Dynamic distribution method and system for power grid risk management authority

Abstract

The invention discloses a dynamic distribution method and system for power grid risk management authority. The method comprises the following steps: periodically obtaining power data corresponding to each power grid base node; calculating operation frequency distribution corresponding to the power data, and performing loss evaluation according to the operation frequency distribution to obtain a node operation loss degree; according to the operation frequency distribution and the node operation loss degree, risk assessment is carried out on each power grid base layer node, and a safety risk area graph is generated according to a node risk assessment value; and generating a risk management authority distribution decision according to the security risk area graph and the historical risk coping decision of each power grid base node, so as to adjust the risk management authority corresponding to each power grid base node. Therefore, the decision permission is flexibly adjusted and distributed, and the timeliness of risk event response is effectively improved.

Description

Technical Field

[0001] This invention relates to the field of permission allocation technology, and in particular to a dynamic allocation method and system for power grid risk management permissions. Background Technology

[0002] With rapid economic development and continuous growth in social electricity demand, modern power grids are becoming increasingly large-scale, exhibiting a complex cross-regional and multi-level architecture. This large-scale power grid covers a wide area, involving different geographical regions, varying electricity load characteristics, and multiple energy access methods, placing extremely high demands on the stable operation and effective management of the power grid.

[0003] In such a large and complex power grid system, the collection and transmission of risk monitoring information faces numerous challenges. Factors such as geographical distance between different regions and differences in communication infrastructure cause delays in the transmission of risk monitoring information from the collection point to the decision-making center. At the same time, in the traditional model, the power grid processing system mainly relies on instructions and scheduling decisions from high-level management departments, making it difficult to respond quickly and accurately when local faults occur in the power grid.

[0004] To improve power grid processing efficiency, existing technologies propose delegating some power grid safety supervision and risk management authority to local power grid management units. However, existing authority delegation schemes typically lack sufficient decision support units. When receiving processing tasks, local power grid management units struggle to quickly obtain comprehensive and accurate decision information, making it difficult to formulate scientific and reasonable response strategies due to insufficient information, thus resulting in untimely response measures. Summary of the Invention

[0005] This invention provides a method and system for dynamically allocating power grid risk management authority, which solves the technical problem that existing authority delegation management schemes are prone to failure to formulate scientific and reasonable response strategies due to insufficient decision-making information, resulting in untimely response measures.

[0006] This invention provides a method for dynamically allocating power grid risk management permissions, comprising:

[0007] Periodically acquire power data corresponding to each basic node of the power grid;

[0008] Calculate the operating frequency distribution corresponding to the power data, and perform loss assessment according to the operating frequency distribution to obtain the degree of node operating loss;

[0009] Based on the operating frequency distribution and the degree of node operating loss, a risk assessment is performed on each of the power grid basic nodes, and a safety risk area map is generated according to the node risk assessment value.

[0010] Based on the security risk area map and the historical risk response decisions of each of the power grid grassroots nodes, a risk management authority allocation decision is generated to adjust the risk management authority corresponding to each of the power grid grassroots nodes.

[0011] Optionally, the step of calculating the operating frequency distribution corresponding to the power data and performing loss assessment according to the operating frequency distribution to obtain the degree of node operating loss includes:

[0012] Perform a Fourier transform on the power data to obtain power frequency domain data and extract the corresponding harmonic frequency amplitude features;

[0013] The power data is decomposed using wavelet transform to determine the characteristics of power variation;

[0014] The harmonic frequency amplitude characteristics and the power variation characteristics are fused together to generate power spectrum characteristics;

[0015] The power spectrum characteristics are statistically analyzed according to preset frequency grouping intervals to generate a frequency distribution map;

[0016] Extract the frequency fluctuation time periods and frequency fluctuation phases from the frequency distribution map to generate the operating frequency distribution;

[0017] Based on the operating frequency distribution and the power grid operating loss formula, the degree of node operating loss is determined.

[0018] Optionally, the formula for power grid operating loss is:

[0019] ;

[0020] in, For the first The degree of operational loss corresponding to each basic node of the power grid This represents the total number of basic nodes in the power grid. ; For the range of runtime loss, For time-varying parameters, For the first Each power grid grassroots node in time Instantaneous voltage at the point, For the first Each power grid grassroots node in time Instantaneous current at the point, For the first Each power grid grassroots node in time The power grid load at the location, For the first Each power grid grassroots node in time The operating frequency of the power grid at that location, For the first The attenuation coefficient of operating state loss for each basic node pair of the power grid For the first Each power grid grassroots node in time Instantaneous active power of the power grid at the location, For the first Each power grid grassroots node in time Instantaneous reactive power of the power grid at the location, This is a correction factor for the degree of power grid operation loss.

