A test method and test system for a power system

Abstract

This invention relates to the field of power system testing technology, specifically to a power system testing method and system. The method includes: determining the voltage stability vulnerability testing objectives and scope; collecting and processing multi-dimensional power system data; constructing a digital twin model; performing power flow calculations using an improved Newton-Raphson method power flow algorithm, and training the digital twin model using historical data in conjunction with a dynamic load model to achieve convergence; designing multi-condition testing scenarios including normal operation, single equipment failure, and extreme weather; determining test indicators for node voltage amplitude, voltage phase angle, voltage stability margin, and reactive power distribution; establishing a vulnerability assessment model; and evaluating the vulnerability of power system nodes. This invention achieves accurate assessment of power system node vulnerability and solves the problem of subjective weighting of existing power system assessment indicators.

Description

Technical Field

[0001] This invention relates to the field of power system testing technology, and specifically to a power system testing method and testing system. Background Technology

[0002] In power system testing, accurately assessing voltage stability vulnerability is crucial for system safety. Traditional methods suffer from insufficient characterization of load dynamics and low model accuracy. Furthermore, the weights of assessment indicators often rely on subjective settings and lack data-driven optimization mechanisms. In addition, test scenarios are mostly limited to normal operating conditions, making it difficult to cover complex operating conditions such as single equipment failures and extreme weather. This results in insufficient accuracy in node vulnerability assessment and an inability to effectively address the diverse risks in actual operation. Therefore, there is an urgent need to address this issue by constructing a vulnerability assessment method for power system nodes that combines dynamic load models with data-driven optimization algorithms. Summary of the Invention

[0003] The purpose of this invention is to address the problems existing in the background art by proposing a testing method and system for power systems.

[0004] The technical solution of the present invention: a testing method for a power system, comprising the following steps:

[0005] Determine the objectives and scope of voltage stability vulnerability testing, collect and process multi-dimensional data of the power system, and construct a digital twin model;

[0006] The construction of the digital twin model includes using a power flow algorithm based on the improved Newton-Raphson method to perform power flow calculations, and combining it with a dynamic load model. The digital twin model is then trained using historical data to achieve convergence.

[0007] Design test scenarios for normal operation, single equipment failure, and extreme weather conditions; determine test indicators for node voltage amplitude, voltage phase angle, voltage stability margin, and reactive power distribution; establish a vulnerability assessment model based on PSO-optimized AHP; and assess the vulnerability value of power system nodes based on the vulnerability assessment model.

[0008] Preferably, the dynamic load model divides the load into constant power, constant impedance, and other power system components, as expressed below:

[0009] ;

[0010] In the formula, , These are the active power and reactive power of the dynamic load, respectively. , These are the active power of a constant power load and the reactive power of the load, respectively. , These are the active power of the constant resistance load and the reactive power of the load, respectively. , These are the active power and reactive power of the load, respectively, based on the other components of the power system.

[0011] Preferably, the power flow algorithm of the Newton-Raphson method is improved by combining a dynamic load model to obtain an improved power flow calculation equation. The expression of the improved power flow calculation equation is as follows;

[0012] ;

[0013] In the formula, , These are the imbalances caused by changes in the total active and reactive power of the node's load, respectively. , These represent the injected active power and reactive power at node i, respectively. , These are the voltage amplitudes at nodes i and j, respectively; , These are the real and imaginary parts of the nodal admittance matrix elements, respectively; , is the voltage phase angle difference between node i and node j; n is the total number of nodes in the system.

[0014] Preferably, a digital twin model is constructed based on the Jacobian matrix and integrated with a dynamic load model. The expression of the digital twin model is as follows:

[0015] ;

[0016] In the formula, , These represent the voltage phase angle increment and voltage magnitude increment at node i, respectively; It is a Jacobian matrix; , These represent the imbalance caused by changes in active and reactive power of the dynamic load, respectively, calculated from the dynamic load model; k is the iteration number, where k is an integer and k≥0; where... , These are the initial phase angle and initial voltage values ​​for node i, respectively;

[0017] The node voltage magnitude and phase angle are updated as follows:

[0018] .

