Power distribution network voltage stability margin evaluation method, system and equipment based on short circuit parameters and medium

Abstract

The invention discloses a power distribution network voltage stability margin assessment method, system and equipment based on short circuit parameters and a medium, and relates to the technical field of voltage stability margin assessment, and the method comprises the steps: obtaining the electrical quantity information of measurement points in a power distribution network, calculating the short circuit capacity of each measurement point based on the electrical quantity information, and carrying out the steady state processing of the short circuit capacity, obtaining a steady-state short-circuit capacity; constructing a voltage stability margin evaluation index according to the steady-state short-circuit capacity, the load power and the power factor, and introducing parameters reflecting the operation state and dynamic characteristics of the system for correction and optimization to obtain a final stability margin index of each measurement point; and determining the weight of each measurement point, and carrying out weighted fusion on the final stability margin index of each measurement point based on the weight to obtain the overall stability margin index of the system. According to the method, the accuracy and response speed of evaluation are improved, and the stability and credibility of the evaluation result can be kept in a scene with incomplete data or severe system operation fluctuation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of voltage stability margin evaluation, and particularly relates to a power distribution network voltage stability margin evaluation method based on short-circuit parameters. BACKGROUND

[0002] With the wide access of distributed energy and the diversified development of power load structure, modern power distribution networks are facing increasingly severe voltage stability challenges. Voltage stability margin, as an index for measuring the ability of a power system to maintain normal voltage level under load disturbance or abnormal operating conditions, has become an important basis for power grid operation monitoring and safety warning. Existing voltage stability margin evaluation methods mainly include two categories: one is the traditional model analysis method based on power flow calculation, such as active power-voltage relationship curve method, continuous power flow method and minimum singular value method, etc. This kind of method has complete theoretical basis, but it depends on the full network parameters, is complex in calculation, and is difficult to meet the real-time monitoring demand; the other is the equivalent modeling method based on local measurement, such as Thevenin equivalent circuit method and local measurement analysis method, which has certain real-time performance, but is strongly dependent on model accuracy and measurement accuracy, and is difficult to adapt to the common topology uncertainty and operating dynamicity problems in power distribution networks.

[0003] In addition, in recent years, some data-driven stability margin estimation methods have also appeared, which attempt to use historical operation data to train the model to quickly predict the system stability. However, this kind of method often has the problem of "black box" model, which is difficult to explain the evaluation results from the physical level, and has high requirements for data quality, and has limited applicability in actual engineering scenarios. Therefore, there is an urgent need for a voltage stability margin fast evaluation method which has physical interpretability, high calculation efficiency, and can adapt to the complex operating state of power distribution networks, so as to improve the system operation reliability and regulation response ability. SUMMARY

[0004] In view of the above problems, the present application is proposed.

[0005] Therefore, the problem to be solved by the present application is that the existing method often has the problem of "black box" model, which is difficult to explain the evaluation results from the physical level, and has high requirements for data quality, and has limited applicability in actual engineering scenarios.

[0006] To solve the above technical problems, the application provides the following technical scheme: a power distribution network voltage stability margin evaluation method based on short-circuit parameters, which comprises the following steps: obtaining electrical quantity information of a measurement point in a power distribution network, calculating short-circuit capacity of each measurement point based on the electrical quantity information, and performing steady-state processing on the short-circuit capacity to obtain steady-state short-circuit capacity; constructing a voltage stability margin evaluation index according to the steady-state short-circuit capacity of each measurement point and corresponding load power and power factor at the measurement point, and introducing parameters reflecting system operation state and dynamic characteristics for correction and optimization to obtain a final stability margin index of each measurement point; determining the weight of each measurement point in combination with electrical distance, voltage sensitivity to reactive power and measurement reliability, weighting and fusing the final stability margin index of each measurement point based on the weight to obtain a system overall stability margin index; and in the case of missing measurement data, compensating for the missing data through an information completion and estimation mechanism.

[0007] As a preferred scheme of the power distribution network voltage stability margin evaluation method based on short-circuit parameters, wherein: the calculation of the short-circuit capacity of each measurement point based on the electrical quantity information comprises the following steps: constructing a measurement variable set based on the electrical quantity information; and for the measurement variable set, an estimation strategy based on system response characteristics and network parameter characteristics is adopted to calculate the short-circuit capacity of each measurement point.

