Circuit breaker maloperation detection method of power transformation and distribution system

Abstract

The invention relates to the technical field of electrical variable measurement, in particular to a circuit breaker maloperation detection method for a power transformation and distribution system, and the method comprises the steps: constructing a current data sequence, a voltage data sequence, a temperature data sequence and a vibration data sequence of a to-be-detected circuit breaker; based on the current data sequence, the voltage data sequence and the temperature data sequence, constructing a current data sequence abnormal fluctuation coefficient used for representing the possibility that the current of the circuit breaker to be detected is in normal fluctuation; based on the vibration data sequence, constructing a circuit breaker abnormal coefficient used for representing the health condition of the operation state of the to-be-detected circuit breaker; and based on the abnormal fluctuation coefficient of the current data sequence and the abnormal coefficient of the circuit breaker, realizing circuit breaker maloperation detection. According to the scheme, the influence of external and internal factors of the circuit breaker is considered at the same time, and the accuracy of circuit breaker maloperation behavior detection is improved by analyzing and processing the multi-source data.

Description

Technical Field

[0001] The invention relates to the technical field of measuring electrical variables, and in particular to a circuit breaker malfunction detection method for a power transformation and distribution system. Background Art

[0002] In the power system, the power distribution system is the core link of power transmission and distribution, and its stability and reliability are directly related to the normal operation of social production and life. With the rapid development of smart grid technology, higher requirements are put forward for the automation level and fault response capability of the power distribution system. The circuit breaker is an important equipment of the power distribution system. The function of the circuit breaker is to cut off or connect the load circuit, and cut off the fault circuit to prevent the accident from expanding and ensure safe operation. Once a fault occurs in the power distribution system, the circuit breaker can cut off the fault circuit in time to ensure the safe and stable operation of the power distribution system. However, in actual applications, the circuit breaker may malfunction. The circuit breaker malfunction means that when the circuit does not actually fail, the circuit breaker is opened, causing the circuit to be interrupted, affecting the stability of the power distribution system.

[0003] In the prior art, the opening and closing of circuit breakers are mainly completed by mechanical structures. In order to achieve fast action, the mechanical structure is required to transmit high power and heavy load. As the number of actions increases and the remaining service life decreases, the mechanical properties of the circuit breaker will inevitably deteriorate. Studies have shown that mechanical failure is the main factor in circuit breaker failure, and most mechanical failures occur in the mechanical structure (operating mechanism). In addition to solving the problem of false operation, circuit breakers used for high voltage also need to solve the problem of arc extinguishing.

[0004] One existing technology is to evaluate the remaining life of the circuit breaker and whether it is prone to failure by analyzing the data of the monitoring circuit breaker itself and the environmental data. However, this method makes it difficult to determine whether the circuit breaker has malfunctioned in real time.

[0005] Another existing technology is to monitor the status of the circuit breaker in real time, and once a fault occurs, it can remind the operator to eliminate it in time. However, this technology requires the transmission of a large amount of sensing data, and has high requirements for the quality of data transmission. For example, "A Circuit Breaker Status Monitoring System" proposes that the data that needs to be transmitted include: circuit breaker time characteristic curve monitoring, opening and closing coil current monitoring, and mechanical vibration signal online monitoring; it also includes: auxiliary circuit monitoring, motor operation switch device power supply monitoring, trip coil integrity monitoring, circuit breaker energy storage spring status monitoring, gas pressure monitoring, switch cumulative operating time monitoring, contact switching time monitoring, and mechanical operation number monitoring, etc. Although this method can judge the malfunction of the circuit breaker, the amount of data that needs to be collected is huge, and the requirements for real-time data are high. If the data collection and data transmission do not meet the requirements, it is easy to make misjudgments.

[0006] There is also an existing technology that analyzes the arc extinguishing ability of the vacuum circuit breaker based on the arc image data of the vacuum arc extinguishing chamber of the vacuum circuit breaker, and then combines the data of the vacuum circuit breaker itself and the environmental data to judge the remaining life of the vacuum circuit breaker and whether it has malfunctioned. However, this method is difficult to apply in practice. The main reason is that the arc generated by instantaneous discharge is difficult to collect and even more difficult to identify. Even if the acquisition time error is 0.01 seconds, the arc image obtained will be completely different; and the arc images generated by different arc extinguishing technologies and different vacuum environments have different shapes, which increases the amount of image recognition calculations. In addition, high-precision image acquisition equipment and image recognition equipment are expensive. The cost of applying this technology in the distribution system even exceeds the loss caused by the malfunction of the circuit breaker.

[0007] In summary, most of the existing circuit breaker misoperation detection methods only analyze the state of the circuit breaker itself, but do not consider external influencing factors outside the circuit breaker; the few methods that take external factors into consideration only rely on current and voltage thresholds for misoperation detection, ignoring the complexity of circuit breaker misoperation and lacking simultaneous evaluation of external and internal factors, resulting in poor misoperation detection effect. Summary of the invention

[0008] In order to solve the technical problem that the existing circuit breaker malfunction detection method has a poor malfunction detection effect, the purpose of the present invention is to provide a circuit breaker malfunction detection method for a power transformation and distribution system, and the technical solution adopted is as follows: A method for detecting malfunction of a circuit breaker in a power transformation and distribution system, comprising: Constructing a current data sequence, a voltage data sequence, a temperature data sequence and a vibration data sequence of the circuit breaker to be detected; wherein the elements in the current data sequence are the current data when the circuit breaker to be detected is working; the elements in the voltage data sequence are the voltage data when the circuit breaker to be detected is working; the elements in the temperature data sequence are the temperature data when the circuit breaker to be detected is working; the elements in the vibration data sequence are the vibration data when the circuit breaker to be detected is working; Based on the current data sequence, the voltage data sequence and the temperature data sequence, constructing an abnormal fluctuation coefficient of the current data sequence; wherein the abnormal fluctuation coefficient of the current data sequence is used to characterize the possibility that the current of the circuit breaker to be detected is in normal fluctuation; Based on the vibration data sequence, a circuit breaker abnormality coefficient is constructed; wherein the circuit breaker abnormality coefficient is used to characterize the health of the operating state of the circuit breaker to be detected; Based on the abnormal fluctuation coefficient of the current data sequence and the circuit breaker abnormality coefficient, circuit breaker malfunction detection is achieved.

