Method and system for power grid voltage stability analysis and control based on short circuit capacity

Abstract

The application discloses a method and system for power grid voltage stability analysis and control based on short-circuit capacity, relates to the technical field of power grid voltage stability analysis, and comprises the following steps: collecting power grid operation data in real time and preprocessing the data to obtain preprocessed data; performing dynamic evaluation on the preprocessed data in combination with multi-time scale analysis to predict the short-circuit capacity of the power grid in real time; establishing a correlation model between the short-circuit capacity and voltage stability to analyze the voltage stability performance of the power grid; monitoring and giving early warning in real time, and controlling according to the early warning. The application effectively improves the control precision and response speed of the voltage stability of the power grid, optimizes the operation efficiency and safety of the power grid, further enhances the adaptability of the system to the state change of the power grid, and significantly improves the quality of power grid management and stability analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid voltage stability, and in particular to a method and system for analyzing and controlling power grid voltage stability based on short-circuit capacity. Background Art

[0002] In the management and control of power systems, grid voltage stability has always been a core issue in maintaining stable operation. With the liberalization of power markets and the widespread integration of renewable energy, grid structures have become increasingly complex, posing new challenges to traditional voltage stability analysis and control methods. Historically, voltage stability issues have been primarily addressed by adjusting generator excitation systems and transmission line capacity. However, these approaches often fail to fully adapt to rapidly changing grid conditions, particularly when faced with large-scale distributed generation systems and fluctuating load demands.

[0003] Existing technologies typically rely on static analytical models when dealing with grid voltage stability, and these models are difficult to reflect the actual operating status of the grid in real time. For example, many models fail to fully consider the dynamic changes in the grid at multiple time scales, such as short-term load mutations, medium-term seasonal load fluctuations, and long-term load growth trends. In addition, the short-circuit capacity calculation methods commonly used in existing technologies are usually based on outdated data or assumptions and cannot effectively predict future network conditions, which may cause the grid to operate in a non-optimal or dangerous state. In the actual application of existing technologies, grid operators face the problems of slow response speed and rough control strategies, which limit their ability to take effective measures in the face of emergencies.

[0004] To address the shortcomings of existing technologies, the present invention proposes a new method for analyzing and controlling grid voltage stability based on short-circuit capacity. This method collects and preprocesses grid operation data in real time, combines dynamic evaluation with multi-time-scale analysis, and predicts the grid's short-circuit capacity in real time, thereby achieving precise control of grid voltage stability. By establishing a correlation model between short-circuit capacity and voltage stability, the present invention not only provides more accurate voltage stability predictions, but also enables real-time monitoring and early warning based on actual grid conditions, allowing for timely adjustment of control strategies to address potential voltage instability. Summary of the Invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the problem to be solved by the present invention is: how to solve the problems of slow response speed and rough control strategy in the existing methods.

[0007] To solve the above technical problems, the present invention provides the following technical solutions: a method for analyzing and controlling power grid voltage stability based on short-circuit capacity, comprising: real-time collection of power grid operation data and preprocessing to obtain preprocessed data; dynamic evaluation of the preprocessed data combined with multi-time scale analysis to predict the short-circuit capacity of the power grid in real time; establishment of a correlation model between short-circuit capacity and voltage stability to analyze the power grid voltage stability performance; real-time monitoring and early warning, and control based on the early warning.

[0008] As a preferred solution of the method for analyzing and controlling grid voltage stability based on short-circuit capacity described in the present invention, the grid operation data includes voltage data, current data, impedance data and power data; and the preprocessing includes data cleaning and data synchronization.

