Alarm method and device of power distribution network system based on distributed power supply

Abstract

The embodiment of the invention provides an alarm method and device for a power distribution network system based on a distributed power supply. A distributed power supply is arranged in a power distribution network system, and the method comprises the following steps: obtaining power flow data of the power distribution network system and an incremental vector corresponding to the power flow data, and determining a sensitivity matrix of the power flow data; obtaining a first each-order semi-invariant of the injection power of the distributed power supply, and determining a second each-order semi-invariant of the injection power of the node of the power distribution network system; on the basis of a Gram-Charlier algorithm, performing prediction processing on the second-order semi-invariant and the sensitivity matrix to obtain power flow prediction data of the branch of the power distribution network system; according to the power flow prediction data of the branch of the power distribution network system and a preset power flow limit value, safety alarm information is generated and sent out, and the safety alarm information comprises the safety coefficient of the power distribution network system. The method and the device are used for achieving the effect of alarming the safety condition of a power distribution network system with distributed power supplies.

Description

Technical Field

[0001] The present application relates to the field of power safety technology, and in particular to an alarm method and device for a distribution network system based on distributed power sources. Background Art

[0002] As an important part of the power system, the distribution network system directly connects the power generation side and the user side. Its safety is directly related to the power supply reliability, power quality and the stable operation of the social economy.

[0003] In existing technologies, it is possible to analyze the distribution network system and determine its safety status. However, the integration of distributed power sources into the distribution network system leads to uncertainty in the parameter changes of the distribution network system with distributed power sources, making it impossible to accurately determine the safety status of the distribution network system with distributed power sources.

[0004] Therefore, there is an urgent need for a solution that can accurately warn of the safety status of a distribution network system with distributed power sources. Summary of the Invention

[0005] The embodiments of the present application provide an alarm method and device for a distribution network system based on distributed power sources, so as to achieve the effect of accurately issuing an alarm on the safety status of the distribution network system with distributed power sources.

[0006] In a first aspect, an embodiment of the present application provides an alarm method for a distribution network system based on a distributed power supply, wherein the distribution network system is provided with a distributed power supply, and the method includes:

[0007] Obtaining flow data of the distribution network system and an incremental vector corresponding to the flow data, and determining a sensitivity matrix of the flow data; wherein the flow data represents power data on branches and nodes of the distribution network system; the incremental vector represents a change in the flow data within a preset time period; and the sensitivity matrix represents a change in each power data in the flow data;

[0008] Obtaining first-order semi-invariants of the injected power of the distributed power source, and determining second-order semi-invariants of the injected power of the nodes of the distribution network system; wherein the first-order semi-invariants represent the probability distribution of the injected power of the distributed power source; and the second-order semi-invariants represent the probability distribution of the injected power of the nodes of the distribution network;

[0009] Based on the Gram-Charlier algorithm, the second-order semi-invariants and the sensitivity matrix are predictively processed to obtain power flow prediction data of the branches of the distribution network system, wherein the power flow prediction data of the branches of the distribution network system represent the maximum values ​​of the parameters on the branches of the distribution network system; and based on the power flow prediction data of the branches of the distribution network system and preset power flow limits, safety alarm information is generated and issued, wherein the safety alarm information includes a safety factor of the distribution network system.

[0010] In a possible implementation, the sensitivity matrix includes a first sensitivity matrix and a second sensitivity matrix; determining the sensitivity matrix of the power flow data includes:

[0011] Determining the first sensitivity matrix and the second sensitivity matrix based on the power flow data and the incremental vector of the power flow data; wherein the first sensitivity matrix represents the change in node voltage caused by the injected power; and the second sensitivity matrix represents the change in branch voltage caused by the injected power;

[0012] A sensitivity matrix of the power flow data is determined according to the first sensitivity matrix and the second sensitivity matrix.

[0013] In a possible implementation, determining the first sensitivity matrix according to the power flow data and the incremental vector of the power flow data includes:

[0014] Processing the power flow data and the incremental vectors of the power flow data according to a Newton-Raphson algorithm to obtain a Jacobian matrix of a node in the distribution network system; wherein the Jacobian matrix represents a linear relationship between the power flow data and the incremental vectors of the power flow data of a node in the distribution network system;

[0015] The Jacobian matrix is ​​inversely processed to obtain a first sensitivity matrix.

[0016] In a possible implementation, determining the second sensitivity matrix according to the power flow data and the incremental vector of the power flow data includes:

[0017] A second sensitivity matrix is ​​obtained according to the incremental vectors of the injection powers of the branches in the incremental vector and the incremental vectors of the injection powers of the nodes in the incremental vector.

[0018] In one possible implementation, obtaining first-order semi-invariants of the injected power of the distributed power source and determining second-order semi-invariants of the injected power of the node of the distribution network system include:

[0019] Determine, based on the first-order semi-invariants of the injected power of the distributed power source and the historical probability data, third-order semi-invariants of the injected power of the branch of the distributed power source; wherein the third-order semi-invariants represent the probability distribution of the injected power of the branch of the distributed power source; and the historical probability data represent the relationship between the injected power of the distributed power source and the injected power of the node of the distributed power source;

[0020] According to the first order semi-invariants and the third order semi-invariants, second order semi-invariants of the injected power of the nodes of the power distribution network system are determined.

[0021] In one possible implementation, the second order semi-invariants and the sensitivity matrix are processed based on the Gram-Charlier algorithm to obtain power flow prediction data of the branches of the distribution network system, including:

[0022] Multiplying the second order semi-invariants and the first sensitivity matrix to obtain fourth order semi-invariants of the voltage increment vector of the node of the distribution network system; multiplying the third order semi-invariants and the second sensitivity matrix to obtain fifth order semi-invariants of the voltage increment vector of the branch of the distribution network system;

[0023] Based on the Gram-Charlier algorithm, the fourth-order semi-invariants and the fifth-order semi-invariants are processed to obtain the power flow prediction data.

[0024] In a possible implementation, the fourth-order semi-invariants and the fifth-order semi-invariants are processed based on the Gram-Charlier algorithm to obtain the power flow prediction data, including:

[0025] Based on the Gram-Charlier algorithm, the fourth-order semi-invariants and the fifth-order semi-invariants are expanded and calculated to obtain a probability density function of the power flow of the branch of the distribution network system; the probability density function represents the probability density distribution of the semi-invariants of each order;

[0026] The power flow prediction data is determined according to the probability density function.

