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Anomaly Detection In Power Distribution System Measurements Using Machine Learning
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Book Synopsis Anomaly Detection in Power Distribution System Measurements Using Machine Learning by : Arun Abhishek Imayakumar
Download or read book Anomaly Detection in Power Distribution System Measurements Using Machine Learning written by Arun Abhishek Imayakumar and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Sensor measurements of distribution system are uncertain due to sensor malfunctions, communication failure and cyber attacks. This thesis aims to perform anomaly detection on measurements utilizing data-driven approaches. The measurements considered are individual smart meter real power measurements and network-wide primary voltage magnitudes. Anomaly detection in individual smart meter measurements using gaussian probabilistic thresholds is explored. It flags non-anomalous data as verified by the comparison of smart meter real power and individual appliance consumption. To perform a real-time comparison for detection, Non-Intrusive Load Monitoring (NILM) is needed, which is difficult due to the associated consumer privacy issues. Alternatively, forecasting can be used for anomaly detection. So, single layer neural network models such as Multi-Layer Perceptron (MLP), and Long Short Term Memory (LSTM) with different features are tried. Even in training data, a poor performance is seen in these models, due to the smart meter profile variability. Hence, aggregated smart meter forecasting using neural networks can be used to detect anomaly in such aggregated measurements with a reasonable accuracy. Network-wide primary voltage measurements are correlated for a phase of feeder for different buses at a given time-step; this is extensively validated empirically. To leverage this, Principal Component Analysis (PCA) is used to reduce the dimensionality of this input data. Further, residual and subspace based methods are explored for network-level anomaly detection and identification. The results for the residual approach on missing and bad data cases are detailed for IEEE 13 bus and IEEE 8500 node test feeders. It is validated through simulations that residual-based approach on subspace projection matrix for the measurement data successfully performs anomaly detection and identification for primary network voltage measurements for the selected test cases. Further research is needed to validate the applicability and accuracy of the proposed framework during changes in the system operating conditions (topology changes, capacitor bank switching, etc.), and on real-world measurements form sensors deployed in the field.
Book Synopsis Anomaly Detection in Smart Distribution Grids with Deep Neural Network by : Ming Zhou (Computer scientist)
Download or read book Anomaly Detection in Smart Distribution Grids with Deep Neural Network written by Ming Zhou (Computer scientist) and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the rapid development of smart grids, the detection of anomalies is essential to improve the quality and security protection of the grid. The identification of anomalies not only saves valuable time but also reduces maintenance costs. Due to the increasing deployment of distributed energy resources, traditional methods of protecting the grid that rely on simple linear models and manual inspections are no longer sufficient. Meanwhile, the massive amount of data generated by smart meters and phasor measurement units provide opportunities to better monitor and control power grids in real-time. Due to this advantage of data availability, various machine learning and deep learning methods have been proposed and are currently demonstrating successful results in anomaly detection in power systems. While previously proposed artificial intelligence techniques can successfully de- tect anomalies, most of them tend to require large amounts of simulated data of all different types of anomalies for training their framework. However, anomalous data may be rare in power distribution systems. In addition, their static training model makes them vulnerable to new data from different distributions entering the system. To address these drawbacks, we propose data-driven frameworks based on deep learning network models to directly detect anomalies in power distribution systems. Anomalies are generally defined as observations that deviate from the standard, normal or expected values. Specifically, this work is divided into two phases. In the first phase, we consider anomalies as events caused by changes in the distribution system load, such as customer disconnection from the grid. A long short-term memory network is proposed to predict the next time step of the voltage magnitude of all buses in the distribution system. A threshold function based on Euclidean distance is then used to detect voltage anomalies by utilizing only normal data. The results corresponding to this proposed framework have been successfully tested using a real distribution network. In the second phase, we aim to classify faults and locate faulted lines in partially observable distribution systems using convolutional neural networks. To improve the robustness of the classification and localization performance, we extract feature vectors with measurements in the observable buses as inputs to the proposed classifier. In addition, we incorporate an online continuous learning algorithm to accommodate variations in the level of integration of distributed energy resources and changes in the load of the distribution system over time. Unlike previous data-driven approaches, the proposed method also deals with imbalanced learning tasks, as fault data are often rare. The performance of the method has been tested and validated by simulating ten faults on a real distribution feeder model.