[0009] Furthermore, the step of constructing a current data sequence, a voltage data sequence, a temperature data sequence, and a vibration data sequence of the circuit breaker to be detected includes: According to the set sampling frequency, the current data, voltage data, temperature data and vibration data of the circuit breaker to be detected when it is working at the opening moment of the circuit breaker to be detected and within the preset time before the opening moment are synchronously collected; Normalize the collected current data, voltage data, temperature data and vibration data respectively; Using the normalized current data to construct a current data sequence; Using the normalized voltage data to construct a voltage data sequence; Use the normalized temperature data to construct a temperature data series; The vibration data sequence is constructed using the normalized vibration data.

[0010] Furthermore, the abnormal fluctuation coefficient of the current data sequence is constructed based on the current data sequence, the voltage data sequence and the temperature data sequence, including: Based on the fluctuation degree of the current data in the current data sequence, combined with the stationarity of the voltage data in the voltage data sequence and the stationarity of the temperature data in the temperature data sequence, construct a current data sequence fluctuation coefficient; Constructing a fault confidence level based on the current data intensity and the current data intensity rising speed in the current data sequence and in combination with the correlation between the current data sequence and the voltage data sequence; The product of the current data sequence fluctuation coefficient and the fault confidence is used as the current data sequence abnormal fluctuation coefficient.

[0011] Further, the current data sequence fluctuation coefficient is constructed based on the fluctuation degree of the current data in the current data sequence, in combination with the stationarity of the voltage data in the voltage data sequence and the stationarity of the temperature data in the temperature data sequence, including: Detecting the maximum values ​​in the current data sequence, and sorting all the detected maximum values ​​in a time sequence according to the sampling time corresponding to the data, so as to construct a current data maximum value data sequence; Determine the sampling time corresponding to the maximum value in the current data maximum value data sequence, and calculate the difference between the maximum value and the second largest value in the current data maximum value data sequence; averaging all current data in the current data sequence to obtain a current mean; counting the number of elements in the current data maximum value data sequence whose element values ​​are greater than the current mean, and calculating the ratio of the number of elements in the current data maximum value data sequence whose element values ​​are greater than the current mean to the total number of elements in the current data maximum value data sequence; respectively calculating the discreteness of the voltage data sequence and the discreteness of the temperature data sequence, and calculating the sum of the discreteness of the voltage data sequence and the discreteness of the temperature data sequence; The current data sequence fluctuation coefficient is constructed based on the sampling time corresponding to the maximum value in the current data maximum value data sequence, the difference between the maximum value and the second largest value in the current data maximum value data sequence, the ratio of the number of elements in the current data maximum value data sequence whose element values ​​are greater than the current mean value to the total number of elements in the current data maximum value data sequence, and the sum of the discreteness of the voltage data sequence and the discreteness of the temperature data sequence.

[0012] Furthermore, the calculation formula of the current data sequence fluctuation coefficient is: ; in, Represents the fluctuation coefficient of the current data series; Indicates the difference between the maximum value and the second largest value in the current data maximum value data sequence; Indicates the sampling time corresponding to the maximum value in the current data maximum value data sequence; Indicates the ratio of the number of elements in the current data maximum value data sequence whose element values ​​are greater than the current mean value to the total number of elements in the current data maximum value data sequence; represents the sum of the discreteness of the voltage data sequence and the discreteness of the temperature data sequence; Represents the normalization function.

[0013] It should be noted that in order to ensure that the calculation results are meaningful, when performing fractional operations, a parameter adjustment factor greater than 0 must be added to the denominator to prevent the denominator from being 0. The value of the parameter adjustment factor is set by the implementer according to actual conditions, and this application does not impose any restrictions.

[0014] Further, the constructing of the fault confidence based on the current data intensity and the current data intensity rising speed in the current data sequence and the correlation between the current data sequence and the voltage data sequence includes: Detecting mutation points in the current data sequence, dividing the current data sequence according to the detected mutation points, dividing the data between every two adjacent mutation points into a subsequence, so as to divide the current data sequence into a plurality of subsequences, and taking the subsequence where the maximum value in the current data sequence is located as the detection current subsequence; wherein, if there are multiple maximum values ​​in different subsequences, taking the subsequence with the largest range as the detection current subsequence; According to the sampling time corresponding to the data in the detection current subsequence, extracting the voltage data at the same sampling time as the data in the detection current subsequence from the voltage data sequence to form a detection voltage subsequence; Calculating a correlation coefficient between the detection current subsequence and the detection voltage subsequence; The majority element in the current data sequence is recorded as the current axis, the element in the detection current subsequence that is greater than the current axis is recorded as the high axis element, all the high axis elements in the detection current subsequence are counted, and the difference between the total number of elements in the detection current subsequence and the number of high axis elements in the detection current subsequence is calculated and recorded as the time axis deflection coefficient; Using the least square method, a straight line fitting is performed on the data in the detection current subsequence to obtain the slope of the fitting straight line, and the ratio of the slope of the fitting straight line to the number of peak values ​​in the detection current subsequence is calculated and recorded as the rise coefficient; Calculating the range of the detection voltage subsequence; A fault confidence level is constructed based on a correlation coefficient between the detection current subsequence and the detection voltage subsequence, the time axis deflection coefficient, the rise coefficient, and a range of the detection voltage subsequence.