[0027] In a possible implementation, determining the power flow prediction data according to the probability density function includes:

[0028] Randomly initialize the branch power flow prediction data to obtain the first prediction data;

[0029] Repeat the following steps until the preset conditions are obtained: determine the (i+1)th prediction data based on the preset learning rate, probability density function, and the (i)th prediction data; where i is a positive integer greater than or equal to 1; determine the value of i plus 1;

[0030] The prediction data obtained when the preset conditions are met is the power flow prediction data.

[0031] In a second aspect, an embodiment of the present application provides an alarm device for a distribution network system based on a distributed power source, wherein the distribution network system is provided with a distributed power source, and the device includes:

[0032] an acquisition module, configured to acquire the power flow data of the distribution network system and the incremental vectors corresponding to the power flow data, and determine a sensitivity matrix of the power flow data; wherein the power flow data represents the power data on the branches and nodes of the distribution network system; the incremental vectors represent the amount of change in the power flow data within a preset time period; and the sensitivity matrix represents the amount of change in each power data in the power flow data;

[0033] A determination module, configured to obtain first-order semi-invariants of the injected power of the distributed power source, and determine second-order semi-invariants of the injected power of the nodes of the distribution network system; wherein the first-order semi-invariants represent the probability distribution of the injected power of the distributed power source; and the second-order semi-invariants represent the probability distribution of the injected power of the nodes of the distribution network;

[0034] A prediction module is used to perform prediction processing on the second-order semi-invariants and the sensitivity matrix based on the Gram-Charlier algorithm to obtain power flow prediction data of the branches of the distribution network system, wherein the power flow prediction data of the branches of the distribution network system represents the maximum value of the parameters on the branches of the distribution network system; and generate and issue safety alarm information based on the power flow prediction data of the branches of the distribution network system and preset power flow limits, wherein the safety alarm information includes a safety factor of the distribution network system.

[0035] In a possible implementation, the sensitivity matrix includes a first sensitivity matrix and a second sensitivity matrix; the acquisition module includes:

[0036] Determining the first sensitivity matrix and the second sensitivity matrix based on the power flow data and the incremental vector of the power flow data; wherein the first sensitivity matrix represents the change in node voltage caused by the injected power; and the second sensitivity matrix represents the change in branch voltage caused by the injected power;

[0037] A sensitivity matrix of the power flow data is determined according to the first sensitivity matrix and the second sensitivity matrix.

[0038] In a possible implementation, determining the first sensitivity matrix according to the power flow data and the incremental vector of the power flow data includes:

[0039] Processing the power flow data and the incremental vectors of the power flow data according to a Newton-Raphson algorithm to obtain a Jacobian matrix of a node in the distribution network system; wherein the Jacobian matrix represents a linear relationship between the power flow data and the incremental vectors of the power flow data of a node in the distribution network system;

[0040] The Jacobian matrix is ​​inversely processed to obtain a first sensitivity matrix.

[0041] In a possible implementation, determining the second sensitivity matrix according to the power flow data and the incremental vector of the power flow data includes:

[0042] A second sensitivity matrix is ​​obtained according to the incremental vectors of the injection powers of the branches in the incremental vector and the incremental vectors of the injection powers of the nodes in the incremental vector.

[0043] In a possible implementation, the determination module includes:

[0044] Determine, based on the first-order semi-invariants of the injected power of the distributed power source and the historical probability data, third-order semi-invariants of the injected power of the branch of the distributed power source; wherein the third-order semi-invariants represent the probability distribution of the injected power of the branch of the distributed power source; and the historical probability data represent the relationship between the injected power of the distributed power source and the injected power of the node of the distributed power source;

[0045] According to the first order semi-invariants and the third order semi-invariants, second order semi-invariants of the injected power of the nodes of the power distribution network system are determined.

[0046] In a possible implementation, the prediction module includes:

[0047] Multiplying the second order semi-invariants and the first sensitivity matrix to obtain fourth order semi-invariants of the voltage increment vector of the node of the distribution network system; multiplying the third order semi-invariants and the second sensitivity matrix to obtain fifth order semi-invariants of the voltage increment vector of the branch of the distribution network system;

[0048] Based on the Gram-Charlier algorithm, the fourth-order semi-invariants and the fifth-order semi-invariants are processed to obtain the power flow prediction data.

[0049] In a possible implementation, the fourth-order semi-invariants and the fifth-order semi-invariants are processed based on the Gram-Charlier algorithm to obtain the power flow prediction data, including:

[0050] Based on the Gram-Charlier algorithm, the fourth-order semi-invariants and the fifth-order semi-invariants are expanded and calculated to obtain a probability density function of the power flow of the branch of the distribution network system; the probability density function represents the probability density distribution of the semi-invariants of each order;

[0051] The power flow prediction data is determined according to the probability density function.

[0052] In a possible implementation, determining the power flow prediction data according to the probability density function includes:

[0053] Randomly initialize the branch power flow prediction data to obtain the first prediction data;

[0054] Repeat the following steps until the preset conditions are obtained: determine the (i+1)th prediction data based on the preset learning rate, probability density function, and the (i)th prediction data; where i is a positive integer greater than or equal to 1; determine the value of i plus 1;

[0055] The prediction data obtained when the preset conditions are met is the power flow prediction data.

[0056] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a memory, a processor;

[0057] The memory stores computer-executable instructions;

[0058] The processor executes the computer-executable instructions stored in the memory, so that the processor executes the above first aspect and / or various possible implementations of the first aspect.

[0059] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the first aspect above and / or various possible implementation methods of the first aspect.

[0060] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above first aspect and / or various possible implementation methods of the first aspect.

