A wind power system transient power angle stability online early warning method and device

Abstract

The application discloses a kind of containing wind power system transient power angle stability online early warning method and device, belong to electric power system stability analysis technical field, the method includes: when system is switched to certain stable operating state with generator mode etc., first, each expected fault is simulated offline once, obtains key information.Second, the electromagnetic power curve before, during, after cleaning of wind power system fault is calculated, and then the critical equivalent inertia under the expected fault set of system is calculated.Finally, the transient critical inertia index is calculated by the equivalent inertia of the current operating state of the system and the critical equivalent inertia, and the system instability risk is early warned.The application can avoid the defect of insufficient rapidity of a large number of simulations of complex system, provide practical early warning index to measure the current safety state of the system for the operator to refer when the system is not subject to fault, can take control means early to avoid transient power angle instability caused by fault, thereby improving the safety of electric power system, and has strong engineering practicability.

Description

Technical Field

[0001] This invention belongs to the field of power system stability analysis technology, and more specifically, relates to an online early warning method and device for transient power angle stability of wind power systems. Background Technology

[0002] Transient power angle stability is a crucial issue for the safety and stability of power systems. Driven by the "dual carbon" goal, the penetration rate of new energy sources, represented by wind power, is rapidly increasing, altering the grid inertia characteristics and adding complexity to the transient power angle stability problem. Simultaneously, large-scale power outages caused by system synchronism are occurring frequently, resulting in severe losses. After a system failure, there is limited time to assess system stability and implement stabilization measures, and relay protection devices and other measures are at risk of malfunction.

[0003] In recent years, wide-area measurement systems (WAMS) have been widely deployed in actual power grids to collect real-time and rapid power grid operation information. Furthermore, the Northwest Power Grid constructed an online inertia monitoring system in 2019, providing a data foundation for online transient power angle stability assessment. Currently, online transient power angle stability assessment methods mainly fall into several categories: time-domain simulation, response trajectory, artificial intelligence, and direct methods. Due to the complex network structure of new energy systems, the speed of time-domain simulation cannot meet the requirements, and response trajectory and artificial intelligence methods struggle to quantify safety margins. Direct methods include extended equal area criterion (EEAC) and transient energy function, offering good quantification results. However, existing online transient power angle assessment methods utilize post-fault system information, requiring a period of time after the fault occurs. Moreover, the primary application of transient stability assessment methods remains conventional systems.

[0004] Existing research on transient power angle stability of wind-powered systems focuses primarily on qualitative analysis of transient power angle stability after wind power integration, without proposing practical quantitative evaluation methods. In summary, current online transient stability assessments of wind-powered systems suffer from shortcomings, including reliance on post-fault system data, insufficient consideration of wind farm transient characteristics, and a lack of practical quantitative indicators. Summary of the Invention

[0005] To address the aforementioned deficiencies or improvement needs of existing technologies, this invention provides an online early warning method and device for transient power angle stability in wind-powered systems. Its purpose is to calculate the transient critical inertia index using the current equivalent inertia and the critical equivalent inertia before a fault occurs in a wind-powered system; and to quantify and warn of the transient power angle instability risk of the system. This solves the existing technical problems of relying on post-fault system data and insufficient consideration of wind farm transient characteristics in online transient stability assessment of wind-powered systems.

[0006] To achieve the above objectives, according to one aspect of the present invention, an online early warning method for transient power angle stability of wind power systems based on critical inertia is provided, wherein the following steps are performed when the system transitions to a new stable state:

[0007] Data update phase: S1: Update the online dataset and anticipated fault set collected by the power grid wide-area monitoring system to obtain inertia information;

[0008] Offline simulation phase: S2: Use the inertia information to perform offline simulation for each of the expected faults in the expected fault set; the offline simulation involves angle-of-attack instability, thereby obtaining the unit cluster, wind turbine transient response curve, and wind turbine terminal voltage information under each of the expected faults;

[0009] Critical inertia calculation stage: S3: Calculate the current equivalent inertia corresponding to each anticipated fault using the inertia information and the generator grouping results; S4: Calculate the electromagnetic power curves of the wind power system before, during, and after the fault using the generator grouping, the wind turbine transient response curve, and the wind turbine terminal voltage information during the fault, in order to obtain the system's equivalent limit cut-off angle, and then calculate the critical equivalent inertia of the system under the anticipated fault set; S5: Calculate the transient critical inertia index using the current equivalent inertia and the critical equivalent inertia;

[0010] Risk warning stage: S6: Based on the transient critical inertia index, a warning is issued for the risk of transient power angle instability of the system.

