Power distribution network voltage sensitivity identification method, device, equipment and medium

Abstract

The invention discloses a power distribution network voltage sensitivity identification method, device, equipment and medium, and the method comprises the steps: constructing a sensitivity estimation model through collected power distribution network line parameters and power distribution network operation parameters, and then carrying out the global search of the sensitivity estimation model; the method comprises the steps of obtaining a sensitivity estimation model according to a preset convergence condition, obtaining a pre-selected sensitivity result of the sensitivity estimation model, taking the pre-selected sensitivity result as an initial point of a Gaussian-Newton algorithm, carrying out iterative calculation on the sensitivity estimation model through the Gaussian-Newton algorithm, and stopping the iterative calculation until the solved sensitivity result meets the preset convergence condition. And taking the current sensitivity result meeting the convergence condition as a final sensitivity result of the sensitivity estimation model. According to the invention, the finally output sensitivity result can reflect the sensitivity relation between the voltage and the power in the power distribution network more accurately.

Description

Technical Field

[0001] The present invention relates to the field of power system operation and control technology, and in particular to a distribution network voltage sensitivity identification method, device, equipment and medium. Background Art

[0002] Voltage-power sensitivity plays an important role in the control, monitoring, and operation of distribution networks. In the prior art, although the data-driven linear regression (LR) method is widely used due to its simplicity and ease of implementation, it faces significant challenges when dealing with voltage-power sensitivity estimation in distribution networks.

[0003] Specifically, the complexity and dynamics of the distribution network lead to a complex correlation (MD) between voltage-power sensitivity and network parameters. This correlation is often difficult to fully capture and accurately express in models based on linear regression, and in actual situations, this correlation is often nonlinear. Therefore, even if we try to incorporate this correlation into the model for solution, the complexity of the calculation will significantly increase the difficulty of processing, thereby limiting the effectiveness and accuracy of voltage sensitivity identification. Summary of the invention

[0004] The present invention provides a method, device, equipment and medium for identifying voltage sensitivity of a distribution network. The method considers the correlation between sensitivity and network parameters, converts the identification problem into a sensitivity estimation model with multiple decision variables, and solves the sensitivity estimation model to achieve the purpose of improving the identification accuracy of voltage sensitivity.

[0005] An embodiment of the present invention provides a method for identifying voltage sensitivity of a distribution network, comprising:

[0006] Obtain distribution network line parameters and distribution network operation parameters;

[0007] Based on the obtained parameters, a sensitivity estimation model is constructed;

[0008] Performing a global search on the sensitivity estimation model to obtain a preselected sensitivity result of the sensitivity estimation model; wherein the preselected sensitivity result includes a preselected correlation coefficient and a preselected voltage active sensitivity;

[0009] According to the preselected sensitivity result and the preset Gauss-Newton algorithm, the sensitivity estimation model is iteratively calculated until the sensitivity result obtained by solving satisfies the preset convergence condition, the iterative calculation is stopped, and the sensitivity result that currently satisfies the convergence condition is used as the final sensitivity result of the sensitivity estimation model.

[0010] Furthermore, the distribution network line parameters include a topological diagram of the distribution network; the distribution network operation parameters include the voltage amplitude of each node at each time step, the active power of each node at each time step, and the reactive power of each node at each time step;

[0011] The step of constructing a sensitivity estimation model according to the acquired parameters includes:

[0012] According to the topological diagram, the voltage amplitude of each node at each time step, the active power of each node at each time step, and the reactive power of each node at each time step are substituted into a preset power flow linearization equation to obtain a first relational expression for characterizing the relationship between node voltage and power;

[0013] According to the node voltage power sensitivity correlation expression, the ratio relationship between voltage active sensitivity and voltage reactive sensitivity is determined;

[0014] According to the ratio relationship, the parameter used to characterize the voltage reactive power sensitivity in the first relational expression is replaced, and the relational expression after the replacement is used as the second relational expression;

[0015] According to the second relational expression, a sensitivity estimation model is constructed.

[0016] Furthermore, the sensitivity estimation model includes:

[0017]

[0018] In the formula, represents the voltage amplitude at the ith node in a single time step (between t and t-1), Indicates the change value of active power; Indicates the change value of reactive power; α i,k represents the voltage active sensitivity between the voltage amplitude of the i-th node and the active power of the k-th node; m i,k Represents the correlation coefficient.

