Fault identification-based wind power adaptive control optimization method, system and device

Abstract

The application discloses a wind power adaptive control optimization method and system based on fault identification and equipment, monitors the wind turbine terminal voltage curve in the region after different fault disturbances occur, extracts the key features of the wind turbine terminal voltage under two disturbances of direct current commutation failure and alternating current short circuit fault, and obtains the threshold value for distinguishing the direct current fault disturbance and the alternating current fault disturbance; according to the extracted key features of the terminal voltage, the commutation failure disturbance and the alternating current short circuit disturbance are distinguished through two-layer judgment logic through double criteria of the voltage change rate and the voltage entering the low-pass threshold rebound recovery time, and it is judged whether the power angle stability problem or the overvoltage problem is more serious at the moment; the wind power controller parameters are adaptively selected to improve the stable operation ability of the power grid under different disturbances. The technical scheme can automatically select the corresponding converter control parameters in the disturbance process, improves the robustness of the new energy support performance, and improves the system stability level in diversified scenes.

Description

Technical Field

[0001] This invention belongs to the field of new energy in power systems, and relates to a wind power adaptive control optimization method, system, and equipment based on fault identification. Background Technology

[0002] my country's grid-connected renewable energy capacity is growing rapidly, and by 2030, the total installed capacity of wind and solar power will reach over 1.2 billion kilowatts. Currently, the renewable energy penetration rate in the Northwest Power Grid has exceeded 130%, making renewable energy the main power source for installed capacity. Among these, the transient response behavior of wind turbines is increasingly impacting grid stability, becoming a significant factor affecting grid stability and power transmission capacity. However, for ultra-high voltage direct current (UHVDC) transmission lines with a high proportion of renewable energy, multiple stability issues often coexist, including transient overvoltage, power angle, and undervoltage, and these different stability issues place varying requirements on the transient response of wind power.

[0003] To meet the stability requirements of wind power, stability performance can generally be adjusted by regulating controller parameters, specifically by adjusting the low-voltage ride-through active power coefficient K of the wind turbine controller. 2_Id_LV It can change the active power recovery speed after a voltage drop in a wind turbine, K 2_Id_LV The key to selection lies in the different impacts of active power recovery speed on commutation failure and AC system faults. Under commutation failure faults, overvoltage stability is more severe, requiring an increase in K. 2_Id_LV The power angle stability problem is more severe under AC system faults, requiring a reduction in K. 2_Id_LV Therefore, it is necessary to select the appropriate option based on the specific fault.

[0004] Currently, the setting of relevant parameters is limited by the inability to determine the type of fault. In some scenarios, if only a single problem is addressed by optimizing the low-voltage parameters of the wind turbine, good results can be obtained under the condition of a single fault. However, when another type of fault occurs, it may cause grid instability or even collapse.

[0005] Therefore, a new wind power adaptive control optimization strategy based on fault identification is needed to solve the above problems, which leads to this case. Summary of the Invention

[0006] The purpose of this invention is to provide a wind power adaptive control optimization method, system, and equipment based on fault identification. During disturbances, it automatically selects appropriate converter control parameters, fully leveraging the flexible and adaptable characteristics of new energy converter equipment control, improving the robustness of new energy support performance, and enhancing the system stability level under diverse scenarios.

[0007] To achieve the above objectives, the solution of the present invention is:

[0008] A wind power adaptive control optimization method based on fault identification includes the following steps:

[0009] Step 1: Analyze the different stability problems caused by different fault disturbance signals in the power grid;

[0010] Step 2: Monitor the wind turbine terminal voltage curves in the region after different fault disturbances occur, extract the key characteristics of the wind turbine terminal voltage under two disturbances: DC commutation failure and AC short circuit fault, and obtain the threshold value to distinguish between DC fault disturbances and AC fault disturbances in the power grid.

[0011] Step 3: Based on the key characteristics of the terminal voltage extracted in Step 2, the commutation failure disturbance and the AC short circuit disturbance are distinguished by two-layer judgment logic through the dual criteria of voltage change rate and voltage rebound recovery time when entering the low breakdown threshold. Based on this, it is determined whether the power angle stability problem or the overvoltage problem is more serious at this time.

[0012] Step 4: Adaptively select wind power controller parameters based on the types of disturbances and stability problems identified in Step 3, in order to improve the stable operation capability of the power grid under different disturbances.

[0013] In step 1 above, when a DC fault or AC fault disturbance occurs in the power grid, the wind turbine generator is disturbed and its voltage drops, entering a low-voltage ride-through state. In the low-voltage ride-through state, reducing the active current can improve the power angle stability characteristics. Adjusting the active current and reactive current can reduce the overvoltage extreme value.