[0061] An embodiment of the present application provides an alarm method and device for a distribution network system based on distributed power sources. The method obtains the power flow data of the distribution network system and the incremental vector corresponding to the power flow data, and determines the sensitivity matrix of the power flow data. At the same time, the method obtains the first-order semi-invariants of the injected power of the distributed power source, and determines the second-order semi-invariants of the injected power of the nodes of the distribution network system. Based on the Gram-Charlier algorithm, the second-order semi-invariants and the sensitivity matrix are predicted and processed to obtain the power flow prediction data of the branches of the distribution network system. Finally, according to the power flow prediction data of the branches of the distribution network system and the preset power flow limit, a means of generating and issuing safety alarm information is used to achieve early warning of the safety situation of the distribution network system including the distributed power source, and effectively ensure the safe and stable operation of the distribution network system. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0063] Figure 1 A schematic diagram of a process for an alarm method for a distribution network system based on a distributed power supply provided in an embodiment of the present application Figure 1 ;

[0064] Figure 2 A schematic diagram of a process for an alarm method for a distribution network system based on a distributed power supply provided in an embodiment of the present application Figure 2 ;

[0065] Figure 3 A schematic diagram of the structure of an alarm device for a distribution network system based on a distributed power supply provided in an embodiment of the present application Figure 1 ;

[0066] Figure 4 A schematic diagram of the structure of an alarm device for a distribution network system based on a distributed power supply provided in an embodiment of the present application Figure 2 ;

[0067] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0068] The above drawings illustrate specific embodiments of the present application, which will be described in more detail below. These drawings and the textual description are not intended to limit the scope of the present application in any way, but rather to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments. DETAILED DESCRIPTION

[0069] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0070] The distribution network system efficiently and stably transmits electricity generated by power generation to various users, including residential, commercial, and industrial users. Any security issues with the distribution network, such as power outages or voltage fluctuations, not only disrupt residents' daily lives, disrupting lighting and home appliance use, but also cause losses to commercial operations, such as shopping malls being unable to operate normally and data loss due to power outages on data center servers. For industrial production, it can even cause production line halts and equipment damage, seriously impacting the stable operation of the social economy.

[0071] While existing technologies can analyze distribution network systems and assess their safety status, these methods are mostly based on the stable structure and parameters of traditional distribution networks. Traditional distribution networks have centralized power sources and relatively fixed parameters, allowing analytical models and algorithms to accurately predict and assess the system's safety status. However, with the integration of distributed power sources (such as solar photovoltaic and wind power), the situation has changed dramatically. Distributed power sources are decentralized, intermittent, and random, leading to uncertainties in the parameters of distribution network systems (such as voltage, current, and power). For example, the output power of solar photovoltaic power generation fluctuates with changes in light intensity and temperature, while the output power of wind power generation depends on the speed and direction of wind. This uncertainty makes it difficult for existing technologies to accurately determine the safety status of distribution network systems with distributed power sources, as existing analytical models and algorithms cannot effectively handle these dynamically changing parameters.

[0072] Due to the parameter uncertainty associated with the integration of distributed power sources, existing technologies can misjudge or miss safety issues in distribution network systems. For example, potential safety hazards such as voltage overshoots and power imbalances may not be detected in a timely manner, hindering timely warning and response measures. This not only impacts power supply reliability and quality but can also lead to more serious safety incidents.

[0073] Therefore, the present application provides an alarm method and device for a distribution network system based on distributed power sources, which can solve the above problems.

[0074] The following specific embodiments describe in detail the technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0075] Figure 1 A schematic diagram of a process for an alarm method for a distribution network system based on a distributed power supply provided in an embodiment of the present application Figure 1 , the distribution network system is equipped with distributed power sources, such as Figure 1 As shown, the method includes:

[0076] S101. Obtain the flow data of the distribution network system and the incremental vector corresponding to the flow data, and determine the sensitivity matrix of the flow data; wherein the flow data represents the power data on the branches and nodes of the distribution network system; the incremental vector represents the change in the flow data within a preset time period; and the sensitivity matrix represents the change in each power data in the flow data.

[0077] For example, distributed power sources are small, distributed at the user end, connected to a lower-voltage grid, and primarily consumed locally. These sources include small wind turbines, photovoltaic panels, and small gas turbines. These sources are typically located near load centers, enabling decentralized energy utilization and efficient supply.

[0078] Power flow data refers to the numerical values ​​of electrical quantities such as voltage, current, and power at each node in a power system, particularly a distribution network. In a distribution network, these data reflect the flow of power. For example, node voltage indicates the potential at that node, current indicates the rate of charge flow through the line, and power includes active power and reactive power. Active power is the actual power consumed or generated, while reactive power is related to the energy exchange between electric and magnetic fields.

[0079] An increment vector is a vector that describes the change in power flow data. When the operating state of the distribution network system changes (such as load changes, generator output adjustments, etc.), the power flow data will also change accordingly. The increment vector is used to quantify this change, and each element corresponds to the change in the corresponding power flow data (such as node voltage, branch power, etc.). For example, if the voltage amplitude of node 1 changes from the original V1 to V1+ΔV1, then ΔV1 is the element in the increment vector corresponding to the node voltage amplitude, the power flow data. For the power flow data of the entire distribution network, the increment vector contains the changes in electrical quantities such as voltage, current, and power at all nodes. These changes can be used to analyze the dynamic characteristics of the power system.

[0080] Power flow calculation methods (such as the Newton-Raphson method and the Gauss-Seidel method) are used to calculate power flow data for distribution networks under specific operating conditions. These methods are based on known information such as the power system topology, component parameters (such as line impedance and transformer ratio), generator output, and load power. They use iterative calculations to determine power flow data such as node voltage and branch power.

[0081] When the operating state of the distribution network system changes (such as load increase, generator output adjustment, network topology change, etc.), the power flow calculation is re-performed to obtain new power flow data. The new power flow data is subtracted from the original power flow data, and the difference vector obtained is the increment vector corresponding to the power flow data. For example, the original voltage amplitude of node 1 is V 10 , after changing to V 11 , then the voltage amplitude increment of node 1 is ΔV1=V 11 -V 10 , the set of all flow data increments constitutes the increment vector.

[0082] The sensitivity matrix describes the relationship between the increment of power flow data and the increment of system operating parameters (such as generator output, load power, etc.). The sensitivity matrix is ​​usually determined by perturbation method or analytical method.

[0083] Take the perturbation method as an example: a small perturbation ΔP is applied to a certain operating parameter of the system (such as the active power of a certain load), and then the power flow calculation is repeated to obtain the increment ΔX of the power flow data (X represents the power flow data, such as node voltage and branch power). By repeatedly changing the size and direction of the perturbation parameter and performing multiple power flow calculations, multiple sets of ΔP and ΔX data are obtained. Then, using the least squares method or other regression analysis method, the linear relationship between ΔX and ΔP is fitted, resulting in a sensitivity matrix S that satisfies ΔX = S ΔP.

[0084] S102. Obtain first-order semi-invariants of the injected power of the distributed power source, and determine second-order semi-invariants of the injected power of the nodes of the distribution network system; wherein the first-order semi-invariants characterize the probability distribution of the injected power of the distributed power source; and the second-order semi-invariants characterize the probability distribution of the injected power of the nodes of the distribution network.

