Power system small interference stability minimum damping ratio margin monitoring method

Abstract

The invention provides a power system small interference stability minimum damping ratio margin monitoring method, which comprises the following steps: firstly, considering load growth mode constraints and power system operation requirements, and establishing a small interference stability minimum damping ratio margin optimization model; secondly, constructing an augmented Lagrangian function by using a Lagrangian multiplier method and an exterior penalty function method, and deriving a first-order optimality condition; and finally, a nested double-layer iteration solving method is designed, the outer layer iteration is solved by adopting a quasi-Newton method, and the inner layer iteration is solved by adopting an extended three-stage strategy. According to the method, the most dangerous load increasing mode and the corresponding small-interference stability minimum damping ratio margin can be found while the system operation requirement is met, and the method is easy to implement and has high application value.

Description

Technical Field

[0001] The invention relates to the field of power system stability analysis and control, and in particular to a method for monitoring a minimum damping ratio margin of small disturbance stability of a power system. Background Art

[0002] With the high proportion of new energy access to the grid, the rapid development of high-voltage direct current transmission technology, and the rapid growth of load demand, the operation modes of modern power systems are becoming more and more diverse, and the switching between operation modes is becoming more frequent. In order to ensure the stable operation of modern power systems, the national standard "Power System Safety and Stability Calculation Specification" requires that the power system should not only be stable under small disturbances, but also that the damping ratio should meet certain requirements. However, the switching of operation modes may cause the system to change from an operation state with a relatively high damping ratio to an operation state where the minimum damping ratio is on the preset damping ratio line (i.e., a critical operation state), or even a weakly damped operation state. The damping ratio margin is used to describe the relationship between the current high damping ratio operation state and the critical operation state of the system. In order to avoid the change in operation mode causing the system to be in a critical operation state or even a weakly damped operation state, it is necessary to monitor the damping ratio margin of the system.

[0003] Existing research mainly focuses on the calculation of damping ratio margin under a given load growth mode, and has achieved a lot of research results. However, in the actual operation of the power system, the load growth mode is difficult to accurately determine, resulting in the unreliable damping ratio margin. In response to this problem, domestic and foreign scholars have carried out some research on the monitoring, updating and improvement of the damping ratio margin considering the fluctuation of the load growth mode (such as Murilo EC Bento, Rodrigo A. Ramos, An approach for monitoring and updating the load margin of power systems in dynamic security assessment, Electric Power Systems Research 198 (2021) 107365). But in fact, since the minimum damping ratio margin under the most dangerous load growth mode can provide more effective information within the range of possible operation of the system, it is more reasonable and more concerned. However, from the perspective of optimization theory, research on the most dangerous load growth mode from the current operating point to the small disturbance stability dynamic performance boundary and the corresponding minimum damping ratio margin has not yet been found. Summary of the invention

[0004] The purpose of the present invention is to propose a method for monitoring the minimum damping ratio margin of small disturbance stability of a power system by constructing a small disturbance stability minimum damping ratio margin optimization model with the goal of minimizing the distance between the current operating point and the small disturbance stability dynamic performance boundary.

[0005] The objective of the present invention is achieved by the following technical solutions:

[0006] In a first aspect, the present invention provides a method for monitoring a minimum damping ratio margin of small disturbance stability of a power system, comprising the following steps:

[0007] Considering the load growth mode constraints and power system operation requirements, an optimization model for the minimum damping ratio margin of small disturbance stability is established;

[0008] The augmented Lagrangian function is constructed using the Lagrange multiplier method and the exterior point penalty function method, and the KKT condition is derived;

[0009] The nested double-layer iterative method is used to solve the KKT condition and output the most dangerous load growth mode and the minimum damping ratio margin for small disturbance stability.

