Efficient CNN‐XGBoost technique for classification of power transformer internal faults against various abnormal conditions

Abstract

To increase the classification accuracy of a protection scheme for power transformer, an effective convolution neural network (CNN) extreme gradient boosting (XGBoost) combination is proposed in this work. Data generated from various test cases are fed to one‐dimensional CNN for high‐level feature extraction. After that, an efficient classifier tool XGBoost is used to properly discriminate different transformer internal faults against outside abnormalities. A portion of an Indian power system is considered and simulated in PSCAD software using the multi‐run feature to collect a large number of data for various fault/abnormal situations. The generated data are used in MATLAB software where the proposed algorithm is programmed. A high‐performance CPU is used for training and testing purpose of the projected artificial intelligent technique. The obtained results for classification accuracy as well as discrimination time shows that the proposed scheme is competent enough to properly discriminate transformer operational conditions. Further, the combined CNN‐XGBoost technique is compared with existing relevance vector machine and hierarchical ensemble of extreme learning machine classifier techniques. Moreover, a hardware experiment is performed in a laboratory prototype of 50 kVA, 440/220 V transformer to verify the authenticity of the developed protective scheme. After analyzing a variety of experiments, the authors note that the presented method provides promising classification accuracy within a short time period.

Intelligent Identification over Power Big Data: Opportunities, Solutions, and Challenges

“…Target Reference Method Advantage [91] Generation and identification model with GAN Generate samples to train the identification model [92] Identification and correction method for drilling data Correct the abnormal drilling data [93] Extreme gradient boosting framework…”

“…To improve the classification accuracy of the scheme for protecting the power transformer, Raichura et al [93] designed the extreme gradient boosting framework to distinguish the outside faults and inner anomalies. Moreover, a convolutional neural network (CNN) was employed to classify the faults.…”

The real‐time state identification of the electricity‐heat system based on Borderline‐SMOTE and XGBoost

“…XGBoost is a machine learning algorithm based on Boosting integration learning. It takes a series of tree models as basic classifiers and recombines them with different weights to synthesise a strong classifier, which can realize the complementary advantages of each classifier [22,23]. Compared with Gradient Boosting Decision Tree (GBDT), the XGBoost algorithm performs second-order Taylor expansions for the objective function and adds regular term, which effectively reduces the risk of overfitting and improves the accuracy and operation speed of the algorithm.…”

Fault detection and classification of an HVDC transmission line using a heterogenous multi‐machine learning algorithm

“…The main question on the threshold is about the process of choosing an appropriate value; most of the researchers have selected it according to the worst‐case study. In this research, we use no threshold value, and the task of classification and detection is fulfilled by comparing the similarities between query data and training dataset. Other research projects such as [37–39] have used ML for fault detection and classification too, but the main contribution of this paper and the proposed algorithm is a new insight into integrated ML system architecture where a combination of two or more different ML models through a new system architecture can provide salient capabilities as it can be seen in [40]. We consider different ML models for detection and classification and connect them with an appropriate property which results in ensembled learning and a more accurate protection model.…”