Flexible DC power transmission line lightning stroke interference identification method based on Pearson coefficient

Abstract

The invention discloses a flexible DC power transmission line lightning stroke interference identification method based on a Pearson coefficient, and the method specifically comprises the following steps: 1, collecting voltage and current data in a time window from 0.25 ms after a protection element is started to 1.25 ms after the protection element is started, and solving a line-mode current; step 2, calculating a Pearson correlation coefficient gamma between the line mode current obtained in the step 1 and the corresponding time quantum; step 3, if gamma gt; if gamma set is greater than gamma set, a fault is judged; if gamma lt; and judging that the lightning stroke interference occurs if gamma set exists. According to the method, faults and lightning stroke interference can be accurately distinguished by obtaining the Pearson correlation coefficient between the line mode current and the time quantity at the protection measuring point.

Description

Technical Field

[0001] The invention belongs to the technical field of power systems and relates to a method for identifying lightning interference of flexible direct current transmission lines based on a Pearson coefficient. Background Art

[0002] Compared with traditional DC transmission technology, flexible DC transmission technology has shown obvious advantages in the access of new energy. It can not only avoid commutation failure and reactive power compensation problems, but also provide active and reactive power to the AC system at the same time, thereby realizing the efficient integration and complementarity of large-scale and multi-type new energy, improving energy utilization efficiency, and enhancing the stability of power supply. Since the flexible DC transmission system adopts modular multilevel converter (MMC) technology, the system has a high degree of electronicization, so it is called a "low inertia" system. When a system line fails, overcurrent may occur within a few milliseconds. If the faulty line cannot be detected and isolated in a timely and accurate manner, the converter station may be locked due to the valve hall overcurrent protection, which puts higher requirements on the accuracy and response speed of the protection system.

[0003] In the line protection of flexible DC transmission system, accurate and rapid removal of faulty lines is the core of the protection system. However, lightning interference is one of the common external interferences in the power system. If a certain area is struck by lightning but no fault occurs, the protection device may malfunction. In order to ensure the correct operation of the protection device, it is necessary to conduct in-depth research on the transient traveling wave waveform characteristics of the flexible DC transmission system under different types of faults and lightning interference, and propose a fast and reliable lightning interference identification standard.

[0004] The existing lightning interference identification methods are mainly based on the analysis and identification of traveling wave transient quantities and their frequency domain characteristics. However, the existing identification methods still have many defects and limitations in practical applications, which affect the accuracy and reliability of lightning interference identification.

[0005] First, the traditional identification methods based on traveling waves and transient quantities are highly sensitive to high-frequency interference signals and are easily affected by lightning interference, resulting in false operations. For example, lightning interference signals usually contain a large number of high-frequency components, and existing traveling wave protection devices have difficulty distinguishing between lightning interference and actual faults, resulting in misjudgment. Secondly, although the identification method based on frequency domain features can improve the recognition accuracy to a certain extent, it still has the problems of difficult threshold setting and poor noise robustness when facing complex electromagnetic environments. These methods require debugging and optimization of a large number of parameters in practical applications, which increases the complexity and maintenance cost of the system.

[0006] In addition, existing lightning interference identification methods are also insufficient in terms of speed and real-time performance. For highly sensitive application scenarios such as flexible DC transmission systems, existing methods are difficult to quickly determine the nature of lightning interference and short-circuit faults before the main protection is activated, which affects the protection performance of the system. Under conditions of abnormal data transmission or noise interference, the identification performance of existing methods also needs to be further improved.

[0007] In summary, the existing lightning interference identification methods have obvious defects and limitations in terms of high-frequency interference sensitivity, threshold setting, identification speed and real-time performance. Summary of the invention

[0008] The purpose of the present invention is to provide a method for identifying lightning disturbance of flexible direct current transmission lines based on the Pearson coefficient. The method can accurately distinguish between faults and lightning disturbances by obtaining the Pearson correlation coefficient between the line mode current and the time quantity at the protection measuring point.

[0009] The technical solution adopted by the present invention is a method for identifying lightning interference of flexible direct current transmission lines based on the Pearson coefficient, which specifically includes the following steps:

[0010] Step 1, collecting data in the time window from 0.25ms after the protection element is started to 1.25ms after the protection element is started, and calculating the line mode current Δi1(k);

[0011] Step 2, for the line mode current obtained in step 1, calculate the Pearson correlation coefficient γ between the line mode current and the corresponding time quantity;

[0012] Step 3: Determine whether lightning interference occurs based on the Pearson correlation coefficient γ calculated in step 2.

