Monitoring performance evaluation method and system for power equipment on-line monitoring device

Abstract

The invention relates to a monitoring performance evaluation method and system for an on-line monitoring device of power equipment. The method comprises the following steps: acquiring an initial state operation set when the on-line monitoring device of the power equipment operates for the first time; judging whether the running time of the on-line monitoring device of the power equipment reaches a preset verification moment, if so, executing the next step, and if not, not performing any operation; obtaining a state operation set of the on-line monitoring device of the power equipment at the verification moment and a plurality of moments after the verification moment, and taking the state operation set as a verification data set; and based on the initial state operation set and the verification data set, processing by adopting a performance evaluation model so as to calculate the deviation degree between the power equipment online monitoring device and the initial state, and obtaining a quantitative monitoring performance evaluation result of the power equipment online monitoring device. Compared with the prior art, the method has the advantages that the monitoring result can be effectively and quantitatively evaluated, various abnormal types can be evaluated, and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of online monitoring of electric power equipment, and in particular to a monitoring performance evaluation method and system of an online monitoring device of electric power equipment. Background Art

[0002] Online monitoring technology for power equipment is one of the effective means to timely discover hidden dangers of defects in power equipment. In recent years, a large number of online monitoring devices have been installed on some important lines. However, after the online monitoring system has been running for a period of time, a series of hardware or software program problems often occur. Relevant data reports show that after the online monitoring system has been running continuously for 2 to 3 years, about 50% of the online monitoring systems are in a faulty state, and in the systems that are still in working state, only 30% of the data belongs to effective monitoring. Therefore, after the online monitoring device of power equipment has been working continuously for 2 to 3 years, due to problems such as harsh environment, aging of the device and hardware quality, only a few system devices will continue to work effectively. This shows that the online monitoring system device itself cannot be separated from maintenance and regular calibration.

[0003] At present, the only way to confirm the status of online monitoring system devices is to arrange personnel to check and calibrate the monitoring devices one by one. For security reasons, personnel are generally not allowed to enter or approach the line site in operation (except for staff), so the status confirmation of online monitoring devices becomes difficult to carry out.

[0004] On the other hand, in order to maintain the moisture-proof and waterproof performance of the on-site equipment, the communication cables between the sensors and the local signal processing device (LS) are all sealed with waterproof tubes. Even if you enter the site, it is difficult to use standard signal injection or replacement of input signals to calibrate and verify the monitoring device without dismantling the equipment.

[0005] The more effective approach at present is to install a verification signal device to regularly output a pulse signal for verification to the on-site sensors and acquisition devices. However, this relies more on human judgment and analysis, and it is currently impossible to automatically judge and analyze the verification results.

[0006] Online monitoring of power equipment is a long-term, unmanned, automatic monitoring system. Data analysis and display use multi-dimensional graphs represented by orthogonal combinations of multiple parameters, such as Figure 2As shown in the figure, there are phase resolved partial discharge (PRPD) spectrum, phase resolved pulse sequence (PRPS) spectrum, frequency distribution spectrum, signal amplitude change trend qt diagram, discharge frequency change trend nt diagram and time domain waveform spectrum. At present, the commonly used alarm judgment methods of online monitoring systems mostly use multiple logic gates + neural networks. The multiple logic gate judgment is to judge the threshold value by forming multiple logics (such as q+t, n+t, q+n, etc.) based on the physical quantity discharge quantity q, density n, signal duration t and signal phase characteristics; plus the judgment of the neural network, the final judgment result of partial discharge or noise is obtained. However, if the signal detected by the online monitoring device is biased or problematic, the amount of data used by the multiple logic gates and the neural network is also biased, or even invalid, which means that the judgment of the online monitoring system is misjudged or even missed, which directly loses the effectiveness of the system.

[0007] In summary, if the online monitoring system of power equipment has not been effectively calibrated after long-term operation, the following problems may exist:

[0008] 1. Lack of quantitative analysis methods to judge the effectiveness of the system. Relying only on simple equipment status indications or single parameters to judge that the online monitoring device is damaged, it is impossible to deeply understand the actual performance of the device during long-term operation. Failure to timely discover problems through quantitative analysis methods and failure to analyze the degree of abnormality will mislead the operation and maintenance personnel in judging the operating status of the power equipment.

[0009] 2. Lack of methods to evaluate various types of abnormalities. Different types of abnormalities require different treatment methods and response strategies. Lack of abnormality type evaluation methods will affect the ability to warn and predict abnormal situations, fail to discover potential problems in time, and fail to take corresponding maintenance measures, resulting in low maintenance efficiency of the device.

[0010] 3. Waste of investment. An online monitoring system for power equipment that has not been calibrated for a long time may eventually become useless. Because it is impossible to accurately judge the effectiveness of the system and evaluate the type of abnormalities, the monitoring system cannot play its due role, wasting the huge amount of money invested in the early stage. Summary of the invention

[0011] The purpose of the present invention is to provide a monitoring performance evaluation method and system for an online monitoring device of electric power equipment for quantitatively evaluating monitoring performance.

[0012] The purpose of the present invention can be achieved by the following technical solutions:

[0013] A monitoring performance evaluation method for an online monitoring device of an electric power equipment comprises the following steps:

[0014] Obtaining an initial state operation set when the online monitoring device of the electric power equipment is operated for the first time;

[0015] Determine whether the operation time of the online monitoring device of the electric power equipment reaches the preset verification time, if yes, execute the next step, if no, do nothing;

[0016] Obtaining a state operation set of the online monitoring device of the electric power equipment at the verification time and multiple times thereafter as a verification data set;

[0017] Based on the initial state operation set and the verification data set, a performance evaluation model is used for processing to calculate the degree of deviation of the online monitoring device of the electric equipment from the initial state, and obtain a quantitative monitoring performance evaluation result of the online monitoring device of the electric equipment.