[0008] As a preferred scheme of the power distribution network voltage stability margin evaluation method based on short-circuit parameters, wherein: the steady-state processing of the short-circuit capacity to obtain the steady-state short-circuit capacity comprises the following steps: pre-processing the short-circuit capacity data, eliminating abnormal values in the calculation process by using noise suppression technology; based on the pre-processed data, sample smoothing processing is performed by using a sliding time window mechanism to generate intermediate estimates of the short-circuit capacity in a continuous time period; and based on the weight relationship between the intermediate estimates and the observation values, an exponential weighted average calculation is performed to obtain the steady-state short-circuit capacity estimation value at the current time.

[0009] The preferred technical scheme has the beneficial effect that by introducing noise suppression technology and sliding window smoothing mechanism, the measurement noise and data mutation influence in the short-circuit capacity estimation value are effectively eliminated, and the stability and continuity of the short-circuit capacity estimation value under dynamic operation conditions are improved.

[0010] As a preferred scheme of the power distribution network voltage stability margin evaluation method based on short-circuit parameters, wherein: the construction of the voltage stability margin evaluation index comprises the following steps: taking the steady-state short-circuit capacity of each measurement point and the corresponding load power and power factor at the measurement point as input parameters; based on the ratio relationship between the steady-state short-circuit capacity and the load power, an initial voltage stability margin evaluation index is constructed in combination with the power factor influence to reflect the static matching level between the node voltage support capability and the actual load; and the voltage stability margin evaluation index is standardized to a dimensionless form.

[0011] As a preferred scheme of the power distribution network voltage stability margin evaluation method based on short-circuit parameters, the final stability margin index of each measurement point comprises: based on the electrical structure characteristics at the measurement point, selecting a structural parameter for representing the local voltage support capability of the measurement point, and performing static correction on the voltage stability margin index; monitoring the time variation trend of the load of each measurement point, and performing dynamic correction on the voltage stability margin index according to the load variation rate; introducing a voltage deviation factor based on the deviation degree between the current voltage and the rated voltage, and compensating the voltage stability margin index after static correction and dynamic correction to finally form the final stability margin index of each measurement point.

[0012] The beneficial effect of the preferred technical scheme is that multiple dynamic factors related to system operation state are fused, the static margin index is optimized step by step, the finally constructed stability margin index has both static accuracy and dynamic adaptability, and can more truly reflect the node stability state.

[0013] As a preferred scheme of the power distribution network voltage stability margin evaluation method based on short-circuit parameters, the final stability margin index of each measurement point comprises: based on the electrical structure characteristics at the measurement point, selecting a structural parameter for representing the local voltage support capability of the measurement point, and performing static correction on the voltage stability margin index; monitoring the time variation trend of the load of each measurement point, and performing dynamic correction on the voltage stability margin index according to the load variation rate; introducing a voltage deviation factor based on the deviation degree between the current voltage and the rated voltage, and compensating the voltage stability margin index after static correction and dynamic correction to finally form the final stability margin index of each measurement point.

[0014] The beneficial effect of the preferred technical scheme is that the electrical distance, the voltage sensitivity to reactive power, the measurement reliability and other factors are introduced to weight the measurement points, the interference of single-point abnormality on the overall judgment is avoided, and the importance of the measurement points and the data quality are double-considered.

[0015] As a preferred scheme of the power distribution network voltage stability margin evaluation method based on short-circuit parameters, the final stability margin index of each measurement point comprises: based on the electrical structure characteristics at the measurement point, selecting a structural parameter for representing the local voltage support capability of the measurement point, and performing static correction on the voltage stability margin index; monitoring the time variation trend of the load of each measurement point, and performing dynamic correction on the voltage stability margin index according to the load variation rate; introducing a voltage deviation factor based on the deviation degree between the current voltage and the rated voltage, and compensating the voltage stability margin index after static correction and dynamic correction to finally form the final stability margin index of each measurement point.

[0016] To solve the above technical problems, the application provides the following technical scheme: a system of a power distribution network voltage stability margin evaluation method based on short-circuit parameters, comprising: a short-circuit capacity data acquisition module, a stability margin index construction module, a weighting module and a compensation module; the short-circuit capacity data acquisition module is used to acquire electrical quantity information of a measurement point in a power distribution network, calculate the short-circuit capacity of each measurement point based on the electrical quantity information, and perform steady-state processing on the short-circuit capacity to obtain a steady-state short-circuit capacity; the stability margin index construction module constructs a voltage stability margin evaluation index according to the steady-state short-circuit capacity of each measurement point and the corresponding load power and power factor at the measurement point, and introduces parameters reflecting the system operation state and dynamic characteristics for correction and optimization to obtain the final stability margin index of each measurement point; the weighting module is used to determine the weight of each measurement point in combination with the electrical distance, the sensitivity of voltage to reactive power and the measurement reliability, and perform weighted fusion on the final stability margin index of each measurement point based on the weight to obtain the overall system stability margin index; and the compensation module compensates for the missing data through an information completion and estimation mechanism in the case of missing measurement data.

