Offshore wind farm grid-connected random harmonic impact analysis method

Abstract

This invention relates to a method for analyzing the random harmonic impact of offshore wind farms connected to the grid. It addresses the numerous uncertainties inherent in harmonic impact analysis of offshore wind farms, which introduces a degree of risk into the analysis results. Therefore, this invention employs a random approach to provide more reliable results. It establishes a random mathematical model of key parameters, including active components contributing harmonic currents and passive components influencing harmonic flow, external grid background harmonics, and the wind farm's electrical architecture. The analysis results reflect the actual harmonic voltage situation at the grid connection point. Furthermore, this method yields a large amount of simulated harmonic voltage data for offshore wind farms connected to the grid. This extensive data allows for statistical analysis of harmonic voltage data at each order at the grid connection point, referencing international standards, and determining representative representations. The resulting representative representations of harmonic voltages are more representative and can serve as a reference for determining appropriate harmonic mitigation schemes.

Description

[Technical Field]

[0001] This invention relates to a power engineering method for wind power generation, and more particularly to a method for analyzing random harmonic impacts of offshore wind farms connected to the grid. [Previous Technology]

[0002] In offshore wind farm harmonic analysis, harmonic power flow analysis mainly analyzes the impact of wind farms on the system's harmonic load power flow after they are connected to the power system. The sources of uncertainty that need to be considered when establishing a stochastic harmonic analysis model for offshore wind farm grid connection can generally be categorized as follows:

[0003] 1. Wind turbine power generation variation: The variation in power generation will have a certain impact on the system harmonic power flow. The wind turbine output harmonic behavior varies depending on the number of wind turbines in the wind farm, the actual and virtual power settings of the wind turbines at different wind speeds, the modulation settings of the wind turbine power converter, the fundamental frequency phase, etc.

[0004] 2. Impedance variation of submarine cable transmission lines: The data for medium-voltage submarine cables and main high-voltage submarine cables in offshore wind farm turbine arrays are usually calculated based on a specific length and the arrangement of the lines to determine the line's self-impedance and mutual impedance. In addition, the ambient temperature of the submarine cable, such as 15°C at a water depth of 30 meters, will also affect the line impedance due to temperature variations, as the external ambient temperature is a random variable.

[0005] 3. Changes in the external power grid background harmonic model: The harmonic power flow calculation required to establish the harmonic analysis model of offshore wind farm grid connection must specify the equivalent background harmonic voltage and harmonic impedance values ​​of the external power grid at the connection point of the wind farm. Due to the addition and expansion of transmission line structures or the failure and maintenance of power equipment, coupled with the grid connection of other offshore wind farms or other renewable energy and energy storage systems near the connection point, the estimated external power grid background harmonic impedance model becomes a source of uncertainty in establishing the harmonic analysis model of offshore wind farm grid connection.

[0006] 4. Changes in the internal power architecture of offshore wind farms: The main sources of changes in the submarine cable network structure are network switching operations performed by wind farm developers for the maintenance and troubleshooting of offshore power equipment, such as high-voltage transformers, power switches, and submarine cable fault repairs. These operations also include line switching strategies implemented in response to contingencies. When power transmission for a faulty line segment is diverted to other lines, the network structure changes accordingly. Changes in network structure data have a significant impact on the establishment of harmonic analysis models for offshore wind farms. When establishing random harmonic analysis models, the uncertainties causing the wind farm network structure must be considered.

[0007] Of course, the uncertainty of the input data for harmonic analysis of offshore wind farms can be modeled through randomization. However, there are not many studies on harmonic analysis that consider the uncertainty of power systems [1-7]. The parameter variations considered mainly include line parameters, power generation and load variations [1-4,7]. The methods used are mostly Monte Carlo simulation, sensitivity analysis, and Markov chain method. The random models used are normal distribution function and uniform distribution function. Applications of random harmonic analysis mode include filter design [1,2], harmonic resonance assessment [3-5], harmonic distortion estimation [6], and harmonic source output estimation [7]. Its analysis applications are mainly in transmission and distribution systems, and there are few cases of random harmonic analysis of offshore wind power systems.

[0008] Furthermore, the harmonic impulse analysis of offshore wind farms is generally conducted using the traditional worst-case scenario simulation analysis, and the traditional simulation method is used [8]. Usually, only the comparison of harmonic voltage analysis results under a single scenario is considered, and the maximum, minimum and average values ​​of grid-connected harmonic voltages are not fully grasped.