[0018] Furthermore, the initial state operation set includes:

[0019] 1) PRPD atlas dataset:

[0020]

[0021] Wherein, subscript 0 indicates the initial state, f indicates the passband monitoring frequency band, f1~f4 represent four different passband monitoring frequency bands, φ indicates the synchronization phase, Q indicates the signal charge, N indicates the signal frequency, T indicates the signal duration, and D is the PRPD spectrum;

[0022] 2) Time domain waveform data set:

[0023] B0=[V0 t0 τ0]

[0024] In the formula, V represents the maximum amplitude of the waveform, t represents the rising edge time of the waveform, and τ represents the slope of the waveform;

[0025] 3) Frequency domain dataset:

[0026]

[0027] Where F0 is the frequency domain data set at the initial state;

[0028] 4) Continuous operation coefficient β:

[0029] β=1+exceeded years / 8;

[0030] 5) Ambient temperature Et0 and humidity Eh0 during operation;

[0031] 6) Vibration degree Ev0 during operation.

[0032] Furthermore, the execution steps of the performance evaluation model include:

[0033] The initial state operation set and the verification data set are combined to construct a performance evaluation set, wherein the performance evaluation set includes a PRPD spectrum evaluation data set, a time domain waveform evaluation data set, a frequency domain evaluation data set, a continuous operation coefficient evaluation data set, an ambient temperature and humidity evaluation data set, and a vibration evaluation data set, and the expression of the performance evaluation set is:

[0034]

[0035] Where Z is the performance evaluation set, subscript 0 represents the initial state, subscript 1-n represents the state at the verification time and multiple times thereafter, f represents the passband monitoring frequency band, f1~f4 represent 4 different passband monitoring frequency bands, D is the PRPD spectrum data set, B is the time domain waveform data set, F is the frequency domain data set, β is the continuous operation coefficient, Et and Eh are the ambient temperature and humidity during operation, respectively, and Ev is the vibration degree during operation;

[0036] Calculate the maximum deviation value Δ of the performance evaluation set max ;

[0037] Based on the maximum deviation value Δ max The deviation index of all feature parameters in the performance evaluation set is calculated, wherein the calculation expression of the deviation index is:

[0038]

[0039] Where Ad is the deviation index, Δ max is the maximum deviation value, n is the number of verifications, Δ i is the i-th deviation value;

[0040] Determine whether the deviation index is greater than a preset threshold value. If so, it indicates that the online monitoring device for electric power equipment operates abnormally, and the next step is executed. If not, it indicates that the online monitoring device for electric power equipment operates normally, and no operation is performed.

[0041] Each evaluation data set in the performance evaluation set is processed separately to obtain the monitoring performance evaluation result of the online monitoring device of the power equipment.

[0042] Furthermore, the process of processing the PRPD atlas evaluation data set to obtain a monitoring performance evaluation result includes:

[0043] 1) Processing of synchronization phase φ in PRPD spectrum evaluation dataset:

[0044] Calculate the deviation value of the synchronization phase φ and the range coefficient nφ , where the calculation expression is:

[0045] Δφ 偏离(f1，f2，f3，f4) =|φ n -φ0|,n φ =φ n / 180°

[0046] Where, subscript 0 indicates the initial state, subscript n indicates the operating state at the nth acquisition after the calibration time, f indicates the passband monitoring frequency band, f1~f4 represent 4 different passband monitoring frequency bands, Δφ 偏离(f1，f2，f3，f4) is the deviation value of the synchronization phase;

[0047] If 1.05<n φ ≤1.15, 10°<Δφ 偏离(f1，f2，f3，f4) ≤30° or 0.85≤n φ <0.95, it indicates that the phase acquisition and processing module in the online monitoring device of the power equipment has aged;

[0048] If φ <0.85 or n φ >1.15, Δφ 偏离(f1，f2，f3，f4) >30°, it means that the phase acquisition and processing module is damaged;

[0049] 2) Processing of signal charge quantity Q in PRPD spectrum evaluation data set:

[0050] Calculate the deviation value of the signal charge Q and the range coefficient n Q , where the calculation expression is:

[0051] ΔQ 偏离(f1，f2，f3，f4) =|Q n -Q0| / Q0*100%,n Q =Q n / Q0

[0052] Where ΔQ 偏离(f1，f2，f3，f4) is the deviation value of the signal charge;

[0053] If 1.2<n Q ≤1.4, 20%<ΔQ 偏离(f1，f2，f3，f4) ≤40% or 0.6≤n Q <0.8, it means that the PRPD module and sensor in the online monitoring device of the power equipment are aged;

[0054] If Q >1.4, ΔQ 偏离(f1，f2，f3，f4) >40% or n Q <0.6, it means that the PRPD module is operating abnormally and the sensor is aging;

[0055] 3) Processing of signal frequency N in PRPD spectrum evaluation data set:

[0056] Calculate the deviation value and range coefficient n of the signal frequency N N , where the calculation expression is:

[0057] ΔN 偏离(f1，f2，f3，f4) =|N n -N0| / N0*100%,n N =N n / N0

[0058] Where, ΔN 偏离(f1，f2，f3，f4) is the deviation value of the signal frequency;

[0059] If 1.15<n N ≤1.4, 15%<ΔN 偏离(f1，f2，f3，f4) ≤40% or 0.6≤n N <0.85, it means that the PRPD module in the online monitoring device of the power equipment has aged;

[0060] If N >1.4, ΔN 偏离(f1，f2，f3，f4) >40% or n N <0.6, it means that the PRPD module is operating abnormally;

[0061] 3) Processing of signal duration T in PRPD atlas evaluation dataset:

[0062] Calculate the deviation value of the signal duration T and the range coefficient n T , where the calculation expression is:

[0063] ΔT 偏离(f1，f2，f3，f4) =|T n -T0| / T0*100%,n T =Tn / T0

[0064] Where, ΔT 偏离(f1，f2，f3，f4) is the deviation value of the signal duration;

[0065] If 0.85≤n T <1,ΔT 偏离(f1，f2，f3，f4) <15%, it indicates that the PRPD module in the online monitoring device for the power equipment has aged;

[0066] If T <0.85, ΔT 偏离(f1，f2，f3，f4) >15%, it indicates that the PRPD module operates abnormally.

