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<p>In smart substations, equipment protection and abnormal warning are crucial to the safe and stable operation of the power grid. In existing research, traditional methods such as LSTM (Long Short-Term Memory Network) are time-consuming and require large computing resources, and SVM (Support Vector Machine) is easy to fall into local optimization and limited generalization ability when processing high-dimensional data, making it difficult to efficiently realize deep feature mining and accurate early warning of multi-source data. In this study, it is proposed to achieve accurate early warning through “synchronization data of similar equipment” (i.e., simultaneous collection of operating data of the same type of equipment, such as load current and oil temperature of multiple transformers on the same bus, so as to facilitate mutual verification in case of abnormality) and multi-source data fusion technology. Firstly, high-precision sensors are used to collect electrical and non-electric data such as voltage, current, equipment temperature, and vibration in real time, and various data features are integrated into a unified vector (such as combining power trend and vibration frequency characteristics) through feature-level fusion, and then redundancy is removed by dimensionality reduction algorithms such as PCA. The core model uses the “Whale Optimization Extreme Learning Machine” (WOA-ELM): WOA simulates the initial parameters of the Whale Predation Behavior Optimization Extreme Learning Machine (ELM), and the ELM exerts fast learning and strong generalization capabilities to deeply mine the processed multi-source data features. Experiments show that compared with the traditional model, the accuracy of equipment anomaly identification is improved by about 20%, and the early warning response time is shortened by 30%, which significantly improves the efficiency and reliability of equipment protection in intelligent substation. It not only provides a stronger guarantee for the safe operation of equipment, but also shows potential application value in the early abnormal warning system, which can help the power system achieve more efficient preventive maintenance.</p>

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Equipment protection and anomaly warning method of intelligent substation based on homologous recording and multi-source data

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月26日

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  • Haibo Zhang,
  • Hongying Xing,
  • Shicheng Duan

摘要

In smart substations, equipment protection and abnormal warning are crucial to the safe and stable operation of the power grid. In existing research, traditional methods such as LSTM (Long Short-Term Memory Network) are time-consuming and require large computing resources, and SVM (Support Vector Machine) is easy to fall into local optimization and limited generalization ability when processing high-dimensional data, making it difficult to efficiently realize deep feature mining and accurate early warning of multi-source data. In this study, it is proposed to achieve accurate early warning through “synchronization data of similar equipment” (i.e., simultaneous collection of operating data of the same type of equipment, such as load current and oil temperature of multiple transformers on the same bus, so as to facilitate mutual verification in case of abnormality) and multi-source data fusion technology. Firstly, high-precision sensors are used to collect electrical and non-electric data such as voltage, current, equipment temperature, and vibration in real time, and various data features are integrated into a unified vector (such as combining power trend and vibration frequency characteristics) through feature-level fusion, and then redundancy is removed by dimensionality reduction algorithms such as PCA. The core model uses the “Whale Optimization Extreme Learning Machine” (WOA-ELM): WOA simulates the initial parameters of the Whale Predation Behavior Optimization Extreme Learning Machine (ELM), and the ELM exerts fast learning and strong generalization capabilities to deeply mine the processed multi-source data features. Experiments show that compared with the traditional model, the accuracy of equipment anomaly identification is improved by about 20%, and the early warning response time is shortened by 30%, which significantly improves the efficiency and reliability of equipment protection in intelligent substation. It not only provides a stronger guarantee for the safe operation of equipment, but also shows potential application value in the early abnormal warning system, which can help the power system achieve more efficient preventive maintenance.