A method for detecting the breakdown phenomenon when a shunt capacitor bank is put into operation

Abstract

The present invention discloses a method for detecting breakdown phenomena when a shunt capacitor bank is put into operation, which relates to the field of shunt capacitor bank fault detection and includes the following steps: putting a group of capacitor banks into a certain phase and extracting the current waveform data of one cycle after the capacitor banks are put in; obtaining the high-frequency frequency at the time of putting in according to the current waveform data by using the FFT algorithm; extracting the signal with the high-frequency frequency being a specified frequency at the time of putting in by using wavelet transform and connecting the peaks of the signal to form an envelope; judging whether there is a component breakdown phenomenon in the capacitor bank according to the number of wave peaks of the envelope, thus completing the detection of breakdown phenomena when the shunt capacitor bank is put into operation. The present invention solves the problems existing in the prior art, such as low detection accuracy, high false alarm rate, complex equipment and data processing, and limited application scope.

Description

Technical Field

[0001] The present invention relates to the field of fault detection of shunt capacitor banks, and particularly to a method for detecting breakdown phenomena when shunt capacitor banks are put into operation. Background Art

[0002] Shunt capacitor banks are widely used in system reactive power compensation and voltage regulation due to their low unit capacity cost, flexible operation, and convenient maintenance. Due to complex operating conditions and long-term exposure to various adverse factors, breakdown problems may occur during the input process of shunt capacitor banks, that is, the capacitor banks may experience partial or complete breakdown. When the number of breakdown components reaches a certain level, it will cause protection tripping and unplanned shutdown. If the protection fails to act in time, a large number of component breakdowns will lead to through-short circuit faults of the capacitors, and in severe cases, it will cause malignant accidents such as capacitor explosion and fire, posing a threat to the safe operation of the system and equipment.

[0003] Currently, the research on shunt capacitor bank faults mainly focuses on post-event cause analysis and relay protection setting, and there is less research on the diagnosis of internal component breakdown faults of capacitor banks. However, the existing technologies have problems such as low detection accuracy, high false alarm rate, complex equipment and data processing, and limited application scope. Summary of the Invention

[0004] In view of the above deficiencies in the prior art, the method for detecting breakdown phenomena when shunt capacitor banks are put into operation provided by the present invention solves the problems of low detection accuracy, high false alarm rate, complex equipment and data processing, and limited application scope existing in the prior art.

[0005] To achieve the above-mentioned invention objective, the technical solution adopted by the present invention is: A method for detecting breakdown phenomena when shunt capacitor banks are put into operation, comprising the following steps:

[0006] S1: Put a group of capacitor banks into a certain phase, and extract the current waveform data of one cycle after the capacitor banks are put into operation;

[0007] S2: Use the FFT algorithm to obtain the high-frequency frequency at the time of input according to the current waveform data;

[0008] S3: Use wavelet transform to extract the signal with a high-frequency frequency of a specified frequency at the time of input, and connect the peaks of the signal to form an envelope;

[0009] S4: Judge whether there is a component breakdown phenomenon in the capacitor bank according to the number of wave peaks of the envelope, and complete the detection of the breakdown phenomenon when the shunt capacitor bank is put into operation.

[0010] The beneficial effects of the above solution are as follows: The present invention proposes a method for detecting the breakdown of capacitor elements when a shunt capacitor bank is put into operation. It determines whether there is a breakdown of elements based on the waveform data of three-phase voltage and current, improving the detection efficiency of capacitor element breakdown faults and the accuracy of identification results, and solving the problems existing in the prior art, such as low detection accuracy, high false alarm rate, complex equipment and data processing, and limited application scope.

[0011] Further, in S2, the high-frequency frequency at the time of input is obtained from the current waveform data by using the FFT algorithm, including the following steps:

[0012] S2-1: The signal is decomposed into a linear combination of sine waves and cosine waves of different frequencies by using the FFT algorithm to obtain signals of different frequencies. The signal is expressed in the frequency domain as:

[0013]

[0014]

[0015] where X[k] is the complex value corresponding to the k-th frequency in the frequency domain, |·| is the amplitude, e is the exponent, θ k is the phase corresponding to the k-th frequency, j is the imaginary unit, f k is the frequency corresponding to the k-th frequency, N is the number of sampling points, and f s is the sampling rate;

[0016] S2-2: According to the signals of different frequencies, each frequency signal is sorted according to the amplitude size to obtain the high-frequency frequency.