[0019] Preferably, it is determined whether the convergence condition is met. The expression for the convergence condition is as follows: ;

[0020] If the conditional expression is true, the iteration ends, and the voltage magnitude and phase angle of each node are output; otherwise, the iteration returns to the digital twin model. In the formula, The convergence accuracy was preset and obtained by fitting based on historical big data.

[0021] Preferably, the methods for determining the test indicators of node voltage amplitude, voltage phase angle, voltage stability margin, and reactive power distribution include:

[0022] Voltage stability margin is calculated using a singular value decomposition-based method.

[0023] The node voltage magnitude, voltage phase angle, voltage stability margin based on singular value decomposition, and reactive power distribution are used as primary evaluation indicators, denoted as... Each primary evaluation indicator is further subdivided into several secondary evaluation indicators;

[0024] Based on the multiple secondary indicators of each primary evaluation indicator, the overall evaluation indicator system is constructed as follows:

[0025] In the formula, n1, n2, n3, and n4 are the total number of secondary indicators for each primary indicator.

[0026] Preferably, each set of indicators is treated as a particle, and secondary indicators are statistically analyzed for the overall evaluation indicator system. The total number of secondary indicators is calculated using the formula N=n1+n2+n3+n4, and this total number is used as the dimension N of each particle.

[0027] Particle h can be represented as ;in, Let be the weight of the z-th secondary index in particle h, and satisfy . .

[0028] Preferably, historical failure cases are obtained, and the Spearman correlation coefficient between the vulnerability ranking and the actual failure severity in the historical failure cases is used as the objective function f(X) to measure the degree of agreement between the evaluation results and the actual situation.

[0029] The expression for the objective function is: ;

[0030] In the formula, m represents the number of historical failure cases; denoted as the difference between the vulnerability ranking obtained based on the assessment model and the actual failure severity ranking in the c-th case;

[0031] The objective function is optimized based on the PSO algorithm. By iteratively optimizing the particle h, the velocity of particle h at the (t+1)th iteration is obtained using the update formula. and location The updated formula is as follows:

[0032] ;

[0033] ;

[0034] In the formula, Inertial weights are used to balance global and local search capabilities; , For learning factors; , is a random number uniformly distributed in the interval [0,1]. Let h be the individual optimal position of particle h in the t-th iteration; This represents the globally optimal position of the entire particle swarm at the t-th iteration.

[0035] The velocity and position of the particles are continuously updated iteratively until a preset stopping condition is met, at which point the globally optimal position is obtained. This refers to the optimal combination of index weights obtained through optimization; the preset stopping condition is reaching the maximum number of iterations or the convergence of the objective function value.

[0036] Preferably, for the combination of indicator weights, the vulnerability value CZ of each node is calculated using a weighted summation method, and the calculation formula is as follows: In the formula, Let be the weight of the node in the e-th secondary indicator; Let be the standardized value of the node under the e-th secondary index;

[0037] Based on the vulnerability values ​​CZ of each node, the nodes are sorted in descending order to obtain the vulnerability ranking of each node under each failure case based on the evaluation model.

[0038] The severity of actual faults was determined by inviting experts in the power system field to score historical fault cases, and then ranking the scores in descending order to obtain a ranking of the severity of actual faults.

[0039] This invention also discloses a power system testing system, which applies the aforementioned power system testing method, specifically including:

[0040] The data acquisition and processing module is used to determine the voltage stability vulnerability test targets and scope, collect and process multi-dimensional data of the power system, and build a digital twin model;

[0041] The construction of the digital twin model includes using a power flow algorithm based on the improved Newton-Raphson method to perform power flow calculations, and combining it with a dynamic load model. The digital twin model is then trained using historical data to achieve convergence.

