Article: Power grid safety monitoring system based on machine learning algorithms Journal: International Journal of Energy Technology and Policy (IJETP) 2026 Vol.21 No.2 pp.115 - 135 Abstract: This article presents a power grid safety monitoring system based on embedded machine learning algorithms to improve the accuracy and real-time performance of power grid operations. A module for analysing and determining changes in optical channel performance was designed, and a long short-term memory (LSTM) model was used for analysis and prediction; a module for analysing and predicting the types of hidden danger degradation in optical channel performance was constructed, and a random forest model was used for identification and prediction. By integrating the outputs of the above modules using a cascaded model, the operational time of the optical channel was predicted. Thirty sets of comparative tests were conducted between traditional monitoring systems and embedded algorithms in the experiment. Experimental results showed that the embedded algorithm achieved anomaly detection accuracy of 89.1% to 99.2%, an error rate of 0.26% to 0.87%, and a response time of 0.71 seconds to 1.27 seconds, all of which were better than those of traditional monitoring systems. Inderscience Publishers - linking academia, business and industry through research

Title: Power grid safety monitoring system based on machine learning algorithms

Authors: Yaoshan Zhang; Zhuangwei Chen; Meihong Wang; Liang Zhang; Yue Zhou

Addresses: Hainan Power Grid Co., Ltd., Haikou, 570100, Hainan, China ' Hainan Power Grid Co., Ltd., Haikou, 570100, Hainan, China ' Hainan Power Grid Co., Ltd., Haikou, 570100, Hainan, China ' Hainan Power Grid Co., Ltd., Construction Branch, Haikou, 570100, Hainan, China ' Hainan Power Grid Co., Ltd., Construction Branch, Haikou, 570100, Hainan, China

Abstract: This article presents a power grid safety monitoring system based on embedded machine learning algorithms to improve the accuracy and real-time performance of power grid operations. A module for analysing and determining changes in optical channel performance was designed, and a long short-term memory (LSTM) model was used for analysis and prediction; a module for analysing and predicting the types of hidden danger degradation in optical channel performance was constructed, and a random forest model was used for identification and prediction. By integrating the outputs of the above modules using a cascaded model, the operational time of the optical channel was predicted. Thirty sets of comparative tests were conducted between traditional monitoring systems and embedded algorithms in the experiment. Experimental results showed that the embedded algorithm achieved anomaly detection accuracy of 89.1% to 99.2%, an error rate of 0.26% to 0.87%, and a response time of 0.71 seconds to 1.27 seconds, all of which were better than those of traditional monitoring systems.

Keywords: power grid safety monitoring system; embedded machine learning algorithms; LSTM model; random forest model; optical channel performance.

DOI: 10.1504/IJETP.2026.153598

International Journal of Energy Technology and Policy, 2026 Vol.21 No.2, pp.115 - 135

Received: 29 Aug 2025
Accepted: 17 Nov 2025
Published online: 18 May 2026 *

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Title: Power grid safety monitoring system based on machine learning algorithms

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