Typical partial discharge fault type identification method and system for switch cabinet

Abstract

The invention discloses a typical partial discharge fault type identification method and system for a switch cabinet. The method comprises the following steps: building a switch cabinet partial discharge fault simulation and detection platform; simulating a fault and acquiring data by adopting an ultrasonic sensor; extracting the transient impact quantity of the ultrasonic signal during partial discharge; drawing a transient impact quantity-probability density distribution curve; and summarizing the characteristics of transient impact quantity-probability density distribution curves of different discharge models under different voltage grades, and realizing discharge type distinguishing according to the characteristics of the transient impact quantity-probability density distribution curves. According to the method, the problems of large data volume, tedious data processing steps, too long calculation time and the like of a previous algorithm are solved, and in the field detection process, the fault types can be distinguished according to the fault type judgment basis in the invention, so that the diagnosis accuracy is improved, and prospective help is provided for subsequent maintenance work.

Description

Technical Field

[0001] This invention relates to the electrical field, specifically to a method and system for identifying typical partial discharge fault types in switchgear. Background Technology

[0002] High-voltage switchgear is a crucial component of power distribution networks, playing a vital role in distributing electrical energy within the power system. Due to their sheer number, a power outage caused by their failure results in customer power outages and significant economic losses. Over long-term operation, high-voltage switchgear inevitably experiences insulation degradation, leading to partial discharge. Partial discharge is one of the main manifestations of insulation deterioration in power equipment. Therefore, strengthening early detection of partial discharge is a crucial prerequisite for ensuring the safe and stable operation of switchgear.

[0003] Currently, methods for detecting partial discharge in switchgear include pulsed current method, ultra-high frequency method, transient ground voltage method, and ultrasonic method. Among these, ultrasonic signals are widely used in the detection of partial discharge in power equipment due to their excellent anti-electrical interference characteristics. In the partial discharge area of ​​electrical equipment, the air continuously undergoes thermal expansion and cooling contraction processes, and the continuous change in air volume forms compression-sparse waves that propagate outward in the form of sound waves. The ultrasonic frequency is concentrated in the range of 40kHz to 2MHz. Since the background noise frequency of most substations in actual operation is concentrated below 20kHz, the ultrasonic method has a significant advantage in detecting partial discharge in switchgear. However, current research on the characteristic quantities of partial discharge mainly focuses on time-frequency and phase aspects, extracting the time-frequency spectrum as a characteristic quantity and combining it with various machine learning algorithms to achieve signal recognition. Although this significantly improves the recognition rate, the algorithm steps are too cumbersome, and the large amount of data leads to excessively long computation time. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for identifying partial discharge based on ultrasonic signal characteristics. The aim is to determine the signal characteristics of different types of partial discharge ultrasonic waves, thereby distinguishing the discharge type. The algorithm is simple, computationally efficient, and has a short identification time, which can help improve the accuracy of on-site partial discharge diagnosis and provide forward-looking assistance for subsequent maintenance work.

[0005] The present invention adopts the following technical solution:

[0006] A method for identifying typical partial discharge fault types in switchgear includes:

[0007] S1: Establish an experimental platform for simulating and detecting partial discharge faults in switchgear;

[0008] S2: Simulate a fault and collect data using an ultrasonic sensor;

[0009] S3: Extract the transient impact quantity of the ultrasonic signal during partial discharge;

[0010] S4: Plot the transient impact quantity-probability density distribution curve;

[0011] S5: Summarize the characteristics of the transient impact quantity-probability density distribution curves of different discharge models at different voltage levels, and use this to distinguish the discharge types.

[0012] Alternatively, in step S1, the experimental platform includes an experimental transformer, a step-up transformer, a 10kV switchgear, an ultrasonic sensor, an electrical signal acquisition instrument, and a handheld partial discharge detector.

[0013] Alternatively, in step S2, the four typical defects simulated are tip discharge, internal discharge, floating discharge, and surface discharge, and experiments are conducted at three voltage levels: 5.77kV, 8kV, and 9kV.