[0017] A computer device comprises a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above-mentioned power distribution network voltage stability margin evaluation method based on short-circuit parameters when executing the computer program.

[0018] A computer readable storage medium stores a computer program, and the computer program implements the steps of the above-mentioned power distribution network voltage stability margin evaluation method based on short-circuit parameters when executed by a processor.

[0019] The application has the following beneficial effects: the application introduces a voltage stability margin evaluation mechanism based on short-circuit parameters, constructs a full-process method system from measurement point level steady-state short-circuit capacity extraction, node margin index construction and dynamic correction, multi-measurement point weighted fusion to missing data compensation and early warning, and has the advantages of simple calculation, clear physical meaning and strong real-time performance. Compared with the existing method, the application not only improves the accuracy and response speed of the evaluation, but also maintains the stability and reliability of the evaluation results in the case of incomplete data or severe system operation fluctuations, and is suitable for stable operation monitoring and early warning control of modern power distribution networks. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor.

[0021] Figure 1 A flow chart of a power distribution network voltage stability margin evaluation method based on short-circuit parameters in Example 1. DETAILED DESCRIPTION

[0022] In order to make the above objectives, features and advantages of the present application more apparent, specific embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0023] In the following description, a large number of specific details are set forth in order to facilitate a thorough understanding of the present application, but the present application can also be implemented in other ways different from those described herein, and those skilled in the art can make similar generalizations without departing from the spirit of the present application, therefore the present application is not limited to the specific embodiments disclosed below.

[0024] Example 1, refer to Figure 1 , the first embodiment of the present application, the embodiment provides a kind of power distribution network voltage stability margin evaluation method based on short-circuit parameters, including, as shown in Figure 1

[0025] S1: obtain the electrical quantity information of the measuring point in the power distribution network, calculate the short-circuit capacity of each measuring point based on the electrical quantity information, and perform steady-state processing on the short-circuit capacity to obtain the steady-state short-circuit capacity.

[0026] S2: according to the steady-state short-circuit capacity of each measuring point and the corresponding load power and power factor at the measuring point, construct a voltage stability margin evaluation index, and introduce a parameter reflecting the system operating state and dynamic characteristics for correction and optimization, to obtain the final stability margin index of each measuring point.

[0027] S3: determine the weight of each measuring point by combining the electrical distance, the sensitivity of voltage to reactive power and the measurement credibility, and perform weighted fusion on the final stability margin index of each measuring point based on the weight to obtain the overall system stability margin index.

[0028] S4: in the case of missing measurement data, compensate for the missing data through information completion and estimation mechanism.

[0029] ​It should be explained that in recent years, although data-driven based voltage stability evaluation methods have appeared, these methods need a large amount of historical data for training, and it is difficult to explain the physical meaning of the evaluation results, and the engineering application value is limited, the present application proposes a power distribution network voltage stability margin evaluation method based on short-circuit parameters, aiming to solve the following technical problems: (1) Establish a quantitative relationship between short-circuit parameters and voltage stability margin, realize fast voltage stability evaluation based on short-circuit capacity; (2) Design a stability margin grading standard suitable for the characteristics of the distribution network to support state classification and early warning; (3) Cooperatively use short-circuit parameter information of multiple measurement points to improve the reliability of the evaluation results; (4) In the case of incomplete or erroneous measurement data, ensure the accuracy of the evaluation results.

[0030] Therefore, in order to solve the above problems, through the steps of S1-S4, the short-circuit capacity of each key node is derived by real-time acquisition of electrical quantity information, and dynamic smoothing and steady-state extraction are performed, forming the basic parameters that can be used for evaluation. On this basis, the static margin index representing voltage stability is generated by combining the node load level and power factor and other operating characteristics. Subsequently, multi-dimensional factors reflecting the system structure characteristics and operating changes are introduced to dynamically correct the initial index, enhancing its sensitivity and adaptability. In view of the system requirement of evaluation, further combined with the importance and data reliability of each measurement point, a multi-point weighted fusion mechanism is constructed to output the comprehensive stability margin evaluation results at the global level. At the same time, in order to deal with the case of missing or incomplete measurement data, a compensation method based on neighborhood information and state estimation is designed to ensure the continuity and accuracy of the evaluation under complex working conditions.

