A power line model parameter adaptability analysis method and system

Abstract

The application relates to the technical field of power systems, and discloses a power line model parameter adaptability analysis method and system, which comprises the following steps: S100, acquiring electrical parameters reflecting actual operation characteristics of a power line as a characteristic parameter set and acquiring electrical parameters calculated by a power line model as a model parameter set; S200, performing statistical analysis on the model parameter set and the characteristic parameter set; and S300, calculating an adaptability index I of the model parameter set, I=I1*I2, wherein I1 represents expected adaptability of the model parameter set, and I2 represents interval adaptability of the model parameter set. Through the above technical scheme, adaptability analysis and defect analysis of the overall line parameters can be realized, overall review and evaluation of the effectiveness of various key parameters of the power line can be performed, common defects of the line parameters can be identified, reference bases for re-measuring the line parameters can be provided, the risk control level of a relay protection system can be improved, and the stable and economic operation of a power system can be ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system, in particular, to a power line model parameter adaptability analysis method and system. BACKGROUND

[0002] The power device parameters are the basis of power system analysis and calculation, and the accuracy of the power line positive sequence and zero sequence impedance parameters is particularly important for the effectiveness of the relay protection setting calculation result. At present, such parameters are mainly measured and recorded at the initial stage of the formation of the power line. However, due to the limitations of the test conditions and test methods, and the changes of the parameters with the increase of the operation time, there is a difference between the electrical characteristic quantity of the power grid fault state and the theoretical calculation value based on the line parameters, which cannot provide accurate reference for the analysis and calculation of the power system.

[0003] There are a large number of power lines, and the parameter measurement workload is huge, which will also affect the normal operation and power supply reliability of the power system. The existing parameter review means mainly forms the review rules by mining the parameter data of a large number of lines to identify possible abnormalities of individual parameters. However, when the overall accuracy of the existing data itself is defective, the review result obtained therefrom also does not have enough reference value. Therefore, it is urgent to provide a method for accurately analyzing the abnormality of the power line parameters. SUMMARY

[0004] The present application provides a power line model parameter adaptability analysis method and system, which solves the problem that the electrical characteristic quantity of the power grid fault state and the theoretical calculation value based on the line parameters exist difference, which cannot provide accurate reference for the analysis and calculation of the power system.

[0005] The technical scheme of the present application is as follows:

[0006] A power line model parameter adaptability analysis method, comprising the following steps,

[0007] S100: obtaining electrical parameters reflecting part of the actual operation characteristics of the power line as a characteristic parameter set, and obtaining the electrical parameters calculated by the power line model as a model parameter set, the electrical parameters including the unit length positive sequence resistance, the unit length positive sequence reactance, the unit length zero sequence resistance and the unit length zero sequence reactance of the power line;

[0008] S200: statistically analyzing the model parameter set and the characteristic parameter set;

[0009] S300: calculating the adaptability index I of the model parameter set, I = I1 x I2, wherein I1 represents the expected adaptability of the model parameter set, and I2 represents the interval adaptability of the model parameter set; the adaptability index of the model parameter set is used to represent the adaptability of the power line model;

[0010] S400: adaptability analysis of the model parameter set, I min represents the lower limit of the adaptability of the model parameter set, when I min , it indicates that the current model parameter set is not adaptive enough and needs to be updated.

[0011] S500: defect analysis of the model parameter set, I 1,min represents the lower limit of the expected adaptability of the model parameter set,

[0012] When E(G c )>E(G b ) and I1 1,min , the model parameters are generally large.

[0013] When E(G c )<E(G b ) and I1 1,min , the model parameters are generally small.

[0014] When I1>I 1,min and I min , the model parameters are not accurate enough.

[0015] Further, the step S200 comprises,

[0016] S201: calculating the expected E(G b ) and variance D(G b ) of the feature parameter set;

[0017] S202: calculating the confidence interval C(G b ) = {X | U b X < V b} of the feature parameter set,

[0018] S203: calculating the expected E(G c ) and variance D(G c ) of the model parameter set;

[0019] S204: calculating the confidence interval C(G c ) = {X | U c X < V c} of the model parameter set.

[0020] Further, the lower limit of the confidence interval U b = exp[μ(G b )-zσ(G b )], and the upper limit of the confidence interval V b = exp[μ(G b )+zσ(Gb )], wherein,

[0021]

[0022] a lower confidence limit U of the model parameter set c = exp [μ(G c ) - zσ(G c )], an upper confidence limit V of the model parameter set c = exp [μ(G c ) + zσ(G c )], wherein,

[0023]

[0024] further, an expected adaptability of the model parameter set

[0025]

[0026] an interval adaptability of the model parameter set

[0027]

[0028] Further, the electrical parameters reflecting the actual operation characteristics of the partial power line include a calculation result obtained by parameter identification according to fault recording data possessed by the partial power line.

