Overview of Defect Diagnosis Methods for High-Voltage AC Circuit Breakers
1. What are the characteristic parameters of the coil current waveform in high-voltage circuit breaker operating mechanisms? How to extract these characteristic parameters from the original trip coil current signal?
Answer: The characteristic parameters of the coil current waveform in high-voltage circuit breaker operating mechanisms may include the following:
Steady-state peak current: The maximum steady-state current value in the electromagnet coil waveform, representing the position where the electromagnet core moves to and briefly stays at its limit position.
Duration: The duration of the electromagnet coil current waveform, typically ranging from a dozen to over a hundred milliseconds.
Rise time before core activation: The time required for the current waveform to rise from zero to the first peak current.
Fall time: The time required for the current waveform to fall from the first peak current back to the second trough. This corresponds to the time when the armature plunger starts moving, strikes the trip mechanism, and drives it to the electromagnet armature's limit position.
Waveform shape: The overall shape of the waveform, such as single pulse, multi-pulse, or periodic waveform.
Frequency: If the waveform is periodic, its frequency is an important parameter.
To extract these characteristic parameters from the original trip coil current signal, the following steps are typically required:
Sampling: Use appropriate sampling equipment with sufficient sampling rate to continuously sample the coil current and convert the signal to digital form.
Filtering: Filter the sampled data to remove high-frequency noise for better identification of waveform features.
Peak detection: Find the maximum value from the filtered signal to determine the peak current.
Duration measurement: Calculate the duration by detecting the time points where the waveform starts and ends from zero current.
Rise time and fall time measurement: Calculate the rise time and fall time by detecting the time points from zero current to peak current and from peak current back to zero current, respectively.
Shape analysis: Use mathematical methods or waveform fitting techniques to analyze the waveform shape.
Frequency analysis: If the waveform is periodic, use Fourier transform or autocorrelation function to estimate the frequency.
These steps typically require signal processing and data analysis tools (such as MATLAB, Python's NumPy and SciPy libraries, etc.). Extracting these characteristic parameters helps monitor and analyze the performance of high-voltage circuit breaker operating mechanisms. Note that appropriate safety measures must be taken when handling high-voltage currents to prevent accidental electric shock or other hazards.
2. What algorithms can be used to extract characteristic parameters such as peak and trough amplitudes and their corresponding time points from coil current waveforms? Please list them specifically.
Answer: To extract characteristic parameters such as peak and trough amplitudes and their corresponding time points from coil current waveforms, various signal processing and analysis algorithms can be used. Waveform segmentation and segment-by-segment comparison can be used to obtain characteristic parameters. The following are some commonly used algorithms and methods:
Peak detection algorithms: These algorithms can detect peaks in waveforms, including maximum peaks and minimum troughs. Common algorithms include threshold method, sliding window method, gradient-based methods, etc.
Zero crossing detection algorithms: These algorithms can detect transitions in waveforms from positive to negative or from negative to positive, typically used together with peak and trough detection.
Fourier transform: Can convert the coil current waveform to the frequency domain, extract peak and trough information in the frequency domain, and then map it back to the time domain through inverse transformation to obtain time information.
Integration and differentiation algorithms: Integration can be used to estimate waveform amplitude, while differentiation can be used to estimate the slope of peaks and troughs, thereby inferring their time points.
Waveform fitting: By fitting waveform models such as Gaussian models, S-curves, etc., to estimate the positions and amplitudes of peaks and troughs. Adjusting theoretical parameters of electromagnets to generate coil current waveforms that continuously approach actual measurement data, thereby obtaining waveform characteristic parameters of actual coil currents from theoretical parameters.
Windowed analysis: Segment the waveform into small windows and extract characteristic parameters within each window to capture changes in peaks and troughs.
Derivative-based methods: Calculate the derivative of the waveform to find the positions of peaks and troughs; points where the derivative becomes zero are extreme points.
These algorithms can be used individually or in combination, with the specific choice depending on the nature of the waveform and the requirements of the specific application. In practical applications, domain knowledge and data analysis tools are typically combined to ensure accurate extraction of characteristic parameters from coil current waveforms.
3. What characteristic parameters does the vibration acceleration signal of high-voltage circuit breaker operating mechanisms have during opening and closing operations? How to extract these characteristic parameters from the measured mechanical vibration signals of high-voltage circuit breakers?
Answer: The vibration acceleration signal of high-voltage circuit breaker operating mechanisms during opening and closing operations may contain many characteristic parameters that provide important information about mechanism performance and condition. The following are some possible characteristic parameters and methods to extract them:
Peak acceleration: The maximum acceleration value in the vibration signal, typically expressed in g units (gravity acceleration).
Duration: The duration of the vibration event, typically in milliseconds or seconds.
Frequency components: Through Fourier transform or fast Fourier transform (FFT) and other spectral analysis methods, frequency components in the vibration signal can be extracted to identify the occurrence of any frequency components.
Vibration amplitude: The amplitude of the vibration signal, which can be expressed as the distance from peak to zero.
Peak-to-peak value: The vibration amplitude of a complete cycle in the vibration signal, typically used to identify periodic vibrations.
Number of pulses: For multi-pulse vibrations, the number of pulses within a given time period can be calculated.
Shape of acceleration waveform: The waveform of the vibration signal can be used to analyze the start, end, and duration of vibration.
High-frequency components: Identify high-frequency vibration components, which may indicate mechanism instability or damage.
To extract these characteristic parameters, the following steps are typically required:
Vibration signal acquisition: Use appropriate sensors (such as accelerometers) to collect vibration signals from the operating mechanism of the high-voltage circuit breaker.
Signal digitization: Convert the analog vibration signal to digital form for subsequent analysis.
