A partial discharge monitoring method and system for GIS

Abstract

The application relates to the technical field of partial discharge monitoring, in particular to a GIS partial discharge monitoring method and system, which comprises the following steps: collecting electromagnetic data in GIS equipment, time-aligning the collected electric field data and magnetic field data to generate electromagnetic data time series; and performing time series analysis according to the electromagnetic data time series. The application synchronously processes electromagnetic data in the GIS equipment, forms electromagnetic data time series, improves the analysis accuracy of electromagnetic signals through electromagnetic field characteristic analysis, and eliminates interference caused by asynchronous data; further, the positioning processing of abnormal peak values through differential analysis refines the recognition accuracy of the discharge source position and improves the accuracy of the discharge source position information; meanwhile, the curvature and torsion of the electric field line are calculated by using the differential geometry mode, and the recognition sensitivity to the internal electric field structure change of the GIS equipment is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of partial discharge monitoring, and in particular to a partial discharge monitoring method and system for GIS. Background Art

[0002] Partial discharge monitoring technology belongs to the field of insulation condition monitoring and fault diagnosis for power equipment. Specifically, it uses the electrical, magnetic, acoustic, and optical characteristics associated with partial discharge within electrical equipment to monitor and analyze the location, intensity, nature, and development trends of potential insulation defects in real time. GIS partial discharge monitoring methods are used to detect and locate partial discharge in gas-insulated switchgear (GIS), pinpointing the source and characteristics of the discharge.

[0003] Existing technologies lack differential analysis of the discharge source location, resulting in large errors in source location and difficulty accurately identifying the source location and the actual boundary of the discharge area. Furthermore, the lack of in-depth analysis of the topological changes in the electric field lines leads to large errors in the assessment of discharge intensity and impact range, resulting in inaccurate predictions of partial discharge trends. Therefore, improvements are needed. Summary of the Invention

[0004] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a partial discharge monitoring method and system for GIS.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a method for monitoring partial discharge of a GIS, comprising the following steps:

[0006] Collecting electromagnetic data from GIS equipment, time-aligning the collected electric field data with the magnetic field data to generate an electromagnetic data time series; performing time series analysis based on the electromagnetic data time series to obtain an electromagnetic field characteristic sequence;

[0007] Based on the electromagnetic field characteristic sequence, a statistical method is applied to detect abnormal peaks of the partial discharge signal, identify and classify the abnormal signals, and generate preliminary discharge signal characteristics; differential analysis is performed on the preliminary discharge signal characteristics, and the location of the discharge source is determined based on the signal strength and phase difference of the electric and magnetic fields to obtain discharge source location information;

[0008] Differential geometry is used to calculate the curvature and torsion of the electric field lines within the GIS equipment. Based on the location information of the discharge source, the topological structure changes of the electric field lines in the partial discharge area are described to generate electric field topological characteristic analysis results. Based on the electric field topological characteristic analysis results, the discharge intensity and impact range are derived to obtain discharge property analysis results.

[0009] Based on the discharge property analysis result, the development trend of partial discharge is predicted, and a discharge development trend prediction result is generated.

[0010] Preferably, the steps of acquiring the electromagnetic data time series are:

[0011] Collect electric field data and magnetic field data from GIS equipment to form electric field data sequences and magnetic field data sequences with timestamps. By matching the same timestamps, the two sequences are aligned to obtain a preliminarily aligned electromagnetic data sequence.

[0012] Based on the preliminarily aligned electromagnetic data sequence, setting a maximum allowable time difference threshold, calculating the time difference between the electric field data sequence and the magnetic field data sequence, and screening valid data points whose time difference is less than the maximum time difference threshold to obtain a synchronized electromagnetic data sequence;

[0013] Based on the synchronized electromagnetic data sequence, the electric field data and the magnetic field data are rearranged in a unified timestamp sequence to obtain an electromagnetic data time series.

[0014] Preferably, the steps of acquiring the electromagnetic field characteristic sequence are:

[0015] According to the electromagnetic data time series, continuous data segments are intercepted at fixed intervals, and the mean and variance of the electric field data and the magnetic field data in each segment are calculated to obtain the statistics of each segment of data;

[0016] Based on the statistics of each segment of data, the electromagnetic field coupling strength value is calculated using the following formula:

[0017] ;

[0018] in, is the electromagnetic field coupling strength value, For the The electric field strength at each data point, For the The magnetic field strength of each data point, is the number of data points in the electromagnetic data time series;

[0019] Based on the electromagnetic field coupling strength values, the electromagnetic data are arranged in time order to generate an electromagnetic field feature sequence.

[0020] Preferably, the steps of obtaining the preliminary discharge signal characteristics are:

[0021] Determining the time width of an analysis window according to the electromagnetic data time series, and intercepting data segments within the sequence by sliding the analysis window to obtain a segment sequence of the electromagnetic data time series;

[0022] Based on the segment sequence of the electromagnetic data time series, the electromagnetic field fluctuation consistency value is calculated, and the calculation formula is:

[0023] ;

[0024] in, is the electromagnetic field fluctuation consistency value, 、 The first The electric field data and magnetic field data of each point, 、 are the average values ​​of the electric field data and magnetic field data in the corresponding analysis window, 、 are the phase values ​​of the electric field data and the magnetic field data, is the number of data points within the analysis window;

[0025] According to the electromagnetic field fluctuation consistency value, the data position exceeding the threshold is determined to be an abnormal peak point, the abnormal peak point is marked and classified, and a preliminary discharge signal feature is obtained.

[0026] Preferably, the step of obtaining the discharge source location information is:

[0027] Extracting the electric field intensity difference, the magnetic field intensity difference and the phase difference according to the preliminary discharge signal characteristics to form a preliminary discharge signal characteristic differential sequence;

[0028] According to the preliminary discharge signal characteristic differential sequence, the positioning correlation value of the discharge source is calculated, and the calculation formula is:

[0029] ;

[0030] in, is the location correlation value of the discharge source, is the difference value of the electric field data, is the difference value of the magnetic field data, is the phase difference after the electromagnetic signal is differentiated, For the The duration of the electromagnetic signal corresponding to the data point is For the The intensity difference value of the electromagnetic signal of the data point, is the total number of differential data points;

[0031] The discharge source position is determined based on the positioning correlation value of the discharge source to obtain discharge source position information.

[0032] Preferably, the steps of obtaining the electric field topological characteristic analysis results are:

[0033] According to the discharge source location information, the spatial coordinate points of the electric field lines in the discharge area are intercepted from the electric field line data inside the GIS equipment, and the position coordinate change of each coordinate point in the three-dimensional space is calculated to obtain the electric field line spatial coordinate sequence;

[0034] Based on the electric field line spatial coordinate sequence, the local topological deformation intensity value of the electric field line is calculated, and the calculation formula is:

[0035] ;

[0036] in, is the local topological deformation intensity value of the electric field line, For the The curvature value of the electric field line at the coordinate point is For the The torsion value of the electric field line at a spatial coordinate point, For the The torsion value of the electric field line at a spatial coordinate point, is the total number of spatial coordinate points of the intercepted electric field lines;

[0037] Based on the local topological deformation intensity value of the electric field lines, a topological correlation relationship between the deformation degree of the electric field lines and the location of the discharge source is established to describe the structural change characteristics of the electric field lines in the discharge area and generate the electric field topological characteristic analysis results.

[0038] Preferably, the steps for obtaining the discharge property analysis results are:

[0039] Based on the analysis results of the electric field topology characteristics, the discharge intensity index is calculated using the following formula:

[0040] ;

[0041] in, is the discharge intensity index, is the maximum curvature value of the electric field line in the electric field topology characteristic analysis results, is the maximum value of the electric field line torsion rate in the electric field topological characteristic analysis results;

[0042] Based on the discharge intensity index and combined with the trend of the topological structure change of the electric field lines inside the GIS equipment, the impact radius and regional range of the discharge are determined to generate the discharge property analysis results.

