Reactive voltage risk assessment method and device based on full-pure embedding algorithm

Abstract

The invention discloses a reactive voltage risk assessment method and device based on a full-pure embedding algorithm, and the method comprises the steps: obtaining and carrying out the standardized processing of source load historical data, and calculating the power probability density functions of a power supply and a load, a typical operation scene is extracted according to an improved K-means clustering method based on PDF, and upper and lower limit scenes of operation are obtained through an envelope line method; constructing a multi-dimensional full-pure embedded power flow model, and deducing the full-pure embedded power flow model according to a Maclaurin expansion principle; the two scenes are substituted into a multi-dimensional full-pure embedded power flow model for analysis, and voltage analytic solutions of all buses are obtained; and combining the power probability density function to analyze the bus voltage out-of-limit probability in the typical operation scene and the operation upper and lower limit scenes, and evaluating the reactive voltage risk of the regional power grid by using the bus voltage out-of-limit probability. According to the method, the problems that for reactive voltage risk assessment, massive historical data of a traditional power flow algorithm is difficult to process, and an intelligent algorithm is poor in interpretability are solved.

Description

Technical Field

[0001] The present invention belongs to the field of power systems, and in particular relates to a reactive voltage risk assessment method and device based on a holonomic embedded algorithm. Background Art

[0002] With the access of a large number of clean energy sources, the power system is facing increasingly complex operating scenarios, and the importance of ensuring its safe and stable operation has become increasingly prominent. In this context, evaluating the transmission capacity of the regional power system and the operating range of reactive voltage can help system operation and maintenance personnel clearly understand the potential operating risks. However, the current online data volume of historical power grid data is extremely large, and the calculation cost of continuously analyzing various types of data through power flow algorithms is too high. At the same time, traditional iterative power flow algorithms, such as the Newton-Raphson algorithm, often have numerical problems in large-scale power systems or pathological systems, such as initial value dependence problems and high and low voltage solution problems. Therefore, it is necessary to propose a method that can quickly and effectively extract key scenario information of the power system, so as to more stably and reliably realize the analysis and evaluation of the reactive voltage operating range of the power system.

[0003] The current reactive voltage risk assessment methods mainly rely on two types of methods. One is to continuously analyze and calculate massive power system operation data based on traditional power flow algorithms, and the other is to train massive data based on intelligent algorithms to achieve the assessment of system reactive voltage risks. The traditional power flow algorithm has clear physical meaning and high accuracy. However, when faced with massive data, the traditional power flow algorithm may take a long time to calculate, especially in complex systems, it is difficult to handle the nonlinear and multivariable relationships of complex systems, the calculation time is further increased and the convergence is poor. In addition, for new power system architectures (such as VSC-HVDC, etc.), the traditional power flow algorithm needs to be rebuilt compared to the intelligent algorithm model. Intelligent algorithms can mine potential patterns and laws from a large amount of data, handle the dynamic and nonlinear characteristics of complex systems, and have strong adaptability. However, intelligent algorithms rely on high-quality and well-labeled data. If the data quality is poor, the accuracy of the model may decrease. At the same time, there is no clear physical mechanism for intelligent algorithms. Many intelligent algorithms have "black-box" characteristics, the model decision process is opaque, and it is difficult to explain their results. Therefore, for the risk assessment of reactive voltage, a method is also needed that can quickly process massive historical data, analyze and evaluate the results reliably and with clear physical meaning, so as to adapt to the development of new power systems.

