Power system voltage stability analysis method and related equipment

Abstract

The invention discloses a power system voltage stability analysis method and related equipment, relates to the technical field of voltage evaluation, and solves the problem of low accuracy of an existing analysis scheme. According to the method, the first-order and second-order change information of the voltage deviation and the voltage fluctuation is fully considered in the new energy reactive power control model, and the first-order and second-order change information amplifies the perception capability of the model for the voltage fluctuation of the power system based on the voltage deviation, so that the response speed of the model is improved, and the control accuracy is improved. The operation of the new energy nodes in the power system can be simulated more accurately, so that the voltage stability margin of the power system can be analyzed more accurately based on the model, and the accuracy of voltage stability analysis of the power system is improved. Besides, sensitive parameters are analyzed based on a new energy control parameter-sensitivity curve, a corresponding optimal control scheme is provided, reactive compensation can be accurately carried out on the power grid in real time by adopting the optimal control scheme, and the voltage stability margin of the power grid is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of voltage assessment, and particularly to a method for analyzing voltage stability of a power system and related equipment. Background Art

[0002] With the transformation of the global energy structure, the proportion of renewable energy (such as wind power and photovoltaic power generation) in the power system is continuously increasing. However, new energy power generation has the characteristics of intermittency and volatility, bringing new challenges to the stable operation of the power system. Especially the voltage stability problem. Due to the different control characteristics of new energy generating units from traditional synchronous generators, traditional voltage stability analysis methods cannot accurately evaluate the impact of high-proportion new energy access on the voltage stability of the power grid.

[0003] Existing voltage stability analysis methods usually assume that the control parameters of generating units are fixed, and do not fully consider the dynamic control characteristics and parameter changes of new energy generating units on voltage stability. The insufficient analysis accuracy leads to insufficient response of the system to voltage fluctuations, thereby reducing the voltage stability margin.

[0004] In view of this, a method for analyzing voltage stability of a power system and related equipment is needed. Summary of the Invention

[0005] Aiming at the problem of low accuracy of the existing power system voltage stability analysis scheme, the present invention provides a method for analyzing voltage stability of a power system and related equipment, which can improve the accuracy of power system voltage stability analysis. The specific technical solutions are as follows:

[0006] In the first aspect, an embodiment of the present application provides a method for analyzing voltage stability of a power system, including:

[0007] Establish a simulation model of the power system, where the simulation model includes a new energy reactive power control model, and the new energy reactive power control model is used to adjust the reactive power output of new energy nodes in the power system based on the bus voltage, voltage first-order change rate, and voltage second-order change rate of the power system; through the continuous power flow algorithm, based on the simulation model, calculate the power flow operation data of each node in the power system; through the control variable method and the Jacobian matrix, based on the power flow operation data, calculate the new energy control parameter - voltage stability margin curve, where the curve is used to represent the corresponding relationship between the new energy control parameters of the new energy nodes and the voltage stability margin of the power system, and the voltage stability margin is the minimum singular value of the Jacobian matrix; based on the curve, analyze the sensitive parameters in the new energy control parameters; based on the sensitive parameters, output the optimal control scheme of the power system.

[0008] Preferably, the control equation of the new energy reactive power control model includes:

[0009] Q (k+1) = Q (k) + γ (k) ·K Q (V ref - V (k) ) - Q (k) + δ (k) ·ΔV (k) + η (k) ·Δ 2 V (k) ;

[0010] Among them, Q (k) and Q (k+1) are the reactive power outputs of the new energy node at the k-th and (k + 1)-th iterations respectively. K Q is the gain of the reactive power regulator. V (k) is the bus voltage obtained through power flow calculation at the k-th iteration. ΔV (k) is the first-order voltage change rate at the k-th iteration. Δ 2 V (k) is the second-order voltage change rate at the k-th iteration. V ref is the reference voltage. γ (k) , δ (k) and η (k) are the feedback regulation coefficient, the first-order compensation coefficient, and the second-order compensation coefficient at the k-th iteration respectively.

[0011] Preferably, the power flow operation data includes the bus voltage, the first-order voltage change rate, and the second-order voltage change rate, and the coefficients of the control equation are dynamically updated based on the power flow operation data; the expressions for dynamic coefficient update include:

[0012] γ (k+1) = γ (k) + α(|V (k) - V ref | + β|ΔV (k) | - γ (k) );

[0013] δ (k+1) = δ (k) + μ(|ΔV (k) | - δ (k) );

[0014] η (k+1) = η (k) + υ(|Δ 2 V (k) | - η (k) );

[0015] Among them, γ (k+1) , δ (k+1) and η(k+1) They are the feedback regulation coefficient, the first-order compensation coefficient, and the second-order compensation coefficient at the (k + 1)-th iteration respectively; α, μ, and ν are learning step parameters, and β is the first-order change rate weight parameter.

