Method and system for analyzing and controlling power grid voltage stability based on short-circuit capacity

Abstract

The invention discloses a power grid voltage stability analysis and control method and system based on short-circuit capacity, and relates to the technical field of power grid voltage stability analysis, and the method comprises the steps: collecting power grid operation data in real time, and carrying out the preprocessing of the data, and obtaining the preprocessed data; carrying out dynamic evaluation on the preprocessed data and combining 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, and analyzing the voltage stability performance of the power grid; real-time monitoring and early warning are carried out, and control is carried out according to early warning. According to the method, the control precision and the response speed of the power grid voltage stability are effectively improved, the operation efficiency and the safety of the power grid are optimized, the adaptive capacity of the system to the power grid state change is enhanced, and the quality of power grid management and stability analysis is remarkably improved.

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, the voltage stability of the power grid has always been one of the core issues in maintaining the stable operation of the power system. With the liberalization of the power market and the widespread access to renewable energy, the power grid structure has become more and more complex, and traditional voltage stability analysis and control methods are facing new challenges. Historically, the problem of voltage stability has been mainly solved by adjusting the excitation system of the generator and the capacity of the transmission line. However, these methods often cannot fully adapt to rapidly changing grid conditions, especially when faced with large-scale distributed generation systems and changing load demands.

[0003] Existing technologies usually 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 of the grid at multiple time scales, such as load mutations in the short term, seasonal load fluctuations in the medium term, and load growth trends in the long term. 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 limits their ability to take effective measures in the face of emergencies.

[0004] In view of the shortcomings of the prior art, the present invention proposes a new method for analyzing and controlling the voltage stability of power grids based on short-circuit capacity. The method collects power grid operation data in real time and performs preprocessing, combines dynamic evaluation and multi-time scale analysis, and predicts the short-circuit capacity of the power grid in real time, thereby achieving precise control of the voltage stability of the power grid. By establishing a correlation model between short-circuit capacity and voltage stability, the present invention can not only provide more accurate voltage stability predictions, but also perform real-time monitoring and early warning according to the actual power grid status, and timely adjust the control strategy to deal with possible voltage instability problems. 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; establishing 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 the voltage stability of a power grid based on short-circuit capacity of 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] Among them, 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 forecast 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 the voltage stability of the power grid based on short-circuit capacity of 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 combined with 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, which is expressed as:

[0017]

[0018] Among them, ETS(V,t) is the exponential smoothing predicted value of voltage V at time t, ETS(I,t) is the exponential smoothing predicted value of current I at time t, and ETS(Z,t) is the exponential smoothing predicted value of impedance Z at time t; the long-term short-circuit capacity prediction value includes the method of using seasonal adjustment and trend decomposition, which 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 of the present invention, the association 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 of 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 solution of the method for analyzing and controlling power grid voltage stability based on short-circuit capacity of the present invention, the early warning includes setting a high threshold value θ of the voltage stability index. 1 , the threshold value θ in the voltage stability index 2 and voltage stability index low threshold θ 3 , if VS(t) is greater than or equal to the voltage stability index high threshold θ 1 , no alarm is given, normal monitoring is performed, and the existing operation is maintained. If VS(t) is greater than or equal to the threshold value θ in the voltage stability index 2 and is less than the voltage stability index high threshold θ 1 , 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, the primary response measures are initiated. If VS(t) is greater than or equal to the voltage stability index low threshold θ 3 And is less than the threshold value θ in the voltage stability index 2 , an orange alarm is issued, capacitors or FACTS equipment are started, backup generators are dispatched or fast response energy storage systems are activated, slight load reduction or emergency load transfer plans are considered, and if VS(t) is less than or equal to the voltage stability index low threshold θ 3 , a red alert is issued and widespread load reduction is implemented to disconnect some non-critical loads.