[0004] The Chinese patent with the publication number CN111541246A discloses a method for the full-pure embedded calculation of AC and DC power flow in a power system, including: constructing a full-pure AC power flow calculation model; considering different control modes of the converter station, constructing full-pure control models under various control modes, and performing VSC converter station-related calculations based on the converter station equivalent model and the above AC network power flow results, and obtaining relevant data such as converter loss and active power size of DC nodes; determining the type of DC network node based on the active control mode of the converter station, constructing full-pure equations for each type of DC node, forming a full-pure DC power flow calculation model, and performing DC network power flow calculation; updating the AC system PCC active power based on the DC network power flow data, and subtracting it from the previous result of the value, if the difference is greater than the set threshold, returning to the initial step, if the difference is less than the set threshold, obtaining the overall power flow distribution of the AC / DC hybrid system, and the process ends. Although the full-pure embedded calculation method can provide accurate results in the power flow calculation of the AC / DC hybrid system, it may face challenges in terms of calculation time, convergence and calculation efficiency when facing large-scale complex systems. Especially in the context of nonlinearity, multivariable coupling, and multiple control methods of the system, problems such as poor convergence, high computational complexity, and difficulty in real-time calculation may be encountered. Summary of the invention

[0005] The present invention provides a reactive voltage risk assessment method and device based on a holomorphic embedded algorithm, aiming to solve the problem that for reactive voltage risk assessment, a large amount of historical data of a traditional power flow algorithm is difficult to process and the interpretability of an intelligent algorithm is poor.

[0006] In order to solve the above technical problems, the present invention provides a reactive voltage risk assessment method based on a full pure embedded algorithm, comprising the following steps:

[0007] The historical data of source and load are acquired and standardized, and the change process of power supply and load is analyzed by calculating the power probability density function of power supply and load. The typical operation scenarios of the power system are extracted according to the improved K-means clustering method based on PDF, and the upper and lower limit operation scenarios are obtained through the envelope method.

[0008] A multi-dimensional holomorphic embedded power flow model is constructed, and a unified framework for the calculation of the holomorphic embedded power flow model is derived based on the Maclaurin expansion principle.

[0009] The typical operation scenarios and the upper and lower limit operation scenarios are brought into the multi-dimensional holomorphic embedded power flow model for analysis to obtain the analytical solutions for the bus voltages in the typical operation scenarios and the upper and lower limit operation scenarios.

[0010] The power probability density function is combined to analyze the bus voltage over-limit probability in typical operation scenarios and upper and lower limit operation scenarios, and the bus voltage over-limit probability is used to evaluate the reactive voltage risk of the regional power grid.

[0011] Preferably, the power probability density functions of the power source and the load are specifically:

[0012]

[0013] In the formula, f X (x) is the power probability density function; x is the target power point for calculating the probability density; q is the number of samples of the source-load historical data, i∈[1,q]; h is the bandwidth parameter, which controls the degree of smoothing; K is the kernel function; X i is the i-th observation value in the source-load historical data, representing the parameters of the source-load historical data.

[0014] Preferably, the typical operation scenarios of the power system extracted according to the improved K-means clustering method based on PDF are as follows:

[0015] S1: Select R centers based on the calculated PDF.

[0016] S2: Assign a cluster to each data sample based on the PDF-weighted distance.

[0017] S3: The calculated mean of the data within each cluster is used as the new center.

[0018] S4: Repeat steps S2 and S3 until the clustering results are stable. According to the feature center of each cluster in the clustering results and the distribution of samples within the cluster, define the key features of the operation scenario, and summarize the typical operation scenario corresponding to each cluster.

[0019] Preferably, constructing a multidimensional holomorphic embedded power flow model is specifically as follows:

[0020] According to the network division area of ​​the power system, a multi-dimensional full-pure embedded power flow model is constructed, specifically:

[0021]

[0022] Where s is the embedding factor, which represents the multidimensional variable that affects the power system state; D is the number of network partitions, d1∈[1,D], d2∈[1,D]; V i is the voltage of node number i; V i SL is the given voltage of the equilibrium node; N is the total number of nodes, i, k∈[1, N] represents the node number; Y ik is the element in the i-th row and k-th column of the node admittance matrix; S i is the power of node i; P i is the active power of node i; j is the imaginary unit; Q i is the reactive power of node i; the upper horizontal line is the conjugate of the variable; W i To represent Vi The reciprocal form of |V i PV | is the voltage amplitude of the PV node.