[0067] Furthermore, the process of processing the time domain waveform evaluation data set to obtain a monitoring performance evaluation result includes:

[0068] 1) Calculate the deviation value of the maximum amplitude V of the waveform in the time domain waveform evaluation data set and the range coefficient n v , where the calculation expression is:

[0069] ΔV 偏离(f1，f2，f3，f4) =|V n -V0| / V0*100%,n v =V n / V0

[0070] Where, subscript 0 indicates the initial state, subscript n indicates the operating state at the nth acquisition after the calibration time, f indicates the passband monitoring frequency band, f1~f4 represent 4 different passband monitoring frequency bands, ΔV 偏离(f1，f2，f3，f4) is the deviation value of the maximum amplitude of the waveform;

[0071] If 1.15<n v ≤1.4,15%<ΔV 偏离(f1，f2，f3，f4) ≤40% or 0.6≤n v <0.85, it means that the waveform acquisition and processing module and the filter module in the online monitoring device of the power equipment are aged and damaged;

[0072] If Q >1.4,ΔV 偏离(f1，f2，f3，f4) >40% or n Q <0.6, it means that the waveform acquisition and processing module and the filtering module are operating abnormally;

[0073] 2) Calculate the range coefficient of the waveform rising edge time t in the time domain waveform evaluation data set, where the calculation expression is:

[0074] n t =t n / t0

[0075] If n ≤20ns, n t ≤1, it means that the waveform acquisition and processing module and the filtering module in the online monitoring device for electric power equipment operate well;

[0076] If n >20ns, n t >1, it means that the waveform acquisition and processing module and the filtering module are operating abnormally;

[0077] 3) Calculate the deviation value and range coefficient n of the waveform slope τ in the time domain waveform evaluation data set τ , where the calculation expression is:

[0078] Δτ 偏离(f1，f2，f3，f4) =|τ n-τ0| / τ0*100%, n τ =τ n / τ0

[0079] In the formula, Δτ 偏离(f1，f2，f3，f4) is the deviation value of the waveform slope;

[0080] If 1.15<n τ ≤1.4, 15%<Δτ 偏离(f1，f2，f3，f4) ≤40% or 0.6≤n τ <0.85, it means that the waveform acquisition and processing module and the filter module in the online monitoring device of the power equipment are aged and damaged;

[0081] If τ >1.4, Δτ 偏离(f1，f2，f3，f4) >40% or n τ <0.6, it means that the waveform acquisition and processing module and the filtering module are operating abnormally.

[0082] Furthermore, the process of processing the frequency domain evaluation data set to obtain the monitoring performance evaluation result includes:

[0083] Calculate the deviation value in the frequency domain, where the calculation expression is:

[0084] ΔF 偏离 =|F n -F0| / F0*100%

[0085] In the formula, subscript 0 represents the initial state, subscript n represents the operating state at the nth acquisition after the calibration time, F is the frequency domain, ΔF 偏离 is the deviation value in the frequency domain;

[0086] If 15%<ΔF 偏离 <30%, it indicates that the filter module in the online monitoring device for the power equipment has aged;

[0087] If ΔF 偏离 >30%, it indicates that the filtering module operates abnormally.

[0088] Furthermore, the process of processing the continuous operation coefficient evaluation data set to obtain the monitoring performance evaluation result includes:

[0089] The maximum continuous operation coefficient is extracted. The larger the maximum continuous operation coefficient is, the longer the continuous operation time of the online monitoring device of the electric power equipment is, and the greater the aging loss of the PRPD module, sensor, and filter module is.

[0090] Furthermore, the process of processing the environmental temperature and humidity evaluation data set to obtain the monitoring performance evaluation result includes:

[0091] 1) Calculate the average value of the ambient temperature Et over a period of time based on the ambient temperature and humidity evaluation data set and standard deviation Δ Et ;

[0092] Determine whether the ambient temperature of the online monitoring device of the power equipment exceeds If yes, it means that the phase acquisition and processing module and the waveform acquisition and processing module in the online monitoring device for electric power equipment will be affected, resulting in abnormal operation; if no, it means that the ambient temperature of the online monitoring device for electric power equipment is normal;

[0093] 2) Determine whether the humidity Eh of the online monitoring device for electric power equipment is within a preset humidity range. If so, it indicates that static electricity accumulation or moisture is present in the online monitoring device for electric power equipment.

[0094] Furthermore, the process of processing the vibration evaluation data set to obtain a monitoring performance evaluation result includes:

[0095] Based on the vibration evaluation data set, the average value of the vibration level over a period of time is calculated. Based on the average Determine the vibration standard range;

[0096] Determine whether the vibration degree of the online monitoring device of the electric power equipment at a certain moment exceeds the vibration standard range. If so, it means that it will affect the phase acquisition and processing module and the waveform acquisition and processing module in the online monitoring device of the electric power equipment, resulting in abnormal operation. If not, it indicates that the vibration degree of the online monitoring device of the electric power equipment is normal.

