New energy large base alternating current collection line multi-interval fault detection method

Abstract

The invention discloses a multi-interval fault detection method for an alternating current collection line of a large new energy base, and belongs to the technical field of line fault detection. A new energy large base AC collection line multi-interval fault detection method comprises the steps of analyzing abrupt change current characteristics of power electronic sources in a new energy large base after a fault, and determining abrupt change current characteristic differences of different types of power electronic sources; by means of the difference of the abrupt change current characteristics, the characteristic value decomposition algorithm is adopted to extract the abrupt change current characteristics; and measuring errors and abrupt change current characteristic differences of a fault collection line, a non-fault collection line and a main transformer low-voltage side line are analyzed, a fault detection fixed value setting calculation mode is established, and accurate identification of internal and external faults of the area is realized. By adopting the multi-interval fault detection method for the alternating current collection line of the new energy large base, the problems that an existing detection method is not suitable for detection of a 35kV alternating current collection line of the new energy large base, the fault detection efficiency is low, and the fault detection precision is low are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of line fault detection, and in particular to a multi-interval quantity fault detection method for an AC collection line of a large new energy base. BACKGROUND

[0002] The construction of large new energy bases such as Shaguo Desert has become a new form and important measure for the construction of new power systems. Flexible direct current transmission is an important way to transmit power from large new energy bases. When a 35kV AC collection line of a large new energy base fails, the short-circuit current is provided by power electronic conversion equipment of different types, which presents the characteristics of limited amplitude and waveform distortion, making it difficult for overcurrent fault detection to act correctly.

[0003] To solve the problem that traditional overcurrent fault detection cannot correctly act due to the limited amplitude and waveform distortion of the fault characteristics of multiple types of power electronic sources, existing solutions can be divided into two categories: time-domain quantity fault detection and frequency-domain quantity fault detection. In the frequency-domain quantity-based fault detection, the non-power frequency quantity of the fault characteristics of new energy sources is considered, and a fault detection method based on the frequency difference of multi-interval current is proposed. However, in a system without synchronous power sources, the fault detection performance decreases because different power electronic converters have non-power frequency quantities. A new fault detection principle based on high-frequency impedance can also be used, but it requires a high sampling rate and is not resistant to noise. Considering that the sampling frequency of the fault detection device of the AC system is usually within 2.4kHz, the fault detection based on fault transient time-domain quantities is more suitable for engineering applications. By analyzing the current fault component, a new fault detection principle is proposed based on the characteristics of the current fault component. However, this method is not resistant to noise and abnormal values. In existing methods, the fault detection setting value is corrected by a step setting value or the real-time calculation of the system impedance. However, this method is only suitable for scenarios where new energy is connected to a large system and is not suitable for 35kV AC collection lines in large new energy bases without synchronous power sources. Artificial intelligence algorithms can also be used to identify fault lines, but they have long computation times and their fault detection performance is highly dependent on data quality, making them difficult to apply in practical engineering. SUMMARY

[0004] The purpose of the present application is to provide a multi-interval quantity fault detection method for an AC collection line of a large new energy base, which solves the problem of low fault detection efficiency and low precision of existing detection methods for 35kV AC collection lines in large new energy bases.

[0005] To achieve the above purpose, the present application provides a multi-interval quantity fault detection method for an AC collection line of a large new energy base, comprising the following steps: S1, analyze the sudden current characteristics of power electronic sources in a large new energy base after a fault, and determine the differences in sudden current characteristics of different types of power electronic sources; S2, using the difference of the sudden change current characteristics, using the algorithm based on eigenvalue decomposition to extract the sudden change current characteristics on the multi-interval line; S3, analyzing the measurement error and the fault gathering line, the non-fault gathering line, and the sudden change current characteristic difference of the low voltage side line of the main transformer, establishing the fault detection setting calculation method, and realizing the accurate identification of the intra-area and extra-area faults.