[0009] Given the increasing number of large-scale offshore wind power systems connected in parallel with transmission systems, many factors exist within the overall system that can affect the accuracy of the system operation modes required for harmonic analysis. Uncertainties or errors in system parameters and various input data will prevent the accurate representation of the actual harmonic distortion of offshore wind farms connected to the grid, leading to reduced reliability and increased risk in the analysis results of wind farm electrical infrastructure and system parallel operation. This, in turn, increases the system design margin and may increase system design and grid connection costs. Since the operation of transmission systems containing offshore wind farms introduces numerous sources of uncertainty for harmonic analysis, considering the uncertainties in system operation is an important and necessary step when performing harmonic analysis of offshore wind farms connected to transmission systems in parallel. This is particularly important for the upcoming completion and grid connection of offshore wind power development projects in Taiwan. [Summary of the Invention]

[0010] In view of the problems of the prior art, the inventors believe that there should be a method for analyzing the random harmonic impact of offshore wind farms connected to the grid, which includes at least the following steps:

[0011] Step 1: Input the basic parameter data of the offshore wind farm grid-connected system;

[0012] Step 2: Set the shape parameter K=1;

[0013] Step 3: Determine the internal electrical architecture of the offshore wind farm using Formula 1; Formula 1 is:

[0014] Step 4: Use Formula 2 to determine the wind turbine input power generation; Formula 2 is: where v is the wind speed; C is the polynomial coefficient; m is the polynomial order.

[0015] Step 5: Use Formula 3 to determine the wind turbine output harmonic current; Formula 3 is...

[0016] Step 6: Determine the harmonic impedance of the submarine cable using Formula 4; Formula 4 is: where RAC is the resistance of the conductor at a certain operating temperature (Ω / m); RDC is the resistance of the conductor at a certain operating temperature (Ω / m); ys is the skin effect; yp is the proximity effect;

[0017] Step 7: Use the first formula to determine the background power grid harmonic impedance;

[0018] Step 8: Determine the background power grid harmonic voltage using Formula 5; Formula 5 is: where μ is the mean and σ is the standard deviation.

[0019] Step 9: Perform harmonic power flow analysis of the offshore wind farm connected to the grid;

[0020] Step 10: Execute the Kth offshore wind farm grid-connected harmonic power flow analysis results;

[0021] Step 11: Then set Knew=K+1. If Knew is not greater than the preset maximum value, continue to execute steps 3 to 10;

[0022] Step 12: When the new K value is greater than the preset maximum value; proceed to step 13;

[0023] Step 13: Output the results of the random harmonic impact analysis of the off-site wind field connected to the grid.

[0024] This invention firstly uses the probability of each possible network architecture for the research target as the basis for the number of samplings. In each simulation, uncertainties such as time-varying wind turbine harmonic output, submarine cable line parameter variations, and external power grid background harmonics are considered. Based on the aforementioned established probability models, these possible uncertain variables are randomly sampled, and the randomly sampled data is loaded into the harmonic power flow formula for calculation. The calculation process simultaneously considers different wind turbine capacities and array submarine cable specifications. In addition to calculating harmonic voltage and power flow, the calculation results also perform harmonic impedance frequency sweeping at different locations in the system. Then, the harmonic power flow solutions obtained for each network architecture are combined, and finally, the random harmonic impulse analysis results of the wind farm under the future grid connection scenario are output.