[0019] Furthermore, the globally searching the sensitivity estimation model to obtain a preselected sensitivity result of the sensitivity estimation model includes:

[0020] The sensitivity estimation model is solved by a preset particle swarm algorithm and a preset least squares method until the total residual of the sensitivity estimation model is less than a first preset threshold, thereby obtaining a preselected sensitivity result of the sensitivity estimation model.

[0021] Further, the sensitivity estimation model is iteratively calculated according to the preselected sensitivity result and the preset Gauss-Newton algorithm until the sensitivity result obtained by solving satisfies the preset convergence condition, the iterative calculation is stopped, and the sensitivity result that currently satisfies the convergence condition is used as the final sensitivity result of the sensitivity estimation model, including:

[0022] The norm solution of the residual vector corresponding to the sensitivity result is less than a second preset threshold as a convergence condition of the Gauss-Newton algorithm;

[0023] Using the preselected sensitivity results as initial parameter estimates for the Gauss-Newton algorithm;

[0024] According to the Gauss-Newton algorithm, the sensitivity estimation model is iteratively calculated until the norm solution of the residual vector corresponding to the sensitivity result obtained is less than a second preset threshold, the iterative calculation is stopped, and the sensitivity result in which the norm solution of the current residual vector is less than the second preset threshold is used as the final sensitivity result of the sensitivity estimation model.

[0025] The present application also provides a distribution network voltage sensitivity identification device, comprising: a parameter acquisition module, a sensitivity estimation model construction module, a pre-selected sensitivity result calculation module, and a sensitivity result determination module;

[0026] The parameter acquisition module is used to obtain the distribution network line parameters and distribution network operation parameters;

[0027] The sensitivity estimation model construction module is used to construct a sensitivity estimation model according to the acquired parameters;

[0028] The preselected sensitivity result calculation module is used to perform a global search on the sensitivity estimation model to obtain a preselected sensitivity result of the sensitivity estimation model; wherein the preselected sensitivity result includes a preselected correlation coefficient and a preselected voltage active sensitivity;

[0029] The sensitivity result determination module is used to iteratively calculate the sensitivity estimation model according to the pre-selected sensitivity result and the preset Gauss-Newton algorithm until the sensitivity result obtained by solving satisfies the preset convergence condition, stop the iterative calculation, and use the sensitivity result that currently satisfies the convergence condition as the final sensitivity result of the sensitivity estimation model.

[0030] Furthermore, the distribution network line parameters include a topological diagram of the distribution network; the distribution network operation parameters include the voltage amplitude of each node at each time step, the active power of each node at each time step, and the reactive power of each node at each time step;

[0031] The step of constructing a sensitivity estimation model according to the acquired parameters includes:

[0032] According to the topological diagram, the voltage amplitude of each node at each time step, the active power of each node at each time step, and the reactive power of each node at each time step are substituted into a preset power flow linearization equation to obtain a first relational expression for characterizing the relationship between node voltage and power;

[0033] According to the node voltage power sensitivity correlation expression, the ratio relationship between voltage active sensitivity and voltage reactive sensitivity is determined;

[0034] According to the ratio relationship, the parameter used to characterize the voltage reactive power sensitivity in the first relational expression is replaced, and the relational expression after the replacement is used as the second relational expression;

[0035] According to the second relational expression, a sensitivity estimation model is constructed.

[0036] Further, the sensitivity estimation model is iteratively calculated according to the preselected sensitivity result and the preset Gauss-Newton algorithm until the sensitivity result obtained by solving satisfies the preset convergence condition, the iterative calculation is stopped, and the sensitivity result that currently satisfies the convergence condition is used as the final sensitivity result of the sensitivity estimation model, including:

[0037] The norm solution of the residual vector corresponding to the sensitivity result is less than a second preset threshold as a convergence condition of the Gauss-Newton algorithm;

[0038] Using the preselected sensitivity results as initial parameter estimates for the Gauss-Newton algorithm;

[0039] According to the Gauss-Newton algorithm, the sensitivity estimation model is iteratively calculated until the norm solution of the residual vector corresponding to the sensitivity result obtained is less than a second preset threshold, the iterative calculation is stopped, and the sensitivity result in which the norm solution of the current residual vector is less than the second preset threshold is used as the final sensitivity result of the sensitivity estimation model.