[0097] The present invention also provides a monitoring performance evaluation system for an online monitoring device of an electric power equipment, comprising:

[0098] Initial state acquisition module: used to obtain the initial state operation set when the online monitoring device of the power equipment is operated for the first time;

[0099] Verification time judgment module: used to judge whether the operation time of the online monitoring device of the electric power equipment reaches the preset verification time, if yes, execute the next step, if no, do nothing;

[0100] Verification data acquisition module: used to obtain the state operation set of the online monitoring device of the power equipment at the verification time and multiple times thereafter as a verification data set;

[0101] Evaluation module: used to process the initial state operation set and the verification data set using a performance evaluation model to calculate the degree of deviation of the online monitoring device for electric equipment from the initial state, and obtain a quantitative monitoring performance evaluation result of the online monitoring device for electric equipment.

[0102] Compared with the prior art, the present invention has the following beneficial effects:

[0103] (1) The present invention collects the initial state operation data, the state operation data at the time of verification and thereafter, and uses a performance evaluation model to calculate the degree of deviation of the online monitoring device of the power equipment from the initial state, thereby achieving accurate quantitative evaluation of the monitoring performance of the online monitoring device.

[0104] (2) The status operation data collected by the present invention includes multiple dimensions, which can comprehensively evaluate the monitoring performance of the online monitoring device. At the same time, through precise calculation and analysis of multi-dimensional data, it can accurately judge the various performance indicators of the online monitoring device, thereby determining whether the online monitoring device has an abnormality and the type of abnormality.

[0105] (3) The present invention helps maintenance personnel quickly and effectively restore the normal operation of the device. According to the abnormal fault type prompted, they can immediately prepare to replace the corresponding module and go to the site, which greatly shortens the time required for fault elimination and enables the monitoring system to play its due role without wasting the huge amount of money invested in the early stage. BRIEF DESCRIPTION OF THE DRAWINGS

[0106] Figure 1 It is a schematic diagram of the method flow of the present invention;

[0107] Figure 2 It is a signal discrimination multi-dimensional spectrum of the online monitoring system of the present invention;

[0108] Figure 3 is a performance evaluation model of the present invention;

[0109] Figure 4 This is the evaluation process of scenario 1 in the embodiment of the present invention;

[0110] Figure 5 This is the evaluation process of scenario 2 in the embodiment of the present invention;

[0111] Figure 6 This is the evaluation process of scenario 3 in the embodiment of the present invention. DETAILED DESCRIPTION

[0112] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0113] Example 1

[0114] In view of the problem that the current online monitoring system of power equipment has not been effectively verified after long-term operation, this embodiment provides a monitoring performance evaluation method for an online monitoring device of power equipment, which mainly solves the problems of data analysis, comparison, and evaluation of defects and abnormalities after the on-site online monitoring device is verified. Figure 1 As shown, the method comprises the following steps:

[0115] S1. Generate the initial state dataset for the first run.

[0116] When the online monitoring device for power equipment is operated for the first time, a correction signal is injected, data is collected and an initial state data set is generated, which includes:

[0117] (1) PRPD Atlas Dataset

[0118]

[0119] Where 0 represents the initial state, f represents the passband monitoring frequency band, f1 to f4 represent four different passband monitoring frequency bands, φ represents the synchronization phase, Q represents the signal charge, N represents the signal frequency, and T represents the signal duration.

[0120] (2) Time domain waveform data set

[0121] B0=[V0t0τ0]

[0122] Where V represents the maximum amplitude of the waveform, t represents the rising edge time of the waveform, and τ represents the slope of the waveform.

[0123] (3) Frequency domain dataset

[0124]

[0125] Where f represents the passband monitoring frequency band, and Q represents the signal charge.

[0126] (4) Continuous operation coefficient β: the ratio of continuous operation time to device life. Assuming that the general life of the device is 8 years, within 8 years after commissioning, the coefficient β = 1; if the system has been in operation for more than 8 years, the continuous operation coefficient β = 1 + the number of years exceeded / 8. For example: the system has been in operation for 9 years, the continuous operation coefficient β = 1 + 1 / 8 = 1.125;

[0127] (5) Ambient temperature Et0 and humidity Eh0 of the device

[0128] (6) Vibration degree of device operation Ev0

[0129] S2. When the preset verification time is reached, the automatic verification program is started to obtain the current status data set.

[0130] After the monitoring device has been running for a certain period of time, it reaches the preset verification time. The system automatically starts the verification program and records the collected data at that time and generates the verification data set (1) PRPD spectrum data set D1, (2) time domain waveform data set B1, (3) frequency domain data set T1, (4) continuous operation coefficient β, (5) ambient temperature Et1 humidity Eh1, and (6) ambient vibration degree Ev1.

[0131] Verify again, record the collected data at that moment and generate verification data sets (1) PRPD spectrum data set D2, (2) time domain waveform data set B2, (3) frequency domain data set F2, (4) continuous operation coefficient β, (5) ambient temperature Et2 humidity Eh2, (6) ambient vibration Ev2.

[0132] After starting the calibration procedure several times in succession, the analysis data and judgment results obtained are more accurate.

[0133] S3. Input the characteristic values ​​of previous verification data into the performance evaluation model to determine whether the current device is operating normally. If there is an abnormality, determine the type of abnormal defect.

[0134] According to the status data set of the online monitoring system, Figure 3 The performance evaluation model shown includes data set construction, feature parameter extraction, deviation difference calculation and anomaly prediction analysis.

[0135] 1) Construct a data set. According to the weights between the parameters, construct a performance evaluation set, as shown in the following formula (1):

[0136]

[0137] 2) Calculate the maximum deviation value Δ of the data set max

[0138] 3) Calculate the all characteristic parameter deviation index Ad (All characteristic parameters deviation) of the data set, as shown in formula (2):

[0139]

[0140] The deviation is acceptable and no adjustment or repair is required.

[0141] If Ad>0.15, it is considered that the system may have detection bias, that is, hidden defect problems;

[0142] At this point, it is necessary to classify the various possible hidden defect problems: a) phase acquisition and processing module abnormality; b) PRPD module abnormality; c) sensor abnormality; d) filter module abnormality; e) waveform acquisition and processing module abnormality.