[0014] In step 2 above, key features of the generator terminal voltage are extracted, including extracting a large number of generator terminal voltage curves of wind turbines under commutation failure and AC short circuit faults in the power grid and performing normalization processing, thereby extracting key features of the generator terminal voltage.

[0015] In step 3 above, the commutation failure disturbance and the AC short-circuit disturbance are distinguished through two layers of judgment logic using both the voltage change rate and the voltage recovery time to the low breakdown threshold.

[0016] First, the voltage change rate is compared with a voltage change rate threshold. If the voltage change rate exceeds the threshold, an AC short-circuit fault is preliminarily determined, and the low-voltage ride-through active power factor K is reduced. 2_Id_LV This reduces the active power generated by the wind turbine during fault ride-through; otherwise, a DC commutation failure is preliminarily determined, and the low-voltage ride-through active power factor K is increased. 2_Id_LV Reduce the low-voltage ride-through reactive power coefficient K 2_Iq_LV This increases the active power generated by the wind turbine during fault ride and reduces the reactive power generated by the wind turbine during fault ride.

[0017] Then, when the voltage rebounds to a preset percentage from the lowest point, the recovery time of the voltage entering the low breakdown threshold is calculated, and the recovery time of the voltage entering the low breakdown threshold is compared with the time threshold value. If the recovery time of the voltage entering the low breakdown threshold is greater than the time threshold value, a secondary determination is made that an AC short circuit fault has occurred; otherwise, a secondary determination is made that a DC commutation failure fault has occurred.

[0018] Compare the two judgment results to see if they are consistent. If they are consistent, maintain the low-voltage ride-through active power coefficient K set during the initial judgment. 2_Id_LV Low-voltage ride-through reactive power coefficient K 2_Iq_LV The low-voltage ride-through active power coefficient K will remain unchanged; otherwise, it will be changed. 2_Id_LV Low-voltage ride-through reactive power coefficient K 2_Iq_LV Set as a compromise value.

[0019] In step 3 above, when the disturbance is determined to be an AC short circuit, the power angle stability problem is considered to be more serious; when the disturbance is determined to be a commutation failure, the overvoltage problem is considered to be more serious.

[0020] In step 3 above, the voltage change rate v and the voltage recovery time t after entering the low breakdown threshold are... re The calculation method is as follows:

[0021]

[0022] t re =t u＝1.05umin -t u＝umin

[0023] In the formula, u0, u min These are the initial and minimum voltage values, t. u＝1.05umin and t u＝umin These represent the moments when the voltage reaches its minimum and the moment when it rebounds by 5% from its minimum, respectively, while t represents the time it takes for the voltage to drop from its initial value to its minimum point.

[0024] A wind power adaptive control optimization system based on fault identification includes:

[0025] The analysis module is used to analyze the different stability problems caused by different fault disturbance signals in the power grid;

[0026] The key feature extraction module is used to extract key features of the wind turbine terminal voltage under two types of disturbances: DC commutation failure and AC short circuit fault, based on the wind turbine terminal voltage curves in the region after different fault disturbances occur.

[0027] The logic judgment module is used to distinguish between commutation failure disturbances and AC short-circuit disturbances based on the key features of the terminal voltage extracted by the key feature extraction module, by measuring the voltage change rate and the recovery time of the voltage entering the low breakdown threshold, and to determine whether the power angle stability problem or the overvoltage problem is more serious at this time; and,

[0028] The parameter selection module is used to adaptively select wind power controller parameters based on the type of disturbance and stability problem determined by the logic judgment module, so as to improve the stable operation capability of the power grid under different disturbances.

[0029] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor; when the processor executes the computer program, it implements the steps of the wind power adaptive control optimization method based on fault identification as described above.

[0030] A computer-readable storage medium storing a computer program; when executed by a processor, the computer program implements the steps of the wind power adaptive control optimization method based on fault identification as described above.

[0031] After adopting the above scheme, considering the diverse operating scenarios, complex stability issues, and varying requirements for the response and support behavior of renewable energy converters under different disturbances in high-proportion renewable energy power grids, this invention analyzes and proposes the control response requirements of converters under different dominant stability problems under different disturbances. By analyzing and evaluating key characteristic quantities of disturbance signals, the control parameters required for the current disturbance are identified, and a parameter adaptive system is designed to automatically select appropriate converter control parameters during the disturbance process. This fully leverages the flexible and adaptable characteristics of renewable energy converter equipment control, improves the robustness of renewable energy support performance, and enhances the system stability level under diverse scenarios. The effectiveness of the method is verified using a provincial power grid in Northwest China as an example. The proposed method can be used for adaptive optimization of actual renewable energy converter control strategies, improving the grid stability level and renewable energy transmission capacity after large-scale renewable energy integration. Attached Figure Description

[0032] Figure 1 This is a block diagram of the adaptive control strategy for new energy controller parameters;

