Abstract
Aiming at the uncertainty of fault type reasoning based on fault data in transformer fault diagnosis model, this paper proposed a hierarchical diagnosis model based on neighborhood rough set and XGBoost. The model used arctangent transformation to preprocess the DGA data, which could reduce the distribution span of data features and the complexity of model training. Using 5 characteristic gases and 16 gas ratios as the input characteristic parameters of the XGBoost model at all levels, reduction was performed on these 21 input feature attributes, features that had a high contribution to fault classification were retained, and redundant features were removed to improve the accuracy and efficiency of model prediction. Taking advantage of XGBoost's strong ability to extract a few features, the output of the model was the superposition of leaf node scores for each type of fault, the maximum score was the type of failure the sample belonged to, and its value was also the probability value. The obtained probability was used as one of the evidence sources to use D-S evidence theory for information fusion to verify the reliability of the model. Experiments have proved that the XGBoost graded diagnosis model proposed in this article has the highest overall accuracy rate comparing with the traditional model, reaching 93.01%, the accuracy of XGBoost models at all levels has reached more than 90%, the average accuracy rate is higher than that of the traditional model by an average of more than 2.7%, and the average time-consuming is only 0.0695 s. After D-S multi-source information fusion, the reliability of the prediction results of the model proposed in this paper has been improved.
Highlights
Transformers are important equipment for transforming and transmitting electrical energy
In order to find early faults inside the transformer, combining with the characteristics of real-time, online, no electricity, and magnetic field interference based on DGA diagnosis[5], This paper proposes XGBoost's multi-level transformer fault diagnosis based on neighborhood rough set
Based on the XGBoost model, this paper uses the neighborhood rough set theory to reduce its input, and uses its output as one of the evidence sources to employ D-S evidence theory for information fusion, and establish the XGBoost graded diagnosis model, which is compared with the non-code ratio method, BPNN and RandomForest, the prediction accuracy of the transformer graded diagnosis model proposed in this paper is higher than 2.7% on average, and the average time from training model to prediction is less than 0.07 s
Summary
Transformers are important equipment for transforming and transmitting electrical energy. Reference [1] introduces a correction factor to the nearest neighbor component analysis algorithm, and maps the K nearest neighbors in combination with the training metric matrix, thereby improving the classification performance of the K nearest neighbor algorithm on unbalanced data sets; reference [2] inputs the DC transient excitation into the transformer winding, and takes the oscillating wave response at the end of the winding as the analysis object, and proposes a winding fault diagnosis technology; reference [3] quantifies the change characteristics of condition monitoring data over time, calculates the control limit of T 2 and Q statistics, and determines the samples that exceed the control limit as fault samples, proposes unsupervised concept drift recognition and dynamic graph embedded transformer fault detection method; reference [4] uses deep belief network for unsupervised training, extracts features from DGA data and combines D-S evidence theory to solve the uncertainty problem of transformer fault diagnosis. In order to find early faults inside the transformer, combining with the characteristics of real-time, online, no electricity, and magnetic field interference based on DGA diagnosis[5], This paper proposes XGBoost's multi-level transformer fault diagnosis based on neighborhood rough set. This paper uses DS evidence theory[8] for information fusion to solve the uncertainty[9] and imprecision of diagnosis knowledge and methods, thereby improving the reliability of model diagnosis
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