[0009] As a preferred solution of the method for analyzing and controlling power grid voltage stability based on short-circuit capacity according to the present invention, the predicted short-circuit capacity of the power grid includes calculating the short-circuit capacity at time t based on the electrical parameters of the power grid, which is expressed as:

[0010]

[0011] Where SCC(t) is the short-circuit capacity at time t, V(t) is the voltage at time t, I(t) is the short-circuit current at time t, and Z(t) is the impedance of the line. The short-circuit capacity of the power grid is predicted using multi-time scale analysis, which is expressed as:

[0012]

[0013] in, is the predicted short-circuit capacity value, α is the short-term weight coefficient, SCC short (t) is the short-term short-circuit capacity forecast value, β is the medium-term weight coefficient, SCC medium (t) is the medium-term short-circuit capacity prediction value, γ is the long-term weight coefficient, SCC long (t) is the predicted value of long-term short-circuit capacity.

[0014] As a preferred solution of the method for analyzing and controlling power grid voltage stability based on short-circuit capacity according to the present invention, the short-term short-circuit capacity prediction value includes using an autoregressive model to predict the instantaneous change of each parameter, which is incorporated into the calculation of the short-circuit capacity and expressed as:

[0015]

[0016] Among them, i is the i-th time point, p is the number of historical data points, that is, the total number of time points, α i is the voltage coefficient of the i-th historical data point, V(ti) is the voltage value at time ti, β iis the current coefficient of the i-th historical data point, I(ti) is the short-circuit current value at time ti, γ i is the impedance coefficient of the i-th historical data point, Z(ti) is the impedance value at time ti; the medium-term short-circuit capacity prediction value includes smoothing the trend and periodicity in the data, and is expressed as:

[0017]

[0018] Wherein, ETS(V,t) is the exponentially smoothed predicted value of voltage V at time t, ETS(I,t) is the exponentially smoothed predicted value of current I at time t, and ETS(Z,t) is the exponentially smoothed predicted value of impedance Z at time t. The long-term short-circuit capacity prediction value includes the use of seasonal adjustment and trend decomposition methods, and is expressed as:

[0019]

[0020] Among them, Trend(V,t) is the trend component of voltage, Trend(I,t) is the trend component of current, and Trend(Z,t) is the trend component of impedance.

[0021] As a preferred solution of the method for analyzing and controlling power grid voltage stability based on short-circuit capacity according to the present invention, the correlation model between short-circuit capacity and voltage stability includes constructing an exponential linear mixed function and a logarithmic exponential mixed function; the exponential linear mixed function is expressed as:

[0022]

[0023] in, is an exponential linear mixing function, a is an exponential linear mixing function adjustment coefficient, and P(t) is the total power of the power grid at time t. The logarithmic exponential mixing function is expressed as:

[0024] g(V load (t),Z(t))=log(V load (t)+1)·e b·Z(t)

[0025] Among them, g(V load (t), Z(t)) is a log-exponential mixing function, V load (t) is the load voltage, and b is the log-exponential mixing function adjustment coefficient.

[0026] As a preferred solution of the method for analyzing and controlling power grid voltage stability based on short-circuit capacity according to the present invention, the correlation model between the short-circuit capacity and voltage stability is expressed as follows:

[0027]

[0028] Where VS(t) is the voltage stability index at time t, and λ is the regulation coefficient.

[0029] As a preferred embodiment of the method for analyzing and controlling power grid voltage stability based on short-circuit capacity according to the present invention, the early warning includes setting a high threshold value θ1 of the voltage stability index, a medium threshold value θ2 of the voltage stability index, and a low threshold value θ3 of the voltage stability index. If VS(t) is greater than or equal to the high threshold value θ1 of the voltage stability index, no alarm is issued, normal monitoring is performed, and existing operations are maintained. If VS(t) is greater than or equal to the medium threshold value θ2 of the voltage stability index and less than the high threshold value θ1 of the voltage stability index, a yellow alarm is issued, the monitoring frequency of the power grid is increased, load forecasts and power grid configuration are reviewed, and adjustments are prepared. If the voltage continues to decline, primary response measures are initiated. If VS(t) is greater than or equal to the low threshold value θ3 of the voltage stability index and less than the medium threshold value θ2 of the voltage stability index, an orange alarm is issued, capacitors or FACTS equipment are activated, backup generators are dispatched, or fast-response energy storage systems are activated, and minor load reduction or emergency load transfer plans are considered. If VS(t) is less than or equal to the low threshold value θ3 of the voltage stability index, a red alarm is issued, extensive load reduction is implemented, and some non-critical loads are disconnected.

