Data-driven section non-transferable transmission power boundary multi-stage accurate characterization method

Abstract

The invention relates to the technical field of power systems, and particularly discloses a data-driven multi-stage accurate characterization method for a section non-transferable transmission power boundary, and the method comprises the following steps: S1, carrying out the unsupervised learning of power grid operation mode data through employing a clustering algorithm based on a Gaussian mixture model, and generating an operation mode sub-mode cluster set; s2, in each sub-mode cluster set, calculating an initial boundary of a single section according to a conservative principle; and S3, searching an instability domain boundary by using an Alpha Shape algorithm, constructing a coupling boundary, and solving a union set with the initial boundary to form a final non-transferable transmission power boundary constraint. The method has the advantages that the power grid operation mode is divided into a plurality of sub-mode clusters, the non-transferable transmission power conservative calculation method in the operation mode clusters is provided, and the problem that a traditional single non-transferable transmission power calculation method is excessively conservative is solved.

Description

Technical Field

[0001] The invention relates to the technical field of electric power systems, and in particular to a data-driven multi-level accurate characterization method for a section that cannot cross a transmission power boundary. Background Art

[0002] The uneven distribution of energy in different countries and regions in the world, coupled with the large differences in their energy structure and load demand, has led to an imbalance between supply and demand. For example, China's energy resources are mostly concentrated in the western region with high altitude and underdeveloped economy, while the load demand is mainly concentrated in the central and eastern coastal areas with developed economy and dense population. The unbalanced energy resource ratio makes the interconnection of large power grids an inevitable development trend of modern new power systems. In countries and regions such as Europe, Africa, and North America, cross-border power grid interconnection has even been achieved. With the rapid development of my country's power grid from inter-provincial power grids to cross-regional power grids, and the continuous construction and operation of ultra-high voltage AC and DC transmission channels, the country has completed the strategic layout of "west-to-east power transmission, north-south mutual supply, and national networking". However, the safety hazards of cross-regional power transmission cannot be ignored. Long-distance transmission channels have become the weak link of the power grid, and there is a risk of instability when the transmission power is large.

[0003] The calculation of transmission channel transmission limit relies on the calculation of total transfer capacity (TTC) of typical operation mode, and takes the most conservative value as the "checkpoint" of dispatching operation in the project to ensure the absolute safe and stable operation of the power grid. The "checkpoint" is the section limit, which is also the boundary of the power that cannot be crossed in the system operation. In small systems, TTC and the power that cannot be crossed in the section may be unified due to the limitations of the typical mode. With the increase of new energy penetration, the power grid operation mode has a "combination explosion" phenomenon, the typical mode is difficult to identify, and the calculation of TTC is difficult. Although there are artificial intelligence solutions at present, the "black box" characteristics with poor explanatory power make such solutions difficult to be widely trusted by the engineering community. With the increasing complexity of the operation mode of the power grid, the calculation of the power that cannot be crossed in the section in the project still relies on the experience of power grid experts and manual simulation calculations. In order to ensure the absolute bottom line of safe operation, the power that cannot be crossed will be restricted too conservatively, resulting in the obstruction of new energy consumption, and it is a labor-intensive and low-precision calculation. Some studies have proposed a method for calculating transmission power in tiers that cannot be crossed in order to tap the transmission potential, but the tiers are still based on expert experience and historical operating methods, which makes it difficult to adapt to systems with a high proportion of new energy.

[0004] In general, the existing methods of non-crossable transmission power classification and conditional mapping provide a direction for exploring the transmission potential of sections. However, there is still a lack of systematic and scientific computational theory on how to effectively reduce the computational complexity, how to consider the joint distribution information of multiple sections, and how to calculate the precise coupled non-crossable transmission power boundary. Summary of the invention

[0005] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a data-driven multi-level accurate characterization method for a cross-section that cannot cross a transmission power boundary.

