Abstract：The accurate recognition of power quality disturbance（PQD）is one of the main problems to be

solved after PQD occurrence，which is of great importance for responsibility dividing and power market reform

process accelerating. Massive quantities of power quality monitoring data prepare the ground for the recognition of

PQD. Since the electrical characteristic is different for different PQD，the waveform difference between different

power quality disturbances can be employed for the recognition of PQD. Combing the deep learning，the method

for the recognition of complex PQD via bidirectional independently recurrent neural network（Bi-IndRNN）was

proposed. In this way，the intrinsic characteristic of PQD was extracted，the internal correspondence between the

input sequence and the output sequence was established，the dependence of the analysis result on the physical

characteristic quantity was overcome，and the recognition accuracy of PQD was improved. The results illustrate

that the diversity of complex PQD can be effectively responded，where the intrinsic characteristic hidden in

complex PQD signal can be extracted directly，resulting in high accuracy.

      Key words：power quality disturbance（PQD）recognition；bidirectional independently recurrent neural network （Bi-IndRNN）；deep Learning