[0011] In one embodiment, the expression for the current equivalent inertia in S3 is:

[0012]

[0013] Among them, H OMIB S is the equivalent inertia of a single-machine infinite system. B As the baseline capacity, S sys This is the equivalent capacity for the transient power angle stability of the system under a grouping condition.

[0014] In one embodiment, the S sys The expression is:

[0015]

[0016] Among them, S Ni S Nj These are the rated capacities of the i-th generator in the leading group S and the j-th generator in the lagging group A, respectively.

[0017] In one embodiment, S4 includes:

[0018] Electromagnetic power curves before, during, and after a wind farm fault are calculated using power system stability analysis and control EEAC theory, taking into account the characteristics of the wind farm.

[0019] The equivalent limit cut-off angle of the system is obtained by using a piecewise approximate linear method, and then the critical equivalent inertia H of the system under the expected fault set is calculated. sysmin .

[0020] In one embodiment, S5 includes:

[0021] The formula for calculating the transient critical inertia index TCiI is:

[0022] In one embodiment, the online dataset in the data update phase includes: the inertia of each unit, the terminal voltage and current phasors of each synchronous unit collected by the PMU, the currently operating system admittance matrix, and the input power of each unit.

[0023] In one embodiment, S6 includes:

[0024] When the transient critical inertia index TCiI < 0, that is, the current equivalent inertia of the system is less than the critical equivalent inertia, the power angle difference during the acceleration phase exceeds the limit cut-off angle, and the system transient power angle becomes unstable.

[0025] When the transient critical inertia index TCiI>0, the current equivalent inertia of the system is greater than the critical equivalent inertia, the power angle difference during the acceleration phase is less than the limit cut-off angle, and the transient power angle of the system is stable.

[0026] Among them, the smaller the TCiI, the weaker the transient power angle stability of the system.

[0027] According to another aspect of the present invention, a transient power angle stability online early warning device for a wind power system based on critical inertia is provided, for executing the aforementioned transient power angle stability online early warning method for a wind power system based on critical inertia when transitioning to a new stable state, comprising:

[0028] The data update module is used to update the online dataset and anticipated fault set collected by the power grid wide-area monitoring system to obtain inertia information;

[0029] The offline simulation module is used to perform offline simulation of each expected fault in the expected fault set using the inertia information; the offline simulation involves angle-of-attack instability, thereby obtaining the unit cluster, wind turbine transient response curve, and wind turbine terminal voltage information during the fault under each of the expected faults.

[0030] The critical inertia calculation module is used to calculate the current equivalent inertia corresponding to each anticipated fault using the inertia information and the generator grouping results; to calculate the electromagnetic power curves of the wind power system before, during, and after the fault using the generator grouping, the wind turbine transient response curve, and the wind turbine terminal voltage information during the fault, in order to obtain the system's equivalent limit cut-off angle, and then calculate the critical equivalent inertia of the system under the anticipated fault set; and to calculate the transient critical inertia index using the current equivalent inertia and the critical equivalent inertia.

[0031] The risk warning module is used to provide early warning of the system's transient power angle instability risk based on the transient critical inertia index.

[0032] According to another aspect of the invention, an electronic device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of the method.

[0033] According to another aspect of the invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the method.