[0006] The object of the present invention is achieved by the following technical solution: a data-driven multi-level accurate characterization method for a cross-section that cannot cross a transmission power boundary, the method comprising the following steps:

[0007] Step S1, using a clustering algorithm based on a Gaussian mixture model to perform unsupervised learning on the power grid operation mode data to generate operation mode sub-mode clusters;

[0008] Step S2, in each sub-pattern cluster, calculating the initial boundary of a single section according to the conservative principle;

[0009] Step S3: Use the Alpha Shape algorithm to search for the boundary of the instability region, construct a coupling boundary, and find the union with the initial boundary to form the final transmission power boundary constraint that cannot be crossed.

[0010] Specifically, a clustering algorithm of a Gaussian mixture model is used to decompose the operation mode sample space into several models formed based on Gaussian probability density functions, including an N-dimensional Gaussian mixture model with K components, as shown in the following formula:

[0011]

[0012] Among them, N(x|μ k ,∑ k ) is the N-dimensional Gaussian probability density function; μ k represents the mean vector; is the operation mode set; k is the N×N dimensional covariance matrix; p(x) is the probability density function of the Gaussian mixture model; w k is the mixed weight, which represents the weight coefficient of the kth single Gaussian function in the mixed model in GMM; x is the sample vector set of the operation mode, in which the vector x i Including traditional unit active output P G 、Active power output of new energy units P Re 、Load active power P Load and DC transmission P DC .

[0013] Specifically, when performing cluster analysis, the maximum likelihood function is used for parameter estimation:

[0014]

[0015] Among them, L(X|θ) is the likelihood function, and the parameter θ that makes L(X|θ) reach the maximum value is found through the maximum likelihood function.* ,θ * = argmaxL(X|θ), θ = (μ k ,∑ k ,w k ), finally, we get the cluster of operation mode sub-modes Specifically, when performing parameter estimation, the EM algorithm is used for calculation. The specific method is:

[0016] According to the specified μ k ,∑ k and w k Initial value, calculate the probability that each data point belongs to different categories, data point x j The posterior probability of belonging to class k is:

[0017]

[0018] According to the obtained posterior probability results, the maximum likelihood method is used to obtain the new μ k ,∑ k and w k , the calculation formula is as follows:

[0019]

[0020] Repeat the calculation of the posterior probability and the new μ k ,∑ k and w k , until the log-likelihood function meets the convergence condition |lnL(X|θ) (t) -lnL(X|θ) (t-1) ∣<ε, get the Gaussian mixture model parameter value.

[0021] Specifically, the initial insurmountable transmission power boundary of a single section in each operation mode sub-mode cluster is calculated as follows:

[0022]

[0023] in, For the expected accident set; A collection of different mode clusters for operating modes; is the jth operation mode sub-mode cluster; is the safety label of the i-th working condition after the safety and stability check of the h-th anticipated accident, 0 means unsafe, and 1 means otherwise; P (i) is the section transmission power under the i-th working condition; P is,j , P se,j They are respectively the minimum active power flow of the section in the unstable operation mode and the maximum active power flow of the section in the stable operation mode in the jth operation mode sub-mode cluster.

[0024] Specifically, the specific steps of step S3 include:

[0025] Step S31, input cross-sectional power sample data: S = [point j,1 ,…,point j,m ], Boundary adhesion control parameter: α>0;

[0026] Step S32: construct a Delaunay triangulation, calculate the Delaunay triangulation of the power sample data S, generate a set of triangles and their edge sets, denoted by T = {Δ1, Δ2, ..., Δ m} is the set of all triangles in the triangulation;

[0027]

[0028] Where Conv(S) represents the convex hull of the power sample data S;

[0029] Step S33, calculate the empty sphere radius of all Delaunay edges, for each edge e = (point i ,point j ), calculate the circumscribed circle radius r of each triangle, and take the smallest r as the radius of the empty sphere;

[0030] Step S34: for each edge e=(point i ,piont j ), if the radius of the empty sphere Then keep e; otherwise delete it;