[0023] Preferably, according to the principle that the corresponding power series coefficients on both sides of the McLaughlin expansion are equal, the recursive relationship of the multidimensional holomorphic embedded power flow model is derived, and then the equation equivalent transformation method is used to place all known quantities on the right side of the recursive calculation equation and the quantities to be calculated are placed on the left side of the recursive calculation equation, and the matrix form of the multidimensional holomorphic embedded power flow model can be obtained, which is specifically:

[0024] AX=b

[0025] Where A is the coefficient matrix of the linear equation generated by the recursive calculation process; X is the power series coefficient vector to be calculated for the multidimensional holomorphic embedded power flow model; b is a known vector for calculating the power series coefficients of order no greater than n-1, and n represents the order to be calculated.

[0026] Preferably, in a power system including any number of SL nodes, PQ nodes, and PV nodes, the multi-dimensional holomorphic embedded power flow model expansion is specifically expressed as:

[0027] The matrix A is expressed as:

[0028]

[0029] In the formula, G i，k With B i，k Y i，k The real and imaginary parts of the variable; the subscripts SL, PQ and PV correspond to the node types of nodes i and k, namely, the balancing node, the load node and the power generation node; the subscripts Im and Re are the imaginary and real parts of the parameter variables; V PV，Re [0, 0, ..., 0] is the known real component of the PV node voltage at the initial state; V PV，Im [0, 0, ..., 0] is the known imaginary component of the PV node voltage at the initial state; W PV，Re [0, 0, ..., 0] is V PV，Re The reciprocal of [0, 0, ..., 0]; W PV，Im [0, 0, ..., 0] is V PV，Im The reciprocal of [0, 0, ..., 0]; auxiliary variable It is expressed as:

[0030]

[0031]

[0032] In the formula, is the conjugate form of the inverse value of the voltage at node i in the known initial state; Q i [0, 0, ..., 0] is the reactive power value of PV node i in the known initial state.

[0033] The vector X to be determined is expressed as:

[0034]

[0035] Where V SL [n] represents the nth-order voltage coefficient of the equilibrium node, V PQ [n], V PV [n] Similarly, Q PV [n] represents the nth-order reactive power coefficient of the PV node to be determined.

[0036] The vector b is represented by:

[0037]

[0038] Where K = k1 + k2 + ... + k D =1; n = n1 + n2 + ... n D ; D is the number of network partitions.

[0039] Preferably, the typical operation scenario and the upper and lower limit operation scenario are brought into the multi-dimensional holomorphic embedded power flow model for analysis to obtain the voltage analytical functions of the typical operation scenario and the upper and lower limit operation scenario as follows:

[0040]

[0041] Where s is the embedding factor, which represents the multidimensional variable that affects the power system state; D is the number of network partitions; V i is the voltage of node number i; n all Indicates the total order of the power flow when it converges, that is, the value of n when it converges; n i The order of the network partition numbered i.

[0042] According to a typical operation scenarios and 2 upper and lower limit operation scenarios, a+2 voltage analytical solutions can be obtained for each bus.

[0043] Preferably, the probability of bus voltage exceeding the limit in typical operation scenarios and upper and lower limit operation scenarios is analyzed in combination with the probability density function as follows:

[0044] According to the power probability density function of the power source and load, the embedding factors s1, s2, ..., s are adjusted according to the maximum power probability density of the power source and load in each region. D The size of the bus voltage in the regional power grid is obtained under the typical operation scenario and the upper and lower limit operation scenarios, as well as the corresponding probability.

[0045] The number of crossing-limit points and the corresponding probability value are weighted to obtain the crossing-limit probability of the bus voltage.

[0046] Preferably, the risk of reactive voltage in the regional power grid is evaluated by using the bus voltage over-limit probability as follows:

[0047] According to the obtained bus voltage over-limit probability P in the key scenario and upper and lower limit scenarios 越上限 and P 越下限 , the risk index G of the reactive voltage of the regional power grid can be obtained by weighting the typical degree of each scenario. The expression of the risk index is:

[0048]

[0049] Where O is the number of regional power grid buses; T is the typicality of the operation scenario; b∈[1, a+2].

