Method and device for monitoring transient voltage stability of tracking-networking hybrid new energy station

Abstract

The invention discloses a tracking-construction network hybrid new energy station transient voltage stability monitoring method and device, and the method comprises the steps: constructing a voltage trajectory variation equation in a normal operation mode and a low voltage ride through mode of a hybrid new energy station, and bringing the key operation parameters of a system into an index input range; constructing a switching compensation matrix during operation mode switching of the hybrid new energy station, wherein the switching compensation matrix is used for determining a required compensation gradient; carrying out numerical integration on the voltage trajectory variation equation in the integration step length, and calculating an SSMLE evaluation index according to an integral final value matrix eigenvalue; the integral result is corrected by using the switching compensation matrix at the operation mode switching moment; according to the method, an SSMLE evaluation index evolution curve is obtained, a transient voltage stability criterion suitable for online evaluation is given based on the SSMLE evaluation index evolution curve and a maximum Lyapunov exponent stability discrimination mechanism, and transient voltage discrimination is realized on a detection device. The device comprises a processor and a memory.

Description

Technical Field

[0001] This invention relates to the field of power systems, and in particular to a method and device for monitoring transient voltage stability at hybrid renewable energy power stations connected to the grid. Background Technology

[0002] With the large-scale grid connection of new energy power sources and the development of power electronic equipment control technology, a hybrid grid connection method for new energy power plants, which includes both grid-following (GFL) and grid-forming (GFM) generation units, has been proposed to enable new energy power plants to have rapid power response characteristics and frequency / voltage support capabilities. However, under severe grid faults, it is difficult for the units in the hybrid new energy power plant to achieve self-stabilization, and the dynamic interactions between the units and between the source and the grid will further deteriorate the voltage stability characteristics of the system. [1-2] Furthermore, due to the weak overcurrent capacity of power electronic equipment, it is prone to triggering the limiting circuit under conditions such as sudden voltage drops, which will further threaten the stable operation of hybrid renewable energy power plants. [3] .

[0003] Currently, voltage stability analysis and monitoring of hybrid renewable energy power plants are mainly based on full-order system models, using trajectory eigenvalues ​​or phase plane methods to analyze the influence trends and thresholds of generator injection current and control system parameters on system stability. However, with increasing system complexity and the switching behavior of converters in multiple control modes during transients, these methods have revealed more and more limitations. These limitations are mainly reflected in the following aspects: these methods are mostly used for static stability analysis and to guide control parameter design, but under large disturbances, hybrid renewable energy power plants exhibit strong time-varying nonlinear characteristics, making it difficult to quickly and accurately describe the evolution of the system voltage trajectory; furthermore, due to the switching of control strategies during transients, a single system model is difficult to accurately characterize the system's time-varying characteristics, resulting in low accuracy of voltage stability monitoring results and difficulty in online application.

[0004] To meet the engineering requirements for real-time monitoring of transient voltage stability, the Maximum Lyapunov exponent (MLE), derived from the ergodic theory of nonlinear dynamical systems, has been used in recent years for monitoring the transient voltage stability of power systems. [4] Depending on the solution method, MLE can be divided into time-delay embedding (TMLE). [5] MLE based on discrete mapping model [6] And MLE based on QR decomposition (QRMLE) [7]Phase-space embedding-based MLE has been applied to power system transient voltage stability monitoring. [8-9] However, it often suffers from sign characteristic oscillations, requiring a preset observation time window to determine voltage stability. While the Mode Optimization Leakage (MLE) based on the eigenvalues ​​of the Jacobian matrix can be used to analyze the impact of control parameters on system stability and obtain the system's stable operating boundary, it is typically used for static stability analysis and verification. It has not yet been used to obtain transient voltage stability by real-time calculation of the MLE curve and mining its dynamic evolution characteristics. Furthermore, due to the switching of control strategies in hybrid renewable energy power plants during transients, the construction of transient voltage stability evaluation indices must simultaneously consider the dynamic characteristics of the system under different control strategies and the non-smooth characteristics at the switching point. Therefore, it is necessary to construct a transient voltage evaluation index that simultaneously considers the control characteristics of hybrid renewable energy power plants, monitors the transient voltage stability of renewable energy power plants in real time, and ensures the safe and stable operation of the renewable energy power system.

[0005] References

[0006] [1] Zhan Changjiang, Wu Heng, Wang Xiongfei, et al. A review of stability studies of grid converters [J]. Proceedings of the CSEE, 2023, 43(06): 2339-2359. Yuan Xiaoming, Zhang Meiqing, Chi Yongning, et al.

[0007] [2] Basic challenges and technical routes for dynamic problems in power electronic power systems [J]. Proceedings of the CSEE, 2022, 42(5): 1904-1916. Xie Xiaorong, He Jingbo, Mao Hangyin, et al.

[0008] [3] New problems and classification of stability of “high-voltage and high-efficiency” power systems [J]. Proceedings of the CSEE, 2021, 41(2):461-474. Ma Zihan, Huang Meng, Fu Xikun, et al. Evaluation of weak grid operation capability of new energy power plants under grid-type power access [J / OL]. Automation of Electric Power Systems, 1-11 [2024-11-10].