[0042] The indicator determination and evaluation module is used to design test scenarios for normal operation, single equipment failure and extreme weather conditions, determine the test indicators of node voltage amplitude, voltage phase angle, voltage stability margin and reactive power distribution, establish a vulnerability assessment model based on PSO optimized AHP, and evaluate the vulnerability value of power system nodes based on the vulnerability assessment model.

[0043] Compared with the prior art, the above-mentioned technical solution of the present invention has the following beneficial technical effects:

[0044] This invention constructs a digital twin model that integrates a dynamic load model and an improved Newton-Raphson power flow algorithm. The model is trained to convergence using historical data to improve its accuracy. A multi-condition test scenario is designed, and a vulnerability assessment model for AHP is optimized by combining PSO. The Spearman correlation coefficient is used as the objective function to optimize the index weights, thereby achieving an accurate assessment of the vulnerability of power system nodes and solving the problem of subjective weights in existing power system assessment indicators. Attached Figure Description

[0045] Figure 1 This is a layer structure diagram of Embodiment 1 of the present invention. Detailed Implementation

[0046] Example 1, as Figure 1 As shown, the present invention proposes a testing method for a power system, comprising the following steps:

[0047] Determine the objectives and scope of voltage stability vulnerability testing, collect and process multi-dimensional data of the power system, and construct a digital twin model;

[0048] Data collection includes power system connection structure, equipment parameters, load characteristics, historical operation and equipment failure data; data processing includes cleaning, filtering, missing data completion, classification and normalization.

[0049] The construction of the digital twin model includes using a power flow algorithm based on the improved Newton-Raphson method to perform power flow calculations, and combining it with a dynamic load model. The digital twin model is then trained using historical data to achieve convergence.

[0050] The dynamic load model divides the load into constant power, constant impedance, and other power system components, and its expression is as follows:

[0051] ;

[0052] In the formula, , These are the active power and reactive power of the dynamic load, respectively. , These are the active power of a constant power load and the reactive power of the load, respectively. , These are the active power of the constant resistance load and the reactive power of the load, respectively. , These are the active power and reactive power of the load, respectively, based on the components of the remaining power system.

[0053] The power flow algorithm of the Newton-Raphson method is improved by combining the dynamic load model, and the improved power flow calculation equation is obtained. The expression of the improved power flow calculation equation is as follows;

[0054] ;

[0055] In the formula, , These are the imbalances caused by changes in the total active and reactive power of the node's load, respectively. , These represent the injected active power and reactive power at node i, respectively. , These are the voltage amplitudes at nodes i and j, respectively; , These are the real and imaginary parts of the nodal admittance matrix elements, respectively; , is the voltage phase angle difference between node i and node j; n is the total number of nodes in the system;

[0056] A digital twin model is constructed based on the Jacobian matrix and integrated with a dynamic load model. The expression of the digital twin model is as follows:

[0057] ;

[0058] In the formula, , These represent the voltage phase angle increment and voltage magnitude increment at node i, respectively; It is a Jacobian matrix; , These represent the imbalance caused by changes in active and reactive power of the dynamic load, respectively, calculated from the dynamic load model; k is the iteration number, where k is an integer and k≥0; where... , These are the initial phase angle and initial voltage values ​​for node i, respectively;

[0059] The node voltage magnitude and phase angle are updated as follows:

[0060] ;

[0061] To determine whether the convergence condition is met, the expression for the convergence condition is as follows: ;

[0062] If the conditional expression is true, the iteration ends, and the voltage magnitude and phase angle of each node are output; otherwise, the iteration returns to the digital twin model. In the formula, The convergence accuracy was determined by fitting based on historical big data.

[0063] Design test scenarios for normal operation, single equipment failure, and extreme weather conditions; determine test indicators for node voltage amplitude, voltage phase angle, voltage stability margin, and reactive power distribution; establish a vulnerability assessment model based on PSO-optimized AHP; and assess the vulnerability value of power system nodes based on the vulnerability assessment model.