[0021] Optionally, the step of conducting a risk assessment on each of the power grid's basic nodes based on the operating frequency distribution and the degree of node operating loss, and generating a safety risk area map according to the node risk assessment values, includes:

[0022] Calculate the mean frequency and standard deviation of each of the power grid basic nodes according to the operating frequency distribution;

[0023] Calculate the frequency kurtosis corresponding to the operating frequency distribution based on the frequency mean and the frequency standard deviation;

[0024] The node risk assessment value is obtained by substituting the frequency mean, frequency standard deviation, frequency kurtosis, and node operational loss degree into the risk assessment formula.

[0025] Power grid grassroots nodes whose node risk assessment values ​​are greater than the risk threshold are selected as nodes to be determined;

[0026] Based on the historical power data of each node to be determined and the associated power grid topology, a corresponding security risk area map is generated.

[0027] Optionally, the risk assessment formula is:

[0028] ;

[0029] in, For the first The node risk assessment value corresponding to each basic node of the power grid. For the first The average frequency corresponding to each basic node of the power grid For the first The frequency standard deviation corresponding to each basic node of the power grid For the first Frequency fluctuation kurtosis corresponding to each basic node of the power grid For the first The degree of operational loss corresponding to each basic node of the power grid For the first The power grid operation risk weights corresponding to each basic node of the power grid This is the correction factor for the node risk assessment value.

[0030] Optionally, generating risk management authority allocation decisions based on the security risk area map and the historical risk response decisions of each of the power grid's basic nodes includes:

[0031] Acquire power grid line distribution data, substation location data, and land cover resistivity within the safety risk area map, and perform risk prediction to determine the scope of safety risk impact.

[0032] Identify risk propagation information within the security risk area map, and perform emergency deployment prediction based on the risk propagation information to determine risk emergency deployment response information;

[0033] Key influencing factors were extracted from the historical risk response decisions of each of the aforementioned power grid grassroots nodes;

[0034] Based on the key influencing factors, the scope of the safety risks, and the emergency response information, a hierarchical model is used to assign weights to the risk management authority corresponding to each of the power grid's basic nodes, resulting in a risk management authority allocation decision.

[0035] Optionally, identifying risk propagation information within the security risk area map and performing emergency deployment prediction based on the risk propagation information to determine risk emergency deployment response information includes:

[0036] Obtain the region topology map corresponding to the security risk region map;

[0037] Risk propagation information is identified by combining the regional topology map with the corresponding power information; wherein, the risk propagation information includes the propagation path and propagation speed;

[0038] Simulate the emergency response path according to the propagation path and the propagation speed, and calculate the risk emergency deployment duration according to the emergency response path;

[0039] Based on the obtained emergency reserve resource quantity and the aforementioned risk emergency allocation duration, emergency allocation prediction is performed to determine risk emergency allocation response information.

[0040] Optionally, the method further includes:

[0041] Once the risk management authority allocation decision is executed, the power change information of each of the power grid grassroots nodes is obtained in real time.

[0042] The power change information is subjected to fuzzy logic control according to a preset fuzzy rule base to generate new risk management permission allocation decisions.

[0043] Optionally, before performing the periodic acquisition of power data corresponding to each basic node of the power grid, the method further includes:

[0044] Various data acquisition devices are deployed at each grassroots node of the power grid.

[0045] Based on the geographical location and terrain of each of the aforementioned power grid grassroots nodes, a node transmission framework is constructed;

[0046] The power data of the power grid's basic nodes are collected in real time by each of the aforementioned data acquisition devices and uploaded according to the node transmission framework.

[0047] The present invention also provides a dynamic allocation device for power grid risk management authority, comprising:

[0048] The data acquisition module is used to periodically acquire power data corresponding to each basic node of the power grid;

[0049] The node operation loss calculation module is used to calculate the operating frequency distribution corresponding to the power data, and to perform loss assessment according to the operating frequency distribution to obtain the node operation loss degree.

[0050] The safety risk area map generation module is used to perform risk assessment on each of the power grid basic nodes based on the operating frequency distribution and the degree of node operating loss, and generate a safety risk area map according to the node risk assessment value.

[0051] The decision generation module is used to generate risk management permission allocation decisions based on the security risk area map and the historical risk response decisions of each of the power grid grassroots nodes, so as to adjust the risk management permissions corresponding to each of the power grid grassroots nodes.