[0010] Based on the above, the construction method of the optimization model of the minimum damping ratio margin for small disturbance stability considering the load growth mode constraints and the power system operation requirements includes:

[0011] Assume that the objective function of the small disturbance stability minimum damping ratio margin optimization model is to minimize the Euclidean distance from the current operating point to the critical operating point on the small disturbance stability dynamic performance boundary, expressed as:

[0012]

[0013] In formula (1), is the damping ratio margin; ||·||2 is the 2-norm; P L,N and P L,C are the load active power column vectors of the current operating point and the critical operating point respectively;

[0014] The constraints of the small disturbance stability minimum damping ratio margin optimization model include: equation constraints of the critical operating point on the dynamic performance boundary, power change constraints between the current operating point and the critical operating point, load and power generation growth mode constraints, upper and lower limits of the active power of the generator at the critical point, upper and lower limits of the reactive power of the generator at the critical point, upper and lower limits of the node voltage at the critical point, and branch flow constraints under the critical state, wherein,

[0015] The equality constraint that must be satisfied at any critical operating point on the dynamic performance boundary is:

[0016] F(x C ,m C ,μ C ,B)=0 (2)

[0017] A C U Re,C -σ M,C URe,C +ω M,C U Im,C =0 (3)

[0018] A C U Im,C -σ M,C U Im,C -ω M,C U Re,C =0 (4)

[0019]

[0020]

[0021]

[0022] In equations (2) to (7), the subscript C represents the critical operating point; the superscript T represents the matrix transpose; x and m are the dynamic state vector and the non-state vector, respectively; μ is the step size; B is the power growth mode; A is the dynamic state matrix; σ M and ω M are the eigenvalues ​​λ corresponding to the minimum damping ratio M The real and imaginary parts of U Re and U Im The eigenvalues ​​λ are M The real and imaginary parts of the right eigenvector of ; ζ M is the damping ratio threshold;

[0023] The power variation constraint between the current operating point and the critical operating point is:

[0024]

[0025] In formula (8), Q L,N and Q L,C are the load reactive power column vectors of the current operating point and the critical operating point respectively; P G,N and P G,C are the generator active power column vectors of the current operating point and the critical operating point respectively; B L,P and B L,Q are the column vectors of the load active power and reactive power growth modes respectively; B G,P is the column vector of the generator active power growth mode;

[0026] The load and generation growth constraints are:

[0027] P Li,C ·P Li,N ≥0 (9)

[0028]

[0029]

[0030] In formula (9)-formula (11), P Li,C and P Li,N are the load active power of node i at the current operating point and the critical operating point respectively; B Li,P and B Li,Q are the growth modes of active power and reactive power of the load at node i respectively; is the power factor of node i; B Gi,P is the growth mode of the active power of the generator at node i; N G and N L are the number of generator nodes and the number of load nodes respectively; P Gi,N is the active power of the generator at node i at the current operating point;

[0031] The upper and lower limits of the generator active power at the critical operating point are:

[0032]

[0033] In formula (12), P Gi,C is the active power of the generator at node i at the critical operating point; P Gi and are the lower and upper limits of the active power of the generator at node i respectively;

[0034] The upper and lower limits of the generator reactive power at the critical operating point are:

[0035]

[0036] In formula (13), Q Gi,C is the generator reactive power at node i at the critical operating point; Q Gi and are the lower and upper limits of the reactive power of the generator at node i respectively;

[0037] The upper and lower limits of node voltage at the critical operating point are:

[0038]

[0039] In formula (14), V i,C is the voltage amplitude of node i at the critical operating point; V i and are the lower and upper limits of the voltage amplitude at node i respectively;

[0040] The branch power flow constraint under critical operation state is:

[0041]

[0042] In formula (15), I ij,Cis the current between node i and node j under critical operation state; is the current limit between node i and node j.

[0043] Based on the above, the augmented Lagrangian function is constructed using the Lagrangian multiplier method and the exterior point penalty function method, and the method for deriving the KKT condition is:

[0044] Assume that the compact form of the minimum damping ratio margin model for small disturbance stability is:

[0045] min f(u,y)(16)

[0046] st h(u,y)=0 (17)

[0047] g(u,y)≤0 (18)

[0048] In equations (16)-(18), f(·) is the objective function, representing equation (1); h(·) is the equality constraint vector, including equations (2)-(8) and (10)-(11); g(·) is the inequality constraint vector, including equations (9) and (12)-(15); u is the control vector, which is determined by the active power growth mode B of each load node. Li,P Composition; y is the mixed state vector,

[0049] The augmented Lagrangian function constructed using the Lagrangian multiplier method and the exterior point penalty function method is:

[0050] L(u,y,z)=f(u,y)+z T h(u,y)+Φ(u,y) (19)

[0051]

[0052] In formulas (19)-(20), L(·) is the augmented Lagrangian function; Φ(·) is the penalty term; g i (·) is the i-th inequality constraint; z is the Lagrange multiplier vector; l i is the penalty factor of the i-th inequality constraint; r is the number of inequality constraints;

[0053] The KKT condition for eliminating z is:

[0054]

[0055]

[0056] In formula (21)-formula (22), and are the gradients of the augmented Lagrangian function L with respect to the control vector u and the Lagrangian multiplier vector z respectively; and are the gradients of the objective function f with respect to u and z respectively; and are the gradients of the objective function f and the penalty term Φ with respect to the mixed state vector y respectively; and are the gradients of Φ and y with respect to u respectively.