[0050] On the other hand, the present invention also provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the reactive voltage risk assessment method based on a fully pure embedded algorithm as described in any embodiment of the present invention is implemented.

[0051] Compared with the prior art, the present invention has the following technical effects:

[0052] The present invention proposes a reactive voltage risk assessment method based on full pure embedding, combining the advantages of data and mechanism, overcoming the problems of poor interpretability, strong data dependence and poor accuracy of intelligent algorithm methods. At the same time, it avoids the problem that traditional power flow algorithms are difficult to handle large amounts of data analysis and difficult to analyze complex networks. The method effectively improves the analysis efficiency of reactive voltage risks, has clear physical meaning, and has strong engineering applicability. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is an overall flow chart of the reactive voltage risk assessment method based on the holopure embedded algorithm described in the present invention. DETAILED DESCRIPTION

[0054] In order to make the objectives, technical solutions and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in combination with specific embodiments of the present application and with reference to the accompanying drawings.

[0055] Embodiment 1

[0056] This embodiment provides a reactive voltage risk assessment method based on a full pure embedded algorithm. First, a probability density function is constructed according to the source-load historical data. Clustering methods are used to extract typical operating scenarios to deal with the problem of massive data being difficult to process. Then, a multidimensional fully pure embedded power flow model is constructed to effectively deal with the initial value dependence problem of traditional power flow algorithms. The algorithm embedding factor is set according to the partition characteristics of the grid. The change in the embedding factor reflects the change in load conditions. Combined with typical operating scenarios, the source-load operating range under which the voltage does not exceed the limit under the source-load distribution range can be analyzed, as well as the risk probability of voltage exceeding the limit in the power grid, to achieve a rapid and effective assessment of reactive voltage risk. Figure 1 As shown, the following steps are included:

[0057] Acquire and standardize historical source and load data, including current, voltage, power, load and other parameters, calculate the power probability density function of the power supply and load, analyze the change process of the power supply and load, and extract the typical operation scenarios of the power system according to the improved K-means clustering method based on PDF, and obtain the upper and lower limit operation scenarios through the envelope method.

[0058] A multi-dimensional holomorphic embedded power flow model is constructed, and a unified framework for the calculation of the holomorphic embedded power flow model is derived based on the Maclaurin expansion principle.

[0059] The typical operation scenarios and the upper and lower limit operation scenarios are brought into the multi-dimensional holomorphic embedded power flow model for analysis to obtain the analytical solutions for the bus voltages in the typical operation scenarios and the upper and lower limit operation scenarios.

[0060] The power probability density function is combined to analyze the bus voltage over-limit probability in typical operation scenarios and upper and lower limit operation scenarios, and the bus voltage over-limit probability is used to evaluate the reactive voltage risk of the regional power grid.

[0061] As a preferred implementation of this embodiment, the acquired source-load historical data is organized into a high-dimensional feature space, and the power probability density function (PDF) of the power source and the load is calculated as follows:

[0062]

[0063] In the formula, f X (x) is the power probability density function; x is the target power point for calculating the probability density; q is the number of samples of the source-load historical data, i∈[1,q]; h is the bandwidth parameter, which controls the degree of smoothing. The choice of bandwidth will affect the estimation effect, and can be optimized using the Silverman formula or cross-validation method; F is the kernel function, and the Gaussian kernel is commonly used; X i is the i-th observation value in the source-load historical data, representing the parameters of the source-load historical data.

[0064] Specifically, the Gaussian kernel function is expressed as:

[0065]

[0066] Where F(u) represents the Gaussian kernel function; u is the power probability density function

[0067] After obtaining the power PDF of the power supply and load, the PDF can be used to analyze the change process of the power supply and load and identify the values ​​that occur with high frequency, such as maximum power output and maximum load.

[0068] Before extracting key operating scenarios, historical data needs to be standardized to eliminate the impact of different feature scales. This patent uses Z-score for parameter standardization:

[0069]

[0070] Where X is the initial source load historical data; X′ is the standardized source load historical data; μ is the sample mean; σ is the standard deviation.