[0029] Further, the method for obtaining the characteristic parameters includes parameter measurement or parameter derivation.

[0030] A power line model parameter adaptability analysis system, comprising a parameter acquisition module, a statistical analysis module, an adaptability index calculation module and an adaptability judgment module, the output of the parameter acquisition module is connected to the input of the statistical analysis module, the output of the statistical analysis module is connected to the input of the adaptability index calculation module, the output of the adaptability index calculation module is connected to the input of the adaptability judgment module, and the adaptability judgment module is used for outputting the analysis result of the power line model.

[0031] The working principle and beneficial effects of the present application are as follows:

[0032] 1. The present application can realize the adaptability analysis and defect analysis of the overall line model parameters by statistical analysis method under the condition of only obtaining partial power line characteristic parameters, can perform overall review and evaluation on the effectiveness of each key parameter of the power line, can be used for identifying the common defects of the line model parameters, can provide reference basis for line parameter re-measurement work, and has important role in improving the risk control level of the relay protection system, and guaranteeing the stable and economic operation of the power system.

[0033] 2、The application can realize the analysis of the overall line model parameters through statistical analysis on the premise of only obtaining the characteristic parameters of part of the power line, and overcomes the difficulty that the complete line parameters are difficult to be obtained for comprehensive parameter review by the prior art whether through fault recording parameter identification or actual measurement, and provides review basis for the basic parameters of various analysis and calculation systems in the power line. DETAILED DESCRIPTION

[0034] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments of the application, all other embodiments obtained by a person of ordinary skill in the art without creative work are within the protection scope of the application.

[0035] The application provides a power line model parameter adaptability analysis method, which predicts the deviation of the model parameters (i.e. the parameters used in the current calculation system) of the entire regional power grid from the actual values of the parameters by statistical analysis between the characteristic parameters and the model parameters, so as to provide a reference basis for the line parameter re-measurement work, improve the risk management and control level of the relay protection system, and ensure the stable and economic operation of the power system.

[0036] The characteristic parameters are obtained by means such as Parameter measurement, parameter identification or parameter derivation The model parameters are actual values recorded and applied in various calculation and analysis systems, and can be directly obtained from the power calculation system; the electrical parameters include the positive sequence resistance per unit length of the power line, the positive sequence reactance per unit length, the zero sequence resistance per unit length, and the zero sequence reactance per unit length;

[0037] (1) Obtaining of the characteristic parameters;

[0038] At present, a complete fault recording system has been built in each regional power grid, and a large amount of recording data has been accumulated in the system. The fault current of the power grid is directly related to the primary parameters of the power grid. For any line, the short-circuit current flowing through the line and the node voltages on both sides can be obtained from the recording file when the nearby equipment fails. Since the electrical quantities are less affected by the operation mode and more characteristic when the power grid fails, the line parameter identification result obtained based on the fault recording data has high accuracy.

[0039] Therefore, the feature parameter acquisition of the application is based on the power line parameter identification of the fault recording data. Since a single fault event triggers multiple recording devices near the fault point to generate recording data, the time information provided by the recording results of multiple devices may not be based on the same time, and therefore, waveform alignment needs to be performed first, and then the line positive sequence and zero sequence impedance identification is realized in combination with the equivalent model of the power line. The specific steps include:

[0040] 1) Data analysis. The recording files in COMTRADE format are grouped and arranged according to the fault recording time, that is, the recording information on both sides of the power line generated in the same fault event is associated, the CFG file and the DAT file contained in the recording file are analyzed, and the fault waveform data is extracted.

[0041] 2) Waveform alignment. The waveforms are roughly aligned according to the zero sequence current mutation points of the waveform files on both sides of the power line, and then the waveforms are accurately aligned according to the phase angle difference of the voltages at the beginning and end of the line.

[0042] 3) Frequency unification. When the sampling frequencies of the waveform files on both sides of the power line are inconsistent, interpolation or sampling is performed for pretreatment to make the frequencies of the waveforms on both sides consistent.

[0043] 4) Fourier transformation. The fundamental wave components of the voltages and currents on both sides of the power line are extracted by applying the difference Fourier algorithm, and then the effective values and phases thereof are solved.

[0044] 5) Sequence component decomposition. The positive sequence and zero sequence components of the voltages and currents on both sides of the power line are solved by applying the symmetrical component method.