[0085] For example, in a power system, injected power refers to the power provided by a power source to the grid or the power absorbed by a load from the grid. For a distributed power source, injected power is the active and reactive power it outputs to the distribution network. For a distribution network node, injected power is the algebraic sum of the injected power of all power sources at that node and the power absorbed by the load.

[0086] Semi-invariant is an important parameter that describes the probability distribution characteristics of random variables. For random variable X, its characteristic function is φ(t)=E(eitX )(E represents the mathematical expectation), take the logarithm of the characteristic function to get Inφ(t), and then perform Taylor series expansion on it. In the expansion, t n The coefficient of the term (divided by i n ) is the nth-order semi-invariant K of the random variable X n ,Right now The semi-invariant is additive, if X1, X2, ..., X m are independent random variables, Then the nth-order semi-invariant of Y is

[0087] The first order semi-invariants refer to the semi-invariants of the random variable of the distributed power injection power. In probability distribution, the first order semi-invariants usually correspond to the mean (mathematical expectation) of the random variable. For the injection power P of the distributed power DG , its first-order semi-invariant k 1,DG Indicates the average level of power injected by distributed power generation. For example, in a distributed power generation system including wind power generation and photovoltaic power generation, k 1,DG This reflects the average power injected into the distribution network by these distributed power sources as a whole. It comprehensively considers the output characteristics of different types of distributed power sources and their operation in the system, and is an important indicator for measuring the concentration trend of distributed power injection.

[0088] First, we need to collect the power output data of distributed power sources (such as solar photovoltaic panels, small wind turbines, etc.). For example: the power output data is P1, P2, ..., P n , where n is the number of data points. Based on the collected power data for distributed generation, select an appropriate probability calculation formula to estimate its probability distribution. For example, photovoltaic output power typically follows a Beta distribution because it is affected by sunlight intensity; wind turbine output power typically follows a Weibull distribution because it is affected by wind speed. Use historical data (such as sunlight intensity and wind speed) to determine the distribution parameters using maximum likelihood estimation or moment estimation.

[0089] Obtain the first-order semi-invariants of the injected power of the distributed power source, and determine the third-order semi-invariants of the injected power of the distributed power source branch using the first-order semi-invariants of the injected power of the distributed power source and the historical probability data of the distributed power source. Based on the first-order semi-invariants and the third-order semi-invariants, determine the second-order semi-invariants of the injected power of the nodes of the distribution network system. The calculation formula is as follows:

[0090]

[0091] Where, ΔS (k)A second order semi-invariant characterizing the injected power at a node of the distribution network system; Characterize the first order semi-invariants of the injected power of the distributed generation; represents the third order semi-invariant of the injected power of the branch of the distributed power source; k represents the kth order of the semi-invariant; g represents the number of the distributed power source; I represents the branch number of the distributed power source.

[0092] S103. Based on the Gram-Charlier algorithm, the second-order semi-invariants and the sensitivity matrix are predicted and processed to obtain the power flow prediction data of the branches of the distribution network system, wherein the power flow prediction data of the branches of the distribution network system represent the maximum value of the parameters on the branches of the distribution network system; based on the power flow prediction data of the branches of the distribution network system and the preset power flow limit, a safety alarm information is generated and issued, wherein the safety alarm information includes the safety factor of the distribution network system.

[0093] For example, the Gram-Charlier algorithm is a probability density function approximation method based on series expansion. It constructs an approximate expression close to the true probability distribution through the moments (such as mean, variance, skewness, kurtosis, etc.) or semi-invariants of known random variables. In this embodiment, the Gram-Charlier algorithm is used to map the randomness of the node injection power (described by the semi-invariant) to the branch flow and predict its probability distribution. Through the Gram-Charlier series expansion, the probability density function of the branch flow can be reconstructed, and then its statistical characteristics (such as mean, variance, quantiles, etc.) can be calculated.

[0094] The power flow data and the incremental vectors of the power flow data are processed using the Newton-Raphson algorithm to obtain the Jacobian matrix of the nodes in the distribution network system. The Jacobian matrix represents the linear relationship between the power flow data and the incremental vectors of the power flow data at the nodes in the distribution network system. The Jacobian matrix is ​​then inverted to obtain a first sensitivity matrix. A second sensitivity matrix is ​​obtained based on the incremental vectors of the injected power of the branches in the incremental vectors and the incremental vectors of the injected power of the nodes in the incremental vectors.

[0095] The second order semi-invariants and the first sensitivity matrix are multiplied to obtain fourth order semi-invariants of the voltage increment vector of the node of the distribution network system; the third order semi-invariants and the second sensitivity matrix are multiplied to obtain fifth order semi-invariants of the voltage increment vector of the branch of the distribution network system;

[0096] The fourth-order semi-invariants and fifth-order semi-invariants are processed by the Gram-Charlier algorithm to obtain the power flow prediction data.

[0097] An embodiment of the present application provides an alarm method for a distribution network system based on distributed power sources, which obtains the power flow data of the distribution network system and the incremental vector corresponding to the power flow data, and determines the sensitivity matrix of the power flow data, and at the same time obtains the first-order semi-invariants of the injected power of the distributed power source, and determines the second-order semi-invariants of the injected power of the nodes of the distribution network system. Based on the Gram-Charlier algorithm, the second-order semi-invariants and the sensitivity matrix are predicted and processed to obtain the power flow prediction data of the branches of the distribution network system. Finally, according to the power flow prediction data of the branches of the distribution network system and the preset power flow limit, a means of generating and issuing safety alarm information is used to achieve early warning of the safety situation of the distribution network system including the distributed power source, and effectively ensure the safe and stable operation of the distribution network system.

[0098] Figure 2 A schematic diagram of a process for an alarm method for a distribution network system based on a distributed power supply provided in an embodiment of the present application Figure 2 ,like Figure 2 As shown, this embodiment Figure 1 Based on the embodiment, an alarm method for a distribution network system based on a distributed power source is described in detail. The method includes:

[0099] S201. Determine a first sensitivity matrix and a second sensitivity matrix based on the power flow data and the incremental vector of the power flow data; wherein the first sensitivity matrix represents the change in the voltage of the node caused by the injected power; the second sensitivity matrix represents the change in the voltage of the branch caused by the injected power; determine the sensitivity matrix of the power flow data based on the first sensitivity matrix and the second sensitivity matrix.