[0052] As can be seen from the above technical solutions, the present invention has the following advantages:

[0053] This invention acquires power data corresponding to each basic node of the power grid periodically; calculates the operating frequency distribution corresponding to the power data, and performs loss assessment according to the operating frequency distribution to obtain the degree of node operating loss; based on the operating frequency distribution and the degree of node operating loss, it conducts risk assessment for each basic node of the power grid and generates a safety risk area map according to the node risk assessment value; based on the safety risk area map and the historical risk response decisions of each basic node of the power grid, it generates risk management authority allocation decisions to adjust the risk management authority corresponding to each basic node of the power grid. This allows for flexible adjustment and allocation of decision-making authority, effectively improving the timeliness of risk event response. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 A flowchart illustrating the steps of a method for dynamically allocating power grid risk management permissions, as provided in an embodiment of the present invention;

[0056] Figure 2 This is a structural block diagram of a dynamic allocation system for power grid risk management permissions provided in an embodiment of the present invention. Detailed Implementation

[0057] This invention provides a method and system for dynamically allocating power grid risk management authority, which addresses the technical problem that existing authority delegation management schemes are prone to delays in response due to insufficient decision-making information, making it impossible to formulate scientific and reasonable response strategies.

[0058] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0059] Please see Figure 1 , Figure 1 A flowchart illustrating the steps of a dynamic allocation method for power grid risk management permissions provided in an embodiment of the present invention.

[0060] This invention provides a method for dynamically allocating power grid risk management permissions, comprising:

[0061] Step 101: Periodically acquire power data corresponding to each basic node of the power grid;

[0062] In this embodiment, the system periodically obtains corresponding power data from each basic node of the power grid to serve as the data basis for subsequent processing.

[0063] In one example of the present invention, before performing the periodic acquisition of power data corresponding to each power grid grassroots node, the method further includes the following steps:

[0064] Various data acquisition devices are deployed at each grassroots node of the power grid.

[0065] Based on the geographical location and terrain of each power grid grassroots node, construct the node transmission framework;

[0066] Power data from the basic nodes of the power grid is collected in real time by various data acquisition devices and uploaded according to the node transmission framework.

[0067] In this embodiment, the data acquisition devices include, but are not limited to, voltage transformers (VT), current transformers (CT), and load sensors. Real-time data acquisition of the power grid is achieved by deploying voltage transformers (VT), current transformers (CT), and load sensors at various basic nodes of the power grid. Voltage transformers are responsible for collecting voltage data from each power grid node, current transformers are responsible for monitoring current changes at the node, and load sensors collect load information from the power grid node in real time. Through precise sensor deployment and data acquisition, the voltage, current, and load data of each power grid node are transmitted to the edge computing device periodically and continuously. This data undergoes preprocessing, including data denoising and timestamp calibration, to ensure data accuracy and consistency. After preliminary processing and analysis by the edge computing node, all acquired data is periodically uploaded to this system along the transmission path of the node transmission framework via a reliable communication network. Encryption technology is used during data transmission to ensure data security and integrity.

[0068] It should be noted that by installing voltage transformers, current transformers, and load sensors at the basic nodes of the power grid, the installation of voltage transformers should ensure that their accuracy meets the requirements of real-time voltage monitoring. They are typically installed on the high-voltage side of the power grid, and monitoring of the grid voltage is achieved by measuring the voltage at the contact points. Current transformers are installed in the main current circuit to accurately measure the current intensity in the power grid. Load sensors are set at the load points of the power grid, collecting data by monitoring changes in the grid load to reflect the grid load situation in real time. All sensors should be networked with the power grid's data transmission system, connecting to the data acquisition module through standardized interfaces to ensure accurate data transmission and real-time updates. The installation process should ensure the electrical safety of the equipment, and the installation location should be chosen to facilitate maintenance and data acquisition, avoiding the influence of interference sources.