[0014] Alternatively, step S3 may include:

[0015] The amplitude change of a signal between two adjacent extreme points is considered a fluctuation. The transient impact is the rate of change of the signal during one fluctuation. Its positive or negative sign represents the polarity of the fluctuation, and its magnitude serves as a standard for separating the discharge signal from the background noise. The specific calculation method for the transient impact quantity K is as follows:

[0016]

[0017] In equation (1): FA(k+1) is the amplitude at the end of the (k+1)th fluctuation; FA(k) is the amplitude at the end of the kth fluctuation; FT(k+1) is the point number at the end of the (k+1)th fluctuation; FT(k) is the point number at the end of the kth fluctuation.

[0018] To extract the wave characteristics from the raw signal measured by the ultrasonic sensor, it is necessary to calculate the local wave quantity Spf(n) from the first data point, as shown in equation (2).

[0019] spf(n)=SA(n+1)-SA(n) (2)

[0020] In equation (2): SA(n+1) is the value of the (n+1)th point in the original data; SA(n) is the value of the nth point in the original data.

[0021] Let the cumulative volatility AcF be the sum of the nth local volatility in the original data.

[0022] When n = 1, let AcF = Spf(1). If AcF × Spf(n) > 0, it indicates that the polarity of the signal change has not changed, that is, there is no fluctuation. Then add this Spf(n) to the cumulative fluctuation amount AcF and continue the calculation of the next point. If AcF × Spf(n) < 0, it indicates that the signal turns at this point, that is, it has experienced a fluctuation. The sum AcF of all local fluctuation amounts before the current point is the amplitude FA(k) of this fluctuation, and the starting point of this local fluctuation is the ending point FT(k) of this fluctuation. Record the fluctuation time T(k), the fluctuation amplitude FA(k), and the ending point of the fluctuation. After the recording is completed, clear AcF to facilitate the calculation of the next fluctuation amplitude.

[0023] Optionally, in step S4, obtain the transient impulse amount of the ultrasonic signal within 20 s for each type of partial discharge, draw the transient impulse amount - probability density distribution curve, and compare it with the transient impulse amount - probability density distribution curve of the background noise under the current voltage.

[0024] Optionally, in step S5, based on the transient impulse amount - probability density distribution curves of different discharge models at different voltage levels drawn in S4, summarize the characteristics and obtain the discrimination criteria for the fault type. Specifically:

[0025] For tip discharge, the transient impulse amount - probability density distribution curve at 5.77 kV voltage shows a "V" - shaped peak and the peak value is greater than the background noise. At 8 kV voltage, the transient impulse amount - probability density distribution curve shows a "V" - shaped peak and the peak value is less than the background noise. At 9 kV voltage, the transient impulse amount - probability density distribution curve shows a "four - peak type" and the second peak is the highest.

[0026] For internal discharge, the transient impulse amount - probability density distribution curve at 5.77 kV voltage shows a "V" - shaped peak and the peak value is less than the background noise. At 8 kV voltage, the transient impulse amount - probability density distribution curve shows a "five - peak type" and the second peak is the highest. At 9 kV voltage, the transient impulse amount - probability density distribution curve shows a "six - peak type" and the second peak is the highest.

[0027] For floating discharge, the transient impulse amount - probability density distribution curve at 5.77 kV voltage shows a "V" - shaped peak and the peak value is approximately equal to the background noise. At 8 kV voltage, the transient impulse amount - probability density distribution curve shows a "four - peak type" and the second peak is the highest. At 9 kV voltage, the transient impulse amount - probability density distribution curve shows a "five - peak type" and the second peak is the highest.

[0028] The transient impulse probability density distribution curve of surface discharge at 5.77kV shows a "four-peak" shape with the second peak being the highest. At 8kV, the transient impulse probability density distribution curve shows a "five-peak" shape with the second and third peaks being the highest. At 9kV, the transient impulse probability density distribution curve shows a "five-peak" shape with the third peak being the highest.

[0029] A typical partial discharge fault type identification system for switchgear includes:

[0030] The experimental platform construction module is used to build an experimental platform for simulating and detecting partial discharge faults in switchgear.

[0031] The data acquisition module is used to simulate faults and collect data using ultrasonic sensors;

[0032] The transient impact quantity extraction module is used to extract the transient impact quantity of ultrasonic signals during partial discharge;

[0033] The distribution curve plotting module is used to plot the transient impact quantity-probability density distribution curve;

[0034] The discharge type differentiation module summarizes the characteristics of the transient impact quantity-probability density distribution curves of different discharge models at different voltage levels, and uses this to differentiate discharge types.