[0064] For example, single equipment failure conditions may include generator tripping, generator excitation system failure, transformer winding short circuit, transformer bushing damage, single-phase ground fault in transmission line, and three-phase short circuit in transmission line; extreme weather conditions may include high temperature weather, strong wind weather, and heavy rain weather.

[0065] Methods for determining node voltage amplitude, voltage phase angle, voltage stability margin, and reactive power distribution test parameters include:

[0066] The voltage stability margin is calculated using a singular value decomposition (SVD)-based method. Specifically, the Jacobian matrix is ​​subjected to SVD to obtain the minimum singular value on the diagonal of the singular matrix. The voltage stability margin is then calculated by comparing the minimum singular value with the norm of the Jacobian matrix. It should be noted that SVD and norm calculation are existing techniques and will not be elaborated on here.

[0067] The node voltage magnitude, voltage phase angle, voltage stability margin based on singular value decomposition, and reactive power distribution are used as primary evaluation indicators, denoted as... Each primary evaluation indicator is further subdivided into several secondary evaluation indicators; for example, voltage amplitude... It is further subdivided into average voltage amplitude. Standard deviation Minimum value etc.; for voltage stability margin Subdivided into the minimum value of voltage stability margin at each node. rate of change wait;

[0068] Based on the multiple secondary indicators of each primary evaluation indicator, the overall evaluation indicator system is constructed as follows:

[0069] In the formula, n1, n2, n3, and n4 are the total number of secondary indicators for each primary indicator;

[0070] The method for establishing a vulnerability assessment model based on PSO-optimized AHP specifically includes the following steps:

[0071] Treat each set of indicators as a particle, perform secondary indicator statistics on the overall evaluation indicator system, calculate the total number of secondary indicators using the formula N=n1+n2+n3+n4, and use it as the dimension N of each particle;

[0072] Particle h can be represented as ;in, Let be the weight of the z-th secondary index in particle h, and satisfy . ;

[0073] Historical failure cases are obtained, and the Spearman correlation coefficient between the vulnerability ranking and the actual failure severity in the historical failure cases is used as the objective function f(X) to measure the degree of agreement between the assessment results and the actual situation.

[0074] The expression for the objective function is: ;

[0075] In the formula, m represents the number of historical failure cases; denoted as the difference between the vulnerability ranking obtained based on the assessment model and the actual failure severity ranking in the c-th case;

[0076] The objective function is optimized based on the PSO algorithm. By iteratively optimizing the particle h, the velocity of particle h at the (t+1)th iteration is obtained using the update formula. and location The updated formula is as follows:

[0077] ;

[0078] ;

[0079] In the formula, Inertial weights are used to balance global and local search capabilities; , This is the learning factor, which is usually a constant greater than 0; , is a random number uniformly distributed in the interval [0,1]. Let h be the individual optimal position of particle h in the t-th iteration; This represents the globally optimal position of the entire particle swarm at the t-th iteration.

[0080] The velocity and position of the particles are continuously updated iteratively until a preset stopping condition is met, at which point the globally optimal position is obtained. This refers to the optimal combination of indicator weights obtained through optimization; the preset stopping condition is reaching the maximum number of iterations or the convergence of the objective function value.

[0081] For the combination of indicator weights, the vulnerability value CZ of each node is calculated using a weighted summation method, and the calculation formula is as follows: In the formula, Let be the weight of the node in the e-th secondary indicator; Let be the standardized value of the node under the e-th secondary index;

[0082] Based on the vulnerability values ​​CZ of each node, the nodes are sorted in descending order to obtain the vulnerability ranking of each node under each failure case based on the evaluation model.

[0083] The severity of actual faults was determined by inviting experts in the power system field to score historical fault cases, and then ranking the scores in descending order to obtain a ranking of the severity of actual faults; the scoring was implemented using a 9-scale method.

