A Data Imputation Model in Phasor Measurement Units Based on Bagged Averaging of Multiple Linear Regression

Le, Ngoc Thien; Benjapolakul, Watit

doi:10.1109/access.2018.2856768

Cited by 20 publications

(10 citation statements)

References 21 publications

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“…Lagrange interpolating polynomial is described in [4], [15], [16] and can be represented as follows-…”

Section: Fig 6 Input and Output From Foh B Lagrange Polynomial Methodsmentioning

confidence: 99%

Methods of Missing Data Handlingin One Shot Response based Power System Control

Dahal¹

2019

IJEAT

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The work done in this paper addresses various methods of handling missing phasor samples obtained from power flow simulations using DSA tools like TSAT and PSAT. Pseudorandom numbers in MATLAB are used to simulate 0-10% of missing samples and are recovered using different extrapolation techniques. After recovery, samples are subjected to decision trees to assess the performance of one shot stabilizing controls like in [1], [2].The power system model used is the 176 bus model of Western Electrical Coordinating Council (WECC).

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“…Lagrange interpolating polynomial is described in [4], [15], [16] and can be represented as follows-…”

Section: Fig 6 Input and Output From Foh B Lagrange Polynomial Methodsmentioning

confidence: 99%

Methods of Missing Data Handlingin One Shot Response based Power System Control

Dahal¹

2019

IJEAT

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“…It defines a reference load before the target time and collects past loads similar to the reference load to render the data vector and condition matrix observed. Lee and Benjapolakul in (176) present a Bagged Averaging of Multiple Linear Regression model, handling data from the phasor measurement unit. The proposed model handles and manages the missing values quickly and efficiently in synchronized frequency data calculation.…”

Section: Multiple Linear Regressionmentioning

confidence: 99%

Machine Learning and Deep Learning in smart manufacturing: The Smart Grid paradigm

Kotsiopoulos

Sarigiannidis

Ioannidis

et al. 2021

Computer Science Review

194

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Industry 4.0 is the new industrial revolution. By connecting every machine and activity through network sensors to the Internet, a huge amount of data is generated. Machine Learning (ML) and Deep Learning (DL) are two subsets of Artificial Intelligence (AI), which are used to evaluate the generated data and produce valuable information about the manufacturing enterprise, while introducing in parallel the Industrial AI (IAI). In this paper, the principles of the Industry 4.0 are highlighted, by giving emphasis to the features, requirements, and challenges behind Industry 4.0. In addition, a new architecture for AIA is presented. Furthermore, the most important ML and DL algorithms used in Industry 4.0 are presented and compiled in detail. Each algorithm is discussed and evaluated in terms of its features, its applications, and its efficiency. Then, we focus on one of the most important Industry 4.0 fields, namely the smart grid, where ML and DL models are presented and analyzed in terms of efficiency and effectiveness in smart grid applications. Lastly, trends and challenges in the field of data analysis in the context of the new Industrial era are highlighted and discussed such as scalability, cybersecurity, and big data.

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“…An imputation method based on a denoising autoencoder was presented in [ 22 ]. An imputation model named “bagged averaging of multiple linear regression” was discussed in [ 23 ] for imputing missing data in phasor measurement units. A two-stage deep autoencoder-based data imputation method was discussed in [ 24 ] for imputing missing data in wind farms.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Ensemble Classifiers for Anomaly Handling in Smart Home Energy Consumption Data

Prakash

Kumar

Moganti

et al. 2022

Sensors

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Addressing data anomalies (e.g., garbage data, outliers, redundant data, and missing data) plays a vital role in performing accurate analytics (billing, forecasting, load profiling, etc.) on smart homes’ energy consumption data. From the literature, it has been identified that the data imputation with machine learning (ML)-based single-classifier approaches are used to address data quality issues. However, these approaches are not effective to address the hidden issues of smart home energy consumption data due to the presence of a variety of anomalies. Hence, this paper proposes ML-based ensemble classifiers using random forest (RF), support vector machine (SVM), decision tree (DT), naive Bayes, K-nearest neighbor, and neural networks to handle all the possible anomalies in smart home energy consumption data. The proposed approach initially identifies all anomalies and removes them, and then imputes this removed/missing information. The entire implementation consists of four parts. Part 1 presents anomaly detection and removal, part 2 presents data imputation, part 3 presents single-classifier approaches, and part 4 presents ensemble classifiers approaches. To assess the classifiers’ performance, various metrics, namely, accuracy, precision, recall/sensitivity, specificity, and F1 score are computed. From these metrics, it is identified that the ensemble classifier “RF+SVM+DT” has shown superior performance over the conventional single classifiers as well the other ensemble classifiers for anomaly handling.

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A Data Imputation Model in Phasor Measurement Units Based on Bagged Averaging of Multiple Linear Regression

Cited by 20 publications

References 21 publications

Methods of Missing Data Handlingin One Shot Response based Power System Control

Methods of Missing Data Handlingin One Shot Response based Power System Control

Machine Learning and Deep Learning in smart manufacturing: The Smart Grid paradigm

Machine Learning-Based Ensemble Classifiers for Anomaly Handling in Smart Home Energy Consumption Data

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