[0017] The beneficial effects of the above further solution are as follows: FFT efficiently calculates the spectrum of the signal by applying the butterfly operation and the iterative divide-and-conquer method, and obtains the spectrum result of the signal through the above formula.

[0018] Further, S3 includes the following sub-steps:

[0019] S3-1: The signal is decomposed into wavelet coefficients of different scales and frequencies. The formula for the wavelet coefficient C x (a,N / 2πa) is:

[0020]

[0021]

[0022] where a is a continuous variable, x(t) is the input signal, ψ * is the conjugate of the wavelet function, t is the time, d is the integral symbol, and f c is the center frequency;

[0023] S3-2: Select a wavelet function, set the center frequency, find the wavelet coefficient indices close to the high-frequency frequency, and extract the wavelet coefficients of the specified frequency;

[0024] S3-3: Using time as the abscissa and the wavelet coefficients of the specified frequency as the ordinate, connect the peaks to form an envelope curve.

[0025] The beneficial effects of the above further solution are as follows: Through wavelet transform, the frequency characteristics of the signal at different scales can be analyzed. Then, based on the high-frequency frequency f obtained by the FFT algorithm, the signal with frequency f is extracted. By selecting appropriate wavelet basis functions and scale parameters, the corresponding detail coefficients are then extracted.

[0026] Further, in S4, it is determined whether there is a component breakdown in the capacitor bank according to the number of peaks of the envelope curve. The specific determination is as follows:

[0027] When the number of peaks of the envelope curve > 1, there is a component breakdown in the capacitor bank;

[0028] When the number of peaks of the envelope curve ≤ 1, there is no component breakdown in the capacitor bank.

[0029] The beneficial effects of the above further solution are as follows: Through the above technical solution, it is determined whether there is a breakdown in the capacitor bank according to the number of peaks of the envelope curve, and the breakdown phenomenon at low levels can be accurately identified, improving the reliability of detection. Description of the Drawings

[0030] Figure 1 It is a flowchart of a method for detecting the breakdown phenomenon when a shunt capacitor bank is put into operation. Detailed Embodiments

[0031] The present invention will be further described below with reference to the drawings and specific embodiments.

[0032] As Figure 1 shown, a method for detecting the breakdown phenomenon when a shunt capacitor bank is put into operation includes the following steps:

[0033] S1: Put a group of capacitor banks into a certain phase, and extract the current waveform data of one cycle after the capacitor banks are put into operation;

[0034] S2: Use the FFT algorithm to obtain the high-frequency frequency at the time of input according to the current waveform data;

[0035] S3: Use wavelet transform to extract the signal with the high-frequency frequency at the time of input as the specified frequency, and connect the peaks of the signal to form an envelope curve;

[0036] S4: Determine whether there is a component breakdown in the capacitor bank according to the number of peaks of the envelope curve, and complete the detection of the breakdown phenomenon when the shunt capacitor bank is put into operation.

[0037] The FFT (Fast Fourier Transform) is an efficient method for calculating the DFT (Discrete Fourier Transform), which is based on the ideas of the divide-and-conquer method and the butterfly operation.

[0038] First, the input signal is regarded as a sequence of length N, where N is a power of 2, and a rearrangement operation is performed so that the odd-indexed and even-indexed elements of the input signal are arranged in the second half and the first half respectively. This operation ensures that the signal can be divided into smaller subsequences during recursive calculation, thus achieving the efficiency of the FFT algorithm.

[0039] Next, the FFT algorithm calculates the spectrum of the signal by applying the "butterfly operation" multiple times. The butterfly operation is a combined operation that performs addition and multiplication on two complex inputs according to a specific formula to obtain two outputs. The results of the butterfly operation are used as the inputs for the next-level iteration.

[0040] The core idea of the FFT is to reduce the computational complexity of the DFT from O(n^2) to O(n log n), where n is the signal length. This is achieved by recursively dividing the signal length in half.