[0030] Another object of the present invention is to provide a system for a method of analyzing and controlling grid voltage stability based on short-circuit capacity, which can solve the problem of analyzing and controlling grid voltage stability based on short-circuit capacity by constructing a grid voltage stability analysis and control system.

[0031] To solve the above technical problems, the present invention provides the following technical solutions: a system for analyzing and controlling power grid voltage stability based on short-circuit capacity, comprising a data acquisition module, a short-circuit capacity prediction module, a voltage stability performance analysis module and an alarm module; the data acquisition module is used to collect power grid operation data in real time and perform preprocessing to obtain preprocessed data; the short-circuit capacity prediction module is used to dynamically evaluate the preprocessed data and combine it with multi-time scale analysis to predict the short-circuit capacity of the power grid in real time; the voltage stability performance analysis module is used to establish a correlation model between short-circuit capacity and voltage stability and analyze the power grid voltage stability performance; the alarm module is used to monitor and issue early warnings in real time, and to perform control based on the early warnings.

[0032] A computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the method for analyzing and controlling power grid voltage stability based on short-circuit capacity are implemented.

[0033] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for analyzing and controlling power grid voltage stability based on short-circuit capacity as described above.

[0034] The beneficial effects of the present invention are as follows: the method for analyzing and controlling power grid voltage stability based on short-circuit capacity provided by the present invention effectively improves the control accuracy and response speed of power grid voltage stability, optimizes the operating efficiency and safety of the power grid, enhances the system's adaptability to changes in power grid status, and significantly improves the quality of power grid management and stability analysis by real-time acquisition and preprocessing of power grid data, combining multi-time scale analysis for dynamic evaluation, constructing a correlation model between short-circuit capacity and voltage stability, and implementing a real-time monitoring and early warning system. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0036] Figure 1 This is a flow chart of a method for analyzing and controlling grid voltage stability based on short-circuit capacity provided in the first embodiment of the present invention.

[0037] Figure 2 This is a structural diagram of a system for analyzing and controlling grid voltage stability based on short-circuit capacity according to a second embodiment of the present invention. DETAILED DESCRIPTION

[0038] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0039] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0040] Example 1, reference Figure 1For the first embodiment of the present application, the embodiment provides a method for power grid voltage stability analysis and control based on short-circuit capacity, comprising: collecting power grid operation data in real time and preprocessing to obtain preprocessed data; performing dynamic evaluation on the preprocessed data in combination with multi-time scale analysis to predict the short-circuit capacity of the power grid in real time; establishing a correlation model between the short-circuit capacity and the voltage stability to analyze the voltage stability performance of the power grid; monitoring and warning in real time, and controlling according to the warning.

[0041] S1, collecting power grid operation data in real time and preprocessing to obtain preprocessed data.

[0042] The power grid operation data includes voltage data, current data, impedance data and power data, etc.

[0043] The voltage data includes the voltage values of each node and the voltage data of specific load points, and the data comes from the monitoring system of the transformer substation and the distribution network.

[0044] The current data involves real-time measurement of line current, which is obtained through CT (current transformer).

[0045] The impedance data includes line impedance, transformer impedance, etc., which is calculated based on the electrical characteristics and operating conditions of the system.

[0046] The power data includes total power and power quality data of the power grid.

[0047] The preprocessing includes data cleaning and data synchronization.

[0048] S2, performing dynamic evaluation on the preprocessed data in combination with multi-time scale analysis to predict the short-circuit capacity of the power grid in real time.

[0049] The short-circuit capacity at time t is calculated based on the basic electrical parameters of the power grid, which is represented as:

[0050]

[0051] Wherein, SCC(t) is the short-circuit capacity at time t, V(t) is the voltage at time t, I(t) is the short-circuit current at time t, and Z(t) is the impedance of the line.

[0052] Time series analysis is applied to each parameter (voltage, current, impedance) to identify its trend and periodic changes over time.