[0031] Step S35: Connect all the retained edges to form the boundary of the Alpha Shape, and the remaining boundary points constitute the final boundary polygon, which is recorded as

[0032] Step S36: Initial boundary generated by formula (5) Find the union to form the transmission power boundary constraint that cannot be crossed: Ω j is the unstable operation mode set in operation mode cluster j; Represents approximation to Ω j The conservative transmission power boundary of the set real boundary cannot be crossed;

[0033] Step S37: Output the multi-section under the current mode type that cannot cross the transmission power boundary

[0034] The present invention has the following advantages:

[0035] The method proposed in the present invention introduces more operating characteristics, divides the power grid operation mode into multiple sub-mode clusters, and proposes a conservative calculation method for the non-crossable transmission power within the operation mode cluster, solving the problem of over-conservatism of the traditional single non-crossable transmission power calculation method. In addition, considering the multi-section coupling relationship, a method for calculating the non-crossable transmission power boundary with accurate description is constructed to fully tap the transmission potential of the section. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 Schematic diagram of the characterization method of the present invention. DETAILED DESCRIPTION

[0037] The present invention is further described below in conjunction with the accompanying drawings, but the protection scope of the present invention is not limited to the following description. Figure 1 As shown, a data-driven multi-level precise characterization method for the non-crossable transmission power boundary of a section is provided, and the method comprises the following steps: Step S1, using a clustering algorithm based on a Gaussian mixture model to perform unsupervised learning on the power grid operation mode data to generate operation mode sub-mode clusters; using a clustering algorithm to learn the similarities and differences between the operation modes from massive samples to identify typical modes of characteristics such as load, direct current, and new energy, and calculate the non-crossable transmission power in each sub-mode cluster, thereby exploring the transmission potential under a specific operation mode, and indirectly characterizing the non-crossable transmission power boundary more accurately in a higher-dimensional feature space.

[0038] Step S2, in each sub-pattern cluster, calculating the initial boundary of a single section according to the conservative principle;

[0039] Step S3: Use the Alpha Shape algorithm to search for the boundary of the instability region, construct a coupling boundary, and find the union with the initial boundary to form the final transmission power boundary constraint that cannot be crossed.

[0040] Furthermore, a clustering algorithm of a Gaussian mixture model is used to decompose the operation mode sample space into several models formed based on Gaussian probability density functions, including an N-dimensional Gaussian mixture model with K components, as shown in the following formula:

[0041]

[0042] Among them, N(x|μ k ,∑ k ) is the N-dimensional Gaussian probability density function; μ k represents the mean vector; is the operation mode set; k is the N×N dimensional covariance matrix; p(x) is the probability density function of the Gaussian mixture model; w kis the mixed weight, which represents the weight coefficient of the kth single Gaussian function in the mixed model in GMM; x is the sample vector set of the operation mode, in which the vector x i Including traditional unit active output P G 、Active power output of new energy units P Re 、Load active power P Load and DC transmission P DC .

[0043] Furthermore, when performing cluster analysis, the maximum likelihood function is used for parameter estimation:

[0044]

[0045] Among them, L(X|θ) is the likelihood function, and the parameter θ that makes L(X|θ) reach the maximum value is found through the maximum likelihood function. * ,θ * = argmaxL(X|θ), θ = (μ k ,∑ k ,w k ), finally, we get the cluster of operation mode sub-modes Furthermore, when performing parameter estimation, the EM algorithm is used for calculation. The specific method is as follows:

[0046] Expected step (E-step): According to the specified μ k ,∑ k and w k Initial value, calculate the probability that each data point belongs to different categories, data point x j The posterior probability of belonging to class k is:

[0047]

[0048] Maximization step (M-step): Based on the obtained posterior probability results, the new μ is obtained using the maximum likelihood method k ,∑ k and w k , the calculation formula is as follows:

[0049]

[0050] Repeat the calculation of E-step and M-step until the log-likelihood function meets the convergence condition |lnL(X|θ) (t) -lnL(X|θ) (t-1) ∣<ε, get the Gaussian mixture model parameter value.