[0100] For example, the node voltage amplitude and phase angle, branch power, and other parameters of the distribution network under steady-state operating conditions are obtained through power flow calculation. Small changes in injected power (such as load fluctuations or changes in distributed generation output) are simulated to form incremental vectors ΔP and ΔQ of node injected power (corresponding to changes in active and reactive power, respectively). The first sensitivity matrix H0 describes the sensitivity of node voltage to changes in injected power. The second sensitivity matrix T0 describes the sensitivity of branch power flows to changes in injected power.

[0101] A first sensitivity matrix and a second sensitivity matrix are determined according to the power flow data and the incremental vector of the power flow data.

[0102] In one example, the flow data and the incremental vector of the flow data are processed according to the Newton-Raphson algorithm to obtain the Jacobian matrix of the nodes in the distribution network system; wherein the Jacobian matrix represents the linear relationship between the flow data of the nodes in the distribution network system and the incremental vector of the flow data; the Jacobian matrix is ​​inversely processed to obtain a first sensitivity matrix.

[0103] For example, the Newton-Raphson method is a core method for calculating power system power flows. Its core is to iteratively solve the nonlinear power flow equation and construct a Jacobian matrix in each iteration to update the state variables (voltage amplitude and phase angle). According to the Newton-Raphson algorithm, the power flow data and the incremental vectors of the power flow data are processed to obtain the Jacobian matrix of the nodes in the distribution network system. This is a prior art method and will not be elaborated in detail in this embodiment.

[0104] The Jacobian matrix J is a 2n×2n matrix (n is the number of nodes), divided into blocks:

[0105]

[0106] Among them, J0 represents the Jacobian matrix; J PP Characterizes the partial derivative of active power with respect to phase angle; J PQ Characterizes the partial derivative of active power with respect to voltage amplitude; J QP Characterizes the partial derivative of reactive power with respect to phase angle; J QQ Characterizes the partial derivative of reactive power with respect to voltage amplitude.

[0107] Perform inverse processing on the Jacobian matrix to obtain the first sensitivity matrix. The calculation formula is:

[0108]

[0109] Among them, H0 represents the first sensitivity matrix; Characterizes the inverse of the Jacobian matrix.

[0110] In one example, the second sensitivity matrix is ​​obtained according to the incremental vector of the injection power of the branch in the incremental vector and the incremental vector of the injection power of the node in the incremental vector.

[0111] Exemplarily, the second sensitivity matrix is ​​obtained according to the incremental vector of the injection power of the branch in the incremental vector and the incremental vector of the injection power of the node in the incremental vector. The calculation formula is:

[0112] ΔZ=T0ΔS

[0113] Wherein, ΔZ represents the incremental vector of the injection power of the branch in the incremental vector; T0 represents the second sensitivity matrix; and ΔS represents the incremental vector of the injection power of the node in the incremental vector.

[0114] S202. Determine the third order semi-invariants of the injected power of the branches of the distributed power source based on the first order semi-invariants of the injected power of the distributed power source and the historical probability data; wherein the third order semi-invariants characterize the probability distribution of the injected power of the branches of the distributed power source; the historical probability data characterizes the relationship between the injected power of the distributed power source and the injected power of the nodes of the distributed power source; determine the second order semi-invariants of the injected power of the nodes of the distribution network system based on the first order semi-invariants and the third order semi-invariants.

[0115] Exemplarily, third-order semi-invariants of the injected power of the branches of the distributed power are fitted by combining historical probability data (such as wind speed, light intensity, etc.) with first-order semi-invariants of the injected power of the distributed power.

[0116] According to the first-order semi-invariants and the third-order semi-invariants, the second-order semi-invariants of the injected power of the nodes of the distribution network system are determined. The calculation formula is:

[0117]

[0118] Where, ΔS (k) A second order semi-invariant characterizing the injected power at a node of the distribution network system; Characterize the first order semi-invariants of the injected power of the distributed generation; represents the third order semi-invariant of the injected power of the branch of the distributed power source; k represents the kth order of the semi-invariant; g represents the number of the distributed power source; I represents the branch number of the distributed power source.

[0119] S203. Multiply the second-order semi-invariants and the first sensitivity matrix to obtain the fourth-order semi-invariants of the voltage increment vector of the node of the distribution network system; multiply the third-order semi-invariants and the second sensitivity matrix to obtain the fifth-order semi-invariants of the voltage increment vector of the branch of the distribution network system; based on the Gram-Charlier algorithm, process the fourth-order semi-invariants and the fifth-order semi-invariants to obtain the power flow prediction data.

[0120] For example, the second order semi-invariant and the first sensitivity matrix are multiplied to obtain the fourth order semi-invariant of the voltage increment vector of the node of the distribution network system. The calculation formula is:

[0121]

[0122] Where ΔX (k) Characterize the fourth order semi-invariants; Characterize the first sensitivity matrix; ΔS (k)The second order semi-invariant representing the injected power of the node of the distribution network system; k represents the kth order of the semi-invariant.

[0123] Multiplying the third order semi-invariant and the second sensitivity matrix, we can get the fifth order semi-invariant of the voltage increment vector of the branch of the distribution network system. The calculation formula is:

[0124]

[0125] Where ΔZ (k) Characterize the fifth order semi-invariants; Characterizing a second sensitivity matrix;

[0126] ΔS (k) The second order semi-invariant representing the injected power of the node of the distribution network system; k represents the kth order of the semi-invariant.

[0127] Based on the Gram-Charlier algorithm, the fourth-order semi-invariants and the fifth-order semi-invariants are expanded to obtain the power flow prediction data.

[0128] In one example, based on the Gram-Charlier algorithm, the fourth-order semi-invariants and the fifth-order semi-invariants are expanded and calculated to obtain the probability density function of the power flow of the branches of the distribution network system; the probability density function represents the probability density distribution of each order semi-invariant; and the power flow prediction data is determined based on the probability density function.

[0129] Exemplarily, based on the Gram-Charlier algorithm, the fourth-order semi-invariants and the fifth-order semi-invariants are expanded and calculated to obtain the probability density function of the power flow of the branch of the distribution network system.

[0130]

[0131] Wherein, F(x) represents the distribution function of each order semi-invariant of the power flow of the branch of the distribution network system; f(x) represents the probability density function of each order semi-invariant of the power flow of the branch of the distribution network system; The distribution function that represents the standard normal distribution; μ(x) represents the probability density function of the standard normal distribution; and Function The first, second, and third derivatives of ;

[0132] μ(x), μ(x), and μ(x) are the first, second, and third derivatives of the function μ(x), respectively; C1, C2, and C3 are the central moments of the first, second, and third derivatives, respectively.