[0057] Based on the above, the nested two-layer iterative method for solving equations (21)-(22) includes:

[0058] Outer layer: Based on the Broyden-Fletcher-Goldfarb-Shanno method, search u using equation (21);

[0059] Inner layer: Once u changes, use the extended three-stage strategy to solve equation (22) and update y.

[0060] Based on the above, the extended three-stage strategy for solving equation (22) includes:

[0061] 1) Estimation phase

[0062]

[0063] In formula (23), is the threshold, j1 and j2 are the number of iterations in the one-dimensional search, and j1 <j2;

[0064] If equation (23) is not satisfied, it is necessary to estimate the initial vector consisting of the steady-state initial value and the critical mode that first crosses the dynamic performance boundary

[0065] If equation (23) is satisfied, no estimation is required and the last calculation result is used as the initial vector, that is,

[0066] 2) Correction phase

[0067] According to the estimation stage Use Newton's method or Broyden's rank 1 method to accurately calculate y (i) ;

[0068] 3) Correction phase

[0069] If y (i) If it is on the dynamic performance boundary, the iteration terminates and the calculation results are output; otherwise, update And return to the correction phase.

[0070] In a second aspect, the present invention provides a power system small disturbance stability minimum damping ratio margin monitoring system, comprising:

[0071] The model building module is used to establish a small disturbance stability minimum damping ratio margin optimization model taking into account the load growth mode constraints and power system operation requirements;

[0072] The construction and derivation module is connected with the model building module and is used to construct the augmented Lagrangian function using the Lagrangian multiplier method and the exterior point penalty function method, and to derive the KKT condition;

[0073] The solution and output module is connected with the construction and derivation module, and is used to solve the KKT condition by using a nested double-layer iteration method, and output the most dangerous load growth mode and the minimum damping ratio margin for small disturbance stability;

[0074] The model building module is connected to the construction and derivation module, and the construction and derivation module is connected to the solution and output module, so as to implement the power system small disturbance stability minimum damping ratio margin monitoring method.

[0075] In a third aspect, the present invention provides a method for assisting power system operation control, comprising:

[0076] The method for monitoring the minimum damping ratio margin for small disturbance stability of a power system is adopted to monitor the most dangerous load growth mode and the minimum damping ratio margin for small disturbance stability of the power system;

[0077] Based on the monitored most dangerous load growth mode and the minimum damping ratio margin for small disturbance stability, a reference is provided for power system operation and regulation.

[0078] In a fourth aspect, the present invention provides a system for assisting in the operation and regulation of a power system, including the power system small disturbance stability minimum damping ratio margin monitoring system;

[0079] The most dangerous load growth mode and the minimum damping ratio margin of small disturbance stability are obtained through the power system small disturbance stability minimum damping ratio margin monitoring system;

[0080] The operation of the power system is regulated and controlled based on the most dangerous load growth mode and the minimum damping ratio margin for small disturbance stability.

[0081] In a fifth aspect, the present invention provides a computer device, comprising a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the method for monitoring the minimum damping ratio margin of small disturbance stability of a power system as described.

[0082] In a sixth aspect, the present invention provides a computer-readable storage medium having a computer program / instruction stored thereon, which, when executed by a processor, implements the method for monitoring the minimum damping ratio margin for small disturbance stability of a power system as described.

[0083] In a seventh aspect, the present invention provides a computer program product, comprising a computer program / instruction, which, when executed by a processor, implements the method for monitoring the minimum damping ratio margin for small disturbance stability of a power system as described.

[0084] Compared with the prior art, the present invention has outstanding substantive features and significant progress, specifically:

[0085] 1. The present invention accurately considers the constraints of load growth mode and the operation requirements of the power system, establishes a small-disturbance stability minimum damping ratio margin optimization model; and designs a nested two-layer iterative solution method, in which the outer iteration adopts the BFGS method to solve, and the inner iteration adopts the extended three-stage strategy to solve; the method is comprehensive, accurate, and easy to implement.