[0041] In S2, the FFT algorithm is used to obtain the high-frequency frequency at the time of input according to the current waveform data, including the following steps:

[0042] S2-1: The signal is decomposed into a linear combination of sine waves and cosine waves of different frequencies through the FFT algorithm to obtain signals of different frequencies. The signal is represented in the frequency domain as:

[0043]

[0044]

[0045] where X[k] is the complex value corresponding to the k-th frequency in the frequency domain of the signal. The amplitude and phase information of different frequency components of the signal can be obtained by iterative tracking. |·| is the amplitude, e is the exponential, θ k is the phase corresponding to the k-th frequency, j is the imaginary unit, f k is the frequency corresponding to the k-th frequency, N is the number of sampling points, and f s is the sampling rate;

[0046] S2-2: According to the signals of different frequencies, the signals of each frequency are sorted by amplitude size to obtain the high-frequency frequency. Among them, the two frequency signals with the highest amplitudes (including the 50 Hz power frequency signal and the high-frequency signal with a frequency of f0).

[0047] Wavelet transform is a time-frequency analysis method that can extract different frequency components from a signal. It has advantages in both the time domain and the frequency domain and is widely used in signal processing. Wavelet transform uses wavelet functions as basis functions and analyzes the characteristics of the signal at different times and frequencies by calculating the inner product of the signal and the wavelet function. Different from the Fourier transform, wavelet transform has time-domain locality and can capture the instantaneous characteristics and frequency changes of the signal.

[0048] Wavelet functions are functions that have locality and rapid decay characteristics between the time domain and the frequency domain. The most common wavelet functions include Morlet wavelets, Daubechies wavelets, Haar wavelets, etc. These wavelet functions have different frequency resolutions and time-domain resolutions and can adapt to the analysis needs of different types of signals.

[0049] S3 includes the following sub-steps:

[0050] S3-1: Decompose the signal into wavelet coefficients of different scales and frequencies. The wavelet coefficient C x (a,N / 2πa) formula is:

[0051]

[0052]

[0053] where a is a continuous variable, x(t) is the input signal, ψ * is the conjugate of the wavelet function, t is time, d is the integral symbol, f c is the central frequency;

[0054] the conjugate function of;

[0055] S3-2: Select a wavelet function, set the central frequency f c = 0.8125, find the index of the wavelet coefficient close to the high-frequency frequency and extract the wavelet coefficient of the specified frequency;

[0056] In this embodiment, wavelet functions such as Haar, Daubechies, Coiflet, Symlet, Morlet, etc. can be selected.

[0057] In this embodiment, the formula for converting the scale to the frequency (a total of 256 frequencies) f is:

[0058] f = f c ×f s / a

[0059] S3-3: Use time as the abscissa and the wavelet coefficient of the specified frequency as the ordinate to connect the peaks and form an envelope.

[0060] In continuous wavelet transform, the signal x(t) is continuously convolved with the wavelet function ψ(t) to obtain continuous wavelet coefficients, which represent the signal energy at the scale parameter a and the translation parameter b.

[0061] In one embodiment of the present invention, through wavelet transform, the frequency characteristics of the signal at different scales can be analyzed. Then, from the high-frequency frequency f obtained by the FFT algorithm, the signal with the frequency of f is extracted. By selecting appropriate wavelet basis functions and scale parameters, the corresponding detail coefficients are then extracted.

[0062] The specific method for extracting high-frequency signals can be selected according to specific application scenarios and requirements. Common methods include threshold processing, frequency band selection, etc.

[0063] After extracting the high-frequency signal, the peak points are connected and called the envelope line.

[0064] In S4, it is judged whether there is an element breakdown in the capacitor bank according to the number of wave peaks of the envelope line. The specific judgment is as follows:

[0065] When the number of wave peaks of the envelope line > 1, there is an element breakdown in the capacitor bank;

[0066] When the number of wave peaks of the envelope line ≤ 1, there is no element breakdown in the capacitor bank.