[0025] The stochastic harmonic analysis model for offshore wind farm grid connection refers to the harmonic analysis model established when considering uncertainties in the specific operating conditions of the offshore wind farm grid-connected system. Since uncertainties in different variables within the system have varying degrees of impact on harmonic power flow calculations, and these uncertainties can be considered by the stochastic model, a stochastic approach can be used to evaluate potential uncertainties in the system during harmonic power flow calculations, providing a more appropriate description of possible future operating conditions. The accuracy of the stochastic model is crucial to the accuracy of the offshore wind farm stochastic harmonic analysis model. Establishing a stochastic harmonic analysis model for offshore wind farm grid connection that accurately reflects potential future harmonic distortions is a critical issue in assessing the impact of large-scale offshore wind power generation on the power quality of the grid-connected power system and on nearby renewable energy grid-connected operators. The analysis results obtained through this method can reflect the actual situation of harmonic voltage at the grid connection point. Furthermore, this method can obtain a large amount of simulated harmonic voltage data for offshore wind farms. This large amount of data allows for the statistical analysis of harmonic voltage data at the grid connection point, referencing international standards, and determining representative values ​​for each order. The representative values ​​for each order of harmonic voltage obtained in this way will be more representative and can serve as a reference for determining appropriate harmonic mitigation schemes. Moreover, this invention further considers the internal electrical equipment of offshore wind farms. Changes in the offshore wind farm's installation environment, maintenance and repair causing changes in the internal electrical structure, and changes in the power harmonic model of the external power grid at the grid connection point, etc., may cause variations in these factors. These variations are stochastically modeled and simulated. This stochastic simulation method obtains more information on the harmonic voltage of offshore wind farms. By considering all uncertainties in the stochastic simulation harmonic voltage analysis results, compared with past harmonic voltage analysis results that only considered a single scenario, the simulation method in this paper can further obtain the maximum, minimum, and average values ​​of the grid-connected harmonic voltage of the research target.

Implementation Method

[0026] The following description, with the aid of drawings, illustrates the structure, features, and embodiments of the present invention, so that your review committee may have a further understanding of the present invention.

[0027] This invention uses an actual offshore wind farm in Taiwan as a case study, and its simplified power line diagram is shown in Figure 2. The OWPP_A wind farm has a capacity of 128 MW and is divided into four wind turbine groups (No.1 Group ~ No.4 Group). No.1 Group and No.3 Group each consist of 6 wind turbines with a capacity of 6 MW, No.2 Group consists of 5 wind turbines with a capacity of 6 MW, and No.4 Group consists of 3 wind turbines with a capacity of 6 MW and 2 wind turbines with a capacity of 4 MW. The rated voltage of each wind turbine group is 33 kV and is connected to an onshore substation via submarine cables. The research objective is to step up the voltage from 33 kV to 161 kV through the onshore substation and finally connect it to the grid connection point via an onshore cable.

[0028] Please refer to Figure 1, which is a flowchart illustrating the offshore wind farm grid-connected random harmonic impact analysis method of the present invention. It includes at least the following steps:

[0029] Step 1: Input the basic parameter data of the offshore wind farm grid-connected system;

[0030] Step 2: Set the shape parameter K=1;

[0031] Step 3: Determine the internal electrical architecture of the offshore wind farm using Formula 1; Formula 1 is:

[0032] Since the harmonics of the external power grid will vary due to the uncertainty of the external power grid and there is no certain regularity, the random model of the external power grid harmonics, including harmonic voltage and impedance, can be considered as a normal distribution function, a uniform distribution function or other distribution functions. First, the random mathematical model of background harmonic impedance. In the probability distribution model of each group of background impedance, it is considered that the difference of each group of harmonic impedance lies in the influence of the maintenance, repair and failure of the power equipment of the external power grid. Among them, the probability of the maintenance of electrical equipment of the external power grid is the largest. Therefore, it is assumed that the maintenance time of each power equipment is fixed and will be repeated. So, the common uniform distribution

[14] can be used to represent the probability distribution of each group of background harmonic impedance. Its continuous uniform random variable probability density function in the interval [a, b] can be expressed as:

[0033] Step 4: Use Formula 2 to determine the wind turbine input power generation; Formula 2 is: where v is the wind speed; C is the polynomial coefficient; m is the polynomial order.

[0034] The output power of offshore wind turbines directly affects the value of wind turbine harmonic current. According to the wind turbine harmonic current source information compiled in [8], it can be seen that from the wind turbine output power from 0% to 100%, there will be a total of 11 groups of harmonic current sources in every 10% interval. In order to calculate the wind turbine harmonic current source corresponding to the wind turbine output power more precisely, this invention introduces polynomial curve fitting to estimate the wind turbine harmonic current source caused by different wind turbine output powers that have not been measured as follows [9]: where is the percentage of wind turbine output power; C is the polynomial coefficient; m is the polynomial order. The reason for the change in the output power of offshore wind turbines is the change in wind speed in the sea area where they are located. The common mathematical model of wind speed can be expressed as follows

[10] : where is D; is the wind speed; is the size parameter.