[0133] Find the maximum value point of the probability density function f(x), and the x value corresponding to this point is the power flow prediction value

[0134] In one example, the branch flow prediction data is randomly initialized to obtain the first prediction data; the following steps are repeated until the preset conditions are obtained: based on the preset learning rate, probability density function, and the i-th prediction data, the i+1-th prediction data is determined; wherein i is a positive integer greater than or equal to 1; the value of i is determined plus 1; wherein the prediction data obtained when the preset conditions are met is the flow prediction data.

[0135] Exemplarily, a numerical optimization algorithm (such as Newton's method, gradient descent method, etc.) is used to find the maximum point of f(x). Branch power flow prediction data x0 is randomly initialized to obtain first prediction data.

[0136] Determine the i+1 prediction data based on the preset learning rate, probability density function, and the i-th prediction data. The calculation formula is:

[0137]

[0138] Among them, x n+1 Represents the i+1th prediction data; x n Represents the i-th predicted data; α represents the preset learning rate; f ′ (x n ) represents the first-order derivative of f(x); f ″ (x n ) represents the second-order derivative of f(x).

[0139] Convergence judgment: When |x n+1 -x n When |<∈, the iteration stops, where ∈ is the preset threshold.

[0140] x n+1 That is the maximum value point of the probability density function of f(x), which is the power flow prediction data.

[0141] Optionally, the power flow prediction data of the branch includes one or more of the following: the maximum value of the voltage of the branch, the maximum value of the current of the branch, and the maximum value of the power of the branch.

[0142] S204 . Generate and issue safety warning information based on the power flow prediction data of the branches of the distribution network system and the preset power flow limit, wherein the safety warning information includes the safety factor of the distribution network system.

[0143] For example, the safety factor is calculated based on the power flow prediction data of the branch of the distribution network system and the preset power flow limit. The calculation formula is:

[0144]

[0145] Among them, A represents the safety factor of the distribution network system; W l Characterizes the weight of the lth branch; B l Power flow forecast data representing the branches of the distribution network system; Representing a preset power flow limit; wherein the preset power flow limit represents the maximum limit value of the power flow data of the branch of the distribution network system;

[0146] The closer the safety factor is to 1, the less secure the distribution network is; the smaller the safety factor, the safer the distribution network is. Based on the safety factor, safety warnings are generated and issued.

[0147] The embodiment of the present application provides an alarm method for a distribution network system based on distributed power sources, which realizes an accurate early warning effect of potential safety situations of the distribution network system through a series of refined calculation and processing means. Specifically, first, according to the flow data and the incremental vector of the flow data, the first sensitivity matrix and the second sensitivity matrix are determined, the first sensitivity matrix characterizes the change in the voltage of the node caused by the injection power, and the second sensitivity matrix characterizes the change in the voltage of the branch caused by the injection power, and then the sensitivity matrix of the flow data is determined based on these two sensitivity matrices. Then, based on the first order semi-invariants of the injection power of the distributed power source and the historical probability data, the third order semi-invariants of the injection power of the branch of the distributed power source are determined, wherein the historical probability data characterizes the relationship between the injection power of the distributed power source and the injection power of the node of the distributed power source, and then based on the first order semi-invariants and the third order semi-invariants, the second order semi-invariants of the injection power of the node of the distribution network system are determined. Afterwards, the second-order semi-invariants and the first sensitivity matrix are multiplied to obtain the fourth-order semi-invariants of the voltage increment vector of the node of the distribution network system; the third-order semi-invariants and the second sensitivity matrix are multiplied to obtain the fifth-order semi-invariants of the voltage increment vector of the branch of the distribution network system. Based on the Gram-Charlier algorithm, the fourth-order semi-invariants and the fifth-order semi-invariants are processed to obtain the flow prediction data. Finally, according to the flow prediction data of the branch of the distribution network system and the preset flow limit, a safety alarm information is generated and issued, wherein the safety alarm information includes the safety factor of the distribution network system. Through the above means, the operating status of the distribution network system can be monitored and predicted more accurately, effectively improving the operating safety and reliability of the distribution network system including distributed power sources.

[0148] Figure 3 A schematic diagram of the structure of an alarm device for a distribution network system based on a distributed power supply provided in an embodiment of the present application Figure 1 , the distribution network system is equipped with distributed power sources, such as Figure 3As shown, the alarm device 30 of the distributed power distribution network system provided in this embodiment includes:

[0149] Acquisition module 301 is used to acquire power flow data and the corresponding increment vectors of the power flow data of the distribution network system, and determine the sensitivity matrix of the power flow data; wherein the power flow data represents the power data on the branches and nodes of the distribution network system; the increment vector represents the change in the power flow data within a preset time period; and the sensitivity matrix represents the change in each power data in the power flow data;

[0150] Determination module 302 is configured to obtain first-order semi-invariants of the injected power of the distributed generation and determine second-order semi-invariants of the injected power of the nodes of the distribution network system; wherein the first-order semi-invariants represent the probability distribution of the injected power of the distributed generation; and the second-order semi-invariants represent the probability distribution of the injected power of the nodes of the distribution network;

[0151] The prediction module 303 is used to perform prediction processing on the second-order semi-invariants and the sensitivity matrix based on the Gram-Charlier algorithm to obtain the power flow prediction data of the branches of the distribution network system, wherein the power flow prediction data of the branches of the distribution network system represents the maximum value of the parameters on the branches of the distribution network system; and generate and issue safety alarm information based on the power flow prediction data of the branches of the distribution network system and the preset power flow limit, wherein the safety alarm information includes the safety factor of the distribution network system.

[0152] This embodiment provides a distribution network equipment maintenance device based on the maintenance time of distribution network equipment, which can execute the method provided by the above method embodiment. Its implementation principle and technical effects are similar, and this embodiment will not be repeated here.

[0153] Figure 4 A schematic diagram of the structure of an alarm device for a distribution network system based on a distributed power supply provided in an embodiment of the present application Figure 2 , the distribution network system is equipped with distributed power sources, such as Figure 4 As shown, the alarm device 40 of the distributed power distribution network system provided in this embodiment includes:

[0154] Acquisition module 401 is used to acquire power flow data and the corresponding increment vectors of the power flow data of the distribution network system, and determine the sensitivity matrix of the power flow data; wherein the power flow data represents the power data on the branches and nodes of the distribution network system; the increment vector represents the change in the power flow data within a preset time period; and the sensitivity matrix represents the change in each power data in the power flow data;

[0155] Determination module 402 is configured to obtain first-order semi-invariants of the injected power of the distributed generation and determine second-order semi-invariants of the injected power of the nodes of the distribution network system; wherein the first-order semi-invariants represent the probability distribution of the injected power of the distributed generation; and the second-order semi-invariants represent the probability distribution of the injected power of the nodes of the distribution network.