[0040] The following beneficial effects are achieved by implementing the present invention:

[0041] The present invention provides a method, device, equipment and medium for identifying voltage sensitivity of a distribution network. The method constructs a sensitivity estimation model based on the collected relevant data of the distribution network; then a global search is performed on the sensitivity estimation model to find a rough solution of the sensitivity estimation model, that is, a pre-selected sensitivity result, and then the pre-selected sensitivity result is used as the initial point of the Gauss-Newton algorithm, and the sensitivity result close to the real one is continuously calculated through iterative calculation, thereby, the sensitivity result finally outputted can more accurately reflect the sensitivity relationship between voltage and power in the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solution of the present application, the drawings required for use in the implementation manner will be briefly introduced below. Obviously, the drawings described below are only some implementation manners of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0043] Figure 1 It is a flow chart of a method for identifying voltage sensitivity of a distribution network provided in a certain embodiment of the present application;

[0044] Figure 2 It is a structural schematic diagram of a distribution network voltage sensitivity identification device provided by a certain embodiment of the present application;

[0045] Figure 3 It is a structural diagram of a terminal device provided in a certain embodiment of the present application. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solutions and advantages of this application clearer, the technical solutions in this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0047] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" in the specification and claims of this application and the above-mentioned figure descriptions and any variations thereof are intended to cover non-exclusive inclusions.

[0048] In the description of the embodiments of the present application, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "multiple" is more than two, unless otherwise clearly and specifically defined.

[0049] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0050] In the description of the embodiments of the present application, the term "and / or" is only a description of the association relationship of the associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0051] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0052] In the description of the embodiments of the present application, unless otherwise clearly specified and limited, technical terms such as "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, and it can be the internal connection of two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0053] See also Figure 1 , is a flow chart of a method for identifying voltage sensitivity of a distribution network provided by an embodiment of the present invention, comprising:

[0054] S1. Obtaining distribution network line parameters and distribution network operation parameters;

[0055] S2. Construct a sensitivity estimation model based on the acquired parameters;

[0056] In a preferred embodiment, the distribution network line parameters include a topological diagram of the distribution network; the distribution network operation parameters include the voltage amplitude of each node at each time step, the active power of each node at each time step, and the reactive power of each node at each time step;

[0057] The step of constructing a sensitivity estimation model according to the acquired parameters includes:

[0058] According to the topological diagram, the voltage amplitude of each node at each time step, the active power of each node at each time step, and the reactive power of each node at each time step are substituted into a preset power flow linearization equation to obtain a first relational expression for characterizing the relationship between node voltage and power;

[0059] According to the node voltage power sensitivity correlation expression, the ratio relationship between voltage active sensitivity and voltage reactive sensitivity is determined;

[0060] According to the ratio relationship, the parameter used to characterize the voltage reactive power sensitivity in the first relational expression is replaced, and the relational expression after the replacement is used as the second relational expression;

[0061] According to the second relational expression, construct a sensitivity estimation model;

[0062] Specifically, the active power flow and reactive power flow are determined by the voltage amplitude and phase angle of the node. By linearizing the power flow, the following equations can be derived:

[0063]

[0064] Where P represents the active power of all nodes; Q represents the reactive power of all nodes; θ represents the phase angle of all nodes; V represents the vector of voltage amplitudes of all nodes; and J represents the Jacobian matrix.

[0065] By calculating J(J -1 ), ΔV can be expressed as:

[0066]

[0067] In the formula, Indicates the sensitivity of voltage to active power; Indicates the sensitivity of voltage to reactive power.

[0068] Although you can use J -1 To obtain these sensitivities, data-driven estimates are required. If the number of nodes is N, the preset power flow linearization equation can be expressed as:

[0069]

[0070] In the formula, represents the voltage amplitude at the i-th node within a single time step (between t and t-1); Represents the change in active power at the i-th node within a single time step (between t and t-1); Represents the change in reactive power at the i-th node within a single time step (between t and t-1); α i,k represents the voltage active sensitivity between the voltage amplitude of the i-th node and the active power of the k-th node; β i,k It represents the voltage-reactive sensitivity between the voltage amplitude of the i-th node and the active power of the k-th node.

[0071] If you collect enough and Then, according to the topological diagram, the voltage amplitude of each node at each time step, the active power of each node at each time step, and the reactive power of each node at each time step are substituted into the preset power flow linearization equation to obtain a first relational expression for characterizing the relationship between node voltage and power:

[0072]

[0073] Where n represents the last time step.