[0043] Preferably, the steps for obtaining the discharge development trend prediction result are:

[0044] Based on the results of the discharge property analysis, the internal spatial structure data of the GIS equipment is called to extract the three-dimensional spatial volume, spatial area and topological structure changes corresponding to different discharge levels, and a historical parameter set of the discharge level and the corresponding regional spatial structure changes is established;

[0045] Based on the historical parameter set of discharge levels and corresponding regional spatial structure changes, a time series model is used to construct the sequence characteristics of spatial structure parameters changing over time based on the continuous characteristics of three-dimensional spatial volume and area changes in the historical parameter set, and generate a sequence feature set of spatial structure parameter changes;

[0046] Based on the feature set of the spatial structure parameter change sequence, the spatial structure parameter sequence is fitted through the regression analysis model, the future change trend parameters of the discharge intensity level and the discharge impact area range are calculated, the fitting relationship between the discharge level and the regional range change is established, and the discharge development trend prediction results are generated.

[0047] The present invention provides a partial discharge monitoring system, comprising:

[0048] The electromagnetic data acquisition module collects electric and magnetic field data from GIS equipment, performs time alignment, and generates electromagnetic data time series;

[0049] The feature extraction module performs time series analysis on the electromagnetic data time series, extracts the electric field and magnetic field intensity characteristics at each time point, and obtains the electromagnetic field feature sequence;

[0050] The anomaly detection and classification module detects abnormal peaks in the data of the electromagnetic field feature sequence, identifies abnormal signals, classifies different types of discharge signals, and generates preliminary discharge signal features;

[0051] The discharge source location module performs differential analysis on the initial discharge signal characteristics, calculates the signal strength and phase difference of the electric and magnetic fields, determines the location of the discharge source, and obtains the discharge source location information;

[0052] The topological feature analysis module uses the discharge source location information to calculate the curvature and torsion of the electric field lines inside the GIS equipment, describe the topological structure changes of the electric field lines in the local discharge area, deduce the discharge intensity and impact range, and generate electric field topological feature analysis results.

[0053] Compared with the prior art, the advantages and positive effects of the present invention are:

[0054] The present invention performs time synchronization processing on electromagnetic data in GIS equipment to form an electromagnetic data time series, and improves the analysis accuracy of electromagnetic signals through electromagnetic field feature analysis, eliminating interference caused by data asynchrony. Furthermore, the positioning processing of abnormal peaks through differential analysis refines the identification accuracy of the discharge source position and improves the accuracy of the discharge source location information. At the same time, the curvature and torsion of the electric field lines are calculated using differential geometry, which enhances the recognition sensitivity of changes in the electric field structure inside the GIS equipment, more accurately evaluates the discharge intensity and impact range, makes the prediction of discharge properties and development trends more reliable, and ensures the safe operation of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 Schematic diagram of the steps of the present invention. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0057] See also Figure 1 The present invention provides a technical solution, a partial discharge monitoring method for GIS, comprising the following steps:

[0058] Collect electromagnetic data from GIS equipment, time-align the collected electric field data with the magnetic field data to generate an electromagnetic data time series; perform time series analysis based on the electromagnetic data time series to obtain an electromagnetic field feature sequence;

[0059] Based on the electromagnetic field feature sequence, statistical methods are applied to detect abnormal peaks in partial discharge signals, identify and classify abnormal signals, and generate preliminary discharge signal features. Differential analysis is performed on the preliminary discharge signal features to determine the location of the discharge source based on the signal strength and phase difference of the electric and magnetic fields, thereby obtaining discharge source location information.

[0060] Differential geometry is used to calculate the curvature and torsion of the electric field lines within the GIS equipment. Based on the location of the discharge source, the topological structure changes of the electric field lines in the local discharge area are described, generating electric field topological characteristic analysis results. Based on the electric field topological characteristic analysis results, the discharge intensity and impact range are derived, and the discharge property analysis results are obtained.

[0061] Based on the discharge property analysis results, the development trend of partial discharge is predicted and the discharge development trend prediction results are generated.

[0062] The steps for obtaining electromagnetic data time series are:

[0063] Collect electric field data and magnetic field data from GIS equipment to form electric field data sequences and magnetic field data sequences with timestamps. By matching the same timestamps, the two sequences are aligned to obtain a preliminarily aligned electromagnetic data sequence.

[0064] Based on the preliminarily aligned electromagnetic data sequences, a maximum time difference threshold is set, the time difference between the electric field data sequence and the magnetic field data sequence is calculated, and valid data points with a time difference less than the maximum time difference threshold are screened to obtain a synchronized electromagnetic data sequence;

[0065] Based on the synchronized electromagnetic data sequence, the electric field data and magnetic field data are rearranged in a unified timestamp order to obtain the electromagnetic data time series.

[0066] Specifically, based on the collected electric field data and magnetic field data of the GIS equipment, and with reference to previous maintenance records and the signal response time standards provided by the manufacturer, a preliminary check is first performed on each data record. For example, the electric field strength range is limited to the interval of 0V / m to 50kV / m, and the magnetic field strength range is limited to the interval of 0A / m to 10A / m, and invalid values ​​outside this interval are excluded. Then, the timestamp attached to each valid data is read, divided into hours, minutes and milliseconds as basic units, and the timestamp format is checked against a comparison table constructed based on the error distribution obtained by empirical statistics. When incomplete or inconsistently labeled records are found, they are removed or re-labeled. Then, the retained records are timestamp matched, and the electric field data with the same timestamp or within the minimum deviation range are matched with the magnetic field data. The minimum deviation range here is given based on the average transmission delay after multiple on-site sampling tests and combined with the actual layout distance. The example is set as , if the timestamps of the two records differ by no more than If the matching is completed, it is considered to be matched, otherwise it is determined that the matching is not possible and the unqualified record is discarded. After the matching is completed, all the electric field data and magnetic field data corresponding to the timestamp are summarized to obtain a preliminary aligned electromagnetic data sequence.

[0067] Based on the preliminary aligned electromagnetic data sequence obtained above, we first need to set a maximum time difference threshold to filter out data points with excessive differences. The specific value of this threshold can be obtained by calculating the average and standard deviation of transmission delay samples obtained by multiple field measurements and then performing weighted processing. For example, it can be set to Then, the difference between the electric field sampling time and the magnetic field sampling time of each record in the preliminary aligned electromagnetic data sequence is calculated. If the difference exceeds Then exclude or mark the record as a non-synchronous record, and then The records of the conditions are retained, and the electric field strength and magnetic field strength within these records are compared with the valid range set previously. For example, the electric field strength is still compared with the range of 0V / m to 50kV / m, and the magnetic field strength is compared with the range of 0A / m to 10A / m. If they are all within the valid range, they are included in the available data set. Subsequently, the data that meet the time difference limit and are within the valid range are sorted and summarized into a new sequence to obtain a synchronized electromagnetic data sequence.

[0068] Based on the synchronized electromagnetic data sequence, first check whether the timestamp format of each record is unified, such as ensuring that "year-month-day hour: minute: second. millisecond" is used or the same precision unit is not missing. Then arrange them in sequence from early to late or small to large according to the timestamp. If multiple records appear with the same timestamp, sort them in sequence or make additional comparisons, such as giving priority to retaining data with higher sampling accuracy to avoid duplication. Then place the electric field intensity and magnetic field intensity side by side in the same time dimension, keep the indexes corresponding to each other, and retain the electric field value and magnetic field value of each data as well as its timestamp in the record. After that, these sorted data can be continuously stored as a time series data set to obtain the electromagnetic data time series.

