Intelligent substation relay protection setting verification method based on deep learning
CAO Haiou
CHEN Peng
Abstract：[Objective]During verifying settings of relay protection equipment in substations,traditional methods mainly rely on manual verification or simple program verification.The manual verification accuracy varies,with relatively low verification efficiency.Simple program verification improves its efficiency to some extent,but there is room for further accuracy enhancement.To this end,a setting verification method for relay protection of intelligent substations based on deep learning was proposed.[Methods]Firstly,an improved convolutional recurrent neural network(CRNN)was employed to identify relay protection settings.Specifically,the convolutional neural network(CNN)was adopted to convert text images into feature sequences,followed by leveraging the recurrent neural network(RNN)to identify the feature sequences.Finally,the identification results were transcribed by adopting a dictionary-based connectionist temporal classification(CTC)loss function to obtain the setting text information.On this basis,the RNN module was enhanced by utilizing a convert gate unit,thus building a bidirectional convert gate long short-term memory(Bi-CGLSTM)model to achieve adaptive adjustment of data weights.Then,the setting verification was carried out by combining Chinese word segmentation technology.A complete dictionary of setting names was constructed,with the Levenshtein distance algorithm adopted to calculate the similarity between the text to be verified and the standard text.Additionally,an improved forward maximum matching algorithm was applied to match the setting text,thus completing the one-by-one setting verification of relay protection equipment in substations.[Results]240 relay protection setting sheets from a power supply company that cover ten common equipment models were selected as experimental samples to validate the feasibility and effectiveness of the proposed method.The training parameter setting of the deep learning model was as follows:the iteration count of 100,learning rate of 0.001,and the Adam optimizer for adjusting weights and biases.The experimental results show that the identification accuracy of the improved CRNN model exceeds 97％,while the verification accuracy of the proposed method reaches 97.07％,with relatively shorter verification time and better overall performance than that of other comparative methods.[Conclusions]The identification accuracy of the setting text of substation relay protection in the context of big data can be effectively enhanced by the improved deep neural network.Additionally,verification accuracy can be ensured and verification efficiency can be significantly enhanced by the combination of the Levenshtein distance algorithm and the improved forward maximum matching algorithm.Powerful technical support is provided by the proposed method for the intelligent operation and maintenance of intelligent substations.
Keywords：intelligent substationrelay protection equipmentsetting verificationconvolutional recurrent neural networkdictionaryimproved forward maximum matching algorithmattention mechanism
Publication Date：2025-11-25
Online Publishing Date：2025-12-29(First online date of this platform, not the publication date of the document)
Pages：7( 704-710 )