[0051] The operation mode sub-mode clusters are obtained through the GMM clustering algorithm, and the non-crossing transmission power of the sections is further calculated in each cluster according to the conservative principle. For the multi-section non-crossing transmission power with coupling effect, if the correlation between sections is not considered, the feasible domain of the multi-section non-crossing transmission power division may be reduced, and even there may be no feasible method. In this regard, it is necessary to describe the multi-section non-crossing transmission power with logical association constraints on the basis of considering the correlation, and calculate the coupled non-crossing transmission power boundary for the related sections, which can be specifically divided into initial boundary and coupling boundary calculation. The initial boundary calculation of a single section in each operation mode sub-mode cluster is shown in the following formula:

[0052]

[0053] in, is the set of anticipated accidents, including static, transient and Nk faults; A collection of different mode clusters for operating modes; is the jth operation mode sub-mode cluster; is the safety label of the i-th working condition after the safety and stability check of the h-th anticipated accident, 0 means unsafe, and 1 means otherwise; P (i) is the section transmission power under the i-th working condition; P is,j , P se,j They are respectively the minimum active power flow of the section in the unstable operation mode and the maximum active power flow of the section in the stable operation mode in the jth operation mode sub-mode cluster.

[0054] Furthermore, the specific steps of step S3 include:

[0055] Step S31, input cross-sectional power sample data: Boundary adhesion control parameter: α>0;

[0056] Step S32: construct a Delaunay triangulation, calculate the Delaunay triangulation of the power sample data S, generate a set of triangles and their edge sets, denoted by T = {Δ1, Δ2, ..., Δ m} is the set of all triangles in the triangulation;

[0057]

[0058] Where Conv(S) represents the convex hull of the power sample data S;

[0059] Step S33, calculate the empty sphere radius of all Delaunay edges, for each edge e = (point i ,point j ), calculate the circumscribed circle radius r of each triangle, and take the smallest r as the radius of the empty sphere;

[0060] Step S34: for each edge e=(point i ,point j , if the radius of the empty sphere is Then keep e; otherwise delete it;

[0061] Step S35: Connect all the retained edges to form the boundary of the Alpha Shape, and the remaining boundary points constitute the final boundary polygon, which is recorded as

[0062] Step S36: Initial boundary generated by formula (5) Find the union to form the transmission power boundary constraint that cannot be crossed: Ω j is the unstable operation mode set in operation mode cluster j; Represents approximation to Ω j The conservative transmission power boundary of the set real boundary cannot be crossed;

[0063] Step S37: Output the multi-section under the current mode type that cannot cross the transmission power boundary

[0064] The above method can generate multi-section coupled non-crossable transmission power condition mapping rules, which can be applied to optimization models such as unit combination and economic dispatch, assist the dispatching department to explore the potential of transmission channels and help the transmission and consumption of new energy.

[0065] Any person skilled in the art can make many possible changes and modifications to the technical solution of the present invention by using the above-mentioned technical contents without departing from the scope of the technical solution of the present invention, or modify it into an equivalent embodiment of equivalent changes. Therefore, any changes, modifications, equivalent changes and modifications made to the above embodiments according to the technology of the present invention without departing from the content of the technical solution of the present invention shall fall within the protection scope of the technical solution.

Claims

1. A data-driven multi-level accurate characterization method for cross-section transmission power boundary, characterized by: The method comprises the following steps: Step S1, using a clustering algorithm based on a Gaussian mixture model to perform unsupervised learning on the power grid operation mode data to generate operation mode sub-mode clusters; Step S2, in each sub-pattern cluster, calculating the initial boundary of a single section according to the conservative principle; Step S3: Use the Alpha Shape algorithm to search for the boundary of the instability region, construct a coupling boundary, and find the union with the initial boundary to form the final transmission power boundary constraint that cannot be crossed.

