The　growing　penetration　of　renewable　energy　sources　leads　to　power　quality　disturbances　(PQDs)　being　multiple.　The　real-time　monitoring　of　the　parameters　of　multiple　power　quality　disturbances　(MPQDs)　can　help　electricity　distributors　better　control　power　quality　problems.　In　this　paper,　an　adaptive　spectral　trend-based　optimized　empirical　wavelet　transform　(EWT)　is　proposed　to　analyze　MPQDs.　First,　the　upper　and　lower　envelopes　of　the　signal　frequency　spectrum　are　fitted　by　piecewise　cubic　Hermite　interpolation.　Then　the　sum　of　the　upper　and　lower　envelopes　is　halved　as　a　spectral　trend.　These　steps　are　repeated　until　all　peak-to-peak　distances　are　greater　than　the　key　threshold,　indicating　that　the　spectral　trend　has　already　been　optimized.　Third,　main　frequency　components　are　calculated　based　on　peaks　of　the　optimal　spectral　trend,　which　can　be　used　as　a　basis　for　spectrum　segmentation.　Eventually,　the　components　decomposed　by　EWT　represent　different　disturbance　elements,　whose　parameters　can　be　further　calculated　by　Hilbert　transform　(HT).　To　verify　the　effectiveness　of　the　proposed　algorithm,　two　databases　are　applied　for　analysis:　synthetic　signals,　and　recording　signals　by　the　experimental　platform.　At　the　same　time,　four　advanced　algorithms　are　used　for　comparison,　which　are　scale-space　optimized　EWT　(SOEWT),　improved　EWT　(IEWT),　variational　mode　decomposition　(VMD)　and　fast　Stockwell　transform　(FST).　The　results　showed　that　the　proposed　method　can　accurately　segment　the　spectrum　and　prevent　the　inappropriate　segmentation　of　the　original　EWT　so　that　it　can　monitor　the　disturbance　parameters　with　high　precision.　In　addition,　the　proposed　algorithm　has　a　simple　computation.　©　2022　Elsevier　Ltd