[0031] Embodiment 2 is a second embodiment of the present application, which is different from the first embodiment in that: a power distribution network voltage stability margin evaluation method based on short-circuit parameters further comprises the following steps A1-A2 in step S1:

[0032] A1: Based on the electrical quantity information, a set of measurement variables is constructed.

[0033] A2: For the set of measurement variables, an estimation strategy based on system response characteristics and network parameter characteristics is used to calculate the short-circuit capacity of each measurement point.

[0034] Specifically, the electrical quantity information includes voltage amplitude, current amplitude and phase angle information.

[0035] In the present application embodiment, in step A2, the method for calculating the short-circuit capacity of each measurement point adopts the perturbation response method, including the following steps A211-A212:

[0036] A211: Introduce a small disturbance signal (e.g. short-time switching load or adjusting local voltage reference value) during system operation, and collect voltage amplitude, current amplitude and node impedance before and after the disturbance in real time;

[0037] A212: Calculate the apparent power change caused by the disturbance response, i.e. short-circuit capacity, which is expressed as,

[0038]

[0039] where S sc,i is the short-circuit capacity of measurement point i, V i is the voltage amplitude of measurement point i, ΔV i is the voltage change of measurement point i, ΔI i is the current change of measurement point i, and Z i is the measurement point impedance.

[0040] In an optional embodiment, the method for calculating the short-circuit capacity of each measurement point can also use impedance matrix mapping method, including the following steps A221-A223:

[0041] A221: Based on system model or online identification technology, construct the equivalent impedance matrix corresponding to the measurement node, and extract the diagonal element of measurement point i to represent its equivalent impedance;

[0042] A222: According to the voltage amplitude of the measurement point, calculate the short-circuit capacity formula expressed as,

[0043]

[0044] where Z ii is the diagonal element of the impedance matrix of measurement point i.

[0045] A223: Periodically update the impedance matrix data or perform real-time impedance correction to adapt to dynamic conditions such as topology changes and power fluctuations, to ensure the timeliness and accuracy of short-circuit capacity estimation.

[0046] In another optional embodiment, the method for calculating the short-circuit capacity of each measurement point can also use data-driven regression model method, including the following steps A231-A233:

[0047] A231: Collect system operation history data, construct input variable set (such as measurement point voltage amplitude, current amplitude, phase angle difference, node position, etc.) and corresponding short-circuit capacity label, form training sample set;

[0048] A232: Based on the training sample set, use support vector regression, multilayer feedforward neural network or other regression model to train the nonlinear function mapping, and get the estimation model after offline learning;

[0049] A233: In the online evaluation stage, the input feature vector measured in real time is sent into the estimation model, the node short-circuit capacity estimation value is output, and a confidence score can be introduced to assist in anomaly detection.

[0050] It should be noted that the ability to calculate the short-circuit capacity of each measuring point based on actual measurement data is achieved, avoiding the dependence on global topology model and static parameter configuration, and being highly adaptable; at the same time, multiple calculation paths such as the disturbance method and the impedance matrix method are supported, having certain flexibility and robustness under the premise of ensuring accuracy; key physical quantity support is provided for subsequent voltage stability margin evaluation, and the interpretability and physical meaning of the evaluation results are strengthened.

[0051] Further, in step S1, the steady-state processing of the short-circuit capacity to obtain the steady-state short-circuit capacity includes the following steps A3-A5:

[0052] A3: The short-circuit capacity data is preprocessed, and a noise suppression technique is used to eliminate abnormal values in the calculation process.

[0053] A4: Based on the preprocessed data, a sliding time window mechanism is used for sample smoothing processing to generate intermediate estimates of the short-circuit capacity in a continuous time period.

[0054] A5: Based on the weight relationship between the intermediate estimates and the observed values, an exponential weighted average calculation is performed to obtain the steady-state short-circuit capacity estimation value at the current time.

[0055] In the embodiments of the present application, in step A3, the noise suppression technique uses a multi-scale weighted filtering method, including the following steps A311-A313:

[0056] A311: The original short-circuit capacity time series is input into multiple filters of different frequency scales (such as low-pass, wavelet basis function, sub-band filter, etc.), and response signals under multiple frequency components are obtained respectively.

[0057] A312: Each frequency component is assigned a preset weight to emphasize the main frequency information and suppress high-frequency noise or low-frequency drift.