[0006] Preferably, in S1, the power electronic source includes wind and light units and flexible direct current converter stations, and the wind and light units adopt current inner loop control after the fault occurs, showing a controlled current source characteristic, and the short-circuit current sudden change characteristic is represented as: ; In the formula, the subscript FPRES represents the full power type power source, is the short-circuit current of the full power type power source, and respectively, the full power type power source fault d axis, q axis current command value, is the actual value of the full power type power source fault d axis current, is the three-phase phase sequence, is the damping ratio of the second-order system, ω d is the damping oscillation frequency, β is the damping angle, is the initial phase angle after the fault, ω is the power frequency angle frequency, t is the time, A is the calculation coefficient related to the damping oscillation frequency, is the calculation coefficient related to the damping ratio of the second-order system; The flexible direct current converter station adopts constant voltage-frequency control, and the sudden change current is represented as: ; In the formula, ω + represents the positive sequence angle frequency, ω - represents the negative sequence angle frequency, Z eq is the equivalent impedance related to the short-circuit type, is the proportional coefficient related to the short-circuit type, is the current of the flexible direct current converter station in the φ phase, is the electromotive force of the flexible direct current converter station, is the positive sequence initial phase angle, is the negative sequence initial phase angle, is the short-circuit current provided by the wind and light unit, represents the wind and light unit.

[0007] Preferably, the maximum current-carrying capacity of the wind-solar unit is 1.5 times its rated current.

[0008] Preferably, the HVDC converter station provides a maximum short-circuit current of 1.5 times its rated current to prevent overcurrent lockout.

[0009] Preferably, in S2, the algorithm based on eigenvalue decomposition is used to extract the sudden current characteristics on the multi-interval line, specifically including the following steps: S21, constructing a current sampling array within a time window Constructing into an inverse Toeplitz matrix form: ; In the formula, N is the number of current sample of the current sampling array I , is the current sample value of the N th current sample; S22, Huber weighting processing is performed on each sample value in the matrix to reduce the weight of abnormal values and reduce the influence of abnormal values; S23, standardizing the matrix after weighting processing to obtain a standardized matrix; the maximum eigenvalue is extracted by eigenvalue decomposition to represent the short-circuit current sudden change characteristics.

[0010] Preferably, in S22, the Huber weighting factor is: ; In the formula, is the Huber weighting factor of the matrix , is the current sample value of the matrix , is the Huber weighting factor of the matrix , Me is the median.

[0011] Preferably, in S23, the standardized matrix is: ; Eigenvalue decomposition is performed on the standardized matrix: ; In the formula, B is the eigenvalue matrix after eigenvalue decomposition, and the superscript T represents the transpose of the matrix, is the eigenvalue of the matrix B , λ 1> λ 2>… λ N .

[0012] Preferably, in the S3, the fixed value setting calculation method is based on the size relationship of the short-circuit current mutation characteristics of the fault collection line, the non-fault collection line and the low-voltage side line of the main transformer after the fault occurs, combined with the influence of the new energy zero output scene, to form a fault detection criterion, and the fault detection criterion is: ; In the formula, λ 1( I L ) is the maximum eigenvalue of the current sampling value on the collection line obtained by eigenvalue decomposition, is the fault detection setting value of the first fault detection criterion, is the fault detection setting value of the second fault detection criterion, I MTL is the current sampling value on the low-voltage side line of the main transformer, is the maximum eigenvalue obtained by eigenvalue decomposition after setting processing of the current sampling value on the low-voltage side line of the main transformer; When the fault detection criterion is met, it is determined as an intra-zone fault; otherwise, it is an extra-zone fault.

[0013] Preferably, the S3 is λ set is 10, is 0.7.

[0014] The advantages and positive effects of the new energy large base alternating current collection line multi-interval fault detection method are: 1. The application utilizes the difference in the mutation current characteristics of multiple types of power electronic sources, extracts fault characteristics by combining the eigenvalue decomposition algorithm, and can accurately distinguish intra-zone faults and extra-zone faults through the detection criterion, avoiding misoperation and refusal to operate, and improving the detection accuracy.