[0033] Figure 2 This is a diagram of a hybrid system architecture;

[0034] Figure 3 It is a two-machine equivalent system;

[0035] Figure 4 This is the judgment logic diagram of the present invention;

[0036] Figure 5 These are the terminal voltage curves of the wind turbine under different fault disturbances;

[0037] Figure 6 This is the distribution of wind turbine terminal voltage drops under different fault disturbances;

[0038] Figure 7 These are the power angle curves of thermal power plants under three parameter schemes in the near-field area during AC fault disturbances;

[0039] Figure 8 This is a comparison of wind power overvoltage curves under three parameter schemes during DC fault disturbances;

[0040] Figure 9 These are the power angle curves of thermal power plants under three parameter schemes in the near-field under multiple fault disturbances (AC fault disturbance occurs first);

[0041] Figure 10 These are the power angle curves of thermal power plants under three parameter schemes in the near-field under multiple fault disturbances (DC fault disturbance occurs first);

[0042] Figure 11 This is a comparison of wind power overvoltage curves under three parameter schemes in the near-field under multiple fault disturbances (AC fault occurs first);

[0043] Figure 12 This is a comparison of wind power overvoltage curves under three parameter schemes in the near-field under multiple fault disturbances (DC fault occurs first). Detailed Implementation

[0044] The present invention will be further described below with reference to the accompanying drawings. The present invention will be further illustrated below with reference to the accompanying drawings and specific embodiments. It should be understood that these embodiments are merely preferred embodiments of the present invention. After reading this invention, any modifications made by those skilled in the art that do not depart from the principles of the invention fall within the scope defined by the appended claims.

[0045] This invention provides a wind power adaptive control optimization method based on fault identification, comprising the following steps:

[0046] Step 1: Analyze the different stability problems caused by different fault disturbance signals in the power grid;

[0047] We analyzed the low-voltage ride-through active power coefficient K of the wind turbine controller. 2_Id_LV Relationship with the angle of attack, K 2_Id_LV The relationship with overvoltage was further used to determine the low-voltage ride-through active power factor K. 2_Id_LV The impact of different voltage disturbance signals on different stability problems.

[0048] 1) Analysis of the active power coefficient K during low-voltage ride-through 2_Id_LV Relationship with the angle of attack

[0049] The relationship between power angle and voltage is as follows:

[0050]

[0051] In the formula, P e P represents the electromagnetic power of the wind turbine. m denoted as , where is the mechanical power of the wind turbine, E is the electromotive force of the generator, U is the node voltage, x is the system impedance, and δ is the power angle.

[0052] according to Figure 2 The system architecture is a three-zone system containing multiple units, including three thermal power units, one wind power unit, and one photovoltaic unit. In the diagram, U... j and θ j Let M be the magnitude and phase angle of the voltage of the j-th bus, respectively, where j = 1, 2, 3, 4. i δ i P Gi These are the moment of inertia, power angle, and transmitted power of the i-th thermal power unit, where i = 1, 2, 3, and P. W P PV The active power transmitted by wind power and photovoltaic power respectively, P S Let x be the active power delivered by the system tie line; and let x be the line impedance. Combining the above equation, we can obtain:

[0053]

[0054]

[0055] Assuming that regions 1 and 2 are weakly interconnected, their tie line reactance (per unit value) is much larger than the equivalent generator reactance (per unit value), and can be approximated as δ1≈θ1≈θ4, δ2≈θ2. Combining the above two equations, we can obtain:

[0056]

[0057] Where δ=δ1-δ2, For equivalent mechanical power, This is the equivalent electromagnetic power.

[0058] As can be seen from the above formula, when DC faults and AC fault disturbances occur in the system, after the voltage of the renewable energy source drops and it enters the low-voltage ride-through state, the larger the active current coefficient of the low-voltage ride-through, the faster the active power recovery, the more active power generated during the process, the greater the equivalent mechanical power, the faster the sending-end unit accelerates, and the larger the acceleration area, which is not conducive to power angle stability. Reducing K... 2_Id_LV This will cause the active power of new energy sources to recover more slowly, resulting in a smaller acceleration area, which is more conducive to power angle stability. In other words, new energy units need to reduce active current during low-voltage periods to reduce equivalent mechanical power and line power flow, thereby improving power angle stability characteristics. It can also be seen that the magnitude of reactive power has a relatively small impact on power angle stability.