[0034] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects:

[0035] 1. This invention fully considers the equivalent external characteristics of multi-stage wind farm faults and combines the WAMS system with a small amount of simulation to obtain key data information. Simultaneously, based on the system inertia level, this invention proposes a practical inertia index to quantitatively warn of the risk of transient power angle instability in the system, thus providing important reference for operators to understand the system's stability level and take timely stabilization measures.

[0036] 2. This invention innovatively integrates the transient power angle instability risk early warning method with online rolling updates. It takes the fixed interval of running time or the system transitioning to a new stable state due to changes in startup mode as the starting point, and takes actual system application as the starting point. It fully combines and utilizes the real-time operating data of the system, so that the analysis results are more consistent with reality and can be applied to engineering practice.

[0037] 3. This invention utilizes the piecewise equivalence theory to simplify the complex and unsolvable nonlinear stage into a multi-segment uniform acceleration process. It employs an iterative method to fully connect the initial and final values ​​of the segmented process, greatly reducing the complexity of analysis and calculation.

[0038] 4. This invention comprehensively considers the impact of wind farms on the transient power angle stability of the system at multiple stages: before, during, and after a fault. It makes reasonable classifications and assumptions, and discusses special problems in a special way. It quantitatively determines the accurate early warning range of the method described in this invention, making the analysis targeted, reliable, and accurate. Attached Figure Description

[0039] Figure 1 This is a flowchart of an online early warning method for transient power angle stability of a wind power system based on critical inertia, provided in an embodiment of the present invention.

[0040] Figure 2 This is a network diagram of a four-unit, two-area wind farm access system provided in an embodiment of the present invention;

[0041] Figure 3a To provide an embodiment of the present invention, a power angle swing curve of each unit corresponding to a fault clearing time of 0.50s is provided; Figure 3b To provide an embodiment of the present invention, a power angle swing curve of each unit corresponding to a fault clearing time of 0.51s is provided;

[0042] Figure 4 This is a transient power output diagram of a doubly-fed wind turbine provided in an embodiment of the present invention;

[0043] Figure 5 This is a schematic diagram of the equivalent segmentation of the acceleration stage according to an embodiment of the present invention;

[0044] Figure 6a The equivalent work angle characteristic curve under critical stability corresponding to a barrier clearing time of 0.50s is provided in an embodiment of the present invention.

[0045] Figure 6b The diagram shows the equivalent work angle characteristic curve under critical stability corresponding to a barrier clearing time of 0.51s, as provided in an embodiment of the present invention. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0047] See Figure 1 This invention provides an online early warning method for transient power angle stability of wind power systems based on critical inertia. When the system transitions to a new stable state at fixed intervals or due to changes in operating conditions or system startup methods, the method includes the following steps:

[0048] S1, update the input data by combining the online data information collected by the WAMS system with the set of anticipated faults established based on research and experience;

[0049] Specifically, the anticipated fault type is selected as the most severe three-phase metallic short circuit, and the fault location is selected at the sending end of the tie line, i.e., at node 8 of the tie line 8-9 near the busbar. The fault duration is 1 second. After the anticipated fault lasts for a period of time, one of the double-circuit lines 8-9 is disconnected, i.e., the fault clearing stage. The fault clearing time is described here using critical stability conditions of 0.50 seconds and 0.51 seconds to illustrate the accuracy of the invention.

[0050] S2, perform an offline simulation for each anticipated fault for 3 to 5 seconds, set a longer fault clearing time, this simulation needs to cause power angle instability, and obtain the unit group, wind turbine transient response curve, and wind turbine terminal voltage information under the anticipated fault.

[0051] Specifically, based on the anticipated fault, a 3-second offline simulation was first performed to determine the following information: (1) Under this fault, the leading groups are synchronous generators G1 and G2, and the lagging groups are G3 and G4, based on... Figure 3a and Figure 3b (2) The wind farm terminal voltage is 0.49 pu during the initial stage of the fault, so that the wind turbine can be converted from a constant power source to a constant impedance during the calculation in this stage; (3) The transient response characteristics of the wind farm in the three stages of the fault, before, during and after the fault is cleared, and the output power of the wind farm after the fault is cleared are obtained by fitting the function to obtain the formula. Undetermined coefficients k Δ1 =1.033, k Δ2 =13.78.