[0058] A313: The multi-scale signal after weighted fusion is output as the preprocessing result, serving as the basis data for subsequent sliding window steady-state processing.

[0059] In an optional embodiment, the noise suppression technique can also use a multi-scale weighted filtering method, including the following steps A321-A323:

[0060] A321: A sliding window median filter is used on the short-circuit capacity time series, replacing each window center point with the median value in the window to smooth local disturbances.

[0061] A322: Determine outliers by combining statistical indicators (such as mean ± 3 standard deviation range), replace or interpolate the points that deviate from the normal value range.

[0062] A323: Output the time series after removing outliers and completing median smoothing, for downstream steps to call.

[0063] In another optional implementation, the noise suppression technique can also use a multi-scale weighted filtering method, including the following steps A331-A333:

[0064] A331: Perform empirical mode decomposition on the short-circuit capacity sequence to extract several intrinsic mode functions, and decompose the original signal into multiple components with different frequency characteristics.

[0065] A332: Identify and remove intrinsic mode function components containing major high-frequency noise or low-frequency trend drift.

[0066] A333: Reconstruct the remaining components to superimpose a stationary signal, which is output as the de-noised short-circuit capacity data.

[0067] It should be noted that by introducing multi-scale filtering and sliding window smoothing mechanism, the influence of measurement noise and data mutation in short-circuit capacity estimation is effectively eliminated; the stability and continuity of short-circuit capacity estimation under dynamic operating conditions are improved, which helps to support the margin evaluation in real-time scenarios; the output steady-state short-circuit capacity can be used as high-quality input for subsequent index construction, fusion and prediction model, improving the system evaluation accuracy.

[0068] Further, in step S2, constructing the voltage stability margin evaluation index includes the following steps B1-B3:

[0069] B1: The steady-state short-circuit capacity of each measurement point and the corresponding load power and power factor at the measurement point are used as input parameters.

[0070] B2: Based on the ratio relationship between the steady-state short-circuit capacity and the load power, and combined with the influence of the power factor, an initial voltage stability margin evaluation index is constructed to reflect the static matching level between the node voltage support capability and the actual load.

[0071] B3: The voltage stability margin evaluation index is standardized to dimensionless form.

[0072] Specifically, the formula for constructing the initial voltage stability margin evaluation index is

[0073]

[0074] where VSI i is the initial voltage stability margin evaluation index of measurement point i, S iFor measuring the load power of the measuring point i, including active power and reactive power, cosφ i For measuring the power factor of the measuring point i.

[0075] Further, in step S2, obtaining the final stability margin index of each measuring point includes the following steps B4-B6:

[0076] B4: Based on the electrical structure characteristics at the measuring point, selecting a structural parameter for representing the local voltage support capability of the measuring point, and performing static correction on the voltage stability margin index.

[0077] B5: Monitoring the time-varying trend of the load of each measuring point, and performing dynamic correction on the voltage stability margin index according to the load change rate.

[0078] B6: Based on the deviation degree between the current voltage and the rated voltage, introducing a voltage deviation factor to compensate the voltage stability margin index after static correction and dynamic correction, and finally forming the final stability margin index of each measuring point.

[0079] In the embodiments of the present application, in step B4, the static correction adopts a correction method based on impedance ratio, including the following steps B411-B413:

[0080] B411: Obtain the equivalent impedance parameters at each measuring point, decompose them into reactance and resistance, and calculate the impedance ratio of the node.

[0081] B412: Set an empirical coefficient, and correct the initial voltage stability margin evaluation index VSI i by a correction factor to obtain the margin index after structural correction, which is expressed as,

[0082]

[0083] wherein VSI mod,i is the voltage margin index after static correction, β is the empirical coefficient, X th,i is the equivalent reactance of the measuring point i, R th,i is the equivalent resistance of the measuring point i.

[0084] B413: Taking VSI mod,i as the input of subsequent dynamic correction (such as load change rate, voltage deviation).

[0085] In an optional embodiment, the static correction can also adopt a correction method based on impedance modulus, including the following steps B421-B423:

[0086] B421: Obtain the equivalent impedance complex form of the measuring point, and calculate its modulus.

[0087] B422: Perform correction calculation according to the size of the modulus value to set the structure attenuation factor, which is expressed as,

[0088] VSI mod,i = VSI i · (1 - κ · |Z| )

[0089]

[0090] Wherein, |Z| is the modulus value, and κ is the structure attenuation factor.