[0045] 6) Parameter identification. The Π-type equivalent model is adopted to solve the positive sequence impedance Z1, the zero sequence impedance Z0, the positive sequence admittance Y1, and the zero sequence admittance Y0 of the power line according to the positive sequence and zero sequence components of the voltages and currents,

[0046]

[0047] Then, in combination with the length information of the power line, the unit length positive sequence impedance z1, the zero sequence impedance z0, the positive sequence conductance y1, and the zero sequence conductance y0 corresponding to the line type are solved.

[0048] (2) Cleaning of feature parameters and model parameters;

[0049] Ideally, the unit length impedance and admittance of power lines are constant. Affected by production process, operation condition, environment climate and other factors, the above parameters have a certain range of changes in actual application scenarios. Abnormal recording device or complex power grid fault may cause large deviation of power line parameter identification results based on fault recording data; at the same time, power grid reconstruction, new equipment operation and other projects are accompanied by maintenance or migration of device parameter records in power system analysis and calculation system, which may cause some parameter records to deviate seriously from the normal value.

[0050] In order to correctly identify the common deviation of power line parameters, the abnormal values in characteristic parameters and model parameters should be removed first. In the present application, the quartile method is used to clean the characteristic parameters and model parameters. Taking the positive sequence impedance as an example, the same method can be used for data processing and analysis of the rest of the parameters, and the specific steps include:

[0051] 1) solving the lower quartile Q1 and the upper quartile Q3 of the data set;

[0052] 2) according to formula (2), the upper limit of normal value W max and the upper limit of normal value W min of the data set are solved;

[0053] In the formula, k is usually 1.5.

[0054] 3) when the value of the element in the data set is greater than W max or less than W min , it is determined that it is an abnormal value, which is not used for the following statistical analysis.

[0055] (3) statistical analysis of characteristic parameter set and model parameter set

[0056] Under ideal conditions, the distribution of the characteristic parameter set and the model parameter set of the power line in a certain regional power grid should be approximately the same. When there is a significant difference between the distribution of the two parameter sets, it indicates that the model parameter set cannot accurately reflect the electrical quantity characteristics of the power line in the fault state.

[0057] The electrical parameters per unit length of the power line approximately obey the lognormal distribution. First, the expectation E(G b ) and variance D(G b ) of the characteristic parameter set are solved by statistical analysis; the expectation E(G c ) and variance D(G c ) of the model parameter set are solved.

[0058] The confidence interval of the characteristic parameter set is: C(G b ) = {X | U b X < V b},

[0059] wherein:

[0060] Similarly, the confidence interval of the model parameter set is: C(G c ) = {X | U c < X < V c},

[0061] wherein,

[0062] According to the different confidence levels considered to be delineated, the corresponding different confidence coefficients z are adopted, when the 95% confidence level is adopted, the confidence coefficient z is 1.96.

[0063] (4) Model parameter set adaptability index calculation

[0064] The higher the coincidence degree of the model parameter set and the expected value and confidence interval of the characteristic parameter set, the better the adaptability of the model parameter set, and the adaptability index I = I1 x I2 is defined, wherein I1 represents the expected adaptability of the model parameter set, and I2 represents the interval adaptability of the model parameter set.

[0065]

[0066]

[0067] (5) Adaptability analysis of model parameters

[0068] The closer the value of the adaptability index I to 1, the higher the coincidence degree of the model parameter set and the expected value and confidence interval of the characteristic parameter set, and the better the adaptability of the model parameter set. The threshold value I min is set as the lower limit of the adaptability index of the power line target parameter set, and when I < I min , it indicates that the current target parameter is not adaptive enough, cannot correctly reflect the performance of the power line, and is not suitable for parameter characteristic analysis, and the measured value record needs to be updated.

[0069] (6) Defect analysis of model parameters

[0070] The threshold value I 1,min represents the lower limit of the expected adaptability of the model parameter set,

[0071] When E(G c ) > E(G b ) and I1 < I 1,min , the model parameters are overall large;

[0072] When E(G c ) < E(G b ) and I1 < I 1,min , the model parameters are overall small;

[0073] When I1>I 1,min and I min , then the model parameter precision is insufficient.

[0074] The adaptability index lower limit I min in the application and the expected adaptability lower limit I 1,min can be adjusted according to actual application scenarios.

[0075] Embodiment 1

[0076] 503 pieces of power line model parameters of a certain type in a certain region's relay protection setting calculation system were adopted, and part of the data is shown in Table 1, which is referred to as the to-be-analyzed data below.

[0077] Table 1 Example of to-be-analyzed data

[0078]

[0079] The fault recording parameter identification method was adopted to obtain the power line parameter identification, and the required recording data needed to meet the following requirements:

[0080] 1) The conductor type adopted by the line where the recorder is located is consistent with the to-be-analyzed data;

[0081] 2) The fault recording files of the recorders on both sides of the line have the same time.