[0035] This invention performs probability density statistics on the transient impact quantity of ultrasonic signals from partial discharge in switchgear, and plots transient impact quantity-probability density distribution curves. It reveals significant differences in the curves generated by different types of faults, thereby extracting the signal characteristics of different types of partial discharge ultrasonic waves. This method features a simplified algorithm, low computational load, and no excessively high sampling rate requirements on the detection instrument, solving the problems of large data volume, cumbersome data processing steps, and long computation time associated with previous algorithms. Attached Figure Description

[0036] Figure 1 The flowchart illustrates a method for identifying typical partial discharge fault types in switchgear, as provided by this invention.

[0037] Figure 2 This is a detailed structural diagram of the partial discharge experimental platform for switchgear provided by the present invention.

[0038] Figure 3 The diagram shows four typical partial discharge defect models in the simulation test provided by this invention.

[0039] Figure 4 The following is a flowchart of the initial fluctuation feature extraction process provided by the present invention.

[0040] Figure 5 This is a distribution diagram of the transient impact quantity-probability density curve of the ultrasonic signal for a tip discharge fault in an embodiment of the present invention.

[0041] Figure 6 This is a distribution diagram of the transient impact quantity-probability density curve of the ultrasonic signal for internal discharge fault in an embodiment of the present invention.

[0042] Figure 7 This is a distribution diagram of the transient impact quantity-probability density curve of the ultrasonic signal in the embodiment of the present invention for a suspended discharge fault.

[0043] Figure 8 This is a distribution diagram of the transient impact quantity-probability density curve of ultrasonic signal for surface discharge faults according to an embodiment of the present invention.

[0044] Figure 9 This is a flowchart of a typical partial discharge fault type identification system for switchgear provided by the present invention. Detailed Implementation

[0045] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0046] This invention provides a method for identifying typical partial discharge fault types in switchgear in the power industry, with reference to... Figure 1 As shown, it includes:

[0047] S1: Establish a simulation and detection platform for partial discharge faults in switchgear;

[0048] To simulate partial discharge in actual switchgear and obtain ultrasonic signal characteristics of partial discharge caused by different types of insulation defects, a real-world experimental platform for partial discharge in switchgear was built to simulate the fault in a real 10kV switchgear.

[0049] The experimental platform includes: a GY-5 / 50 transformer with a capacity of 5 kVA, a high voltage of 50 kV, a high voltage of 100 mA, and a voltage regulation range of 1–25 kV; a 10 kV switchgear; a 400SR160 non-contact ultrasonic sensor (center frequency of 40 kHz); an electrical signal acquisition instrument (sampling frequency of 1.25 MS / s); and a handheld partial discharge detector PDT-110. Typical faults were simulated in the busbar room of a decommissioned 10 kV switchgear that had been in operation for many years. The single-phase transformer was controlled by the step-up transformer to apply voltage to the copper busbar of phase B. The ultrasonic sensor was attached to the gap in the switchgear door near the busbar room. The ultrasonic signals during different types of partial discharge were collected using the electrical signal acquisition instrument. (See attached image for experimental platform details.) Figure 2 .

[0050] S2: Simulate a fault and collect data using an ultrasonic sensor;