[0091] B423: Output the corrected static margin index VSI mod,i for subsequent processing.

[0092] In another optional embodiment, the static correction can also adopt a correction method based on the voltage sensitivity factor, including the following steps B431-B433:

[0093] B431: Calculate the sensitivity of the measurement point voltage to the reactive power injection, which is expressed as,

[0094]

[0095] Wherein, ES i is the sensitivity of the voltage of measurement point i to the reactive power injection, indicating the degree of change of the voltage amplitude caused by unit reactive injection, Q i is the reactive power of measurement point i, is the partial derivative.

[0096] B432: Introduce the sensitivity value as a correction factor, and correct the initial margin index in the following way, which is expressed as,

[0097]

[0098] Wherein, λ is an empirical adjustment coefficient.

[0099] B433: Finally output the corrected margin index considering the local voltage regulation difficulty.

[0100] It should be noted that the multiple dynamic factors related to the system operating state (such as impedance structure, load change rate, voltage deviation) are fused to optimize the static margin index step by step; The final stability margin index constructed has both static accuracy and dynamic adaptability, and can more truly reflect the node stability state; It can improve the response capability of the evaluation model to abnormal working conditions such as power grid disturbance and load severe fluctuation, and realize a more practical margin evaluation framework.

[0101] Further, in step S3, obtaining the system overall stability margin index includes the following steps C1-C3:

[0102] C1: Determine the electrical distance between each measurement point and the system source point, and the sensitivity parameter of the measurement point voltage to the reactive power, and obtain the initial measurement point weight w according to the product of the two. i .

[0103] C2: According to the data fluctuation of each measurement point in the historical observation process, the measurement reliability is evaluated, the initial measurement point weight is corrected according to the measurement reliability, and the measurement point weight is formed.

[0104] C3: The voltage stability margin index of the whole system is obtained by weighted average of the measurement point weight and the corresponding measurement point.

[0105] In the embodiment of the application, in step C2, the initial measurement point weight is corrected according to the measurement reliability, and a measurement reliability correction method based on standard deviation is used, including steps C211-C213:

[0106] C211: The standard deviation of the historical data sequence of each measurement point is calculated to reflect the data fluctuation amplitude. C212: Set the maximum allowed standard deviation, calculate the reliability factor of the measurement point, and the formula is,

[0107]

[0108] Wherein, CI i is the reliability factor, σ i is the standard deviation of the historical data sequence of each measurement point i, σ max is the maximum allowed standard deviation, and min( ) is the minimum value in the parentheses.

[0109] C213: Multiply the initial electrical sensitivity weight w i and the reliability factor CI i to obtain the corrected composite weight and normalize it.

[0110] In an optional embodiment, the initial measurement point weight can also be corrected according to the measurement reliability using a correction method based on the data integrity rate, including steps C221-C223:

[0111] C221: Count the actual uploaded data of each measurement point in the target evaluation period, and compare it with the total data, calculate the data integrity ratio of the measurement point.

[0112] C221: Take the data integrity ratio as the basis for measuring the data reliability of the measurement point, and the higher the integrity, the more stable and reliable the measurement.

[0113] C221: The initial weight is corrected by using the ratio, so as to improve the weight contribution of long-term online and stable nodes, and weaken the influence of discontinuous nodes.

[0114] In another optional embodiment, the initial measurement point weight is also corrected according to the measurement reliability, which can also adopt a correction method based on the abnormal rate evaluation, including steps C231-C233:

[0115] C231: Detect and count the mutation, jump or data beyond the reasonable physical range of each measurement point in the historical data to obtain the proportion of abnormal data.

[0116] C232: According to the abnormal proportion, the reliability of the measurement point data is evaluated. The higher the abnormal proportion, the worse the data quality and the lower the reliability.

[0117] C233: The abnormal proportion result is used to adjust the weight distribution of the measurement point in the weighted fusion, so that the influence of the node with frequent data anomalies is weakened in the system stability margin evaluation.

[0118] It should be noted that the electrical distance, voltage against reactive power sensitivity, measurement reliability and other factors are introduced to weight the measurement points, so as to realize the double consideration of the importance of the measurement points and the data quality; effectively improve the representativeness and robustness of the system evaluation results, avoid the interference of single point anomaly on the overall judgment; support the distributed margin fusion and weak point identification in the multi-measurement point heterogeneous environment, which is conducive to supporting regional regulation and hierarchical decision-making.