[0009] [4]Eckmann JP, Ruelle D. Ergodic theory of chaos and strange attractors[J]. Reviews of modern physics, 1985, 57(3):617.

[0010] [5]Rosenstein M T, Collins J J, De Luca C J. A practical method for calculating largest Lyapunov exponents from small data sets[J]. Physica D: Nonlinear Phenomena, 1993, 65(1-2): 117-134.

[0011] [6]Aniszewska D, Rybaczuk M. Lyapunov type stability and Lyapunov exponent for exemplary multiplicative dynamical systems[J]. Nonlinear Dynamics, 2008, 54: 345-354.

[0012] [7]Ramasubramanian K, Sriram M S. A comparative study of computation of Lyapunov spectra with different algorithms[J]. Physica D: Nonlinear Phenomena, 2000, 139(1-2): 72-86.

[0013] [8]Dasgupta S, Paramasivam M, Vaidya U, et al. Real-time monitoring of short-term voltage stability using PMU data[J]. IEEE Transactions on Power Systems, 2013, 28(4): 3702-3711.

[0014] [9]Pinzón J D, Colomé D G. PMU-based online monitoring of short-term voltage stability using Lyapunov exponents[J]. IEEE Latin America Transactions, 2019, 17(10): 1578-1587. Summary of the Invention

[0015] This invention provides a method and device for monitoring transient voltage stability at hybrid renewable energy power plants connected to the grid. Based on the Switching System Maximum Lyapunov Exponent (SSMLE), this invention achieves rapid and accurate monitoring of transient voltage stability and can determine the impact of the grid connection capacity and distance of GFL and GFM generator units on transient voltage stability. Details are described below:

[0016] A first aspect is a method for monitoring transient voltage stability at a hybrid renewable energy power station connected to a grid, the method comprising:

[0017] Construct voltage trajectory variational equations for the normal operation mode and low voltage ride-through mode of hybrid new energy power stations, and incorporate key system operation parameters into the index input range;

[0018] A switching compensation matrix is ​​constructed for the switching of operation modes of hybrid new energy power stations to determine the required compensation gradient;

[0019] Numerical integration is performed on the voltage trajectory variational equation within the integration step, and the SSMLE evaluation index is calculated based on the eigenvalues ​​of the final integral matrix.

[0020] The integration results are corrected using a switching compensation matrix when the operating mode is switched.

[0021] The evolution curve of the SSMLE evaluation index is obtained. Based on the evolution curve of the SSMLE evaluation index and the maximum Lyapunov exponential stability discrimination mechanism, a transient voltage stability criterion suitable for online evaluation is given, and the transient voltage discrimination is implemented on the detection device.

[0022] The voltage trajectory variational equation is: the voltage differential equation is transformed into the voltage trajectory variational equation.

[0023] The switching compensation matrix is ​​as follows:

[0024] Construct the switching compensation matrix J based on the system solution vectors before and after the switching. S ;

[0025] The switching surface equation is written as s(u gd ,u gq )=u gd -u gd0 When switching from subsystem 1 to system 2, the unit vector perpendicular to the switching surface is n = [1, 0]. T When switching back from subsystem 2 to subsystem 1, n = [-1, 0] T .

[0026] The step of correcting the integral result using a switching compensation matrix at the time of operating mode switching includes:

[0027] When the PCC voltage drops below 0.9 times the reference value, LVRT control switching occurs. The gradient correction of the integral final value matrix is ​​performed by switching the compensation matrix, and its eigenvalues ​​are calculated using QR decomposition technology.

[0028] The transient voltage stability criterion applicable to online evaluation is as follows:

[0029] Based on the maximum Lyapunov exponential voltage stability criterion and the evolution characteristics of the SSMLE curve, a transient voltage stability criterion based on SSMLE is given:

[0030] Criterion 1: After the fault is cleared, monitor the amplitude and inflection point of the SSMLE curve in real time. If the inflection point of the SSMLE curve is less than zero, the transient voltage is determined to be stable.

[0031] Criterion 2: After the fault is cleared, monitor the amplitude and inflection point of the SSMLE curve in real time. If the amplitude of the SSMLE curve is greater than zero and lasts for 0.4s, then transient voltage instability is determined.

[0032] In a second aspect, a transient voltage stability monitoring device for a hybrid renewable energy power station with integrated grid and substation, the device comprising: a processor and a memory, wherein the memory stores program instructions, and the processor invokes the program instructions stored in the memory to cause the device to execute the method described in any one of the first aspects.

[0033] Third aspect, a computer-readable storage medium storing a computer program, the computer program including program instructions that, when executed by a processor, cause the processor to perform the method described in any one of the first aspects.

[0034] The beneficial effects of the technical solution provided by this invention are:

[0035] 1. This invention constructs voltage trajectory variational equations for hybrid new energy power stations under different operating conditions, fully considers the transient voltage dynamic characteristics under multiple types of control actions, and accurately reflects the transient voltage evolution behavior.