[0015] Furthermore, the calculation formula of the fault confidence is: ; in, Indicates the fault confidence level; Indicates the time axis bias coefficient; represents a correlation coefficient between the detection current subsequence and the detection voltage subsequence; It represents the rise coefficient; Indicates the extreme difference of the detection voltage subsequence.

[0016] Furthermore, the construction of the circuit breaker abnormality coefficient based on the vibration data sequence includes: Detecting all peaks and valleys in the vibration data sequence; Record the vibration data corresponding to the opening moment as the opening element, record the peak value located before the opening element and adjacent to the opening element as the tripping peak value, and record the difference between the sampling time of the opening element and the tripping peak value as the response time; A normal vibration sequence is constructed by using all elements in the vibration data sequence that are located before the tripping peak value; a switch-off vibration sequence is constructed by using all elements in the vibration data sequence that are located after the tripping peak value and the tripping peak value; Traverse all the peaks in the normal vibration sequence, and for the current peak traversed, calculate the absolute value of the difference between the current peak and its adjacent valley value, recorded as the peak-to-valley value; and calculate the absolute value of the difference in sampling time between the current peak and its adjacent valley value, recorded as the subscript difference; wherein, if there is a valley value after the current peak, its adjacent valley value is the nearest valley value after it, and if there is no valley value after the current peak, its adjacent valley value is the nearest valley value before it; Calculating the mean of the subscript differences in the normal vibration sequence, and calculating the ratio of the response time to the mean of the subscript differences, which is recorded as the response time difference; Calculate the degree of dispersion of all peak-to-valley values ​​in the normal vibration sequence, and record it as the peak-to-valley value difference; Calculating the degree of dispersion of all peak values ​​in the normal vibration sequence, and recording it as peak value difference; Calculating the absolute value of the difference between the gate opening element and the maximum value in the gate opening vibration data sequence; A circuit breaker abnormality coefficient is constructed based on the response time difference, the peak-to-valley value difference, the peak value difference, and the absolute value of the difference between the opening element and the maximum value in the opening vibration data sequence.

[0017] Furthermore, the calculation formula of the circuit breaker abnormality coefficient is: ; in, Indicates the circuit breaker abnormality coefficient; Indicates the difference between peak and valley values; represents the peak difference; represents the absolute value of the difference between the opening element and the maximum value in the opening vibration data sequence; Indicates the response time difference.

[0018] Furthermore, the circuit breaker malfunction detection is implemented based on the abnormal fluctuation coefficient of the current data sequence and the circuit breaker abnormality coefficient, including: Calculating the ratio of the circuit breaker abnormality coefficient to the abnormal fluctuation coefficient of the current data sequence, and recording it as a misoperation index; The malfunction index is compared with a preset threshold value. If the malfunction index is greater than the preset threshold value, it is determined that the circuit breaker to be detected currently malfunctions. Otherwise, it is determined that the circuit breaker to be detected currently does not malfunction.

[0019] The present invention has the following beneficial effects: The present invention constructs a current data sequence, a voltage data sequence, a temperature data sequence and a vibration data sequence of the circuit breaker to be detected; based on the current data sequence, the voltage data sequence and the temperature data sequence, the abnormal fluctuation coefficient of the current data sequence is constructed to evaluate whether the mutation data in the current data sequence is normal fluctuation data; based on the vibration data sequence, the circuit breaker abnormal coefficient is constructed to evaluate the circuit breaker state; based on the abnormal fluctuation coefficient of the current data sequence and the circuit breaker abnormal coefficient, the circuit breaker malfunction detection is realized. Therefore, when performing the circuit breaker malfunction detection, the influence of the external factors and the internal factors of the circuit breaker are taken into account at the same time, and the accuracy of the circuit breaker malfunction behavior detection is effectively improved by analyzing and processing the multi-source data. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1 The present invention is a schematic diagram of the execution flow of the circuit breaker malfunction detection method of the power transformation and distribution system. DETAILED DESCRIPTION

[0022] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the circuit breaker misoperation detection method of a power distribution system proposed by the present invention, its specific implementation method, structure, characteristics and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form. Unless otherwise defined, all technical and scientific terms used in this article have the same meaning as those generally understood by technicians in the technical field of the present invention.

[0023] Example 1 A circuit breaker malfunction detection method for a power transformation and distribution system provided by the present invention is described in detail below with reference to the accompanying drawings.

[0024] A circuit breaker malfunction detection method for a power distribution system, such as Figure 1 As shown, the execution steps of this method are as follows: S1, constructing a current data sequence, a voltage data sequence, a temperature data sequence and a vibration data sequence of a circuit breaker to be detected; wherein the elements in the current data sequence are the current data of the circuit breaker to be detected when it is working; the elements in the voltage data sequence are the voltage data of the circuit breaker to be detected when it is working; the elements in the temperature data sequence are the temperature data of the circuit breaker to be detected when it is working; and the elements in the vibration data sequence are the vibration data of the circuit breaker to be detected when it is working; In this embodiment, the specific implementation process of the above S1 is as follows: S11, synchronously collecting the current data, voltage data, temperature data and vibration data of the circuit breaker to be detected at the opening moment of the circuit breaker to be detected and the preset time before the opening moment according to the set sampling frequency; It should be noted that the current, voltage and vibration data can intuitively reflect the working condition of the circuit breaker; therefore, in order to detect whether the circuit breaker has malfunctioned, it is necessary to collect the current, voltage and vibration data; and, considering that if an abnormality occurs in the power distribution system, the temperature of the circuit breaker will generally fluctuate significantly, therefore, in order to accurately determine whether the circuit breaker has malfunctioned, this embodiment further collects the temperature data of the circuit breaker. Specifically, this embodiment collects the current data, voltage data, temperature data and vibration data of the circuit breaker to be detected at the time of opening the circuit breaker to be detected and within 300 seconds before the opening time; wherein, each type of data is collected synchronously, and the collection frequency is 0.1 seconds.