Book Synopsis Monitoring and Control of Electrical Power Systems using Machine Learning Techniques by : Emilio Barocio Espejo
Download or read book Monitoring and Control of Electrical Power Systems using Machine Learning Techniques written by Emilio Barocio Espejo and published by Elsevier. This book was released on 2023-01-11 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: Monitoring and Control of Electrical Power Systems using Machine Learning Techniques bridges the gap between advanced machine learning techniques and their application in the control and monitoring of electrical power systems, particularly relevant for heavily distributed energy systems and real-time application. The book reviews key applications of deep learning, spatio-temporal, and advanced signal processing methods for monitoring power quality. This reference introduces guiding principles for the monitoring and control of power quality disturbances arising from integration of power electronic devices and discusses monitoring and control of electrical power systems using benchmark test systems for the creation of bespoke advanced data analytic algorithms. Covers advanced applications and solutions for monitoring and control of electrical power systems using machine learning techniques for transmission and distribution systems Provides deep insight into power quality disturbance detection and classification through machine learning, deep learning, and spatio-temporal algorithms Includes substantial online supplementary components focusing on dataset generation for machine learning training processes and open-source microgrid model simulators on GitHub
Book Synopsis Artificial Intelligence Applications in Electrical Transmission and Distribution Systems Protection by : Almoataz Y. Abdelaziz
Download or read book Artificial Intelligence Applications in Electrical Transmission and Distribution Systems Protection written by Almoataz Y. Abdelaziz and published by CRC Press. This book was released on 2021-10-22 with total page 395 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence (AI) can successfully help in solving real-world problems in power transmission and distribution systems because AI-based schemes are fast, adaptive, and robust and are applicable without any knowledge of the system parameters. This book considers the application of AI methods for the protection of different types and topologies of transmission and distribution lines. It explains the latest pattern-recognition-based methods as applicable to detection, classification, and location of a fault in the transmission and distribution lines, and to manage smart power systems including all the pertinent aspects. FEATURES Provides essential insight on uses of different AI techniques for pattern recognition, classification, prediction, and estimation, exclusive to power system protection issues Presents an introduction to enhanced electricity system analysis using decision-making tools Covers AI applications in different protective relaying functions Discusses issues and challenges in the protection of transmission and distribution systems Includes a dedicated chapter on case studies and applications This book is aimed at graduate students, researchers, and professionals in electrical power system protection, stability, and smart grids.
Book Synopsis Application of Machine Learning and Deep Learning Methods to Power System Problems by : Morteza Nazari-Heris
Download or read book Application of Machine Learning and Deep Learning Methods to Power System Problems written by Morteza Nazari-Heris and published by Springer Nature. This book was released on 2021-11-21 with total page 391 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book evaluates the role of innovative machine learning and deep learning methods in dealing with power system issues, concentrating on recent developments and advances that improve planning, operation, and control of power systems. Cutting-edge case studies from around the world consider prediction, classification, clustering, and fault/event detection in power systems, providing effective and promising solutions for many novel challenges faced by power system operators. Written by leading experts, the book will be an ideal resource for researchers and engineers working in the electrical power engineering and power system planning communities, as well as students in advanced graduate-level courses.
Book Synopsis Fundamentals of Machine Learning for Predictive Data Analytics, second edition by : John D. Kelleher
Download or read book Fundamentals of Machine Learning for Predictive Data Analytics, second edition written by John D. Kelleher and published by MIT Press. This book was released on 2020-10-20 with total page 853 pages. Available in PDF, EPUB and Kindle. Book excerpt: The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice. Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. This second edition covers recent developments in machine learning, especially in a new chapter on deep learning, and two new chapters that go beyond predictive analytics to cover unsupervised learning and reinforcement learning.
Book Synopsis Anomaly Detection in Power System Datasets Using Machine Learning in R-programming by : Adeyemi Taylor
Download or read book Anomaly Detection in Power System Datasets Using Machine Learning in R-programming written by Adeyemi Taylor and published by . This book was released on 2018 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Handbook of Smart Energy Systems by : Michel Fathi
Download or read book Handbook of Smart Energy Systems written by Michel Fathi and published by Springer Nature. This book was released on 2023-08-04 with total page 3382 pages. Available in PDF, EPUB and Kindle. Book excerpt: This handbook analyzes and develops methods and models to optimize solutions for energy access (for industry and the general world population alike) in terms of reliability and sustainability. With a focus on improving the performance of energy systems, it brings together state-of-the-art research on reliability enhancement, intelligent development, simulation and optimization, as well as sustainable development of energy systems. It helps energy stakeholders and professionals learn the methodologies needed to improve the reliability of energy supply-and-demand systems, achieve more efficient long-term operations, deal with uncertainties in energy systems, and reduce energy emissions. Highlighting novel models and their applications from leading experts in this important area, this book will appeal to researchers, students, and engineers in the various domains of smart energy systems and encourage them to pursue research and development in this exciting and highly relevant field.