[0143] The phase acquisition and processing module is mainly used to acquire phase data, perform preprocessing, and extract phase eigenvalues; the PRPD module is mainly used to acquire and process PRPD spectrum data; the sensor is mainly used to acquire partial discharge data, ambient temperature, humidity, and vibration of the monitoring device; the filtering module is mainly used to acquire and process frequency domain data; the waveform acquisition and processing module is mainly used to acquire and process time domain waveform data.

[0144] Data processing of phase φ in PRPD spectrum dataset:

[0145] Δφ 偏离(f1，f2，f3，f4) =|φ n -φ0|,n φ =φ n / 180°

[0146] where n φ is the range coefficient, if 0.85≤n φ <0.95 or 1.05 <n φ ≤1.15, 10°<Δφ 偏离(f1，f2，f3，f4) ≤30°, indicating that the phase acquisition and processing modules are aging and have small deviations. The module life is decreasing, but the module can continue to operate and requires periodic inspection and attention.

[0147] If φ <0.85 or n φ >1.15, Δφ 偏离(f1，f2，f3，f4) >30°, it means that the phase acquisition and processing module is damaged and needs to be repaired or replaced.

[0148] Data processing of charge quantity Q in PRPD spectrum data set:

[0149] ΔQ 偏离(f1，f2，f3，f4) =|Qn-Q0| / Q0*100%, n Q =Qn / Q0

[0150] where n Q is the range coefficient, if 0.6≤n Q <0.8 or 1.2<n Q ≤1.4, 20%<ΔQ 偏离(f1，f2，f3，f4) ≤40%, indicating that the PRPD module and sensor are aging and have small deviations, and the component life is decreasing. Self-calibration can be performed to adjust to the normal range;

[0151] If Q<0.6 or n Q >1.4, ΔQ 偏离(f1，f2，f3，f4) >40%, indicating that the PRPD module is abnormal, the sensor is aging, and the module needs to be repaired or replaced.

[0152] Data processing of frequency N in PRPD atlas dataset:

[0153] ΔN 偏离(f1，f2，f3，f4) =|Nn-N0| / N0*100%, n N =Nn / N0

[0154] where n N is the range coefficient, if 0.6≤n N <0.85 or 1.15 <n N ≤1.4, 15%<ΔN 偏离(f1，f2，f3，f4) ≤40%, indicating that the PRPD module is aging and has a small deviation. The component life is decreasing. It can continue to operate and needs periodic inspection and focus.

[0155] If N <0.6 or n N >1.4, ΔN 偏离(f1，f2，f3，f4) >40%, indicating that the PRPD module is abnormal and needs to be repaired or replaced.

[0156] Data processing of duration T in the PRPD atlas dataset:

[0157] ΔT 偏离(f1，f2，f3，f4) =|T n -T0| / T0*100%,n T =Tn / T0,

[0158] If 0.85≤n T <1,ΔT 偏离(f1，f2，f3，f4) <15%, indicating that the PRPD module is aging and has a small deviation. The component life is decreasing. It can continue to operate and needs periodic inspection and focus.

[0159] If T <0.85, ΔT 偏离(f1，f2，f3，f4) >15%, it means the PRPD module is abnormal and needs to be repaired or replaced.

[0160] Data processing of the maximum amplitude V of the waveform in the time domain waveform data set:

[0161] ΔV 偏离(f1，f2，f3，f4) =|Vn-V0| / V0*100%, n v =Vn / V0

[0162] where n v is the range coefficient, if 0.6≤n v<0.85 or 1.15 <n v ≤1.4,15%<ΔV 偏离(f1，f2，f3，f4) ≤40%, indicating that the waveform acquisition and processing module and the filter module are aging and wearing out, with small deviations, and the component life is decreasing. Self-calibration can be performed to adjust them to the normal range;

[0163] If Q <0.6 or n Q >1.4,ΔV 偏离(f1，f2，f3，f4) >40%, it means that the waveform acquisition and processing module and the filtering module are abnormal and need to be repaired or replaced.

[0164] Data processing of waveform rising edge time t in the time domain waveform data set:

[0165] n t =t n / t0, if t n ≤20ns, n t ≤1, indicating that the waveform acquisition and processing module and the filtering module are in good condition; if t n >20ns, n t >1 means that the waveform acquisition and processing module and the filter module are abnormal and need to be repaired or replaced.

[0166] Data processing of waveform pulse width τ in time domain waveform data set:

[0167] Δτ 偏离(f1，f2，f3，f4) =|τ n -τ0| / τ0*100%, n τ =τ n / τ0

[0168] where n τ is the range coefficient, if 0.6≤n τ <0.85 or 1.15 <n τ ≤1.4, 15%<Δτ 偏离(f1，f2，f3，f4) ≤40%, indicating that the waveform acquisition and processing module and the filter module are aging and losing, with small deviations, and the component life is decreasing. The module can continue to operate and needs periodic inspection and focus.

[0169] If τ <0.6 or n τ >1.4, Δτ 偏离(f1，f2，f3，f4) >40%, it means that the waveform acquisition and processing module and the filtering module are abnormal and need to be repaired or replaced.

[0170] Frequency domain dataset

[0171] Data processing of frequency domain data set F: Since the amplitude response of the same detection signal in different frequency bands is different, according to the initial state setting of the online monitoring device: there are 4 fixed frequency bands for data acquisition and preprocessing, namely 1MHz, 5MHz, 10MHz, and 20MHz. What needs to be collected and recorded are: the amplitude average of the detection signal in each frequency band, calculate the detection amplitude difference between every two adjacent frequency bands, and then divide it by the frequency difference to get the steepness value of the frequency band.