[0069] Power data can include, but is not limited to, voltage, current, and grid load. Voltage transformers convert high-voltage signals in the grid into low-voltage signals for subsequent monitoring and data acquisition. Voltage transformers should be physically connected to the voltage lines of each grid's basic nodes to ensure they accurately reflect the voltage value of that node. After the voltage data from each grid's basic nodes is collected by the voltage transformers, it undergoes preliminary data filtering and verification by a processing unit to remove abnormal data caused by environmental or equipment problems. During grid operation, the voltage transformers periodically measure the voltage and transmit it to the data processing unit in real time to ensure the timeliness and accuracy of the grid voltage data, ultimately yielding the voltage corresponding to each grid's basic nodes. By installing current transformers in the main current circuit and directly connecting them to the load current of the power grid, the current transformers can convert the current signal into a proportionally low current signal through the principle of magnetic field induction, ensuring the accuracy and stability of current measurement. Current data from each power grid grassroots node is collected by the current transformers and transmitted to the monitoring system for real-time recording and processing. The data processing unit analyzes the collected current data in real time, promptly detecting any abnormalities such as overload or short circuit, ensuring that the power grid current data reflects the grid load and operating status in real time, ultimately obtaining the current corresponding to each power grid grassroots node. Load sensors obtain real-time data on the power grid load through combined current and voltage measurements. Load sensors are generally installed at the load end of the power grid and connected to the grid load. When the current and voltage in the power grid change, the load sensors can accurately calculate the actual load of the power grid based on these changes. Load information from each power grid grassroots node is collected by the load sensors and transmitted in real time to the central data processing unit. During data processing, the load data is compared with historical data and benchmark loads to identify abnormal fluctuations in the power grid load and promptly send alarm information to maintenance personnel, ultimately obtaining the power grid load corresponding to each power grid grassroots node.

[0070] Simultaneously, precise geographic coordinates of each power grid grassroots node are obtained through GPS positioning technology, and topographic data of the surrounding terrain of each power grid node are obtained in conjunction with Geographic Information System (GIS). This information helps to analyze the environment in which the node is located and the natural disaster risks it faces. Secondly, based on the power grid topology, the connection relationship between each node and its neighboring nodes and the upper-level data processing unit is analyzed, and a connection diagram of the power grid network is drawn. This process requires the use of power grid design drawings, topology analysis tools, and power grid load and sensor data to calculate the communication link and bandwidth requirements of each node. Based on the above information, an edge computing node adaptive transmission framework is constructed. The core of this framework is based on the data acquisition capabilities and network connectivity of each power grid grassroots node. The edge computing unit performs preliminary processing, analysis, and filtering of sensor data to reduce data transmission latency and improve data transmission reliability. To ensure the stability of data transmission, multi-link redundancy technology is adopted to ensure that the system can automatically switch to the backup link when a link fails, ensuring that no data is lost. The edge computing node also incorporates adaptive retransmission technology to ensure that data is not lost or delayed during transmission due to network congestion or instability.

[0071] It should be noted that Geographic Information System (GIS) technology and load dispatching systems can be used to model and classify the electricity load of each grassroots node in the power grid. Based on parameters such as time period, region, and electricity demand, load types can be divided into different categories such as industrial load, commercial load, and residential load. Next, based on the topology and interconnection relationships of the upper-level power grid, and combined with data from the power grid's main control and dispatching system, the connection hierarchy between the grassroots nodes and the upper-level power grid can be further analyzed. For example, if a grassroots node is identified as belonging to a high-voltage ring network or a downstream branch of a low-voltage substation, power transmission and dispatching information can be extracted through the power grid load monitoring system and communication network to confirm the connection hierarchy. A node transmission framework related to each grassroots node can then be designed, including the installation location and quantity of voltage, current, and load sensors, and their connection methods with the power grid communication system. The node transmission framework needs to consider factors such as load fluctuations, substation equipment capacity, and power grid stability to ensure that the sensor deployment can comprehensively and accurately collect power grid operation data. The design of the node transmission framework is based on system capacity, sensor power requirements, and the connection hierarchy between the node and the upper-level power grid.

[0072] In this embodiment of the invention, the electromagnetic environment around each power grid grassroots node is monitored. Specifically, electromagnetic field strength data of the area where the power grid grassroots node is located is collected using an electromagnetic field strength tester and a spectrum analyzer, and frequency component analysis is performed. These devices can accurately measure the strength and frequency characteristics of electric and magnetic fields. For example, strong electromagnetic interference exists around certain industrial areas or substations, requiring special attention. Through spectrum analysis, the main frequency bands of electromagnetic interference, such as low-frequency or high-frequency components, are identified. Combined with local environmental characteristics, the frequency bands that may be affected by interference to power grid equipment are determined. Then, based on the measured electromagnetic field strength and frequency characteristics, electromagnetic compatibility (EMC) is performed. Electromagnetic compatibility (EMC) calibration involves using an EMC calibration system to adapt and debug equipment such as voltage transformers, current transformers, and load sensors used in the power grid. By comparing with electromagnetic field analysis data, the frequency response range, filtering settings, and anti-interference capabilities of the sensors are adjusted to ensure stable operation in different electromagnetic environments. For example, in areas with severe high-frequency interference, filters need to be added or sensor models resistant to high-frequency interference need to be used. These EMC calibration parameters will help ensure the long-term stable operation of subsequent equipment, avoid measurement errors or equipment damage caused by electromagnetic interference, and ultimately obtain the EMC calibration parameters corresponding to each basic node of the power grid.