[0071] K-means clustering aims to divide data into R clusters. First, R initial centers are selected, and then the centers are iteratively updated and the clusters are assigned until convergence to seek the internal structure of the data. As a preferred implementation of this embodiment, after obtaining the PDF, this embodiment extracts typical operating scenarios of the power system according to the improved K-means clustering method based on PDF. The improved K-means clustering method selects the peak position of PDF (such as the maximum probability position of each parameter) as the initial center, which can better reflect the trend of the data set and ensure that the initial clustering center is located in the high-density area of ​​the data, thereby accelerating convergence and improving clustering quality. At the same time, in the improved K-means clustering method, when assigning weights to samples, it can be based on the probability value of the sample in PDF. Considering that samples in high-density areas in the data are more likely to have important features, this embodiment assigns higher weights to samples with high probability values ​​during the clustering process, so that more attention is paid to high-probability sample values ​​during clustering. Specifically:

[0072] S1: Select R centers based on the calculated PDF.

[0073] S2: Assign a cluster to each data sample based on the PDF-weighted distance.

[0074] S3: The calculated mean of the data within each cluster is used as the new center.

[0075] S4: Repeat steps S2 and S3 until the clustering results are stable. According to the feature center of each cluster in the clustering results and the distribution of samples within the cluster, define the key features of the operation scenario, and summarize the typical operation scenario corresponding to each cluster.

[0076] The upper and lower limit scenarios of operation are obtained by using the envelope method for the power history data in the source and load history data. Specifically, the power curve of the power history data is synthesized in one day in units of days, and the high and low points of the data are identified through drawing and analysis to determine the basic shape of the envelope. Then, an envelope that can cover the data fluctuations is fitted according to the basic shape of the envelope to obtain the upper and lower limit scenarios of power operation.

[0077] As a preferred implementation of this embodiment, constructing a multi-dimensional holomorphic embedded power flow model is specifically as follows:

[0078] According to the network division area of ​​the power system, a multi-dimensional full-pure embedded power flow model is constructed, specifically:

[0079]

[0080] Where s is the embedding factor, which represents the multidimensional variable that affects the power state of the power system; D is the number of network partitions. In this embodiment, each branch feeder is divided into a partition, d1∈[1, D], d2∈[1, D]; V i is the voltage of node number i; V i SL is the given voltage of the equilibrium node; N is the total number of nodes, i, k∈[1, N] represents the node number; Y ik is the element in the i-th row and k-th column of the node admittance matrix; S i is the power of node i; P i is the active power of node i; j is the imaginary unit; Q i is the reactive power of node i; the upper horizontal line is the conjugate of the variable; W i To represent V i The reciprocal form of |V i PV | is the voltage amplitude of the PV node.

[0081] As a preferred implementation of this embodiment, according to the principle that the corresponding power series coefficients on both sides of the McLaughlin expansion are equal, the recursive relationship of the multidimensional holomorphic embedded power flow model is derived, and then the equation equivalent transformation method is used to place all known quantities on the right side of the recursive calculation equation and the quantities to be calculated on the left side of the recursive calculation equation, and the matrix form of the multidimensional holomorphic embedded power flow model can be obtained, which is specifically:

[0082] AX=b

[0083] Where A is the linear equation coefficient matrix generated by the recursive calculation process; X is the power series coefficient vector to be calculated for the multidimensional holomorphic embedded power flow model; b is a known vector for calculating the power series coefficients of order not greater than n-1, n represents the order to be calculated, and 1 to n-1 represent known parameters.