[0015] 2. The application amplifies the mutation characteristics by constructing an inverse Toeplitz matrix, reduces the influence of abnormal values by combining Huber weighting, reduces the misoperation caused by noise and abnormal values, and further filters out interference components by standardizing processing and eigenvalue decomposition, thereby improving the anti-interference ability.

[0016] 3. The application is aimed at the new energy zero output scene, and the setting value λ set ensures that the extra-zone fault detection does not misoperate; considering the measurement error and phase angle difference, the setting value k set ensures that the intra-zone fault detection does not refuse to operate, adapts to the complex environment of no synchronous power source and multiple power electronic sources, and meets the detection needs of the new energy large base 35kV alternating current collection line.

[0017] 4. The present invention realizes rapid fault identification in the transient stage through current sampling with a 10ms window length and rapid calculation of matrix eigenvalues, shortens fault response time, and improves fault detection efficiency.

[0018] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 Flowchart of the present invention; Figure 2 This is the topology diagram of the new energy flexible direct current grid connection used in the simulation experiment of the present invention; Figure 3 Calculated value for fault detection in the simulation experiment area; Figure 4 It is the calculated value of fault detection outside the simulation experiment area. DETAILED DESCRIPTION

[0020] In this application, unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. In the event of any inconsistency, the meaning described in this specification or the meaning derived from the contents recorded in this specification shall prevail. In addition, the terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0021] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0022] like Figure 1 A method for detecting multi-bay faults in AC collection lines of a large-scale new energy base includes the following steps: S1. Analyze the sudden current characteristics of power electronic sources in large new energy bases after faults, and determine the differences in sudden current characteristics of different types of power electronic sources.

[0023] The power electronic source includes a wind-solar generator set and a flexible DC converter station. After a fault occurs, the wind-solar generator set adopts current inner loop control and exhibits controlled current source characteristics. The short-circuit current mutation characteristic is expressed as: ; Where, the subscript FPRES represents the full power supply, is the short-circuit current of the full-power power supply, and After full power failure d axis, q Axis current command value, Before full power failure d Actual value of shaft current, For the three-phase sequence, is the damping ratio of the second-order system, ω d is the damped oscillation frequency, β is the damping angle, is the initial phase angle after the fault, ω is the power frequency angle frequency, t is the time, A is the calculation coefficient related to the damped oscillation frequency, is the calculation coefficient related to the damping ratio of the second-order system; The short-circuit current jump characteristic provided by the wind-solar generator is affected by the low-pass control strategy. The jump current amplitude is related to the maximum current-carrying capacity of the wind-solar generator. Considering the characteristics of the power electronic elements of the wind-solar generator, the maximum current-carrying capacity is 1.5 times the rated current.

[0024] The HVDC converter station adopts constant voltage-frequency control, and needs to prevent excessive short-circuit current by reducing the voltage command value. The jump current is represented as: ; In the formula, ω + represents the positive sequence angle frequency, ω - represents the negative sequence angle frequency, Z eq is the equivalent impedance related to the type of short circuit, is the proportional coefficient related to the type of short circuit, is the current of the HVDC converter station in the φ phase, is the electromotive force of the HVDC converter station, is the positive sequence initial phase angle, is the negative sequence initial phase angle, is the short-circuit current provided by the wind-solar generator, represents the wind-solar generator.

[0025] The jump current amplitude provided by the HVDC converter station is affected by the maximum current of the HVDC converter station after the fault. In order to prevent overcurrent from causing the HVDC converter station to be locked out, the maximum short-circuit current provided by the HVDC converter station is 1.5 times the rated current of the HVDC converter station, and compared with the new energy jump current, due to the differences in control characteristics and maximum current-carrying capacity characteristics, it presents different jump current characteristics.

[0026] S2, using the difference in jump current characteristics, an algorithm based on eigenvalue decomposition is used to extract the jump current characteristics on the multi-interval line.