[0035] After the wind speed is determined, the height of the wind turbine wheel casing will be different from the height of the wind speed measurement. Therefore, in general, the wind speed at the height of the target offshore wind turbine wheel casing can be estimated according to the power law, which is expressed as

[10] : (3) Where Z1 is the height of the wind speed measurement; Z2 is the height of the wind speed to be estimated; α is the friction coefficient under different terrains, as shown in Table 1: Table 1: Friction coefficient α under various terrain types

[11] Terrain type α Lake, ocean and smooth hard ground 0.10 foot high grass on level ground 0.15 Tall erops, hedges, and shrubs 0.20 Wooded country with many trees 0.25 Small town with some trees and shrubs 0.30 City area with tall buildings 0.40

[0036] After estimating the wind speed, the output power of the wind turbine can be estimated by the wind speed. However, the ideal calculation of the wind turbine output power needs to take into account the conversion efficiency of the wind turbine. Since the conversion efficiency of a specific wind turbine has not been obtained, it is calculated through the power curve with actual measurement. However, considering that the data of the wind turbine power curve cannot measure the wind speed and its output power data of the wind turbine, it is necessary to introduce polynomial curve fitting to estimate the wind speed that has not been measured. Substituting the wind speed into the polynomial fitting equation can be expressed as the following formula [9]: where v is the wind speed; C is the polynomial coefficient; m is the polynomial order.

[0037] Step 5: Use Formula 3 to determine the wind turbine output harmonic current; This Formula 3 is

[0038] The output power of offshore wind turbines directly affects the value of wind turbine harmonic current. According to the wind turbine harmonic current source information compiled in [8], it can be seen that from the wind turbine output power from 0% to 100%, there will be a total of 11 groups of harmonic current sources in intervals of 10%. In order to calculate the wind turbine harmonic current sources corresponding to the wind turbine output power more precisely, this invention introduces polynomial curve fitting to estimate the wind turbine harmonic current sources caused by different wind turbine output powers that have not been measured, as shown in the following formula [9]: where is the percentage of wind turbine output power; C is the polynomial coefficient; m is the polynomial order.

[0039] Step 6: Determine the harmonic impedance of the submarine cable using Formula 4; Formula 4 is:

[0040] After considering the random sampling seawater temperature model, the total AC resistance of the submarine cable to a specific external ambient temperature can be obtained according to the international standard IEC 60287-1-1

[12] : where RAC is the resistance of the conductor at a certain operating temperature (Ω / m); RDC is the resistance of the conductor at a certain operating temperature (Ω / m); ys is the skin effect; yp is the proximity effect.

[0041] Step 7: Use the first formula to determine the background power grid harmonic impedance;

[0042] Step 8: Determine the background power grid harmonic voltage using Formula 5; Formula 5 is: where μ is the mean and σ is the standard deviation.

[0043] Step 9: Perform harmonic power flow analysis of the offshore wind farm connected to the grid;

[0044] Step 10: Execute the Kth offshore wind farm grid-connected harmonic power flow analysis results;

[0045] Step 11: Then set Knew=K+1. If the value of Knew is not greater than the preset maximum value, continue to execute steps 3 to 10.

[0046] Step 12: When the new K value is greater than the preset maximum value; proceed to step 13;

[0047] Step 13: Output the results of the random harmonic impact analysis of the off-site wind field connected to the grid.

