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Author

Aleena Swetapadma

Bio: Aleena Swetapadma is an academic researcher from KIIT University. The author has contributed to research in topics: Fault (power engineering) & Fault indicator. The author has an hindex of 15, co-authored 66 publications receiving 792 citations. Previous affiliations of Aleena Swetapadma include National Institute of Technology, Raipur.

Papers published on a yearly basis

Papers
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Proceedings ArticleDOI
15 May 2019
TL;DR: This paper highlights the kNN method and its modified versions available in previously done researches and suggests variants that remove the weaknesses of kNN and provide a more efficient method.
Abstract: k-Nearest Neighbor (kNN) algorithm is an effortless but productive machine learning algorithm. It is effective for classification as well as regression. However, it is more widely used for classification prediction. kNN groups the data into coherent clusters or subsets and classifies the newly inputted data based on its similarity with previously trained data. The input is assigned to the class with which it shares the most nearest neighbors. Though kNN is effective, it has many weaknesses. This paper highlights the kNN method and its modified versions available in previously done researches. These variants remove the weaknesses of kNN and provide a more efficient method.

214 citations

Journal ArticleDOI
TL;DR: In this paper, three separate fuzzy inference systems are designed for complete protection scheme for transmission line, which is able to accurately detect the fault (both forward and reverse), locate and also identify the faulty phase(s) involved in all ten types of shunt faults that may occur in a transmission line under different fault inception angle, fault resistances and fault location.
Abstract: This study aims to improve the performance of transmission line directional relaying, fault classification and fault location schemes using fuzzy system. Three separate fuzzy inference system are designed for complete protection scheme for transmission line. The proposed technique is able to accurately detect the fault (both forward and reverse), locate and also identify the faulty phase(s) involved in all ten types of shunt faults that may occur in a transmission line under different fault inception angle, fault resistances and fault location. The proposed method needs current and voltage measurements available at the relay location and can perform the fault detection and classification in about a half-cycle time. The proposed fuzzy logic based relay has less computation complexity and is better than other AI based methods such as artificial neural network, support vector machine, and decision tree (DT) etc. which require training. The percentage error in fault location is within 1 km for most of the cases. Fault location scheme has been validated using χ2 test with 5% level of significance. Proposed scheme is a setting free method and is suitable for wide range of parameters, fault detection time is less than half cycle and relay does not show any reaching mal-operation so it is reliable, accurate and secure.

92 citations

Proceedings ArticleDOI
01 Feb 2019
TL;DR: This paper surveys the various concepts of support vector machines, some of its real life applications and future aspects of SVM.
Abstract: The best way to acquire knowledge about an algorithm is feeding it data and checking the result. In a layman's language machine learning can be called as an ideological child or evolution of the idea of understanding algorithm through data. Machine learning can be subdivided into two paradigms, supervised learning and unsupervised learning. Supervised learning is implemented to classify data using algorithms like support vector machines (SVM), linear regression, logistic regression, neural networks, nearest neighbor etc. Supervised learning algorithm uses the concepts of classification and regression. Linear classification was earlier used to form the decision plane but was bidimensional. But a particular dataset might have required a non linear decision plane. This gave the idea of the support vector machine algorithm which can be used to generate a non linear decision boundary using the kernel function. SVM is a vast concept and can be implemented on various real world problems like face detection, handwriting detection and many more. This paper surveys the various concepts of support vector machines, some of its real life applications and future aspects of SVM.

87 citations

Journal ArticleDOI
TL;DR: In this article, a fault location algorithm which does not need to classify the fault type before location estimation is presented, which can locate all types of shunt faults including the cross-country and evolving faults.