[0073] Based on the previously designed node transmission framework and the obtained electromagnetic compatibility interference calibration parameters, specific equipment installation and commissioning were carried out. The installation process first ensured that each sensor was accurately positioned according to design requirements. For example, in the installation of voltage transformers, it was necessary to ensure that their measurement points were located at critical locations where the grid load changed. Current transformers, on the other hand, needed to be installed in suitable locations based on the line load conditions to ensure measurement accuracy. The installation of load sensors should be arranged according to the user types and power load density in the area to ensure that they could collect load change information in real time. During installation, the communication ports of all sensors needed to be connected to the grid monitoring system to ensure that data could be transmitted to the central control platform in real time. Furthermore, at each grid base... During node deployment, electromagnetic compatibility (EMC) verification is also required to confirm that the installed sensors are not affected by external electromagnetic interference and that their data transmission is stable. After installation, preliminary testing and debugging are performed on each sensor device to check whether it can accurately respond to changes in the external electromagnetic field and collect power grid operation data. During the debugging process, EMC calibration parameters are used to further optimize the equipment, adjust the sensor's response threshold and acquisition accuracy to ensure its stability and accuracy in the actual operating environment, and ensure that all sensor data can be accurately uploaded to the power grid monitoring platform for real-time monitoring and risk warning. Finally, voltage transformers, current transformers, and load sensors are deployed at each power grid grassroots node.

[0074] Step 102: Calculate the operating frequency distribution corresponding to the power data, and conduct a loss assessment according to the operating frequency distribution to obtain the degree of node operating loss;

[0075] In this embodiment, frequency analysis is performed on the voltage and current data collected from each grid grassroots node. Algorithms such as Fourier Transform (FFT) are used to convert the time-domain signals into frequency-domain signals, thereby obtaining the grid operating frequency distribution corresponding to each grid grassroots node. By comparing historical data with standard thresholds, it can be determined whether there are frequency anomalies or excessive fluctuations in the grid. The operating frequency distribution is combined with grid load data to assess grid operating losses. The loss assessment model analyzes the decline in grid operating efficiency based on current and voltage fluctuations and load changes, quantifying the degree of grid operating losses corresponding to each grid grassroots node.

[0076] In one example of the present invention, step 102 may include the following sub-steps:

[0077] Perform Fourier transform on the power data to obtain power frequency domain data and extract the corresponding harmonic frequency amplitude features;

[0078] Wavelet transform decomposition is performed on power data to determine power variation characteristics;

[0079] The harmonic frequency amplitude characteristics and power variation characteristics are fused together to generate power spectrum characteristics;

[0080] Frequency statistics are performed on the power spectrum characteristics according to the preset frequency grouping intervals to generate a frequency distribution map;

[0081] Extract the frequency fluctuation periods and phases from the frequency distribution map to generate the operating frequency distribution;

[0082] The degree of node operation loss is determined by combining the operating frequency distribution with the power grid operation loss formula.

[0083] In this embodiment, power data is collected from various power grid grassroots nodes, and a Discrete Fourier Transform (DFT) is performed on the collected power data. By sampling the collected time-domain signals according to a set sampling frequency, discrete signal sequences are formed. By applying the Discrete Fourier Transform to these sequences, the frequency domain representation of the voltage and current of each power grid grassroots node can be obtained, that is, the distribution of frequency and amplitude. Finally, the voltage frequency domain representation and current frequency domain representation of each power grid grassroots node are obtained.