[0069] The steps for obtaining the electromagnetic field characteristic sequence are:

[0070] According to the electromagnetic data time series, continuous data segments are intercepted at fixed intervals, and the mean and variance of the electric field data and magnetic field data in each segment are calculated to obtain the statistics of each segment of data;

[0071] Based on the statistics of each segment of data, the electromagnetic field coupling strength value is calculated using the following formula:

[0072] ;

[0073] in, is the electromagnetic field coupling strength value, For the The electric field strength at each data point, For the The magnetic field strength of each data point, is the number of data points in the electromagnetic data time series;

[0074] Based on the electromagnetic field coupling intensity value, the electromagnetic data time series is arranged in chronological order to generate an electromagnetic field feature sequence.

[0075] Specifically, based on the electromagnetic data time series obtained above, a fixed time range is first selected as the interval and the entire sequence is divided into several continuous segments. Then, the electric field record and magnetic field record in each segment are read. If the electric field value of any record is lower than 0V / m or higher than 50kV / m, it is marked as out-of-limit data and excluded from the segment. If the magnetic field value is lower than 0A / m or higher than 10A / m, it is also marked and excluded. After all out-of-limit data are eliminated, mean calculation and variance calculation are performed on the remaining data in this segment, and the electric field mean and magnetic field mean are recorded. At the same time, the values ​​of the electric field variance and the magnetic field variance are recorded through specific calculation forms. The above four values ​​together constitute the statistics of this segment. In order to determine the setting method of the fixed time range, it is necessary to consult the device generation data. The technical specifications provided by the manufacturer and the sampling accuracy recorded in the on-site monitoring are used to organize these sampling accuracy data. For example, in 24-hour monitoring, 60 electric field and magnetic field records are collected every minute. Each record corresponds to a stable time period. Based on this, the data every 10 minutes or 20 minutes can be grouped into a segment for calculation. If there are 600 electric field records and 600 magnetic field records in a 10-minute segment, of which 580 samples meet the range specified in the previous article, then only the mean and variance operations are performed on these 580 valid records, so that the statistics of the segment are more consistent in value. If it is found that the number of valid records in the segment is too small, the time range can be appropriately changed or the data collection accuracy can be reconfirmed. After processing all segments in this way, the statistics of each segment of data are obtained.

[0076] The benefit of this formula is that it takes into account the combined effects of electric field strength and magnetic field strength. By calculating the ratio of the product of the two to the modulus length, it can reflect the degree of coupling between the two and provide a quantifiable indicator for subsequent electromagnetic field characteristic analysis.

[0077] The steps for obtaining parameters are as follows: The electric field strength value of each data point needs to be continuously monitored at a certain interval in the field acquisition device. The specific electric field strength can be obtained by a high-voltage sensor installed near the GIS equipment. The sensor can measure within the range of 0V / m to 50kV / m. Each measurement data is recorded with a timestamp. If the measured result exceeds 50kV / m or a negative value appears, it will be excluded to avoid interference with the subsequent calculation process. In order to determine the actual production environment , can continuously collect electric field information within 24 hours and record it into several pieces. If 80 valid electric field records collected in a certain period of statistics are V / m, V / m, V / m and other values, then keep them in the corresponding positions one by one, corresponding order.

[0078] The steps to obtain the parameters are to record The magnetic field strength value at the same time point needs to be measured in the same monitoring period. The measurement range is generally set to 0A / m to 10A / m. If a negative value or a record greater than 10A / m appears, it will also be filtered to keep the data within the valid range. If 80 magnetic field records are obtained during the collection, they are A / m, A / m, A / m, etc., correspond to arrive Store them separately, and then accurately find the corresponding Matched .

[0079] The steps to obtain the parameters are as follows: The total number of records selected is the number of data points that meet the time synchronization and value range requirements in the corresponding segment. If there are 90 electric field records and 90 magnetic field records in a segment, and only 80 can be paired after over-limit filtering or time deviation filtering, then .

[0080] Calculation process:

[0081] Set a segment to have Valid records, respectively V / m, A / m, V / m, A / m, V / m, A / m, first calculate each pair The numerator and denominator of :

[0082] ;

[0083] ;

[0084] Similarly, calculate the second and third records:

[0085] ;

[0086] ;

[0087] Then add these three numbers and divide by : The results show that The numerical value can be regarded as a reference for the overall coupling strength of these three records. If the calculation results of subsequent segments are larger than this value, it means that the electric field and the magnetic field are more highly coupled; if the calculation results of subsequent segments are smaller than this value, it means that the coupling degree between the electric field and the magnetic field is relatively lower.

[0088] Based on the electromagnetic field coupling intensity value, it is necessary to sort it out in the time sequence of the electromagnetic data time series, and arrange the coupling intensity values ​​matched at each moment one by one in chronological order, and then retain the corresponding time indexes in the aggregation process, such as recording the specific moment with millisecond accuracy, and saving the electromagnetic field coupling intensity value at the same moment in the corresponding sequence position. In order to complete this sorting process, it is necessary to first check the previously obtained coupling intensity calculation results one by one. If there is a timestamp conflict in the data generated in different fragments, it is necessary to compare the status record of the acquisition device during field monitoring or the repeated measurement record to confirm whether there are really redundant or abnormal results at the same time. If there is an abnormal measurement, it will be eliminated or marked as an abnormal entry, and then the remaining normal coupling intensity values ​​will be arranged from early to late according to time. Into an ordered queue, and indicate its paragraph index value in the original data in the record index. In order to determine the subdivision or merging of the final sequence, an analysis cycle reference can be set, for example, a batch of data is divided every five minutes or ten minutes, and all coupling strength records in the same batch are summarized in the management system, and then time sorting and superposition are performed to generate a continuous array. If some data points have abnormal deviations exceeding 1ms in timestamp accuracy, they can be excluded at this step or placed in a separate reference list. In order to avoid confusion, it is necessary to check after all data are sorted to confirm that there are no breakpoints or jumps in the continuous time period of the sequence. The ordered data summarized in this way can be used as the basis for further analysis of the electromagnetic field distribution. Through this series of operations, an electromagnetic field characteristic sequence is generated.

[0089] The steps for obtaining the preliminary discharge signal characteristics are:

[0090] According to the electromagnetic data time series, the time width of the analysis window is determined, and the data segments in the sequence are intercepted by sliding the analysis window to obtain a segment sequence of the electromagnetic data time series;

[0091] Based on the fragment sequence of the electromagnetic data time series, the electromagnetic field fluctuation consistency value is calculated using the following formula:

[0092] ;

[0093] in, is the electromagnetic field fluctuation consistency value, 、 The first The electric field data and magnetic field data of each point, 、 are the average values ​​of the electric field data and magnetic field data in the corresponding analysis window, 、 are the phase values ​​of the electric field data and the magnetic field data, is the number of data points within the analysis window;

[0094] According to the consistency value of electromagnetic field fluctuation, the data position exceeding the threshold is determined to be an abnormal peak point. The abnormal peak point is identified and classified to obtain the preliminary discharge signal characteristics.

[0095] Specifically, based on the electromagnetic data time series obtained previously, first consult the existing data for the high-voltage transmission system's analysis window duration setting specifications, and compare them with the on-site monitoring sampling frequency and data fluctuation frequency to determine the appropriate analysis window time width. Subsequently, continuous sliding is performed on the time axis with the time width as a step size. Each time a slide is performed, the electromagnetic data within the corresponding range is intercepted as a segment. If there are less than 10 records in a certain segment or there is a lack of timestamp continuity, the segment is marked in the recording system and temporarily not included in subsequent processing. In order to ensure that the time width of the analysis window is consistent with the actual on-site operation conditions, the basic time dimension range can be determined based on the electric field and magnetic field change curves collected by the sensor within a day, and then, combined with the example instructions given by the equipment manufacturer in the maintenance manual, an initial time width threshold is set, such as the 50 millisecond to 200 millisecond interval, and an attempt is made within this interval. Try to select a specific value, such as 100 milliseconds, for sliding segmentation. By repeating the sliding segmentation multiple times and comparing the number and continuity of the obtained fragments, the time width that can ensure the integrity of the data within the fragment is finally selected. When the time width of the analysis window is fixed, it is gradually slid along the time series from the beginning to the end. During this period, a data fragment is extracted for each set time step, and the serial number information and start and end timestamp information are attached to the fragment in the recording system. In this way, several fragments that are interconnected and cover the entire sequence can be formed in the time series. Each fragment contains relatively sufficient electric and magnetic field data with continuous timestamps within a fixed time length. If the signal acquisition at a certain period of time on site is missing, resulting in insufficient fragments, the corresponding blank area will be retained in the record list for subsequent comparison. After the last sliding operation is completed at the end of the time series, the fragment sequence of the electromagnetic data time series can be obtained.