[0052] S3, using the inertia information in S1 and the grouping results obtained in S2, calculate the current equivalent inertia of the operating system under different anticipated faults;

[0053] Specifically, calculate the equivalent capacity of the transient power angle stability of the system under this group:

[0054]

[0055] Calculate the current equivalent capacity S of the system. sys It is 900MVA. Base capacity S B Further calculate the current equivalent inertia of the system for 100 MVA: Calculate the current equivalent inertia H of the system sys The value is calculated to be 6.346s based on the clustering situation.

[0056] S4. Using the information obtained from S1, calculate the electromagnetic power curves of the wind power system before, during, and after the fault is cleared, obtain the equivalent limit cut-off angle of the system, and then calculate the critical equivalent inertia of the system under the expected fault set.

[0057] Specifically, a segmented equivalent method is used to keep the equivalent electromagnetic power constant in each segment of the acceleration process, meaning each segment is a uniformly accelerated motion. This is achieved by solving for the segment time and the final rotational speed segment by segment. Figure 5 As shown in the figure. If the transient fluctuation characteristics of the wind farm are not considered, the calculation using the formula will yield t. c At 0.50s, the critical inertia of the system is H. sysmin It is 5.546s; t c =0.51s, H sysmin It is 5.768s. Considering the transient fluctuation characteristics of the wind farm, t c The critical inertia H of the system at 0.50s sysmin It is 6.334s; t c =0.51s, H sysmin It is 6.588s.

[0058] S5, the transient critical inertia index TCiI is calculated using the current equivalent inertia calculated in S3 and the critical equivalent inertia calculated in S4. For different anticipated faults, the transient critical inertia index TCiI of the system is... sys The minimum value of each TCiI;

[0059] Specifically, through the formula Calculate the transient critical inertia index TCiI of the system. If the transient fluctuation characteristics of the wind farm are not considered, t c =0.50s, TCiI is 0.126; t c At 0.51s, TCiI is 0.091. Considering the transient fluctuation characteristics of the wind farm, t c At 0.50s, TCiI is 0.002; t c At 0.51s, TCiI is -0.038.

[0060] S6, based on the transient critical inertia index TCiI of S5, the system is warned of transient power angle instability risk;

[0061] Specifically, when TCiI < 0, the current equivalent inertia of the system is less than the critical equivalent inertia, the power angle difference during the acceleration phase exceeds the limit cutoff angle, and the system's transient power angle becomes unstable. When TCiI > 0, the current equivalent inertia of the system is greater than the critical equivalent inertia, the power angle difference during the acceleration phase is less than the limit cutoff angle, and the system's transient power angle is stable. The smaller the TCiI, the weaker the system's transient power angle stability. The multi-level safety warning risk of power system transient power angle stability is divided into four levels, as shown in Table 1.

[0062] Table 1 Risk Classification for Transient Power Angle Stability Early Warning

[0063] TCiI>C2 high inertia Low risk <![CDATA[C1<TCiI ≤ C2]]> Medium inertia Medium risk <![CDATA[0<TCiI ≤ C1]]> low inertia High risk <![CDATA[TCiI ≤ 0]]> Ultra-low inertia Extremely high risk

[0064] The system's risk classification threshold coefficients C1 and C2 are set to 0.10 and 0.40, respectively. If the transient fluctuation characteristics of the wind farm are not considered, t c At 0.50s, the system is determined to be stable and in a medium-risk state; t c At 0.51s, the system is determined to be stable but in a high-risk state. Considering the transient fluctuation characteristics of the wind farm, t c At 0.50s, the system is determined to be stable but in a high-risk state; t c At 0.51s, the system is determined to be unstable and in an extremely high-risk state.