[0016] Preferably, based on this curve, sensitive parameters in the new energy control parameters are analyzed, including: calculating the slope of each data point on this curve based on this curve; determining the inflection point of the curve based on this slope; obtaining the maximum slope of the data points in the sensitive area where the inflection point of this curve is located, and this sensitive area is the data interval composed of the inflection point of this curve and the N data points closest to the inflection point of this curve; where N is a preset positive integer; in the case where this maximum slope is greater than the first preset threshold, determining this new energy control parameter as this sensitive parameter; or, obtaining the maximum value among the maximum slopes corresponding to each of these new energy control parameters, and determining the new energy control parameter corresponding to this maximum value as this sensitive parameter.

[0017] Preferably, by using the control variable method and the Jacobian matrix, based on this power flow operation data, a new energy control parameter - voltage stability margin curve is calculated, including: keeping the parameters other than the target new energy control parameter unchanged, changing the target new energy control parameter with a preset step length, and this target new energy control parameter is any one of these new energy control parameters; calculating this voltage stability margin based on the changed target new energy control parameter and this power flow operation data through the Jacobian matrix; generating this curve based on this target new energy control parameter and this voltage stability margin.

[0018] Preferably, after calculating the power flow operation data of each node in this power system based on this simulation model through the continuation power flow algorithm, this method further includes: calculating the voltage stability margin of this power system based on this power flow operation data; in the case where the difference between this voltage stability margin and the preset voltage stability margin is less than the second preset threshold, triggering the step of calculating a new energy control parameter - voltage stability margin curve by using the control variable method and the Jacobian matrix based on this power flow operation data.

[0019] Preferably, based on this sensitive parameter, an optimal control scheme for this power system is output, including: in the case where this sensitive parameter includes the reference voltage, outputting an optimal control scheme of raising the reference voltage of this new energy node to 1.05 pu; in the case where this sensitive parameter includes the reactive power regulator gain, outputting an optimal control scheme of adjusting the reactive power regulator gain of this new energy node to the range of 0.8 - 1.2.

[0020] In a second aspect, an embodiment of the present application provides a power system voltage stability analysis device, which is applied to the method as described in the first aspect. This device includes:

[0021] A simulation module for establishing a simulation model of a power system, the simulation model including a new energy reactive power control model for regulating the reactive power output of new energy nodes in the power system based on the bus voltage, the first-order voltage change rate, and the second-order voltage change rate of the power system;

[0022] A first calculation module for calculating the power flow operation data of each node in the power system based on the simulation model through a continuation power flow algorithm;

[0023] A second calculation module for calculating a new energy control parameter - voltage stability margin curve based on the power flow operation data through a control variable method and a Jacobian matrix, where the curve is used to represent the correspondence between the new energy control parameters of the new energy nodes and the voltage stability margin of the power system, and the voltage stability margin is the minimum singular value of the Jacobian matrix;

[0024] An analysis module for analyzing the sensitive parameters in the new energy control parameters based on the curve;

[0025] An output module for outputting an optimized control scheme for the power system based on the sensitive parameters.

[0026] In a third aspect, an embodiment of the present application provides a computing device, including: a memory for storing a program; a processor for loading the program to execute the method as described in the first aspect.

[0027] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, the computer-readable storage medium including a stored program, wherein when the program runs, it controls the device where the computer-readable storage medium is located to execute the method as described in the first aspect.

[0028] Compared with the prior art, the beneficial effects of the present invention are: By fully considering the voltage deviation and the first-order and second-order change information of voltage fluctuations in the new energy reactive power control model, and based on the voltage deviation and the first-order and second-order change information, the model's perception ability of voltage fluctuations in the power system is amplified, enabling more accurate simulation of the operation of new energy nodes in the power system, thus enabling more accurate analysis of the voltage stability margin of the power system and improving the accuracy of voltage stability analysis of the power system. In addition, the present invention also analyzes sensitive parameters based on the new energy control parameter - sensitivity curve and provides a corresponding optimized control scheme. Using this optimized control scheme can perform reactive power compensation on the power grid in real time and accurately, significantly improving the voltage stability margin of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0030] Figure 1 Schematic flow chart of a method for analyzing the voltage stability of a power system provided by an embodiment of the present application;

[0031] Figure 2 Schematic structural diagram of an IEEE 30-node system provided by an embodiment of the present application;

[0032] Figure 3 Schematic structural diagram of a device for analyzing the voltage stability of a power system provided by an embodiment of the present application;

[0033] Figure 4 Schematic structural diagram of a computing device provided by an embodiment of the present application. Specific embodiments

[0034] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0035] It should be understood that when used in this specification and the appended claims, the terms "comprises" and "comprising" indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0036] It should also be understood that the terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include the plural forms.

[0037] It should be further understood that the term " / and / " used in the specification of the present invention and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0038] To solve the problem of low accuracy in the existing power system voltage stability analysis solutions, the present invention provides a power system voltage stability analysis method and related devices, which can improve the accuracy of power system voltage stability analysis.

[0039] Please refer to Figure 1 , Figure 1 which is a schematic flowchart of a power system voltage stability analysis method provided by an embodiment of this application. This method can be applied to a computing device; as Figure 1 shown, this method includes the following steps:

[0040] Step 101, the computing device establishes a simulation model of the power system.

[0041] Among them, the computing device can be a computing node in the power system, or a computing device in the total control room of the power system, or a computing device communicatively connected to the control module and data acquisition module of the power system; specifically, this computing device can be a server, or a personal computer or tablet directly operated by power system management personnel or maintenance personnel.