[0082] In the past two years, there have been 264 power failures of 110kV and above in the region, and a total of 6573 fault recording files have been generated, of which 177 groups of recording files meet the conditions. After data processing and parameter identification, 177 groups of positive sequence resistance, positive sequence reactance, zero sequence resistance, zero sequence reactance and corresponding line length data were obtained, and part of the results are shown in Table 2.

[0083] Table 2 Example of identification results

[0084]

[0085] The unit length positive sequence resistance, unit length positive sequence reactance, unit length zero sequence resistance and unit length zero sequence reactance of the to-be-analyzed data and the identification results were calculated respectively, and data cleaning was performed to form a model parameter set and a feature parameter set. The expected value, variance and confidence upper and lower limits of the model parameter set and the feature parameter set were solved, and the results are shown in Table 3.

[0086] Table 3 Data analysis results

[0087]

[0088] The adaptability index lower limit I min was set to 0.8, and the expected adaptability lower limit I 1,min was 0.9,

[0089] The model parameter adaptability analysis result is that the fitness of the unit length positive sequence resistance and the unit length zero sequence reactance is insufficient.

[0090] The model parameter defect analysis result is that the unit length positive sequence resistance parameter precision is insufficient and overall is too small, and the unit length zero sequence reactance precision is insufficient.

[0091] The application further provides a power line model parameter adaptability analysis system, which comprises a parameter acquisition module, a statistical analysis module, an adaptability index calculation module and an adaptability judgment module, the output of the parameter acquisition module is connected with the input of the statistical analysis module, the output of the statistical analysis module is connected with the input of the adaptability index calculation module, the output of the adaptability index calculation module is connected with the input of the adaptability judgment module, and the adaptability judgment module is used for outputting the analysis result of the power line model.

[0092] The above only is the preferred embodiment of the application, and does not limit the application, and any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the application should be included in the protection scope of the application.

Claims

1. A method of power line model parameter adaptability analysis, characterized by, The method comprises the following steps: S100: obtaining electrical parameters partially reflecting actual operation characteristics of the power line as a characteristic parameter set, and obtaining electrical parameters calculated by a power line model as a model parameter set, the electrical parameters comprising unit length positive sequence resistance, unit length positive sequence reactance, unit length zero sequence resistance, and unit length zero sequence reactance of the power line; S200: performing statistical analysis on the model parameter set and the characteristic parameter set; S300: calculating the fitness index of the model parameter set according to the statistical analysis result wherein, denotes the expected fitness of the model parameter set, denotes the interval fitness of the model parameter set; the fitness index of the model parameter set is used to represent the fitness of the power line model; The step S200 comprises, S201: calculate the expectation of the set of characteristic parameters and variance ; S202: calculate a confidence interval of the feature parameter set , S203: compute expectation of the model parameter set and variance ; S204: calculate a confidence interval for the set of model parameters ; the expected adaptability of the model parameter set ; interval adaptability of the model parameter set 。 2. The method of claim 1, wherein, The electrical parameters partially reflecting actual operation characteristics of the power line comprise calculation results obtained by parameter identification according to fault recording data possessed by part of the power lines.

3. The method of claim 1, wherein the power line model parameter adaptability analysis is performed by: The method for obtaining the characteristic parameters comprises parameter measurement or parameter derivation.

4. The method for analyzing adaptability of a power line model parameter according to claim 1, characterized in that, a lower confidence limit of the set of characteristic parameters , upper confidence limit of the set of characteristic parameters , wherein , ; a lower confidence limit for the model parameter set a upper confidence limit for the model parameter set wherein, ， 。 5. The method of claim 1, wherein, further comprising, S400: Perform adaptability analysis on the model parameter set, represents the lower limit of the adaptability of the model parameter set, and when it indicates that the current model parameter set is not adaptable enough and the model parameters need to be updated.​ 6. The method of claim 1, wherein, further comprising, S500: performing a defect analysis on the model parameter set, represents a lower bound of adaptability of the model parameter set, represents a lower bound of expected adaptability of the model parameter set, When > And < If so, the model parameters are overall too large. When E ( G c )< E ( G b ) and < , the model parameters are overall small; When > And < If so, the model parameters are not accurate enough.

7. A power line model parameter adaptability analysis system for implementing the power line model parameter adaptability analysis method according to any one of claims 1 to 6, characterized by The system comprises a parameter acquisition module, a statistical analysis module, an adaptability index calculation module, and an adaptability judgment module, an output of the parameter acquisition module is connected to an input of the statistical analysis module, an output of the statistical analysis module is connected to an input of the adaptability index calculation module, an output of the adaptability index calculation module is connected to an input of the adaptability judgment module, and the adaptability judgment module is used for outputting an analysis result of the power line model.

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