[0027] The algorithm based on eigenvalue decomposition is used to extract the jump current characteristics on the multi-interval line, which specifically includes the following steps: S21, in order to quickly identify the mutation characteristics of short-circuit current in the transient phase, the current sampling array in a time window is constructed as a two-dimensional array, in order to amplify the mutation feature difference, the current sampling array is multiplied by a Toeplitz matrix, and the Toeplitz matrix is constructed as follows: In the formula, N is the number of current sample of the current sampling array I, is the current sampling value of the first N

[0028] After the fault occurs, with the update of the sampling points in the time window, the fault sampling points are updated along the left diagonal sampling points of the constructed inverse Toeplitz matrix, and the change speed of the fault sampling points of the matrix is higher than that of the one-dimensional array.

[0029] S22, Huber weighting is performed on each sampling value in the matrix to reduce the weight of abnormal value and reduce the influence of abnormal value.

[0030] By Huber weighting of each sampling value, the weight of abnormal value is reduced to reduce the influence of abnormal value, and the Huber weighting factor is: In the formula, is the Huber weighting factor of the matrix row column, is the current sampling value of the matrix row column, Me is the median.

[0031] S23, the weighted matrix is standardized to obtain a standardized matrix, and the maximum eigenvalue is extracted by eigenvalue decomposition to represent the short-circuit current mutation feature.

[0032] The inverse Toeplitz matrix is standardized, and the mean operator and Huber weighting factor are introduced to obtain an improved standardized matrix: By eigenvalue decomposition of the matrix, the eigenvalue reflects the mutation characteristics of the current signal under different frequency modes. The eigenvalue decomposition of the standardized matrix is as follows: In the formula, B is the eigenvalue matrix after eigenvalue decomposition, the superscript T represents the transpose of the matrix, is the eigenvalue of the matrix B , and λ 1>​​​​​​λ 2>…> λ N .

[0033] The larger eigenvalues ​​typically correspond to the main mutation characteristics of the current signal, while the smaller eigenvalues ​​may be related to noise components. Therefore, the largest eigenvalue can be used to characterize the short-circuit current mutation characteristics of the corresponding line. The eigenvalues ​​calculated by performing eigenvalue decomposition on this improved normalized matrix can effectively reduce the impact of outliers and avoid fault detection malfunctions caused by outliers.

[0034] S3. Analyze the measurement errors and the differences in the sudden current characteristics of the fault collection line, non-fault collection line, and main transformer low-voltage side line, establish a fault detection constant setting calculation method, and realize accurate identification of faults inside and outside the area.

[0035] According to the magnitude relationship of the short-circuit current mutation characteristics of the three lines after the fault occurs, and considering the impact of scenarios such as zero output of renewable energy on the proposed fault detection, a multi-interval information fault detection criterion based on eigenvalue decomposition is constructed as follows: ; Where, λ 1( I L ) is the maximum eigenvalue obtained by eigenvalue decomposition of the current sampling value on the collection line, λ set is the fault detection setting value of the first fault detection criterion, is the fault detection setting value of the second fault detection criterion, I MTL The current sampling value on the low-voltage side of the main transformer, It is the maximum eigenvalue obtained by characteristic decomposition after the current sampling value on the low-voltage side of the main transformer is set and processed.

[0036] The calculation of the setting value of the fault detection criterion needs to ensure that the proposed fault detection can be carried out without false action under the fault outside the zone and the fault detection does not refuse to operate under the fault inside the zone. Among them, the first fault detection criterion mainly ensures that the fault detection does not falsely operate under the fault outside the zone in the scenario of zero output of new energy of non-fault line. Considering the maximum amplitude error of 10% caused by the current transformer transmission, for the setting value The selection of the short-circuit current amplitude I that the non-fault line can provide needs to be considered. F Provide 1.2 times the rated current for the new energy source on this line, and consider the influence of the current transformer amplitude error. The maximum eigenvalue calculated is: ; Where: ω represents the angular frequency, t Represents time,φ represents the initial phase angle. Therefore, a certain margin is reserved, and the setting value of the first fault detection criterion λ set is set to 10.