[0096] The benefit of the formula is that it not only takes into account the difference interaction factors between the electric field data and the magnetic field data, but also incorporates the phase difference component into the overall calculation, so that it can quantitatively describe the common offset of the electric field and magnetic field during the fluctuation process in a single calculation, helping to make a more intuitive quantitative assessment of the fluctuation consistency under the electromagnetic coupling state.

[0097] The steps to obtain the parameter are: it represents the first The electric field data value of each point can be set in V / m. High-voltage electric field sensors can be deployed on site to continuously monitor the electric field strength, collect data several times per second, and record the collected values ​​with a timestamp. In order to ensure data accuracy, the electric field range will be set between 0V / m and 50kV / m during the sensor calibration process. If a record exceeds the range, it will be removed from the recording system. In an analysis window, all electric field records that meet the timestamp continuity and amplitude within this range are marked as For example, in a test, 50 electric field readings were obtained in a 5-second window, concentrated in the range of 500V / m to 3000V / m. These 50 readings can be numbered and stored in sequence.

[0098] The steps to obtain the parameter are: it represents the first The magnetic field data value of each point can be set in A / m. Similarly, it is necessary to obtain data on site through a magnetic field sensing device and pair the results with the corresponding timestamp. The magnetic field sensing device can be set to a range between 0A / m and 10A / m. If a single monitoring record exceeds this range, it will be excluded from the recording system. Correspondingly, it is necessary to ensure that the difference with its timestamp does not exceed 0.5 milliseconds, and then number them in sequence. For example, within a 5-second window, 50 valid magnetic field readings are obtained, with values ​​between 1.0 A / m and 1.5 A / m.

[0099] The steps to obtain the parameter are: It represents the average value of the electric field data within the analysis window, and the unit is the same as Same. If you get Effective electric field data , we can get it by the following calculation formula : , in specific implementation, all effective electric field values ​​can be added up first, and then divided by , if there are 50 electric field values ​​in the window, such as V / m, V / m, , V / m, just add them up and divide by 50. The exact value of .

[0100] The steps to obtain the parameter are: It represents the average value of the magnetic field data within the analysis window, and the unit is the same as The calculation method is the same as Similarly, through If the 50 magnetic field values ​​in a window are between 1.0A / m and 1.5A / m, add them up and divide by 50 to get .

[0101] The steps to obtain the parameter are as follows: It represents the phase value of the electric field data within the analysis window and can be expressed in radians or degrees. When the sensor acquires the electric field, it is necessary to configure an additional phase measurement device to track the time-varying sine wave or other periodic waveform of the electric field. This phase value is then stored in the recording system along with a timestamp. If 50 instantaneous electric field data points are acquired within a 5-second window, the phase measurement device can determine the phase of each data point. If the electric field fluctuation period is 20 milliseconds, the specific phase angle information can be recorded at each sampling point. For example, the phase of one electric field record is 33.5 degrees.

[0102] The steps to obtain the parameter are: it represents the phase value of the magnetic field data within the analysis window, and Similarly, a phase measurement device needs to be configured in the magnetic field acquisition to record the waveform phase of each magnetic field data and store it in the same time stamp management method. If the magnetic field signal also shows periodic fluctuations during specific monitoring, the phase value can be measured at each sampling point, and then these phase values ​​can be compared with the previously acquired electric field phase values. If measured in degrees, the phase of a magnetic field record may be 31.2 degrees, and the timestamp can be used to determine whether the record is , then assign this phase to .

[0103] The steps for obtaining the parameter are as follows: It represents the number of data points in the analysis window. If 50 electromagnetic records that meet the standard are generated in a 5-second window, then .

[0104] Calculation process: Set within a 5-second window, obtain Valid records, electric field data V / m, V / m, V / m, magnetic field data A / m, A / m, A / m, phase data Spend, Spend, Spend, Spend, Spend, Degrees. Calculate first and :

[0105] ;

[0106] ;

[0107] Then calculate The value of each item, only the first item is shown below:

[0108] ;

[0109] ;

[0110] ;

[0111] Calculate the second and third terms in the same way, add them together and divide by , and finally take the square root of the result to get If all the values ​​are summed up and calculated, we can get .

[0112] The results show that It means that the electric field and magnetic field fluctuations in this window have a high level of consistency. If the value calculated in other windows is larger, it means higher fluctuation synchronization. If the value is too small, it means that the phase offset or amplitude difference between the electric field and the magnetic field is more significant.

[0113] According to the electromagnetic field fluctuation consistency value obtained above, a threshold needs to be set to filter out abnormal peak points. In actual scenarios, the number of abnormal data that appear during high failure rates in the field monitoring can be counted based on the previous statistical analysis and compared to obtain a numerical value as a reference. For example, when the electromagnetic field fluctuation consistency value is concentrated between 1.0 and 10.0, the failure probability is extremely low. When it exceeds 15.0, the failure probability increases significantly. Therefore, the threshold can be set to 15.0, and then each consistency value is compared. If a record If it is greater than 15.0, it is marked as an abnormal peak point. To ensure accuracy, data that are repeatedly judged to be beyond 15.0 and have close time periods can be classified as the same type of abnormality in the recording system, and a specific abnormality number or type code can be attached to the location. If the interval between certain peak points is too short and the amplitude continuously exceeds the expected range, it can also be set that 5 consecutive abnormalities are regarded as a more serious category. During the execution process, it is necessary to first check whether the consistency value has calculation errors and duplicate records. Once confirmed to be correct, the threshold is used for screening one by one, and the exceeding limit value is associated with the corresponding timestamp and original record to form an abnormal peak point set. Finally, category labels are attached to all peak points in the system. For example, label A refers to the high-frequency large fluctuation type, and label B refers to the short-term strong impact type. After such a screening and classification action, the preliminary discharge signal characteristics can be obtained.

[0114] The steps for obtaining the discharge source location information are as follows:

[0115] According to the preliminary discharge signal characteristics, the electric field intensity difference, magnetic field intensity difference and phase difference are extracted respectively to form a preliminary discharge signal characteristic difference sequence;

[0116] According to the preliminary discharge signal characteristic differential sequence, the location correlation value of the discharge source is calculated. The calculation formula is:

[0117] ;

[0118] in, is the location correlation value of the discharge source, is the difference value of the electric field data, is the difference value of the magnetic field data, is the phase difference after the electromagnetic signal is differentiated, For the The duration of the electromagnetic signal corresponding to the data point is For the The intensity difference value of the electromagnetic signal of the data point, is the total number of differential data points;

[0119] The discharge source position is determined based on the positioning correlation value of the discharge source to obtain the discharge source position information.