[0065] like Figure 2 As shown, the test system is a doubly-fed induction generator (DFIG) wind farm connected to a four-unit, two-area system. The synchronous generator uses a third-order model, and the DFIG model includes both turbine-side and grid-side converter control. The turbine-side converter uses MPPT control. The DFIG wind farm uses multiple 2MW rated single-unit DFIGs aggregated into an equivalent model, and the load uses a constant impedance model. Bus 6 connects to a 300MW rated DFIG wind farm, using a fifth-order DFIG model. It connects to node 5 via a transformer and synchronous generator G2, with a load power of 1700MW and a wind power penetration rate of 17.65%.

[0066] Under the aforementioned preset fault condition, when the fault clearing time is 0.50s, the initial swing of the power angles of each synchronous generator unit shows a certain degree of separation, which then gradually converges, and the system's transient power angle stabilizes. Figure 3a When the fault clearing time is 0.51s, the power angles of each synchronous generator unit gradually approach separation, and the system's transient power angle becomes unstable, such as... Figure 3b The transient response characteristics of the wind farm in the three stages before, during, and after the fault are as follows: Figure 4 As shown.

[0067] like Figure 5 As shown, P eIII and P' eIII These are the electromagnetic power theoretical calculation curves for the fault clearing phase, excluding and considering the transient fluctuation characteristics of the wind farm, respectively. e The actual electromagnetic power curve is shown, and the area A3 represents the reduction in deceleration area due to the transient fluctuation characteristics of the wind farm. The actual electromagnetic power curve shows that the system is stable at a clearance time of 0.50s and unstable at 0.51s. The above warning steps do not consider the possibility of missed instability detection due to the transient fluctuation characteristics of the wind farm, while this invention fully considers the characteristics of the wind farm, resulting in an accurate warning method. Figure 6aThe equivalent work angle characteristic curve under critical stability when the fault clearing time is 0.50s; Figure 6b The equivalent work angle characteristic curve under critical stability when the fault clearing time is 0.51s.

[0068] To further verify the reliability of the proposed method, the rated power output of the wind farm was adjusted to 100MW, 300MW, and 600MW by changing the number of connected DFIGs, corresponding to wind power penetration rates of 5.88%, 17.65%, and 35.29% for the system, respectively. Transient power angle stability was determined under different wind power penetration rates, and the results are shown in Table 2.

[0069] Table 2 Stability early warning results under different wind power penetration rates

[0070]

[0071] The results show that, without considering the transient fluctuation characteristics of wind farms, the system exhibits missed detections at different penetration rates. The critical inertia judgment method proposed in this invention can accurately predict the system stability and quantify the transient power angle stability safety margin based on the system inertia, thus defining the system safety risks. Furthermore, the test results analysis shows that as the penetration rate of wind farms connected to the interconnected system's sending end continues to increase, the impact on the system's transient stability is not monotonic.

[0072] This invention presents an online early warning method for transient power angle stability in wind power systems based on critical inertia. This method eliminates the need for post-fault assessment. Regarding transient power angle stability, wind farm output power exhibits short-term fluctuations after fault clearance, making transient power angle stability analysis and assessment crucial. The system's equivalent inertia is closely related to transient power angle stability. Numerical system tests demonstrate that the proposed transient critical inertia index has high accuracy in stability assessment. The proposed method, based on anticipated faults and the WAMS system, features real-time performance, predictive capabilities, and indexation, providing system safety indicators for controllers. This invention, combining data and models, holds great promise for large-scale power grid applications.

[0073] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for online early warning of transient power angle stability in wind power systems based on critical inertia, characterized in that, When transitioning to a new stable state, the following steps are performed: Data update phase: S1: Update the online dataset and anticipated fault set collected by the power grid wide-area monitoring system to obtain inertia information; Offline simulation phase: S2: Use the inertia information to perform offline simulation for each anticipated fault in the anticipated fault set; The offline simulation involves angle-of-attack instability, thereby obtaining the unit clusters, transient response curves of the wind turbine, and terminal voltage information of the wind turbine during the fault under each of the anticipated faults. Critical inertia calculation stage: S3: Calculate the current equivalent inertia under each anticipated fault using the inertia information and the unit grouping results; S4: Calculate the electromagnetic power curves of the wind power system before, during, and after the fault using the unit group, the transient response curve of the wind turbine, and the terminal voltage information of the wind turbine during the fault, in order to obtain the equivalent limit cut-off angle of the system, and then calculate the critical equivalent inertia of the system under the expected fault set; S5: Calculate the transient critical inertia index using the current equivalent inertia and the critical equivalent inertia; Risk warning stage: S6: Based on the transient critical inertia index, a warning is issued for the risk of transient power angle instability of the system.