[0025] Specifically, a temporary register may be provided to automatically and cyclically store the working data of the circuit breaker within 300.1 seconds, and the stored data may be automatically uploaded once a circuit breaker occurs.

[0026] S12, normalizing the collected current data, voltage data, temperature data, and vibration data respectively; It should be noted that, since different types of data have different units, in order to avoid the influence of data dimension in subsequent calculations, this embodiment normalizes the collected data to eliminate the dimensional influence; when normalizing each type of data, this embodiment directly adopts the existing normalization algorithm (such as Z-score normalization algorithm) to implement it.

[0027] S13, constructing a current data sequence using the normalized current data; S14, constructing a voltage data sequence using the normalized voltage data; S15, constructing a temperature data sequence using the normalized temperature data; S16, constructing a vibration data sequence using the normalized vibration data.

[0028] S2, constructing an abnormal fluctuation coefficient of the current data sequence based on the current data sequence, the voltage data sequence and the temperature data sequence; wherein the abnormal fluctuation coefficient of the current data sequence is used to characterize the possibility that the current of the circuit breaker to be detected is in normal fluctuation; Specifically, in this embodiment, the implementation process of the above S2 is as follows: S21, constructing a current data sequence fluctuation coefficient based on the fluctuation degree of the current data in the current data sequence, combined with the stationarity of the voltage data in the voltage data sequence and the stationarity of the temperature data in the temperature data sequence; Among them, it should be noted that, under normal circumstances, the current of the circuit breaker will always fluctuate, but these fluctuations are normal fluctuations. If the current data fluctuates normally, there is no excitation inrush current or short-circuit fault, but the circuit breaker has opened, it means that the possibility of malfunction caused by the circuit breaker's own fault is very high. Therefore, in order to detect whether the circuit breaker has malfunctioned, it is first necessary to detect whether the circuit breaker current data is in a normal fluctuation state. However, during the normal operation of the power distribution system, the start and stop of various different equipment will cause the circuit breaker current to produce a more obvious instantaneous mutation. Therefore, if the stability of the current data sequence is evaluated only by the discrete degree or extreme difference of the current data, it may cause the normal current data sequence to be mistakenly detected as abnormal fluctuation data or excitation inrush current, thereby affecting the subsequent circuit breaker malfunction evaluation.

[0029] In view of the above problems, although some mutation points may appear in the current data, the mutation points appear instantaneously, and under normal circumstances, the current fluctuation peak should be within the normal range, and the influence on the voltage and temperature is very small, so the voltage data sequence and the temperature data sequence should still have stability. Therefore, this embodiment is based on the current data fluctuation degree in the current data sequence, combined with the voltage data stability in the voltage data sequence and the temperature data stability in the temperature data sequence, to construct the current data sequence fluctuation coefficient, so as to determine whether the fluctuation of the current data is normal fluctuation. The specific process is as follows: S211, detecting the maximum values ​​in the current data sequence, and sorting all the detected maximum values ​​in a time sequence according to the sampling time corresponding to the data, so as to construct a current data maximum value data sequence; S212, determining the sampling time corresponding to the maximum value in the current data maximum value data sequence, and calculating the difference between the maximum value and the second largest value in the current data maximum value data sequence; S213, averaging all current data in the current data sequence to obtain a current mean value; counting the number of elements in the current data maximum value data sequence whose element values ​​are greater than the current mean value, and calculating the ratio of the number of elements in the current data maximum value data sequence whose element values ​​are greater than the current mean value to the total number of elements in the current data maximum value data sequence; S214, respectively calculating the discreteness of the voltage data sequence and the discreteness of the temperature data sequence, and calculating the sum of the discreteness of the voltage data sequence and the discreteness of the temperature data sequence; S215, based on the sampling time corresponding to the maximum value in the current data maximum value data sequence, the difference between the maximum value and the second largest value in the current data maximum value data sequence, the ratio of the number of elements in the current data maximum value data sequence whose element values ​​are greater than the current mean value to the total number of elements in the current data maximum value data sequence, and the sum of the discrete degree of the voltage data sequence and the discrete degree of the temperature data sequence, construct the current data sequence fluctuation coefficient, the formula is as follows: ; in, represents the fluctuation coefficient of the current data series, The smaller the value, the more likely it is that the current data is still fluctuating normally; Indicates the difference between the maximum value and the second largest value in the current data maximum value data sequence; Indicates the sampling time corresponding to the maximum value in the current data maximum value data sequence; Indicates the ratio of the number of elements in the current data maximum value data sequence whose element values ​​are greater than the current mean value to the total number of elements in the current data maximum value data sequence; It represents the sum of the discreteness of the voltage data sequence and the discreteness of the temperature data sequence; Represents the normalization function.

[0030] Among them, it should be noted that if the fluctuation of current data is normal, even if the current increases instantaneously due to the start and stop of the equipment, the degree of increase is far less than the degree of current increase caused by a short-circuit fault or an excitation surge. Therefore, in the case of normal fluctuation of current data, the difference B between the maximum value and the second largest value in the current data maximum value data sequence should not be very large. Therefore, the smaller the B value, the greater the possibility that the current data is still fluctuating normally. At the same time, if a short-circuit fault or an excitation surge causes the circuit breaker to open, the maximum value in the current data sequence must be the current data collected at the time of opening, that is, the maximum value must be at the end of the sequence. At this time, the sampling time C corresponding to the maximum value should be very large. Therefore, the larger the C value, the greater the possibility of an excitation surge or a short-circuit fault, that is, the smaller the possibility that the current data is still fluctuating normally. In addition, the ratio of the number of elements in the current data maximum value data sequence whose element values ​​are greater than the current mean value to the total number of elements in the current data maximum value data sequence The larger the value, the more peak values ​​are greater than the current mean. If most of the current peak values ​​are greater than the current mean, this can to a certain extent explain that the differences between the peak values ​​in the current data are not large. Therefore, The larger the value is, the greater the possibility that the current current data is still fluctuating normally; further, since the voltage or temperature will change significantly when an excitation inrush current or a short circuit fault occurs, the smaller the sum of the discrete degrees of the voltage data sequence and the discrete degrees of the temperature data sequence E is, the higher the stability of the current voltage and temperature data is, that is, the smaller the possibility of an excitation inrush current or a short circuit fault occurring at this time, that is, the greater the possibility that the current current data is still fluctuating normally.