[0042] Among them, the power system is a distribution network system accessing new energy, and this power system includes traditional generator set nodes (hereinafter referred to as traditional power generation nodes) and new energy generator set nodes (hereinafter referred to as new energy nodes).

[0043] The computing device can obtain data such as grid topology, generator set parameters, new energy control parameters, and load data from the power system or the management device of the power system, and establish a simulation model of the power system based on these data.

[0044] Among them, this simulation model can include the active power model and reactive power model of traditional power generation nodes, the active power control model and reactive power control model of new energy nodes (hereinafter referred to as new energy active power control model and new energy reactive power control model), grid topology structure, and load model.

[0045] Specifically, this grid topology structure can be represented by the node admittance matrix Y, and its definition is as follows:

[0046]

[0047] Among them: y ij represents the line admittance connecting node i and j, and y sh,i : the shunt admittance of node i.

[0048] The self - admittance of node \(i\) is equal to the sum of the admittances of all branches connected to node \(i\), which reflects the ability of node \(i\) to inject current into the power network under the action of unit voltage. The mutual - admittance between node \(i\) and node \(j\) is equal to the negative of the admittance of the branch connecting node \(i\) and node \(j\), which reflects the current coupling effect between node \(i\) and node \(j\).

[0049] The nodal admittance matrix is the basis for power flow calculation and voltage stability analysis, and is used to describe the electrical connection and impedance relationship of the power grid.

[0050] The equation expressions of the active - power model and reactive - power model of traditional power - generation nodes can be:

[0051]

[0052] where, \(P\) i , \(Q\) i represent the active power and reactive power of node \(i\) respectively, \(G\) ij , \(B\) ij are the real part and imaginary part of the nodal admittance matrix respectively, \(V\) i , \(V\) j represent the voltage amplitudes of node \(i\) and node \(j\) respectively, \(\theta\) ij represents the voltage phase - angle difference between node \(i\) and node \(j\), and \(N\) is the number of traditional power - generation nodes.

[0053] The active - power model and reactive - power model of traditional power - generation nodes can provide the basic power - balance constraint for power flow calculation.

[0054] The expression of the load model can be:

[0055]

[0056] where, \(P_0, Q_0\) are the rated active power and reactive power of the power system, \(V_0\) is the rated voltage of the power system, and \(\alpha,\beta\) are the exponents of power varying with voltage.

[0057] The load model can be used to describe the response characteristics of the load in the power grid with voltage fluctuations. In the subsequent continuation power - flow algorithm, through the load model, the dependence of the actual load on voltage can be simulated, ensuring that the solved voltage distribution is closer to the actual operation state, thereby improving the accuracy of voltage stability analysis.

[0058] The control equations of the new - energy active - power control model can include:

[0059] \(P\) gen \( = P\) max \(\times C\) P (\(\lambda,\beta\)); (5)

[0060] where, \(P\) genFor the active power output, P, of a new energy generating unit max is the rated power of the new energy generating unit

[0061] C P is the power coefficient, λ is the tip speed ratio, and β is the pitch angle.

[0062] The active power control model of the embodiments of the present application takes into account the influence of natural resources such as wind speed and light, as well as corresponding control strategies, on new energy generating units. By introducing parameters related to new energy resources, the active power output can more realistically reflect the intermittency and volatility of new energy power generation; the improved active power output can be used as the input for subsequent power flow calculations, which helps to accurately calculate the node voltage, thereby providing reliable data for subsequent reactive power adjustment and voltage stability margin analysis.

[0063] Among them, the new energy reactive power control model is used to adjust the reactive power output of new energy nodes in the power system based on the bus voltage, the first-order voltage change rate, and the second-order voltage change rate of the power system. The control equation of the new energy reactive power control model includes:

[0064] Q (k+1) = Q (k) + γ (k) ·K Q (V ref - V (k) ) - Q (k) + δ (k) ·ΔV (k) + η (k) ·Δ 2 V (k) ; (6)

[0065] Among them, Q (k) and Q (k+1) are the reactive power outputs of the new energy node at the k-th and (k + 1)-th iterations respectively. K Q is the reactive power regulator gain. V (k) is the bus voltage obtained through power flow calculation at the k-th iteration. ΔV (k) is the first-order voltage change rate at the k-th iteration. Δ 2 V (k) is the second-order voltage change rate at the k-th iteration. V ref is the reference voltage. γ (k) , δ (k) and η (k) are the feedback regulation coefficient, the first-order compensation coefficient, and the second-order compensation coefficient at the k-th iteration respectively.

[0066] Equation (6) divides the reactive power output adjustment into four parts: maintaining the previous state, direct feedback based on voltage deviation, first-order dynamic compensation, and second-order dynamic compensation. After introducing the first-order and second-order voltage change information, the system can capture the dynamic fluctuations and acceleration effects of the power grid more precisely, ensure the real-time and accuracy of reactive power compensation, and achieve online adaptive control through closed-loop feedback.

[0067] Based on the above expressions (1) - (6), the computing device can establish a simulation model of the power system.

