The online monitoring system for cable partial discharge is a complex process that integrates multiple technologies to distinguish between normal and abnormal, requiring hardware detection, signal processing, and intelligent algorithms to work together. With the development of sensing technology, artificial intelligence, and Internet of Things technology, the accuracy and reliability of partial discharge monitoring will continue to improve, providing stronger guarantees for the safe operation of power cables. In the future, the application of new technologies such as adaptive learning and digital twins will enable partial discharge monitoring systems to have stronger environmental adaptability and more accurate fault warning capabilities.
1、 Basic concepts of partial discharge
Partial discharge refers to the non penetrating discharge phenomenon that occurs in a localized area of a cable insulation system. This discharge will not immediately cause insulation breakdown, but long-term existence will gradually deteriorate the insulation material, which may ultimately lead to serious faults. The cable partial discharge online monitoring system evaluates the insulation status of the cable by detecting these discharge signals in real time.
2、 Differences in Characteristics between Normal and Abnormal Signals
1. Signal amplitude characteristics: Under normal operating conditions, electromagnetic interference signals usually have low and stable amplitudes, while abnormal partial discharge signals have high amplitudes and may show an increasing trend.
2. Phase distribution characteristics: Normal interference signals are often randomly distributed throughout the power frequency cycle, while abnormal partial discharge signals are mostly concentrated in specific phase intervals of the power frequency voltage (such as voltage rise or fall stages).
3. Repetition frequency characteristics: Normal noise signals have unstable repetition rates, while abnormal partial discharge signals usually have relatively stable repetition rates.
4. Waveform characteristics: Abnormal partial discharge pulse waveforms usually have specific rise time and attenuation characteristics, which are significantly different from the waveform of normal interference signals.
5. Frequency domain characteristics: Through spectral analysis, it can be found that abnormal partial discharge signals have significant energy concentration in specific frequency bands, while normal noise signals have a wide spectral distribution and no significant peaks.
3、 The main technical methods for distinguishing between normal and abnormal
1. Threshold comparison method: The system sets threshold values for parameters such as discharge capacity and number of discharges. If the threshold is exceeded, it is judged as abnormal. This method is simple and direct, but it requires setting thresholds reasonably based on factors such as cable type and operating environment.
2. Pattern recognition technology: Using artificial intelligence algorithms (such as neural networks, support vector machines, etc.) to classify and recognize discharge signals. The system establishes a recognition model through training with a large number of normal and abnormal samples.
3. Phase Resolution Analysis (PRPD): Distinguishing between normal and abnormal by analyzing the phase distribution pattern of the discharge signal within the power frequency cycle. Different types of partial discharge defects (such as air gap discharge, surface discharge, etc.) have PRPD pattern characteristics.
4. Multi sensor data fusion: Multiple sensors are used to detect signals from different locations, and methods such as signal arrival time difference and correlation analysis are used to distinguish between real partial discharge signals and external interference.
5. Trend analysis method: Long term monitoring of signal changes at the same location, sudden increases or patterns of regular growth often indicate abnormal conditions.
6. Pulse waveform analysis method: Extract features from captured individual pulse waveforms, such as rise time, pulse width, oscillation frequency, etc., and compare them with a typical partial discharge pulse feature library.
4、 Auxiliary measures to improve differentiation accuracy
1. Environmental noise benchmark test: The system records the environmental noise characteristics under the condition of no partial discharge in the cable, which serves as the benchmark for subsequent signal analysis.
2. Multi point joint monitoring: By comparing signals from multiple monitoring points, local interference that may be experienced by a single monitoring point can be eliminated.
3. Correlation analysis of operating conditions: Analyze the correlation between monitoring data and operating parameters such as load changes and temperature changes to eliminate misjudgments caused by changes in operating conditions.
4. Regular calibration and verification: Regularly calibrate the monitoring system with a standard signal source to ensure detection sensitivity and accuracy.
5. Expert system assisted decision-making: Encode the experience and knowledge of domain experts into a rule base, providing a second opinion for the automatic judgment system.
5、 Challenges and Countermeasures in Practical Applications
1. Complex electromagnetic interference environment: The electromagnetic interference in places such as substations is complex, and a combination of hardware filtering and digital signal processing is needed to improve the signal-to-noise ratio.
2. Diversity of cable types: Different types of cables (such as XLPE and oil filled cables) have different partial discharge characteristics, and the monitoring system needs to have adjustable parameter settings.
3. Progressive defect recognition: Early partial discharge signals are weak, requiring high-sensitivity sensors and advanced signal enhancement algorithms.
4. Big data processing: Online monitoring generates massive amounts of data, requiring efficient data compression and feature extraction techniques.