[0084] Then, by identifying the main frequency components contained in the voltage and current frequency domain representations of each grid base node, frequency domain analysis methods are used to extract and statistically analyze each frequency component, obtaining the amplitude and frequency of each frequency component in the voltage and current signals. In specific implementation, a frequency threshold can be set to filter out significant harmonic frequencies and their corresponding amplitudes, and statistical analysis can be performed. This process can be accelerated by Fast Fourier Transform (FFT), ultimately obtaining the voltage and current harmonic frequency and amplitude characteristics corresponding to each grid base node. By performing wavelet transform decomposition on the voltage and current signals of the power grid's basic nodes, wavelet transform can simultaneously provide local information of the signal in both the time and frequency domains. It is particularly suitable for analyzing transient signals, such as sags (short-term voltage drops) and sudden changes (abrupt changes in voltage or current) in the power grid. Wavelet transform decomposes voltage and current signals by selecting appropriate mother wavelets (such as Daubechies wavelets, Morlet wavelets, etc.) to obtain components at different scales. These components reflect different frequencies and temporal local characteristics of the signal. For power change characteristics, the results of wavelet decomposition can be used to analyze their change characteristics, duration, and amplitude. After obtaining the power change characteristics, these two types of characteristics are integrated with the previously obtained harmonic frequency and amplitude characteristics of voltage and current to form a comprehensive power spectrum characteristic. The two types of characteristics can be fused according to certain weights to obtain the power spectrum characteristic set of each power grid basic node. These characteristic sets contain frequency domain information and transient change characteristics in the time domain of the voltage and current signals, ultimately yielding the power spectrum characteristics corresponding to each power grid basic node.

[0085] Next, the operating frequency of the power grid is analyzed according to the power spectrum characteristics to identify the normal operating frequency and potential fault frequencies. During normal operation, the voltage and current spectra exhibit specific regularities. However, during faults, anomalies, or load fluctuations, the high-frequency components or specific frequency components in the spectrum change. By analyzing the power spectrum characteristics, the operating frequency distribution of the power grid can be obtained. Specifically, this can be achieved by statistically analyzing the spectrum characteristics of each basic node of the power grid to obtain its frequency distribution map. The amplitude and phase information in the frequency domain can be obtained through Fast Fourier Transform (FFT) or other spectrum analysis algorithms. Next, the slope change of the spectrum is calculated. The instantaneous slope of the spectrum change is calculated using numerical differentiation methods. By setting a slope change threshold, the moments when the spectrum slope changes abruptly are identified. These moments represent abnormal spectrum changes occurring during power grid operation. Based on the moments of slope abrupt changes, the moments of abrupt changes in the operating spectrum slope of each basic node of the power grid are obtained to reflect sudden events occurring in the power grid during certain periods. Finally, the moments of abrupt changes in the operating spectrum slope of the power grid corresponding to each basic node are obtained. By further analyzing the voltage and current spectral feature sets, the extreme points in the operating spectrum are identified. First, by utilizing the relationship between voltage and current amplitude and frequency in the spectrum diagram, the local maxima and minima of the voltage and current spectrum in the power grid are further extracted. These extreme points represent significant feature points of frequency change trends in power grid operation, indicating changes in the stability of the power grid system or potential faults. Specifically, peak detection algorithms (such as differential methods or peak detection algorithms) are applied to scan the spectrum signal. By setting thresholds or performing sliding window processing, all extreme points in the voltage and current spectrum are screened out. Based on these extreme points, the operating status and abnormal fluctuations of the power grid can be further analyzed, and finally, the extreme points of the power grid operating spectrum corresponding to each basic node of the power grid are obtained. By combining previously acquired extreme points of the power grid operating spectrum with the moments of abrupt changes in the spectral slope, the frequency fluctuation periods of the power grid are determined. Specifically, by analyzing the correlation between the time of the extreme points and the moments of abrupt changes in the spectral slope, the frequency fluctuation intervals accompanying the moments of abrupt changes in the spectral slope are identified. These fluctuation periods represent the amplitude of frequency changes in the power grid within a specific time period. To achieve this, time series analysis methods can be used to analyze the distribution of extreme points before and after the moments of abrupt changes in the slope, and the intensity and duration of frequency fluctuations can be determined based on the amplitude changes in the spectrum. In this way, the operating frequency fluctuation periods of each basic node of the power grid can be obtained, and finally, the power grid operating frequency fluctuation periods corresponding to each basic node of the power grid can be obtained.

[0086] By extracting frequency and phase information from the previously acquired power grid voltage and current spectrum feature set, phase demodulation technology is used to extract the phase components in the power grid spectrum signal in order to accurately analyze the phase changes of the power grid operating frequency. This can be done by using phase analysis methods in digital signal processing (DSP) technology, or by extracting the phase information of the spectrum after using Fast Fourier Transform (FFT). For each spectrum point, the phase angle change is calculated to further determine the phase fluctuation of the power grid at different frequency points. By analyzing the phase characteristics of the power grid voltage and current spectrum, the phase relationship between frequency fluctuations and different parts of the power grid can be identified, thereby revealing synchronization problems, harmonic interference or other operational anomalies in the power grid, and finally obtaining the power grid operating frequency fluctuation phase corresponding to each power grid grassroots node.