[0068] In the embodiment of the present application, the new energy unit model is integrated into the overall power system model, including the power grid topology structure (represented by the nodal admittance matrix), the traditional generator set model, and the load model, forming a complete power flow calculation framework, so that the computing device can execute step 102.

[0069] Step 102: The computing device calculates the power flow operation data of each node in the power system based on this simulation model through the continuation power flow algorithm.

[0070] The continuation power flow (CPF) algorithm is a numerical calculation method for solving the power flow problem of the power system and is used to analyze the voltage stability of the power system. The continuation power flow algorithm solves the steady-state operating point of the power system, that is, calculates the control parameters of each node, based on the given load and power supply conditions, and a continuous parameter (usually the load growth factor or other parameters related to the system operating state). The continuation power flow algorithm can track the power flow solutions of the system under different operating states from the initial operating state to the voltage stability limit, so as to obtain the power flow change situation of the system within the entire operating range.

[0071] It can be understood that during the iteration process of the continuation power flow algorithm, the power flow solutions (node control parameters) of the power system under different states and the electrical parameters corresponding to the power flow solutions are collectively referred to as power flow operation data in this article.

[0072] The active power balance equation and reactive power balance equation of the continuation power flow algorithm can include the following expressions:

[0073]

[0074] Among them, P i and Q i are the active and reactive power injections of node i respectively, V i and θ i are the voltage amplitude and phase angle of the node, G ij and B ij are the real and imaginary parts of the nodal admittance matrix.

[0075] Preferably, the power flow calculation data includes the bus voltage, the first-order voltage change rate, and the second-order voltage change rate, and the coefficients of the control equation are dynamically updated based on the power flow calculation data; the expression for the dynamic update of the coefficients includes:

[0076] γ (k+1) = γ (k) + α(|V (k) -V ref |+β|ΔV (k) |-γ (k) ); (9)

[0077] δ (k+1) = δ (k) + μ(|Δ V (k) |-δ (k) ); (10)

[0078] η (k+1) = η (k) + υ(|Δ 2 V (k) |-η (k) ); (11)

[0079] where γ (k+1) , δ (k+1) and η (k+1) are the feedback regulation coefficient, the first-order compensation coefficient, and the second-order compensation coefficient at the (k + 1)-th iteration, respectively; α, μ, and ν are learning step parameters, and β is the first-order change rate weight parameter.

[0080] By establishing a detailed control model of the new energy generating unit, the active power output and reactive power regulation are described. The new energy active power control model considers the influence of natural resources such as wind speed and light and control strategies; the new energy reactive power control model incorporates the first-order voltage change rate and the second-order change amount as incremental compensation on the basis of the traditional voltage deviation feedback, and adopts a self-optimizing update mechanism of the feedback gain and compensation coefficient to realize the online closed-loop update using the voltage deviation, first-order, and second-order change information.

[0081] Exemplarily, in the initial stage, the computing device can calculate the initial reactive power output according to the initial bus voltage V (0) :

[0082] Q (0) =K Q (V ref -V (0) ); (12)

[0083] After each round of power flow calculation, calculate through the latest and previous moment voltage data:

[0084] ΔV (k) =V(k) -V (k-1) ; (13)

[0085] Δ 2 V (k) = ΔV (k) -ΔV (k-1) ; (14)

[0086] Substitute the above data and the current feedback coefficient into the formula, as well as formula (6), to calculate the new reactive power output Q (k+1) .

[0087] After each round of iteration, according to the latest measured V (k) , ΔV (k) and Δ 2 V (k) data, use the above formulas (9) - (11) to update each feedback and compensation coefficient. When the voltage deviation or fluctuation is large, the corresponding coefficient automatically increases to enhance the adjustment strength; when the system is stable, each coefficient automatically decreases to avoid over-adjustment. The updated coefficients will be used for the next round of reactive power adjustment to achieve closed-loop adaptive control.

[0088] Preferably, during the iterative calculation by the continuation power flow algorithm, after each iteration, based on the power flow operation data obtained from the latest iteration, calculate the voltage stability margin of the power system; when the difference between the voltage stability margin and the preset voltage stability margin is less than the second preset threshold, execute step 103.

[0089] Among them, the continuation power flow algorithm is to solve the voltage stability limit state of the power system, and the preset voltage stability margin is the voltage stability margin under the pre-estimated voltage stability limit state. When the difference (i.e., the absolute value of the difference) between the voltage stability margin and the preset voltage stability margin is less than the second preset threshold, the computing device can consider that the optimal solution of the continuation power flow algorithm has been obtained. At this time, the computing device can use this optimal solution as a reference to perform a sensitivity analysis on the control parameters of the new energy node (hereinafter referred to as the new energy control parameters).

[0090] Step 103: Based on the power flow operation data, calculate the new energy control parameter - voltage stability margin curve through the control variable method and the Jacobian matrix.

[0091] Among them, this curve is used to represent the corresponding relationship between the new energy control parameters of the new energy node and the voltage stability margin of the power system, and this voltage stability margin is the minimum singular value of the Jacobian matrix.