[0074] The correlation between the sensitivities can be analyzed by the Thevenin equivalent model of the power network at the i-th node, and the voltage at the i-th node is written as:

[0075]

[0076] In the formula, ~ represents the complex number of the corresponding variable; Ω G Represents a collection of interface nodes; Ω L Represents a set of normal nodes; represents the transfer impedance between the i-th and j-th nodes; Represents the driving impedance of the i-th node. Rearranging the above equation, we can get the following:

[0077]

[0078] According to equations (6)-(9), the network is represented by the Thevenin equivalent model, and the current in the equation is replaced by the injected complex power It means that:

[0079]

[0080] In the formula,

[0081] Furthermore, regarding P k and Q k of The partial derivative formula is as follows:

[0082]

[0083] In the formula,

[0084] It should be noted that the parameters with subscript 0 represent the reference values ​​of the corresponding variables.

[0085] Since the voltage angle difference in the power system is very small, θ k Assuming that is close to zero, the voltage-power sensitivity satisfies the following equation:

[0086]

[0087] Consider only the P k and Q k of The real part of the partial derivative formula, assuming that θ k ≈0, therefore, the node voltage power sensitivity correlation expression can be written as:

[0088]

[0089] Therefore, according to the node voltage power sensitivity correlation expression, the ratio relationship between voltage active sensitivity and voltage reactive sensitivity is determined as follows:

[0090]

[0091] According to the ratio relationship, Defined as m i,k (correlation coefficient), the parameter β used to characterize the voltage reactive power sensitivity in the first relational expression (4) i,k Replace the relational expression after replacement and use it as the second relational expression; and calculate f according to the second relational expression i (a i ,m i ) is minimized as the goal, and a sensitivity estimation model is constructed;

[0092] In a preferred embodiment, the sensitivity estimation model includes:

[0093]

[0094] In the formula, represents the voltage amplitude at the i-th node in a single time step (between t and t-1), Indicates the change value of active power; Indicates the change value of reactive power; α i,k represents the voltage active sensitivity between the voltage amplitude of the i-th node and the active power of the k-th node; m i,k Represents the correlation coefficient.

[0095] S3, performing a global search on the sensitivity estimation model to obtain a preselected sensitivity result of the sensitivity estimation model; wherein the preselected sensitivity result includes a preselected correlation coefficient and a preselected voltage active sensitivity;

[0096] In a preferred embodiment, the globally searching the sensitivity estimation model to obtain a preselected sensitivity result of the sensitivity estimation model includes:

[0097] The sensitivity estimation model is solved by using a preset particle swarm algorithm and a preset least squares method until the total residual of the sensitivity estimation model is less than a first preset threshold, thereby obtaining a preselected sensitivity result of the sensitivity estimation model;

[0098] Specifically, the particle swarm optimization algorithm cannot find solutions involving many decision variables, so it is assumed that m i,k is m0, which is independent of nodes i and k. Therefore, the sensitivity estimation model is transformed into the following formula:

[0099]

[0100] In the formula, the parameters with superscript 0 represent approximate estimation exponents.

[0101] In order to find m 0 and sensitivity, combining the particle swarm algorithm and the least squares method, wherein the sensitivity estimation model is solved by the least squares method, the partial derivative of the objective function is calculated, and the partial derivative formula is set to zero to obtain the fitting parameters The solution, and through the initial m 0 The objective function is optimized and solved to obtain more accurate sensitivity and correlation coefficient.

[0102] First, initialize m 0 and PSO particles, for a given m 0 , The above formula (20) is obtained by the least squares method. If m 0 With the optimal value, the total residual for all i is minimized, so it is necessary to recursively update m 0 to minimize the total residual of PSO; if the particle swarm algorithm calculation result is less than a first preset threshold, a preselected sensitivity result of the sensitivity estimation model is obtained.

[0103] It should be noted that due to m 0 Determined by PSO, so m 0 The search space of is important and can be set to 0-3.7 considering the X / R ratio in the transmission network and the line impedance of the cable.

[0104] S4. According to the pre-selected sensitivity result and the preset Gauss-Newton algorithm, the sensitivity estimation model is iteratively calculated until the sensitivity result obtained satisfies the preset convergence condition, the iterative calculation is stopped, and the sensitivity result that currently satisfies the convergence condition is used as the final sensitivity result of the sensitivity estimation model.