[0086] 2. The minimum damping ratio margin optimization model constructed by the method of the present invention can find the most dangerous load growth mode and the corresponding small disturbance stable minimum damping ratio margin while meeting the system operation requirements, and has high application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] Figure 1 A three-phase strategy for expansion.

[0088] Figure 2 This is a 24-point power system diagram for 8 machines.

[0089] Figure 3 To augment the changing trend of the Lagrangian function value and the damping ratio margin. DETAILED DESCRIPTION

[0090] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0091] Example 1

[0092] This embodiment provides a method for monitoring the minimum damping ratio margin of small disturbance stability of a power system, and the implementation steps are as follows:

[0093] (1) Considering the constraints of load growth mode and the operation requirements of the power system, an optimization model of the minimum damping ratio margin for small disturbance stability is established.

[0094] Assume that the objective function of the small disturbance stability minimum damping ratio margin optimization model is to minimize the Euclidean distance from the current operating point to the critical operating point on the small disturbance stability dynamic performance boundary, expressed as:

[0095]

[0096] In formula (1), is the damping ratio margin; ||·||2 is the 2-norm; P L,N and P L,C are the load active power column vectors of the current operating point and the critical operating point respectively.

[0097] The constraints of the small disturbance stability minimum damping ratio margin optimization model include equation constraints of the critical operating point on the dynamic performance boundary, power change constraints between the current operating point and the critical operating point, load and power generation growth mode constraints, upper and lower limit constraints on the active power of the generator at the critical point, upper and lower limit constraints on the reactive power of the generator at the critical point, upper and lower limit constraints on the node voltage at the critical point, and branch flow constraints under the critical state.

[0098] The equality constraint that must be satisfied at any critical operating point on the dynamic performance boundary is:

[0099] F(x C ,m C ,μ C ,B)=0 (2)

[0100] A C U Re,C -σ M,C U Re,C +ω M,C U Im,C =0 (3)

[0101] A C U Im,C -σ M,C U Im,C -ω M,C U Re,C =0 (4)

[0102]

[0103]

[0104]

[0105] In equations (2) to (7), the subscript C represents the critical operating point; the superscript T represents the matrix transpose; x and m are the dynamic state vector and the non-state vector, respectively; μ is the step size; B is the power growth mode; A is the dynamic state matrix; σ M and ω M are the eigenvalues ​​λ corresponding to the minimum damping ratio M The real and imaginary parts of U Re and U Im The eigenvalues ​​λ are MThe real and imaginary parts of the right eigenvector of ; ζ M is the damping ratio threshold. Equations (2) to (7) require that the critical operating point is located on the dynamic performance boundary. Specifically, Equation (2) is the steady-state equation that the critical operating point should satisfy; Equations (3) to (4) are the characteristic values ​​λ M and the real-domain expression of the characteristic equation that its right eigenvector should satisfy; Equations (5) and (6) require that the right eigenvector is non-zero; Equation (7) is the minimum damping ratio requirement.

[0106] The power variation constraint between the current operating point and the critical operating point is:

[0107]

[0108] In formula (8), Q L,N and Q L,C are the load reactive power column vectors of the current operating point and the critical operating point respectively; P G,N and P G,C are the generator active power column vectors of the current operating point and the critical operating point respectively; B L,P and B L,Q are the column vectors of the load active power and reactive power growth modes respectively; B G,P is the column vector of the generator active power growth mode.

[0109] The load and generation growth constraints are:

[0110] P Li,C ·P Li,N ≥0 (9)

[0111]

[0112]

[0113] In formula (9)-formula (11), P Li,C and P Li,N are the load active power of node i at the current operating point and the critical operating point respectively; B Li,P and B Li,Q are the growth modes of active power and reactive power of the load at node i respectively; is the power factor of node i; B Gi,P is the growth mode of the active power of the generator at node i; N G and N L are the number of generator nodes and the number of load nodes respectively; P Gi,N is the active power of the generator at node i at the current operating point.

[0114] The upper and lower limits of the generator active power at the critical operating point are:

[0115]

[0116] In formula (12), P Gi,C is the active power of the generator at node i at the critical operating point; P Gi and are the lower and upper limits of the generator active power at node i respectively.