[0051] The four typical simulated defects were point discharge, internal discharge, floating discharge, and surface discharge. Considering various factors such as the experimental environment, the values ​​of the handheld partial discharge detector, and the breakdown voltage values ​​of different defect models under different parameters, the experiment was conducted at three voltage levels: 5.77kV, 8kV, and 9kV. 5.77kV is the phase voltage of a 10kV switchgear, serving as the starting voltage to reflect the switchgear's operation under rated voltage and to verify the invention's testing and differentiation capabilities under rated conditions. The parameters of each defect model were adjusted so that all four defect models could slightly discharge at 5.77kV. Data was collected from the same location outside the switchgear using a handheld partial discharge detector. When the values ​​were consistent, the discharge levels of the four defect models were considered to be nearly identical, thus determining the parameters of the defect models. As the voltage was further increased, the discharge levels of each defect model became increasingly intense, producing obvious discharge sounds. When the voltage reached approximately 8kV, the ultrasonic signal values ​​detected by the handheld partial discharge detector for the four defect models were the most similar. Therefore, 8kV was determined as the typical voltage for the discharge development process in this experiment. Under the current defect model parameters, each defect model breaks down sequentially when the voltage level exceeds 9kV. Therefore, 9kV is used as the breakdown critical voltage to characterize the severe discharge state. The four typical partial discharge defect models simulated in the experiment are shown below. Figure 3 The needle tip discharge model is shown in [reference needed]. Figure 3 (a) The high-voltage end electrode is a copper wire with a diameter of 2mm and a cone angle of 30°. The grounding end plate electrode is the metal shell of the switchgear busbar compartment, and the distance between the pins and the plate is 12mm. See the surface discharge model. Figure 3 (b) Its high-voltage end is a copper rod with a diameter of 13mm, and the grounding end is the metal outer shell of the switchgear busbar compartment, with a composite insulator tightly clamped between the two. The insulator has a height of 130mm and a creepage distance of 244mm; the internal discharge model is shown in [reference needed]. Figure 3 (c) The model consists of three layers of square insulating rubber sheets with sides of 200mm. A rectangular gap of 40mm long and 20mm wide is located in the center of the middle insulating rubber sheet. Each insulating rubber sheet is 8mm thick, and the breakdown voltage of a single layer is 20kV. The high-voltage end of the model is a 13mm diameter copper rod in good contact with the insulating rubber sheet, and the grounding end is the metal casing of the switchgear busbar compartment. See the floating discharge model below. Figure 3 (d) The high-voltage end is a copper rod with a diameter of 13mm. The grounding end is the metal shell of the switch cabinet busbar compartment. A square insulating rubber plate with a thickness of 8mm, a breakdown voltage of 20kV, and a side length of 200mm is laid on the grounding end. A metal screw commonly used to fix the busbar in the switch cabinet is placed on it. The screw is 76mm long, has a tip diameter of 12mm, and is 5mm away from the copper rod at the high-voltage end.

[0052] S3: Extract the transient impact quantity of the ultrasonic signal during partial discharge;

[0053] The amplitude change of a signal between two adjacent extreme points is considered a fluctuation. The transient impulse is the rate of change of the signal during one fluctuation. Its positive or negative sign represents the polarity of the fluctuation, and its magnitude can be used as a standard to separate the discharge signal from the background noise. The specific calculation method for the transient impulse quantity K is as follows:

[0054]

[0055] In equation (1): FA(k+1) is the amplitude at the end of the (k+1)th fluctuation; FA(k) is the amplitude at the end of the kth fluctuation; FT(k+1) is the point number at the end of the (k+1)th fluctuation; FT(k) is the point number at the end of the kth fluctuation.

[0056] To extract the wave characteristics from the raw signal measured by the ultrasonic sensor, it is necessary to calculate the local wave quantity Spf(n) from the first data point, as shown in equation (2).

[0057] spf(n)=SA(n+1)-SA(n) (2)

[0058] In equation (2): SA(n+1) is the value of the (n+1)th point in the original data; SA(n) is the value of the nth point in the original data.

[0059] Let the cumulative volatility AcF be the sum of the nth local volatility in the original data.

[0060] When n=1, let AcF=Spf(1). If AcF×Spf(n)>0, it means that the polarity of the signal change has not changed, that is, no fluctuation has been experienced. Then add Spf(n) to the accumulated fluctuation amount AcF and continue the calculation for the next point. If AcF×Spf(n)<0, it means that the signal has turned at this point, that is, it has experienced a fluctuation. The sum of all local fluctuations before the current point AcF is the fluctuation amplitude FA(k). The starting point of this local fluctuation is the ending point FT(k) of this fluctuation. Record the fluctuation time T(k), fluctuation amplitude FA(k), and fluctuation ending point. After recording, clear AcF to facilitate the calculation of the next fluctuation amplitude.

[0061] The specific flowchart for the initial fluctuation feature extraction is as follows: Figure 4 As shown.