[0084] As a preferred implementation of this embodiment, in a power system including any number of SL nodes, PQ nodes, and PV nodes, the multi-dimensional holomorphic embedded power flow model expansion is specifically expressed as:

[0085] The matrix A is expressed as:

[0086]

[0087] In the formula, G i，k With B i，k Y i，k The real and imaginary parts of the variable; the subscripts SL, PQ and PV correspond to the node types of nodes i and k, namely, the balancing node, the load node and the power generation node; the subscripts Im and Re are the imaginary and real parts of the parameter variables; V PV，Re [0, 0, ..., 0] is the known real component of the PV node voltage at the initial state; V PV，Im [0, 0, ..., 0] is the known imaginary component of the PV node voltage at the initial state; W PV，Re [0, 0, ..., 0] is V PV，Re The reciprocal of [0, 0, ..., 0]; W PV，Im [0, 0, ..., 0] is V PV，Im The reciprocal of [0, 0, ..., 0]; auxiliary variable It is expressed as:

[0088]

[0089]

[0090] In the formula, is the conjugate form of the inverse value of the voltage at node i in the known initial state; Q i [0, 0, ..., 0] is the reactive power value of PV node i in the known initial state.

[0091] The vector X to be determined is expressed as:

[0092]

[0093] Where V SL [n] represents the nth-order voltage coefficient of the equilibrium node, V PQ [n], V PV [n] Similarly, Q PV [n] represents the n-th order reactive power coefficient to be calculated for the PV node; the number of n-th order coefficients is M.

[0094] Specifically, the calculation formula of M is:

[0095]

[0096] The vector b is represented by:

[0097]

[0098] Where K = k1 + k2 + ... + k D =1; n = n1 + n2 + ... n D ; D is the number of network partitions.

[0099] As a preferred implementation of this embodiment, the typical operation scenario and the upper and lower limit operation scenario are brought into the multi-dimensional holomorphic embedded power flow model for analysis to obtain the voltage analytical functions of the typical operation scenario and the upper and lower limit operation scenario:

[0100]

[0101] Where s is the embedding factor, which represents the multidimensional variable that affects the power system state; D is the number of network partitions; V i is the voltage of node number i; n all Indicates the total order of the power flow when it converges, that is, the value of n when it converges; n i The order of the network partition numbered i.

[0102] According to a typical operation scenarios and 2 upper and lower limit operation scenarios, a+2 voltage analytical solutions can be obtained for each bus.

[0103] As a preferred implementation of this embodiment, the probability of bus voltage exceeding the limit in typical operation scenarios and upper and lower limit operation scenarios is analyzed in combination with the probability density function:

[0104] According to the power probability density function of the power source and load, the embedding factors s1, s2, ..., s are adjusted according to the maximum power probability density of the power source and load in each region. D The size of the bus voltage in the regional power grid is obtained under the typical operation scenario and the upper and lower limit operation scenarios, as well as the corresponding probability.

[0105] The number of over-limit points and the corresponding probability value are weighted to obtain the over-limit probability of the bus voltage. The over-limit judgment can be based on GB / T 12325-2008 "Power Quality Supply Voltage Deviation". For example, the voltage s1 of a bus is set from 0.3-0.8 with a step size of 0.01 and 50 points are set. When s1 = 0.76 ~ 0.80 is the over-limit point, there are a total of five over-limit points. Then the over-limit probability P of the bus within the set range is 越上限 The specific calculation method is:

[0106]

[0107] P越上限 Calculate in the same way.

[0108] As a preferred implementation of this embodiment, the use of bus voltage over-limit probability to evaluate the reactive voltage risk of the regional power grid is specifically as follows:

[0109] According to the obtained bus voltage over-limit probability P in the key scenario and upper and lower limit scenarios 越上限 and P 越下限 , the risk index G of the reactive voltage of the regional power grid can be obtained by weighting the typical degree of each scenario. The expression of the risk index is:

[0110]

[0111]

[0112] Where O is the number of regional power grid buses; T is the typicality of the operation scenario; b∈[1, a+2].

[0113] The reactive voltage risk analysis of the regional power grid can be realized through the bus voltage over-limit probability and the reactive voltage over-limit risk index of the regional power grid. At the same time, the partial derivative coefficient of the analytical function with respect to the embedded factor can also reflect the sensitivity of the bus voltage to the power change of a certain regional power grid. This sensitivity can provide a basis for the management of reactive voltage risks, the installation of reactive compensation devices and voltage regulation equipment.

