Review of machine learning applications to power systems studies

The complexity of electric power networks from generation, transmission and distribution stations in modern times has resulted to generation of big and more complex data that requires more technical and mathematical analysis because it deals with monitoring, supervisory control and data acquisition all in real time. This has necessitated the need for more accurate analysis and predictions in power systems studies especially under transient, uncertainty or emergency conditions without interference of humans. This is necessary so as to minimize errors with the aim targeted towards improving the overall performance and the need to use more technical but very intelligent predictive tools has become very relevant. Machine learning (ML) is a powerful tool which can be utilized to make accurate predictions about the future nature of data based on past experiences. ML algorithms operate by building a model (mathematical or pictorial) from input examples to make data driven predictions or decisions for the future. ML can be used in conjunction with big data to build effective predictive systems or to solve complex data analytic problems. Electricity generation forecasting systems that could predict the amount of power required at a rate close to the electricity consumption have been proposed in several works. This study seeks to review machine learning applications to power system studies. This paper reviewed applications of ML tools in power systems studies.

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Transparency and Fairness in Machine Learning Applications

10.37419/jpl.v4.i5.2 ◽

2018 ◽

Vol 4 (5) ◽

pp. 443-463

Author(s):

Jim Shook ◽

Robyn Smith ◽

Alex Antonio

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Reading Comprehension ◽

New Technology ◽

Human Intervention ◽

Daily Work ◽

Comprehension Test ◽

The Future ◽

Machine Learning Applications ◽

Self Driving Cars

Businesses and consumers increasingly use artificial intelligence (“AI”)— and specifically machine learning (“ML”) applications—in their daily work. ML is often used as a tool to help people perform their jobs more efficiently, but increasingly it is becoming a technology that may eventually replace humans in performing certain functions. An AI recently beat humans in a reading comprehension test, and there is an ongoing race to replace human drivers with self-driving cars and trucks. Tomorrow there is the potential for much more—as AI is even learning to build its own AI. As the use of AI technologies continues to expand, and especially as machines begin to act more autonomously with less human intervention, important questions arise about how we can best integrate this new technology into our society, particularly within our legal and compliance frameworks. The questions raised are different from those that we have already addressed with other technologies because AI is different. Most previous technologies functioned as a tool, operated by a person, and for legal purposes we could usually hold that person responsible for actions that resulted from using that tool. For example, an employee who used a computer to send a discriminatory or defamatory email could not have done so without the computer, but the employee would still be held responsible for creating the email. While AI can function as merely a tool, it can also be designed to act after making its own decisions, and in the future, will act even more autonomously. As AI becomes more autonomous, it will be more difficult to determine who—or what—is making decisions and taking actions, and determining the basis and responsibility for those actions. These are the challenges that must be overcome to ensure AI’s integration for legal and compliance purposes.

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A review of machine learning applications in IoT-integrated modern power systems

The Electricity Journal ◽

10.1016/j.tej.2020.106879 ◽

2021 ◽

Vol 34 (1) ◽

pp. 106879

Author(s):

Matin Farhoumandi ◽

Quan Zhou ◽

Mohammad Shahidehpour

Keyword(s):

Machine Learning ◽

Power Systems ◽

Machine Learning Applications

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Optimization Techniques for Mining Power Quality Data and Processing Unbalanced Datasets in Machine Learning Applications

Energies ◽

10.3390/en14020463 ◽

2021 ◽

Vol 14 (2) ◽

pp. 463

Author(s):

Alvaro Furlani Bastos ◽

Surya Santoso

Keyword(s):

Machine Learning ◽

Power Systems ◽

Power Quality ◽

Optimization Techniques ◽

Machine Learning Algorithms ◽

Quality Data ◽

Successful Performance ◽

Data Mining Approach ◽

Machine Learning Applications ◽

Learning Frameworks

In recent years, machine learning applications have received increasing interest from power system researchers. The successful performance of these applications is dependent on the availability of extensive and diverse datasets for the training and validation of machine learning frameworks. However, power systems operate at quasi-steady-state conditions for most of the time, and the measurements corresponding to these states provide limited novel knowledge for the development of machine learning applications. In this paper, a data mining approach based on optimization techniques is proposed for filtering root-mean-square (RMS) voltage profiles and identifying unusual measurements within triggerless power quality datasets. Then, datasets with equal representation between event and non-event observations are created so that machine learning algorithms can extract useful insights from the rare but important event observations. The proposed framework is demonstrated and validated with both synthetic signals and field data measurements.