Book Synopsis Intelligent Data Mining and Analysis in Power and Energy Systems by : Zita A. Vale
Download or read book Intelligent Data Mining and Analysis in Power and Energy Systems written by Zita A. Vale and published by John Wiley & Sons. This book was released on 2022-12-13 with total page 500 pages. Available in PDF, EPUB and Kindle. Book excerpt: Intelligent Data Mining and Analysis in Power and Energy Systems A hands-on and current review of data mining and analysis and their applications to power and energy systems In Intelligent Data Mining and Analysis in Power and Energy Systems: Models and Applications for Smarter Efficient Power Systems, the editors assemble a team of distinguished engineers to deliver a practical and incisive review of cutting-edge information on data mining and intelligent data analysis models as they relate to power and energy systems. You’ll find accessible descriptions of state-of-the-art advances in intelligent data mining and analysis and see how they drive innovation and evolution in the development of new technologies. The book combines perspectives from authors distributed around the world with expertise gained in academia and industry. It facilitates review work and identification of critical points in the research and offers insightful commentary on likely future developments in the field. It also provides: A thorough introduction to data mining and analysis, including the foundations of data preparation and a review of various analysis models and methods In-depth explorations of clustering, classification, and forecasting Intensive discussions of machine learning applications in power and energy systems Perfect for power and energy systems designers, planners, operators, and consultants, Intelligent Data Mining and Analysis in Power and Energy Systems will also earn a place in the libraries of software developers, researchers, and students with an interest in data mining and analysis problems.
Book Synopsis Planning and Operation of Active Distribution Networks by : Antonio Carlos Zambroni de Souza
Download or read book Planning and Operation of Active Distribution Networks written by Antonio Carlos Zambroni de Souza and published by Springer Nature. This book was released on 2022-01-31 with total page 523 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a broad and detailed view about how traditional distribution systems are evolving smart/active systems. The reader will be able to share the view of a number of researchers directly involved in this field. For this sake, philosophical discussions are enriched by the presentation of theoretical and computational tools. A senior reader may incorporate some concepts not available during his/her graduation process, whereas new Engineers may have contact with some material that may be essential to his/her practice as professionals.
Book Synopsis Smart Grid Sensors by : Hamed Mohsenian-Rad
Download or read book Smart Grid Sensors written by Hamed Mohsenian-Rad and published by . This book was released on 2022-04-06 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover the ever-growing field of smart grid sensors, covering traditional and state-of-the-art sensor technologies, as well as data-driven and intelligent methods for using sensor measurements in support of innovative smart grid applications. Covers recent and emerging topics, such as smart meters, synchronized phasor measurements, and synchronized waveform measurements. Additional advanced topics and future trends are also discussed, such as situational awareness, probing, and working with off-domain measurements. Including real-world examples, exercise questions, and sample data sets, this is an essential text for students, researchers, and scientists, as well as field engineers and practitioners in the areas of smart grid and power systems.
Book Synopsis Advanced Anomaly Detection Technologies and Applications in Energy Systems by : Tinghui Ouyang
Download or read book Advanced Anomaly Detection Technologies and Applications in Energy Systems written by Tinghui Ouyang and published by Frontiers Media SA. This book was released on 2022-10-14 with total page 628 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis The Official (ISC)2 SSCP CBK Reference by : Mike Wills
Download or read book The Official (ISC)2 SSCP CBK Reference written by Mike Wills and published by John Wiley & Sons. This book was released on 2022-03-03 with total page 839 pages. Available in PDF, EPUB and Kindle. Book excerpt: The only official body of knowledge for SSCP—(ISC)2’s popular credential for hands-on security professionals—fully revised and updated 2021 SSCP Exam Outline. Systems Security Certified Practitioner (SSCP) is an elite, hands-on cybersecurity certification that validates the technical skills to implement, monitor, and administer IT infrastructure using information security policies and procedures. SSCP certification—fully compliant with U.S. Department of Defense Directive 8140 and 8570 requirements—is valued throughout the IT security industry. The Official (ISC)2 SSCP CBK Reference is the only official Common Body of Knowledge (CBK) available for SSCP-level practitioners, exclusively from (ISC)2, the global leader in cybersecurity certification and training. This authoritative volume contains essential knowledge practitioners require on a regular basis. Accurate, up-to-date chapters provide in-depth coverage of the seven SSCP domains: Security Operations and Administration; Access Controls; Risk Identification, Monitoring and Analysis; Incident Response and Recovery; Cryptography; Network and Communications Security; and Systems and Application Security. Designed to serve as a reference for information security professionals throughout their careers, this indispensable (ISC)2 guide: Provides comprehensive coverage of the latest domains and objectives of the SSCP Helps better secure critical assets in their organizations Serves as a complement to the SSCP Study Guide for certification candidates The Official (ISC)2 SSCP CBK Reference is an essential resource for SSCP-level professionals, SSCP candidates and other practitioners involved in cybersecurity.