[0156] The prediction module 403 is used to perform prediction processing on the second-order semi-invariants and the sensitivity matrix based on the Gram-Charlier algorithm to obtain the power flow prediction data of the branches of the distribution network system, wherein the power flow prediction data of the branches of the distribution network system represents the maximum value of the parameters on the branches of the distribution network system; and generate and issue safety alarm information based on the power flow prediction data of the branches of the distribution network system and the preset power flow limit, wherein the safety alarm information includes the safety factor of the distribution network system.

[0157] In a possible implementation, the sensitivity matrix includes a first sensitivity matrix and a second sensitivity matrix; the acquisition module 401 includes:

[0158] Determine a first sensitivity matrix and a second sensitivity matrix based on the power flow data and the incremental vector of the power flow data; wherein the first sensitivity matrix represents the change in node voltage caused by the injected power; and the second sensitivity matrix represents the change in branch voltage caused by the injected power;

[0159] A sensitivity matrix of the power flow data is determined according to the first sensitivity matrix and the second sensitivity matrix.

[0160] In a possible implementation, determining the first sensitivity matrix according to the power flow data and the incremental vector of the power flow data includes:

[0161] The power flow data and the incremental vectors of the power flow data are processed according to the Newton-Raphson algorithm to obtain the Jacobian matrix of the nodes in the distribution network system. The Jacobian matrix represents the linear relationship between the power flow data and the incremental vectors of the power flow data of the nodes in the distribution network system.

[0162] The Jacobian matrix is ​​inverted to obtain the first sensitivity matrix.

[0163] In a possible implementation, determining the second sensitivity matrix according to the power flow data and the incremental vector of the power flow data includes:

[0164] A second sensitivity matrix is ​​obtained according to the increment vectors of the injection powers of the branches in the increment vector and the increment vectors of the injection powers of the nodes in the increment vector.

[0165] In a possible implementation, the determining module 402 includes:

[0166] Determine, based on the first-order semi-invariants of the injected power of the distributed power source and the historical probability data, the third-order semi-invariants of the injected power of the branch of the distributed power source; wherein the third-order semi-invariants represent the probability distribution of the injected power of the branch of the distributed power source; and the historical probability data represent the relationship between the injected power of the distributed power source and the injected power of the node of the distributed power source;

[0167] According to the first order semi-invariants and the third order semi-invariants, second order semi-invariants of the injected power of the nodes of the distribution network system are determined.

[0168] In a possible implementation, the prediction module 403 includes:

[0169] The second order semi-invariants and the first sensitivity matrix are multiplied to obtain fourth order semi-invariants of the voltage increment vector of the node of the distribution network system; the third order semi-invariants and the second sensitivity matrix are multiplied to obtain fifth order semi-invariants of the voltage increment vector of the branch of the distribution network system;

[0170] Based on the Gram-Charlier algorithm, the fourth-order semi-invariants and the fifth-order semi-invariants are processed to obtain the power flow prediction data.

[0171] In one possible implementation, based on the Gram-Charlier algorithm, the fourth-order semi-invariants and the fifth-order semi-invariants are processed to obtain power flow prediction data, including:

[0172] Based on the Gram-Charlier algorithm, the fourth-order semi-invariants and the fifth-order semi-invariants are expanded and calculated to obtain the probability density function of the power flow of the branches of the distribution network system; the probability density function represents the probability density distribution of each order semi-invariant;

[0173] According to the probability density function, the flow prediction data is determined.

[0174] In one possible implementation, determining the power flow prediction data according to the probability density function includes:

[0175] Randomly initialize the branch power flow prediction data to obtain the first prediction data;

[0176] Repeat the following steps until the preset conditions are obtained: determine the (i+1)th prediction data based on the preset learning rate, probability density function, and the (i)th prediction data; where i is a positive integer greater than or equal to 1; determine the value of i plus 1;

[0177] Among them, the forecast data obtained when the preset conditions are met is the flow forecast data.

[0178] This embodiment provides a distribution network equipment maintenance device based on the maintenance time of distribution network equipment, which can execute the method provided by the above method embodiment. Its implementation principle and technical effects are similar, and this embodiment will not be repeated here.

[0179] Figure 5 This is a schematic diagram of the structure of the electronic device provided in the embodiment of the present application. Figure 5 As shown, the electronic device 50 provided in this embodiment includes: at least one processor 501 and a memory 502. Optionally, the device 50 further includes a communication component 503. The processor 501, the memory 502 and the communication component 503 are connected via a bus 504.

[0180] In a specific implementation process, at least one processor 501 executes the computer-executable instructions stored in the memory 502, so that the at least one processor 501 performs the above method.

[0181] The specific implementation process of the processor 501 can be found in the above method embodiment. Its implementation principle and technical effects are similar and will not be repeated here in this embodiment.

[0182] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the present invention may be directly implemented by a hardware processor or implemented by a combination of hardware and software modules in the processor.

[0183] The memory may include a high-speed memory (Random Access Memory, RAM), and may also include a non-volatile memory (NVM), such as at least one disk memory.

[0184] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. Buses can be classified into address buses, data buses, and control buses. For ease of illustration, the buses in the drawings of this application are not limited to just one bus or just one type of bus.

[0185] The present application also provides a computer program product, including a computer program, which implements the above method when executed by a processor.

[0186] The present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the above method is implemented.

[0187] The above-mentioned readable storage medium can be implemented by any type of volatile or non-volatile memory device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0188] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be an integral part of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (ASIC). Of course, the processor and the readable storage medium can also exist in the device as discrete components.

[0189] The division of units is merely a logical functional division; actual implementations may employ alternative divisions, such as combining or integrating multiple units or components into another system, or omitting or disabling certain features. Furthermore, any direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between devices or units, either through an interface, electrical, mechanical, or other means.

[0190] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0191] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0192] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program code.