[0120] Specifically, according to the preliminary discharge signal characteristics obtained above, first browse each identified abnormal peak point data in the recording system one by one, read the corresponding electric field value and magnetic field value and their respective phase information, and arrange the electric field strength, magnetic field strength, phase and other data in chronological order, and then perform differential operations on the data in adjacent or continuous time periods. The specific method is to first intercept adjacent electric field strength values ​​from the same time index range, calculate their difference and add the result to the electric field strength difference list. Similarly, perform the same differential operation on the magnetic field strength value and put the result into the magnetic field strength difference list. Then, obtain the phase difference list based on the difference between the electric field phase and the magnetic field phase in each record. In order to make the differential process more accurate, it is necessary to first eliminate any possible Data jumps occur, such as excluding records with electric field strength exceeding 0V / m to 50kV / m or magnetic field strength exceeding 0A / m to 10A / m. Repeated detection is also required when the timestamp accuracy is insufficient. If the data source is found to be missing in the same time index, the record is marked as missing. Then, the data summary table is traversed according to the marked abnormal peak point sequence number. By summarizing the differential list and phase difference list one by one, an overall preliminary discharge signal feature differential sequence is formed. Each differential record carries relevant information and time index of the electric field, magnetic field and phase. Through this differential sequence, the amplitude fluctuation and phase shift caused by the change of various indicators over time can be more intuitively observed in the subsequent analysis link, thereby completing the preliminary discharge signal feature differential sequence.

[0121] The formula is useful in that 、 、 、 、 The comprehensive calculation of multiple differential information, such as the differential amplitude, intensity change and phase relationship of the electric and magnetic fields, is taken into account in the calculation. The differences reflected by partial discharge in the time domain and phase domain can be concentrated into a positioning correlation value, so as to more accurately identify the location of the discharge source.

[0122] The parameter acquisition steps are as follows: It represents the differential value of the electric field data. The specific method is to first check the difference in electric field strength at each adjacent sampling moment from the preliminary discharge signal feature differential sequence. The timestamps must be aligned and the sensor range must be between 0V / m and 50kV / m. The differential value can be positive or negative. If adjacent records with electric field strengths of 2000V / m and 2200V / m appear in a certain sampling period, the difference is calculated as V / m, and recorded as In the collection process, in order to ensure accuracy, the time stamp difference of adjacent records needs to be controlled within 1ms. For example, if a differential sequence contains 50 effective electric field differences, they can be recorded in sequence as .

[0123] The steps to obtain the parameter are as follows: It represents the differential value of the magnetic field data. Similarly, it is necessary to find the magnetic field strength at adjacent sampling moments in the preliminary discharge signal feature differential sequence and subtract the two to obtain the difference. The on-site magnetic field range can be controlled between 0A / m and 10A / m. If adjacent records appear in a certain acquisition, A / m and A / m, then the difference is recorded as A / m. In subsequent statistics, the value is marked as , in order to distinguish the location of corresponding records.

[0124] The acquisition steps of the parameter are as follows: It represents the phase difference after the electromagnetic signal is differentiated, usually described in degrees or radians. The phase measurement device installed around the device needs to obtain the phase values ​​of the electric field and magnetic field at the same time when recording, and then perform differential processing. If the electric field phase in a certain record is 30 degrees and the magnetic field phase is 28 degrees, then the phase difference of the electromagnetic signal is 2 degrees. To express.

[0125] The steps to obtain the parameters are: The duration of the electromagnetic signal corresponding to each data point can be set in milliseconds or microseconds and needs to be recorded in the sensor sampling process or subsequent statistics. , combined with the differential value and phase value, is used to evaluate the impact of the discharge feature on positioning. When acquiring, the accurate duration can be obtained by marking the start of the discharge signal and measuring its end time, or the length of the section exceeding a certain reference value in the electric field and magnetic field strength curve can be regarded as For example, if an abnormal peak is found to start at 200ms and end at 210ms during one day of monitoring, the duration of the corresponding record is =10ms.

[0126] The steps to obtain the parameters are: The intensity difference value of the electromagnetic signal of each data point needs to be obtained by subtracting the electromagnetic signal intensity at the current moment from the electromagnetic signal intensity at the previous moment. If the total intensity of the electric field and magnetic field is recorded as a certain value under the same index, and the difference is made after comparing it with the previous index, the final difference can be collectively referred to as For example, if the total intensity at a certain moment is 300 and the total intensity at the next sampling moment is 250, then .

[0127] The parameter acquisition steps are as follows: It represents the total number of differential data points, which is generally determined by counting the number of all valid records in the initial discharge signal feature differential sequence. If, in the case of 24-hour monitoring, 200 data with matching timestamps and valid differential operation results are obtained after screening, then .

[0128] Calculation process:

[0129] In a certain monitoring, set , and record each difference Collect as follows:

[0130] Article 1: V / m, A / m, , , ms;

[0131] Article 2: V / m, A / m, , , ms;

[0132] Article 3: V / m, A / m, , , ms;

[0133] Calculate first The corresponding values ​​are:

[0134] Article 1: ;

[0135] Article 2: The result is approximately = 7.26×108×0.12835≈9.32×10^7

[0136] Article 3: The result is approximately = 4.8×108×0.38835≈1.86×10^8;

[0137] Adding the three together: ; take the cube root and divide by : ;therefore .

[0138] The results show that when When the value is large, it means that the electric field and magnetic field differential amplitude and phase change corresponding to these differential records have a higher degree of comprehensive influence, which in turn reflects that the discharge source here has obvious intensity characteristics or location information characteristics. In subsequent comparisons, a critical value such as 200 can be set in advance. A value greater than 200 indicates a concentrated and significant discharge area, while a value less than 100 indicates a low overall impact of the differential record. Based on this, the specific location of the discharge source can be determined in combination with the actual spatial layout of the GIS equipment.

[0139] Based on the previously obtained location correlation values ​​of the discharge source, they need to be placed in the coordinate system of the GIS equipment for coordinate comparison. Using the distribution table of sensors pre-installed throughout the equipment, we can determine whether the location correlation values ​​corresponding to certain locations are significantly higher. First, we perform a horizontal comparison of the location correlation values ​​obtained during multiple monitoring periods of the day or week. If the location correlation values ​​recorded by a sensor or group of sensors are significantly higher than those of other sensor locations within the same time range, these locations are recorded as potential discharge sources. The equipment schematic is then consulted and the specific nodes of the three-dimensional coordinate system are marked one by one. The numbers of electrical connections or sealing interfaces are checked. If these numbers overlap with discharge locations in previous maintenance records, the corresponding locations are recorded in the index column of the inspection report. If the corresponding locations have not been discovered before, they can be focused on in subsequent maintenance processes. These locations are then summarized in the recording system along with the time evolution of the location correlation values. These locations are then compared one by one with the GIS equipment coordinate data confirmed on-site to verify the height, flange interface type, and insulation model of these coordinate points where discharge may occur. Each identified coordinate is written into the discharge source location information list, ultimately obtaining the discharge source location information.

[0140] The steps for obtaining the electric field topological characteristic analysis results are as follows:

[0141] According to the discharge source location information, the spatial coordinate points of the electric field lines in the discharge area are intercepted from the electric field line data inside the GIS equipment, and the position coordinate change of each coordinate point in three-dimensional space is calculated to obtain the electric field line spatial coordinate sequence;

[0142] Based on the spatial coordinate sequence of the electric field lines, the local topological deformation intensity value of the electric field lines is calculated. The calculation formula is:

[0143] ;

[0144] in, is the local topological deformation intensity value of the electric field line, For the The curvature value of the electric field line at the coordinate point is For the The torsion value of the electric field line at a spatial coordinate point, For the The torsion value of the electric field line at a spatial coordinate point, is the total number of spatial coordinate points of the intercepted electric field lines;

[0145] Based on the local topological deformation intensity value of the electric field lines, a topological correlation relationship between the deformation degree of the electric field lines and the location of the discharge source is established to describe the structural change characteristics of the electric field lines in the discharge area and generate the electric field topological characteristic analysis results.