69 citations

Journal ArticleDOI
TL;DR: In this article, a decision tree regression (DTR)-based fault distance estimation scheme for double-circuit transmission lines is presented, where three-phase current and voltage signals measured at one end of the line are used as inputs to a fault-location network.
Abstract: In this paper, a decision tree regression (DTR)-based fault distance estimation scheme for double-circuit transmission lines is presented. Fault location is estimated using the information obtained from fault events data. The DTR was chosen because it requires less training time, offers greater accuracy with a large data set, and robustness than all other techniques like artificial neural networks, support vector machines, adaptive neurofuzzy inference systems, etc. Hitherto, DT has been used for fault detection/classification, but it has not been used for fault location. Three-phase current and voltage signals measured at one end of the line are used as inputs to a fault-location network. The proposed method does not require a communication link as it uses only one-end measurements. Signals are processed with two signal-processing techniques-discrete Fourier transforms and discrete wavelet transform. A comparative study of both techniques has been carried out to observe the effect of signal processing on the fault-location estimation method. The proposed method is tested on three test systems, namely: 1) the 2-bus; 2) the WSCC-9-bus; and 3) the IEEE 14-bus test systems. The test results confirm that the proposed DTR-based algorithm is not affected by the variation in fault type, fault location, fault inception angle, fault resistance, prefault load angle, SCC, load variation, and line parameters. The proposed scheme is relatively simple and easy in comparison with complex equation-based fault-location estimation methods.

67 citations


Cited by
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Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

Journal ArticleDOI
TL;DR: In this article, an early fault diagnostic technique based on acoustic signals was used for a single-phase induction motor, which can be also used for other types of rotating electric motors.

286 citations

Journal ArticleDOI
01 Apr 2016
TL;DR: A comprehensive review on the methods used for fault detection, classification and location in transmission lines and distribution systems is presented in this article, where fault detection techniques are discussed on the basis of feature extraction.
Abstract: A comprehensive review on the methods used for fault detection, classification and location in transmission lines and distribution systems is presented in this study. Though the three topics are highly correlated, the authors try to discuss them separately, so that one may have a more logical and comprehensive understanding of the concepts without getting confused. Great significance is also attached to the feature extraction process, without which the majority of the methods may not be implemented properly. Fault detection techniques are discussed on the basis of feature extraction. After the overall concepts and general ideas are presented, representative works as well as new progress in the techniques are covered and discussed in detail. One may find the content of this study helpful as a detailed literature review or a practical technical guidance.

248 citations

Journal ArticleDOI
TL;DR: This paper classified textual clinical reports into four classes by using classical and ensemble machine learning algorithms, and Logistic regression and Multinomial Naïve Bayes showed better results than other ML algorithms by having 96.2% testing accuracy.
Abstract: Technology advancements have a rapid effect on every field of life, be it medical field or any other field. Artificial intelligence has shown the promising results in health care through its decision making by analysing the data. COVID-19 has affected more than 100 countries in a matter of no time. People all over the world are vulnerable to its consequences in future. It is imperative to develop a control system that will detect the coronavirus. One of the solution to control the current havoc can be the diagnosis of disease with the help of various AI tools. In this paper, we classified textual clinical reports into four classes by using classical and ensemble machine learning algorithms. Feature engineering was performed using techniques like Term frequency/inverse document frequency (TF/IDF), Bag of words (BOW) and report length. These features were supplied to traditional and ensemble machine learning classifiers. Logistic regression and Multinomial Naive Bayes showed better results than other ML algorithms by having 96.2% testing accuracy. In future recurrent neural network can be used for better accuracy.

207 citations

Journal ArticleDOI
TL;DR: The characterizations of big data, smart grids as well as huge amount of data collection are discussed as a prelude to illustrating the motivation and potential advantages of implementing advanced data analytics in smart grids.
Abstract: Data analytics are now playing a more important role in the modern industrial systems. Driven by the development of information and communication technology, an information layer is now added to the conventional electricity transmission and distribution network for data collection, storage and analysis with the help of wide installation of smart meters and sensors. This paper introduces the big data analytics and corresponding applications in smart grids. The characterizations of big data, smart grids as well as huge amount of data collection are firstly discussed as a prelude to illustrating the motivation and potential advantages of implementing advanced data analytics in smart grids. Basic concepts and the procedures of the typical data analytics for general problems are also discussed. The advanced applications of different data analytics in smart grids are addressed as the main part of this paper. By dealing with huge amount of data from electricity network, meteorological information system, geographical information system etc., many benefits can be brought to the existing power system and improve the customer service as well as the social welfare in the era of big data. However, to advance the applications of the big data analytics in real smart grids, many issues such as techniques, awareness, synergies, etc., have to be overcome.

189 citations