[0059] 2) Analyze the active power coefficient K during low-voltage ride-through. 2_Id_LV Relationship with overvoltage

[0060] Figure 3 For an equivalent two-region interconnection model, based on Figure 3 Based on the system model, the nodal equations for nodes 3 and 4 after the fault ends are listed as follows:

[0061]

[0062] In the formula, V is the voltage at node 3, E1 is the voltage at generator 1, E2 is the voltage at generator 2, E4 is the voltage at node 4, θ is the phase angle of the voltage at node 4, and X... l and X l B′ represents the tie line impedance before and after the fault, X1 and X2 represent the generator outlet line impedance, and B′ represents the tie line impedance before and after the fault. l B is the line admittance after the fault. c Let I be the line-to-ground equivalent admittance during a fault, δ be the current, and δ be the generator power angle. Rearranging, we get:

[0063]

[0064] Let I=I d -jI q Substituting into the previous equation, the Euler equation can be split into its real and imaginary parts as follows:

[0065]

[0066]

[0067] Shifting the phase in the above equation, we get:

[0068]

[0069] To simplify the calculation, the two-machine system is transformed into a single-machine infinite system, where δ1≈δ and δ2≈0, resulting in:

[0070]

[0071] Will Substituting into the above formula, we get:

[0072]

[0073]

[0074] Will Substituting into the above equation, we get:

[0075]

[0076] In the formula, I q For reactive current, I d For active current, I′ m β is the equivalent current, and β is the equivalent reactance coefficient.

[0077] Because of I q (U1) is generally a monotonically decreasing function of U1, I d (U1) is generally a monotonically increasing function of U1. The implicit expression of U1 above determines that the voltage U1 is a monotonically increasing function of cosδ, that is, the extreme value of transient overvoltage occurs at the transient minimum value of δ. min At this point, the maximum value of the transient overvoltage can be determined as:

[0078]

[0079] As can be seen from the formula, I d The smaller, I q The larger the value, the larger the maximum transient voltage. After the new energy unit is connected, the voltage can be adjusted by regulating the K value of the new energy unit. 2_Id_LV and K 2_Iq_LV Adjust the active current I during the low-voltage ride-through period respectively d and reactive current I q This can effectively reduce the extreme value of overvoltage.

[0080] Therefore, when DC and AC faults occur in the system, after the voltage of the renewable energy source drops and it enters a low-voltage ride-through state, the more active power the generating unit generates and the less reactive power it generates, the lower the system overvoltage; conversely, the less active power the generating unit generates and the more reactive power it generates, the higher the system overvoltage. This can be addressed by adjusting the K... 2_Id_LV To increase active current I d This can effectively reduce the extreme value of overvoltage.

[0081] Step 2: Monitor the wind turbine terminal voltage curves in the region after different fault disturbances occur, extract the key characteristics of the wind turbine terminal voltage under two disturbances: DC commutation failure and AC short circuit fault, and obtain the threshold value to distinguish between DC fault disturbances and AC fault disturbances in the power grid.

[0082] In step 2, when extracting key features of the generator terminal voltage, a large number of generator terminal voltage curves under commutation failure and AC short-circuit faults in the power grid are extracted and normalized to extract key features of the generator terminal voltage.

[0083] In step 2, the voltage drop rate and voltage recovery time when the fault enters the low breakdown threshold are determined by analyzing the terminal voltage curves of a large number of wind turbine units under statistical analysis, with k1 = 8 p.u. / s and k2 = 43 ms.

[0084] Preferably, the voltage curves of wind turbine terminals under a large number of different types of faults were analyzed. It was found that during AC system short-circuit faults, the voltage sag rate was between 10.03 pu / s and 14.23 pu / s, the voltage sag value was between 0.328 pu and 0.587 pu, and the voltage rebound time was between 18 ms and 34 ms. During commutation failure faults, the voltage sag rate was between 4.43 pu / s and 6.11 pu / s, the voltage sag value was between 0.058 pu and 0.147 pu, and the voltage rebound time was between 63 ms and 91 ms. Therefore, the threshold value for the voltage sag rate can be set to 8 p.u. / s, which serves as the first criterion for distinguishing between short-circuit faults and commutation failure faults; the threshold value for the low-break-through threshold rebound recovery time is selected as 45 ms, which serves as the second criterion for distinguishing between short-circuit faults and commutation failure faults.

[0085] Step 3: Based on the key characteristics of the terminal voltage extracted in Step 2, the commutation failure disturbance and the AC short circuit disturbance are distinguished by two-layer judgment logic through the dual criteria of voltage change rate and voltage rebound recovery time when entering the low breakdown threshold. Based on this, it is determined whether the power angle stability problem or the overvoltage problem is more serious at this time.

[0086] Among them, the voltage change rate v and the voltage recovery time t after entering the low breakdown threshold. re The calculation method is as follows:

[0087]

[0088] t re =t u＝1.05umin -t u＝umin

[0089] In the formula, u0, u min These are the initial and minimum voltage values, t. u＝1.05umin and t u＝umin These represent the moments when the voltage reaches its minimum and when it rebounds by 5% from its minimum, respectively, while t represents the time it takes for the voltage to drop from its initial value to its minimum.