[0146] Specifically, according to the discharge source location information obtained above, it is necessary to retrieve the corresponding electric field line distribution list in the internal coordinate data of the GIS equipment, find the electric field line numbers marked as those that may have large discharge behaviors in the specified area, and determine the spatial nodes of each electric field line according to the marking in the equipment structure diagram. Then, extract the three-dimensional coordinate data of these nodes one by one. If it is found that the coordinates of some nodes are repeated or are not within the specified equipment outline range, exclude them from the record and mark them as invalid nodes. Then, read and organize the coordinates of the remaining valid nodes. Check whether there is any offset by comparing them point by point with the reference position coordinates configured during on-site installation, and record the numerical changes of each node in the x, y, and z directions, with millimeters or centimeters as the basic measurement units. If the distance between nodes exceeds the equipment structure tolerance specified by the original manufacturer, for example, the tolerance can be set in the range of 1mm to 5mm, and the actual data shows an offset greater than 5mm, it will be registered as a coordinate abnormality point and retained in a special list for subsequent investigation. Then the three-dimensional coordinate data of all normal nodes are arranged, and these data are sorted in the order of electric field lines and the order of nodes. The final coordinate array is then associated by the electric field line number plus the node number to form a sequence composed of several electric field line space points. Each electric field line space coordinate sequence has several three-dimensional points, and a certain position information is maintained between points for subsequent further analysis. After this process operation, the electric field line space coordinate sequence can be obtained in the recording system.

[0147] The benefit of the formula is that it integrates the three spatial geometric characteristics of curvature, torsion and torsion into the same calculation framework, and combines the absolute value of their product in the numerator and the form of the sum of their squares in the denominator, which can reflect the comprehensive influence of electric field lines when they are twisted and deformed in three-dimensional space.

[0148] The steps to obtain the parameters are: The curvature value of the electric field line at the coordinate point can be in units of , it is necessary to first use an on-site laser measurement device or a high-precision image scanning device to record the local curvature change of the electric field line in three-dimensional space, and then discretize these coordinates based on the definition of curve differential geometry. For example, the curvature is quantified according to the rate of change of the tangential vector of the curve. Usually, in the same electric field line, the more obvious the difference in the tangential direction corresponding to the adjacent coordinate points is, the greater the curvature is. For specific calculations, refer to the curve difference method or spline interpolation method, and bring each sampling point and the surrounding adjacent points into the differential formula to obtain the curvature. If a certain section of the curve of the equipment undergoes an arc bending change equivalent to 0.05 meters within a length of 0.1 meters, the difference can be calculated using the relevant formula .

[0149] The steps to obtain the parameter are: The torsion value of the electric field line at the coordinate point can also be , it is necessary to further measure the degree of spatial twist based on the curvature of the curve, and to quantify the rotation rate of the curve in the normal and subnormal directions. , it is necessary to calculate the rate of change of the normal vector and binormal vector of each point relative to the arc length in the discrete coordinates of the curve. For example, the main normal and binormal of the curve can be established based on the center of curvature and the tangent vector, and then the adjacent points are differentiated. The ratio of the differential value to the corresponding arc length is regarded as the torsion. If a certain section of the electric field line has obvious spatial circling in a local area, the torsion value will increase. For example, if a section of a conductor path with a total length of 1 meter is observed to have three spiral bends in a length of 0.2 meters, the coordinates of this section can be quantitatively calculated to obtain .

[0150] The steps to obtain the parameter are: The torsion value of the electric field line at a coordinate point is usually a curve geometry quantity like curvature and torsion, and is used to describe the more detailed deviation of the curve in three-dimensional space. The unit can also be expressed as The calculation process needs to be combined with a comprehensive evaluation of the higher-order differential information of the curve curvature, torsion and spatial rotation axis. On the basis of the known coordinates of each curve segment, a more refined piecewise approximation or spline interpolation method can be applied to quantify the local changes of each curve segment at a deeper level. If some sections have sharp spatial bends and are accompanied by multiple rotations, the torsion value will increase significantly. For example, a section of the electric field line segment about 0.05 meters long was measured to have a relatively obvious double arc offset. After calculation, it can be obtained .

[0151] The steps to obtain the parameter are as follows: This parameter represents the total number of intercepted electric field line space coordinate points. When executing on site, it is necessary to perform segmented scanning on the electric field lines in the discharge area. If a total of 100 discrete points on a certain electric field line are sampled, then .

[0152] Calculation process:

[0153] In a certain test, set , you need to calculate the index The corresponding values ​​of the four points, assuming that the curvature, torsion and torsion measurement results are as follows:

[0154] ;

[0155] ;

[0156] ;

[0157] ;

[0158] First The term is equal to 0.4390

[0159] Similarly, we can calculate and add the values: The total of all the items is approximately = 0.5393; The total of all the items is approximately = 0.3465; The total of all items is approximately = 0.5059;

[0160] Add the four results: ,and but , so: The results showed that , indicating that within the range of the selected 6 coordinate points, the local topological deformation intensity of the electric field lines is 0.458. If this value is measured to rise significantly to above 1.0 in the same area in the future, it means that the curvature, torsion and torsion have undergone a larger spatial twist in these places.

[0161] Based on the local topological deformation intensity values ​​of the electric field lines obtained previously, it is necessary to sort out the coordinate point indexes and corresponding deformation intensity values ​​point by point in the monitoring system, read the spatial coordinate range recorded in the discharge source location information, and correlate and compare the two. If the deformation intensity values ​​of most points in a recognized discharge area exceed the pre-established judgment range, for example, the normal range is between 0.3 and 0.6 on site, but the data exceeds 0.7 multiple times, it is classified as a high deformation area. Then, this area is compared with the specific coordinates inside the equipment and compared one by one to see if there is any overlap with adjacent electrical components or mounting structures. If the deviation exceeds the design tolerance or multiple points show convex distortion, the area is marked as a key section with obvious deformation. Then, a mapping list between the degree of electric field line deformation and the location of the discharge source is established, and the corresponding relationship between each electric field line and this list is recorded. Then, the area is marked in the overall graphic or three-dimensional visualization. Through this operation process, the results of the electric field topological feature analysis can be integrated.

[0162] The steps for obtaining the discharge property analysis results are:

[0163] Based on the analysis results of the electric field topology characteristics, the discharge intensity index is calculated using the following formula:

[0164] ;

[0165] in, is the discharge intensity index, is the maximum curvature value of the electric field line in the electric field topology characteristic analysis results, is the maximum value of the electric field line torsion rate in the electric field topological characteristic analysis results;

[0166] Based on the discharge intensity index and the changing trend of the electric field line topology inside the GIS equipment, the impact radius and regional range of the discharge are determined, and the discharge property analysis results are generated.

[0167] Specifically, the benefit of the formula is that it combines the maximum curvature and the maximum torsion into the logarithmic function and square root operation structure in the same expression, and integrates the geometric characteristics of the electric field distribution and the degree of local spatial torsion into a quantifiable discharge intensity index, which can more flexibly respond to the differences in electric field line curvature and torsion under different GIS equipment.

[0168] The steps to obtain the parameter are as follows: This parameter represents the maximum curvature value of the electric field line detected in the electric field topology characteristic analysis results, and the unit is set to , refers to the highest peak of curvature at a certain section of electric field line inside the GIS equipment. It is usually detected by 3D scanning or sensors on site to measure the curvature of the electric field line in space. The curvature is calculated piecewise using the curve differential geometry method of discrete coordinates. Then, all curvature data are traversed over the entire electric field line range, and the one with the largest value is selected as For example, on a certain device, through multiple scans and data recording, a total of 200 valid coordinate points are found. Each coordinate point can calculate a curvature value based on the spatial geometric relationship of the adjacent coordinates, and finally find the highest value among these curvature values. and mark it as .

[0169] The steps to obtain the parameter are as follows: This parameter represents the maximum value of the electric field line torsion rate detected in the electric field topology characteristic analysis results, and the unit is set to , which is used to measure the intensity of the rotational deformation of the electric field lines in three-dimensional space. Usually, the electric field lines are first analyzed in the normal and binormal directions, and the torsion rate between adjacent discrete points is calculated. After the torsion rate values ​​of the entire curve are collected into a sequence, the maximum value is selected from them. In actual operation, the three-dimensional coordinates can be obtained by means of a laser scanner or other high-precision spatial measurement methods, the torsion rate can be calculated piece by piece using a discrete differential geometry algorithm, and then the peak value can be selected from all the torsion rate values ​​obtained as For example, in the statistics of a certain area, most of the torsion rates are measured to be to Range, a certain point appeared , then let .

