1、 Technical Status: Multimodal Fusion and Intelligent Upgrade

1. Maturity and differentiation of mainstream detection technologies

Ultra high frequency (UHF) technology: With its anti-interference advantage in the 300MHz-3GHz frequency band, it has become the preferred solution for high-voltage scenarios such as GIS equipment and transformers. For example, the GIS full scene monitoring system developed by State Grid Jiangsu Electric Power has realized the precise positioning of sporadic partial discharge signals through the combination of UHF sensors and edge computing, with an accuracy rate of more than 95%. This technology has been validated for its high reliability in ultra-high voltage projects such as the Baihetan Jiangsu ± 800kV project.

High frequency current method (HFCT): It captures nanoampere level signals through broadband sensors and is widely used in scenarios such as cable joints and switchgear. The HFC System verification system of Nanjing Gubei Electric improves the verification accuracy to ± 0.5dB and reduces the false detection rate from 12.6% to 1.8% through full frequency band simulation (2-30MHz) and intelligent algorithms.

Ultrasonic and optical detection: Ultrasonic technology performs stably in strong electromagnetic interference environments and is commonly used for partial discharge positioning of switchgear and cables; Optical detection achieves complete anti-interference through fiber optic sensors and is suitable for gas insulated equipment.

2. Deep penetration of intelligent technology

AI driven signal analysis: Convolutional neural networks (CNN) and Transformer models are used for partial discharge signal classification and trend prediction. For example, the ultra-high frequency monitoring device can automatically identify 12 types of defects such as corona and surface discharge through an improved CNN model, with a diagnostic accuracy of 98%. The high-voltage cable partial discharge diagnosis system of State Grid Electric Power Research Institute, combined with the multi parameter intensity ratio method, has achieved "fingerprint" identification of discharge types.

Edge computing and the Internet of Things: 95% of the signal preprocessing is completed locally, and the fault response time is reduced to minutes. For example, the intelligent sensor for high-voltage cables improves the signal-to-noise ratio by 1.625 times and reduces power consumption by 47% through adaptive threshold noise reduction and dynamic monitoring strategies.

3. Continuous expansion of application scenarios

In the field of new energy, the demand for partial discharge detection of wind power inverters and photovoltaic inverters has surged under the 800V high-voltage platform. The redundant insulation design of traditional 400V platforms has failed, forcing manufacturers to adopt new detection technologies such as UHF.

Industry and Rail Transit: Partial discharge detection of high-frequency pulse equipment (such as frequency converters) has become a key quality control issue. For example, the vibration partial discharge joint monitoring of high-speed rail traction motors is achieved through multimodal sensors.

2、 Core Challenge: Accuracy, Standardization, and Complex Scene Adaptation

1. Detection accuracy and anti-interference bottleneck

Environmental noise interference: In ultra-high voltage scenarios, interference sources such as corona and arc can cause a false detection rate of up to 12%. Traditional filtering methods are difficult to balance sensitivity and stability, for example, UWB technology requires complex algorithm separation due to the mixing of excitation signals and partial discharge signals.

Lack of verification system: There is a lack of unified standards for high-frequency detectors in China, and deviations in parameters such as sensor transmission impedance and system sensitivity are difficult to trace. The error rate of manual verification reaches ± 1.5dB, and the comparability of cross regional data is insufficient.

2. Difficulties in multi-source data fusion and localization

Single signal feature dimension: A single sensor is easily limited by the device structure, for example, partial discharge of transformer windings needs to be analyzed in combination with UHF, ultrasonic, and oil chromatography data.

Insufficient 3D positioning accuracy: The positioning error of discharge points inside GIS equipment is usually in the centimeter level, and the discrimination of complex structures (such as cross connected cables) still relies on manual experience.

3. Technological adaptation to emerging scenarios

High frequency pulse environment: The electromagnetic noise generated by high-frequency switches (such as IGBT modules) in new energy equipment poses a challenge to traditional detection techniques. For example, the partial discharge signal of a wind power converter needs to be extracted in the MHz level carrier background.

Extreme environmental adaptability: the radiation environment of nuclear power equipment and the low pressure environment of aerospace require the testing equipment to be anti-aging and stable in a wide temperature range.

3、 Future trend: from precise detection to intelligent operation and maintenance

1. Direction of technological innovation

Multi modal fusion detection: Multi dimensional data fusion of sound, light, electricity, temperature and humidity will become mainstream. For example, the GIS monitoring system of State Grid Jiangsu Electric Power integrates ultra-high frequency, ultrasonic, and vibration sensors to construct a digital twin of the equipment and achieve three-dimensional visualization of defects.

Quantum sensing and terahertz technology: Laboratory level quantum sensing technology can break through the traditional sensitivity limit, while terahertz imaging is expected to achieve non-contact detection of internal defects in insulating materials.

Self calibration and self repair: Intelligent sensors achieve real-time calibration through adaptive algorithms, such as high-frequency current sensors that automatically compensate for temperature drift and maintain accuracy within ± 1% for a long time.

2. Standardization and Ecological Construction

Industry standard standardization: The technical specifications for intelligent ultra-high frequency partial discharge online monitoring devices (T/CES 114-2022) and other standards promote the standardization of calibration processes, requiring sensor bandwidth ≥ 1GHz and detection sensitivity ≤ 5pC.

Open platform and data sharing: The power Internet of Things platform (such as PMS3.0) integrates partial discharge data and equipment ledger, constructs a regional power grid health index map, and guides the optimization and allocation of operation and maintenance resources.

3. Deepening and extending application scenarios

Distributed new energy: High frequency partial discharge detection for photovoltaic inverters and energy storage converters will become standard, such as monitoring insulation defects in 1500V photovoltaic systems through UHF sensors.

Industrial predictive maintenance: High voltage motors in the chemical and metallurgical industries can achieve 3-6 month advance warning of faults through partial discharge vibration joint monitoring, reducing operation and maintenance costs by more than 30%.

4. Innovation in Materials and Processes

Nanocomposite insulation material: Its corona resistance performance is verified through partial discharge testing, for example, the lifespan of nano SiO ₂ modified epoxy resin is extended to twice that of traditional materials.

3D printed insulation components: Customized sensors are integrated into the device structure to achieve in-situ sensing of partial discharge signals, such as 220kV cable joints with built-in partial discharge sensors.

4、 Summary: From "Discovering Defects" to "Predicting Risks"

The current partial discharge detection technology is transitioning from "single parameter measurement" to "multi-dimensional state assessment", and its core value has surpassed defect positioning, shifting towards equipment life prediction and risk prevention. In the future, with the deep integration of technologies such as artificial intelligence and quantum sensing, partial discharge detection will become a key cornerstone for building a "self sensing, self decision-making, and self optimization" smart grid. Driven by the "dual carbon" goal, this technology will also play a greater role in areas such as new energy grid connection and energy storage security, ultimately achieving a fundamental change in the operation and maintenance mode of power equipment from "passive response" to "active pre control".