Power Grid Contingency Analysis with Machine Learning: A Brief Survey and Prospects

2020 ◽  
Author(s):  
Sam Yang ◽  
Bjorn Vaagensmith ◽  
Deepika Patra
Keyword(s):  
Machine Learning ◽  
Power Grid ◽  
Contingency Analysis

scholarly journals Data-Driven Prediction Method for Power Grid State Subjected to Heavy-Rain Hazards

2020 ◽  
Vol 10 (14) ◽  
pp. 4693
Author(s):  
Seongmun Oh ◽  
Junhyuk Kong ◽  
Minhee Choi ◽  
Jaesung Jung
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Power Grid ◽  
Prediction Method ◽  
Heavy Rain ◽  
Weather Data ◽  
Support Vector ◽  
Rain Event ◽  
Mean Values ◽  
Svm Model

This study presents a machine learning-based method for predicting the power grid state subjected to heavy-rain hazards. Machine learning models can recognize key knowledge from a dataset without any preliminary knowledge about the dataset. Hence, machine learning methods have been utilized for solving power grid-related problems. Two sets of historical data were used herein: Local weather data and power grid outage data. First, we investigated the heavy-rain-related outage distribution and analyzed the correlated characteristics between weather and outages to characterize the heavy rain events. The analysis results show that multiple weather effects are significant in causing power outages, even under heavy-rain conditions. Furthermore, this study proposes a cost-sensitive prediction method using a support vector machine (SVM) model. The accuracy of the model was improved by applying a cost-sensitive learning algorithm to the SVM model, which was subsequently used to predict the state of the grid. The developed model was evaluated using G-mean values. The proposed method was verified via actual data of a heavy rain event that occurred in South Korea.

scholarly journals Complex Power System Status Monitoring and Evaluation Using Big Data Platform and Machine Learning Algorithms: A Review and a Case Study

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-21 ◽  
Author(s):  
Yuanjun Guo ◽  
Zhile Yang ◽  
Shengzhong Feng ◽  
Jinxing Hu
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Smart Grid ◽  
Power System ◽  
Data Management ◽  
Power Grid ◽  
Technical Solution ◽  
Data Platform ◽  
Complex Power ◽  
Big Data Technology

Efficient and valuable strategies provided by large amount of available data are urgently needed for a sustainable electricity system that includes smart grid technologies and very complex power system situations. Big Data technologies including Big Data management and utilization based on increasingly collected data from every component of the power grid are crucial for the successful deployment and monitoring of the system. This paper reviews the key technologies of Big Data management and intelligent machine learning methods for complex power systems. Based on a comprehensive study of power system and Big Data, several challenges are summarized to unlock the potential of Big Data technology in the application of smart grid. This paper proposed a modified and optimized structure of the Big Data processing platform according to the power data sources and different structures. Numerous open-sourced Big Data analytical tools and software are integrated as modules of the analytic engine, and self-developed advanced algorithms are also designed. The proposed framework comprises a data interface, a Big Data management, analytic engine as well as the applications, and display module. To fully investigate the proposed structure, three major applications are introduced: development of power grid topology and parallel computing using CIM files, high-efficiency load-shedding calculation, and power system transmission line tripping analysis using 3D visualization. The real-system cases demonstrate the effectiveness and great potential of the Big Data platform; therefore, data resources can achieve their full potential value for strategies and decision-making for smart grid. The proposed platform can provide a technical solution to the multidisciplinary cooperation of Big Data technology and smart grid monitoring.

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