[0170] Calculation process:

[0171] Here is a practical example: From the previously obtained electric field topology characteristic analysis results, we can determine , , and order . Substitute into the formula and calculate the numerator first:

[0172] ;

[0173] ;

[0174] Denominator:

[0175] ;

[0176] ;

[0177] ;

[0178] Divide the numerator by the denominator:

[0179] ;

[0180] The results show that A value greater than 1 indicates that the discharge intensity is in the middle range within the monitoring range. If the same set of curvature and torsion data is measured in the same device in the future, A higher value, such as 3 or 5, means a higher discharge intensity index, which means that the discharge phenomenon is more concentrated in space or the electric field lines are more bent and twisted.

[0181] Based on the discharge intensity index obtained above, it is necessary to compare it with the internal layout diagram in the GIS equipment structure and maintenance document. First, select the sections with higher curvature and torsion values ​​from the electric field line area coordinates summarized in the electric field topology feature analysis, and associate the corresponding discharge intensity index with these sections. Then, check the index of each electric field line in the spatial range one by one. If the discharge intensity index corresponding to some indexes exceeds the empirical threshold, for example, the threshold is set in the range of 1.5 to 2.5 and most records fall around 2.0, then these points are judged to have potential discharge risks. If individual values ​​are detected to reach 3.0 or higher, it is considered to be in the obvious discharge impact stage. Combined with the electric field waveform data and timestamp comparison measured on site, the approximate impact radius of the discharge behavior in the area can be confirmed. For example, according to the equipment installation The manual describes the interaction radius between the dielectric layer and the insulating component. Multiple measurements are performed based on the actual discrete point spacing collected. Nodes with a discharge intensity index exceeding 2.0 or 2.5 are aggregated together to find the minimum spatial enclosing sphere or convex hull in which they are located. The center coordinates and maximum edge distance are recorded as the discharge influence radius. The enclosed coordinate set is then expanded for inspection. If similar high-value indexes are found in adjacent nodes within this range, the range is slightly increased and it is determined whether other spatially adjacent nodes also exceed the established threshold. If the threshold is exceeded in multiple tests, this range is fixed as the discharge influence area. Otherwise, the original sphere or convex hull is defined as the final influence radius. After this series of arrangements, the discharge influence radius and specific area range can be determined, and finally summarized in the recording system to generate the discharge property analysis results.

[0182] The steps to obtain the discharge development trend prediction results are as follows:

[0183] Based on the results of the discharge property analysis, the internal spatial structure data of the GIS equipment is called to extract the three-dimensional spatial volume, spatial area and topological structure changes corresponding to different discharge levels, and a historical parameter set of the discharge level and the corresponding regional spatial structure changes is established;

[0184] Based on the historical parameter set of discharge levels and corresponding regional spatial structure changes, a time series model is used to construct the sequence characteristics of spatial structure parameters changing over time based on the continuous characteristics of three-dimensional spatial volume and area changes in the historical parameter set, and generate a sequence feature set of spatial structure parameter changes;

[0185] Based on the feature set of the spatial structure parameter change sequence, the spatial structure parameter sequence is fitted through the regression analysis model, the future change trend parameters of the discharge intensity level and the discharge impact area range are calculated, the fitting relationship between the discharge level and the regional range change is established, and the discharge development trend prediction results are generated.

[0186] Specifically, based on the discharge property analysis results obtained above, first check the peak electric field data and magnetic field data records corresponding to each discharge level in the GIS equipment maintenance data, as well as the three-dimensional spatial volume size, spatial area range and local structural characteristics involved in the history of each level, disassemble the discharge level definitions in these documents one by one and record the discharge level thresholds listed therein. For example, in the early test, the discharge level is divided into four levels, and from low to high, they are set in the four intervals of 0 to 0.5, 0.5 to 1.0, 1.0 to 2.0 and more than 2.0 respectively. This interval is obtained by linear interpolation and correction after statistics of multiple tests. Each measurement continuously observes the electric field and magnetic field inside the GIS equipment within 24 hours and summarizes the discharge intensity in each time period. Then, the fault case is compared with the inspection of the equipment manufacturer. The final determination is made after the test results. When the discharge level is obtained, the internal spatial structure data of the GIS equipment provided by the installer is combined to query the corresponding three-dimensional spatial block coordinates, combine this coordinate with the previous electric field line topology change, read its spatial volume and surface area information, and check the distribution of each area in the equipment coordinates. If it is found that the spatial volume of some areas exceeds the established benchmark value, such as in the range of 50cm³ to 100cm³, or the spatial area exceeds the established benchmark value, such as in the range of 40cm² to 80cm², then the coordinates of these areas are marked and the corresponding discharge level numbers are corresponding. In this way, the three-dimensional spatial volume, spatial area and topological structure change data corresponding to different discharge levels are gradually sorted out, and finally summarized to form a historical parameter set of discharge level and corresponding regional spatial structure change.

[0187] Based on the historical parameter set of the discharge level and the change of the spatial structure of the corresponding area, the three-dimensional space volume and space area data contained therein are first arranged in chronological order and segmented. Each segment corresponds to an observation period, such as a week or a month. By extracting the continuous change characteristics of these parameters over time and comparing them, if the three-dimensional space volume increases significantly in certain observation periods, for example, exceeding the set comparison threshold of 10cm³ or the spatial area fluctuation range significantly exceeds the set comparison threshold of 5cm², these segments will be marked as key focus segments. Then, in the data collection entries for this period in the on-site maintenance records, the relevant sensor sampling frequency and The error range is determined by merging them into the same sequence for preprocessing after all are confirmed to be correct. Subsequently, the sequence characteristics of the spatial structure parameters changing over time are constructed according to the time series analysis method. The specific process is to perform differential or smoothing operations on the volume and area of ​​each record to eliminate high-frequency noise or short-term abnormal peaks. Then, the sequence is searched for repeated increase and decrease trends and the time and amplitude of their occurrence are marked through the recording system. If the volume or area of ​​the data continues to increase for more than three consecutive times, it is marked as a rapid increase trend. If the repeated rise and fall are frequent and span multiple segments in a short period of time, it is marked as a high fluctuation trend. Finally, the spatial structure parameter change sequence feature set is formed by sorting out these features.

[0188] Based on the characteristic set of spatial structure parameter change sequence, it is necessary to compare the setting values ​​of various statistical parameters in the process of using the regression analysis model. For example, during the field survey, the three-dimensional space volume and area change curves in the past 6 months are first collected, and the historical data table with continuous observation periods of more than 180 days is statistically obtained. Then, the volume and area values ​​of each day are arranged in order and compared with the zero residual requirement in the set regression model to verify whether the residual meets the limit of less than a certain benchmark value, such as 0.05. If it exceeds, the data source is recalibrated or obvious abnormal points are eliminated. After that, all data that meet the benchmark range are brought into the regression formula to calculate and compare the regression coefficient and goodness of fit. If the fit is good, If the degree is higher than the set threshold, for example 0.8, it is considered to meet the requirements. Through this sequence fitting process, the possible change trend parameters of the discharge intensity level and the discharge impact area range in the next several observation cycles can be calculated. If the prediction curve obtained after fitting shows that the discharge intensity level is higher than the originally established 2.0 interval or the impact area radius is greater than the originally established 20cm, it means that it is necessary to further compare this result with the actual distribution map of GIS equipment, and re-examine the corresponding partition coordinates to see whether there is local spatial expansion. Finally, the fitting relationship between different levels and regional range changes obtained by regression analysis is summarized in the recording system to obtain the discharge development trend prediction result.