[0036] 2. This invention solves the SSMLE index by integrating the final value matrix of the voltage trajectory variational equation, effectively avoiding the sign characteristic oscillation problem of TMLE, and can quickly and accurately determine the transient voltage steady state.

[0037] 3. This invention improves the voltage stability monitoring accuracy of SSMLE in non-smooth systems by introducing a switching compensation matrix to correct the integration error during system transient switching.

[0038] 4. This invention can provide grid operation and dispatch personnel with faster and more accurate transient voltage stability information of hybrid new energy power plants, and intuitively analyze the impact of the access capacity and control parameters of GFL and GFM generation units on transient voltage stability, providing a reference for the implementation of emergency control and ensuring the safe and stable operation of the new energy power system. Attached Figure Description

[0039] Figure 1 A flowchart of a method for monitoring transient voltage stability in a hybrid renewable energy power station based on a grid;

[0040] Figure 2 The diagram shows the MLE discrimination results for the transient voltage stability scenario;

[0041] Figure 3 The graph shows the MLE (Mean Leakage) discrimination results for transient voltage instability scenarios.

[0042] Figure 4 This diagram illustrates the impact of the connection distance of the power generation unit on transient voltage stability.

[0043] Figure 5 The diagram shows the impact of the connected capacity of the GFM generator unit on transient voltage stability. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below.

[0045] To address the shortcomings of the prior art regarding the difficulty in real-time and accurate monitoring of transient voltage stability in GFL-GFM hybrid renewable energy power plants, this invention provides a method and device for monitoring transient voltage stability in grid-connected hybrid renewable energy power plants. First, the voltage dynamic characteristics are characterized by constructing voltage trajectory variational equations under normal operation and low voltage ride-through modes, and key system operating parameters are included in the index input range. Then, this invention constructs an evaluation index based on the eigenvalues ​​of the final integral matrix of the voltage trajectory variational equations, effectively avoiding the characteristic oscillation problem of the TMLE sign. Furthermore, this invention introduces a switching compensation matrix to correct the integral results during operating state switching, further improving the accuracy of transient voltage stability judgment results. Finally, the proposed monitoring method can be used to determine the impact of the connection distance and capacity of GFL and GFM generation units on transient voltage stability, providing rapid and accurate transient voltage stability information for hybrid renewable energy power plants while offering a reference for implementing emergency control strategies, ensuring the safe and stable operation of the renewable energy power system.

[0046] Example 1

[0047] A method for monitoring transient voltage stability in hybrid renewable energy power plants using a grid-connected system, see [link / reference]. Figure 1 The method includes the following steps:

[0048] 101: Construct voltage trajectory variational equations for the normal operation mode and low voltage ride-through mode of hybrid new energy power stations, and incorporate key system operating parameters into the index input range;

[0049] 102: Constructing a switching compensation matrix for the operation mode switching of hybrid new energy power stations J S , used to determine the required compensation gradient;

[0050] 103: Numerically integrate the variational equation of the voltage trajectory within the integration step size Δt, and then use the eigenvalue λ of the final integral matrix to determine the final value. i Calculate the SSMLE evaluation metrics;

[0051] 104: Utilizing the switching compensation matrix J during operation mode switching. S Correct the integral results to reduce the index calculation error caused by non-smoothness of the system;

[0052] 105: Convert steps 101-104 into a computer-readable program, and repeat steps 103-104 several times to obtain the evolution curve of the SSMLE evaluation index. In the formula, i is the dimension of the voltage trajectory variational equation; λ i (jΔt) represents the eigenvalue of the final integral matrix at time jΔt; M is a constant.

[0053] 106: Based on the evolution curve of the SSMLE evaluation index and the maximum Lyapunov exponential stability discrimination mechanism, a transient voltage stability criterion suitable for online evaluation is given, and the transient voltage stability is rapidly discriminated on the detection device, effectively improving the transient voltage stability monitoring efficiency of hybrid new energy power stations.

[0054] In summary, the embodiments of the present invention obtain transient voltage stability information of hybrid new energy power stations through the above steps 101-105, propose an evaluation index calculation method based on the variational equation of voltage trajectory under multiple operating conditions, effectively avoid TMLE sign characteristic oscillation, and introduce a switching compensation matrix to correct integral error at the time of operating state switching, thereby improving the accuracy of voltage stability discrimination of evaluation index in non-smooth switching system, effectively improving the transient voltage stability monitoring efficiency of hybrid new energy power stations, and thus have high practical application value.

[0055] Example 2

[0056] The scheme in Example 1 will be further described below with specific calculation formulas and examples:

[0057] 201: Establish the PCC voltage differential equations for various operating conditions of hybrid renewable energy power plants, including:

[0058] 1) Simplified conditions. The topology and control block diagram of a hybrid renewable energy power station are as follows: Figure 2 As shown. For the GFL generator unit, since the phase-locked loop (PLL) response speed is much lower than that of the inner current loop, when analyzing the transient voltage stability problem on the PLL time scale, the fast-scale adjustment process of the inner current loop can be reasonably ignored. It can be assumed that the inner current loop response has reached a quasi-steady state, and the injected PCC current of the GFL generator unit is equal to the current reference value, i.e., i 1d =i 1dref i 1q =i 1qref Similarly, since the voltage and current loop response speed of the GFM generator unit is much greater than that of the power outer loop, its dynamics can be ignored in transient voltage stability analysis, and u2 = U can be assumed. v δ GFM =θ v .