[0117] The upper and lower limits of the generator reactive power at the critical operating point are:

[0118]

[0119] In formula (13), Q Gi,C is the generator reactive power at node i at the critical operating point; Q Gi and are the lower and upper limits of the reactive power of the generator at node i respectively.

[0120] The upper and lower limits of node voltage at the critical operating point are:

[0121]

[0122] In formula (14), V i,C is the voltage amplitude of node i at the critical operating point; V i and are the lower and upper limits of the voltage amplitude at node i respectively.

[0123] The branch power flow constraint under critical operation state is:

[0124]

[0125] In formula (15), I ij,C is the current between node i and node j under critical operation state; is the current limit between node i and node j.

[0126] (2) The augmented Lagrangian function is constructed using the Lagrange multiplier method and the exterior point penalty function method, and the Karush-Kuhn-Tucker (KKT) condition is derived.

[0127] Assume that the compact form of the minimum damping ratio margin model for small disturbance stability is:

[0128] min f(u,y)(16)

[0129] st h(u,y)=0 (17)

[0130] g(u,y)≤0 (18)

[0131] In equations (16)-(18), f(·) is the objective function, representing equation (1); h(·) is the equality constraint vector, including equations (2)-(8) and (10)-(11); g(·) is the inequality constraint vector, including equations (9) and (12)-(15); u is the control vector, which is determined by the active power growth mode B of each load node. Li,P Composition; y is the mixed state vector,

[0132] The augmented Lagrangian function constructed using the Lagrangian multiplier method and the exterior point penalty function method is:

[0133] L(u,y,z)=f(u,y)+z T h(u,y)+Φ(u,y) (19)

[0134]

[0135] In formulas (19)-(20), L(·) is the augmented Lagrangian function; Φ(·) is the penalty term; g i (·) is the i-th inequality constraint; z is the Lagrange multiplier vector; l i is the penalty factor of the i-th inequality constraint; r is the number of inequality constraints;

[0136] The KKT condition for eliminating z is:

[0137]

[0138]

[0139] In formula (21)-formula (22), and are the gradients of the augmented Lagrangian function L with respect to the control vector u and the Lagrangian multiplier vector z respectively; and are the gradients of the objective function f with respect to u and z respectively; and are the gradients of the objective function f and the penalty term Φ with respect to the mixed state vector y respectively; and are the gradients of Φ and y with respect to u respectively.

[0140] (3) The nested double-layer iterative method is used to solve the problem and output the most dangerous load growth mode and the minimum damping ratio margin for small disturbance stability.

[0141] The nested two-layer iterative method for solving equation (21)-equation (22) is:

[0142] Outer layer: Based on the Broyden-Fletcher-Goldfarb-Shanno (BFGS) method, use equation (21) to search for u; Inner layer: Once u changes, use the extended three-stage strategy to solve equation (22) and update y;

[0143] Inner layer: Once u changes, use the extended three-stage strategy to solve equation (22) and update y.

[0144] The extended three-stage strategy for solving equation (22) is as follows Figure 1 As shown, specifically including:

[0145] 1) Estimation phase

[0146]

[0147] In formula (23), is the threshold, j1 and j2 are the number of iterations in the one-dimensional search, and j1 <j2;

[0148] If equation (23) is not satisfied, it is necessary to estimate the initial vector consisting of the steady-state initial value and the critical mode that first crosses the dynamic performance boundary

[0149] If equation (23) is satisfied, no estimation is required and the last calculation result can be used as the initial vector, that is,

[0150] 2) Correction phase

[0151] According to the estimation stage Use Newton's method or Broyden's rank 1 method to accurately calculate y (i) .

[0152] 3) Correction phase

[0153] If y (i) If it is on the dynamic performance boundary, the iteration terminates and the calculation results are output; otherwise, update And return to the correction phase.

[0154] Simulation test analysis

[0155] This embodiment selects Figure 2 The 8-machine 24-node power system shown in the figure is modeled and simulated for test analysis. All generators are modeled as sixth-order dynamic models, and the load adopts a constant impedance model. In addition to the equivalent machine, all generators are equipped with an excitation system, a speed control system, and a power system stabilizer. The damping ratio threshold ζ M Set to 3% and the convergence accuracy to 10 -6 .

