State Estimation in Power Engineering Using the Huber Robust Regression Technique

Kyriakides, Elias; Suryanarayanan, Siddharth; Heydt, G.T.

doi:10.1109/tpwrs.2005.846157

Cited by 14 publications

(12 citation statements)

References 2 publications

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“…In the conducted experiments, linear regression and ANN-based AQI prediction were performed. Furthermore, the presented study also found that the customized linear regression methodology outperformed other machine-learning methods, such as the linear [ 54 ], ridge [ 55 ], Lasso [ 56 ], Bayes [ 57 ], Huber [ 58 ], Lars [ 59 ], Lasso-lars [ 60 ], stochastic gradient descent (SGD) [ 61 ], and ElasticNet [ 62 ] regression methodologies, and the customized ANN regression methodology used in the conducted experiments. (iv) In the end, the web and mobile interface was developed to display the air pollution prediction values of a variety of air pollutants.…”

Section: The Necessity Of the Pwp Systemmentioning

confidence: 82%

“…ESP8266 12E/NodeMCU is a cost-effective controller module equipped with a 32-bit microcontroller with 4MB memory. According to [ 58 ], a NodeMCU controller consists of GPIOs, I2C, UART, ADC, and PWM pins to interface with various sensor modules. Figure 1 a represents a microcontroller unit used in the conducted experiments.…”

Section: The Necessity Of the Pwp Systemmentioning

confidence: 99%

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Pollution Weather Prediction System: Smart Outdoor Pollution Monitoring and Prediction for Healthy Breathing and Living

Pandya

Ghayvat²,

Sur

et al. 2020

Sensors

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Air pollution has been a looming issue of the 21st century that has also significantly impacted the surrounding environment and societal health. Recently, previous studies have conducted extensive research on air pollution and air quality monitoring. Despite this, the fields of air pollution and air quality monitoring remain plagued with unsolved problems. In this study, the Pollution Weather Prediction System (PWP) is proposed to perform air pollution prediction for outdoor sites for various pollution parameters. In the presented research work, we introduced a PWP system configured with pollution-sensing units, such as SDS021, MQ07-CO, NO2-B43F, and Aeroqual Ozone (O3). These sensing units were utilized to collect and measure various pollutant levels, such as PM2.5, PM10, CO, NO2, and O3, for 90 days at Symbiosis International University, Pune, Maharashtra, India. The data collection was carried out between the duration of December 2019 to February 2020 during the winter. The investigation results validate the success of the presented PWP system. In the conducted experiments, linear regression and artificial neural network (ANN)-based AQI (air quality index) predictions were performed. Furthermore, the presented study also found that the customized linear regression methodology outperformed other machine-learning methods, such as linear, ridge, Lasso, Bayes, Huber, Lars, Lasso-lars, stochastic gradient descent (SGD), and ElasticNet regression methodologies, and the customized ANN regression methodology used in the conducted experiments. The overall AQI values of the air pollutants were calculated based on the summation of the AQI values of all the presented air pollutants. In the end, the web and mobile interfaces were developed to display air pollution prediction values of a variety of air pollutants.

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Section: The Necessity Of the Pwp Systemmentioning

confidence: 82%

Section: The Necessity Of the Pwp Systemmentioning

confidence: 99%

Pollution Weather Prediction System: Smart Outdoor Pollution Monitoring and Prediction for Healthy Breathing and Living

Pandya

Ghayvat²,

Sur

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…where r is called the residue vector. By introducing the nonnegative slack variable vectors (s, u, v ) into the inequality constraints, and incorporating the slack variables in the logarithmic barrier terms of the objective function, (19) can be reformulated as 20) where s j denotes the j th element of s, p and q stand for the number of rows of h(x) and f(x), respectively, and μ > 0 is called the barrier parameter. Its value will decrease towards zero when x approaches the solution.…”

Section: Problem Formulationmentioning

confidence: 99%

“…After considering the Lagrangian function of (20) and the Karush-Kuhn-Tucker (KKT) optimality conditions, the state estimation problem can be written as…”

Section: Problem Formulationmentioning

confidence: 99%

“…It is known that, although the conventional state estimation method, which is usually based on WLS criteria, is suitable for data with Gaussian noises, it is not robust in the presence of outliers or bad data [4,11]. Many robust estimation techniques have been proposed for the state estimation problem of power system without FACTS devices [12][13][14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

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WLAV state estimation for power system containing multitype FACTS devices

Rakpenthai¹,

Uatrongjit²,

Ngamroo³

2013

IEEJ Transactions Elec Engng

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This paper presents the state estimation of power system in which not only the bus voltages but also the states of the flexible AC transmission system (FACTS) are considered as the state variables. By using the rectangular form of state variables and equivalent measurement techniques, a linear measurement model with constraints of FACTS device is obtained. The predictor-corrector interior point method based on the weighted least absolute value criterion is developed for solving the optimization problem. Simulation results on the modified IEEE 14-bus and 118-bus test systems are provided. The numerical results indicate the effectiveness of the proposed approach.

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Digital H∞ filter design for a low frequency multiple‐input multiple‐output system with multirate measurements

Yang

Wang

2022

Intl J Robust & Nonlinear

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In this article, a digital H∞ filter is designed for a low frequency multiple‐input multiple‐output (MIMO) system with multirate measurements from multiple samplers. A delay compensation is introduced to reduce estimation errors from the multiple samplers and a zero‐order holder. A single‐rate error system is obtained by converting the low frequency MIMO system using a discrete‐time lifting technique. A digital H∞ filter is proposed based on a delay‐partitioning method to guarantee asymptotic stability and H∞ performance for the single‐rate error system. A numerical example is provided to show effectiveness and superiority of the digital H∞ filter in low frequency domain.

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State Estimation in Power Engineering Using the Huber Robust Regression Technique

Cited by 14 publications

References 2 publications

Pollution Weather Prediction System: Smart Outdoor Pollution Monitoring and Prediction for Healthy Breathing and Living

Pollution Weather Prediction System: Smart Outdoor Pollution Monitoring and Prediction for Healthy Breathing and Living

WLAV state estimation for power system containing multitype FACTS devices

Digital H∞ filter design for a low frequency multiple‐input multiple‐output system with multirate measurements

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