　the　six　case　studies　covered　in　this　study　present　the　combined　performance　of　the　Newton　Raphson　method　and　Drone　Squadron　Optimisation　to　extract　the　accurate　parameters　of　a　photovoltaic　module.　©　2021,　The　Korean　Institute　of　Electrical　and　Electronic　Material　Engineers.