Pattern Recognition of Time-Varying Signals Using Ensemble Classifiers
A new classification approach for time-varying power quality (PQ) signals using ensemble classifiers (EC) is proposed in this paper. To achieve high performance, existing expert systems require several signal features so that these systems have more compu
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Abstract A new classification approach for time-varying power quality (PQ) signals using ensemble classifiers (EC) is proposed in this paper. To achieve high performance, existing expert systems require several signal features so that these systems have more computational complexity. In order to reduce the computational cost and to improve the accuracy further, a new set of features called moments and cumulants are introduced in this paper to classify PQ events. Further, the performance of various ensemble classifiers is analyzed with the proposed feature set. Moreover, the analysis is carried out with different training and testing rates. Finally, the performance comparison is made with that of the existing techniques to prove the superiority of the proposed features and classifiers. Keywords PQ signals · Ensemble classifiers · Moments and cumulants · Boosting · Bagging
1 Introduction The exploitation of sensitive electronic components in applications of smart cities, smart buildings, and homes is growing exponentially [1]. These sensitive devices are easily affected by PQ disturbances, such as sags, surges, interruptions. [2, 3]. In real-time applications, failure of these sensitive devices may cause serious damage, especially in smart applications [4]. An automatic or blind recognition system is required to detect and identify the occurrence of PQ problem, so that devices can be M. V. Subbarao (B) · S. K. Terlapu Department of Electronics and Communication Engineering, Shri Vishnu Engineering College for Women, Bhimavaram, India e-mail: [email protected] V. V. S. S. S. Chakravarthy Department of Electronics and Communication Engineering, Raghu Institute of Technology, Visakhapatnam, India S. C. Satapaty Department of Computer Science and Engineering, KIIT, Bhubaneswar, India © Springer Nature Singapore Pte Ltd. 2021 P. S. R. Chowdary et al. (eds.), Microelectronics, Electromagnetics and Telecommunications, Lecture Notes in Electrical Engineering 655, https://doi.org/10.1007/978-981-15-3828-5_76
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prevented from damage [5]. In the last two decades, the researchers have developed different classification methods such as rough set-based [6], discrete wavelet-based [7, 8], neural network-based S-transform and modified ST-based [9–12], etc., for recognition of PQ events. Recently, artificial neural networks, rule-based expert systems, data mining-based classifiers, and fuzzy logic classification systems [13] are developed for classification of PQ events. All these methods extracted some specific features such as skewness, energy, mean, kurtosis, standard deviation, variance, and an average of the squared absolute values, etc., from the single and multiple disturbances for classification. Proper selection of features from the feature vector set is required to get more accuracy in classification and to reduce the classification time of the classifier. Some other approaches for classification of PQ events are discussed in Table 1. To overcome the drawbacks of existing PQ classifica
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