[0084] The objective function guides the iterative update of the PSO algorithm. The PSO algorithm searches for the objective function value with the goal of maximizing the objective function value. In the power system voltage stability vulnerability assessment model, the objective function is the Spearman correlation coefficient between the vulnerability ranking in historical fault cases and the actual fault severity. The PSO algorithm attempts to find the index weight combination that maximizes the correlation coefficient by continuously updating the velocity and position of the particles, which is the optimal solution.

[0085] The vulnerability assessment model based on PSO-optimized AHP achieves objective and accurate assessment and testing of the voltage stability vulnerability of power systems through scientific weight determination and comprehensive evaluation.

[0086] Example 2: A power system testing system proposed in this invention is applied to a power system testing method proposed in Example 1, specifically including:

[0087] The data acquisition and processing module is used to determine the voltage stability vulnerability test targets and scope, collect and process multi-dimensional data of the power system, and build a digital twin model;

[0088] The construction of the digital twin model includes using a power flow algorithm based on the improved Newton-Raphson method to perform power flow calculations, and combining it with a dynamic load model. The digital twin model is then trained using historical data to achieve convergence.

[0089] The indicator determination and evaluation module is used to design test scenarios for normal operation, single equipment failure and extreme weather conditions, determine the test indicators of node voltage amplitude, voltage phase angle, voltage stability margin and reactive power distribution, establish a vulnerability assessment model based on PSO optimized AHP, and evaluate the vulnerability value of power system nodes based on the vulnerability assessment model.

[0090] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited thereto. Various changes can be made within the scope of knowledge possessed by those skilled in the art without departing from the spirit of the present invention.

Claims

1. A testing method for a power system, characterized in that, Includes the following steps: Determine the objectives and scope of voltage stability vulnerability testing, collect and process multi-dimensional data of the power system, and construct a digital twin model; The construction of the digital twin model includes using a power flow algorithm based on the improved Newton-Raphson method to perform power flow calculations, and combining it with a dynamic load model. The digital twin model is then trained using historical data to achieve convergence. The dynamic load model divides the load into constant power, constant impedance, and other power system components, and its expression is as follows: ； In the formula, , These are the active power and reactive power of the dynamic load, respectively. , These are the active power of a constant power load and the reactive power of the load, respectively. , These are the active power of the constant resistance load and the reactive power of the load, respectively. , These are the active power and reactive power of the load, respectively, based on the components of the remaining power system. The power flow algorithm of the Newton-Raphson method is improved by combining the dynamic load model, and the improved power flow calculation equation is obtained. The expression of the improved power flow calculation equation is as follows; ； In the formula, , These are the imbalances caused by changes in the total active and reactive power of the node's load, respectively. , These represent the injected active power and reactive power at node i, respectively. , These are the voltage amplitudes at nodes i and j, respectively; , These are the real and imaginary parts of the nodal admittance matrix elements, respectively; , is the voltage phase angle difference between node i and node j; n is the total number of nodes in the system; A digital twin model is constructed based on the Jacobian matrix and integrated with a dynamic load model. The expression of the digital twin model is as follows: ； In the formula, , These represent the voltage phase angle increment and voltage magnitude increment at node i, respectively; It is a Jacobian matrix; , These represent the imbalance caused by changes in active and reactive power of the dynamic load, respectively, calculated from the dynamic load model; k is the iteration number, where k is an integer and k≥0; where... , These are the initial phase angle and initial voltage values ​​for node i, respectively; The node voltage magnitude and phase angle are updated as follows: ； Design test scenarios for normal operation, single equipment failure, and extreme weather conditions; determine test indicators for node voltage amplitude, voltage phase angle, voltage stability margin, and reactive power distribution; establish a vulnerability assessment model based on PSO-optimized AHP; and assess the vulnerability value of power system nodes based on the vulnerability assessment model.