[0156] Eigenvalue analysis was performed at the optimized initial operating point, and 114 eigenvalues ​​were found, including 62 real eigenvalues ​​and 26 pairs of complex eigenvalues. The minimum damping ratio is 3.30%. Assuming that the initial load active power growth mode is consistent with the injected load active power, the initial load reactive power growth mode and the initial generation active power growth mode can be obtained by equations (10) and (11) respectively, then the damping ratio margin of the optimized initial operating point is 2.2531.

[0157] The constructed minimum damping ratio margin optimization model is solved by using a nested double-layer iteration method. The trend of the augmented Lagrangian function value and the damping ratio margin with the number of iterations during the optimization process is shown in the following figure: Figure 3 As shown. Figure 3 It can be seen that at the optimized initial operating point, some constraints are not satisfied, resulting in the augmented Lagrangian function value being significantly higher than the damping ratio margin value. This means that it is necessary to analyze the damping ratio margin considering the power system operation requirements. After 10 iterations, the minimum damping ratio margin that meets the power system operation requirements is obtained, which is 2.05. Compared with the optimized initial operating point, the augmented Lagrangian function value and the damping ratio margin are reduced by 28.12% and 9.03%, respectively. The simulation results show that the system's damping ratio margin may be overestimated if the worst load growth mode and operation constraints are not considered.

[0158] Table 1 Normalized initial and worst load active power growth patterns

[0159]

[0160] Table 1 shows the normalized initial and worst load active power growth results. It can be seen that the worst mode is significantly different from the initial mode, and the active power growth mode of each load node has changed.

[0161] Table 1 and Figure 3 The results show that the method of this embodiment can find the most dangerous load growth mode and the corresponding small disturbance stability minimum damping ratio margin while meeting the system operation requirements.

[0162] Based on the same inventive concept, the embodiment of the present application also provides a power system small disturbance stability minimum damping ratio margin monitoring system. The implementation scheme for solving the problem provided by the monitoring system is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more power system small disturbance stability minimum damping ratio margin monitoring system embodiments provided below can refer to the limitations of the method above, and will not be repeated here.

[0163] In an exemplary embodiment, a power system small disturbance stability minimum damping ratio margin monitoring system is provided, comprising:

[0164] The model building module is used to establish a small disturbance stability minimum damping ratio margin optimization model taking into account the load growth mode constraints and power system operation requirements;

[0165] The construction and derivation module is connected with the model building module and is used to construct the augmented Lagrangian function using the Lagrangian multiplier method and the exterior point penalty function method, and to derive the KKT condition;

[0166] The solution and output module is connected with the construction and derivation module, and is used to solve the KKT condition by using a nested double-layer iteration method, and output the most dangerous load growth mode and the minimum damping ratio margin for small disturbance stability;

[0167] The model building module is connected to the construction and derivation module, and the construction and derivation module is connected to the solution and output module, so as to implement the power system small disturbance stability minimum damping ratio margin monitoring method described in Example 1.

[0168] In an exemplary embodiment, a method for controlling operation of an auxiliary power system is provided, comprising:

[0169] The method for monitoring the minimum damping ratio margin for small disturbance stability of the power system described in Example 1 is used to monitor the most dangerous load growth mode and the minimum damping ratio margin for small disturbance stability of the power system;

[0170] Based on the monitored most dangerous load growth mode and the minimum damping ratio margin for small disturbance stability, a reference is provided for power system operation and regulation.

[0171] In an exemplary embodiment, a system for assisting power system operation regulation is provided, comprising the power system small disturbance stability minimum damping ratio margin monitoring system described in embodiment 2;

[0172] The most dangerous load growth mode and the minimum damping ratio margin of small disturbance stability are obtained through the power system small disturbance stability minimum damping ratio margin monitoring system;

[0173] The operation of the power system is regulated and controlled based on the most dangerous load growth mode and the minimum damping ratio margin for small disturbance stability.

[0174] Each module in the above system can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module above.

[0175] In an exemplary embodiment, a computer device is provided, which may be a terminal. The computer device further includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, the memory, and the input / output interface are connected via a system bus, and the communication interface, the display unit, and the input device are connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, the steps of the minimum damping ratio margin monitoring method for small disturbance stability of the power system disclosed in Example 1 of the present application are implemented. The display unit of the computer device is used to form a visually visible picture, which may be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0176] Those skilled in the art will appreciate that a specific computer device may include more or fewer components, or combine certain components, or have a different arrangement of components.