[0030] Another object of the present invention is to provide a system for a method of analyzing and controlling power grid voltage stability based on short-circuit capacity, which can solve the problem of analyzing and controlling power grid voltage stability based on short-circuit capacity by constructing a power 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 voltage stability performance of the power grid; the alarm module is used to monitor in real time and issue early warnings, and to control according to 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 as described above 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 accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0036] Figure 1 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 A structural diagram of a system for analyzing and controlling grid voltage stability based on short-circuit capacity provided in 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 implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0039] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and 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 1, which is the first embodiment of the present invention, provides a method for analyzing and controlling power grid voltage stability based on short-circuit capacity, including: 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; establishing 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.

[0041] S1. Collect power grid operation data in real time and preprocess it to obtain preprocessed data.

[0042] Grid operation data includes voltage data, current data, impedance data and power data.

[0043] Voltage data: includes the voltage value of each node and the voltage data of specific load points. The data comes from the monitoring system of the substation and distribution network.

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

[0045] Impedance data: including line impedance, transformer impedance, etc., which are calculated based on the system's electrical characteristics and operating conditions.

[0046] Power data: total grid power and power quality data.

[0047] Preprocessing includes data cleaning and data synchronization.

[0048] S2. 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.

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

[0050]

[0051] Among them, 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 was applied to each parameter (voltage, current, impedance) to identify its trend and periodic changes over time.

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

[0054]

[0055] 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 forecast value, γ is the long-term weight coefficient, SCC long (t) is the long-term short-circuit capacity prediction value. The 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 the prediction error and ensure the accuracy and practicality of the model.

[0056] For short-term prediction, the focus is on the instantaneous changes of the power grid or changes within a few hours. The autoregressive model (AR) is used to predict the instantaneous 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, 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 ith 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. Using an exponential smoothing model can better predict changes in this time scale, smooth out trends and periodicity in the data, and provide robust forecasts, expressed as:

[0060]

[0061] ETS(x,t)=μx(t)+(1-μ)ETS(x,t-1)

[0062] Among them, EST(V,t) is the exponential smoothing predicted value of voltage V at time t, ETS(I,t) is the exponential smoothing predicted value of current I at time t, and ETS(Z,t) is the exponential smoothing predicted value of impedance Z at time t; ETS(x,t) is the predicted value of the exponential smoothing model, x can be voltage V, current I or impedance Z, and t is the predicted time point, μ is the smoothing parameter, which is between 0 and 1 and is used to adjust the 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), which can decompose and predict trend, seasonality, and residual components, expressed as:

[0064]

[0065] Among them, 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 power grid parameter x (which can be voltage, current or impedance), x(t) represents the measured value at time t, select a polynomial degree n, and fit a polynomial trend line based on historical data, which is expressed as:

[0067] Trend(x, t) = a 0 +a 1 t+a 2 t 2 +..+a n t n

[0068] Among them, a 0 , a 1 , a 2 , ..., a n are the polynomial coefficients, estimated from historical data by 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 point is t.

[0069] The polynomial regression analysis of voltage V(t), current I(t) and impedance Z(t) is expressed as:

[0070] Trend(V, t) = a 0 +a 1 t+a 2 t 2 +...+a m tm

[0071] Trend(I, t) = b 0 +b 1 t+b 2 t 2 +...+b p t p

[0072] Trend(Z, t) = c 0 +c 1 t+c 2 t 2 +...+c q t q

[0073] Among them, a 0 , a 1 , a 2 , ..., a m is the polynomial coefficient of voltage, b 0 , b 1 , b 2 , ..., b p is the polynomial coefficient of current, c 0 , c 1 , c 2 , ..., 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 association model between short-circuit capacity and voltage stability includes constructing exponential linear mixing function and logarithmic exponential mixing function.