[0067] By introducing new detection methods and technologies, the entire element breakdown detection process is processed in real time by sensors and computers in the present invention, avoiding the disadvantages that the manual visual method is easily affected by subjective and objective factors, greatly improving the accuracy of the element breakdown determination result, being able to accurately identify low-level breakdown phenomena, and improving the reliability of detection; by optimizing the detection algorithm and eliminating interference factors, reducing the situation of misjudging a normally operating shunt capacitor bank as a breakdown event, thereby reducing unnecessary maintenance and intervention operation costs; simplifying the equipment and data processing flow for shunt capacitor bank breakdown detection, reducing the complexity of the system by introducing more concise and efficient equipment and algorithms, and improving the convenience of installation, maintenance, and operation; reducing the cost of shunt capacitor bank breakdown detection and improving the applicability in different systems and application scenarios, expanding the commercial application scope of this technology by optimizing the scheme and adopting cost-effective technologies, making it more competitive and feasible; laying a foundation for building a new power system. By detecting breakdowns accompanying the input of capacitors, a safer and more reliable operation of the power system can be achieved, accidents can be prevented, and the reliability and stability of the system can be improved.

[0068] Those of ordinary skill in the art will realize that the embodiments described herein are provided to assist the reader in understanding the principles of the present invention, and it should be understood that the scope of protection of the present invention is not limited to such specific statements and embodiments. Those of ordinary skill in the art can make various other specific deformations and combinations that do not depart from the essence of the present invention based on the technical revelations disclosed in the present invention, and these deformations and combinations are still within the scope of protection of the invention.

Claims

1. A method for detecting the breakdown phenomenon when a shunt capacitor bank is put into operation, characterized in that, It includes the following steps: S1: Put a group of capacitor banks into a certain phase and extract the current waveform data for one cycle after the capacitor banks are put in; S2: Use the FFT algorithm to obtain the high-frequency frequency at the time of input according to the current waveform data; S3: Use wavelet transform to extract the signal with the high-frequency frequency at the time of input being the specified frequency, and connect the peaks of the signal to form an envelope; S4: Judge whether there is an element breakdown phenomenon in the capacitor banks according to the number of wave peaks of the envelope, and complete the detection of the breakdown phenomenon when the shunt capacitor banks are put in.

2. The method for detecting the breakdown phenomenon when a shunt capacitor bank is put into operation according to claim 1, characterized in that, The step of using the FFT algorithm in S2 to obtain the high-frequency frequency at the time of input according to the current waveform data includes the following steps: S2-1: Decompose the signal into a linear combination of sine waves and cosine waves of different frequencies through the FFT algorithm to obtain signals of different frequencies, and the signal is expressed in the frequency domain as: where X[k] is the complex value corresponding to the k-th frequency in the frequency domain, |·| is the amplitude, e is the exponent, θ k is the phase corresponding to the k-th frequency, j is the imaginary unit, f k is the frequency corresponding to the k-th frequency, N is the number of sampling points, f s is the sampling rate; S2-2: According to the signals of different frequencies, sort each frequency signal according to the amplitude size to obtain the high-frequency frequency.

3. The method for detecting the breakdown phenomenon when a shunt capacitor bank is put into operation according to claim 2, characterized in that, The following sub-steps are included in S3: S3-1: Decompose the signal into wavelet coefficients of different scales and frequencies, where the wavelet coefficient C x (a, N / 2πa) formula is: where a is a continuous variable, x(t) is the input signal, ψ * is the conjugate of the wavelet function, t is time, d is the integral symbol, f c is the center frequency; S3-2: Select a wavelet function, set the center frequency, find the wavelet coefficient index close to the high-frequency frequency and extract the wavelet coefficients of the specified frequency; S3-3: Use time as the abscissa and the wavelet coefficients of the specified frequency as the ordinate to connect the peaks to form an envelope.

4. The method for detecting the breakdown phenomenon when a shunt capacitor bank is put into operation according to claim 1, characterized in that, In S4, judge whether there is an element breakdown phenomenon in the capacitor banks according to the number of wave peaks of the envelope. The specific judgment is as follows: When the number of wave peaks of the envelope > 1, there is an element breakdown phenomenon in the capacitor banks; When the number of wave peaks of the envelope ≤ 1, there is no element breakdown phenomenon in the capacitor banks.

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