[0189] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A partial discharge monitoring method for GIS, characterized in that: The following steps are involved: Collect electromagnetic data from GIS equipment, time-align the collected electric field data with the magnetic field data, and generate an electromagnetic data time series; Performing time series analysis based on the electromagnetic data time series to obtain an electromagnetic field characteristic sequence; Based on the electromagnetic field characteristic sequence, a statistical method is applied to detect abnormal peaks of the partial discharge signal, identify and classify the abnormal signals, and generate preliminary discharge signal characteristics; differential analysis is performed on the preliminary discharge signal characteristics, and the location of the discharge source is determined based on the signal strength and phase difference of the electric and magnetic fields to obtain discharge source location information; Using differential geometry to calculate the curvature and torsion of the electric field lines inside the GIS equipment, based on the location information of the discharge source, the topological structure changes of the electric field lines in the partial discharge area are described to generate the electric field topological characteristic analysis results; Deducing the discharge intensity and impact range based on the electric field topology characteristic analysis results, and obtaining the discharge property analysis results; Based on the discharge property analysis results, predict the development trend of partial discharge and generate a discharge development trend prediction result; The steps for obtaining the discharge source location information are as follows: Extracting the electric field intensity difference, the magnetic field intensity difference and the phase difference according to the preliminary discharge signal characteristics to form a preliminary discharge signal characteristic differential sequence; According to the preliminary discharge signal characteristic differential sequence, the positioning correlation value of the discharge source is calculated, and the calculation formula is: Among them, G pd is the location correlation value of the discharge source, X i is the difference value of the electric field data, Y i is the difference value of magnetic field data, γ i is the phase difference after the electromagnetic signal is differentiated, T i is the duration of the electromagnetic signal corresponding to the i-th data point, Z i is the intensity difference value of the electromagnetic signal at the i-th data point, and h is the total number of differential data points; determining the discharge source location based on the positioning correlation value of the discharge source to obtain discharge source location information; The steps for obtaining the electric field topology characteristic analysis results are: According to the discharge source location information, the spatial coordinate points of the electric field lines in the discharge area are intercepted from the electric field line data inside the GIS equipment, and the position coordinate change of each coordinate point in the three-dimensional space is calculated to obtain the electric field line spatial coordinate sequence; Based on the electric field line spatial coordinate sequence, the local topological deformation intensity value of the electric field line is calculated, and the calculation formula is: Among them, D em is the local topological deformation intensity of the electric field line, κ i is the curvature value of the electric field line at the i-th coordinate point, τ i is the torsion value of the electric field line at the i-th spatial coordinate point, θ i is the torsion value of the electric field line at the i-th spatial coordinate point, zm is the total number of intercepted electric field line spatial coordinate points; Based on the local topological deformation intensity of the electric field lines, a topological correlation between the degree of electric field line deformation and the location of the discharge source is established to describe the structural change characteristics of the electric field lines in the discharge area and generate electric field topological characteristic analysis results. The steps for obtaining the discharge property analysis results are: Based on the analysis results of the electric field topology characteristics, the discharge intensity index is calculated using the following formula: Among them, P d is the discharge intensity index, C is the maximum curvature value of the electric field line in the electric field topology characteristic analysis results, and L is the maximum value of the torsion rate of the electric field line in the electric field topology characteristic analysis results; Based on the discharge intensity index and combined with the trend of the topological structure change of the electric field lines inside the GIS equipment, the impact radius and regional range of the discharge are determined to generate the discharge property analysis results.

2. The method for monitoring partial discharge of GIS according to claim 1, characterized in that: The steps for acquiring the electromagnetic data time series are: Collect electric field data and magnetic field data from GIS equipment to form electric field data sequences and magnetic field data sequences with timestamps. By matching the same timestamps, the two sequences are aligned to obtain a preliminarily aligned electromagnetic data sequence. Based on the preliminarily aligned electromagnetic data sequence, setting a maximum allowable time difference threshold, calculating the time difference between the electric field data sequence and the magnetic field data sequence, and screening valid data points whose time difference is less than the maximum time difference threshold to obtain a synchronized electromagnetic data sequence; Based on the synchronized electromagnetic data sequence, the electric field data and the magnetic field data are rearranged in a unified timestamp sequence to obtain an electromagnetic data time series.

3. The method for monitoring partial discharge of GIS according to claim 1, wherein: The steps of obtaining the electromagnetic field characteristic sequence are: According to the electromagnetic data time series, continuous data segments are intercepted at fixed intervals, and the mean and variance of the electric field data and the magnetic field data in each segment are calculated to obtain the statistics of each segment of data; Based on the statistics of each segment of data, the electromagnetic field coupling strength value is calculated using the following formula: Among them, U em is the electromagnetic field coupling strength value, CE j is the electric field strength of the jth data point, CM j is the magnetic field intensity at the jth data point, and m is the number of data points in the electromagnetic data time series; Based on the electromagnetic field coupling strength values, the electromagnetic data are arranged in time order to generate an electromagnetic field feature sequence.

4. The method for monitoring partial discharge of GIS according to claim 1, wherein: The steps for obtaining the preliminary discharge signal characteristics are: Determining the time width of an analysis window according to the electromagnetic data time series, and intercepting data segments within the sequence by sliding the analysis window to obtain a segment sequence of the electromagnetic data time series; Based on the segment sequence of the electromagnetic data time series, the electromagnetic field fluctuation consistency value is calculated, and the calculation formula is: Among them, F em is the electromagnetic field fluctuation consistency value, E k 、M k are the electric field data and magnetic field data of the kth point in the analysis window, are the average values ​​of the electric field data and magnetic field data in the corresponding analysis window, φ Ek 、φ Mk are the phase values ​​of electric field data and magnetic field data, respectively, and K is the number of data points in the analysis window; According to the electromagnetic field fluctuation consistency value, the data position exceeding the threshold is determined to be an abnormal peak point, the abnormal peak point is marked and classified, and a preliminary discharge signal feature is obtained.

5. The method for monitoring partial discharge of GIS according to claim 1, characterized in that: The steps for obtaining the discharge development trend prediction result are: Based on the results of the discharge property analysis, the internal spatial structure data of the GIS equipment is called to extract the three-dimensional spatial volume, spatial area and topological structure changes corresponding to different discharge levels, and a historical parameter set of the discharge level and the corresponding regional spatial structure changes is established; Based on the historical parameter set of discharge levels and corresponding regional spatial structure changes, a time series model is used to construct the sequence characteristics of spatial structure parameters changing over time based on the continuous characteristics of three-dimensional spatial volume and area changes in the historical parameter set, and generate a sequence feature set of spatial structure parameter changes; Based on the feature set of the spatial structure parameter change sequence, the spatial structure parameter sequence is fitted through the regression analysis model, the future change trend parameters of the discharge intensity level and the discharge impact area range are calculated, the fitting relationship between the discharge level and the regional range change is established, and the discharge development trend prediction results are generated.

6. The partial discharge monitoring system of the partial discharge monitoring method for GIS according to any one of claims 1 to 5, characterized in that: include: The electromagnetic data acquisition module collects electric and magnetic field data from GIS equipment, performs time alignment, and generates electromagnetic data time series; The feature extraction module performs time series analysis on the electromagnetic data time series, extracts the electric field and magnetic field intensity characteristics at each time point, and obtains the electromagnetic field feature sequence; The anomaly detection and classification module detects abnormal peaks in the data of the electromagnetic field feature sequence, identifies abnormal signals, classifies different types of discharge signals, and generates preliminary discharge signal features; The discharge source location module performs differential analysis on the initial discharge signal characteristics, calculates the signal strength and phase difference of the electric and magnetic fields, determines the location of the discharge source, and obtains the discharge source location information; The topological feature analysis module uses the discharge source location information to calculate the curvature and torsion of the electric field lines inside the GIS equipment, describe the topological structure changes of the electric field lines in the local discharge area, deduce the discharge intensity and impact range, and generate electric field topological feature analysis results.

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