Power flow system ill-conditioned evaluation method and system based on power network parameters

Abstract

The invention discloses a power network parameter-based power flow system ill-conditioned evaluation method and system, and mainly relates to the technical field of data calculation of a power system. Comprising the following steps: generating a multi-dimensional evaluation index set comprising a network topology structure index, an admittance matrix characteristic index and branch impedance parameter statistics according to basic network parameters of a power system; through comprehensive analysis of the generated multi-dimensional evaluation index set, traditional Jacobian matrix singular value calculation is replaced, and the ill-conditioned condition number of power flow calculation of the power system is evaluated; according to the power grid type and the operation scene, dynamically adjusting the judgment threshold value of each index, and outputting the ill-conditioned risk level and the optimization suggestion. The method has the advantages that calculation independent of Jacobian matrix singular values is achieved, dynamic quantitative evaluation is carried out on bad operation conditions of the system, and meanwhile the load flow calculation stability evaluation precision of a large-scale power grid is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data calculation of power systems, and in particular to a method and system for evaluating pathological conditions of a power flow system based on power network parameters. Background Art

[0002] With the rapid development of smart grid and renewable energy technologies, modern power systems are gradually evolving towards multi-level, multi-physics-field deep coupling. Especially driven by the "dual carbon" goals, the high proportion of new energy access and the large-scale application of new power electronic equipment have made the topology, operating characteristics, and parameter correlations of the main distribution network increasingly complex. Traditional single-physics-field modeling methods can no longer meet the needs of refined system analysis and control. Currently, domestic and foreign scholars have conducted extensive research in the field of multi-physics-field coupled modeling of power systems, but they still face challenges such as insufficient quantification of cross-level parameter sensitivity and weak collaborative analysis capabilities of multi-dimensional parameter clusters.

[0003] The continued growth in load demands on existing infrastructure, coupled with the widespread integration of renewable energy sources (RES) such as solar, wind, and energy storage, as well as dynamic loads such as electric vehicles and data centers, has profoundly changed the operational characteristics of modern power systems. This integration exacerbates the challenges of large-scale ill-conditioned power flow calculations, stemming from the inherent characteristics of distribution networks, such as high R / X ratios, weak grid connections, voltage instability, and network congestion. These factors have been widely demonstrated to be the primary cause of ill-conditioned Jacobian matrices in the Newton-Raphson (NR) algorithm, leading to decreased numerical stability and hindered convergence. Although the intermittent nature and load fluctuations of RES can indirectly increase the risk of ill-conditioning by pushing the system to operational limits such as voltage collapse, the fundamental cause of numerical instability lies in the grid parameters themselves, rather than random fluctuations in RES.

[0004] In power flow calculations, the nonlinear effects of the power flow equation (such as impedance fluctuations caused by sudden changes in network topology) are often linearly simplified in existing models, resulting in instability in power flow calculations under complex working conditions.

[0005] Therefore, there is an urgent need for a power flow system pathological assessment method and system based on power network parameters to solve the above problems. Summary of the Invention

[0006] The purpose of the present invention is to provide a method and system for evaluating the pathological condition of the power flow system based on power network parameters, which realizes the dynamic quantitative evaluation of the system's poor operating conditions while significantly improving the accuracy of the power flow calculation stability evaluation of multi-level power grids.

[0007] To achieve the above-mentioned purpose, the present invention is implemented through the following technical solutions:

[0008] In one aspect, a method for evaluating ill-conditioning of a power flow system based on power network parameters is provided, comprising the following steps:

[0009] S1: Based on the basic network parameters of the power system, a multi-dimensional evaluation index set including network topology index, admittance matrix characteristic index and branch impedance parameter statistics is generated;

[0010] S2: By comprehensively analyzing the multi-dimensional evaluation indicators generated in step S1, the traditional Jacobian matrix singular value calculation is replaced to evaluate the ill-conditioned condition number of the power system power flow calculation;

[0011] S3: Dynamically adjust the judgment threshold of each indicator according to the grid type and operation scenario, and output the pathological risk level and optimization suggestions.

[0012] Preferably, the network topology index includes a mesh porosity coefficient M, which is defined as:

[0013]

[0014] Where m is the number of branches and n is the number of nodes. When M→1, the network is determined to be a high-mesh structure and Jacobian matrix preconditioning is initiated.

[0015] Preferably, the admittance matrix characteristic indicators include:

[0016] Sparsity index and the diagonal dominant sign Dominant;

[0017] The sparsity index

[0018]

[0019] Where N is the number of system nodes, NNZ( ) represents the number of non-zero elements in the matrix. When , the sparse matrix compression algorithm is enabled;

[0020] The diagonal dominant sign Dominant:

[0021]

[0022] That is, when Dominant=0, the matrix condition number correction strategy is triggered.