[0119] Further, in step S4, compensating for the missing data includes steps D1-D2:

[0120] D1: When detecting missing measurement data, identify the adjacent measured nodes of the missing measurement point, and extract the adjacency information of the adjacent measured nodes based on the power grid topology relationship.

[0121] D2: Estimate the short-circuit capacity data of the missing measurement point by using the known short-circuit capacity data of the adjacent measured nodes through an interpolation mechanism.

[0122] Specifically, the formula for estimating the short-circuit capacity data of the missing measurement point is,

[0123]

[0124] wherein, is the short-circuit capacity estimation value of the measurement point i, N i is the adjacent measurement node set of the measurement point i, μ ij is the difference coefficient, d ij is the electrical distance between the measurement point i and the measurement point j, d ikFor measuring the electrical distance between the measurement point i and the measurement point k, k is an index.

[0125] For measurement error, a robust state estimation method can be further combined to perform weighted least square estimation based on mixed observation containing partial estimated value and original measurement value, to obtain a corrected electrical quantity estimation vector, and update the stability margin index based on the result.

[0126] Embodiment 3 is a third embodiment of the present application, which is different from the first two embodiments: a power distribution network voltage stability margin evaluation system based on short-circuit parameters, comprising a short-circuit capacity data acquisition module, a stability margin index construction module, a weighting module and a compensation module; the short-circuit capacity data acquisition module is used to acquire electrical quantity information of measurement points in the power distribution network, calculate the short-circuit capacity of each measurement point based on the electrical quantity information, and perform steady-state processing on the short-circuit capacity to obtain steady-state short-circuit capacity; the stability margin index construction module constructs a voltage stability margin evaluation index according to the steady-state short-circuit capacity of each measurement point and the corresponding load power and power factor at the measurement point, and introduces parameters reflecting the system operating state and dynamic characteristics for correction and optimization to obtain the final stability margin index of each measurement point; the weighting module is used to determine the weight of each measurement point by combining the electrical distance, the sensitivity of voltage to reactive power and the measurement credibility, and to perform weighted fusion on the final stability margin index of each measurement point based on the weight to obtain the overall system stability margin index; the compensation module compensates for the missing data through information completion and estimation mechanism in the case of missing measurement data.

[0127] If the functions are realized in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the parts that contribute to the prior art or parts of the technical solutions can be embodied in the form of software products. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various program code storage media.

[0128] The logic and / or steps represented in the flow diagrams or otherwise described herein, for example, can be considered as a sequence of executable instructions, and can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. In the context of this specification, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The computer-readable medium can be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or propagation medium.

[0129] More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection (electronic) having one or more wires, a portable computer diskette (magnetic), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can also be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for example via an optical scanner, then compiled, interpreted, or otherwise processed, and stored in a computer memory in a form that can be later executed by a computer. In some embodiments, the computer-readable medium can be non-transitory.

[0130] It should be understood that aspects of the application can be implemented in hardware, software, firmware or combinations thereof. In the above embodiments, various steps or methods can be implemented in software or firmware that is stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any of the following technologies, which are known in the art, can be used: a combination of discrete logic circuits having logic gates for implementing logic functions upon data signals, application specific integrated circuits having logic gates, field programmable gate arrays (FPGAs), programmable logic arrays (PLAs), and / or the like.

[0131] It should be noted that the above-mentioned embodiments are merely used to illustrate the technical solutions of the present application but not limit the present application, and although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalent replaced without departing from the spirit and scope of the present application, and all of them should be covered in the scope of the claims of the present application.

Claims

1. A method for evaluating the voltage stability margin of a distribution network based on short-circuit parameters, characterized in that: include, Obtain electrical quantity information of measurement points in the distribution network, calculate the short-circuit capacity of each measurement point based on the electrical quantity information, and perform steady-state processing on the short-circuit capacity to obtain the steady-state short-circuit capacity; Based on the steady-state short-circuit capacity of each measurement point and the corresponding load power and power factor at the measurement point, a voltage stability margin evaluation index is constructed, and parameters reflecting the system operating status and dynamic characteristics are introduced for correction and optimization to obtain the final stability margin index of each measurement point. By combining the sensitivity of electrical distance and voltage to reactive power and the reliability of measurement, the weight of each measurement point is determined. Based on the weight, the final stability margin index of each measurement point is weighted and fused to obtain the overall stability margin index of the system. In the event of missing measurement data, information completion and estimation mechanisms are used to compensate for the missing data.