[0053] The multi-time scale analysis is used to predict the short-circuit capacity of the power grid to improve the accuracy of the prediction, which is represented as:

[0054]

[0055] Wherein, is the predicted short-circuit capacity value, α is the short-term weight coefficient, SCC short (t) is the short-term short-circuit capacity forecast value, β is the medium-term weight coefficient, SCC medium (t) is the medium-term short-circuit capacity prediction value, γ is the long-term weight coefficient, SCC long (t) is the predicted long-term short-circuit capacity. Weight coefficients are used to adjust the influence of prediction results at different time scales. These coefficients are usually obtained through data analysis and optimization algorithms to minimize prediction errors and ensure the accuracy and practicality of the model.

[0056] For short-term prediction, the focus is on the immediate changes of the power grid or changes within a few hours. The autoregressive model (AR) is used to predict the immediate changes of each parameter and combined with the calculation of the short-circuit capacity, which is expressed as:

[0057]

[0058] Among them, i is the i-th time point, p is the model order, which means the number of historical data points considered in the autoregressive model, i.e. the total number of time points, α i is the voltage coefficient of the i-th historical data point, V(ti) is the voltage value at time ti, β i is the current coefficient of the i-th historical data point, I(ti) is the short-circuit current value at time ti, γ i is the impedance coefficient of the i-th historical data point, Z(ti) is the impedance value at time ti; α i , β i , γ i It is the coefficient in the autoregressive model, which is used to describe the weight of the data of the first p time points of voltage V, current I and impedance Z in the short-term prediction model.

[0059] Medium-term forecasts should consider data from a few days to a week. Exponential smoothing models can better predict changes on this time scale, smoothing out trends and periodicity in the data and providing robust forecasts, expressed as:

[0060]

[0061] ETS(x,t)=μx(t)+(1-μ)ETS(x,t-1)

[0062] Where EST(V,t) is the exponentially smoothed predicted value of voltage V at time t, ETS(I,t) is the exponentially smoothed predicted value of current I at time t, and ETS(Z,t) is the exponentially smoothed predicted value of impedance Z at time t; ETS(x,t) is the predicted value of the exponential smoothing model, where x can be voltage V, current I, or impedance Z, and t is the predicted time point. μ is a smoothing parameter between 0 and 1, which is used to adjust the degree of influence of historical data on the prediction. x(t) is the parameter value at time t, and ETS(x,t-1) is the predicted value of the exponential smoothing model at time t-1.

[0063] Long-term forecasts focus on monthly or annual trends and are suitable for seasonal adjustment and trend decomposition methods, such as seasonal trend decomposition time series analysis (STL). This method can decompose and predict trend, seasonality, and residual components, expressed as:

[0064]

[0065] Wherein, Trend(V, t) is the trend component of voltage, Treand(I, t) is the trend component of current, and Trend(Z, t) is the trend component of impedance.

[0066] Assume that there is time series data of a power grid parameter x (which can be voltage, current, or impedance), x(t) represents the measured value at time t, and a polynomial degree n is selected to fit a polynomial trend line based on the historical data, which can be expressed as:

[0067] Trend(x, t) = a0 + a1t + a2t 2 +..+a n t n

[0068] Among them, a0, a1, a2, ..., a n are the polynomial coefficients, estimated from historical data using statistical methods such as the least squares method. Trend(x, t) is the trend component of parameter x in long-term trend analysis, and the calculation time is t.

[0069] Polynomial regression analysis of voltage V(t), current I(t) and impedance Z(t) is performed as follows:

[0070] Trend(V, t)=a0+a1t+a2t 2 +...+a m t m

[0071] Trend(I, t) = b0 + b1t + b2t 2 +...+b p t p

[0072] Trend(Z, t) = c0 + c1t + c2t 2 +...+c q t q

[0073] Among them, a0, a1, a2, ..., a m are the polynomial coefficients of voltage, b0, b1, b2, ..., b p are the polynomial coefficients of current, c0, c1, c2, ..., c q are the polynomial coefficients of impedance.