[0031] Based on the above, this embodiment calculates the fluctuation coefficient of the current data sequence, and realizes the evaluation of whether the internal elements of the current data sequence are normal fluctuations; and this embodiment takes into account the impact of the start and stop of equipment in the substation system on the current fluctuation, avoiding the false detection problem caused by a simple evaluation of a single current data only by the extreme value or discreteness of the current data.

[0032] S22, constructing a fault confidence level based on the current data intensity and the current data intensity rising speed in the current data sequence and the correlation between the current data sequence and the voltage data sequence; Among them, it should be noted that when the transformer in the power distribution system is closed or reclosed, due to the nonlinear characteristics of the core magnetic flux, it is easy to cause the excitation inrush phenomenon. Excitation inrush is a transient current, and its current intensity is much greater than the short-circuit current value set by the power distribution system (due to its short duration, it generally does not cause great harm to the transformer). If the excitation inrush phenomenon occurs, the current data will fluctuate greatly, and the circuit breaker may disconnect the circuit, resulting in circuit breaker malfunction. That is, the abnormal fluctuation of current data may be caused by a short-circuit fault or by the excitation inrush current. Therefore, in order to accurately determine whether the circuit breaker is malfunctioning, it is necessary to distinguish whether the abnormal fluctuation of circuit data is caused by the excitation inrush phenomenon, so as to correct the fluctuation coefficient of the current data sequence so that it can more objectively reflect the current fluctuation. To this end, the cause of the increase in current in the circuit can be analyzed to distinguish whether the abnormal current fluctuation is caused by a short-circuit fault (normal circuit breaker disconnection) or an excitation inrush phenomenon (circuit breaker malfunction).

[0033] Through the characteristic analysis of the excitation surge current and the short-circuit fault, it is found that the current intensity during the excitation surge current is much greater than the current intensity during the short-circuit fault, and the current intensity during the excitation surge current rises more rapidly. At the same time, the short-circuit fault is caused by the insulation damage of the circuit and the reduction of resistance. At this time, the voltage remains unchanged, and the excitation surge current will not only cause current fluctuations, but also cause the voltage to rise and fall sharply; further, the excitation surge current waveform is obviously biased to one side of the time axis, that is, the excitation surge current waveform has a strong monotonicity; and in the case of a short-circuit fault, its current waveform still fluctuates violently around the time axis and does not have monotonicity. Based on this, this embodiment constructs a fault confidence based on the current data intensity and the current data intensity rising speed in the current data sequence, combined with the correlation between the current data sequence and the voltage data sequence. The specific process is as follows: S221, detecting mutation points in the current data sequence, dividing the current data sequence according to the detected mutation points, dividing the data between every two adjacent mutation points into a subsequence, so as to divide the current data sequence into multiple subsequences, and taking the subsequence where the maximum value in the current data sequence is located as the detection current subsequence; wherein, if there are multiple maximum values ​​in different subsequences, taking the subsequence with the largest range as the detection current subsequence; S222, according to the sampling time corresponding to the data in the detection current subsequence, extracting from the voltage data sequence the voltage data having the same sampling time as the data in the detection current subsequence to form a detection voltage subsequence; S223, calculating the correlation coefficient between the detection current subsequence and the detection voltage subsequence; Among them, the correlation coefficient of the two sequences can be obtained by the Pearson correlation coefficient calculation method.

[0034] S224, recording the majority element in the current data sequence as the current axis, recording the element in the detection current subsequence that is greater than the current axis as the high axis element, counting all the high axis elements in the detection current subsequence, and calculating the difference between the total number of elements in the detection current subsequence and the number of high axis elements in the detection current subsequence, and recording it as the time axis deflection coefficient; S225, using the least square method to perform straight line fitting on the data in the detection current subsequence, obtain the slope of the fitting straight line, and calculate the ratio of the slope of the fitting straight line to the number of peaks in the detection current subsequence, which is recorded as the rise coefficient; S226, calculating the range of the detection voltage subsequence; S227: construct a fault confidence level based on the correlation coefficient between the detection current subsequence and the detection voltage subsequence, the time axis deflection coefficient, the rise coefficient, and the range of the detection voltage subsequence. The formula is as follows: ; in, Indicates fault confidence. The smaller the F value, the greater the possibility that the current data is abnormally fluctuating due to the magnetizing inrush current. Indicates the time axis bias coefficient; represents a correlation coefficient between the detection current subsequence and the detection voltage subsequence; It represents the rise coefficient; Indicates the extreme difference of the detection voltage subsequence.