2. The online early warning method for transient power angle stability of wind power systems based on critical inertia as described in claim 1, characterized in that, The expression for the current equivalent inertia in S3 is: ； in, The equivalent inertia of a single-machine infinite system. As the baseline capacity, This is the equivalent capacity for transient power angle stability of the system under a grouping condition.

3. The online early warning method for transient power angle stability of wind power systems based on critical inertia as described in claim 2, characterized in that, The The expression is: ； in, S Ni , S Nj They are respectively the leading group S. i The generator and the lag group A j The rated capacity of the generator.

4. The online early warning method for transient power angle stability of wind power systems based on critical inertia as described in claim 2, characterized in that, S4 includes: Electromagnetic power curves before, during, and after a wind farm fault are calculated using power system stability analysis and control EEAC theory, taking into account the characteristics of the wind farm. The equivalent limit cut-off angle of the system is obtained by using a piecewise approximate linear method, and then the critical equivalent inertia of the system under the anticipated fault set is calculated. H sysmin .

5. The online early warning method for transient power angle stability of wind power systems based on critical inertia as described in claim 4, characterized in that, S5 includes: The formula for calculating the transient critical inertia index TCiI is: .

6. The online early warning method for transient power angle stability of wind power systems based on critical inertia as described in claim 1, characterized in that, The online dataset in the data update phase includes: the inertia of each unit, the terminal voltage and current phasors of each synchronous unit collected by the PMU, the currently operating system admittance matrix, and the input power of each unit.

7. The online early warning method for transient power angle stability of wind power systems based on critical inertia as described in any one of claims 1-6, characterized in that, S6 includes: When the transient critical inertia index TCiI < 0, that is, the current equivalent inertia of the system is less than the critical equivalent inertia, the power angle difference during the acceleration phase exceeds the limit cut-off angle, and the system transient power angle becomes unstable. When the transient critical inertia index TCiI>0, the current equivalent inertia of the system is greater than the critical equivalent inertia, the power angle difference during the acceleration phase is less than the limit cut-off angle, and the transient power angle of the system is stable. Among them, the smaller the TCiI, the weaker the transient power angle stability of the system.

8. An online early warning device for transient power angle stability of a wind power system based on critical inertia, characterized in that, The method for online early warning of transient power angle stability of a wind power system based on critical inertia, as described in any one of claims 1-7, is used to execute the method according to any one of claims 1-7 when transitioning to a new steady state, comprising: The data update module is used to update the online dataset and anticipated fault set collected by the power grid wide-area monitoring system to obtain inertia information; The offline simulation module is used to perform offline simulation of each expected fault in the expected fault set using the inertia information; the offline simulation involves angle-of-attack instability, thereby obtaining the unit cluster, wind turbine transient response curve, and wind turbine terminal voltage information during the fault under each of the expected faults. The critical inertia calculation module is used to calculate the current equivalent inertia corresponding to each anticipated fault using the inertia information and the generator grouping results; to calculate the electromagnetic power curves of the wind power system before, during, and after the fault using the generator grouping, the wind turbine transient response curve, and the wind turbine terminal voltage information during the fault, in order to obtain the system's equivalent limit cut-off angle, and then calculate the critical equivalent inertia of the system under the anticipated fault set; and to calculate the transient critical inertia index using the current equivalent inertia and the critical equivalent inertia. The risk warning module is used to provide early warning of the system's transient power angle instability risk based on the transient critical inertia index.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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