[0177] In an exemplary embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for monitoring the minimum damping ratio margin of small disturbance stability of a power system as disclosed in Example 1 of the present application are implemented.

[0178] In an exemplary embodiment, a computer program product is provided, including a computer program / instruction, which, when executed by a processor, implements the steps of the method for monitoring the minimum damping ratio margin of small disturbance stability of a power system as disclosed in Example 1 of the present application.

[0179] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory or other media used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The processor involved in each embodiment provided in this application can be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, etc., but is not limited thereto.

[0180] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0181] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for monitoring the minimum damping ratio margin of small disturbance stability of a power system, characterized in that: The steps include: Step 1: Considering the load growth mode constraints and power system operation requirements, establish a small disturbance stability minimum damping ratio margin optimization model; Assume that the objective function of the small disturbance stability minimum damping ratio margin optimization model is to minimize the Euclidean distance from the current operating point to the critical operating point on the small disturbance stability dynamic performance boundary, expressed as: In formula (1), is the damping ratio margin; ||·||2 is the 2-norm; P L,N and P L,C are the load active power column vectors of the current operating point and the critical operating point respectively; The constraints of the small disturbance stability minimum damping ratio margin optimization model include: equation constraints of the critical operating point on the dynamic performance boundary, power change constraints between the current operating point and the critical operating point, load and power generation growth mode constraints, upper and lower limits of the active power of the generator at the critical point, upper and lower limits of the reactive power of the generator at the critical point, upper and lower limits of the node voltage at the critical point, and branch flow constraints under the critical state, wherein, The equality constraint that must be satisfied at any critical operating point on the dynamic performance boundary is: F(x C ,m C ,μ C ,B)=0 (2) AND C IN Re,C -σ M,C IN Re,C +ω M,C IN Im,C =0 (3) AND C IN Im,C -σ M,C IN Im,C -ω M,C IN Re,C =0 (4) In equations (2) to (7), the subscript C represents the critical operating point; the superscript T represents the matrix transpose; x and m are the dynamic state vector and the non-state vector, respectively; μ is the step size; B is the power growth mode; A is the dynamic state matrix; σ M and ω M are the eigenvalues ​​λ corresponding to the minimum damping ratio M The real and imaginary parts of U Re and U Im The eigenvalues ​​λ are M The real and imaginary parts of the right eigenvector of ; ζ M is the damping ratio threshold; The power variation constraint between the current operating point and the critical operating point is: In formula (8), Q L,N and Q L,C are the load reactive power column vectors of the current operating point and the critical operating point respectively; P G,N and P G,C are the generator active power column vectors of the current operating point and the critical operating point respectively; B L,P and B L,Q are the column vectors of the load active power and reactive power growth modes respectively; B G,P is the column vector of the generator active power growth mode; The load and generation growth constraints are: P Li,C ·P Li,N ≥0 (9) In formula (9)-formula (11), P Li,C and P Li,N are the load active power of node i at the current operating point and the critical operating point respectively; B Li,P and B Li,Q are the growth modes of active power and reactive power of the load at node i respectively; is the power factor of node i; B Gi,P is the growth mode of the active power of the generator at node i; N G and N L are the number of generator nodes and the number of load nodes respectively; P Gi,N is the active power of the generator at node i at the current operating point; The upper and lower limits of the generator active power at the critical operating point are: In formula (12), P Gi,C is the active power of the generator at node i at the critical operating point; P Gi and are the lower and upper limits of the active power of the generator at node i respectively; The upper and lower limits of the generator reactive power at the critical operating point are: In formula (13), Q Gi,C is the generator reactive power at node i at the critical operating point; Q Gi and are the lower and upper limits of the reactive power of the generator at node i respectively; The upper and lower limits of node voltage at the critical operating point are: In formula (14), V i,C is the voltage amplitude of node i at the critical operating point; V i and are the lower and upper limits of the voltage amplitude at node i respectively; The branch power flow constraint under critical operation state is: In formula (15), I ij,C is the current between node i and node j under critical operation state; is the current limit between node i and node j; Step 2: Use the Lagrange multiplier method and the exterior point penalty function method to construct the augmented Lagrangian function and derive the KKT condition; Assume that the compact form of the minimum damping ratio margin model for small disturbance stability is: min f(u,y)(16) st h(u,y)=0 (17) g(u,y)≤0 (18) In equations (16)-(18), f(·) is the objective function, representing equation (1); h(·) is the equality constraint vector, including equations (2)-(8) and (10)-(11); g(·) is the inequality constraint vector, including equations (9) and (12)-(15); u is the control vector, which is determined by the active power growth mode B of each load node. Li,P Composition; y is the mixed state vector, The augmented Lagrangian function constructed using the Lagrangian multiplier method and the exterior point penalty function method is: L(u,y,z)=f(u,y)+z T h(u,y)+Φ(u,y) (19) In formulas (19)-(20), L(·) is the augmented Lagrangian function; Φ(·) is the penalty term; g i (·) is the i-th inequality constraint; z is the Lagrange multiplier vector; l i is the penalty factor of the i-th inequality constraint; r is the number of inequality constraints; The KKT condition for eliminating z is: In formula (21)-formula (22), and are the gradients of the augmented Lagrangian function L with respect to the control vector u and the Lagrangian multiplier vector z respectively; and are the gradients of the objective function f with respect to u and z respectively; and are the gradients of the objective function f and the penalty term Φ with respect to the mixed state vector y respectively; and are the gradients of Φ and y with respect to u respectively; Step 3: Use the nested double-layer iterative method to solve the KKT condition and output the most dangerous load growth mode and the minimum damping ratio margin for small disturbance stability; The nested two-layer iterative method for solving equation (21)-equation (22) includes: Outer layer: Based on the Broyden-Fletcher-Goldfarb-Shanno method, search u using equation (21); Inner layer: Once u changes, use the extended three-stage strategy to solve equation (22) and update y; The extended three-stage strategy for solving equation (22) includes: 1) Estimation phase In formula (23), is the threshold, j1 and j2 are the number of iterations in the one-dimensional search, and j1 <j2; If equation (23) is not satisfied, it is necessary to estimate the initial vector consisting of the steady-state initial value and the critical mode that first crosses the dynamic performance boundary If equation (23) is satisfied, no estimation is required and the last calculation result is used as the initial vector, that is, 2) Correction phase According to the estimation stage Use Newton's method or Broyden's rank 1 method to accurately calculate y (i) ; 3) Correction phase If y (i) If it is on the dynamic performance boundary, the iteration terminates and the calculation results are output; otherwise, update And return to the correction phase.