[0090] When a wind turbine enters low-voltage ride-through mode, the rate of change of the voltage curve over two cycles is assessed. If the rate of change exceeds 8 p.u. / s, an AC short-circuit fault is preliminarily identified. At this point, the power angle issue becomes more severe, requiring a reduction in the active power generated during the fault ride-through. This can be achieved by reducing K. 2_Id_LV To achieve this, the active low-suspension current coefficient K 2_Id_LVis changed to 0.2; otherwise, it is determined that the system has a commutation failure with a more severe overvoltage problem, which requires increasing the active power output by the unit during the fault ride-through period and reducing the reactive power output during the fault ride-through period, which can be achieved by increasing K 2_Id_LV and decreasing K 2_Iq_LV for implementation, at this time K 2_Id_LV is set to 1, K 2_Iq_LV is set to 0.2; at this time, the voltage is continuously monitored, when the voltage rebounds by 5% from the lowest point, the falling trend is judged to have stopped, and the rebound recovery time t for the voltage to enter the low-voltage ride-through threshold is calculated. If t>45ms, the secondary judgment is an AC short-circuit fault, otherwise it is judged as a commutation failure fault. If the two judgments are consistent, K 2_Id_LV and K 2_Iq_LV remain the values determined for the first time unchanged. When the two judgments are inconsistent, the fault type is unclear at this time, and K 2_Id_LV and K 2_Iq_LV都 are set to 0.6. However, a large number of simulations show that this situation is extremely rare and will not affect the overall operation stability of the unit.

[0091] Step 4: adaptively select wind power controller parameters according to the types of disturbances and stability problems judged in Step 3, so as to improve the stable operation capability of the power grid under different disturbances.

[0092] In Step 4, the adaptive selection of the active low-voltage ride-through current coefficient K of the wind power controller parameters 2_Id_LV includes the following steps:

[0093] 11) When the terminal voltage of the faulty wind turbine unit starts to drop, the voltage drop speed in Step 2 is judged. When v is less than the voltage drop speed threshold k1, it is preliminarily judged that a commutation failure fault occurs in the power grid, and the transient overvoltage problem is more severe; when v>k1, it is preliminarily judged that an AC system fault occurs in the power grid, and the power angle stability problem is more severe.

[0094] 12) Continue to monitor the voltage. When the voltage rebounds by 5% from the lowest point, the falling trend is judged to have ended, and the rebound recovery time t for the voltage at this time to enter the low-voltage ride-through threshold is calculated re .

[0095] 13) Perform a second fault judgment. When t re is greater than the rebound recovery time threshold k2, it is judged that a DC commutation failure fault occurs in the power grid, and the transient overvoltage problem is more severe; when t re <k2, it is judged that an AC short-circuit fault occurs in the power grid, and the power angle stability problem is more severe.

[0096] 14) Judge whether the two fault judgments are consistent. If they are consistent, the fault type is determined. If it is a DC commutation failure fault, since the transient overvoltage problem is more severe at this time, set K 2_Id_LV =1, K2_Iq_LV =0.2 to improve overvoltage stability. If the fault is in the AC system, the power angle stability problem is more serious, so K is set to 0.2. 2_Id_LV =0.2 to improve the stability of the power angle, no need to adjust K 2_Iq_LV If the two fault determinations are inconsistent, the fault is determined to be undetermined, and K... 2_Id_LV and K 2_Iq_LV We take the middle value of 0.6.

[0097] After extensive simulations, we found that there are virtually no unpredictable fault conditions, which do not affect the effectiveness of the method proposed in this invention.

[0098] When a DC fault occurs in the power grid, the overvoltage problem is more serious. At this time, it is necessary to increase the active power output of the units in the power grid, reduce the reactive power, and reduce the overvoltage. When an AC fault occurs in the power grid, the power angle stability problem is more serious. At this time, it is necessary to reduce the active power output of the units in the power grid and improve the power angle stability.

[0099] When the wind power is in fault ride-through mode, increase K. 2_Id_LV This can increase the active power generated by the wind turbine during fault ride-through; reduce K 2_Id_LV This can reduce the active power generated during fault ride-through. Increasing K 2_Iq_LV This can increase the reactive power generated by the wind turbine during fault ride-through; reduce K 2_Id_LV This can reduce the reactive power generated during fault ride-through.

[0100] The wind power adaptive control optimization strategy based on fault identification of the present invention has the ability to adaptively select the low-voltage active power ride-through coefficient of the wind turbine controller according to different fault types, thereby changing the wind power active power recovery speed and achieving the purpose of improving system stability under different faults.

[0101] Example 1:

[0102] Among them, local space frame structures such as Figure 1 As shown, the system includes AC systems, new energy sources, and DC stations. The installed capacity of thermal power in the region will reach 19.6 million kilowatts, and the installed capacity of new energy sources will reach 28 million kilowatts. The power will be transmitted to other regions through one ultra-high voltage DC line and four 750 kV interconnection lines. The system also has problems with power angle stability and overvoltage.