2. The power system testing method according to claim 1, characterized in that, To determine whether the convergence condition is met, the expression for the convergence condition is as follows: ; If the conditional expression is true, the iteration ends, and the voltage magnitude and phase angle of each node are output; otherwise, the iteration returns to the digital twin model. In the formula, The convergence accuracy was preset and obtained by fitting based on historical big data.

3. The power system testing method according to claim 1, characterized in that, Methods for determining node voltage amplitude, voltage phase angle, voltage stability margin, and reactive power distribution test parameters include: Voltage stability margin is calculated using a singular value decomposition-based method. The node voltage magnitude, voltage phase angle, voltage stability margin based on singular value decomposition, and reactive power distribution are used as primary evaluation indicators, denoted as... Each primary evaluation indicator is further subdivided into several secondary evaluation indicators; Based on the multiple secondary indicators of each primary evaluation indicator, the overall evaluation indicator system is constructed as follows: In the formula, n1, n2, n3, and n4 are the total number of secondary indicators for each primary indicator.

4. The power system testing method according to claim 3, characterized in that, Treat each set of indicators as a particle, perform secondary indicator statistics on the overall evaluation indicator system, calculate the total number of secondary indicators using the formula N=n1+n2+n3+n4, and use it as the dimension N of each particle; Particle h can be represented as ;in, Let be the weight of the z-th secondary index in particle h, and satisfy . .

5. The power system testing method according to claim 4, characterized in that, Historical failure cases are obtained, and the Spearman correlation coefficient between the vulnerability ranking and the actual failure severity in the historical failure cases is used as the objective function f(X) to measure the degree of agreement between the assessment results and the actual situation. The expression for the objective function is: ; In the formula, m represents the number of historical failure cases; denoted as the difference between the vulnerability ranking obtained based on the assessment model and the actual failure severity ranking in the c-th case; The objective function is optimized based on the PSO algorithm. By iteratively optimizing the particle h, the velocity of particle h at the (t+1)th iteration is obtained using the update formula. and location The updated formula is as follows: ； ； In the formula, Inertial weights are used to balance global and local search capabilities; , For learning factors; , is a random number uniformly distributed in the interval [0,1]. Let h be the individual optimal position of particle h in the t-th iteration; This represents the globally optimal position of the entire particle swarm at the t-th iteration. The velocity and position of the particles are continuously updated iteratively until a preset stopping condition is met, at which point the globally optimal position is obtained. This refers to the optimal combination of index weights obtained through optimization; the preset stopping condition is reaching the maximum number of iterations or the convergence of the objective function value.

6. The power system testing method according to claim 5, characterized in that, For the combination of indicator weights, the vulnerability value CZ of each node is calculated using a weighted summation method, and the calculation formula is as follows: In the formula, Let be the weight of the node in the e-th secondary indicator; Let be the standardized value of the node under the e-th secondary index; Based on the vulnerability values ​​CZ of each node, the nodes are sorted in descending order to obtain the vulnerability ranking of each node under each failure case based on the evaluation model. The severity of actual faults was determined by inviting experts in the power system field to score historical fault cases, and then ranking the scores in descending order to obtain a ranking of the severity of actual faults.

7. A power system testing system, applied to the power system testing method according to any one of claims 1 to 6, characterized in that, Specifically, it includes: The data acquisition and processing module is used to determine the voltage stability vulnerability test targets and scope, collect and process multi-dimensional data of the power system, and build a digital twin model; The construction of the digital twin model includes using a power flow algorithm based on the improved Newton-Raphson method to perform power flow calculations, and combining it with a dynamic load model. The digital twin model is then trained using historical data to achieve convergence. The indicator determination and evaluation module is used to design test scenarios for normal operation, single equipment failure and extreme weather conditions, determine the test indicators of node voltage amplitude, voltage phase angle, voltage stability margin and reactive power distribution, establish a vulnerability assessment model based on PSO optimized AHP, and evaluate the vulnerability value of power system nodes based on the vulnerability assessment model.

Images