[0092] Specifically, the calculation process of the voltage stability margin of the power system can be as follows:

[0093] First, the computing device can take partial derivatives of the power flow equations with respect to voltage magnitude and phase angle to obtain the Jacobian matrix J of the power system:

[0094]

[0095] where δ represents the phase angle. Then, calculate the minimum singular value of the Jacobian matrix to represent the voltage stability margin:

[0096] σmin = min(singular values of J); (16)

[0097] When σ min is smaller, the system is closer to the voltage stability limit.

[0098] Preferably, parameters other than the target new energy control parameter remain unchanged, and the target new energy control parameter is varied at a preset step size. The target new energy control parameter is any one of the new energy control parameters; based on the changed target new energy control parameter and the power flow operation data, calculate the voltage stability margin; based on the target new energy control parameter and the voltage stability margin, generate the curve.

[0099] Among them, the computing device can sequentially take one of the new energy control parameters as the target new energy control parameter, and through the cycle of changing the target new energy parameter - calculating the voltage stability margin, obtain multiple sets of data of the target new energy control parameter and the voltage stability margin, and then generate the corresponding curve based on the multiple sets of data.

[0100] It can be understood that the target new energy control parameter varies within a preset interval.

[0101] Step 104, the computing device analyzes the sensitive parameters in the new energy control parameters based on the curve.

[0102] Among them, the sensitive parameter refers to a new energy control parameter within a certain value range, where the voltage stability margin changes significantly, that is, a small parameter adjustment will cause a large change in the voltage stability margin, then this new energy control parameter is the sensitive parameter.

[0103] Preferably, based on the curve, calculate the slope of each data point on the curve; determine the inflection point of the curve based on the slope; obtain the maximum slope of the data points in the sensitive region where the inflection point of the curve is located; when the maximum slope is greater than the first preset threshold, determine that the new energy control parameter is the sensitive parameter; or, obtain the maximum value of the maximum slopes corresponding to each new energy control parameter, and determine the new energy control parameter corresponding to the maximum value as the sensitive parameter.

[0104] Among them, the inflection point of the curve is the extreme point on the curve, and the number of inflection points of the curve can be one or more. The sensitive region is the data interval composed of the inflection point of the curve and the N data points closest to the inflection point of the curve; where N is a preset positive integer.

[0105] Among them, the sensitive region refers to the range of values of a certain control parameter where the minimum singular value of the system changes very significantly, that is, a small parameter adjustment will cause a large change in the voltage stability margin. It can be understood that in this embodiment, the nearest N data points of the curve inflection point are used to limit this interval. In addition, other methods can also be used to limit this interval.

[0106] Specifically, the slope of the data point can be calculated based on the data coordinates of the previous and subsequent data points.

[0107] Specifically, when the slope of the i-th data point is 0, and the slopes of the (i - 1)-th data point and the (i + 1)-th data point are not all 0, the computing device can determine that the midpoint coordinates of the i-th data point and the (i + 1)-th data point are the inflection point of the curve; when the slope of the i-th data point is not 0, and the slopes of the (i - 1)-th data point and the (i + 1)-th data point belong to positive and negative values respectively, the computing device can determine that the (i + 1)-th data point is the inflection point of the curve.

[0108] Specifically, the computing device can find the new energy control parameter that has the greatest impact on the voltage stability margin as the sensitive parameter for analysis and optimization; or it can also determine the new energy control parameter whose impact on the voltage stability margin is greater than a certain degree based on the first preset threshold as the sensitive parameter.

[0109] Step 105, the computing device outputs an optimized control scheme for the power system based on the sensitive parameter.

[0110] Among them, the computing device calculates the system power flow and voltage stability margin based on the improved continuous power flow algorithm, and evaluates the impact of the new energy control parameter on voltage stability through parameter sensitivity analysis; after determining the sensitive parameter, it can provide guidance for optimizing the control strategy based on the sensitive parameter.

[0111] Preferably, when the sensitive parameter includes the reference voltage, an optimized control scheme for raising the reference voltage of the new energy node to 1.05 pu is output; when the sensitive parameter includes the reactive power regulator gain, an optimized control scheme for adjusting the reactive power regulator gain of the new energy node to the range of 0.8 - 1.2 is output.

[0112] Among them, pu is the per-unit value, that is, the reference value of the parameter.

[0113] In the embodiments of the present application, by fully considering the voltage deviation and the first-order and second-order change information of voltage fluctuation in the new energy reactive power control model, the perception ability of the model for the voltage fluctuation of the power system is amplified based on the voltage deviation and the first-order and second-order change information, so that the operation of the new energy nodes in the power system can be simulated more accurately, and thus the voltage stability margin of the power system can be analyzed more accurately, improving the accuracy of the voltage stability analysis of the power system. In addition, the present invention also analyzes sensitive parameters based on the new energy control parameter-sensitivity curve and provides a corresponding optimal control scheme. By using this optimal control scheme, the reactive power compensation of the power grid can be carried out in real time and accurately, significantly improving the voltage stability margin of the power grid.

[0114] In the embodiments of the present application, by optimizing the control parameters of the new energy nodes, the voltage stability of the power grid can be effectively improved, ensuring the safe and reliable operation of the power grid. At the same time, this method can also provide a scientific basis for the power grid planning, design and operation, and has broad application prospects.