[0062] S4: Plot the transient impact quantity-probability density distribution curve;

[0063] The transient impact magnitude of ultrasonic signals within 20 seconds for each type of partial discharge was calculated, and the transient impact magnitude-probability density distribution curve was plotted. This curve was then compared with the transient impact magnitude-probability density distribution curve under the current voltage noise floor to obtain the transient impact magnitude-probability density curve for the ultrasonic signal of the tip discharge fault, as shown below. Figure 5As shown.

[0064] Under fault-free conditions, the probability distribution curves of transient impulses in each cycle are almost identical across the three voltage levels: the probability density curve is steeper at low transient impulses and flatter at high transient impulses, with a large difference in probability density between high and low transient impulses. Furthermore, the probability density change is very gradual between -0.1 and 0.2 transient impulses, forming a clear "buffer zone." These characteristics of the probability density curves indicate that when there is no partial discharge within the switchgear, the overall transient impulses remain at a low level, with a small amount of high transient impulses mixed in, meaning that the discharge noise energy within the switchgear is generally low. When a tip discharge fault is simulated: the curve shows a clear distribution at higher transient impulses, and becomes steeper in the region between -0.1 and 0.2, with the overall curve shifting to the right. Furthermore, as the applied voltage increases from 5.77kV to 8kV, the curve shifts to the right more significantly, the peak probability density decreases from 1.5 to 1.1, and the maximum transient impulse increases from 1.52 to approximately 1.8, with the overall curve changing from a "tall and thin" shape to a "short and stout" shape. When the voltage rises to 9kV, the curve undergoes a significant change, exhibiting a "four-peak" shape. The second peak of the probability density is concentrated around 0.39, with a peak value reaching 2.4. The fourth peak of the transient impulse is concentrated around 1.18, with a peak value of 0.23. It is evident that as the voltage level increases, the discharge intensity increases, and the peak value of the transient impulse probability density begins to shift to a higher level. Moreover, the probability density exhibits four relatively prominent peaks, indicating that under the current voltage and fault condition, the partial discharge exists in four relatively stable states.

[0065] 1) The state of zero transient impact, also known as the "first peak", is characterized by the transient impact concentrated around the value of 0. This indicates that almost no discharge occurs in this state and is the accumulation of transient impact at all moments when the tip is not discharged.

[0066] 2) Noise floor state, also known as the "second peak". In this state, the fault peak and the noise floor peak are highly coincident, only the probability density is different. The probability density of the fault peak is significantly higher. It is determined that the peak is caused by weak partial discharge of the original insulation defect in the switch cabinet and the noise in the detection environment, i.e., the noise floor component.

[0067] 3) Weak discharge state, also known as the "third peak". In this state, the fault peak appears at a higher transient impact level and is significantly shifted to the right compared to the noise floor peak. It is determined that this peak is composed of a large number of low-frequency ultrasonic signal components generated by the partial discharge of the simulated spike plate model in the switch cabinet.

[0068] 4) Strong discharge state, also known as the "fourth peak". In this state, the fault peak is concentrated at the highest transient impact in the image. The peak value is relatively low, and it is determined to be composed of a small amount of high-frequency ultrasonic signals generated by partial discharge of the tip fault model.

[0069] The probability density curves of the transient impulse amounts of the tip discharge at voltage levels of 5.77 kV and 8 kV are relatively similar in shape to the background noise curve, both showing a "V" shape. The difference brought about by increasing the voltage level is only manifested in the amplitude of the right shift of the curve. When reaching 9 kV, the probability density curve of the transient impulse amount shows a "four-peak" shape, and the maximum probability density is located at the "second peak".