[0114] Embodiment 2

[0115] This embodiment provides an electronic device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the reactive voltage risk assessment method based on a fully pure embedded algorithm as described in any embodiment of the present invention is implemented.

[0116] In the embodiments of the present application, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. Among them, A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c can be represented by: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, c can be single or multiple.

[0117] Those of ordinary skill in the art will appreciate that the various units and algorithm steps described in the embodiments disclosed herein can be implemented in a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0118] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0119] In several embodiments provided in the present application, if any 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 application, 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, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory; hereinafter referred to as: ROM), random access memory (Random Access Memory; hereinafter referred to as: RAM), disk or optical disk, and other media that can store program codes.

[0120] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A reactive voltage risk assessment method based on a holomorphic embedded algorithm, characterized in that: The following steps are involved: Acquire and standardize the historical data of source and load, analyze the change process of power supply and load by calculating the power probability density function of power supply and load, and extract the typical operation scenarios of the power system according to the improved K-means clustering method based on PDF and obtain the upper and lower limit scenarios of operation through the envelope method; Construct a multi-dimensional holomorphic embedded power flow model and derive a unified framework for the calculation of the holomorphic embedded power flow model based on the Maclaurin expansion principle; The typical operation scenarios and the upper and lower limit operation scenarios are brought into the multi-dimensional holomorphic embedded power flow model for analysis to obtain the analytical solutions for the bus voltages in the typical operation scenarios and the upper and lower limit operation scenarios. The power probability density function is combined to analyze the bus voltage over-limit probability in typical operation scenarios and upper and lower limit operation scenarios, and the bus voltage over-limit probability is used to evaluate the reactive voltage risk of the regional power grid.

2. The reactive voltage risk assessment method based on the holomorphic embedded algorithm according to claim 1 is characterized in that: The power probability density functions of the power source and the load are specifically: In the formula, f X (x) is the power probability density function; x is the target power point for calculating the probability density; q is the number of samples of the source-load historical data, i∈[1,q]; h is the bandwidth parameter, which controls the degree of smoothing; K is the kernel function; X i is the i-th observation value in the source-load historical data, representing the parameters of the source-load historical data.

3. The reactive voltage risk assessment method based on the holomorphic embedded algorithm according to claim 1 is characterized in that: The typical operation scenarios of the power system extracted according to the improved K-means clustering method based on PDF are as follows: S1: Select R centers based on the calculated PDF; S2: Assign a cluster to each data sample based on the PDF weighted distance; S3: The calculated mean of the data in each cluster is used as the new center; S4: Repeat steps S2 and S3 until the clustering results are stable. According to the feature center of each cluster in the clustering results and the distribution of samples within the cluster, define the key features of the operation scenario, and summarize the typical operation scenario corresponding to each cluster.

4. The reactive voltage risk assessment method based on the holomorphic embedded algorithm according to claim 1 is characterized in that: The specific steps of constructing a multi-dimensional holomorphic embedded power flow model are as follows: According to the network division area of ​​the power system, a multi-dimensional full-pure embedded power flow model is constructed, specifically: Where s is the embedding factor, which represents the multidimensional variable that affects the power system state; D is the number of network partitions, d1∈[1,D], d2∈[1,D]; V i is the voltage of node number i; is the given voltage of the equilibrium node; N is the total number of nodes, i, k∈[1,N] represents the node number; Y ik is the element in the i-th row and k-th column of the node admittance matrix; S i is the power of node i; P i is the active power of node i; j is the imaginary unit; Q i is the reactive power of node i; the upper horizontal line is the conjugate of the variable; W i To represent V i The reciprocal form of is the voltage amplitude of the PV node.

5. The reactive voltage risk assessment method based on the holomorphic embedded algorithm according to claim 4 is characterized in that: According to the principle that the coefficients of the corresponding power series on both sides of the McLaughlin expansion are equal, the recursive relationship of the multidimensional holomorphic embedded power flow model is derived. Then, using the method of equation equivalent transformation, all known quantities are placed on the right side of the recursive calculation equation, and the quantities to be calculated are placed on the left side of the recursive calculation equation. The matrix form of the multidimensional holomorphic embedded power flow model can be obtained, which is: AX=b Where A is the coefficient matrix of the linear equation generated by the recursive calculation process; X is the power series coefficient vector to be calculated for the multidimensional holomorphic embedded power flow model; b is a known vector for calculating the power series coefficients of order no greater than n-1, and n represents the order to be calculated.

