Technology for Power Outage Research and Judgment-dependent Data Feature Noise Analysis-Reference-Cited by-同舟云学术

Technology for Power Outage Research and Judgment-dependent Data Feature Noise Analysis

Published:2023-09-22 Issue: Volume:10 Page:
ISSN:2032-944X
Container-title:EAI Endorsed Transactions on Energy Web
language:
Short-container-title:EAI Endorsed Trans Energy Web

Author:

Li Xiang

Abstract

INTRODUCTION: Power grid blackouts occur frequently, which significantly impacts social impact. Because these accidents are dynamic and random, predicting and evaluating them is challenging. OBJECTIVES: To explore the complexity of the power grid itself, analyzes the critical changes of the self-organizing model during power grid fault, extracts the data characteristics related to the steady-state maintenance of abnormal systems, and puts forward an effective outage prediction model. METHODS: Starting with cluster analysis, The authors can reduce data fluctuation and eliminate noise interference to optimize data. The evaluation indexes of initial fault occurrence possibility and fault propagation speed in the power grid are constructed. RESULTS: The validation of the outage forecasting model has produced promising results, achieving 96.4% forecasting accuracy and a meager error rate. In addition, the evaluation index developed in this study accurately reflects the possibility and spread speed of power outage accidents. CONCLUSION: The research proves the feasibility of establishing an outage prediction model based on the power grid system data characteristics. The model has high accuracy and reliability and is a valuable tool for power outage research and judgment.

Publisher

European Alliance for Innovation n.o.

Subject

Marketing,Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment

Reference21 articles.

1. Fan, M., Yang, Q., Guo, X.F., Liu, H., Xia J.L., Peng Y.W.. (2023). Prediction method of power outage in a distribution network for unbalanced data, Power System Protection and Control, 51(8): 96-106

2. Li, G.Q., Liu, D.G., Xiao, G.L., Zhang, B., Wang, G.W., Ren, H., Zhen, Z.. (2022).Risk Prediction of Node Outage in High Proportion New Energy Grid, Power System and Clean Energy, 38(10), 106-115.

3. Nan, D.L., Feng, C.Y., Cao, H., Wang, X., Li, Y.D.. (2021). Data-driven predictive model of distribution system blackout， Advanced Technology of Electrical Engineering and Energy， 40(12), 56-63.

4. Yu, Q., Qu, Y.Q., Shi, L.. (2018). Self-correlation Analysis of Power Grid Blackouts Based on Relative Value Method and Hurst Exponent." Automation of Electric Power Systems, 42(01), 55-60,124.

5. Hassani, H., Razavi-Far, R., & Saif, M.. (2022). Real-time out-of-step prediction control to prevent emerging blackouts in power systems: A reinforcement learning approach.Applied Energy,314, 118861.