[0105] In a preferred embodiment, the sensitivity estimation model is iteratively calculated according to the preselected sensitivity result and the preset Gauss-Newton algorithm until the sensitivity result obtained by solving satisfies the preset convergence condition, the iterative calculation is stopped, and the sensitivity result that currently satisfies the convergence condition is used as the final sensitivity result of the sensitivity estimation model, including:

[0106] The norm solution of the residual vector corresponding to the sensitivity result is less than a second preset threshold as a convergence condition of the Gauss-Newton algorithm;

[0107] Using the preselected sensitivity results as initial parameter estimates for the Gauss-Newton algorithm;

[0108] According to the Gauss-Newton algorithm, the sensitivity estimation model is iteratively calculated until the norm solution of the residual vector corresponding to the sensitivity result obtained is less than a second preset threshold, the iterative calculation is stopped, and the sensitivity result whose norm solution of the current residual vector is less than the second preset threshold is used as the final sensitivity result of the sensitivity estimation model;

[0109] Specifically, the Gauss-Newton method finds the solution to the nonlinear LS by formulating the residual using a first-order Taylor series, solving the problem, and sequentially updating the solution. The residual r of the sensitivity estimation model i This is written in a first-order Taylor series as follows:

[0110]

[0111] Where h(=0,1,...) represents the number of iterations; the parameter with ^ represents the perturbation of the corresponding variable.

[0112] The sensitivity estimation model is approximately expressed as:

[0113]

[0114] Minimize the solution obtained by Become the search direction, next, determine the optimal step size d to minimize the objective function To find the data a subiteration based golden section search (GSS) is used and the set point is updated as:

[0115]

[0116] After updating the set point, continue to solve the problem using the Gauss-Newton method until the norm solution of the residual vector corresponding to the sensitivity result obtained is less than a second preset threshold, stop the iterative calculation, and use the sensitivity result in which the norm solution of the current residual vector is less than the second preset threshold as the final sensitivity result of the sensitivity estimation model.

[0117] See also Figure 2 , is a distribution network voltage sensitivity identification device provided by an embodiment of the present invention, comprising: a parameter acquisition module, a sensitivity estimation model construction module, a pre-selected sensitivity result calculation module and a sensitivity result determination module;

[0118] The parameter acquisition module is used to obtain the distribution network line parameters and distribution network operation parameters;

[0119] The sensitivity estimation model construction module is used to construct a sensitivity estimation model according to the acquired parameters;

[0120] The preselected sensitivity result calculation module is used to perform a global search on the sensitivity estimation model to obtain a preselected sensitivity result of the sensitivity estimation model; wherein the preselected sensitivity result includes a preselected correlation coefficient and a preselected voltage active sensitivity;

[0121] The sensitivity result determination module is used to iteratively calculate the sensitivity estimation model according to the pre-selected sensitivity result and the preset Gauss-Newton algorithm until the sensitivity result obtained by solving satisfies the preset convergence condition, stop the iterative calculation, and use the sensitivity result that currently satisfies the convergence condition as the final sensitivity result of the sensitivity estimation model.

[0122] In a preferred embodiment, the distribution network line parameters include a topological diagram of the distribution network; the distribution network operation parameters include the voltage amplitude of each node at each time step, the active power of each node at each time step, and the reactive power of each node at each time step;

[0123] The step of constructing a sensitivity estimation model according to the acquired parameters includes:

[0124] According to the topological diagram, the voltage amplitude of each node at each time step, the active power of each node at each time step, and the reactive power of each node at each time step are substituted into a preset power flow linearization equation to obtain a first relational expression for characterizing the relationship between node voltage and power;

[0125] According to the node voltage power sensitivity correlation expression, the ratio relationship between voltage active sensitivity and voltage reactive sensitivity is determined;

[0126] According to the ratio relationship, the parameter used to characterize the voltage reactive power sensitivity in the first relational expression is replaced, and the relational expression after the replacement is used as the second relational expression;

[0127] According to the second relational expression, a sensitivity estimation model is constructed.