2. The data-driven multi-level accurate characterization method for cross-section transmission power boundary according to claim 1 is characterized in that: The clustering algorithm of Gaussian mixture model is used to decompose the operation mode sample space into several models based on Gaussian probability density function, including an N-dimensional Gaussian mixture model with K components, as shown in the following formula: Among them, N(x|μ k ,∑ k ) is the N-dimensional Gaussian probability density function; μ k represents the mean vector; is the operation mode set; k is the N×N dimensional covariance matrix; p(x) is the probability density function of the Gaussian mixture model; w k is the mixed weight, which represents the weight coefficient of the kth single Gaussian function in the mixed model in GMM; x is the sample vector set of the operation mode, in which the vector x i Including traditional unit active output P G 、Active power output of new energy units P Re 、Load active power P Load and DC transmission P DC .

3. The data-driven multi-level accurate characterization method for cross-section transmission power boundary according to claim 2 is characterized in that: When performing cluster analysis, the maximum likelihood function is used for parameter estimation: Among them, w j is the mixed weight, L(X|θ) is the likelihood function, and the parameter θ that makes L(X|θ) reach the maximum value is obtained through the maximum likelihood function. * ,θ * = argmaxL(X|θ), θ = (μ k ,∑ k ,w k ), finally, we get the cluster of operation mode sub-modes j=1,…,K.

4. The data-driven multi-level accurate characterization method for cross-section transmission power boundary according to claim 3 is characterized in that: When performing parameter estimation, the EM algorithm is used for calculation. The specific method is: According to the specified μ k ,∑ k and w k Initial value, calculate the probability that each data point belongs to different categories, data point x j The posterior probability of belonging to class k is: According to the obtained posterior probability results, the maximum likelihood method is used to obtain the new μ k ,∑ k and w k , the calculation formula is as follows: Repeat the calculation of the posterior probability and the new μ k ,∑ k and w k , until the log-likelihood function meets the convergence condition |lnL(X|θ) (t) -lnL(X|θ) (t-1) ∣<ε, get the Gaussian mixture model parameter value.

5. The data-driven multi-level accurate characterization method for cross-section transmission power boundary according to claim 4 is characterized in that: The initial boundary calculation of a single section in each operating mode cluster is as follows: in, For the expected accident set; A collection of clusters of different modes of operation; is the jth operation mode sub-mode cluster; is the safety label of the i-th working condition after the safety and stability check of the h-th anticipated accident, 0 means unsafe, and 1 means otherwise; P (i) is the section transmission power under the i-th working condition; P is,j , P se,j They are respectively the minimum active power flow of the section in the unstable operation mode and the maximum active power flow of the section in the stable operation mode in the jth operation mode sub-mode cluster.

6. The data-driven multi-level accurate characterization method for cross-section transmission power boundary according to claim 5 is characterized in that: The specific steps of step S3 include: Step S31, input cross-sectional power sample data: Boundary adhesion control parameter: α>0; Step S32: construct a Delaunay triangulation, calculate the Delaunay triangulation of the power sample data S, generate a set of triangles and their edge sets, denoted by T = {Δ1, Δ2, ..., Δ m } is the set of all triangles in the triangulation; Where Conv(S) represents the convex hull of the power sample data S; Step S33, calculate the empty sphere radius of all Delaunay edges, for each edge e = (point i ,point j ), calculate the circumscribed circle radius r of each triangle, and take the smallest r as the radius of the empty sphere; Step S34: for each edge e=(point i ,point j ), if the radius of the empty sphere Then keep e; otherwise delete it; Step S35: Connect all the retained edges to form the boundary of the Alpha Shape, and the remaining boundary points constitute the final boundary polygon, which is recorded as Step S36: Initial boundary generated by formula (5) Find the union to form the transmission power boundary constraint that cannot be crossed: Ω j is the unstable operation mode set in operation mode cluster j; Represents approximation to Ω j The conservative transmission power boundary of the set real boundary cannot be crossed; Step S37: Output the multi-section transmission power boundary under the current mode type.

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