[0013] The present invention is also characterized in that:

[0014] The specific process of step 1 is:

[0015] Step 1.1, after the protection element is activated, set the protection time window. Within this time window, record the positive current i at the installation location of the protection device. p (k) and the negative electrode current i n (k), and respectively compared with the average positive current i within 10ms before the protection is started p0 and the average negative electrode current i n0 Subtract the positive current fault component Δi p (k) and negative electrode current Δi n The fault component of (k) is calculated as shown in formula (1):

[0016]

[0017] Step 1.2, calculate the line mode current fault component Δi1(k) within the time window. The calculation process is shown in formula (2):

[0018]

[0019] In step 1.1, 0.25ms to 1.25ms after the protection element is activated is set as the protection time window.

[0020] The specific process of step 2 is:

[0021] The Pearson correlation coefficient γ between the line mode current fault component Δi1(k) and the time quantity is calculated according to the following formula (3):

[0022]

[0023] Where: k is the time sequence number, 0≤k≤N-1; N is the number of sampling points in the protection time window; is the current mean value; is the mean value of the sampling points.

[0024] The specific process of step 3 is:

[0025] If γ>γ set It is judged as a fault; if γ<γ set It is judged that lightning interference occurs, where γ set is the threshold value.

[0026] The beneficial effect of the present invention is that the present invention identifies lightning interference based on the Pearson correlation coefficient of the line mode current and the time vector measured at the single end of the line. The method can have good tolerance to transition resistance and anti-noise interference. The identification method provided by the present invention can still well identify lightning interference under the conditions of 0Ω and 500Ω transition resistance and 20dB noise interference. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a flow chart of a method for identifying lightning interference of flexible direct current transmission lines based on the Pearson coefficient of the present invention;

[0028] Figure 2 It is a simulation model diagram of a two-terminal true bipolar MMC flexible DC transmission system;

[0029] Figure 3 In the simulation verification stage, the method for identifying lightning interference of flexible direct current transmission lines based on the Pearson coefficient of the present invention is used to identify the ground fault in the area of ​​the bipolar flexible direct current system at 250km and the transition resistance is 500Ω;

[0030] Figure 4 This is a simulation model diagram of a four-terminal MMC flexible DC transmission network;

[0031] Figure 5 In the simulation verification stage, the Pearson coefficient-based flexible DC transmission line lightning interference identification method of the present invention is used to determine the lightning interference shielding failure of the four-terminal flexible DC power grid at 100km.

[0032] Figure 6 This is a simulation model diagram of a four-terminal mesh MMC flexible DC transmission network;

[0033] Figure 7 In the simulation verification stage, the Pearson coefficient-based lightning interference identification method for flexible DC transmission lines of the present invention is used to determine the lightning fault shielding failure of a four-terminal mesh type flexible DC system at 100 km. DETAILED DESCRIPTION

[0034] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0035] The present invention provides a method for identifying lightning interference of flexible direct current transmission lines based on the Pearson coefficient, which specifically comprises the following steps:

[0036] Step 1, collecting data in the time window from 0.25ms after the protection element is started to 1.25ms after the protection element is started, and calculating the line mode current Δi1(k);

[0037] The specific process of step 1 is as follows: Step 1.1, after the protection element is activated, set the protection time window from 0.25ms to 1.25ms after the protection element is activated. Within this time window, record the positive current i at the installation location of the protection device. p (k) and the negative electrode current i n (k), and respectively compared with the average positive current i within 10ms before the protection is started p0 and the average negative electrode current i n0 Subtract the positive current fault component Δi p (k) and negative electrode current Δi n The calculation process is shown in formula (1):

[0038]

[0039] Step 1.2, calculate the line mode current fault component Δi1(k) within the time window. The calculation process is shown in formula (2):

[0040]

[0041] Step 2, for the line mode current obtained in step 1, calculate the Pearson correlation coefficient γ between the line mode current and the corresponding time quantity;

[0042] The specific process of step 2 is:

[0043] The Pearson correlation coefficient between the line mode current fault component Δi1(k) and the time quantity is calculated according to the following formula (3):

[0044]

[0045] Where: k is the time sequence number, 0≤k≤N-1; N is the number of sampling points in the protection time window; is the current mean value; k is the sampling point mean value.