[0128] In a preferred embodiment, the sensitivity estimation model is iteratively calculated according to the preselected sensitivity result and the preset Gauss-Newton algorithm until the sensitivity result obtained by solving satisfies the preset convergence condition, the iterative calculation is stopped, and the sensitivity result that currently satisfies the convergence condition is used as the final sensitivity result of the sensitivity estimation model, including:

[0129] The norm solution of the residual vector corresponding to the sensitivity result is less than a second preset threshold as a convergence condition of the Gauss-Newton algorithm;

[0130] Using the preselected sensitivity results as initial parameter estimates for the Gauss-Newton algorithm;

[0131] According to the Gauss-Newton algorithm, the sensitivity estimation model is iteratively calculated until the norm solution of the residual vector corresponding to the sensitivity result obtained is less than a second preset threshold, the iterative calculation is stopped, and the sensitivity result in which the norm solution of the current residual vector is less than the second preset threshold is used as the final sensitivity result of the sensitivity estimation model.

[0132] See also Figure 3 , a certain embodiment of the present application further provides a terminal device, including:

[0133] one or more processors;

[0134] A memory, coupled to the processor, for storing one or more programs;

[0135] When the one or more programs are executed by the one or more processors, the one or more processors implement the distribution network voltage sensitivity identification method as described above.

[0136] The processor is used to control the overall operation of the terminal device to complete all or part of the steps of the above-mentioned distribution network voltage sensitivity identification method. The memory is used to store various types of data to support the operation of the terminal device, and these data may include, for example, instructions for any application or method used to operate on the terminal device, and data related to the application. The memory can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, referred to as EPROM), programmable read-only memory (Programmable Read-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, disk or optical disk.

[0137] In an exemplary embodiment, the terminal device can be implemented by one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the distribution network voltage sensitivity identification method as described in any of the above embodiments and achieve the same technical effect as the above method.

[0138] In another exemplary embodiment, a computer-readable storage medium including a computer program is also provided, and when the computer program is executed by a processor, the steps of the method for identifying the voltage sensitivity of the distribution network as described in any of the above embodiments are implemented. For example, the computer-readable storage medium may be the above-mentioned memory including the computer program, and the above-mentioned computer program may be executed by a processor of a terminal device to complete the method for identifying the voltage sensitivity of the distribution network as described in any of the above embodiments, and achieve the technical effect consistent with the above-mentioned method.

[0139] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for identifying voltage sensitivity of a distribution network, characterized in that: include: Obtain distribution network line parameters and distribution network operation parameters; Based on the obtained parameters, a sensitivity estimation model is constructed; Performing a global search on the sensitivity estimation model to obtain a preselected sensitivity result of the sensitivity estimation model; wherein the preselected sensitivity result includes a preselected correlation coefficient and a preselected voltage active sensitivity; According to the preselected sensitivity result and the preset Gauss-Newton algorithm, the sensitivity estimation model is iteratively calculated until the sensitivity result obtained by solving satisfies the preset convergence condition, the iterative calculation is stopped, and the sensitivity result that currently satisfies the convergence condition is used as the final sensitivity result of the sensitivity estimation model.

2. The method for identifying voltage sensitivity of a distribution network according to claim 1, characterized in that: The distribution network line parameters include a topological diagram of the distribution network; the distribution network operation parameters include the voltage amplitude of each node at each time step, the active power of each node at each time step, and the reactive power of each node at each time step; The step of constructing a sensitivity estimation model according to the acquired parameters includes: According to the topological diagram, the voltage amplitude of each node at each time step, the active power of each node at each time step, and the reactive power of each node at each time step are substituted into a preset power flow linearization equation to obtain a first relational expression for characterizing the relationship between node voltage and power; According to the node voltage power sensitivity correlation expression, the ratio relationship between voltage active sensitivity and voltage reactive sensitivity is determined; According to the ratio relationship, the parameter used to characterize the voltage reactive power sensitivity in the first relational expression is replaced, and the relational expression after the replacement is used as the second relational expression; According to the second relational expression, a sensitivity estimation model is constructed.

3. The method for identifying voltage sensitivity of a distribution network according to claim 2, characterized in that: The sensitivity estimation model comprises: In the formula, represents the voltage amplitude at the ith node in a single time step (between t and t-1), Indicates the change value of active power; Indicates the change value of reactive power; α i,k represents the voltage active sensitivity between the voltage amplitude of the i-th node and the active power of the k-th node; m i,k Represents the correlation coefficient.

4. The method for identifying voltage sensitivity of a distribution network according to claim 2, characterized in that: The globally searching the sensitivity estimation model to obtain a preselected sensitivity result of the sensitivity estimation model includes: The sensitivity estimation model is solved by a preset particle swarm algorithm and a preset least squares method until the total residual of the sensitivity estimation model is less than a first preset threshold, thereby obtaining a preselected sensitivity result of the sensitivity estimation model.