[0048] This invention considers equipment parameters under different scenarios and performs a large number of random simulations to obtain a large amount of grid connection point harmonic voltage simulation data. Through the simulated data, 95% of each different harmonic voltage order at the grid connection point is further statistically analyzed as a representative value of the harmonic voltage. Table 2 shows a comparison between the random simulation method and the traditional analysis method for grid connection point harmonic voltage. Table 2 shows that the 95% of the 5th harmonic voltage obtained and statistically analyzed using the random simulation method is 1.04%, exceeding the harmonic voltage limit. The 13th harmonic voltage, obtained using the traditional method, exceeds the harmonic voltage limit by 0.794%. Regarding the 50th harmonic voltage analysis, both simulation methods yielded results exceeding the harmonic voltage limit. In summary, it can be seen that the test system used in this invention, in analyzing the harmonic voltage impact at the grid connection point, is more conservative using the random analysis method compared to the traditional analysis method, meaning that the risk of using the random analysis method to examine the harmonic impact of wind farm grid connection is lower. order The random method takes 95% (%) Traditional methods (%) Limit value (%) 2 0.083 0.033 0.376 3 0.288 0.068 0.575 4 0.027 0.005 0.209 5 1.040 0.844 1.008 6 0.021 0.012 0.1055 7 0.426 0.282 0.666 8 0.021 0.020 0.108 9 0.024 0.043 0.271 10 0.016 0.011 0.093 11 0.142 0.259 0.433 12 0.008 0.011 0.082 13 0.179 0.794 0.438 14 0.009 0.009 0.076 15 0.005 0.012 0.08 16 0.004 0.005 0.072 17 0.016 0.082 0.310 18 0.012 0.020 0.068 19 0.012 0.079 0.280 20 0.008 0.000 0.065 21 0.008 0.022 0.053 22 0.001 0.000 0.063 23 0.189 0.225 0.228 24 0.000 0.001 0.062 25 0.000 0.007 0.217 26 0.008 0.041 0.060 27 0.001 0.004 0.052 28 0.003 0.000 0.058 29 0.039 0.056 0.182 30 0.000 0.000 0.057 31 0.001 0.007 0.169 32 0.001 0.000 0.056 33 0.001 0.001 0.052 34 0.000 0.000 0.055 35 0.004 0.027 0.152 36 0.000 0.000 0.054 37 0.000 0.001 0.144 38 0.000 0.000 0.054 39 0.000 0.001 0.053 40 0.000 0.000 0.053 41 0.007 0.006 0.128 42 0.000 0.003 0.053 43 0.001 0.001 0.123 44 0.000 0.000 0.052 45 0.001 0.001 0.052 46 0.000 0.000 0.052 47 0.003 0.003 0.112 48 0.000 0.000 0.051 49 0.009 0.014 0.108 50 0.160 0.160 0.051

[0049] The random harmonic impulse analysis results of this invention, which simultaneously considers all the above-mentioned uncertainties, are shown in Figure 3. Figure 3 shows that the maximum harmonic voltage values ​​at the grid connection point are 1.344% for the 5th harmonic, 0.069% for the 18th harmonic, 0.299% for the 23rd harmonic, and 0.170% for the 50th harmonic, exceeding the limits. Figures 4 to 7 show the probability distribution of the grid connection point harmonic voltage simulation results considering various factors for the 5th, 18th, 23rd, and 50th harmonics, respectively. The analysis results show that the probability of exceeding the limit for the 5th harmonic is 7.3%, for the 18th harmonic is 0.08%, for the 23rd harmonic is 3%, and for the 50th harmonic is 14.85%.

[0050] This invention establishes an offshore wind farm grid-connected harmonic impact analysis model using the Monte Carlo stochastic simulation method. It considers factors such as the variation of harmonic current of offshore wind turbines, the variation of impedance of submarine cables, the background harmonic impedance of the external power grid, the background harmonic voltage of the external power grid, and the variation of the internal power structure of the offshore wind farm. It obtains harmonic voltage data of each order at the offshore wind farm grid connection point from simulation results under various different scenarios. Compared with traditional simulation methods, which can only obtain a set of harmonic voltage values ​​of each order, the Monte Carlo stochastic simulation method will have a larger amount of data to support the reliability of the simulation results. Although traditional simulation methods can take the largest harmonic current source of the offshore wind turbine for simulation, they often ignore the impact of changes in other related equipment parameters of the offshore wind farm. Therefore, the stochastic analysis method proposed in this invention can provide more complete information on the harmonic impact of offshore wind farm grid connection compared with traditional methods, so as to truly reflect the possible distribution range of harmonic impact analysis of the actual wind farm grid connection in the future.

[0051] This invention proposes a set of harmonic analysis models for the grid connection probability of offshore wind farms. It establishes a wind turbine harmonic probability model, a submarine cable impedance probability model, a power grid background harmonic impedance probability model, a power grid background harmonic voltage probability model, and an offshore wind farm internal structure probability model. Through Monte Carlo stochastic simulation, it performs harmonic voltage analysis of actual wind farm grid connection, and the analysis results reflect the actual harmonic voltage situation at the grid connection point. Furthermore, because Monte Carlo stochastic simulation is used, a large amount of simulated harmonic voltage data for offshore wind farm grid connection can be obtained. Through this large amount of data, international standards can be referenced to statistically analyze the harmonic voltage data of each order at the grid connection point and determine representative values. The representative values ​​of each order of harmonic voltage obtained in this way will be more representative and can serve as a reference for determining appropriate harmonic mitigation schemes.