[0046] Step 3, if γ>γ set It is judged as a fault; if γ<γ set It is judged that lightning interference occurs;

[0047] The specific process of step 3 is: if γ>γ set It is judged as a fault; if γ<γ set It is determined that lightning interference occurs. The line mode current waveform in the time window taken by the present invention is a curve that is positively correlated with the time vector when a fault occurs; when lightning interference occurs, it is a curve that is negatively correlated or non-positively correlated with the time vector. Therefore, the present invention believes that the γ measured when a fault occurs is close to 1, and the γ measured when lightning interference occurs is close to 0 or -1. Combined with the simulation data, the threshold value γ is determined. set =0.5.

[0048] As attached Figure 2 As shown in the figure, it is a simulation model diagram of a double-terminal flexible DC transmission system. In the figure, the current limiting reactor L=0.2H, MMC1 and MMC2 are modular converters, P1 and P2 are line boundary protection measurement points, f1 is the disturbance location, the rated voltage of this system is ±500kV, the rated transmission capacity is 3000MVA, the total length of the transmission line is 500km, and the overhead line frequency variable parameter model is adopted. The line wave impedance is Z c0 =320Ω、Z c1 =260Ω. When simulating the system, the sampling frequency is 100kHz, and the 1.2 / 50μs standard lightning current model is used to simulate lightning strikes. The full text takes the line Line1, P1 measuring point as an example, and the fault distance is the distance from the fault point to the measuring point P1. Figure 1 The identification process is used to identify the ground fault.

[0049] Example 1

[0050] like Figure 3 As shown, in Figure 2The line mode current simulation waveform measured at the protection measuring point when a positive grounding fault with a transition resistance of 500Ω occurs in the double-terminal flexible DC transmission system 250km away from the protection measuring point P1 is shown. After the protection element is started, the line mode current data from the 25th sampling point to the 125th sampling point after the start is read, and the Pearson correlation coefficient γ between the line mode current and the time sampling point in the data window is calculated, γ>γ set It is judged as a fault and the protection is activated.

[0051] As attached Figure 4 As shown in the figure, it is a simulation model diagram of a four-terminal MMC flexible DC transmission network. In the figure, the current limiting reactor L = 0.2H, MMC1, MMC2, MMC3, MMC4 are modular converters, P1 and P2 are line boundary protection measurement points, f1 is the disturbance location, the rated voltage between MMC1 and MMC2 is ±500kV, the rated transmission capacity is 3000MVA, the total length of the transmission line is 207.9km, and the overhead line frequency variable parameter model is adopted. The line wave impedance is Z c0 =310Ω、Z c1 =250Ω. When simulating the system, the sampling frequency is 100kHz, and the 1.2 / 50μs standard lightning current model is used to simulate lightning strikes. Since the four-terminal MMC flexible DC network has a highly symmetrical structure except for the MMC parameters. The present invention only takes the protection measuring point P1 of the protection line Line1 as an example to analyze the lightning strike interference identification method. The fault distance is the distance from the fault to the measuring point P1. According to the attached Figure 1 The identification process is used to identify lightning interference.

[0052] Example 2

[0053] As attached Figure 5 As shown, in Figure 4 When a lightning strike interference occurs in a four-terminal MMC flexible DC power grid 100 km away from the protection installation, the line mode current waveform measured at the protection measurement point. After the protection element is started, the line mode current data from the 25th sampling point to the 125th sampling point after the start is read, and the Pearson correlation coefficient γ between the line mode current and the time sampling point in the data window is calculated, γ<γ set It is determined to be lightning interference and protection is returned.

[0054] As attached Figure 6 As shown in the figure, it is a simulation model diagram of a four-terminal mesh MMC flexible DC transmission network. In the figure, the current limiting reactor L = 0.2H, MMC1, MMC2, MMC3, MMC4 are modular converters, P1 and P2 are line boundary protection measurement points, f1 is the disturbance location, the rated voltage between MMC1 and MMC2 is ±500kV, the rated transmission capacity is 3000MVA, the total length of the transmission line is 207.9km, and the overhead line frequency variable parameter model is adopted. The line wave impedance is Zc0 =310Ω、Z c1 =250Ω. When simulating the system, the sampling frequency is 100kHz, and the 1.2 / 50μs standard lightning current model is used to simulate lightning strikes. Since the four-terminal "mesh-type" flexible DC network has a highly symmetrical structure except for the MMC parameters. This paper only takes the protection point P1 of the protection line Line1 as an example to analyze the lightning interference identification method. The fault distance is the distance from the fault point to the measurement point P1. According to the attached Figure 1 The identification process is used to identify lightning faults.