[0103] according to Figure 2 , Figure 3 The system structure derivation and analysis of the influence of new energy unit parameters on different stability problems caused by different fault disturbances.

[0104] The flowchart of the corresponding wind power adaptive control optimization strategy for fault disturbance identification is as follows: Figure 4As shown in the flowchart, after extensive simulations, the voltage curves of wind power and photovoltaic power under different fault conditions are analyzed. Figure 5 As shown; the voltage drop distribution is obtained as follows Figure 6 As shown. Then, the voltage change rate and the voltage rebound recovery time when entering the low-break-threshold are calculated according to the formula.

[0105]

[0106] t re =t u＝1.05umin -t u＝umin

[0107] In the formula, u0, u min These are the initial and minimum voltage values, t. u＝1.05umin and t u＝umin These represent the moment when the voltage reaches its minimum and the moment when it rebounds by 5% from the minimum.

[0108] Based on calculations using voltage drop values, during AC fault disturbances, the voltage drop rate ranges from 10.03 pu / s to 14.23 pu / s, while the voltage rebound time ranges from 18 ms to 34 ms. During DC fault disturbances, the voltage drop rate ranges from 4.43 pu / s to 6.11 pu / s, while the voltage rebound time ranges from 63 ms to 91 ms. Therefore, a threshold value k1 for the voltage drop rate can be set to 8 p.u. / s, serving as the primary criterion for distinguishing between AC and DC fault disturbances. A value greater than the threshold value is initially identified as an AC fault disturbance, while a value less than the threshold value is initially identified as a DC fault disturbance. A threshold value k2 for the low-break-in threshold rebound recovery time is selected as 45 ms, and a voltage threshold value k3 is 0.26 pu, serving as the secondary criterion for distinguishing between AC fault disturbances and commutation DC fault disturbances. By using these criteria to differentiate between different types of faults, short-circuit faults and commutation failure faults can be effectively distinguished.

[0109] Three different parameters were used to compare the optimization effects. Table 1 shows the parameters set for the three methods. Scheme 1 is the parameter selection scheme commonly used by existing manufacturers, which specifically reduces the active current and active current recovery speed, and increases the reactive current to meet the reactive power support capability requirements of national standard grid connection, thus better meeting the grid connection needs of the equipment. Scheme 2, on the other hand, selected a compromise parameter, which increases the ability to cope with overvoltage problems by reducing the robustness to power angle stability.

[0110] Table 1 Comparison of Control Parameters

[0111]

[0112] The voltage rise problem is most severe during periods of high wind power generation. Considering a wind power simulcast rate of 0.7, the renewable energy output of Shuofang, Yuheng, and Xiazhou is 7.5 million kilowatts. Under the 6 million kW DC mode, considering overvoltage constraints (i.e., the turbine terminal voltage does not exceed 1.3 pu), the maximum near-field wind power output capacity is only 5.2 million kilowatts with default parameters. After optimization using a compromise scheme, it can reach 5.8 million kilowatts. With adaptive parameters, the renewable energy output reaches 6.8 million kilowatts. Considering the transient power angle stability problem (i.e., ensuring the power angle does not become unstable after a short-circuit fault), the maximum cross-sectional transmission capacity is only 5 million kilowatts with default parameters. After optimization using a compromise scheme, it can reach 5.2 million kilowatts. With adaptive parameters, the renewable energy output reaches 5.6 million kilowatts. The relevant results are shown in Table 2.

[0113] Table 2. Delivery Capacity under Different Schemes

[0114]

[0115] As can be seen from Table 2, since the adaptive low-voltage switching strategy selects the active and reactive current control values ​​based on the voltage drop and rate of change of the wind turbine terminal, it is better able to adapt to the response requirements after the fault. Under different fault conditions, the effect of the adaptive low-voltage switching strategy is better than that of the traditional optimization scheme.

[0116] After an AC system fault occurs, the controller automatically adjusts the active power undercurrent factor K. 2_Id_LV Set it to 0.2.

[0117] At this point, the power angle curves of near-field thermal power units under different schemes are as follows: Figure 7 As shown. Because the active power output of the unit needs to be reduced when an AC fault disturbance occurs, K needs to be reduced accordingly. 2_Id_LV And increase K 2_Iq_LV It can be seen that the adaptive control strategy performed best, followed by Scheme 1, while Scheme 2 performed worst, because of the active power low-throughput current coefficient K. 2_Id_LV Reducing the angle of attack will decrease the risk of instability.

[0118] After a DC fault disturbance occurs, the controller automatically adjusts the active power undercurrent coefficient K. 2_Id_LV Set to 1, reactive power low-throughput current coefficient K 2_Iq_LV Set it to 0.2.