[0172] ΔF 偏离 =|F n -F0| / F0*100%

[0173] If 15%<ΔF 偏离 <30%, indicating that the filter module is aging and has a small deviation. The component life is decreasing. It can continue to operate and needs periodic inspection and attention.

[0174] If ΔF 偏离 >30%, it means the filter module is abnormal and needs to be repaired or replaced.

[0175] Continuous operation coefficient β data processing: the ratio of continuous operation time to device life, assuming that the general life of the device is 8 years; within 8 years after commissioning, the coefficient β = 1; if it is commissioned for more than 8 years, the continuous operation coefficient β = 1 + exceeded years / 8. For example: the system has been in operation for 9 years, the continuous operation coefficient β = 1 + 1 / 8 = 1.125. The larger the continuous operation coefficient, the longer the continuous operation time of the device, the greater the aging loss of the PRPD module, sensor, and filter module, the greater the possibility of defects, and the need to repair or replace the module.

[0177] Data processing of the ambient temperature Et of the device: Calculate the average value of the ambient temperature over a period of time and standard deviation Δ Et Under normal circumstances, the temperature of the device operating environment changes relatively slowly.

[0178] Suddenly it becomes very large. If the temperature Et at a certain moment n Beyond ±kΔ Et range (k is a coefficient), which exceeds the temperature range required for the normal operation of the device, may have an adverse effect on the device, affecting the phase acquisition and processing module, as well as the waveform acquisition and processing module, causing their functions to malfunction.

[0179] Data processing of the ambient humidity Eh of the device: The high-precision electronic components of the online monitoring device require that the humidity should not be too high or too low. Obtain the change trend of the ambient humidity data and compare the humidity data with the humidity range required for the normal operation of the device. If the humidity Eh n If the humidity is lower than 30% or higher than 80%, it may have adverse effects on the device, such as static electricity accumulation (when the humidity is low) or moisture (when the humidity is high), affecting the performance and life of the device.

[0181] Data processing of the vibration degree Ev of the device operation: Calculate the average value of the vibration degree over a period of time ,If the monitored vibration degree exceeds the design standard of the device, it may have an adverse effect on the device, affecting the phase ,acquisition and processing module, as well as the waveform acquisition and ,processing module, causing abnormal functions.

[0182] Finally, technicians can develop an on-site repair or replacement plan based on the evaluation results.

[0183] This embodiment uses three practical application scenarios for verification of the above method:

[0184] Scenario 1: Figure 4 As shown, the evaluation method of the embodiment of the present invention is applied by manually injecting signals on site to determine whether the online monitoring device has defects and what kind of defects exist.

[0185] Scenario 2: If Figure 5 As shown, for the online monitoring system that has been installed and is running on site, the self-test equipment is installed next to the local signal processing device at each joint of the cable line, connected with the HFCT sensor to form a signal injection loop, and the evaluation method of this embodiment is set in the background MS computer. By regularly injecting verification signals, the online monitoring device is evaluated for detection effectiveness and abnormal prediction, and it is determined whether the online monitoring device has defects and what kind of defects exist.

[0186] Scenario 3: If Figure 6 As shown, for the newly installed online monitoring system, the self-test device can be integrated and installed in the local signal processing device, and the self-test device is connected to the HFCT sensor to form a signal injection loop. The evaluation method of this embodiment is installed in the background MS computer. The online monitoring device is evaluated for detection effectiveness and abnormal prediction by regularly injecting verification signals to determine whether the online monitoring device has defects and what kind of defects exist.

[0187] In summary, the above method mentioned in the embodiment of the present invention mainly solves the problems of data analysis, comparison, and evaluation of defects and abnormalities after the on-site online monitoring device is verified. The specific method is to compare the characteristic values ​​of the initial state data set and the verification data set. By comparing multiple verification data, it can be evaluated whether the online monitoring device is operating normally. If there is an abnormality, the system determines what kind of abnormal defect it is, helping the operation and maintenance personnel to prepare replacement parts in advance, quickly restore the normal operation of the device, and ensure the effectiveness and efficiency of monitoring.

[0188] Example 2

[0189] A monitoring performance evaluation system for an online monitoring device of an electric power equipment, comprising:

[0190] Initial state acquisition module: used to obtain the initial state operation set when the online monitoring device of the power equipment is operated for the first time;

[0191] Verification time judgment module: used to judge whether the operation time of the online monitoring device of the electric power equipment reaches the preset verification time, if yes, execute the next step, if no, do nothing;

[0192] Verification data acquisition module: used to obtain the state operation set of the online monitoring device of the power equipment at the verification time and multiple times thereafter as a verification data set;

[0193] Evaluation module: used to obtain the monitoring performance evaluation result of the online monitoring device of the power equipment based on the initial state operation set and the verification data set by using the performance evaluation model for processing.

[0194] The rest is the same as in Example 1.

[0195] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0196] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A monitoring performance evaluation method for an online monitoring device of an electric power equipment, characterized in that: The following steps are involved: Obtaining an initial state operation set when the online monitoring device of the electric power equipment is operated for the first time; Determine whether the operation time of the online monitoring device of the electric power equipment reaches the preset verification time, if yes, execute the next step, if no, do nothing; Obtaining a state operation set of the online monitoring device of the electric power equipment at the verification time and multiple times thereafter as a verification data set; Based on the initial state operation set and the verification data set, a performance evaluation model is used for processing to calculate the degree of deviation of the online monitoring device of the electric equipment from the initial state, and obtain a quantitative monitoring performance evaluation result of the online monitoring device of the electric equipment.