[0059] 2) During normal operation. First, establish the expression for the output current of the GFL generator unit. Based on the assumptions, neglecting the dynamics of the inner current loop, we can obtain:

[0060] i 1d =i 1dref ,i 1q =i 1qref (1)

[0061] In the formula, i 1d i 1q These are the active and reactive currents injected into the GFL generating unit, respectively; i 1dref and i 1qref These are the reference values ​​for active and reactive currents of the GFL unit, respectively.

[0062] During normal system operation, i 1dref i 1qref Generated by the outer voltage loop, the output current of the GFL generator unit can be expressed as:

[0063]

[0064] In the formula, u dc The DC bus voltage of the GFL generator unit; u dcref This is the reference value for the DC bus voltage; k p1 k is the voltage outer loop proportionality coefficient. i1 x is the voltage outer loop integral coefficient; d1 This is the output of the voltage outer loop integrator.

[0065] Then, the output current expression of the GFM generator unit is established, and its power outer loop adopts virtual synchronous machine control to simulate the operating characteristics of a synchronous generator. The active power-frequency control branch generates the reference angular frequency, and the reactive power-voltage control branch generates the reference voltage amplitude, thereby achieving the autonomous construction of the output voltage amplitude and phase angle. The specific dynamic equation is as follows:

[0066]

[0067] In the formula, ω v To output the reference angular frequency; θ v For its corresponding virtual rotor angle; J and K D These are the rotor inertia coefficient and damping coefficient of the virtual synchronous machine, respectively; T m and T e The torque setpoint and actual output value are obtained from power calculations.

[0068] Based on the assumptions, considering only the outer loop dynamics of the GFM power generation unit, we can obtain:

[0069] u 2d =U v ,u 2q =U v tanθ v (4)

[0070] In the formula, u 2d u 2q These are the dq-axis components of the terminal voltage of the GFM generator unit; U v θ v These are the voltage amplitude and voltage phase angle reference values ​​generated by the power outer loop, respectively.

[0071] u is obtained from equations (3)-(4) 2d u 2q Substituting the expression into equation (5), we obtain the output current of the GFM generator unit as follows:

[0072]

[0073] In the formula, i 2d i 2q R1 and R2 are the active and reactive currents output by the GFM generator unit, respectively; R2 and R3 are the line resistance and reactance collected by the GFM generator unit, respectively.

[0074] Furthermore, the network interface equations are written as follows:

[0075]

[0076] In the formula, u 1d u 1qThese are the dq-axis components of the GFL generator unit terminal voltage; u gd u gq These are the dq-axis components of the grid connection point voltage during normal operation; R1 and X1 are the collection line resistance and reactance of the GFL generator unit, respectively.

[0077] Finally, substituting the output current expressions from equations (1) and (5) into equation (7) and combining them with equation (6), we obtain the PCC voltage differential equation for hybrid new energy power stations during normal operation:

[0078]

[0079] In the formula, C f i is the filter capacitance; gd i gq These are the dq-axis components of the current injected into the PCC point from the power grid; u 1q x represents the q-axis component of the GFL generator unit terminal voltage; pll ω is the output of the phase-locked loop integrator; k is the grid synchronization angular frequency; ω is the output of the phase-locked loop integrator. ppll k is the proportional gain of the phase-locked loop; ipll X represents the integral coefficient of the phase-locked loop; 11 Let X be the variable in the d-axis voltage differential equation. 11 =[u gd ,u gq ,u 1d ,u 2d i 2q i gd ,k i1 ,J,k D ,k Q ,P 2ref Q 2ref ,k ppll ,k ipll ,u dcref C f ,R L2 ]; X 12 Let X be the variable in the q-axis voltage differential equation. 12 =[u gq ,u 2d ,u 2q i 2d ,J,k D ,k Q ,P 2ref Q 2ref ,k ppll ,k ipll C f ,R L2 ].

[0080] 3) During voltage dips. When the voltage at point PCC dips, the GFL generator unit switches to LVRT control mode, prioritizing the injection of reactive current into point PCC to support the voltage. At this time, the active and reactive current command values ​​of the GFL generator unit are calculated using the following formula:

[0081]

[0082] In the formula, i 1ds i 1qs These represent the active and reactive current output by the GFL generator unit during the fault period, respectively; i 1dref i 1qref These represent the reference values ​​of active and reactive current of the GFL generator unit during the fault period, respectively; k is the reactive current control gain. This is the maximum current that the converter can carry.

[0083] During voltage dips, the GFM generator unit regulates the output reactive power of the converter through reactive power-voltage droop control. Its dynamic characteristics can still be modeled as Equations (3)-(4). Similarly, the expression for the terminal voltage u of the GFM generator unit can be obtained. 2ds u 2qs The expression for the output current of the GFM generator unit during the voltage dip is:

[0084]

[0085] In the formula, i 2ds i 2qs These represent the active and reactive current output by the GFM generator unit during the voltage dip, respectively.

