Increasing use of renewable energy sources, liberalized energy markets and most importantly, the integrations of various monitoring, measuring and communication infrastructures into modern power system network offer the opportunity to build a resilient and efficient grid However, it also brings about various threats of instabilities and security concerns in form of cyberattack, voltage instability, power quality (PQ) disturbance among others to the complex network The need for efficient methodologies for quicker identification and detection of these problems have always been a priority to energy stakeholders over the years In recent times, machine learning techniques (MLTs) have proven to be effective in numerous applications including power system studies In the literature, various MLTs such as artificial neural networks (ANN), Decision Tree (DT), support vector machines (SVM) have been proposed, resulting in effective decision making and control actions in the secured and stable operations of the power system Given this growing trend, this paper presents a comprehensive review on the most recent studies whereby MLTs were developed for power system security and stability especially in cyberattack detections, PQ disturbance studies and dynamic security assessment studies The aim is to highlight the methodologies, achievements and more importantly the limitations in the classifier(s) design, dataset and test systems employed in the reviewed publications A brief review of reinforcement learning (RL) and deep reinforcement learning (DRL) approaches to transient stability assessment is also presented Finally, we highlighted some challenges and directions for future studies

/pdf/a-review-of-machine-learning-approaches-to-power-system-4q2b9d8uxr.pdf

A Review of Machine Learning Approaches to Power System Security and Stability

Data analytics are now playing a more important role in the modern industrial systems. Driven by the development of information and communication technology, an information layer is now added to the conventional electricity transmission and distribution network for data collection, storage and analysis with the help of wide installation of smart meters and sensors. This paper introduces the big data analytics and corresponding applications in smart grids. The characterizations of big data, smart grids as well as huge amount of data collection are firstly discussed as a prelude to illustrating the motivation and potential advantages of implementing advanced data analytics in smart grids. Basic concepts and the procedures of the typical data analytics for general problems are also discussed. The advanced applications of different data analytics in smart grids are addressed as the main part of this paper. By dealing with huge amount of data from electricity network, meteorological information system, geographical information system etc., many benefits can be brought to the existing power system and improve the customer service as well as the social welfare in the era of big data. However, to advance the applications of the big data analytics in real smart grids, many issues such as techniques, awareness, synergies, etc., have to be overcome.

/pdf/big-data-analytics-in-smart-grids-a-review-4g4lfx70ug.pdf

Big data analytics in smart grids: a review

Smart grids have been gradually replacing the traditional power grids since the last decade. Such transformation is linked to adding a large number of smart meters and other sources of information extraction units. This provides various opportunities associated with the collected big data. Hence, the triumph of the smart grid energy paradigm depends on the factor of big data analytics. This includes the effective acquisition, transmission, processing, visualization, interpretation, and utilization of big data. The paper provides deep insights into various big data technologies and discusses big data analytics in the context of the smart grid. The paper also presents the challenges and opportunities brought by the advent of machine learning and big data from smart grids.

/pdf/smart-grid-big-data-analytics-survey-of-technologies-35sbfpyrnh.pdf

Smart Grid Big Data Analytics: Survey of Technologies, Techniques, and Applications

This review describes a cloud-based intelligent power management system that uses analytics as a control signal and processes balance achievement pointer, and describes operator acknowledgments that must be shared quickly, accurately, and safely. The current study aims to introduce a conceptual and systematic structure with three main components: demand power (direct current (DC)-device), power mix between renewable energy (RE) and other power sources, and a cloud-based power optimization intelligent system. These methods and techniques monitor demand power (DC-device), load, and power mix between RE and other power sources. Cloud-based power optimization intelligent systems lead to an optimal power distribution solution that reduces power consumption or costs. Data has been collected from reliable sources such as Science Direct, IEEE Xplore, Scopus, Web of Science, Google Scholar, and PubMed. The overall findings of these studies are visually explained in the proposed conceptual framework through the literature that are considered to be cloud computing based on storing and running the intelligent systems of power management and mixing.

A Conceptual and Systematics for Intelligent Power Management System-Based Cloud Computing: Prospects, and Challenges

As power systems (PS) move toward smart grids and microgrids in modern times, complexity eventually rises as a result of integration with various distributed energy resources, demand-side management, and cybersecurity issues. Additionally, frequent PS network blackouts will have an impact on a country’s social, economic, and financial position. To ensure the reliability as well as security of the network’s smart-driven power system, more advanced monitoring and measurement technology, which is dominated by SCADA systems, is required. This is where synchrophasor-based phasor measurement units (PMUs) come into play, which process and analyze massive amounts of data in real-time more precisely to identify systemic anomalies. Due to their quick response times, high computing speeds, accuracy, and scalability, machine learning (ML)-based techniques are becoming more popular for handling real-time big data. The many ML algorithms used recently in synchrophasor technology, which enhances cybersecurity, fault detection and classification, transient stability assessment, voltage stability assessment, and forced oscillation localization are thoroughly reviewed in this paper. With the help of more than 190 pertinent papers, this work effectively discusses the ML applications in the synchrophasor technology where PMUs and μPMUs are deployed. The article also shows that several concerns have not yet been resolved and are still up for consideration by researchers in the future. One of them is the detection and observation of oscillation and line-tripping occurrences in the distribution area where μPMUs are installed. To address these problems, sophisticated DL approaches are recommended. Future possibilities to decrease bandwidth usage and improve processing delay using edge computing technology are also mentioned in this paper. The research potential for ML and DL approaches also extends to power line communication, wide area monitoring systems, and 5G and 6G network communications. 

/pdf/a-review-of-machine-learning-approaches-in-synchrophasor-2np1s2vr.pdf

A Review of Machine Learning Approaches in Synchrophasor Technology

With the growth of new energy resources and the increasingly complexity of power gird and operation condition, a method for intelligent recognition of transient instability pattern based on power grid topology is proposed in this paper. On the basis of the recognition of Dominant Instability Generator Group, a transient stability assessment algorithm which merely uses steady-state information is presented. The validity of the method is proved by the test with the simulation example on China Southern Power Grid in 2014. This method can be applied to establish the relationship between operation mode and stability level, and can provide support for preventive control.

A method for transient stability assessment based on pattern recognition

Popular Tasks used by Researchers

How can I help with your research?

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

We encountered an error while loading the panel. Please try refreshing the page.

Something went wrong

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

Anyone with the link can view this chat. You can switch it back to private anytime.

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Lin Guan

Author Tools

Chat about Author

Papers

A method for transient stability assessment based on pattern recognition