[0074] S3. Establish a correlation model between short-circuit capacity and voltage stability, and analyze the voltage stability performance of the power grid.

[0075] The correlation model between short-circuit capacity and voltage stability includes constructing exponential linear mixing function and logarithmic exponential mixing function.

[0076] The exponential linear hybrid function combines exponential and linear terms. Its basic concept comes from the direct relationship between the current carrying capacity and system stability in the power system. A high short-circuit capacity usually means that the system can better handle large currents and thus maintain stability. The exponential function is used to emphasize the strong nonlinear impact of changes in short-circuit capacity on system stability. This nonlinear response is simulated by the exponential function, enabling the model to demonstrate significant improvements in voltage stability when short-circuit capacity increases significantly. The linear term is directly multiplied by the power factor, reflecting the impact of the total grid power on stability. The total power demand of the system is a core parameter in voltage stability analysis.

[0077] The exponential linear mixing function is expressed as:

[0078]

[0079] in, is the exponential linear mixing function, a is the exponential linear mixing function adjustment coefficient, and P(t) is the total power of the grid at time t.

[0080] The log-exponential hybrid function combines logarithmic and exponential functions to describe how load voltage and line impedance affect voltage stability. load (t)+1) is used to describe the sensitivity of the system when the voltage is low. In power system stability analysis, low voltage often indicates potential instability. The logarithmic function provides a way to increase the model response when the voltage is low, so that when the voltage is close to a lower level, the model is more sensitive to the voltage. b·Z(t)Used to emphasize the impact of changes in line impedance on voltage stability. In power systems, high impedance may cause large voltage drops, which in turn affects system stability. Through the exponential form, the model can give greater weight to increases in impedance, especially when the impedance changes significantly.

[0081] The log-exponential mixing function is expressed as:

[0082] g(V load (t),Z(t))=log(V load (t)+1)·e b·Z(t)

[0083] Among them, g(V load (t), Z(t)) is a log-exponential mixing function, V load (t) is the load voltage, and b is the log-exponential mixing function adjustment coefficient.

[0084] The design and improvement of the function are based on traditional power system stability theory. For example, voltage stability is closely related to the grid's short-circuit capacity, load voltage, total system power, and line impedance. By using logarithmic and exponential functions, the actual impact of these factors on system stability can be more accurately simulated. While maintaining the theoretical foundation, the introduction of nonlinear expressions enhances the model's practical application value and predictive accuracy.

[0085] The correlation model between short-circuit capacity and voltage stability is expressed as:

[0086]

[0087] Where VS(t) is the voltage stability index at time t, and λ is the adjustment coefficient, which adjusts the impact of short-circuit capacity and load characteristics on voltage stability.

[0088] S4. Real-time monitoring and early warning, and control based on the early warning.

[0089] The early warning includes setting a high threshold value θ1 of the voltage stability index, a medium threshold value θ2 of the voltage stability index, and a low threshold value θ3 of the voltage stability index. If VS(t) is greater than or equal to the high threshold value θ1 of the voltage stability index, no alarm is issued, normal monitoring is carried out, and existing operations are maintained. If VS(t) is greater than or equal to the medium threshold value θ2 of the voltage stability index and less than the high threshold value θ1 of the voltage stability index, a yellow alarm is issued, the monitoring frequency of the power grid is increased, the load forecast and power grid configuration are reviewed, and adjustments are prepared. If the decline continues, primary response measures are initiated. If VS(t) is greater than or equal to the low threshold value θ3 of the voltage stability index and less than the medium threshold value θ2 of the voltage stability index, an orange alarm is issued, capacitors or FACTS equipment are activated to improve the power factor of the line and reduce system losses, backup generators are dispatched or fast-response energy storage systems are activated, minor load reduction is considered, or emergency load transfer plans are initiated. If VS(t) is less than or equal to the low threshold value θ3 of the voltage stability index, a red alarm is issued, extensive load reduction is implemented, and some non-critical loads are disconnected. Local or extensive power outages may be required to prevent system collapse.