[0076] The exponential linear mixed function combines exponential and linear terms. Its basic concept comes from the direct relationship between the current carrying capacity of the power system and the stability of the system. A high short-circuit capacity usually means that the system can handle large currents better and thus maintain stability. It is used to emphasize the strong nonlinear effect of changes in short-circuit capacity on system stability. The exponential function can simulate this nonlinear response, enabling the model to show a significant improvement in voltage stability when the short-circuit capacity increases significantly. The linear term is directly multiplied by the power factor, reflecting the impact of the total power of the grid on stability, where 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 power 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 a large voltage drop, which in turn affects the stability of the system. Through the exponential form, the model can give greater weight to the increase 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 is based on the traditional theory of power system stability. For example, voltage stability is usually closely related to the short-circuit capacity of the power grid, load voltage, total system power and line impedance. By adopting logarithmic and exponential functions, the actual impact of these factors on system stability can be simulated more accurately. While maintaining the theoretical basis, the practical application value and prediction accuracy of the model are enhanced by introducing nonlinear expressions.

[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 the voltage stability index high threshold θ 1 , the threshold value θ in the voltage stability index 2 and voltage stability index low threshold θ 3 , if VS(t) is greater than or equal to the voltage stability index high threshold θ 1 , no alarm is given, normal monitoring is performed, and the existing operation is maintained. If VS(t) is greater than or equal to the threshold value θ in the voltage stability index 2 and is less than the voltage stability index high threshold θ 1 , 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, the primary response measures are initiated. If VS(t) is greater than or equal to the voltage stability index low threshold θ 3 And is less than the threshold value θ in the voltage stability index 2 , an orange alarm is issued, capacitors or FACTS equipment are started to improve the power factor of the line and reduce system losses, backup generators are dispatched or fast response energy storage systems are activated, slight load reduction or emergency load transfer plans are considered, and if VS(t) is less than or equal to the voltage stability index low threshold θ 3 , a red alert is issued and widespread load shedding is implemented, disconnecting some non-critical loads, and local or widespread 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, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

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

[0097] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a 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, if necessary, processing in another suitable manner, and then stored in a computer memory.

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

[0099] Embodiment 3 is the third embodiment of the present invention. It is different from the first two embodiments in that it is used to verify the technical effects adopted in the present invention so as to verify the real effects of the method.

[0100] As shown in Table 1, it is a comparison table between 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] Simulation is 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 perform detection simultaneously, 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 accuracy of early warning.

[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] The traditional method has moderate voltage stability, long response time and low warning accuracy when the load increases for a long time.

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

[0117] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. 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 power grid voltage stability based on short-circuit capacity, characterized in that: include, Collect power grid operation data in real time and pre-process it to obtain pre-processed data; 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; Establish the correlation model between short-circuit capacity and voltage stability, and analyze the voltage stability performance of the power grid; Real-time monitoring and early warning, and control based on the early warning.

2. The method for analyzing and controlling power grid voltage stability based on short-circuit capacity according to claim 1, characterized in that: The power 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, characterized in that: 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 forecast value, β is the medium-term weight coefficient, SCC medium (t) is the medium-term short-circuit capacity forecast 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, characterized in that: The short-term short-circuit capacity prediction value includes using the autoregressive model to predict the instantaneous change of each parameter, combined with the calculation of the short-circuit capacity, 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, βi is the current coefficient of the i-th historical data point, I(ti) is the short-circuit current value at time ti, γi 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, 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 forecast value includes the method of 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, characterized in that: The correlation model between the short-circuit capacity and the voltage stability includes constructing an exponential linear mixed function and a logarithmic exponential mixed 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 power 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, and b is the log-exponential mixing function adjustment coefficient.

6. The method for analyzing and controlling power grid voltage stability based on short-circuit capacity according to claim 5, characterized in that: The correlation model between the 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.

7. The method for analyzing and controlling power grid voltage stability based on short-circuit capacity according to claim 6, characterized in that: The early warning includes setting a high threshold value θ1 of the voltage stability index, a middle 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 middle 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 middle 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 reductions are 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 reductions are implemented, and some non-critical loads are disconnected.

8. A system using the method for analyzing and controlling power grid voltage stability based on short-circuit capacity as claimed in any one of claims 1 to 7, 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 for control according to the early warning.

9. 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 7 are implemented.

10. 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 7 are implemented.

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