[0070] The transient impulse amount - probability density curve of the ultrasonic signal of the internal discharge fault is as Figure 6 shown. The fault curve and the background noise curve of the internal discharge are significantly different at a voltage of 5.77 kV. The peak of the fault curve is concentrated around a transient impulse amount of 0.3, and the maximum probability density reaches 1.1. When the transient impulse amount exceeds 0.64, the probability density of the fault curve is significantly higher than that of the background noise curve. It is judged that the internal discharge is not intense at low voltages, and the simulated internal "air gap" generates partial discharges with relatively low frequencies and amplitudes at the fault point due to bearing a higher electric field intensity. As the voltage level gradually increases, it shows a "five-peak type" at 8 kV and a "six-peak type" at 9 kV. The curve as a whole moves to a higher transient impulse amount as the voltage increases, and the number of peaks increases. The peaks at low-level transient impulse amounts gradually shift to higher levels, and at the same time, more fine air gaps start to discharge. At 9 kV, the highest peak "second peak" of the transient impulse amount probability density curve of the internal discharge is concentrated around a transient impulse amount of 0.38, and the highest probability density is concentrated around 1.8, which coincides with the position of the highest peak of the background noise only with different probability densities. The highest peak "sixth peak" of the transient impulse amount is concentrated at 1.97, and the probability density is only 0.01. The appearance of the "six-peak type" indicates that there are 6 relatively stable states of the internal discharge under this fault state and this voltage. The first 2 states are similar to the tip discharge, being the zero-value state of the transient impulse amount and the background noise state, and the latter 4 states are judged to be 4 different-level components of the ultrasonic signal generated by the partial discharge after the voltage increases. The larger the transient impulse amount of the latter 4 peaks, the smaller its probability density, indicating that the ultrasonic signal level of the internal discharge at 9 kV is still concentrated at relatively low transient impulse amounts, and there are also 4 types of small amounts of high-level transient impulse amounts.

[0071] The transient impulse amount probability density curve of the internal discharge at 5.77 kV is approximately the same as the background noise probability density curve, both being "V" shapes, but the fault curve has a higher level of transient impulse amount, and at the same time, the highest peak of its probability density is smaller than that of the background noise probability density. At 8 kV and 9 kV voltages, the curves show a "five-peak type" and a "six-peak type" respectively, and the highest probability density is concentrated at the "second peak".

[0072] The transient impulse amount - probability density curve of the ultrasonic signal of the floating discharge fault is as Figure 7As shown, the probability density curve of the transient impact of the suspended discharge at 5.77kV is almost identical to the noise floor curve. The fault curve shifts towards higher transient impact values, and the flat region of the noise floor curve at the transient impact value of 0-0.15 is not observed. It is determined that the suspended discharge was almost non-existent at 5.77kV.

[0073] As the voltage level increases, the curve exhibits a "four-peak" shape at 8kV and a "five-peak" shape at 9kV. The suspended discharge ultrasonic signal shifts from a low-level transient impact quantity to a higher-level transient impact quantity. The increased field strength at the fault model intensifies the discharge, resulting in a higher-level ultrasonic signal, with both the measured signal frequency and amplitude showing a significant increase. At 9kV, the highest probability density peak is concentrated at the transient impact quantity of 0.38, with a peak value of approximately 2.03. The central transient impact quantity is consistent with the noise floor curve, only differing in probability density, indicating that the noise floor component should occupy a large proportion of the signal. The high-level ultrasonic signal is concentrated at three transient impact quantities, forming a "third peak," "fourth peak," and "fifth peak." Among them, the fifth peak, with the highest transient impact quantity, is concentrated at 1.57, with a probability density of only 0.037. The suspended discharge at 5.77kV is similar to the noise floor curve, indicating almost no discharge. The image at 8kV is a "four-peak" shape, and the image at 9kV is a "five-peak" shape, with the highest probability density peak being the "second peak."

[0074] The transient impulse quantity-probability density curve of the ultrasonic signal for surface discharge faults is shown below. Figure 8 As shown, the probability density curve of the transient impulse at 5.77kV for surface discharge differs significantly from the noise floor curve. The fault curve exhibits a "four-peak" shape, with the highest probability density peak, the "second peak," concentrated at the transient impulse level of 0.38, reaching a peak probability density of 2.2. The "fourth peak" is concentrated near the transient impulse level of 1.19, with a peak probability density of 0.5. At 8kV, the image shows a "five-peak" shape. The "second peak" is concentrated near the transient impulse level of 0.38, reaching a peak probability density of approximately 1.6, while the "third peak" is concentrated near the transient impulse level of 0.77, also with a peak probability density of approximately 1.6. The peak values ​​of the second and third peaks are almost identical. At 9kV, the "third peak" is still concentrated around the transient impact quantity of 0.77, while the peak probability density is around 1.6. The "second peak" is concentrated around 0.38, but the peak probability density is only around 1.34. The image as a whole shifts to a higher transient impact quantity. Unlike other types of discharge, the "third peak" of surface discharge is the highest peak of its probability density.