[0035] Among them, it should be noted that the smaller the I value, the more the elements in the detection current subsequence are biased to one side of the current axis, that is, the current waveform has a strong monotonicity at this time. Since the excitation inrush current waveform has a strong monotonicity, and the current waveform of the short-circuit fault does not have monotonicity, therefore, the smaller the I value, the greater the possibility that the current fluctuation is caused by the excitation inrush current. The larger the G value, the stronger the correlation between the detection current subsequence and the detection voltage subsequence, which means that the voltage data is more likely to be affected at this time. Since the short-circuit fault will not cause voltage fluctuations, and the excitation inrush current will not only cause current fluctuations, but also cause the voltage to rise and fall suddenly, therefore, the larger the G value, the greater the possibility that the current fluctuation is caused by the excitation inrush current. The larger the J value, the faster the rise of the elements in the detection current subsequence and the higher the monotonicity. Since the current intensity during the excitation inrush current rises more rapidly than that during the short-circuit fault, therefore, the larger the J value, the greater the possibility that the current fluctuation is caused by the excitation inrush current. The larger the H value is, the greater the fluctuation of the detection voltage subsequence is. Since the current intensity during the excitation inrush current is much greater than the current intensity during the short-circuit fault, the larger the H value is, the greater the possibility that the current fluctuation is caused by the excitation inrush current.

[0036] S23, taking the product of the current data sequence fluctuation coefficient and the fault confidence as the current data sequence abnormal fluctuation coefficient.

[0037] S3, constructing a circuit breaker abnormality coefficient based on the vibration data sequence; wherein the circuit breaker abnormality coefficient is used to characterize the health of the operating state of the circuit breaker to be detected; It should be noted that, in theory, the better the health of the circuit breaker, the lower the possibility of malfunction; the worse the health of the circuit breaker, the greater the possibility of malfunction. The vibration signal of a circuit breaker in a good health state is stable, and the amplitude of the vibration signal corresponding to different moments is not much different. This embodiment creatively proposes that the health of the operating state of the circuit breaker can be evaluated through the vibration data of the circuit breaker during operation, and the data can be used to evaluate the possibility of malfunction of the circuit breaker.

[0038] In addition, by analyzing the opening process of a circuit breaker in good condition, it is found that when the opening electromagnet is energized and the opening latch is triggered to trip, the vibration signal will have an instantaneous peak value; then, under the action of the spring, the three-way connecting rod rotates, and the moving contact begins to accelerate downward. At this time, the amplitude of the vibration signal gradually increases until the moving contact stops colliding (opening is completed), and the vibration signal reaches the maximum value. Based on this, this embodiment constructs the circuit breaker abnormality coefficient through the following process to evaluate the health of the circuit breaker: S31, detecting all peaks and valleys in the vibration data sequence; S32, recording the vibration data corresponding to the opening moment as the opening element, recording the peak value located before the opening element and adjacent to the opening element as the tripping peak value, and recording the difference between the sampling moments of the opening element and the tripping peak value as the response time; S33, constructing a normal vibration sequence using all elements in the vibration data sequence that are located before the tripping peak; constructing a tripping vibration sequence using all elements in the vibration data sequence that are located after the tripping peak and the tripping peak; S34, traverse all peak values ​​in the normal vibration sequence, and for the current peak value traversed, calculate the absolute value of the difference between the current peak value and its adjacent valley value, recorded as the peak-to-valley value; and calculate the absolute value of the difference in sampling time between the current peak value and its adjacent valley value, recorded as the subscript difference; wherein, if there is a valley value after the current peak value, its adjacent valley value is the nearest valley value after it; if there is no valley value after the current peak value, its adjacent valley value is the nearest valley value before it; S35, calculating the mean of the subscript differences in the normal vibration sequence, and calculating the ratio of the response time to the mean of the subscript differences, which is recorded as the response time difference; S36, calculating the degree of dispersion of all peak-to-valley values ​​in the normal vibration sequence, and recording it as the peak-to-valley value difference; S37, calculating the degree of dispersion of all peak values ​​in the normal vibration sequence, and recording it as the peak value difference; S38, calculating the absolute value of the difference between the gate opening element and the maximum value in the gate opening vibration data sequence; S39, based on the response time difference, the peak-to-valley value difference, the peak value difference, and the absolute value of the difference between the trip element and the maximum value in the trip vibration data sequence, the circuit breaker abnormality coefficient is constructed, and the formula is as follows: ; in, Indicates the circuit breaker abnormality coefficient, The smaller the value, the better the health of the circuit breaker and the lower the possibility of false operation; Indicates the difference between peak and valley values; represents the peak difference; represents the absolute value of the difference between the opening element and the maximum value in the opening vibration data sequence; Indicates the response time difference.

[0039] It should be noted that if the circuit breaker is in good health, then in normal operation, the difference between all peak-to-valley values ​​in its vibration data should be small; the difference between all peak values ​​should also be small; the trip element is the maximum value, that is, the absolute value of the difference between the trip element and the maximum value in the trip vibration data sequence should be small (theoretically it should be zero); the response time must be less than the mean of the subscript difference, so the ratio of the response time to the mean of the subscript difference should be small; at this time, the calculated The value should be smaller, otherwise, the calculated The value should be large, indicating that the health of the circuit breaker is poor.

[0040] S4, based on the abnormal fluctuation coefficient of the current data sequence and the circuit breaker abnormality coefficient, circuit breaker malfunction detection is realized.

[0041] Specifically, in this embodiment, the implementation process of the above S4 is as follows: S41, calculating the ratio of the circuit breaker abnormality coefficient to the abnormal fluctuation coefficient of the current data sequence, and recording it as the misoperation index; Among them, it should be noted that the smaller the circuit breaker abnormality coefficient is, the better the health status of the circuit breaker is, and the smaller the possibility that the circuit breaker opening is a false operation is; and the smaller the abnormal fluctuation coefficient of the current data sequence is, the greater the possibility that the current data is in normal fluctuation, and the greater the possibility that the circuit breaker opening is a false operation is. Therefore, this embodiment uses the ratio of the circuit breaker abnormality coefficient to the abnormal fluctuation coefficient of the current data sequence as the false operation index. The larger the false operation index is, the greater the possibility that the current circuit breaker opening is a false operation.