2. A power system small disturbance stability minimum damping ratio margin monitoring system, characterized in that: include: The model building module is used to establish a small disturbance stability minimum damping ratio margin optimization model taking into account the load growth mode constraints and power system operation requirements; The construction and derivation module is connected with the model building module and is used to construct the augmented Lagrangian function using the Lagrangian multiplier method and the exterior point penalty function method, and to derive the KKT condition; The solution and output module is connected with the construction and derivation module, and is used to solve the KKT condition by using a nested double-layer iteration method, and output the most dangerous load growth mode and the minimum damping ratio margin for small disturbance stability; The model building module is connected to the construction and derivation module, and the construction and derivation module is connected to the solution and output module, so as to implement the power system small disturbance stability minimum damping ratio margin monitoring method described in claim 1.

3. A method for assisting power system operation control, characterized in that: The method for monitoring the minimum damping ratio margin for small disturbance stability of a power system as described in claim 1 is used to monitor the most dangerous load growth mode and the minimum damping ratio margin for small disturbance stability of the power system; Based on the monitored most dangerous load growth mode and the minimum damping ratio margin for small disturbance stability, a reference is provided for power system operation and regulation.

4. A system for assisting power system operation and regulation, characterized in that: The system comprises the power system small disturbance stability minimum damping ratio margin monitoring system as claimed in claim 2; The most dangerous load growth mode and the minimum damping ratio margin of small disturbance stability are obtained through the power system small disturbance stability minimum damping ratio margin monitoring system; The operation of the power system is regulated and controlled based on the most dangerous load growth mode and the minimum damping ratio margin for small disturbance stability.

5. A computer device comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the method for monitoring the minimum damping ratio margin of small disturbance stability of a power system as claimed in claim 1.

6. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instruction is executed by a processor, the method for monitoring the minimum damping ratio margin of small disturbance stability of a power system as claimed in claim 1 is implemented.

7. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the method for monitoring the minimum damping ratio margin of small disturbance stability of a power system as claimed in claim 1 is implemented.

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