[0075] Surface discharge was evident at 5.77kV, with the image showing a "four-peak type". At 8 and 9kV, the image showed a "five-peak type", with the third peak being the highest probability density peak.

[0076] S5: Summarize the characteristics of the transient impact quantity-probability density distribution curves of different discharge models at different voltage levels, and use this to distinguish the discharge types;

[0077] Based on the analysis of the transient impact quantity-probability density distribution curves of different discharge defects in S4 at different voltage levels, a table summarizing the criteria for fault type identification is obtained, as shown in Table 1.

[0078] Table 1

[0079]

[0080] After partial discharge ultrasonic signal detection on site, the fault type can be distinguished according to the fault type discrimination criteria described in Table 1, so as to realize the real-time and rapid detection of partial discharge faults.

[0081] A typical partial discharge fault type identification system for switchgear, such as Figure 9 As shown, it includes:

[0082] The experimental platform construction module is used to build an experimental platform for simulating and detecting partial discharge faults in switchgear.

[0083] The data acquisition module is used to simulate faults and collect data using ultrasonic sensors;

[0084] The transient impact quantity extraction module is used to extract the transient impact quantity of ultrasonic signals during partial discharge;

[0085] The distribution curve plotting module is used to plot the transient impact quantity-probability density distribution curve;

[0086] The discharge type differentiation module summarizes the characteristics of the transient impact quantity-probability density distribution curves of different discharge models at different voltage levels, and uses this to differentiate discharge types.

[0087] This invention analyzes the transient impact quantity-probability density distribution curves of partial discharge faults to obtain the transient impact quantity probability density distribution spectra of different discharge models at different voltage levels, and summarizes a fault type discrimination table. This method is simple in algorithm and requires minimal computation. During on-site testing, fault types can be distinguished according to the fault type discrimination criteria described in the table, thereby improving diagnostic accuracy and providing forward-looking assistance for subsequent maintenance work.

[0088] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for identifying typical partial discharge fault types in switchgear, characterized in that, include: Establish an experimental platform for simulating and detecting partial discharge faults in switchgear; Simulate partial discharge faults and collect data using ultrasonic sensors; Extract the transient impact quantity of the ultrasonic signal during partial discharge; Plot the transient impact quantity-probability density distribution curves of different discharge models at different voltage levels; The characteristics of transient impact quantity-probability density distribution curves of different discharge models at different voltage levels are summarized, and the discharge types are distinguished accordingly.

2. The method for identifying typical partial discharge fault types in switchgear according to claim 1, characterized in that, The experimental platform includes an experimental transformer, a step-up transformer, a 10kV switchgear, an ultrasonic sensor, an electrical signal acquisition instrument, and a handheld partial discharge detector.

3. The method for identifying typical partial discharge fault types in switchgear according to claim 1, characterized in that, The process of simulating partial discharge faults and collecting data using ultrasonic sensors simulates four typical defects: tip discharge, internal discharge, floating discharge, and surface discharge, and experiments are conducted at three voltage levels: 5.77kV, 8kV, and 9kV.

4. The method for identifying typical partial discharge fault types in switchgear according to claim 1, characterized in that, The process of extracting the transient impact quantity of the ultrasonic signal during partial discharge includes: The amplitude change of a signal between two adjacent extreme points is considered a fluctuation. The transient impulse is defined as the rate of change of a single fluctuation in the signal, with its sign representing the polarity of the fluctuation. Its magnitude serves as a standard for separating the discharge signal from the background noise. The specific calculation method for the transient impulse quantity K is as follows: In equation (1): FA(k+1) is the amplitude at the end of the (k+1)th fluctuation; FA(k) is the amplitude at the end of the kth fluctuation; FT(k+1) is the point number at the end of the (k+1)th fluctuation; FT(k) is the point number at the end of the kth fluctuation. The wave characteristics of the raw signal measured by the ultrasonic sensor are extracted, and the local wave quantity Spf(n) is calculated from the first data point, as shown in equation (2). spf(n)=SA(n+1)-SA(n) (2) In equation (2): SA(n+1) is the value of the (n+1)th point in the original data; SA(n) is the value of the nth point in the original data; Let the cumulative volatility AcF be the sum of the nth local volatility in the original data; When n=1, let AcF=Spf(1). If AcF×Spf(n)>0, it means that the polarity of the signal change has not changed, that is, no fluctuation has been experienced. Then add Spf(n) to the accumulated fluctuation amount AcF and continue the calculation for the next point. If AcF×Spf(n)<0, it means that the signal has turned at this point, that is, it has experienced a fluctuation. The sum of all local fluctuations before the current point AcF is the fluctuation amplitude FA(k). The starting point of this local fluctuation is the ending point FT(k) of this fluctuation. Record the fluctuation time T(k), fluctuation amplitude FA(k), and fluctuation ending point. After recording, clear AcF to facilitate the calculation of the next fluctuation amplitude.