[0042] S42, comparing the malfunction index with a preset threshold value, if the malfunction index is greater than the preset threshold value, determining that the current circuit breaker to be detected has malfunctioned, otherwise, determining that the current circuit breaker to be detected has not malfunctioned; It should be noted that the threshold value is set to 0.5 in this embodiment, that is, if the malfunction index is greater than 0.5, it is determined that the current opening of the circuit breaker is a malfunction; if the malfunction index is less than or equal to 0.5, it is determined that the current opening of the circuit breaker is a normal opening.

[0043] In summary, this embodiment provides a circuit breaker misoperation detection method for a transformer substation system, which constructs a current data sequence, a voltage data sequence, a temperature data sequence, and a vibration data sequence of the circuit breaker to be detected; constructs an abnormal fluctuation coefficient of the current data sequence based on the current data sequence, the voltage data sequence, and the temperature data sequence to evaluate whether the mutation data in the current data sequence is normal fluctuation data; constructs a circuit breaker abnormality coefficient based on the vibration data sequence to evaluate the circuit breaker state; and realizes circuit breaker misoperation detection based on the abnormal fluctuation coefficient of the current data sequence and the circuit breaker abnormality coefficient. Therefore, when performing circuit breaker misoperation detection, the influence of external and internal factors of the circuit breaker is taken into account at the same time, and the accuracy of detecting circuit breaker misoperation behavior is effectively improved by analyzing and processing multi-source data.

[0044] In addition, it should be noted that the above are only preferred embodiments of the present invention. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-tasking and parallel processing are also possible or may be advantageous. And although the preferred embodiments of the present invention have been described, it is obvious to a person skilled in the art that once the basic creative concept of the present invention is known, several improvements and modifications can be made without departing from the principles of the present invention, and these improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A circuit breaker malfunction detection method for a power transformation and distribution system, characterized in that: include: Constructing a current data sequence, a voltage data sequence, a temperature data sequence and a vibration data sequence of the circuit breaker to be detected; wherein the elements in the current data sequence are the current data when the circuit breaker to be detected is working; the elements in the voltage data sequence are the voltage data when the circuit breaker to be detected is working; the elements in the temperature data sequence are the temperature data when the circuit breaker to be detected is working; the elements in the vibration data sequence are the vibration data when the circuit breaker to be detected is working; Based on the current data sequence, the voltage data sequence and the temperature data sequence, constructing an abnormal fluctuation coefficient of the current data sequence; wherein the abnormal fluctuation coefficient of the current data sequence is used to characterize the possibility that the current of the circuit breaker to be detected is in normal fluctuation; Based on the vibration data sequence, a circuit breaker abnormality coefficient is constructed; wherein the circuit breaker abnormality coefficient is used to characterize the health of the operating state of the circuit breaker to be detected; Based on the abnormal fluctuation coefficient of the current data sequence and the circuit breaker abnormality coefficient, circuit breaker malfunction detection is achieved.

2. The circuit breaker malfunction detection method of the power transformation and distribution system according to claim 1, characterized in that: The step of constructing a current data sequence, a voltage data sequence, a temperature data sequence, and a vibration data sequence of the circuit breaker to be detected includes: According to the set sampling frequency, the current data, voltage data, temperature data and vibration data of the circuit breaker to be detected when it is working at the opening moment of the circuit breaker to be detected and within the preset time before the opening moment are synchronously collected; Normalize the collected current data, voltage data, temperature data and vibration data respectively; Using the normalized current data to construct a current data sequence; Using the normalized voltage data to construct a voltage data sequence; Use the normalized temperature data to construct a temperature data series; The vibration data sequence is constructed using the normalized vibration data.

3. The circuit breaker malfunction detection method of the power transformation and distribution system according to claim 1, characterized in that: The method of constructing an abnormal fluctuation coefficient of a current data sequence based on the current data sequence, the voltage data sequence and the temperature data sequence includes: Based on the fluctuation degree of the current data in the current data sequence, combined with the stationarity of the voltage data in the voltage data sequence and the stationarity of the temperature data in the temperature data sequence, construct a current data sequence fluctuation coefficient; Constructing a fault confidence level based on the current data intensity and the current data intensity rising speed in the current data sequence and in combination with the correlation between the current data sequence and the voltage data sequence; The product of the current data sequence fluctuation coefficient and the fault confidence is used as the current data sequence abnormal fluctuation coefficient.

4. The circuit breaker malfunction detection method of the power transformation and distribution system according to claim 3, characterized in that: The constructing of the current data sequence fluctuation coefficient based on the fluctuation degree of the current data in the current data sequence, in combination with the stationarity of the voltage data in the voltage data sequence and the stationarity of the temperature data in the temperature data sequence, comprises: Detecting the maximum values ​​in the current data sequence, and sorting all the detected maximum values ​​in a time sequence according to the sampling time corresponding to the data, so as to construct a current data maximum value data sequence; Determine the sampling time corresponding to the maximum value in the current data maximum value data sequence, and calculate the difference between the maximum value and the second largest value in the current data maximum value data sequence; averaging all current data in the current data sequence to obtain a current mean; counting the number of elements in the current data maximum value data sequence whose element values ​​are greater than the current mean, and calculating the ratio of the number of elements in the current data maximum value data sequence whose element values ​​are greater than the current mean to the total number of elements in the current data maximum value data sequence; respectively calculating the discreteness of the voltage data sequence and the discreteness of the temperature data sequence, and calculating the sum of the discreteness of the voltage data sequence and the discreteness of the temperature data sequence; The current data sequence fluctuation coefficient is constructed based on the sampling time corresponding to the maximum value in the current data maximum value data sequence, the difference between the maximum value and the second largest value in the current data maximum value data sequence, the ratio of the number of elements in the current data maximum value data sequence whose element values ​​are greater than the current mean value to the total number of elements in the current data maximum value data sequence, and the sum of the discreteness of the voltage data sequence and the discreteness of the temperature data sequence.