[0055] Example 3

[0056] like Figure 7 As shown, in Figure 6 When a lightning shielding failure occurs in a four-terminal "mesh-type" flexible DC power grid 100 km away from the protection installation, the line mode current waveform measured at the protection measurement point. After the protection element is started, read the line mode current data from the 25th sampling point to the 125th sampling point after the start. Calculate the Pearson correlation coefficient γ between the line mode current and the time sampling point in the data window, γ>γ set It is judged as a fault and the protection is activated.

[0057] In order to comprehensively verify the influence of fault distance, lightning type, transition resistance size on the discrimination result, lightning fault, lightning interference and grounding fault with transition resistance of 0Ω and 500Ω are set at 0km, 100km, 250km, 400km and 500km in the bipolar DC transmission system, and the influence of 20dB noise is considered. The lightning interference identification method is verified according to the simulation results.

[0058] From Table 1, we can see that the simulation data of different fault distances on the bipolar flexible DC transmission line shows that when lightning interference occurs, the Pearson correlation coefficient is less than 0.5; when lightning fault and ground fault occur, the Pearson correlation coefficient is greater than 0.5. Lightning interference can be identified at different fault distances.

[0059] Table 1 Simulation verification results of different fault distances

[0060]

[0061] It can be obtained from Table 2 that the simulation data of ground faults with different transition resistances on the bipolar flexible DC transmission line can be obtained from the data in the time window: when a ground fault occurs, the Pearson correlation coefficient is greater than 0.5, and the fault can be accurately identified under ground faults with different transition resistances.

[0062] Table 2 Simulation verification results of different transition resistances

[0063]

[0064] From Table 3, we can get the simulation data of the bipolar flexible DC transmission line under the conditions of shielding failure and counter-attack. From the data in the time window, we can get that when lightning interference occurs, the Pearson correlation coefficient is less than 0.5; whether lightning shielding failure or lightning counter-attack occurs, the lightning interference can be accurately identified.

[0065] Table 3 Simulation verification results of different lightning strike types

[0066]

[0067] From Table 4, we can get the simulation data of the bipolar flexible DC transmission line fault under 20dB noise interference. From the data in the time window, we can get: when lightning interference occurs, the Pearson correlation coefficient is less than 0.5; when lightning fault and ground fault occur, the Pearson correlation coefficient is greater than 0.5. The protection criterion can accurately identify lightning interference under 20dB noise interference.

[0068] Table 4 Simulation verification results under the influence of 20dB noise interference

[0069]

[0070]

Claims

1. A method for identifying lightning interference in flexible DC transmission lines based on the Pearson coefficient, characterized by: The specific steps include: Step 1, collecting data in the time window from 0.25ms after the protection element is started to 1.25ms after the protection element is started, and calculating the line mode current Δi1(k); Step 2, for the line mode current obtained in step 1, calculate the Pearson correlation coefficient γ between the line mode current and the corresponding time quantity; Step 3: Determine whether lightning interference occurs based on the Pearson correlation coefficient γ calculated in step 2.

2. The method for identifying lightning disturbance of flexible direct current transmission lines based on Pearson coefficient according to claim 1 is characterized in that: The specific process of step 1 is as follows: Step 1.1, after the protection element is activated, set the protection time window. Within this time window, record the positive current i at the installation location of the protection device. p (k) and the negative electrode current i n (k), and respectively compared with the average positive current i within 10ms before the protection is started p0 and the average negative electrode current i n0 Subtract the positive current fault component Δi p (k) and negative electrode current Δi n The fault component of (k) is calculated as shown in formula (1): Step 1.2, calculate the line mode current fault component Δi1(k) within the time window. The calculation process is shown in formula (2):

3. The method for identifying lightning disturbance of flexible direct current transmission lines based on Pearson coefficient according to claim 2 is characterized in that: In step 1.1, 0.25 ms to 1.25 ms after the protection element is activated is set as the protection time window.

4. The method for identifying lightning disturbance of flexible direct current transmission lines based on Pearson coefficient according to claim 3 is characterized in that: The specific process of step 2 is: The Pearson correlation coefficient γ between the line mode current fault component Δi1(k) and the time quantity is calculated according to the following formula (3): Where: k is the time sequence number, 0≤k≤N-1; N is the number of sampling points in the protection time window; is the current mean value; is the mean value of the sampling points.

5. The method for identifying lightning disturbance of flexible direct current transmission lines based on Pearson coefficient according to claim 4 is characterized in that: The specific process of step 3 is as follows: If γ>γ set It is judged as a fault; if γ<γ set It is judged that lightning interference occurs, where γ set is the threshold value.

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