Objective With the large-scale application of photovoltaic PV power generation photovoltaic arraysoperating in natural environments are prone to frequent failures. To accurately predict the state of photovoltaic arrays andimprove the operational safety and power generation efficiency of photovoltaic systems a method combining grey relationalanalysis and fuzzy C-means FCM is proposed. Methods First a mathematical simulation model of the photovoltaicarray is established to perform a grey relational analysis between the simulated I-V curve data and measured data. Thehealth index of the photovoltaic array is then calculated and the health status is classified into four levels healthy subhealthy abnormal and faulty. Subsequently for photovoltaic arrays with sub-healthy or abnormal status and similar hcleuaslttehricnednitceerss .tThheesfeuzczlyusCte-rmceeanntsercsluanstdertiensgt daalgtaoraitrhemusiesdaipnptlhieedGtaoucssluiasntermethmebecrosrhreipspfounndcitniognPtoVdaerterarmyidnaetathetostoabtteaionftthheatpthhoetovcoolmtabicinaatriroany.oRfegsruelytsrTelhaetiopnroaploasendalymseisthoadndisFvCalMidanteodttohnrolyughredsiumceuslattihoen adnedmaenxpderfiomrenotpse.raTtihoenarlesudlattsashaonwdcomputational complexity of photovoltaic arrays but also accurately identifies the state of the photovoltaic array.Conclusion The integration of grey relational analysis and FCM addresses the issue of imprecise state assessment ofphotovoltaic arrays using grey relational analysis alone. It also reduces the data requirements and workload associated withfuzzy C-means clustering for state assessment of photovoltaic arrays.