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The Rise of Intelligent High-Voltage Switchgear: How Is AI Transforming Traditional Power Systems?

Release time:

2025-10-16

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Abstract

Driven by the dual forces of the "Dual Carbon" goals and the construction of a new power system, traditional high-voltage switchgear is undergoing a disruptive transformation—from "mechanical switches" to "intelligent hubs." The deep integration of AI technology is not only reshaping the functional boundaries of high-voltage switchgear but also propelling the entire power system toward autonomous sensing, intelligent decision-making, and highly efficient collaboration. Behind this transformative shift lies AI's comprehensive reimagining of the conventional operational logic of traditional power systems.

I. From "Passive Response" to "Proactive Prediction": AI Empowers High-Voltage Switchgear with All-Dimensional Sensing Capabilities

Traditional high-voltage switchgear relies on manual inspections and expert judgment, leading to the persistent challenge of failing to promptly detect potential hazards such as abnormal temperatures and partial discharges. AI-driven smart high-voltage cabinets, by integrating multi-source sensors with advanced deep-learning algorithms, enable real-time, comprehensive monitoring of equipment conditions.

Take Anhui Shageng Smart Substation as an example: The Transformer neural network model deployed there analyzes 100,000 sets of historical operation data to autonomously optimize the timing sequence for circuit breaker opening and closing. As a result, arc-extinguishing time is reduced to just 0.02 seconds, extending equipment lifespan by 40%. Moreover, this model can also predict circuit breaker contact wear up to 30 days in advance through vibration spectrum analysis, cutting unplanned outage rates by 65%. At Chongqing Yongxin Substation, the digital twin system built by the E-Trust Intelligent Operation and Maintenance Platform enables real-time simulation of physical equipment conditions. Maintenance personnel can virtually test fault-handling strategies in advance, boosting emergency response efficiency by 60%.

This leap in perceptual ability essentially represents AI's process of transforming device operational data into actionable knowledge. By constructing dynamic models that link equipment aging patterns with environmental correlation features, the intelligent high-voltage cabinet can precisely detect temperature fluctuations as small as 0.1°C or mechanical displacements as minuscule as 0.01 mm—reducing fault detection time from hours to just seconds.

II. From "Empirical Setting" to "Dynamic Optimization": AI Reconstructs Protection Logic for Power Systems

The large-scale integration of new energy sources has led to increased grid volatility, making it increasingly difficult for traditional protection settings—reliant on empirical formulas—to remain effective. AI, by enabling real-time analysis of meteorological data, power generation equipment operational data, and grid topology, has achieved dynamic, adaptive protection strategies.

The application case at the ±800 kV Qingyang Converter Station demonstrates that the intelligent protection system powered by the Guangming Electric Power Large Model can update differential protection thresholds every 5 minutes. Even under wind power output fluctuations of up to 30%, the system successfully keeps fault isolation time within 100 milliseconds. By leveraging reinforcement learning algorithms, this system effectively prevented 12 instances of regional overload risks during the peak electricity demand period in summer 2024, reducing the misoperation rate by 82% compared to conventional fixed-set protection methods.

More notably, there has been a breakthrough in collaborative swarm intelligence. By leveraging federated learning technology, multiple high-voltage switchgear units can share operational data while ensuring privacy is fully protected, enabling the coordinated optimization of protective strategies. The upcoming campus-level self-organizing distribution network, set to launch in 2026, will triple the capacity for integrating distributed energy resources, while simultaneously reducing the risk of cascading failures to just one-fifth of that seen in conventional systems.

III. From "Energy Consumption Units" to "Energy-Saving Nodes": AI Drives the Energy Efficiency Management Revolution

The energy efficiency leap in intelligent high-voltage switchgear is evident across two dimensions: energy savings at the equipment level and system-level optimization. At the device level, AI dynamically adjusts the energy consumption of the circuit breaker operating mechanism by analyzing parameters such as current harmonics and reactive power. Schneider’s SM6 series medium-voltage switchgear features an AI-driven magnetic actuator, which delivers a 40% energy-saving advantage over traditional spring mechanisms, reducing carbon emissions by 1.2 tons per unit annually.

At the system level, AI integrates high-voltage switchgear into the carbon management ecosystem. The Carbon Ledger System developed by NARI Group can collect equipment operation data in real time and, combined with regional carbon emission factors, provides enterprises with minute-level carbon footprint calculation services. After implementing this system, a steel company achieved annual electricity cost savings of 8 million yuan by optimizing the timing of power supply to its electric arc furnaces, while also earning 1.2 million yuan from carbon trading revenues.

This energy efficiency revolution is reshaping the power value chain. AI-powered Virtual Power Plant (VPP) platforms can aggregate distributed high-voltage cabinet resources, successfully integrating 120 million kilowatt-hours of abandoned wind power during the peak summer demand period in 2025—by optimizing the charge and discharge strategies of 2,000 smart high-voltage cabinets. This achievement is equivalent to reducing standard coal consumption by 36,000 tons.

IV. From "Mechanical Interlocking" to "Intelligent Immunity": AI Builds a New-Type Security Protection System

Traditional high-voltage switchgear's "five-protection" function relies on mechanical interlocks, which carry the risk of human error. AI, by building a three-dimensional spatial perception network, has achieved a qualitative upgrade in safety protection.

The AI-powered visual system integrated into ABB’s MNS low-voltage switchgear can recognize human movements within 0.5 meters. If it detects someone not wearing a safety helmet or engaging in unauthorized operations, the system will cut off the control power within 0.3 seconds. Even more advanced is the behavior-based safety alert system—DeepSafety, developed by Huawei Digital Power. By analyzing 100,000 sets of operational videos, this system can predict the risk of operator errors up to 15 minutes in advance, with an accuracy rate as high as 98.7%.

In the field of cybersecurity, AI has become a critical defense line against emerging threats. The State Grid Corporation has deployed a deep-learning-based intrusion detection system that can identify even the slightest 0.01% fluctuations in network traffic. In the first quarter of 2025, this system successfully thwarted 127 APT attacks targeting substations—representing a 40-fold improvement in detection efficiency compared to traditional rule-based systems.

Key words:

Digital Transformation of Indoor Switchgear: Enabling Remote Monitoring and Fault Early Warning

2025 Switchgear Technology Trends: Smartization, Modularization, and Eco-Friendliness

Hunan Chengyuan Electrical CO.,LTD

The Rise of Intelligent High-Voltage Switchgear: How Is AI Transforming Traditional Power Systems?