[0086] Then, substituting the output current expressions in equations (2) and (8) into equation (9) and combining them with the network interface equation (6), we obtain the PCC voltage differential equation during the voltage sag:

[0087]

[0088] In the formula, u gds u gqs These are the dq-axis components of the PCC point voltage during the fault period; i gds i gqs These are the active and reactive currents injected into the grid during the fault, respectively.

[0089] 202: Based on the voltage differential equations in normal operation mode and low voltage ride-through mode, transform them into voltage trajectory variational equations and construct a switching compensation matrix for system operation mode switching, including:

[0090] 1) Establishment of variational equations for voltage trajectory.

[0091] To preserve the key dynamic characteristics of the system voltage and simplify the calculation, the voltage differential equation is transformed into a voltage trajectory variational equation. Under normal operating conditions, the expression F on the right side of equation (7) in step 201 is... 11 (X 11 ), F 12 (X 12 ) respectively for u gd u gq Take the partial derivative to obtain the elements of the Jacobi matrix J1, and then calculate F. 11 (X 11 ), F 12 (X 12 ) is represented as J1 and voltage vector [u gd ,u gq ] T Multiplying the equations, we obtain the voltage trajectory variational equations for the system's normal operation, specifically:

[0092]

[0093] In the formula, u gd u gq These are the dq-axis components of the PCC voltage, respectively.

[0094] Right now:

[0095]

[0096] In the formula, s(X) is the switching surface equation; E is the identity matrix; and J1 is the Jacobian matrix during normal system operation.

[0097] Similarly, according to equation (10) in step 201, the variational equation for the voltage trajectory during the system voltage dip can be obtained as follows:

[0098]

[0099] In the formula, s(X) is the switching surface equation; E is the identity matrix; and the Jacobian matrix of the system during the J2 voltage sag is as follows:

[0100] 2) Switch to construct the compensation matrix.

[0101] At the moment of system state transition, the MLE based on QR decomposition cannot contain all the dynamic information at the system transition point. Therefore, this embodiment of the invention introduces a transition compensation matrix to correct the MLE calculation error, as described in detail below:

[0102] When the PCC voltage drops below 0.9 times the reference value or recovers to above 0.9 times the reference value after fault clearance, the system switches between normal control mode (subsystem 1) and LVRT control mode (subsystem 2). To measure the degree of non-smooth distortion of the voltage trajectory caused by transient switching, a switching compensation matrix J needs to be constructed based on the system solution vectors before and after the switching. S :

[0103] J S =E + (F2 / ||F1||-e)(e-dtanθ) T (14)

[0104] In the formula, F1 and F2 represent the system solution vectors before and after the switch, i.e., F1 = [u gd ,u gq ] T F2 = [u gds ,u gqs ] T ; e=F1 / ||F1||; d=[(n·e)en] / ||(n·e)en||; θ=arccosn·e.

[0105] For the low-voltage ride-through control switching behavior involved in this system, the switching surface equation can be written as s(u gd ,u gq )=u gd -u gd0 Then, when switching from subsystem 1 to system 2, the unit vector perpendicular to the switching surface is n = [1, 0]. T When switching back from subsystem 2 to subsystem 1, n = [-1, 0] T .

[0106] 203: Calculate the SSMLE of the voltage trajectory based on the variational equation of the voltage trajectory, and correct the SSMLE calculation error using the switching compensation matrix at the time of system control mode switching, including:

[0107] 1) When the voltage does not drop, solve the voltage trajectory variational equation (Equation (11)) during normal operation in step 202 within Δt to obtain the final integral matrix Q. 1end (Δt), then Q 1end (Δt) is decomposed into QR matrix Q and matrix R:

[0108] [Q,R]=qr(Q 1end (Δt)) (15)

[0109] In the formula, qr(·) is the QR decomposition function.

[0110] Then, the n diagonal elements λ1,λ2,...,λ of matrix R are... nSubstituting diag(R) into equation (15) and using equation (16), we obtain the SSMLE of the system at the non-switching time:

[0111]

[0112] SSMLE(kΔt)=max{LE i (Δt)} (17)

[0113] The matrix Q obtained from the decomposition is used as the new Q(0) to calculate the final integral matrix for the next time period. Steps 1)-2) above are repeated to obtain the SSMLE index for each sampling point.

[0114] 2) When the PCC voltage drops below 0.9 times the reference value, the system undergoes LVRT control switching. At this time, the switching compensation matrix J from subsystem 1 to subsystem 2 is calculated using equation (14) in step 202. S12 By switching the compensation matrix J S12 For the final value matrix Q of the integral 1end Gradient correction is performed on (Δt), and its eigenvalues ​​are calculated using QR decomposition technology, specifically as follows:

[0115] [Q,R]=qr(J S12 ·Q 1end (Δt)) (18)

[0116] Then, substituting the diagonal elements of matrix R into equation (15) and obtaining the SSMLE at the voltage drop moment through equation (16), Q is solved at the next moment using the voltage trajectory variational equation (equation (13)) during the voltage drop period in step 202. 2end (Δt), and then use equations (15)-(17) to solve for the SSMLE during the voltage drop period.