[0115] A specific embodiment will be provided below to further understand the method provided by the embodiments of the present application.

[0116] Please refer to Figure 2 , Figure 2 which is a schematic structural diagram of an IEEE 30-node system provided by the embodiments of the present application. Among them, the system includes multiple traditional generating units, distributed loads and new energy generating units.

[0117] Taking the connection of a wind farm to node 8 as an example for illustration, the specific parameters of node 8 are set as follows:

[0118] Rated active power P r = 100 MW, operating efficiency η = 0.92; taking f(λ,β) = 1.0 (standard working condition), so the active power output P8 = 92 MW; reference voltage V ref = 1.05 pu, initial node voltage Initial reactive power regulator gain K Q = 1.0, then the initial reactive power output of node 8 is:

[0119]

[0120] Initial value of dynamic coefficient: γ (0) = 0.3, δ (0) = 0.05, η (0) = 0.02;

[0121] Learning step parameters: α = 0.1, μ = 0.05, ν = 0.05, first-order weight β = 0.5.

[0122] The calculation process of the first round of iteration is as follows:

[0123] After continuous power flow calculation, the voltage of node 8 is updated to Then:

[0124] Calculate the first-order change rate:

[0125]

[0126] Since there is no previous data initially, take the second-order change amount:

[0127]

[0128] Update the reactive power output:

[0129]

[0130] Update the feedback coefficient:

[0131] γ (1) = 0.3 + 0.1(|1.02 - 1.05| + 0.5×|0.02| - 0.3);

[0132] Calculate: |1.02 - 1.05| = 0.03 pu, 0.5×0.02 = 0.01 pu, the sum = 0.04 pu, then:

[0133] γ (1) = 0.3 + 0.1(0.04 - 0.3) = 0.3 - 0.026 = 0.274;

[0134] Update the first-order compensation coefficient:

[0135] δ (1) = 0.05 + 0.05·(0.02 - 0.05) = 0.05 - 0.0015 = 0.0485;

[0136] Update the second-order compensation coefficient:

[0137] η (1) = 0.02 + 0.05·(0 - 0.02) = 0.02 - 0.001 = 0.019.

[0138] After the first round of iteration, substitute the updated and dynamic coefficients into the next round of power flow calculation. Assume the voltage of node 8 is updated to Then:

[0139] Calculate the first-order change rate:

[0140]

[0141] Calculate the second-order change amount:

[0142]

[0143] Repeat the above update of the reactive power output and the dynamic coefficient until the power flow calculation of the system shows that the node voltages of the entire network tend to be stable, and the minimum singular value of the system Jacobian matrix is gradually increased from the initial value of about 0.15 to the preset voltage stability margin (for example, ≥0.18). The simulation results of the IEEE 30-node system show that the proposed scheme can significantly improve the voltage stability margin of the power grid, and at the same time verify the scalability and practical application value of the scheme.

[0144] The simulation verification using the IEEE 30-node system proves the applicability and scalability of the proposed scheme in large-scale power grids, has broad engineering application prospects, and provides a scientific basis for power grid planning, design, operation and protection.

[0145] The method provided by the embodiments of the present application has been described above. Next, the devices provided by the embodiments of the present application will be described.

[0146] Please refer to Figure 3 , Figure 3 which is a power system voltage stability analysis device provided by an embodiment of the present application. As Figure 3 shown, the device 300 includes:

[0147] A simulation module 301 for establishing a simulation model of a power system, the simulation model including a new energy reactive power control model for adjusting the reactive power output of new energy nodes in the power system based on the bus voltage, the first-order voltage change rate, and the second-order voltage change rate of the power system;

[0148] A first calculation module 302 for calculating the power flow operation data of each node in the power system based on the simulation model through a continuous power flow algorithm;

[0149] A second calculation module 303 for calculating a new energy control parameter - voltage stability margin curve based on the power flow operation data through a control variable method and a Jacobian matrix, where the curve is used to represent the correspondence between the new energy control parameters of the new energy nodes and the voltage stability margin of the power system, and the voltage stability margin is the minimum singular value of the Jacobian matrix;

[0150] An analysis module 304 for analyzing the sensitive parameters in the new energy control parameters based on the curve;

[0151] An output module 305 for outputting an optimal control scheme for the power system based on the sensitive parameters.

[0152] Preferably, the control equation of the new energy reactive power control model includes:

[0153] Q(k+1) = Q (k) + γ (k) ·K Q (V ref - V (k) ) - Q (k) + δ (k) ·ΔV (k) + η (k) ·Δ 2 V (k) ;

[0154] Among them, Q (k) and Q (k+1) are the reactive power outputs of the new energy node at the k-th and (k + 1)-th iterations respectively. K Q is the gain of the reactive power regulator. V (k) is the bus voltage obtained through power flow calculation at the k-th iteration. ΔV (k) is the first-order voltage change rate at the k-th iteration. Δ 2 V (k) is the second-order voltage change rate at the k-th iteration. V ref is the reference voltage. γ (k) , δ (k) and η (k) are the feedback regulation coefficient, the first-order compensation coefficient and the second-order compensation coefficient at the k-th iteration respectively.