[0023] Preferably, the branch impedance parameter statistics include:

[0024] High R / X branch set Y high , global R / X mean μ R / X and standard deviation σ R / X ;

[0025] Assuming the branch is k, the R / X ratio calculation process is:

[0026]

[0027] Among them, R k and X k denote the line resistance and reactance of branch k, respectively, and the ratio ρ k Characterizes the relative dominance of the branch impedance and reactive conductance, and sets the threshold τ as the decision node for the high R / X branch. Then:

[0028]

[0029] Among them, ε branch Represents a branch set;

[0030] When μ R / X >0.3 and σ R / X When it is >0.15, the system is judged to be at a pathological risk.

[0031] Preferably, the method for constructing the Jacobian matrix includes:

[0032] The Newton-Raphson method and the Jacobi formula NR method iteratively solve the nonlinear PF equation by linearizing the residual function G(X) in each iteration. Let the state vector of the k-th iteration be: where e (k) and f (k) Indicates voltage, and Represents the power source, then:

[0033] J (k) ΔX (k) =-G(X (k) )

[0034] in, is the Jacobian matrix, ΔX (k) The Jacobian matrix is divided into two parts corresponding to the state variables e, f, P g , Q g The block:

[0035]

[0036] For PQ nodes, and are all 0, and the PV node is excluded The partial derivative block based on admittance and From the node admittance matrix Y bus =G+jB, using I bus =Y busV and V=e+jf, the node derivatives are:

[0037]

[0038] Partial derivative rule for voltage amplitude constraint:

[0039]

[0040] Preferably, the dynamic threshold optimization includes:

[0041] Different τ are set for the transmission network, distribution network and microgrid respectively;

[0042] Update Y according to real-time topology changes high Set and dynamically adjust the enabling conditions of sparse matrix algorithms.

[0043] On the other hand, an evaluation system based on the above-mentioned power flow system pathological evaluation method based on power network parameters is provided, comprising:

[0044] A set generation module is used to generate a multi-dimensional evaluation index set including network topology index, admittance matrix characteristic index and branch impedance parameter statistics based on the basic network parameters of the power system;

[0045] The ensemble analysis module is used to: evaluate the ill-conditioned condition number of power system flow calculations by comprehensively analyzing the generated multi-dimensional evaluation indicators, replacing the traditional Jacobian matrix singular value calculation;

[0046] The data output module is used to dynamically adjust the judgment threshold of each indicator according to the grid type and operation scenario, and output the pathological risk level and optimization suggestions.

[0047] Compared with the prior art, the beneficial effects of the present invention are: greatly reducing labor costs and improving production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is a flow chart of a method for evaluating the ill-condition of a power flow system based on power network parameters according to the present invention;

[0049] Figure 2 This is a test result diagram of a power flow system pathological assessment method based on power network parameters of the present invention;

[0050] Figure 3 This is a schematic structural diagram of a power network parameter-based power flow system pathological assessment system of the present invention. DETAILED DESCRIPTION

[0051] Below in conjunction with specific embodiment, further set forth the present invention.Should be understood that these embodiments are only used to illustrate the present invention and are not used in limiting the scope of the present invention.In addition, should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms fall within the scope limited by the application equally.

[0052] In the present invention, terms such as "upper", "lower", "left", "right", "front", "back", "vertical", "horizontal", "side", "bottom", etc. indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. They are relational words determined only for the convenience of describing the structural relationships of the various parts or elements of the present invention, and do not specifically refer to any part or element in the present invention, and should not be understood as limiting the present invention.

[0053] In the present invention, terms such as "fixed connection," "connected," and "connection" should be interpreted broadly to mean a fixed connection, an integral connection, or a detachable connection; a direct connection or an indirect connection through an intermediary. Relevant researchers or technicians in this field may determine the specific meanings of these terms in the present invention based on specific circumstances, and they should not be construed as limitations of the present invention.

[0054] Example:

[0055] like Figure 1 As shown, this embodiment provides a method for evaluating the pathological state of a power flow system based on power network parameters, comprising the following steps:

[0056] S1: Based on the basic network parameters of the power system, a multi-dimensional evaluation index set including network topology index, admittance matrix characteristic index and branch impedance parameter statistics is generated;

[0057] S2: By comprehensively analyzing the multi-dimensional evaluation indicators generated in step S1, the traditional Jacobian matrix singular value calculation is replaced to evaluate the ill-conditioned condition number of the power system power flow calculation;

[0058] S3: Dynamically adjust the judgment threshold of each indicator according to the grid type and operation scenario, and output the pathological risk level and optimization suggestions.