[0117] 3) After the fault is cleared, the voltage recovers to more than 0.9 times the reference value, at which point the system exits the LVRT operation mode. The switching compensation matrix J from subsystem 2 to subsystem 1 is calculated using equation (14) in step 202. S21 And correct the integral result according to equation (19):

[0118] [Q,R]=qr(J S21 ·Q 2end (Δt)) (19)

[0119] In the formula, J S21 J in equation (18) S12 These are different switching compensation matrices, with the specific difference being that the voltage solution vectors F1 and F2 are different before and after the switching.

[0120] Then, the diagonal elements of matrix R are substituted into equation (16) and the SSMLE at the voltage recovery time is obtained through equation (17).

[0121] Return to step 1 in step 203 and continue to solve for the SSMLE during normal system operation.

[0122] 204: Transient voltage stability criteria based on SSMLE, including:

[0123] Based on the maximum Lyapunov exponential voltage stability criterion and the evolution characteristics of the SSMLE curve, a transient voltage stability criterion based on SSMLE is given:

[0124] Criterion 1: After the fault is cleared, monitor the amplitude and inflection point of the SSMLE curve in real time. If the inflection point of the SSMLE curve is less than zero, the transient voltage is determined to be stable.

[0125] Criterion 2: After the fault is cleared, monitor the amplitude and inflection point of the SSMLE curve in real time. If the amplitude of the SSMLE curve is greater than zero and lasts for 0.4s, then transient voltage instability is determined.

[0126] The specific construction steps described above are well known to those skilled in the art, and will not be elaborated upon in the embodiments of the present invention.

[0127] In summary, the embodiments of the present invention achieve rapid and accurate monitoring of transient voltage stability in hybrid renewable energy power plants through steps 201-204. Evaluation indices are constructed using the eigenvalues ​​of the final integral matrix of the voltage trajectory variational equation, effectively avoiding the sign characteristic oscillation problem of the MLE index. A switching compensation matrix is ​​introduced to correct the integral error at the system operation mode switching time, thus realizing transient voltage stability monitoring of hybrid renewable energy power plants. Furthermore, the impact of the access distance and access capacity of the power generation units on transient voltage stability can be determined. This provides operation and dispatch personnel with fast, accurate, and reliable transient voltage stability information, which is beneficial for improving the system's safety, stability, and emergency control capabilities.

[0128] Example 3

[0129] The following will use specific examples... Figures 2-5 The feasibility of the solutions in Examples 1 and 2 is verified, as detailed below:

[0130] This example uses a hybrid renewable energy power station containing GFL and GFM power generation units connected to an infinite power system to verify the effectiveness of embodiments 1 and 2 of the present invention.

[0131] 1) Transient voltage stability scenario. A three-phase short-circuit fault occurs on the grid side at t=1s, and the fault is cleared at t=1.1s. The sampling frequency is 100Hz, and the simulation duration is 5s. At this point, the transient voltage is stable. Based on voltage measurement information, to more intuitively reflect the evolution characteristics of the voltage trajectory before and after the disturbance, this embodiment of the invention converts the voltage trajectory to Δt-ΔV. mag -V magcr The space is used to analyze whether it can converge to a stable equilibrium point after being disturbed.

[0132] ΔV of the voltage trajectory at time k mag (k)=V(k)-1.0、V magcr (k)=[ΔV mag (k)-ΔV mag (k-1)]·f s Where V(k) is the voltage amplitude at the k-th sampling point, f s The sampling frequency of the PMU is used. Based on the PCC voltage and current data measured by the PMU, the SSMLE and the time-delay embedded MLE (TMLE) are calculated from the fault clearing time to verify the accuracy and effectiveness of the proposed method. Figure 2 This section presents the voltage evolution and MLE (Mean Leakage) determination results under transient voltage stability scenarios.

[0133] Depend on Figure 2 (a) It can be seen that the voltage stabilizes after the fault is cleared, and the graph shows that the voltage trajectory evolves to a post-fault equilibrium point. Calculate SSMLE and TMLE separately from the fault initiation time. Figure 2 (b) It can be seen that the SSMLE curve does not exhibit critical oscillation of sign characteristics and its inflection point is less than zero. According to criterion one in Example 2, the PCC voltage can be determined to be stable at t = 1.10s, with the stability determination time being 0.02s after the fault is cleared. However, the TMLE curve still has a sign characteristic greater than zero after the fault is cleared, and thereafter the sign characteristic oscillates near zero. Only a certain delay observation window can be set to read the TMLE value, which can easily lead to misjudgment of the transient voltage stability state.

[0134] 2) Transient voltage instability scenario. A three-phase short-circuit fault occurs on the grid side at t=1s, and the fault is cleared at t=1.5s. The sampling frequency is 100Hz, and the simulation duration is 5s. Transient voltage instability occurs during this time. SSMLE and TMLE are calculated starting from the moment the fault occurs. Figure 3 This section describes the voltage evolution and MLE (Mean Leakage) determination results under transient voltage instability scenarios.