[0155] Preferably, the power flow operation data includes the bus voltage, the first-order voltage change rate and the second-order voltage change rate, and the coefficients of the control equation are dynamically updated based on the power flow operation data; the expressions for dynamic coefficient update include:

[0156] γ (k+1) = γ (k) + α(|V (k) - V ref | + β|ΔV (k) | - γ (k) );

[0157] δ (k+1) = δ (k) + μ(|ΔV (k) | - δ (k) );

[0158] η (k+1) = η (k) + υ(|Δ 2 V (k) | - η (k) );

[0159] Among them, γ (k+1) , δ (k+1) and η (k+1)They are respectively the feedback adjustment coefficient, the first-order compensation coefficient, and the second-order compensation coefficient at the (k + 1)-th iteration; α, μ, and ν are learning step parameters, and β is the first-order change rate weight parameter.

[0160] Preferably, the analysis module 304 is specifically configured to calculate the slope of each data point on the curve based on the curve; determine the inflection point of the curve based on the slope; obtain the maximum slope of the data points in the sensitive area where the inflection point of the curve is located, and the sensitive area is a data interval formed by the inflection point of the curve and the N data points closest to the inflection point of the curve; where N is a preset positive integer; in the case that the maximum slope is greater than the first preset threshold, determine the new energy control parameter as the sensitive parameter; or, obtain the maximum value among the maximum slopes corresponding to each new energy control parameter, and determine the new energy control parameter corresponding to the maximum value as the sensitive parameter.

[0161] Preferably, the second calculation module 303 is specifically configured to keep the parameters other than the target new energy control parameter unchanged, change the target new energy control parameter at a preset step length, and the target new energy control parameter is any one of the new energy control parameters; calculate the voltage stability margin based on the changed target new energy control parameter and the power flow operation data through the Jacobian matrix; generate the curve based on the target new energy control parameter and the voltage stability margin.

[0162] Preferably, the second calculation module 303 is further configured to calculate the voltage stability margin of the power system based on the power flow operation data; in the case that the difference between the voltage stability margin and the preset voltage stability margin is less than the second preset threshold, trigger the step of calculating the new energy control parameter - voltage stability margin curve based on the power flow operation data by the control variable method and the Jacobian matrix.

[0163] Preferably, the output module 305 is specifically configured to output an optimal control scheme for increasing the reference voltage of the new energy node to 1.05 pu in the case that the sensitive parameter includes the reference voltage; output an optimal control scheme for adjusting the reactive power regulator gain of the new energy node to within the range of 0.8 - 1.2 in the case that the sensitive parameter includes the reactive power regulator gain.

[0164] The power system voltage stability analysis device provided by the embodiments of the present application can be understood by referring to the corresponding content in the foregoing method embodiment part, and will not be repeated here.

[0165] As Figure 4 shown, Figure 4A possible schematic diagram of the logical structure of a computing device provided by an embodiment of the present application. The computing device 400 includes: a processor 401, a communication interface 402, a memory 403, and a bus 404. The processor 401, the communication interface 402, and the memory 403 are interconnected through the bus 404. In the embodiment of the present application, the processor 401 is used to control and manage the operations of the computing device 400. For example, the processor 401 is used to execute Figure 1 the steps in the embodiment and / or other processes for the technologies described herein. The communication interface 402 is used to support the computing device 400 to communicate. The memory 403 is used to store the program code and data of the computing device 400.

[0166] Among them, the processor 401 may be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field-programmable gate array, or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute various exemplary logical blocks, modules, and circuits described in combination with the disclosure of the present application. The processor may also be a combination that realizes computing functions, such as a combination including one or more microprocessors, a combination of a digital signal processor and a microprocessor, and so on. The bus 404 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For the sake of simplicity of representation, Figure 4 only a thick line is shown in the figure, but it does not mean that there is only one bus or one type of bus.

[0167] In another embodiment of the present application, a computer-readable storage medium is further provided. The computer-readable storage medium includes instructions. When the instructions run on a computer, the computer is caused to execute the above Figure 1 method described in the embodiment.

[0168] Those of ordinary skill in the art can realize that the units of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the components of each example have been generally described according to their functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0169] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0170] In several embodiments provided in the embodiments of the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections between each other can be through some interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0171] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0172] In addition, in each embodiment of the present invention, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0173] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present invention. The foregoing storage medium includes: USB flash drives, read-only memories (ROMs), random access memories (RAMs), mobile hard disks, magnetic disks, or optical discs, and other media that can store program codes.

[0174] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and the description of the present invention.

Claims

1. A method for analyzing the voltage stability of a power system, characterized in that, Including: Establish a simulation model of the power system, where the simulation model includes a new energy reactive power control model for adjusting the reactive power output of new energy nodes in the power system based on the bus voltage, the first-order voltage change rate, and the second-order voltage change rate of the power system; Based on the simulation model, calculate the power flow operation data of each node in the power system through the continuation power flow algorithm; Based on the power flow operation data, calculate the new energy control parameter - voltage stability margin curve through the control variable method and the Jacobian matrix, where the curve is used to represent the correspondence between the new energy control parameter of the new energy node and the voltage stability margin of the power system, and the voltage stability margin is the minimum singular value of the Jacobian matrix; Based on the curve, analyze the sensitive parameters in the new energy control parameters; Based on the sensitive parameters, output the optimal control scheme of the power system.