[0037] The setting of the setting value of the second fault detection criterion needs to consider that the fault detection is not rejected under the intra-zone fault, and considering that the short-circuit current of the wind and light generator and the flexible DC converter station reaches a maximum phase angle difference of 130°, since the current sudden change variable calculation does not change the phase angle characteristic, therefore the maximum phase angle difference of the current sudden change variable can still be calculated as 130°, and therefore the sudden change current characteristic of different lines can be obtained as: In the formula: E represents the current transient sudden change characteristic, the subscript NFL represents a non-fault line, FL represents a fault line, MTL represents a main transformer low-voltage side line, and RES represents a wind and light power source.

[0038] Therefore, when the medium-voltage collection line fails, the fault line side sudden change current amplitude is closest to 0.766 times the main transformer low-voltage side sudden change current amplitude, and therefore the setting value cannot be greater than 0.766. In order to prevent the influence of measurement errors, synchronization delays and other factors, a certain margin is reserved, and the final setting value is set to 0.7.

[0039] Therefore, the fault detection process is: after the fault detection is started, the three-phase current sampling signals of different lines in a 10ms window are extracted, and after the inverse Toeplitz matrix preprocessing, the standardization processing and the maximum eigenvalue are calculated; when the maximum eigenvalue meets the fault detection criterion, the fault detection is acted at this time, and it is determined as an intra-zone fault; when the maximum eigenvalue does not meet the fault detection criterion, the fault detection is reset at this time, and it is determined as an extra-zone fault.

[0040] The method described in the application will be described below with a specific simulation example.

[0041] Figure 2 It is a new energy flexible DC grid topology for simulation experiment of the application. As shown in Figure 2 , a new energy large base flexible DC system model is built in the RTDS, and the fault positions are selected as F1-F5 five fault points, wherein F1, F2 and F3 are at the outlet of the collection line 1, the midpoint of the collection line and the end of the collection line respectively, F4 is at the collection line 2, and F5 is at the low-voltage side line of the main transformer, wherein F4 and F5 are extra-zone faults for the collection line 1, the current signal sampling frequency is 1kHz, and the time window is selected as 20ms.

[0042] The simulation experiment results are as follows Figure 3 , Figure 4 ​As shown, when the three-phase fault occurs outside the area, the characteristic value difference is far less than 0 due to the small sudden current of the collection line, and the fault detection is reliable and does not act; when the three-phase fault occurs in the area, the characteristic value difference is far greater than 0 due to the increased sudden current of the collection line, and the proposed fault detection can correctly act.

[0043] Therefore, the new energy large base AC collection line multi-interval fault detection method provided by the application solves the problem that the existing detection method is not suitable for 35kV AC collection line detection of a new energy large base, and the fault detection efficiency and precision are low.

[0044] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application, but not to limit it, although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that: it can still modify or replace the technical solutions of the present application, and these modifications or equivalent replacements cannot make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present application.

Claims

1. A multi-interval fault detection method for AC collection lines in a large new energy base, characterized in that: The following steps are involved: S1. Analyze the sudden current characteristics of power electronic sources in large new energy bases after a fault, and determine the differences in sudden current characteristics of different types of power electronic sources; S2. Utilizing the differences in sudden current characteristics, an algorithm based on eigenvalue decomposition is used to extract sudden current characteristics on multi-interval lines; S3. Analyze the measurement errors and the differences in the sudden current characteristics of the fault collection line, non-fault collection line, and main transformer low-voltage side line, establish a fault detection constant setting calculation method, and realize accurate identification of faults inside and outside the area.