[0135] Depend on Figure 3 (a) It can be seen that after the fault is cleared, the grid-connected node voltage transiently becomes unstable and there is no post-fault equilibrium point. The SSMLE and TMLE curves are calculated separately. Figure 3(b) It can be seen that the SSMLE curve rapidly crosses zero at 1.22s and its amplitude continuously increases with the increase of voltage instability, which accurately reflects the real-time voltage change. According to criterion two in Example 2, transient voltage instability can be identified at 1.62s. However, TMLE has no key feature points to capture after fault clearance, making it difficult to apply widely.

[0136] 3) The impact of the connection distance of GFL and GFM generator units on transient voltage stability.

[0137] The connection distances of GFL and GFM generator units were set within the range of 2-20 km. By varying the connection distance of each generator unit, the impact of the connection distance on transient voltage stability was analyzed. Figure 4 As shown. By Figure 4 (a) It can be seen that when the GFL generator unit is connected at a short distance, the system has a high transient voltage stability margin, and the corresponding SSMLE value is small. When the connection distance of the GFM generator unit increases, its ability to support the transient voltage at the grid connection point weakens, the SSMLE value increases, and reducing the connection distance between the GFL and GFM generator units will help maintain transient voltage stability. For Example 3, when the connection distance between the GFL and GFM generator units reaches more than 16km, the SSMLE is greater than zero, which indicates transient voltage instability.

[0138] 4) The impact of the connected capacity of the GFM generator unit on transient voltage stability.

[0139] In hybrid renewable energy power plants, different control strategies for each generating unit lead to varying response characteristics during transients, collectively affecting PCC voltage stability. To verify that SSMLE can be used to analyze the impact of generating unit access capacity on transient voltage stability, a three-phase short-circuit fault was first introduced on the grid side, with fault initiation and clearing times of 1s and 1.08s, respectively, and grid strength SCR = 3. Then, by changing the integration capacity ratio (ICR) of the GFM generating units from 30% to 50%, the impact of the change in access capacity on transient voltage stability was analyzed. Figure 5 This describes the impact of access capacity on transient voltage stability.

[0140] Depend on Figure 5 (a) It can be seen that changing the connected capacity ratio of GFM generator units will affect the transient voltage recovery characteristics of the system after a disturbance. Increasing the ICR (Internal Reactive Power Regulator) gives the system a stronger reactive power regulation capability, allowing more reactive power to be injected into the PCC (Power Generation Control Center) during a fault to support voltage recovery, which is beneficial for the stable recovery of transient voltage after a disturbance. Figure 5(b) It can be seen that when ICR = 30%, SSMLE is smaller, which indicates that the transient voltage stability is stronger under this access capacity ratio, and is consistent with the transient voltage evolution in the figure.

[0141] The above results show that the proposed SSMLE effectively overcomes the critical oscillation problem of the sign characteristic of TMLE, has high discrimination accuracy and speed for the transient voltage stability of hybrid new energy power plants, and can intuitively analyze the impact of the connection distance and connection capacity of GFL and GFM power generation units on transient voltage stability.

[0142] Example 4

[0143] A transient voltage stability monitoring device for a hybrid grid-connected renewable energy power station includes:

[0144] Storage device, processor, monitor, and computer program that can run on the processor, the processor executing the computer program to calculate the SSMLE index and monitor transient voltage stability.

[0145] Construct voltage trajectory variational equations for the normal operation mode and low voltage ride-through mode of hybrid new energy power stations, and incorporate key system operation parameters into the index input range;

[0146] A switching compensation matrix is ​​constructed for the switching of operation modes of hybrid new energy power stations to determine the required compensation gradient;

[0147] Numerical integration is performed on the voltage trajectory variational equation within the integration step, and the SSMLE evaluation index is calculated based on the eigenvalues ​​of the final integral matrix.

[0148] The integration results are corrected using a switching compensation matrix when the operating mode is switched.

[0149] The evolution curve of the SSMLE evaluation index is obtained. Based on the evolution curve of the SSMLE evaluation index and the maximum Lyapunov exponential stability discrimination mechanism, a transient voltage stability criterion suitable for online evaluation is given, and the transient voltage discrimination is implemented on the detection device.

[0150] The voltage trajectory variational equation is: the voltage differential equation is transformed into the voltage trajectory variational equation.

[0151] The switching compensation matrix is ​​as follows:

[0152] Construct the switching compensation matrix J based on the system solution vectors before and after the switching. S ;

[0153] The switching surface equation is written as s(u gd ,u gq )=u gd -u gd0When switching from subsystem 1 to system 2, the unit vector perpendicular to the switching surface is n = [1, 0]. T When switching back from subsystem 2 to subsystem 1, n = [-1, 0] T .

[0154] The step of correcting the integral result using a switching compensation matrix at the time of operating mode switching includes:

[0155] When the PCC voltage drops below 0.9 times the reference value, LVRT control switching occurs. The gradient correction of the integral final value matrix is ​​performed by switching the compensation matrix, and its eigenvalues ​​are calculated using QR decomposition technology.

