Voltage Stability Prediction Using Active Machine Learning

Malbaša, Vuk; Zheng, Chenghang; Chen, Po-Chen; Popović, Tomo; Kezunovic, Mladen

doi:10.1109/tsg.2017.2693394

Cited by 103 publications

(65 citation statements)

References 31 publications

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“…In the system due to the severity of the faults many long and short duration events are caused by the power quality [74]. To accurately extract the component of fundamental Journal of Power and Energy Engineering frequency from the distorted input signal the filter design and its Q-factor and redundancy are more important [75]. These machines sometimes contradict with actual system conditions [76].…”

Section: Methods Based On Wavelet Transformmentioning

confidence: 99%

A Three Decades of Marvellous Significant Review of Power Quality Events Regarding Detection & Classification

Ahsan¹,

Pan²,

Li³

2018

JPEE

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Around the globe, the necessity of green supply with a dedicated standard quality thrust of consumers is increasing day by day. The advancement in technology urges the electrical power system to deliver a high-quality rated undistorted sinusoidal current, the voltage at a constant desired standard frequency to its consumers. The present paper reveals a complete and inclusive study of power quality events, such as automatic classification and signal processing via creative techniques and the noises effect on the detection and classification of power quality disturbances. It's planned to make a possible list for quick reference to obtain an extensive variety on the condition & status of available research for detection and classification for young engineers, designers and researchers who enter in the power quality field. The current extensive study is supported by a critical review of more than 200 publications on detection and classification techniques of power quality disturbances.

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Section: Methods Based On Wavelet Transformmentioning

confidence: 99%

A Three Decades of Marvellous Significant Review of Power Quality Events Regarding Detection & Classification

Ahsan¹,

Pan²,

Li³

2018

JPEE

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“…Another solution proposed in the literature is active learning [71], [123]. For instance, in [123], the authors propose an active learning solution that consists in updating the model with real samples when the model prediction is not consistent with the actual system condition. More specifically, they train and update the model with data for which the prediction contradicts with the actual stability state of the system.…”

Section: Validating and Maintaining A Machine Learnt Modelmentioning

confidence: 99%

Recent Developments in Machine Learning for Energy Systems Reliability Management

2020

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This paper reviews recent works applying machine learning techniques in the context of energy systems reliability assessment and control. We showcase both the progress achieved to date as well as the important future directions for further research, while providing an adequate background in the fields of reliability management and of machine learning. The objective is to foster the synergy between these two fields and speed up the practical adoption of machine learning techniques for energy systems reliability management. We focus on bulk electric power systems and use them as an example, but we argue that the methods, tools, etc. can be extended to other similar systems, such as distribution systems, micro-grids, and multi-energy systems.

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“…If certain parameters, they are provided to you what would be your reaction because they know in past you have done something like this and you have a set of data your data is with them that every time when you have into such kind of environment, you do certain kind of actions companies can predict it and this is what is done as a part of machine learning, where data is provided feature extraction takes place. Then a predictive model is calculated and this predictive model is then rolled out for the users for which data was taken [21].…”

Section: Figure 1 Machine Learning (Ml) Processmentioning

confidence: 99%

A survey of big data and machine learning

Salkuti¹

2020

IJECE

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This paper presents a detailed analysis of big data and machine learning (ML) in the electrical power and energy sector. Big data analytics for smart energy operations, applications, impact, measurement and control, and challenges are presented in this paper. Big data and machine learning approaches need to be applied after analyzing the power system problem carefully. Determining the match between the strengths of big data and machine learning for solving the power system problem is of utmost important. They can be of great help to plan and operate the traditional grid/smart grid (SG). The basics of big data and machine learning are described in detailed manner along with their applications in various fields such as electrical power and energy, health care and life sciences, government, telecommunications, web and digital media, retailers, finance, e-commerce and customer service, etc. Finally, the challenges and opportunities of big data and machine learning are presented in this paper.

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Voltage Stability Prediction Using Active Machine Learning

Cited by 103 publications

References 31 publications

A Three Decades of Marvellous Significant Review of Power Quality Events Regarding Detection & Classification

A Three Decades of Marvellous Significant Review of Power Quality Events Regarding Detection & Classification

Recent Developments in Machine Learning for Energy Systems Reliability Management

A survey of big data and machine learning

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Voltage Stability Prediction Using Active Machine Learning

Cited by 103 publications

References 31 publications

A Three Decades of Marvellous Significant Review of Power Quality Events Regarding Detection &amp; Classification

A Three Decades of Marvellous Significant Review of Power Quality Events Regarding Detection &amp; Classification

Recent Developments in Machine Learning for Energy Systems Reliability Management

A survey of big data and machine learning

Contact Info

Product

Resources

About

A Three Decades of Marvellous Significant Review of Power Quality Events Regarding Detection & Classification

A Three Decades of Marvellous Significant Review of Power Quality Events Regarding Detection & Classification