Filtering and denoising: Filter and denoise the vibration signal to eliminate noise and improve signal quality.
Feature extraction: Use signal processing tools (such as FFT) and vibration analysis methods to extract the above characteristic parameters. Vibration signals are transformed using Fourier transform; signals of different frequencies are superimposed at different times to generate acceleration vibration waveforms that approximate the actual vibration curve, obtaining characteristic parameters of actual data from theoretical data.
Data analysis: Analyze the extracted characteristic parameters to identify performance issues or abnormalities in the mechanism.
Analysis of these characteristic parameters can be used to monitor the health status of high-voltage circuit breakers, identify potential failures, and take maintenance measures to ensure their proper operation. Vibration monitoring is typically an important task in engineering that can improve equipment reliability and lifespan.
4. What algorithms can be used to extract characteristic parameters from the mechanical vibration acceleration signals during high-voltage circuit breaker operations?
Answer: When extracting characteristic parameters from the mechanical vibration acceleration signals during high-voltage circuit breaker operations, various signal processing and analysis algorithms can be used. The following are some commonly used algorithms and methods:
Peak detection algorithms: These algorithms can detect peaks in vibration signals, including maximum vibration acceleration peaks. Common algorithms include threshold method, sliding window method, gradient-based methods, etc.
Spectral analysis: Fourier transform or fast Fourier transform (FFT) can be used to convert the vibration signal to the frequency domain and extract frequency components and amplitude information of the vibration.
Vibration energy: Estimate the vibration energy by integrating the square of the vibration signal, thereby obtaining information about the total energy of the vibration.
Vibration frequency: Estimate the main frequency components of the vibration using spectral analysis or autocorrelation functions to identify the frequency characteristics of the vibration.
Vibration amplitude: Quantify the size of the vibration by calculating the amplitude of the vibration signal.
Peak-to-peak value: The vibration amplitude of a complete vibration cycle in the vibration signal, typically used to identify periodic vibrations.
Number of pulses: For multi-pulse vibrations, the number of pulses within a given time period can be calculated.
Shape of vibration waveform: The waveform of the vibration signal can be used to analyze the start, end, and duration of vibration.
Peak time: Estimate the time point when the vibration peak occurs to identify the timing of vibration events.
These algorithms can be used individually or in combination, with the specific choice depending on the nature of the vibration signal and the requirements of the specific application. In practical applications, domain knowledge and data analysis tools are typically combined to ensure accurate extraction of characteristic parameters from the mechanical vibration acceleration signals of high-voltage circuit breakers, to monitor equipment performance and health status.
5. How to extract the peak and peak time of vibration energy signals?
Answer: To extract the peak and peak time of vibration energy signals, you can use signal processing and analysis methods. The following is a general method:
Peak extraction of vibration energy signals:
a. Smooth the vibration energy signal: Apply average filtering or other smoothing methods to reduce noise in the signal, making it easier to detect peaks.
b. Find peak points: Perform peak detection on the smoothed signal, typically through the following steps:
c. Record peak amplitudes: Determine the amplitude of the vibration energy signal at each peak point.
Calculate the first derivative or difference of the signal to find extreme points in the signal (points where the gradient becomes zero).
Use thresholds or other conditions to filter out peak points, excluding small fluctuations.
Peak time extraction:
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Record peak moments: For each detected peak point, record its position on the time axis, i.e., the time moment of the peak.
Use time information: The time information of the peak moments can be used to represent the occurrence time of each peak, typically in milliseconds or seconds.
Note that the specific methods for extracting peaks and peak times may vary depending on the characteristics of the signal. Additionally, the degree of signal smoothing and noise level will also affect peak detection. You can use signal processing tools such as NumPy and SciPy libraries in Python, as well as peak detection algorithms such as threshold method, gradient method, or sliding window method to perform these steps. In practical applications, you may need to adjust algorithm parameters to adapt to specific vibration signal requirements.
6. What characteristic parameters does the sound signal have during opening and closing operations of high-voltage circuit breakers? How to extract these parameters to analyze and diagnose latent defects in high-voltage circuit breakers?
Answer: The sound signal during opening and closing operations of high-voltage circuit breakers may contain some characteristic parameters used to analyze and diagnose equipment performance and health status. The following are some possible sound signal characteristic parameters and methods to extract them:
Sound amplitude: The amplitude or volume of the sound signal, typically expressed in decibels (dB).
Sound frequency: The frequency components of the sound signal, used to identify the tone or frequency range of the sound.
Sound duration: The duration of the sound event, typically in milliseconds or seconds.
Sound waveform: The waveform of the sound signal, used to analyze the start, end, and duration of the sound.
Sound spectrogram: A spectral analysis graph of the sound signal, used to identify the occurrence and changes of frequency components.
Number of pulses: For multiple sound pulses, the number of pulses within a given time period can be calculated.
Sound features: Use sound analysis tools to extract sound features, such as energy, spectral average, peaks, etc., of audio signals.
To extract these characteristic parameters, the following steps can be performed:
Sound signal acquisition: Use appropriate microphones or sensors to collect sound signals during opening and closing operations of high-voltage circuit breakers.
Signal digitization: Convert the analog sound signal to digital form for analysis.
Sound signal processing: Filter and denoise the sound signal to eliminate noise and improve signal quality.
Feature extraction: Use audio signal processing tools and algorithms to extract the above characteristic parameters, such as spectral analysis, waveform analysis, etc.
Data analysis: Analyze the extracted characteristic parameters to identify abnormalities or performance issues in the sound signal.
By monitoring and analyzing sound signals, latent defects in high-voltage circuit breakers can be identified, such as abnormal sounds, mechanical problems, or other abnormal operations. This helps prevent equipment failures and take maintenance measures to ensure the reliability and safety of high-voltage circuit breakers.