2. A monitoring performance evaluation method for an online monitoring device of electric power equipment according to claim 1, characterized in that: The initial state operation set includes: 1) PRPD atlas dataset: Wherein, subscript 0 indicates the initial state, f indicates the passband monitoring frequency band, f1~f4 represent four different passband monitoring frequency bands, φ indicates the synchronization phase, Q indicates the signal charge, N indicates the signal frequency, T indicates the signal duration, and D is the PRPD spectrum; 2) Time domain waveform data set: B0=[V0t0τ0] In the formula, V represents the maximum amplitude of the waveform, t represents the rising edge time of the waveform, and τ represents the slope of the waveform; 3) Frequency domain dataset: Where F0 is the frequency domain data set at the initial state; 4) Continuous operation coefficient β: β=1+exceeded years / 8; 5) Ambient temperature Et0 and humidity Eh0 during operation; 6) Vibration degree Ev0 during operation.

3. The monitoring performance evaluation method of an online monitoring device for electric power equipment according to claim 1 is characterized in that: The execution steps of the performance evaluation model include: The initial state operation set and the verification data set are combined to construct a performance evaluation set, wherein the performance evaluation set includes a PRPD spectrum evaluation data set, a time domain waveform evaluation data set, a frequency domain evaluation data set, a continuous operation coefficient evaluation data set, an ambient temperature and humidity evaluation data set, and a vibration evaluation data set, and the expression of the performance evaluation set is: Where Z is the performance evaluation set, subscript 0 represents the initial state, subscript 1-n represents the state at the verification time and multiple times thereafter, f represents the passband monitoring frequency band, f1-f4 represent four different passband monitoring frequency bands, D is the PRPD spectrum data set, B is the time domain waveform data set, F is the frequency domain data set, β is the continuous operation coefficient, Et and Eh are the ambient temperature and humidity during operation, respectively, and Ev is the vibration degree during operation; Calculate the maximum deviation value Δ of the performance evaluation set max ; Based on the maximum deviation value Δ max The deviation index of all feature parameters in the performance evaluation set is calculated, wherein the calculation expression of the deviation index is: Where Ad is the deviation index, Δ max is the maximum deviation value, n is the number of verifications, Δ i is the i-th deviation value; Determine whether the deviation index is greater than a preset threshold value. If so, it indicates that the online monitoring device for electric power equipment operates abnormally, and the next step is executed. If not, it indicates that the online monitoring device for electric power equipment operates normally, and no operation is performed. Each evaluation data set in the performance evaluation set is processed separately to obtain the monitoring performance evaluation result of the online monitoring device of the power equipment.

4. The monitoring performance evaluation method of an online monitoring device for electric power equipment according to claim 3 is characterized in that: The process of processing the PRPD atlas evaluation data set to obtain the monitoring performance evaluation result includes: 1) Processing of synchronization phase φ in PRPD spectrum evaluation dataset: Calculate the deviation value of the synchronization phase φ and the range coefficient n φ , where the calculation expression is: Df 偏离(f1，f2，f3，f4) =|φ n -φ0|,n φ =φ n / 180° Where, subscript 0 indicates the initial state, subscript n indicates the operating state at the nth acquisition after the calibration time, f indicates the passband monitoring frequency band, f1~f4 represent 4 different passband monitoring frequency bands, Δφ 偏离(f1，f2，f3，f4) is the deviation value of the synchronization phase; If 1.05<n φ ≤1.15, 10°<Δφ 偏离(f1，f2，f3，f4) ≤30° or 0.85≤n φ <0.95, it indicates that the phase acquisition and processing module in the online monitoring device of the power equipment has aged; If φ <0.85 or n φ >1.15, Δφ 偏离(f1，f2，f3，f4) >30°, it means that the phase acquisition and processing module is damaged; 2) Processing of signal charge quantity Q in PRPD spectrum evaluation data set: Calculate the deviation value of the signal charge Q and the range coefficient n Q , where the calculation expression is: ΔQ 偏离(f1，f2，f3，f4) =|Q n -Q0| / Q0*100%,n Q =Q n / Q0 Where ΔQ 偏离(f1，f2，f3，f4) is the deviation value of the signal charge; If 1.2<n Q ≤1.4, 20%<ΔQ 偏离(f1，f2，f3，f4) ≤40% or 0.6≤n Q <0.8, it means that the PRPD module and sensor in the online monitoring device of the power equipment are aged; If Q >1.4, ΔQ 偏离(f1，f2，f3，f4) >40% or n Q <0.6, it means that the PRPD module is operating abnormally and the sensor is aging; 3) Processing of signal frequency N in PRPD spectrum evaluation data set: Calculate the deviation value and range coefficient n of the signal frequency N N , where the calculation expression is: ΔN 偏离(f1，f2，f3，f4) =|N n -N0| / N0*100%,n N =N n / N0 Where, ΔN 偏离(f1，f2，f3，f4) is the deviation value of the signal frequency; If 1.15<n N ≤1.4, 15%<ΔN 偏离(f1，f2，f3，f4) ≤40% or 0.6≤n N <0.85, it indicates that the PRPD module in the online monitoring device for the power equipment has aged; If N >1.4, ΔN 偏离(f1，f2，f3，f4) >40% or n N <0.6, it means that the PRPD module is operating abnormally; 3) Processing of signal duration T in PRPD spectrum evaluation dataset: Calculate the deviation value of the signal duration T and the range coefficient n T , where the calculation expression is: ΔT 偏离(f1，f2，f3，f4) =|T n -T0| / T0*100%,n T =Tn / T0 In the formula, ΔT 偏离(f1，f2，f3，f4) is the deviation value of the signal duration; If 0.85≤n T <1,ΔT 偏离(f1，f2，f3，f4) <15%, it indicates that the PRPD module in the online monitoring device for the power equipment has aged; If T <0.85, ΔT 偏离(f1，f2，f3，f4) >15%, it indicates that the PRPD module operates abnormally.