[0156] The transient voltage stability criterion applicable to online evaluation is given as follows:

[0157] Based on the maximum Lyapunov exponential voltage stability criterion and the evolution characteristics of the SSMLE curve, a transient voltage stability criterion based on SSMLE is given:

[0158] Criterion 1: After the fault is cleared, monitor the amplitude and inflection point of the SSMLE curve in real time. If the inflection point of the SSMLE curve is less than zero, the transient voltage is determined to be stable.

[0159] Criterion 2: After the fault is cleared, monitor the amplitude and inflection point of the SSMLE curve in real time. If the amplitude of the SSMLE curve is greater than zero and lasts for 0.4s, then transient voltage instability is determined.

[0160] It should be noted that the device descriptions in the above embodiments correspond to the method descriptions in the embodiments, and the embodiments of the present invention will not be repeated here.

[0161] The execution entities of the aforementioned processor and memory can be devices with computing functions such as computers, microcontrollers, and single-chip microcomputers. In specific implementations, the embodiments of the present invention do not limit the execution entities and can select them according to the needs of actual applications.

[0162] Data signals are transmitted between the memory and the processor via a bus, which will not be elaborated upon in this embodiment of the invention.

[0163] Based on the same inventive concept, embodiments of the present invention also provide a computer-readable storage medium, the storage medium including a stored program, which, when the program is running, controls the device where the storage medium is located to execute the method steps in the above embodiments.

[0164] The computer-readable storage medium includes, but is not limited to, flash memory, hard disk, solid-state drive, etc.

[0165] It should be noted that the description of the readable storage medium in the above embodiments corresponds to the description of the method in the embodiments, and the embodiments of the present invention will not be repeated here.

[0166] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of the present invention is generated.

[0167] A computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in or transmitted through a computer-readable storage medium. A computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium can be magnetic or semiconductor, etc.

[0168] Unless otherwise specified, the model numbers of the various devices in this embodiment of the invention are not limited, and any device that can perform the above functions is acceptable.

[0169] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of a preferred embodiment, and the sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0170] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for monitoring transient voltage stability at a hybrid renewable energy power station using a grid-connected system, characterized in that, The method includes: Construct voltage trajectory variational equations for the normal operation mode and low voltage ride-through mode of hybrid new energy power stations, and incorporate key system operation parameters into the index input range; A switching compensation matrix is ​​constructed for the switching of operation modes of hybrid new energy power stations to determine the required compensation gradient; Numerical integration is performed on the voltage trajectory variational equation within the integration step, and the SSMLE evaluation index is calculated based on the eigenvalues ​​of the final integral matrix. The integration results are corrected using a switching compensation matrix when the operating mode is switched. The evolution curve of the SSMLE evaluation index is obtained. Based on the evolution curve of the SSMLE evaluation index and the maximum Lyapunov exponential stability discrimination mechanism, a transient voltage stability criterion suitable for online evaluation is given, and the transient voltage discrimination is implemented on the detection device.

2. The method for monitoring transient voltage stability of a hybrid renewable energy power station according to claim 1, characterized in that, The voltage trajectory variational equation is: the voltage differential equation is transformed into the voltage trajectory variational equation.

3. The method for monitoring transient voltage stability of a hybrid renewable energy power station according to claim 1, characterized in that, The switching compensation matrix is: Construct the switching compensation matrix J based on the system solution vectors before and after the switching. S ; The switching surface equation is written as s(u gd ,u gq )=u gd -u gd0 When switching from subsystem 1 to system 2, the unit vector perpendicular to the switching surface is n = [1, 0]. T When switching back from subsystem 2 to subsystem 1, n = [-1, 0] T .

4. The method for monitoring transient voltage stability of a hybrid renewable energy power station according to claim 1, characterized in that, The step of correcting the integral result using the switching compensation matrix at the time of operating mode switching includes: When the PCC voltage drops below 0.9 times the reference value, LVRT control switching occurs. The gradient correction of the integral final value matrix is ​​performed by switching the compensation matrix, and its eigenvalues ​​are calculated using QR decomposition technology.

5. The method for monitoring transient voltage stability of a hybrid renewable energy power station according to claim 1, characterized in that, The transient voltage stability criterion applicable to online evaluation is as follows: Based on the maximum Lyapunov exponential voltage stability criterion and the evolution characteristics of the SSMLE curve, a transient voltage stability criterion based on SSMLE is given: Criterion 1: After the fault is cleared, monitor the amplitude and inflection point of the SSMLE curve in real time. If the inflection point of the SSMLE curve is less than zero, the transient voltage is determined to be stable. Criterion 2: After the fault is cleared, monitor the amplitude and inflection point of the SSMLE curve in real time. If the amplitude of the SSMLE curve is greater than zero and lasts for 0.4s, then transient voltage instability is determined.

6. A transient voltage stability monitoring device for a hybrid renewable energy power station, characterized in that, The device includes a processor and a memory, the memory storing program instructions, the processor invoking the program instructions stored in the memory to cause the device to perform the method according to any one of claims 1-5.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, the computer program including program instructions that, when executed by a processor, cause the processor to perform the method described in any one of claims 1-5.

Images