Power Line Communication and Sensing Using Time Series Forecasting

Author:

Huo Yinjia,Prasad GauthamORCID,Lampe LutzORCID,Leung VictorORCID

Abstract

Smart electrical grids rely on data communication to support their operation and on sensing for diagnostics and maintenance. Usually, the roles of communication and sensing equipment are different, i.e., communication equipment does not participate in sensing tasks and vice versa. Power line communication (PLC) offers a cost-effective solution for joint communication and sensing for smart grids. This is because the high-frequency PLC signals used for data communication also reveal detailed information regarding the health of the power lines that they travel through. Traditional PLC-based power line or cable diagnostic solutions are dependent on prior knowledge of the cable type, network topology, and/or characteristics of the anomalies. In this paper, we develop a power line sensing technique that can detect various types of cable anomalies without any prior domain knowledge. To this end, we design a solution that first uses time-series forecasting to predict the PLC channel state information at any given point in time based on its historical data. Under the approximation that the prediction error follows a Gaussian distribution, we then perform chi-squared statistical test to build an anomaly detector which identifies the occurrence of a cable fault. We demonstrate the effectiveness and universality of our sensing solution via evaluations conducted using both synthetic and real-world data extracted from low- and medium-voltage distribution networks.

Funder

Natural Sciences and Engineering Research Council

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Condition Monitoring of Underground Power Cables Via Power-Line Modems and Anomaly Detection;IEEE Transactions on Power Delivery;2024-02

2. Joint Detection Method for Two-Way Automatic Communication System;2024 4th International Conference on Neural Networks, Information and Communication (NNICE);2024-01-19

3. Validation of Machine Learning-Aided and Power Line Communication-Based Cable Monitoring Using Measurement Data;Sensors;2024-01-05

4. PLC Network Integrity Solution;Advances in Information Security;2024

5. Comparison of SVM and LSTM for Power Line Communication State Information Forecasting Using Streamlit Framework;2023 IEEE 14th International Conference on Software Engineering and Service Science (ICSESS);2023-10-17

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