2. The method according to claim 1, characterized in that, The control equation of the new energy reactive power control model includes: Q (k+1) = Q (k) + γ (k) · [K Q (V ref - V (k) ) - Q (k) + δ (k) · ΔV (k) + η (k) · Δ 2 V (k) ; Among them, Q (k) and Q (k+1) are the reactive power outputs of the new energy node at the k-th and (k + 1)-th iterations respectively. K Q is the gain of the reactive power regulator. V (k) is the bus voltage obtained through power flow calculation at the k-th iteration. ΔV (k) is the first-order voltage change rate at the k-th iteration. Δ 2 V (k) is the second-order voltage change rate at the k-th iteration. V ref is the reference voltage. γ (k) , δ (k) and η (k) are the feedback regulation coefficient, the first-order compensation coefficient, and the second-order compensation coefficient at the k-th iteration respectively.

3. The method according to claim 2, characterized in that, The power flow operation data includes the bus voltage, the first-order voltage change rate, and the second-order voltage change rate, and the coefficients of the control equation are dynamically updated based on the power flow operation data; the dynamic update expression of the coefficients includes: γ (k+1) = γ (k) + α(||V (k) - V ref | + β|ΔV (k) |- γ (k) ); δ (k+1) = δ (k) + μ(|ΔV (k) | - δ (k) ); η (k+1) = η (k) + υ(|Δ 2 V (k) | - η (k) ); Among them, γ (k+1) , δ (k+1) and η (k+1) are the feedback adjustment coefficient, the first-order compensation coefficient, and the second-order compensation coefficient at the (k + 1)-th iteration, respectively; α, μ, and ν are learning step parameters, and β is the first-order change rate weight parameter.

4. The method according to any one of claims 1 to 3, characterized in that The step of analyzing the sensitive parameters in the new energy control parameters based on the curve includes: Based on the curve, calculate the slope of each data point on the curve; Determine the curve inflection point based on the slope; Obtain the maximum slope of the data points in the sensitive area where the curve inflection point is located, and the sensitive area is a data interval composed of the curve inflection point and the nearest N data points to the curve inflection point; where N is a preset positive integer; In the case where the maximum slope is greater than the first preset threshold, determine the new energy control parameter as the sensitive parameter; or, Obtain the maximum value of the maximum slopes corresponding to each new energy control parameter, and determine the new energy control parameter corresponding to the maximum value as the sensitive parameter.

5. The method according to any one of claims 1 to 3, characterized in that, The step of calculating the new energy control parameter - voltage stability margin curve based on the power flow operation data through the control variable method and the Jacobian matrix includes: Keep the parameters other than the target new energy control parameter unchanged, and change the target new energy control parameter with a preset step size, where the target new energy control parameter is any one of the new energy control parameters; Based on the changed target new energy control parameter and the power flow operation data, calculate the voltage stability margin through the Jacobian matrix; Based on the target new energy control parameter and the voltage stability margin, generate the curve.

6. The method according to any one of claims 1 to 3, characterized in that, After calculating the power flow operation data of each node in the power system based on the simulation model through the continuation power flow algorithm, the method further includes: Based on the power flow operation data, calculate the voltage stability margin of the power system; In the case where the difference between the voltage stability margin and the preset voltage stability margin is less than the second preset threshold, trigger the step of calculating the new energy control parameter - voltage stability margin curve based on the power flow operation data through the control variable method and the Jacobian matrix.

7. The method according to any one of claims 1 to 3, characterized in that, Output an optimized control scheme for the power system based on the sensitive parameters, including: When the sensitive parameter includes the reference voltage, output an optimized control scheme for raising the reference voltage of the new energy node to 1.05 pu; When the sensitive parameter includes the reactive power regulator gain, output an optimized control scheme for adjusting the reactive power regulator gain of the new energy node within the range of 0.8 - 1.

2.

8. A device for analyzing the voltage stability of a power system, characterized in that, Applied to the method according to any one of claims 1 - 7, the device includes: A simulation module for establishing a simulation model of the power system, the simulation model including a new energy reactive power control model for regulating the reactive power output of new energy nodes in the power system based on the bus voltage, voltage first-order change rate, and voltage second-order change rate of the power system; A first calculation module for calculating the power flow operation data of each node in the power system based on the simulation model through the continuous power flow algorithm; A second calculation module for calculating a new energy control parameter - voltage stability margin curve based on the power flow operation data through the control variable method and the Jacobian matrix, where the curve is used to represent the correspondence between the new energy control parameters of the new energy node and the voltage stability margin of the power system, and the voltage stability margin is the minimum singular value of the Jacobian matrix; An analysis module for analyzing the sensitive parameters in the new energy control parameters based on the curve; An output module for outputting an optimized control scheme for the power system based on the sensitive parameters.

9. A computing device, characterized in that, Including: A memory for storing programs; A processor for loading the program to execute the method according to any one of claims 1 - 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein when the program runs, it controls the device where the computer-readable storage medium is located to execute the method according to any one of claims 1 - 7.

Images