For　minute　time　scale　solar　photovoltaic　(PV)　power　forecasting,　the　motion　of　clouds　over　PV　power　plant　mainly　contribute　to　the　fluctuant　and　intermittent　nature　of　solar　PV　power　output.　Therefore,　research　on　cloud　motion　displacement　(CMD)　calculation　to　realize　cloud　motion　prediction　is　a　key　sub-process　for　minute　time　scale　solar　PV　power　forecasting　approaches.　Fourier　phase　correlation　theory　(FPCT)　is　widely　applied　in　CMD　calculation　for　its　superiority　of　simplicity　and　less　computation,　then　an　improved　algorithm　based　on　image-phase-shift-invariance　(IPSI)　is　proposed　to　reduce　the　outlier　probability　of　CMD　results.　However,　at　present,　the　current　IPSI　algorithm　still　has　limitations　and　cannot　avoid　the　occurrence　of　outliers　altogether.　In　this　paper,　we　presented　a　novel　method,　termed　IPSI　based　multi-transform-fusion　(MTF)　method,　to　further　improve　the　effectiveness　compared　with　traditional　FPCT　and　affine　transform　based　IPSI　method.　First,　three　image　transform　methods　satisfying　IPSI　condition,　respectively　wavelet　transform　(WT),　affine　transform　(AT),　and　convolution　transform　(CT),　are　explored.　Then　the　information　increment　of　the　transformed　sky　images　using　the　above　three　methods　is　analyzed,　respectively.　Second,　we　determine　the　suitable　image　transform　method　for　IPSI　algorithm　under　specific　cloud　condition　according　to　the　corresponding　information　increment.　Third,　an　IPSI　based　MTF　method　for　CMD　calculation　in　sky　images　is　proposed.　The　original　sky　images　are　transformed　through　WT,　AT,　and　CT　to　generate　multiple　images　that　maintain　the　same　object　motion　information,　then　calculate　the　CMDs　in　each　generated　image.　Finally,　we　apply　Gaussian　distribution　to　fit　the　multiple　CMD　values　and　taking　its　mathematical　expectation　as　final　CMD　result.　Various　experimental　results　in　4　different　scenarios　show　that　the　performance　of　the　proposed　approach　is　better　than　FPCT,　AT　based　IPSI,　and　OF　method,　by　reducing　plenty　of　CMD　outliers,　thus　delivering　greater　accuracy　and　robustness.