5. The circuit breaker malfunction detection method of the power transformation and distribution system according to claim 4, characterized in that: The calculation formula of the fluctuation coefficient of the current data sequence is: ； in, Represents the fluctuation coefficient of the current data series; Indicates the difference between the maximum value and the second largest value in the current data maximum value data sequence; Indicates the sampling time corresponding to the maximum value in the current data maximum value data sequence; Indicates the ratio of the number of elements in the current data maximum value data sequence whose element values ​​are greater than the current mean value to the total number of elements in the current data maximum value data sequence; represents the sum of the discreteness of the voltage data sequence and the discreteness of the temperature data sequence; Represents the normalization function.

6. The circuit breaker malfunction detection method for a power transformation and distribution system according to claim 3, characterized in that: The constructing of fault confidence based on the current data intensity and the current data intensity rising speed in the current data sequence and the correlation between the current data sequence and the voltage data sequence includes: Detecting mutation points in the current data sequence, dividing the current data sequence according to the detected mutation points, dividing the data between every two adjacent mutation points into a subsequence, so as to divide the current data sequence into a plurality of subsequences, and taking the subsequence where the maximum value in the current data sequence is located as the detection current subsequence; wherein, if there are multiple maximum values ​​in different subsequences, taking the subsequence with the largest range as the detection current subsequence; According to the sampling time corresponding to the data in the detection current subsequence, extracting the voltage data at the same sampling time as the data in the detection current subsequence from the voltage data sequence to form a detection voltage subsequence; Calculating a correlation coefficient between the detection current subsequence and the detection voltage subsequence; The majority element in the current data sequence is recorded as the current axis, the element in the detection current subsequence that is greater than the current axis is recorded as the high axis element, all the high axis elements in the detection current subsequence are counted, and the difference between the total number of elements in the detection current subsequence and the number of high axis elements in the detection current subsequence is calculated and recorded as the time axis deflection coefficient; Using the least square method, a straight line fitting is performed on the data in the detection current subsequence to obtain the slope of the fitting straight line, and the ratio of the slope of the fitting straight line to the number of peak values ​​in the detection current subsequence is calculated and recorded as the rise coefficient; Calculating the range of the detection voltage subsequence; A fault confidence level is constructed based on a correlation coefficient between the detection current subsequence and the detection voltage subsequence, the time axis deflection coefficient, the rise coefficient, and a range of the detection voltage subsequence.

7. The circuit breaker malfunction detection method of the power transformation and distribution system according to claim 6, characterized in that: The calculation formula of the fault confidence is: ； in, Indicates the fault confidence level; Indicates the time axis bias coefficient; represents a correlation coefficient between the detection current subsequence and the detection voltage subsequence; It represents the rise coefficient; Indicates the extreme difference of the detection voltage subsequence.

8. The circuit breaker malfunction detection method for a power transformation and distribution system according to claim 1, characterized in that: The circuit breaker abnormality coefficient is constructed based on the vibration data sequence, including: Detecting all peaks and valleys in the vibration data sequence; Record the vibration data corresponding to the opening moment as the opening element, record the peak value located before the opening element and adjacent to the opening element as the tripping peak value, and record the difference between the sampling time of the opening element and the tripping peak value as the response time; A normal vibration sequence is constructed by using all elements in the vibration data sequence that are located before the tripping peak value; a tripping vibration sequence is constructed by using all elements in the vibration data sequence that are located after the tripping peak value and the tripping peak value; Traverse all the peaks in the normal vibration sequence, and for the current peak traversed, calculate the absolute value of the difference between the current peak and its adjacent valley value, recorded as the peak-to-valley value; and calculate the absolute value of the difference in sampling time between the current peak and its adjacent valley value, recorded as the subscript difference; wherein, if there is a valley value after the current peak, its adjacent valley value is the nearest valley value after it, and if there is no valley value after the current peak, its adjacent valley value is the nearest valley value before it; Calculating the mean of the subscript differences in the normal vibration sequence, and calculating the ratio of the response time to the mean of the subscript differences, which is recorded as the response time difference; Calculate the degree of dispersion of all peak-to-valley values ​​in the normal vibration sequence, and record it as the peak-to-valley value difference; Calculating the degree of dispersion of all peak values ​​in the normal vibration sequence, and recording it as peak value difference; Calculating the absolute value of the difference between the gate opening element and the maximum value in the gate opening vibration data sequence; A circuit breaker abnormality coefficient is constructed based on the response time difference, the peak-to-valley value difference, the peak value difference, and the absolute value of the difference between the opening element and the maximum value in the opening vibration data sequence.

9. The circuit breaker malfunction detection method of the power transformation and distribution system according to claim 8, characterized in that: The calculation formula of the circuit breaker abnormality coefficient is: ； in, Indicates the circuit breaker abnormality coefficient; Indicates the difference between peak and valley values; represents the peak difference; represents the absolute value of the difference between the opening element and the maximum value in the opening vibration data sequence; Indicates the response time difference.

10. The circuit breaker malfunction detection method of the power transformation and distribution system according to claim 1, characterized in that: The circuit breaker malfunction detection is realized based on the abnormal fluctuation coefficient of the current data sequence and the circuit breaker abnormal coefficient, including: Calculating the ratio of the circuit breaker abnormality coefficient to the abnormal fluctuation coefficient of the current data sequence, and recording it as a misoperation index; The malfunction index is compared with a preset threshold value. If the malfunction index is greater than the preset threshold value, it is determined that the circuit breaker to be detected currently malfunctions. Otherwise, it is determined that the circuit breaker to be detected currently does not malfunction.

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