5. The method for identifying voltage sensitivity of a distribution network according to claim 4, characterized in that: The sensitivity estimation model is iteratively calculated according to the preselected sensitivity result and the preset Gauss-Newton algorithm until the sensitivity result obtained by solving satisfies the preset convergence condition, the iterative calculation is stopped, and the sensitivity result that currently satisfies the convergence condition is used as the final sensitivity result of the sensitivity estimation model, including: The norm solution of the residual vector corresponding to the sensitivity result is less than a second preset threshold as a convergence condition of the Gauss-Newton algorithm; Using the preselected sensitivity results as initial parameter estimates for the Gauss-Newton algorithm; According to the Gauss-Newton algorithm, the sensitivity estimation model is iteratively calculated until the norm solution of the residual vector corresponding to the sensitivity result obtained is less than a second preset threshold, the iterative calculation is stopped, and the sensitivity result in which the norm solution of the current residual vector is less than the second preset threshold is used as the final sensitivity result of the sensitivity estimation model.

6. A distribution network voltage sensitivity identification device, characterized in that: include: Parameter acquisition module, sensitivity estimation model construction module, pre-selected sensitivity result calculation module and sensitivity result determination module; The parameter acquisition module is used to obtain the distribution network line parameters and distribution network operation parameters; The sensitivity estimation model construction module is used to construct a sensitivity estimation model according to the acquired parameters; The preselected sensitivity result calculation module is used to perform a global search on the sensitivity estimation model to obtain a preselected sensitivity result of the sensitivity estimation model; wherein the preselected sensitivity result includes a preselected correlation coefficient and a preselected voltage active sensitivity; The sensitivity result determination module is used to iteratively calculate the sensitivity estimation model according to the pre-selected sensitivity result and the preset Gauss-Newton algorithm until the sensitivity result obtained by solving satisfies the preset convergence condition, stop the iterative calculation, and use the sensitivity result that currently satisfies the convergence condition as the final sensitivity result of the sensitivity estimation model.

7. The distribution network voltage sensitivity identification device according to claim 6, characterized in that: The distribution network line parameters include a topological diagram of the distribution network; the distribution network operation parameters include the voltage amplitude of each node at each time step, the active power of each node at each time step, and the reactive power of each node at each time step; The step of constructing a sensitivity estimation model according to the acquired parameters includes: According to the topological diagram, the voltage amplitude of each node at each time step, the active power of each node at each time step, and the reactive power of each node at each time step are substituted into a preset power flow linearization equation to obtain a first relational expression for characterizing the relationship between node voltage and power; According to the node voltage power sensitivity correlation expression, the ratio relationship between voltage active sensitivity and voltage reactive sensitivity is determined; According to the ratio relationship, the parameter used to characterize the voltage reactive power sensitivity in the first relational expression is replaced, and the relational expression after the replacement is used as the second relational expression; According to the second relational expression, a sensitivity estimation model is constructed.

8. The distribution network voltage sensitivity identification device according to claim 7, characterized in that: The sensitivity estimation model is iteratively calculated according to the preselected sensitivity result and the preset Gauss-Newton algorithm until the sensitivity result obtained by solving satisfies the preset convergence condition, the iterative calculation is stopped, and the sensitivity result that currently satisfies the convergence condition is used as the final sensitivity result of the sensitivity estimation model, including: The norm solution of the residual vector corresponding to the sensitivity result is less than a second preset threshold as a convergence condition of the Gauss-Newton algorithm; Using the preselected sensitivity results as initial parameter estimates for the Gauss-Newton algorithm; According to the Gauss-Newton algorithm, the sensitivity estimation model is iteratively calculated until the norm solution of the residual vector corresponding to the sensitivity result obtained is less than a second preset threshold, the iterative calculation is stopped, and the sensitivity result in which the norm solution of the current residual vector is less than the second preset threshold is used as the final sensitivity result of the sensitivity estimation model.

9. A device, characterized in that: include: one or more processors; A memory, coupled to the processor, for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the distribution network voltage sensitivity identification method as described in any one of claims 1-5.

10. A medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for identifying voltage sensitivity of a distribution network as described in any one of claims 1 to 5 is implemented.

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