When　the　power　grid　presents　an　unobservable　state　due　to　insufficient　measurement　configuration,　traditional　state　estimation　methods　fail　to　accurately　detect　the　power　grid’s　harmonic　distribution.　Therefore,　this　paper　proposes　a　harmonic　state　estimation　method,　which　combines　the　mechanism　of　harmonic　propagation　and　generative　adversarial　networks　(GAN)　to　estimate　the　harmonic　state　of　unobservable　nodes.　Firstly,　the　unobservable　region　is　simplified　using　the　network　topology　equivalent　method,　and　the　harmonic　transfer　equations　between　the　state　variables　of　unobservable　nodes　and　the　virtual　state　variables　of　boundary　nodes　are　derived,　which　is　used　as　the　basis　for　the　integration　of　the　mechanism　and　GAN.　Secondly,　a　GAN-based　harmonic　state　estimation　model　is　constructed,　which　formulates　a　loss　function　based　on　harmonic　state　equations　and　transfer　equations.　The　loss　function　incorporates　measurement　residuals　and　the　mean　square　error　of　virtual　state　quantities　at　boundary　nodes　as　penalization　terms,　thereby　refining　the　training　process　of　the　model　via　harmonic　equations.　Furthermore,　a　residual　model　incorporating　attention　mechanisms　is　used　to　improve　the　structure　of　generator,　and　the　convolutional　neural　networks　are　employed　to　improve　the　the　structure　of　discriminator.　Besides,　the　patch　GAN　is　utilized　for　local　data　discrimination,　so　the　feature　mining　capabilities　of　the　model　can　be　enhanced.　Finally,　the　effectiveness　of　the　proposed　method　is　validated　through　simulation　tests　on　the　IEEE　33-node　system.　©2025　Chin.Soc.for　Elec.Eng.