[0090] Example 2, reference Figure 2 , which is the second embodiment of the present invention, is different from the previous embodiment in that it provides a system for analyzing and controlling power grid voltage stability based on short-circuit capacity, including: a data acquisition module, a short-circuit capacity prediction module, a voltage stability performance analysis module and an alarm module.

[0091] The data acquisition module is used to collect power grid operation data in real time and perform preprocessing to obtain preprocessed data.

[0092] The short-circuit capacity prediction module is used to dynamically evaluate the pre-processed data and combine it with multi-time scale analysis to predict the short-circuit capacity of the power grid in real time.

[0093] The voltage stability performance analysis module is used to establish a correlation model between short-circuit capacity and voltage stability and analyze the voltage stability performance of the power grid.

[0094] The alarm module is used for real-time monitoring and early warning, and for control based on the early warning.

[0095] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0096] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0097] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0098] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0099] Example 3 is the third example of the present invention. It is different from the first two examples in that it is used to verify the technical effects adopted in the present invention in order to verify the real effects of this method.

[0100] As shown in Table 1, it is a comparison table of the traditional method and the method of the present invention.

[0101] Table 1 Comparison table between traditional method and method of the present invention

[0102]

[0103]

[0104] Simulations were performed simultaneously using the traditional method and the method of the present invention.

[0105] This embodiment uses the traditional method and the method of the present invention to simultaneously perform detection, and the detection comparison results are shown in Table 2 below:

[0106] Table 2 Simulation data comparison table

[0107]

[0108] For load mutation:

[0109] When the load changes suddenly, the traditional method has low voltage stability, long response time and low warning accuracy.

[0110] The method of the present invention can maintain high voltage stability when the load changes suddenly, significantly shorten the response time, and greatly improve the early warning accuracy.

[0111] For seasonal load fluctuations:

[0112] Traditional methods have moderate voltage stability, long response time and low early warning accuracy during seasonal load fluctuations.

[0113] The method of the present invention has high voltage stability, short response time and high early warning accuracy when seasonal load fluctuates.

[0114] For long-term load growth:

[0115] Traditional methods have moderate voltage stability, long response time and low early warning accuracy when the load increases over a long period of time.

[0116] The method of the present invention has high voltage stability, short response time and high early warning accuracy when the load increases over a long period of time.

[0117] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention 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 invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for analyzing and controlling grid voltage stability based on short-circuit capacity, characterized by: include, Collect power grid operation data in real time and pre-process it to obtain pre-processed data; Dynamically evaluate the pre-processed data and combine it with multi-time scale analysis to predict the short-circuit capacity of the power grid in real time; Establish a correlation model between short-circuit capacity and voltage stability to analyze the voltage stability performance of the power grid; Real-time monitoring and early warning, and control based on the early warning; The correlation model between the short-circuit capacity and voltage stability includes constructing an exponential linear mixing function and a logarithmic exponential mixing function; The exponential linear mixing function is expressed as, in, is the exponential linear mixing function, a is the exponential linear mixing function adjustment coefficient, P(t) is the total power of the grid at time t; The log-exponential mixing function is expressed as, g(V load (t),Z(t))=log(V load (t)+1)·e b·Z(t) Among them, g(V load (t), Z(t)) is a log-exponential mixing function, V load (t) is the load voltage, b is the log-exponential mixing function adjustment coefficient; The correlation model between short-circuit capacity and voltage stability is expressed as: Where VS(t) is the voltage stability index at time t, and λ is the regulation coefficient.

2. The method for analyzing and controlling power grid voltage stability based on short-circuit capacity according to claim 1, wherein: The grid operation data includes voltage data, current data, impedance data and power data; The preprocessing includes data cleaning and data synchronization.