[0193] Those skilled in the art will appreciate that all or part of the steps in the above-described method embodiments can be implemented using hardware associated with program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0194] Finally, it should be noted that those skilled in the art will readily identify other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art not disclosed herein. The present invention is not limited to the precise structure described above and illustrated in the accompanying drawings, and various modifications and variations may be made without departing from the scope thereof. The scope of the present invention is limited solely by the appended claims.

Claims

1. An alarm method for a distribution network system based on distributed power sources, characterized in that: The distribution network system is provided with a distributed power supply, and the method includes: Obtaining flow data of the distribution network system and an incremental vector corresponding to the flow data, and determining a sensitivity matrix of the flow data; wherein the flow data represents power data on branches and nodes of the distribution network system; the incremental vector represents a change in the flow data within a preset time period; and the sensitivity matrix represents a change in each power data in the flow data; Obtaining first-order semi-invariants of the injected power of the distributed power source, and determining second-order semi-invariants of the injected power of the nodes of the distribution network system; wherein the first-order semi-invariants represent the probability distribution of the injected power of the distributed power source; and the second-order semi-invariants represent the probability distribution of the injected power of the nodes of the distribution network; Based on the Gram-Charlier algorithm, the second-order semi-invariants and the sensitivity matrix are predictively processed to obtain power flow prediction data of the branches of the distribution network system, wherein the power flow prediction data of the branches of the distribution network system represent the maximum values ​​of the parameters on the branches of the distribution network system; and based on the power flow prediction data of the branches of the distribution network system and preset power flow limits, safety alarm information is generated and issued, wherein the safety alarm information includes a safety factor of the distribution network system.

2. The method according to claim 1, characterized in that The sensitivity matrix includes a first sensitivity matrix and a second sensitivity matrix; Determine the sensitivity matrix for power flow data, including: Determining the first sensitivity matrix and the second sensitivity matrix based on the power flow data and the incremental vector of the power flow data; wherein the first sensitivity matrix represents the change in node voltage caused by the injected power; and the second sensitivity matrix represents the change in branch voltage caused by the injected power; A sensitivity matrix of the power flow data is determined according to the first sensitivity matrix and the second sensitivity matrix.

3. The method according to claim 2, characterized in that Determining the first sensitivity matrix according to the power flow data and the incremental vector of the power flow data includes: Processing the power flow data and the incremental vectors of the power flow data according to a Newton-Raphson algorithm to obtain a Jacobian matrix of a node in the distribution network system; wherein the Jacobian matrix represents a linear relationship between the power flow data and the incremental vectors of the power flow data of a node in the distribution network system; The Jacobian matrix is ​​inversely processed to obtain a first sensitivity matrix.

4. The method according to claim 2, characterized in that Determining the second sensitivity matrix according to the power flow data and the incremental vector of the power flow data includes: A second sensitivity matrix is ​​obtained according to the incremental vectors of the injection powers of the branches in the incremental vector and the incremental vectors of the injection powers of the nodes in the incremental vector.

5. The method according to claim 1, wherein Obtaining first-order semi-invariants of the injected power of the distributed power source and determining second-order semi-invariants of the injected power of the node of the distribution network system includes: Determine, based on the first-order semi-invariants of the injected power of the distributed power source and the historical probability data, third-order semi-invariants of the injected power of the branch of the distributed power source; wherein the third-order semi-invariants represent the probability distribution of the injected power of the branch of the distributed power source; and the historical probability data represent the relationship between the injected power of the distributed power source and the injected power of the node of the distributed power source; According to the first order semi-invariants and the third order semi-invariants, second order semi-invariants of the injected power of the nodes of the power distribution network system are determined.

6. The method according to any one of claims 1 to 5, characterized in that The second order semi-invariants and the sensitivity matrix are processed based on the Gram-Charlier algorithm to obtain power flow prediction data of the branches of the distribution network system, including: Multiplying the second order semi-invariants and the first sensitivity matrix to obtain fourth order semi-invariants of the voltage increment vector of the node of the distribution network system; multiplying the third order semi-invariants and the second sensitivity matrix to obtain fifth order semi-invariants of the voltage increment vector of the branch of the distribution network system; Based on the Gram-Charlier algorithm, the fourth-order semi-invariants and the fifth-order semi-invariants are processed to obtain the power flow prediction data.

7. The method according to claim 6, characterized in that The fourth-order semi-invariants and the fifth-order semi-invariants are processed based on the Gram-Charlier algorithm to obtain the power flow prediction data, including: Based on the Gram-Charlier algorithm, the fourth-order semi-invariants and the fifth-order semi-invariants are expanded and calculated to obtain a probability density function of the power flow of the branch of the distribution network system; the probability density function represents the probability density distribution of the semi-invariants of each order; The power flow prediction data is determined according to the probability density function.

8. The method according to claim 7, characterized in that Determining the power flow prediction data according to the probability density function includes: Randomly initialize the branch power flow prediction data to obtain the first prediction data; Repeat the following steps until the preset conditions are obtained: determine the (i+1)th prediction data based on the preset learning rate, probability density function, and the (i)th prediction data; where i is a positive integer greater than or equal to 1; determine the value of i plus 1; The prediction data obtained when the preset conditions are met is the power flow prediction data.

9. An alarm device for a distribution network system based on distributed power sources, characterized in that: The distribution network system is provided with a distributed power supply, and the device includes: an acquisition module, configured to acquire the power flow data of the distribution network system and the incremental vectors corresponding to the power flow data, and determine a sensitivity matrix of the power flow data; wherein the power flow data represents the power data on the branches and nodes of the distribution network system; the incremental vectors represent the amount of change in the power flow data within a preset time period; and the sensitivity matrix represents the amount of change in each power data in the power flow data; A determination module, configured to obtain first-order semi-invariants of the injected power of the distributed power source, and determine second-order semi-invariants of the injected power of the nodes of the distribution network system; wherein the first-order semi-invariants represent the probability distribution of the injected power of the distributed power source; and the second-order semi-invariants represent the probability distribution of the injected power of the nodes of the distribution network; A prediction module is used to perform prediction processing on the second-order semi-invariants and the sensitivity matrix based on the Gram-Charlier algorithm to obtain power flow prediction data of the branches of the distribution network system, wherein the power flow prediction data of the branches of the distribution network system represents the maximum value of the parameters on the branches of the distribution network system; and generate and issue safety alarm information based on the power flow prediction data of the branches of the distribution network system and preset power flow limits, wherein the safety alarm information includes a safety factor of the distribution network system.

10. An electronic device, characterized in that: include: Memory, processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory, so that the processor performs the method according to any one of claims 1 to 8.

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