2. The distribution network voltage stability margin assessment method based on short-circuit parameters as described in claim 1, characterized in that: The calculation of the short-circuit capacity at each measurement point based on the electrical quantity information includes: Based on electrical quantity information, a set of measurement variables is constructed; For the set of measurement variables, an estimation strategy based on system response characteristics and network parameter features is adopted to estimate the short-circuit capacity of each measurement point.

3. The distribution network voltage stability margin assessment method based on short-circuit parameters as described in claim 2, characterized in that: The short-circuit capacity is subjected to steady-state processing to obtain the steady-state short-circuit capacity, which includes: The short-circuit capacity data is preprocessed, and noise suppression techniques are used to eliminate outliers in the calculation process. Based on the preprocessed data, a sliding time window mechanism is used to smooth the samples and generate intermediate estimates of short-circuit capacity over continuous time periods. Based on the weighted relationship between the intermediate estimate and the observed value, an exponentially weighted average calculation is performed to obtain the steady-state short-circuit capacity estimate at the current moment.

4. The distribution network voltage stability margin assessment method based on short-circuit parameters as described in claim 3, characterized in that: The voltage stability margin evaluation metrics include: The steady-state short-circuit capacity at each measurement point, as well as the corresponding load power and power factor at the measurement point, are used as input parameters. Based on the ratio between the steady-state short-circuit capacity and the load power, and combined with the influence of the power factor, an initial voltage stability margin evaluation index is constructed to reflect the static matching level between the node voltage support capacity and the actual load. The voltage stability margin assessment index is standardized into a dimensionless form.

5. The distribution network voltage stability margin assessment method based on short-circuit parameters as described in claim 4, characterized in that: The final stability margin index for each measurement point includes, Based on the electrical structure characteristics at the measurement point, structural parameters are selected to characterize the local voltage support capability at the measurement point, and the voltage stability margin index is statically corrected. Monitor the time-varying trend of load at each measurement point and dynamically correct the voltage stability margin index based on the load change rate; A voltage deviation factor is introduced based on the degree of deviation between the current voltage and the rated voltage to compensate for the voltage stability margin index after static and dynamic correction, and finally form the final stability margin index for each measurement point.

6. The distribution network voltage stability margin assessment method based on short-circuit parameters as described in claim 5, characterized in that: The obtained overall system stability margin index includes, The electrical distance between each measurement point and the system source point, as well as the sensitivity parameter of the voltage at each measurement point to reactive power, are determined respectively. The initial measurement point weights are obtained by multiplying the two. Based on the data fluctuations of each measurement point during the historical observation process, the measurement reliability is assessed, and the initial measurement point weights are corrected according to the measurement reliability to form measurement point weights; The overall voltage stability margin index of the system is obtained by weighting each measurement point with its corresponding final stability margin index.

7. The distribution network voltage stability margin assessment method based on short-circuit parameters as described in claim 6, characterized in that: The compensation for missing data includes... When missing measurement data is detected, the adjacent measured nodes of the missing measurement point are identified, and the adjacency information of the adjacent measured nodes is extracted based on the power grid topology. By using an interpolation mechanism, the short-circuit capacity data of the missing measurement points is estimated using the known short-circuit capacity data of adjacent measured nodes.

8. A distribution network voltage stability margin assessment system based on short-circuit parameters, employing the distribution network voltage stability margin assessment method based on short-circuit parameters as described in any one of claims 1 to 7, characterized in that: It includes a short-circuit capacity data acquisition module, a stability margin index construction module, a weighting module, and a compensation module; The short-circuit capacity data acquisition module is used to acquire electrical quantity information of measurement points in the distribution network, calculate the short-circuit capacity of each measurement point based on the electrical quantity information, and perform steady-state processing on the short-circuit capacity to obtain the steady-state short-circuit capacity. The stability margin index construction module constructs a voltage stability margin evaluation index based on the steady-state short-circuit capacity of each measurement point and the corresponding load power and power factor at the measurement point. It also introduces parameters that reflect the system operating status and dynamic characteristics for correction and optimization to obtain the final stability margin index for each measurement point. The weighting module is used to combine the sensitivity of electrical distance and voltage to reactive power and the measurement reliability to determine the weight of each measurement point, and to perform weighted fusion of the final stability margin index of each measurement point based on the weight to obtain the overall stability margin index of the system. The compensation module compensates for missing measurement data through an information completion and estimation mechanism.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the distribution network voltage stability margin assessment method based on short-circuit parameters as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the distribution network voltage stability margin assessment method based on short-circuit parameters as described in any one of claims 1 to 7.

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