[0087] By combining previously obtained frequency fluctuation periods and phases, a frequency distribution analysis of the power grid is performed. Specifically, firstly, based on the frequency fluctuation periods of each basic node in the power grid at different time intervals, the distribution range of the power grid's operating frequency is calculated. This analysis can be based on probability density functions (PDFs) or statistical distribution analysis methods to determine the amplitude and frequency of power grid frequency fluctuations and compare the frequency differences between nodes. Next, by combining the phase information of the power grid frequency fluctuations, the phase differences and synchronicity between different power grid nodes are analyzed, thereby obtaining the distribution of the power grid frequency. This frequency distribution data helps to determine the stability of the power grid, ultimately yielding the power grid operating frequency distribution corresponding to each basic node.

[0088] By combining the total number of power grid grassroots nodes, the time range of operating losses, instantaneous voltage, instantaneous current, power grid load, power grid operating frequency, operating state loss attenuation coefficient, instantaneous active power of the power grid, instantaneous reactive power of the power grid, and related parameters, a suitable power grid operating loss calculation formula is constructed to assess the power grid operating loss of the corresponding power grid load. In combination with the voltage, current, load and other data of each grassroots node, the degree of power grid loss is calculated, and finally the degree of power grid operating loss corresponding to each power grid grassroots node is obtained.

[0089] The formula for power grid operation loss is as follows:

[0090] ;

[0091] in, For the first The degree of operational loss corresponding to each basic node of the power grid This represents the total number of basic nodes in the power grid. ; For the range of runtime loss, For time-varying parameters, For the first Each power grid grassroots node in time Instantaneous voltage at the point, For the first Each power grid grassroots node in time Instantaneous current at the point, For the first Each power grid grassroots node in time The power grid load at the location, For the first Each power grid grassroots node in time The operating frequency of the power grid at that location, For the first The attenuation coefficient of operating state loss for each basic node pair of the power grid For the first Each power grid grassroots node in time Instantaneous active power of the power grid at the location, For the first Each power grid grassroots node in time Instantaneous reactive power of the power grid at the location, This is a correction factor for the degree of power grid operation loss.

[0092] This embodiment integrates multiple factors such as voltage, current, load, operating frequency, and power to comprehensively assess the operational losses of each basic node in the power grid. By meticulously analyzing the electrical characteristics of each node, a more accurate assessment of the degree of loss can be obtained, helping to identify inefficiencies or anomalies in grid operation. The variables in the formula (such as voltage, current, load, and power) are time-dependent, meaning they change over time. This allows the calculation to reflect the grid's operating status in real-time or dynamically, capturing fluctuations in parameters such as voltage and current, especially losses caused by voltage fluctuations and load changes. By monitoring these variables in real time, grid managers can adjust the grid's operating status promptly to reduce losses. The load parameter in the formula is directly related to grid operating losses. The grid's load status directly affects the distribution of voltage, current, and power, thus influencing grid losses. Calculating grid operating losses under different load conditions helps grid operators optimize load allocation and scheduling, thereby improving grid efficiency and reducing unnecessary losses. The operating frequency of the power grid plays a crucial role in the stability and efficiency of the power system. Frequency fluctuations can lead to abnormal equipment operation, thereby increasing losses. This formula takes into account this impact, enabling timely assessment of the degree of loss based on changes in the power grid operating frequency. This helps improve power grid stability and avoid losses caused by frequency anomalies. Furthermore, the loss attenuation coefficient and correction coefficient included in the formula provide the ability to further adjust the power grid operating status. The attenuation coefficient reflects the impact of power grid equipment aging, environmental factors, or other external factors on losses, while the correction coefficient can adjust the calculation results to adapt to different power grid operating scenarios or optimization needs. It can refine the power grid loss assessment according to actual conditions, providing a more flexible analytical tool. In addition, this formula can realize the power grid operation loss assessment process for the power grid load corresponding to each basic node of the power grid, and simultaneously, through the correction coefficient of the degree of power grid operation loss... The introduction of this formula allows for adjustments based on errors that occur during the calculation process, thereby improving the accuracy and applicability of the power grid operation loss calculation formula.

[0093] Step 103: Based on the operating frequency distribution and the degree of node operating loss, conduct risk assessments on each power grid grassroots node, and generate a safety risk area map according to the node risk assessment values;

[0094] In this embodiment, the frequency fluctuations and loss levels of nodes are weighted based on the operating frequency distribution to obtain a node risk assessment value for each power grid node. This reflects the instability of the power grid's operating state and can indicate potential fault areas. Multiple risk assessment algorithms, including fuzzy logic analysis and decision tree models, are used during the assessment process to quantify the risk assessment value of each node. The system locates the basic nodes of the power grid and generates a safety risk area map. This process relies on a precise Geographic Information System (GIS) and risk level model, combining the spatial location of nodes, real-time power grid operating status, and risk assessment results to generate the safety risk area map.

