Partial Discharge Characteristics and Lifetime Prediction of 36 kV Prefabricated Cable Accessories
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Partial Discharge Characteristics and Lifetime Prediction of 36 kV Prefabricated Cable Accessories

Publish Time: 2026-05-20     Origin: Site

1. Partial Discharge Mechanisms in Prefabricated Accessories

PD typically occurs within voids, gaps, or along contaminated interfaces in the insulation system. For prefabricated silicone rubber or EPDM-based accessories at 36 kV, common PD sources include:

· Interface defects between the cable insulation and the accessory due to improper installation or moisture ingress.

· Internal voids in the stress control layer or insulation body caused by manufacturing imperfections.

· Surface contamination on the stress cone or electrode edges, leading to electric field enhancement.


PD generates charge recombination, ultraviolet radiation, heat, and chemical byproducts, progressively eroding the insulation. At 36 kV, the inception voltage for critical defects often lies between 12 kV and 20 kV, making real-time PD monitoring essential.


2. Typical PD Characteristics

Using phase-resolved partial discharge (PRPD) patterns, engineers can classify defect types:


· Cavity discharge (void) – symmetric pattern with peaks at 45° and 135° phase angles.

· Surface discharge – asymmetric, often with higher magnitude at one polarity.

· Interface tracking – broad phase distribution with fluctuating amplitude.


For 36 kV accessories, PD magnitudes usually range from 10 pC to 500 pC for incipient defects. Once PD exceeds 300 pC consistently, rapid degradation is likely. The PD inception voltage (PDIV) decreases over service time; a 30% drop from the initial value indicates a high-risk condition.


3. Temperature and Humidity Effects

Field data show that at 36 kV, elevated temperature (above 50°C) reduces PDIV by 15–25% due to material thermal expansion and interface pressure loss. High humidity ( > 80% RH) promotes water treeing and surface conductivity, shifting PD activity to lower phase angles and increasing repetition rates by several orders of magnitude.


4. Lifetime Prediction Model

A widely adopted framework for lifetime prediction is the inverse power law (IPL) model, combining voltage and PD severity:


L = L_0 \cdot \left( \frac{V_0}{V} \right)^n \cdot \left( \frac{PD_0}{PD_{meas}} \right)^m


where:

· L = remaining lifetime (hours or years)

· V = operating voltage (36 kV rms)

· PD_{meas} = measured PD magnitude (pC)

· n, m = empirical constants (typically n = 9–12 for rubber insulation, m = 1.5–2)

· L_0, V_0, PD_0 = reference parameters from accelerated aging tests.


For 36 kV accessories, typical L_0 is 200,000 hours at 1.2× rated voltage with PD < 10 pC. Using real-time PD data, the model can estimate remaining life with ±20% accuracy after calibration.


5. Practical Application: PD Trend Analysis

Instead of single absolute values, lifetime prediction relies on PD trend analysis:


· Stable PD ( < 100 pC, low repetition) – normal aging, life > 10 years.

· Incipient (100–300 pC, occasional bursts) – 3–7 years remaining.

· Active (300–800 pC, multiple phase positions) – 6–24 months.

· Pre-breakdown ( > 800 pC or rapid growth) – days to weeks.


6. Recommendations for Field Application

· Perform PD measurement on every 36 kV accessory after installation (baseline PDIV and magnitude).

· Use ultra-high frequency (UHF) or high-frequency current transformer (HFCT) sensors with continuous monitoring for critical feeders.

· Combine PD data with temperature, load current, and humidity for accurate life prediction.

· Set maintenance thresholds: >200 pC for investigation, >500 pC for urgent replacement.


Conclusion

Partial discharge characteristics provide a powerful window into the health of 36 kV prefabricated cable accessories. By understanding typical PD patterns and applying an adapted inverse power law model with trend analysis, utilities can predict remaining life with reasonable accuracy. Implementing continuous PD monitoring not only prevents catastrophic failures but also enables condition-based maintenance, reducing costs and improving grid reliability.

  jonsonchai@chinahaivo.com
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