5. The method for identifying typical partial discharge fault types in switchgear according to claim 1, characterized in that, In the process of plotting the transient impact quantity-probability density distribution curve, the transient impact quantity of ultrasonic signal within 20s for each type of partial discharge is calculated, the transient impact quantity-probability density distribution curve is plotted, and compared with the transient impact quantity-probability density distribution curve of the noise floor under the current voltage.

6. The method for identifying typical partial discharge fault types in switchgear according to claim 1, characterized in that, In the process of differentiating the discharge types, based on the transient impulse quantity - probability density distribution curves of multiple discharge models drawn in the transient impulse quantity - probability density distribution curve, the characteristics are summarized, and the basis for judging the fault types is obtained. Specifically: For tip discharge, the transient impulse quantity - probability density distribution curve at 5.77 kV shows a "Ji" - shaped peak with the peak value greater than the background noise. At 8 kV, the transient impulse quantity - probability density distribution curve shows a "Ji" - shaped peak with the peak value less than the background noise. At 9 kV, the transient impulse quantity - probability density distribution curve shows a "four - peak type" with the second peak being the highest. For internal discharge, the transient impulse quantity - probability density distribution curve at 5.77 kV shows a "Ji" - shaped peak with the peak value less than the background noise. At 8 kV, the transient impulse quantity - probability density distribution curve shows a "five - peak type" with the second peak being the highest. At 9 kV, the transient impulse quantity - probability density distribution curve shows a "six - peak type" with the second peak being the highest. For floating discharge, the transient impulse quantity - probability density distribution curve at 5.77 kV shows a "Ji" - shaped peak with the peak value approximately equal to the background noise. At 8 kV, the transient impulse quantity - probability density distribution curve shows a "four - peak type" with the second peak being the highest. At 9 kV, the transient impulse quantity - probability density distribution curve shows a "five - peak type" with the second peak being the highest. For surface discharge, the transient impulse quantity - probability density distribution curve at 5.77 kV shows a "four - peak type" with the second peak being the highest. At 8 kV, the transient impulse quantity - probability density distribution curve shows a "five - peak type" with the second and third peaks being the highest. At 9 kV, the transient impulse quantity - probability density distribution curve shows a "five - peak type" with the third peak being the highest.

7. The method for identifying typical partial discharge fault types in switchgear according to claim 2, characterized in that, The model of the experimental transformer is GY - 5 / 50, with a capacity of 5 (kVA), a high - voltage of 50 (kV), and a high - voltage current of 100 (mA). The voltage regulation range of the booster is 1 - 25 kV. The model of the ultrasonic sensor is the 400SR160 non - contact ultrasonic sensor with a center frequency of 40 kHz. The sampling frequency of the electrical signal acquisition instrument is 1.25 MS / s, and the model of the handheld partial discharge detector is PDT - 110.

8. A typical partial discharge fault type identification system for switchgear, characterized in that, Including: An experimental platform building module, used to build an experimental platform for simulating and detecting partial discharge faults in switchgear; A data acquisition module, used to simulate faults and collect data using an ultrasonic sensor; A transient impulse quantity extraction module, used to extract the transient impulse quantity of the ultrasonic signal during partial discharge; A distribution curve drawing module, used to draw the transient impulse quantity - probability density distribution curve; A discharge type differentiation module, which summarizes the characteristics of the transient impulse quantity - probability density distribution curves of different discharge models at different voltage levels and realizes the differentiation of discharge types based on this.

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