[0119] At this point, the wind turbine terminal voltage curves under different schemes are as follows: Figure 8 As shown. Since the active power output of the unit needs to be increased when a DC fault disturbance occurs, K needs to be increased accordingly. 2_Id_LV Decrease K 2_Iq_LV It can be seen that the adaptive control strategy performed best, followed by scheme 2, while scheme 1 performed the worst, because of the active power low-throughput current coefficient K. 2_Id_LV Increase the reactive power low-throughput current coefficient K2_Id_LV Reducing the voltage will decrease the risk of overvoltage.

[0120] When multiple fault disturbances occur, the power angle curves of near-field thermal power plants under different schemes are as follows: Figure 9 and Figure 10 As shown, the adaptive parameter optimization scheme quickly adjusts the control parameters according to the type of stability problem at this time. When an AC fault disturbance occurs first and then a DC fault disturbance occurs, K... 2_Id_LV First it becomes 0.2, then it becomes 1, K 2_Id_LV The value changes from 1 to 0.2; when a DC system fault disturbance occurs first, followed by an AC system fault disturbance, K... 2_Id_LV First it becomes 1, then it becomes 0.2. By adaptively adjusting the wind power, the system's power angle stability can be continuously improved after AC fault disturbances occur.

[0121] The wind turbine terminal voltage curves under different schemes when multiple fault disturbances occur are as follows: Figure 11 and Figure 12 As shown, by adaptively adjusting the low-voltage parameters of the wind turbine, the transient overvoltage of the system can be reduced after a DC fault disturbance occurs, thereby improving the stable operation capability.

[0122] It is evident that the wind power converter control parameter adaptive switching method based on disturbance identification of the present invention has the best effect under various conditions and significantly improves the system's power transmission capacity.

[0123] This invention also provides another wind power adaptive control optimization method device based on fault identification, including a processor and a memory configured to store a computer program capable of running on the processor; wherein, when the processor is configured to run the computer program, it executes the method steps in the foregoing embodiments.

[0124] In practical applications, the aforementioned processor includes a Field-Programmable Gate Array (FPGA), and the processor can be a Central Processing Unit (CPU) or a Digital Signal Processor (DSP). It is understood that for different devices, the electronic devices used to implement the functions of the aforementioned processor can also be other types, and this embodiment of the invention does not impose specific limitations.

[0125] The aforementioned memory can be volatile memory, such as random-access memory (RAM); or non-volatile memory, such as read-only memory (ROM), flash memory, hard disk drive (HDD), or solid-state drive (SSD); or a combination of the above types of memory, and provides instructions and data to the processor.

[0126] In an exemplary embodiment, the present invention also provides a computer-readable storage medium for storing a computer program.

[0127] Optionally, the computer-readable storage medium can be applied to any of the methods in the embodiments of the present invention, and the computer program causes the computer to execute the corresponding processes implemented by the processor in the various methods of the embodiments of the present invention. For the sake of brevity, these will not be described in detail here.

[0128] In the several embodiments provided by this invention, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0129] The above embodiments are merely illustrative of the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solutions based on the technical concept proposed in this invention shall fall within the scope of protection of this invention.

Claims

1. A wind power adaptive control optimization method based on fault identification, characterized in that... Includes the following steps: Step 1: Analyze the different stability problems caused by different fault disturbance signals in the power grid; Step 2: Monitor the wind turbine terminal voltage curves in the region after different fault disturbances occur, extract the key characteristics of the wind turbine terminal voltage under two disturbances: DC commutation failure and AC short circuit fault, and obtain the threshold value to distinguish between DC fault disturbances and AC fault disturbances in the power grid. Step 3: Based on the key characteristics of the terminal voltage extracted in Step 2, the commutation failure disturbance and the AC short circuit disturbance are distinguished by two-layer judgment logic through the dual criteria of voltage change rate and voltage rebound recovery time when entering the low breakdown threshold. Based on this, it is determined whether the power angle stability problem or the overvoltage problem is more serious at this time. Step 4: Adaptively select wind power controller parameters based on the types of disturbances and stability problems identified in Step 3, in order to improve the stable operation capability of the power grid under different disturbances. In step 3, the commutation failure disturbance and the AC short-circuit disturbance are distinguished through two layers of judgment logic using both the voltage change rate and the voltage recovery time to the low breakdown threshold. First, the voltage change rate is compared with a voltage change rate threshold. If the voltage change rate exceeds the threshold, an AC short-circuit fault is preliminarily determined, and the low-voltage ride-through active power factor is reduced. This reduces the active power generated by the wind turbine during fault ride-through; otherwise, a DC commutation failure is preliminarily determined, increasing the low-voltage ride-through active power factor. Reduce the reactive power coefficient during low-voltage ride-through. This increases the active power generated by the wind turbine during fault ride and reduces the reactive power generated by the wind turbine during fault ride. Then, when the voltage rebounds to a preset percentage from the lowest point, the recovery time of the voltage entering the low breakdown threshold is calculated, and the recovery time of the voltage entering the low breakdown threshold is compared with the time threshold value. If the recovery time of the voltage entering the low breakdown threshold is greater than the time threshold value, a secondary determination is made that an AC short circuit fault has occurred; otherwise, a secondary determination is made that a DC commutation failure fault has occurred. Compare the two judgment results to see if they are consistent. If they are consistent, maintain the low-voltage ride-through active power coefficient set during the initial judgment. Low-voltage ride-through reactive power coefficient constant; Otherwise, the low-voltage ride-through active coefficient will be reduced. Low-voltage ride-through reactive power coefficient Set as a compromise value.

