Underground power quality disturbance identification system based on improved deep learning algorithm

The invention relates to an underground power quality disturbance identification system based on an improved deep learning algorithm, and the method comprises the following steps: S1, collecting data through a mutual inductor, obtaining nine kinds of single disturbance signals and composite power qu...

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Bibliographic Details
Main Authors HU LIJIA, WANG YU, CHEN YEHUI
Format Patent
LanguageChinese
English
Published 29.07.2022
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
HANDLING RECORD CARRIERS
MEASURING
MEASURING ELECTRIC VARIABLES
MEASURING MAGNETIC VARIABLES
PHYSICS
PRESENTATION OF DATA
RECOGNITION OF DATA
RECORD CARRIERS
TESTING
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Summary:The invention relates to an underground power quality disturbance identification system based on an improved deep learning algorithm, and the method comprises the following steps: S1, collecting data through a mutual inductor, obtaining nine kinds of single disturbance signals and composite power quality disturbance signals through a sampling board card, and uploading the data to an upper computer through an industrial standard RS-485 communication interface by the collection board card; s2, in an upper computer, determining an optimal feature layer and freezing parameters of a pre-trained deep convolutional neural network model Alex Net, and performing model migration; s3, after the model is migrated, replacing a Softmax classifier of the migrated model with an SVM (Support Vector Machine), and obtaining a new full connection layer in the model; and S4, performing fine adjustment on the parameters of the latest model by using the single disturbance signal and the composite power quality disturbance signal af
Bibliography:Application Number: CN202110117257