5. The monitoring performance evaluation method of an online monitoring device for electric power equipment according to claim 3 is characterized in that: The process of processing the time domain waveform evaluation data set to obtain the monitoring performance evaluation result includes: 1) Calculate the deviation value of the maximum amplitude V of the waveform in the time domain waveform evaluation data set and the range coefficient n v , where the calculation expression is: ΔV 偏离(f1，f2，f3，f4) =|V n -V0| / V0*100%,n v =V n / V0 Where, subscript 0 indicates the initial state, subscript n indicates the operating state at the nth acquisition after the calibration time, f indicates the passband monitoring frequency band, f1~f4 represent 4 different passband monitoring frequency bands, ΔV 偏离(f1，f2，f3，f4) is the deviation value of the maximum amplitude of the waveform; If 1.15<n v ≤1.4, 15%<ΔV 偏离(f1，f2，f3，f4) ≤40% or 0.6≤n v <0.85, it means that the waveform acquisition and processing module and the filter module in the online monitoring device of the power equipment are aged and damaged; If Q >1.4,ΔV 偏离(f1，f2，f3，f4) >40% or n Q <0.6, it means that the waveform acquisition and processing module and the filtering module are operating abnormally; 2) Calculate the range coefficient of the waveform rising edge time t in the time domain waveform evaluation data set, where the calculation expression is: n t =t n / t0 If n ≤20ns, n t ≤1, it means that the waveform acquisition and processing module and the filtering module in the online monitoring device for electric power equipment operate well; If n >20ns, n t >1, it means that the waveform acquisition and processing module and the filtering module are operating abnormally; 3) Calculate the deviation value and range coefficient n of the waveform slope τ in the time domain waveform evaluation data set τ , where the calculation expression is: Dt 偏离(f1，f2，f3，f4) =|τ n -τ0| / τ0*100%,n τ =t n / τ0 In the formula, Δτ 偏离(f1，f2，f3，f4) is the deviation value of the waveform slope; If 1.15<n τ ≤1.4, 15%<Δτ 偏离(f1，f2，f3，f4) ≤40% or 0.6≤n τ <0.85, it means that the waveform acquisition and processing module and the filter module in the online monitoring device of the power equipment are aged and damaged; If τ >1.4, Δτ 偏离(f1，f2，f3，f4) >40% or n τ <0.6, it means that the waveform acquisition and processing module and the filtering module are operating abnormally.

6. The monitoring performance evaluation method of an online monitoring device for electric power equipment according to claim 3 is characterized in that: The process of processing the frequency domain evaluation data set to obtain the monitoring performance evaluation result includes: Calculate the deviation value in the frequency domain, where the calculation expression is: ΔF 偏离 =|F n -F0| / F0*100% In the formula, subscript 0 represents the initial state, subscript n represents the operating state at the nth acquisition after the calibration time, F is the frequency domain, ΔF 偏离 is the deviation value in the frequency domain; If 15%<ΔF 偏离 <30%, it indicates that the filter module in the online monitoring device for the power equipment has aged; If ΔF 偏离 >30%, it indicates that the filtering module operates abnormally.

7. The monitoring performance evaluation method of an online monitoring device for electric power equipment according to claim 3 is characterized in that: The process of processing the continuous operation coefficient evaluation data set to obtain the monitoring performance evaluation result includes: The maximum continuous operation coefficient is extracted. The larger the maximum continuous operation coefficient is, the longer the continuous operation time of the online monitoring device of the electric power equipment is, and the greater the aging loss of the PRPD module, sensor, and filter module is.

8. The monitoring performance evaluation method of an online monitoring device for electric power equipment according to claim 3 is characterized in that: The process of processing the environmental temperature and humidity evaluation data set to obtain the monitoring performance evaluation result includes: 1) Calculate the average value of the ambient temperature Et over a period of time based on the ambient temperature and humidity evaluation data set and standard deviation Δ Et ; Determine whether the ambient temperature of the online monitoring device of the power equipment exceeds ±kΔ Et If yes, it means that the phase acquisition and processing module and the waveform acquisition and processing module in the online monitoring device for electric power equipment will be affected, resulting in abnormal operation; if no, it means that the ambient temperature of the online monitoring device for electric power equipment is normal; 2) Determine whether the humidity Eh of the online monitoring device for electric power equipment is within a preset humidity range. If so, it indicates that static electricity accumulation or moisture is present in the online monitoring device for electric power equipment.

9. The monitoring performance evaluation method of an online monitoring device for electric power equipment according to claim 3 is characterized in that: The process of processing the vibration evaluation data set to obtain the monitoring performance evaluation result includes: Based on the vibration evaluation data set, the average value of the vibration level over a period of time is calculated. , and based on the average Determine the vibration standard range; Determine whether the vibration degree of the online monitoring device of the electric power equipment at a certain moment exceeds the vibration standard range. If so, it means that it will affect the phase acquisition and processing module and the waveform acquisition and processing module in the online monitoring device of the electric power equipment, resulting in abnormal operation. If not, it indicates that the vibration degree of the online monitoring device of the electric power equipment is normal.

10. A monitoring performance evaluation system for an online monitoring device of electric power equipment, characterized in that: include: Initial state acquisition module: used to obtain the initial state operation set when the online monitoring device of the power equipment is operated for the first time; Verification time judgment module: used to judge whether the operation time of the online monitoring device of the electric power equipment reaches the preset verification time, if yes, execute the next step, if no, do nothing; Verification data acquisition module: used to obtain the state operation set of the online monitoring device of the power equipment at the verification time and multiple times thereafter as a verification data set; Evaluation module: used to process based on the initial state operation set and the verification data set using a performance evaluation model to calculate the degree of deviation of the online monitoring device of the power equipment from the initial state, and obtain a quantitative monitoring performance evaluation result of the online monitoring device of the power equipment.

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