2. A multi-compartment fault detection method for AC collection lines in a large-scale new energy base according to claim 1, characterized in that: In S1, the power electronic source includes a wind-solar generator set and a flexible DC converter station. After a fault occurs, the wind-solar generator set adopts current inner loop control and exhibits controlled current source characteristics. The short-circuit current mutation characteristic is expressed as: ； Where, the subscript FPRES represents the full power supply, is the short-circuit current of the full-power power supply, and After full power failure d axis, q Axis current command value, is the actual value of the d-axis current before the full power supply failure, For the three-phase sequence, is the damping ratio of the second-order system, ω d is the damped oscillation frequency, β is the damping angle, is the initial phase angle after the fault, ω is the power frequency angular frequency, t For time, A is the calculation coefficient related to the damped oscillation frequency, is the calculation coefficient related to the damping ratio of the second-order system; The flexible DC converter station adopts constant voltage-frequency control, and the sudden change current is expressed as: ； Where, ω + represents the positive sequence angular frequency, ω - represents the negative sequence angular frequency, Z eq is the equivalent impedance related to the short-circuit type, is the proportionality coefficient related to the short circuit type, is the current in the φ phase of the flexible DC converter station, is the electromotive force of the flexible DC converter station, is the positive sequence initial phase angle, is the negative sequence initial phase angle, The short-circuit current provided to the wind and solar generator sets, Represents wind and solar power units.

3. The method for detecting multi-bay faults in AC lines of a large-scale new energy base according to claim 2 is characterized in that: The maximum current carrying capacity of the wind-solar generator set is 1.5 times its rated current.

4. The method for detecting multi-bay faults in AC lines of a large-scale new energy base according to claim 3 is characterized by: To prevent overcurrent blocking, the flexible DC converter station provides a maximum short-circuit current of 1.5 times its rated current.

5. A multi-compartment fault detection method for AC collection lines in a large-scale new energy base according to claim 4, characterized in that: In S2, the sudden change current characteristics on the multi-interval line are extracted using an algorithm based on eigenvalue decomposition, which specifically includes the following steps: S21, the current sampling array within the time window Constructed in inverse Toeplitz matrix form: ； Where N is the current sampling array I The number of current samples, For the N Current sampling value; S22, performing Huber weighting processing on each sampling value in the matrix to reduce the weight of the outlier and reduce the impact of the outlier; S23, normalizing the weighted matrix to obtain a normalized matrix; extracting the maximum eigenvalue by eigenvalue decomposition to characterize the short-circuit current mutation characteristics.

6. The method for detecting multi-bay faults in AC collection lines of a large-scale new energy base according to claim 5 is characterized in that: In the S22, The Huber weighting factor is: ； Where, is a matrix OK The Huber weighting factor of the column, is a matrix OK The current sampling value of the column, Me is the median.

7. A multi-compartment fault detection method for AC collection lines in a large-scale new energy base according to claim 6, characterized in that: In the S23, The normalized matrix is: ； Perform eigendecomposition on a normalized matrix: ； Where, B is the eigenvalue matrix after eigendecomposition, and the superscript T represents the transpose of the matrix. is a matrix B The eigenvalues ​​of .

8. The method for detecting multi-bay faults in AC collection lines of a large-scale new energy base according to claim 7 is characterized by: In S3, the fixed value setting calculation method is based on the relationship between the short-circuit current mutation characteristics of the fault collection line, the non-fault collection line, and the main transformer low-voltage side line after the fault occurs, combined with the impact of the new energy zero output scenario, to form a fault detection criterion. The fault detection criterion is: ； Where, λ 1( I L ) is the maximum eigenvalue obtained by eigenvalue decomposition of the current sampling value on the collection line, λ set is the fault detection setting value of the first fault detection criterion, is the fault detection setting value of the second fault detection criterion, I MTL The current sampling value on the low-voltage side of the main transformer, The maximum eigenvalue obtained by characteristic decomposition after the current sampling value on the low-voltage side of the main transformer is set and processed; If the fault detection criteria are met, it is determined to be an internal fault; otherwise, it is an external fault.

9. A multi-bay fault detection method for AC collection lines in a large-scale new energy base according to claim 8, characterized in that: described λ set is 10, is 0.7.

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