Book Synopsis Applications of Deep Machine Learning in Future Energy Systems by : Mohammad-Hassan Khooban
Download or read book Applications of Deep Machine Learning in Future Energy Systems written by Mohammad-Hassan Khooban and published by Elsevier. This book was released on 2024-08-20 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: Applications of Deep Machine Learning in Future Energy Systems pushes the limits of current Artificial Intelligence techniques to present deep machine learning suitable for the complexity of sustainable energy systems. The first two chapters take the reader through the latest trends in power engineering and system design and operation, before laying out the current AI approaches and our outstanding limitations. Later chapters provide in-depth accounts of specific challenges and the use of innovative third-generation machine learning, including neuromorphic computing, to resolve issues from security to power supply. An essential tool for the management, control, and modelling of future energy systems, Applications of Deep Machine Learning maps a practical path towards AI capable of supporting sustainable energy. Clarifies the current state and future trends of energy system machine learning and the pitfalls facing our transitioning systems Provides guidance on 3rd-generation AI tools for meeting the challenges of modeling and control in modern energy systems Includes case studies and practical examples of potential applications to inspire and inform researchers and industry developers
Book Synopsis Machine Learning Assisted Digital Twin for Event Identification in Electrical Power System by : Xinya Song
Download or read book Machine Learning Assisted Digital Twin for Event Identification in Electrical Power System written by Xinya Song and published by BoD – Books on Demand. This book was released on 2023 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Future Modern Distribution Networks Resilience by : Mohammad Taghi Ameli
Download or read book Future Modern Distribution Networks Resilience written by Mohammad Taghi Ameli and published by Elsevier. This book was released on 2024-02-23 with total page 440 pages. Available in PDF, EPUB and Kindle. Book excerpt: Future Modern Distribution Networks Resilience examines the combined impact of low-probability and high-impact events on modern distribution systems’ resilience. Using practical guidance, the book provides comprehensive approaches for improving energy systems’ resilience by utilizing infrastructure and operational strategies. Divided in three parts, Part One provides a conceptual introduction and review of power system resilience, including topics such as risk and vulnerability assessment in power systems, resilience metrics, and power systems operation and planning. Part Two discusses modelling of vulnerability and resilience evaluation indices and cost-benefit analysis. Part Three reviews infrastructure and operational strategies to improve power system resilience, including robust grid hardening strategies, mobile energy storage and electric vehicles, and networked microgrids and renewable energy resources. With a strong focus on economic results and cost-effectives, Future Modern Distribution Networks Resilience is a practical reference for students, researchers and engineers interested in power engineering, energy systems, and renewable energy. Reviews related concepts to active distribution systems resilience before, during, and after a sudden disaster Presents analysis of risk and vulnerability for reliable evaluation, sustainable operation, and accurate planning of energy grids against low-probability and high-impact events Highlights applications of practical metrics for resilience assessment of future energy networks Provides guidance for the development of cost-effective resilient techniques for reducing the vulnerability of electrical grids to severe disasters
Book Synopsis Sustainable Energy for Smart Cities by : João L. Afonso
Download or read book Sustainable Energy for Smart Cities written by João L. Afonso and published by Springer Nature. This book was released on 2020-04-08 with total page 313 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed post-conference proceedings of the First EAI International Conference on Sustainable Energy for Smart Cities, SESC 2029, held as part of the Smart City 360° Summit event in Braga, Portugal, in December 2019. The 23 revised full papers were carefully reviewed and selected from 38 submissions. They contribute to answer complex societal, technological, and economic problems of emergent smart cities. The papers are organized thematically in tracks, starting with mobile systems, cloud resource management and scheduling, machine learning, telecommunication systems, and network management. The papers are grouped in topical sections on electric mobility; power electronics; intelligent, transportation systems; demand response; energy; smart homes; Internet of Things; monitoring; network communications; power quality; power electronics.