[0052] In summary, the present invention does indeed meet the requirements for industrial applicability, and has not been published or publicly used prior to this application, nor is it known to the public, and it is not readily apparent, thus meeting the requirements for patentability. Therefore, a patent application is filed in accordance with the law. However, the above-described embodiments are preferred embodiments of the present invention in the industrial sector, and all equivalent variations made within the scope of the claims of this application are within the scope of the claims in this case.

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[10] T. -H. Yeh and L. Wang, "A Study on Generator Capacity for Wind Turbines Under Various Tower Heights and Rated Wind Speeds Using Weibull Distribution," in IEEE Transactions on Energy Conversion, vol. 23, no. 2, pp. 592-602, June 2008, doi: 10.1109 / TEC.2008.918626.

[11] E. Golding, The Generation of Electricity by Wind Power. New York:Halsted Press, 1976

[12] IEC 60287-1-1:2023 Electric cables - Calculation of the current rating - Part 1-1: Current rating equations (100 % load factor) and calculation of losses – General

[13] https: / / www.cwb.gov.tw / Data / knowledge / announce / marine7.pdf

[14] Walpole, Ronald; et al. (2012). Probability & Statistics for Engineers and Scientists. Boston, USA: Prentice Hall. pp. 171–176.ISBN 978-0-321-62911-1.

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[16] RY Rubinstein and DP Kroese, Simulation and the Monte Carlo Method, 3rd Edition, John Wiley & Sons, Inc., Nov. 2016.

[17] IEC TR 61000-3-6, “Assessment of emission limits for the connection of distorting installations to MV, HV and EHV power systems,” Feb. 2008.

[18] IEEE 519-2014, "IEEE Recommended Practice and Requirements for Harmonic Control in Electric Power Systems," Jun. 2014. [Simplified Explanation of the Diagram]

[0054] The first figure is a flowchart of the present invention. The second figure is a flowchart of the random harmonic impact analysis of the present invention. The third figure is a schematic diagram of the statistical results of random harmonic voltage at the combined network points. The fourth figure is a schematic diagram of the probability density distribution function of the 5th order harmonic voltage at the combined network points. The fifth figure is a schematic diagram of the probability density distribution function of the 18th order harmonic voltage at the combined network points. The sixth figure is a schematic diagram of the probability density distribution function of the 23rd order harmonic voltage at the combined network points. The seventh figure is a schematic diagram of the probability density distribution function of the 50th order harmonic voltage at the combined network points. The eighth figure is a schematic diagram comparing the results of Monte Carlo random simulation method and general harmonic voltage simulation analysis.

Claims

1. A method for analyzing random harmonic impacts of an offshore wind farm connected to the grid, comprising at least the following steps: Step 1: Input basic parameter data of the offshore wind farm grid-connected system; Step 2: Set shape parameter K=1; Step 3: Use Formula 1 to determine the internal electrical architecture of the offshore wind farm; Formula 1 is: where a and b are the upper and lower bounds of a uniform distribution and X is a random variable with the same probability density for all values ​​in the interval a and b; Step 4: Use Formula 2 to determine the wind turbine input power generation; Formula 2 is: where v is the wind speed; C is the polynomial coefficient; m is the polynomial order; Step 5: Use Formula 3 to determine the wind turbine output harmonic current; Formula 3 is: where p is the percentage of wind turbine output power; C is the polynomial coefficient; m is the polynomial order; Step 6: Use Formula 4 to determine the harmonic impedance of the submarine cable; Formula 4 is: where RAC is the resistance of the conductor at a certain operating temperature (Ω / m); RDC is the resistance of the conductor at a certain operating temperature (Ω / m); ys is the skin effect; yp is the proximity effect; Step 7: Use Formula 1 to determine the background grid harmonic impedance; Step 8: Use Formula 5 to determine the background grid harmonic voltage; Formula 5 is: where μ is the mean and σ is the standard deviation; Step 9: Perform offshore wind farm grid-connected harmonic power flow analysis; Step 10: Perform the Kth offshore wind farm grid-connected harmonic power flow analysis results; Step 11: Then set Knew=K+1. If the Knew value is not greater than the preset maximum value, continue to perform steps 3 to 10; Step 12: When the new K value is greater than the preset maximum value, proceed to step 13; Step 13: Output the offshore wind farm grid-connected random harmonic impact analysis results.

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