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The Education of the Human Factors Engineer in the Age of Data Science

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/1071181320641109 ◽

2020 ◽

Vol 64 (1) ◽

pp. 480-484

Author(s):

Daniel Hannon ◽

Esa Rantanen ◽

Ben Sawyer ◽

Ashley Hughes ◽

Katherine Darveau ◽

...

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Human Factors ◽

Data Science ◽

Panel Discussion ◽

Quantitative Skills ◽

The Future ◽

Relevant Role ◽

Machine Learning Applications

The continued advances in artificial intelligence and automation through machine learning applications, under the heading of data science, gives reason for pause within the educator community as we consider how to position future human factors engineers to contribute meaningfully in these projects. Do the lessons we learned and now teach regarding automation based on previous generations of technology still apply? What level of DS and ML expertise is needed for a human factors engineer to have a relevant role in the design of future automation? How do we integrate these topics into a field that often has not emphasized quantitative skills? This panel discussion brings together human factors engineers and educators at different stages of their careers to consider how curricula are being adapted to include data science and machine learning, and what the future of human factors education may look like in the coming years.

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Advanced machine learning applications to modern power systems

New Technologies for Power System Operation and Analysis ◽

10.1016/b978-0-12-820168-8.00007-9 ◽

2021 ◽

pp. 209-257

Author(s):

Cong Feng ◽

Mucun Sun ◽

Morteza Dabbaghjamanesh ◽

Yuanzhi Liu ◽

Jie Zhang

Keyword(s):

Machine Learning ◽

Power Systems ◽

Machine Learning Applications

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A Study on machine learning methods and applications in genetics and genomics

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i1.7.10653 ◽

2018 ◽

Vol 7 (1.7) ◽

pp. 201

Author(s):

K Jayanthi ◽

C Mahesh

Keyword(s):

Machine Learning ◽

Gene Prediction ◽

Genomic Data ◽

Data Sets ◽

Complex Data ◽

Learning Methods ◽

Machine Learning Methods ◽

Complex Data Sets ◽

Machine Learning Applications ◽

Applications Of Machine Learning

Machine learning enables computers to help humans in analysing knowledge from large, complex data sets. One of the complex data is genetics and genomic data which needs to analyse various set of functions automatically by the computers. Hope this machine learning methods can provide more useful for making these data for further usage like gene prediction, gene expression, gene ontology, gene finding, gene editing and etc. The purpose of this study is to explore some machine learning applications and algorithms to genetic and genomic data. At the end of this study we conclude the following topics classifications of machine learning problems: supervised, unsupervised and semi supervised, which type of method is suitable for various problems in genomics, applications of machine learning and future views of machine learning in genomics.

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Healthcare Prediction and Analysis System with Constant Data Polling

International Journal of Inventive Engineering and Sciences - Regular Issue ◽

10.35940/ijies.k0993.0951120 ◽

2020 ◽

Vol 5 (11) ◽

pp. 1-8

Keyword(s):

Machine Learning ◽

Big Data ◽

Data Extraction ◽

Community Services ◽

Complex Data ◽

Accurate Analysis ◽

Early Disease Detection ◽

Huge Data ◽

Scope Of Application ◽

Analysis System

The applications of Big data and machine learning in the fields of healthcare, bioinformatics and information sciences are the most important things that a researcher takes into consideration when doing predictive analysis. The Data production at this stage has never been higher and it is increasing at an alarming rate. Hence, it is difficult to store, process and visualise this huge data using customary technologies. However, abstract design for a specific massive information application has been restricted. With advancement of big data in the field of biomedical and healthcare domain, accurate analysis of medical data can be proved beneficial for early disease detection, patient care and community services. Machine learning is being used in a wide scope of application domains to discover patterns in huge datasets. Moreover, the results from machine learning drive critical decisions in applications relating healthcare and biomedicine. The transformation of data to actionable insights from complex data remains a key challenge. In this paper we have introduced a new method of polling of data before analysis is conducted on it. This method will be valuable for dealing with the issue of incomplete data and will progressively prompt suitable and more precise data extraction.

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