6. The reactive voltage risk assessment method based on the holomorphic embedded algorithm according to claim 5 is characterized in that: In a power system containing any number of SL nodes, PQ nodes, and PV nodes, the multi-dimensional holomorphic embedded power flow model expansion is specifically expressed as: The matrix A is expressed as: In the formula, G i,k With B i,k Y i,k The real and imaginary parts of the variable; the subscripts SL, PQ and PV correspond to the node types of nodes i and k, namely, the balancing node, the load node and the power generation node; the subscripts Im and Re are the imaginary and real parts of the parameter variables; V PV,Re [0,0,…,0] is the known real component of the PV node voltage at the initial state; V PV,Im [0,0,…,0] is the known imaginary component of the PV node voltage at the initial state; W PV,Re [0,0,…,0] is V PV,Re The reciprocal of [0,0,…,0]; W PV,Im [0,0,…,0] is V PV,Im The reciprocal of [0,0,…,0]; auxiliary variable It is expressed as: In the formula, is the conjugate form of the inverse value of the voltage at node i in the known initial state; Q i [0,0,…,0] is the reactive power value of PV node i in the known initial state; The vector X to be determined is expressed as: Where V SL [n] represents the nth-order voltage coefficient of the equilibrium node, V PQ [n], V PV [n] Similarly, Q PV [n] represents the nth-order reactive power coefficient to be calculated for the PV node; The vector b is represented by: Where K = k1 + k2 + ... + k D =1; n = n1 + n2 + ... n D ; D is the number of network partitions.

7. The reactive voltage risk assessment method based on the holomorphic embedded algorithm according to claim 1 is characterized in that: The typical operation scenarios and the upper and lower limit operation scenarios are brought into the multi-dimensional holomorphic embedded power flow model for analysis, and the voltage analytical functions of the typical operation scenarios and the upper and lower limit operation scenarios are obtained as follows: Where s is the embedding factor, which represents the multidimensional variable that affects the power system state; D is the number of network partitions; V i is the voltage of node number i; n all Indicates the total order of the power flow when it converges, that is, the value of n when it converges; n i is the order corresponding to the partition numbered i in the network; According to a typical operation scenarios and 2 upper and lower limit operation scenarios, a+2 voltage analytical solutions can be obtained for each bus.

8. The reactive voltage risk assessment method based on the holomorphic embedded algorithm according to claim 1 is characterized in that: Combined with the probability density function, the probability of bus voltage exceeding the limit in typical operation scenarios and upper and lower limit operation scenarios is as follows: According to the power probability density function of the power source and load, the embedding factors s1, s2, ..., s are adjusted according to the maximum power probability density of the power source and load in each region. D The size of is obtained to obtain the operating range and corresponding probability of the bus voltage in the regional power grid under the typical operating scenario and the upper and lower limit operating scenarios; The number of crossing-limit points and the corresponding probability value are weighted to obtain the crossing-limit probability of the bus voltage.

9. The reactive voltage risk assessment method based on the holomorphic embedded algorithm according to claim 8 is characterized in that: The specific method of evaluating the reactive voltage risk of regional power grid using bus voltage over-limit probability is as follows: According to the obtained bus voltage over-limit probability P in the key scenario and upper and lower limit scenarios 越上限 and P 越下限 , the risk index G of the reactive voltage of the regional power grid can be obtained by weighting the typical degree of each scenario. The expression of the risk index is: Where O is the number of regional power grid buses; T is the typicality of the operation scenario; b∈[1,a+2].

10. An electronic device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the reactive voltage risk assessment method based on a fully pure embedded algorithm as described in any one of claims 1 to 9 when executing the computer program.

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