2. The method as described in claim 1, characterized in that: In step 1, when a DC fault or AC fault disturbance occurs in the power grid, the wind turbine is disturbed and its voltage drops, entering a low-voltage ride-through state. In the low-voltage ride-through state, reducing the active current can improve the power angle stability characteristics. Adjusting the active current and reactive current can reduce the overvoltage extreme value.

3. The method as described in claim 1, characterized in that: In step 2, key features of the generator terminal voltage are extracted, including extracting a large number of generator terminal voltage curves of wind turbines under commutation failure and AC short circuit faults in the power grid and performing normalization processing, thereby extracting key features of the generator terminal voltage.

4. The method as described in claim 1, characterized in that: In step 3, if the disturbance is determined to be an AC short circuit, the power angle stability problem is considered to be more serious; if the disturbance is determined to be a commutation failure, the overvoltage problem is considered to be more serious.

5. The method as described in claim 1, characterized in that: In step 3, the voltage change rate and voltage recovery time after entering the low breakdown threshold The calculation method is as follows: ， ， In the formula, , These are the initial and minimum voltage values, respectively. and These represent the moments when the voltage reaches its minimum and when it rebounds by 5% from the minimum. This is the time it takes for the voltage to drop from its initial value to its lowest point.

6. A wind power adaptive control optimization system based on fault identification, characterized in that... include: The analysis module is used to analyze the different stability problems caused by different fault disturbance signals in the power grid; The key feature extraction module is used to extract key features of wind turbine terminal voltage under two types of disturbances: DC commutation failure and AC short circuit fault, based on the wind turbine terminal voltage curves in the region after different fault disturbances occur. The logic judgment module is used to distinguish between commutation failure disturbance and AC short circuit disturbance based on the key features of the terminal voltage extracted by the key feature extraction module, by the voltage change rate and the voltage rebound recovery time when the voltage enters the low breakdown threshold, and to determine whether the power angle stability problem or the overvoltage problem is more serious at this time. as well as, The parameter selection module is used to adaptively select wind power controller parameters based on the type of disturbance and stability problem determined by the logic judgment module, so as to improve the stable operation capability of the power grid under different disturbances. Among them, the voltage change rate and the voltage recovery time to the low breakdown threshold are used to distinguish between commutation failure disturbances and AC short-circuit disturbances, including, First, the voltage change rate is compared with a voltage change rate threshold. If the voltage change rate exceeds the threshold, an AC short-circuit fault is preliminarily determined, and the low-voltage ride-through active power factor is reduced. This reduces the active power generated by the wind turbine during fault ride-through; otherwise, a DC commutation failure is preliminarily determined, increasing the low-voltage ride-through active power factor. Reduce the reactive power coefficient during low-voltage ride-through. This increases the active power generated by the wind turbine during fault ride and reduces the reactive power generated by the wind turbine during fault ride. Then, when the voltage rebounds to a preset percentage from the lowest point, the recovery time of the voltage entering the low breakdown threshold is calculated, and the recovery time of the voltage entering the low breakdown threshold is compared with the time threshold value. If the recovery time of the voltage entering the low breakdown threshold is greater than the time threshold value, a secondary determination is made that an AC short circuit fault has occurred; otherwise, a secondary determination is made that a DC commutation failure fault has occurred. Compare the two judgment results to see if they are consistent. If they are consistent, maintain the low-voltage ride-through active power coefficient set during the initial judgment. Low-voltage ride-through reactive power coefficient Keep it unchanged; otherwise, the low-voltage ride-through active power factor will be changed. Low-voltage ride-through reactive power coefficient Set as a compromise value.

7. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor; characterized in that: When the processor executes the computer program, it implements the steps of the wind power adaptive control optimization method based on fault identification as described in any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program; characterized in that: When the computer program is executed by the processor, it implements the steps of the wind power adaptive control optimization method based on fault identification as described in any one of claims 1 to 5.

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