[0059] The construction method of the Jacobian matrix is as follows:

[0060] The Newton-Raphson method and the Jacobi formula NR method iteratively solve the nonlinear PF equation by linearizing the residual function G(X) in each iteration. Let the state vector of the k-th iteration be: where e (k) and f (k) Indicates voltage, and Represents the power source, then:

[0061] J (k) ΔX (k) =-G(X (k) )

[0062] in, is the Jacobian matrix, ΔX (k) The Jacobian matrix is divided into two parts corresponding to the state variables e, f, P g , Q g The block:

[0063]

[0064] For PQ nodes, and are all 0, and the PV node is excluded The partial derivative block based on admittance and From the node admittance matrix Y bus =G+jB, using I bus =Y bus V and V=e+jf, the node derivatives are:

[0065]

[0066] Partial derivative rule for voltage amplitude constraint:

[0067]

[0068] Used to generate Jacobian matrix constraint rows for PV nodes to improve sparsity calculation efficiency.

[0069] Network topology indicators, specifically:

[0070] Radial networks exhibit a triangular sparsity pattern and generally have better numerical stability. Meshed networks, on the other hand, have dense interconnections or parallel lines, which can lead to near-linear dependencies in the matrix, thereby increasing the sensitivity of the Jacobian matrix to numerical instability. Network topologies with low connectivity (such as radial networks with long feeders) are susceptible to adverse conditions because their structural fragility makes the network more susceptible to adverse conditions. Network topology indicators include the mesh porosity coefficient M, which is defined as:

[0071]

[0072] Where m is the number of branches and n is the number of nodes. When M → 1, the network is considered highly meshed, triggering Jacobian matrix preconditioning. M ranges from 0 to 1: When M = 0, the network exhibits a radial tree structure; when M = 1, the network reaches maximum meshing. A higher meshing coefficient means the network has more interconnected and parallel lines, increasing the Jacobian matrix's sensitivity to numerical instability and worsening the matrix's condition number.

[0073] The characteristic indicators of the admittance matrix include:

[0074] In power distribution systems, high R / X ratios are common. This ratio leads to strong coupling between the real and imaginary power equations, distorting the block structure of the Jacobian matrix.

[0075] For branch k, its R / X ratio is calculated as follows:

[0076]

[0077] Among them, R k and X k denote the line resistance and reactance of branch k, respectively, and the ratio ρ k Characterizes the relative dominance of the branch impedance and reactive conductance;

[0078] If ρ k >>1, indicating that the resistive component is dominant, which is very typical in power distribution systems. k →1, it means that the branch is purely resistive (X k =0) At this time, the virtual power effect can be ignored. At this time, the diagonal dominance flag Dominant is introduced:

[0079]

[0080] When Dominant = 0, it indicates that the system has a high risk of pathological condition. In power system analysis, the diagonal dominance of the Jacobian matrix is crucial for the stability and convergence of numerical methods. However, in actual power grids, due to the influence of various factors such as network structure and load distribution, the diagonal dominance of the Jacobian matrix may be destroyed, resulting in instability or convergence difficulties in the solution process. Therefore, other indicators are needed to supplement the explanation:

[0081] Includes: admittance matrix sparsity and branch impedance ratio statistics;

[0082] Among them: sparsity index

[0083]

[0084] Where N is the number of system nodes, NNZ( ) represents the number of non-zero elements in the matrix, and the higher the sparsity, the better. The more suitable it is for sparse matrix acceleration algorithms. Sparsity: directly affects the storage efficiency of the Jacobian matrix and the iterative solution speed (CSR compression format can be enabled when sparsity > 90%);

[0085] Branch impedance parameter statistics include:

[0086] High R / X branch set Y high , global R / X mean μ R / X and standard deviation σ R / X ;

[0087] Assuming the branch is k, the R / X ratio calculation process is:

[0088]

[0089] Among them, R k and X k denote the line resistance and reactance of branch k, respectively, and the ratio ρ k This characterizes the relative dominance of the branch's impedance and reactive conductance, setting a threshold value, τ. τ can be set based on the power system voltage level, for example, considering high-voltage transmission grids, distribution grids, and microgrids. The R / X ratio, τ, can be set by multiplying it by the average value of different grid types and a custom ratio. High R / X branches: Identify weak links (such as cable lines and SVG access points) that are prone to electromagnetic coupling oscillations. In this case:

[0090]

[0091] Among them, ε branch Represents a branch set;

[0092] When μ R / X When it is >1000, the system is judged to be at pathological risk.

[0093] like Figure 2 As shown, for the synthetic U.S. power grid model and the actual or test power grid model (the number of nodes is 70k, 25k, and 10k respectively, covering the high-voltage transmission network to distribution network scenario), combined with the estimation method of this embodiment, the test results are given; according to Figure 2 The test results show that:

[0094] 1. Pathological risk determination: All cases do not meet the diagonal dominance (Dominant = 0), and the condition number is extremely high (> 10 8 ), verifying the sensitivity of the method of this embodiment to pathological systems.