3. The method for analyzing and controlling power grid voltage stability based on short-circuit capacity according to claim 2, wherein: The predicted short-circuit capacity of the power grid includes calculating the short-circuit capacity at time t based on the electrical parameters of the power grid, which is expressed as: Where SCC(t) is the short-circuit capacity at time t, V(t) is the voltage at time t, I(t) is the short-circuit current at time t, and Z(t) is the impedance of the line. The short-circuit capacity of the power grid is predicted using multi-time scale analysis, expressed as, in, is the predicted short-circuit capacity value, α is the short-term weight coefficient, SCC short (t) is the short-term short-circuit capacity prediction value, β is the medium-term weight coefficient, SCC medium (t) is the medium-term short-circuit capacity prediction value, γ is the long-term weight coefficient, SCC long (t) is the predicted value of long-term short-circuit capacity.

4. The method for analyzing and controlling power grid voltage stability based on short-circuit capacity according to claim 3, wherein: The short-term short-circuit capacity prediction value includes using the autoregressive model to predict the instantaneous change of each parameter and combining it into the calculation of the short-circuit capacity, which is expressed as: Among them, i is the i-th time point, p is the number of historical data points, that is, the total number of time points, α i is the voltage coefficient of the i-th historical data point, V(ti) is the voltage value at time ti, β i is the current coefficient of the i-th historical data point, I(ti) is the short-circuit current value at time ti, γ i is the impedance coefficient of the i-th historical data point, and Z(ti) is the impedance value at time ti; The medium-term short-circuit capacity forecast includes smoothing the trend and periodicity in the data and is expressed as, Where, ETS(V,t) is the exponentially smoothed predicted value of voltage V at time t, ETS(I,t) is the exponentially smoothed predicted value of current I at time t, and ETS(Z,t) is the exponentially smoothed predicted value of impedance Z at time t. The long-term short-circuit capacity forecast value includes a method using seasonal adjustment and trend decomposition, which is expressed as: Among them, Trend(V,t) is the trend component of voltage, Trend(I,t) is the trend component of current, and Trend(Z,t) is the trend component of impedance.

5. The method for analyzing and controlling power grid voltage stability based on short-circuit capacity according to claim 4, wherein: The early warning includes setting a high threshold value θ1 of the voltage stability index, a medium threshold value θ2 of the voltage stability index and a low threshold value θ3 of the voltage stability index. If VS(t) is greater than or equal to the high threshold value θ1 of the voltage stability index, no alarm is issued, normal monitoring is carried out, and existing operations are maintained. If VS(t) is greater than or equal to the medium threshold value θ2 of the voltage stability index and less than the high threshold value θ1 of the voltage stability index, a yellow alarm is issued, the monitoring frequency of the power grid is increased, the load forecast and the power grid configuration are reviewed, and adjustments are prepared. If the voltage continues to decline, primary response measures are initiated. If VS(t) is greater than or equal to the low threshold value θ3 of the voltage stability index and less than the medium threshold value θ2 of the voltage stability index, an orange alarm is issued, capacitors or FACTS equipment are started, backup generators are dispatched or fast response energy storage systems are activated, minor load reduction is considered, or emergency load transfer plans are initiated. If VS(t) is less than or equal to the low threshold value θ3 of the voltage stability index, a red alarm is issued, extensive load reduction is implemented, and some non-critical loads are disconnected.

6. A system using the method for analyzing and controlling power grid voltage stability based on short-circuit capacity according to any one of claims 1 to 5, characterized in that: Including data acquisition module, short-circuit capacity prediction module, voltage stability performance analysis module and alarm module; The data acquisition module is used to collect power grid operation data in real time and perform preprocessing to obtain preprocessed data; The short-circuit capacity prediction module is used to dynamically evaluate the pre-processed data and combine it with multi-time scale analysis to predict the short-circuit capacity of the power grid in real time; The voltage stability performance analysis module is used to establish a correlation model between short-circuit capacity and voltage stability and analyze the voltage stability performance of the power grid; The alarm module is used for real-time monitoring and early warning, and performs control according to the early warning.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for analyzing and controlling power grid voltage stability based on short-circuit capacity according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for analyzing and controlling power grid voltage stability based on short-circuit capacity according to any one of claims 1 to 5 are implemented.

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