[0095] 2. Indicator correlation:

[0096] High μ R / C For example, (0.193) and high σ R / X When (0.128) appears together, the condition number deteriorates significantly (e.g. 5.565×10 8 ), in line with patent criteria v R / X >0.3 and σ R / X >0.15 risk warning logic.

[0097] Cases with a low topological connectivity coefficient M (<0.15) (e.g., a power grid model with 70k nodes) may still be pathological due to the presence of high R / X branches, reflecting the necessity of multidimensional indicators.

[0098] like Figure 3 As shown, this embodiment further provides an evaluation system based on the above-mentioned power flow system pathological evaluation method based on power network parameters, including:

[0099] A set generation module is used to generate a multi-dimensional evaluation index set including network topology index, admittance matrix characteristic index and branch impedance parameter statistics based on the basic network parameters of the power system;

[0100] The ensemble analysis module is used to: evaluate the ill-conditioned condition number of power system flow calculations by comprehensively analyzing the generated multi-dimensional evaluation indicators, replacing the traditional Jacobian matrix singular value calculation;

[0101] The data output module is used to dynamically adjust the judgment threshold of each indicator according to the grid type and operation scenario, and output the pathological risk level and optimization suggestions.

[0102] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the described embodiments. Those skilled in the art can make various equivalent modifications or substitutions to the transaction features between nodes without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. A method for evaluating the pathological state of a power flow system based on power network parameters, characterized in that: The following steps are involved: S1: Based on the basic network parameters of the power system, a multi-dimensional evaluation index set including network topology index, admittance matrix characteristic index and branch impedance parameter statistics is generated; S2: By comprehensively analyzing the generated multi-dimensional evaluation index set, we can replace the traditional Jacobian matrix singular value calculation to evaluate the ill-conditioned condition number of power system power flow calculation; S3: Dynamically adjust the judgment threshold of each indicator according to the grid type and operation scenario, and output the pathological risk level and optimization suggestions.

2. The method for evaluating the pathological state of a power flow system based on power network parameters according to claim 1, characterized in that: The network topology index includes the mesh degree coefficient M, which is defined as: Where m is the number of branches and n is the number of nodes. When M→1, the network is determined to be a high-mesh structure and Jacobian matrix preconditioning is initiated.

3. The method for evaluating the pathological state of a power flow system based on power network parameters according to claim 1, characterized in that: The admittance matrix characteristic indicators include: Sparsity index and the diagonal dominant sign Dominant; The sparsity index Where N is the number of system nodes, NNZ() represents the number of non-zero elements in the matrix. When , the sparse matrix compression algorithm is enabled; The diagonal dominant sign Dominant: That is, when Dominant=0, the matrix condition number correction strategy is triggered.

4. The method for evaluating the pathological state of a power flow system based on power network parameters according to claim 1, characterized in that: The branch impedance parameter statistics include: High R / X branch set Y high , global R / X mean μ R / X and standard deviation σ R / X ; Assuming the branch is k, the R / X ratio calculation process is: Among them, R k and X k denote the line resistance and reactance of branch k, respectively, and the ratio ρ k Characterizes the relative dominance of the branch impedance and reactive conductance, and sets the threshold τ as the decision node for the high R / X branch. Then: Among them, ε branch Represents a branch set; When μ R / X >0.3 and σ R / X When >0.15, the system is judged to be at high pathological risk.

5. The method for evaluating the pathological state of a power flow system based on power network parameters according to claim 1, characterized in that: In step S2, the method for constructing the Jacobian matrix includes: Partial derivative rule for voltage amplitude constraint: Used to generate Jacobian matrix constraint rows for PV nodes to improve sparsity calculation efficiency.

6. The method for evaluating the pathological state of a power flow system based on power network parameters according to claim 1, characterized in that: In step S3, dynamically adjusting the determination threshold of each indicator includes: For the transmission network, distribution network and microgrid, τ=0.5, 5, 10 are set respectively; Update Y according to real-time topology changes high Set and dynamically adjust the enabling conditions of sparse matrix algorithms.

7. An evaluation system based on the power network parameter-based power flow system pathological evaluation method according to claim 1, characterized in that: include: A set generation module is used to generate a multi-dimensional evaluation index set including network topology index, admittance matrix characteristic index and branch impedance parameter statistics based on the basic network parameters of the power system; The ensemble analysis module is used to: evaluate the ill-conditioned condition number of power system flow calculations by comprehensively analyzing the generated multi-dimensional evaluation indicators, replacing the traditional Jacobian matrix singular value calculation; The data output module is used to dynamically adjust the judgment threshold of each indicator according to the grid type and operation scenario, and output the pathological risk level and optimization suggestions.

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