[0095] In one example of the present invention, step 103 may include the following sub-steps:

[0096] Calculate the mean frequency and standard deviation of each power grid node based on the operating frequency distribution;

[0097] Calculate the frequency kurtosis corresponding to the operating frequency distribution based on the frequency mean and frequency standard deviation;

[0098] By substituting the frequency mean, frequency standard deviation, frequency kurtosis, and node operational loss into the risk assessment formula, the node risk assessment value is obtained.

[0099] Grid-level nodes whose node risk assessment value is greater than the risk threshold are selected as nodes to be determined.

[0100] Based on the historical power data of each undetermined node and the associated power grid topology, a corresponding security risk area map is generated.

[0101] In this embodiment, the operating frequency distribution of the power grid is constructed by collecting the operating frequency data of each basic node of the power grid. The operating frequency data of the power grid comes from the real-time monitoring system of the power grid. These systems usually deploy frequency sensors or obtain frequency-related data through the data acquisition module of the dispatch center. Then, data analysis tools, such as MATLAB or the NumPy library in Python, are used to calculate the frequency mean and frequency standard deviation of each basic node of the power grid.

[0102] Next, volatility analysis is performed on the frequency mean and standard deviation of each grid node. This volatility analysis focuses on assessing the sharpness and tail behavior of the frequency distribution, i.e., calculating the kurtosis. Kurtosis reflects extreme cases of frequency fluctuations; higher kurtosis indicates frequent abnormal frequency fluctuations in the grid. Using the SciPy library in Python or other professional data analysis tools, probability density estimation (PDF) is first performed on the frequency data, and then the kurtosis value of the frequency data is calculated according to the formula:

[0103] Frequency fluctuation kurtosis = ;

[0104] in For the first The power grid operating frequency data values ​​corresponding to each basic node of the power grid. For the first The average operating frequency of the power grid corresponding to each basic node of the power grid. For the first The standard deviation of the operating frequency of each basic node in the power grid is used to obtain the kurtosis of the frequency fluctuation for each node. The mean frequency, standard deviation of the frequency, kurtosis of the frequency fluctuation, and the degree of node operating loss are then substituted into the risk assessment formula to obtain the node risk assessment value.

[0105] After calculating the node risk assessment value, a risk threshold (such as 0.75) can be determined based on historical risk data. The node risk assessment value of each basic node is then compared. If the node risk assessment value of a node is less than the threshold, the power grid operation safety of that node is considered relatively high, and it is identified as a "non-risk basic node". If the node risk assessment value of a node is greater than or equal to the threshold, the node is determined to be a "risk basic node", and necessary safety early warning measures are taken. This judgment process can be processed by an automated data processing system. By comparing the node risk assessment value with the threshold, a safety early warning report is generated in real time to indicate whether there are potential risks.

[0106] A subset of basic power grid nodes with risk assessment values ​​exceeding the risk threshold are selected as potential nodes. Based on historical power data and the associated power grid topology of each potential node, a corresponding safety risk area map is generated. Detailed power grid operation data for the risky basic nodes is obtained through a power grid monitoring system, including current, voltage, and power data for each node, as well as relevant operational history records. This data is input into an intelligent data processing platform. This platform utilizes advanced algorithms (such as machine learning-based anomaly detection algorithms and physical model-based fault diagnosis algorithms) to analyze the potential causes and locations of faults at the node. Furthermore, combining the power grid topology and operating status, a digital power grid model is used to precisely locate possible fault areas, thereby determining specific safety risk areas. This location process can be achieved through various means, such as trend analysis based on abnormal current and voltage fluctuations and spatial positioning technology based on sensor data. The safety risk location results are presented through a visual interface, ultimately yielding the safety risk area map corresponding to the basic power grid node.

[0107] Furthermore, the risk assessment formula is as follows:

[0108] ;

[0109] in, For the first The node risk assessment value corresponding to each basic node of the power grid. For the first The average frequency corresponding to each basic node of the power grid For the first The frequency standard deviation corresponding to each basic node of the power grid For the first Frequency fluctuation kurtosis corresponding to each basic node of the power grid For the first The degree of operational loss corresponding to each basic node of the power grid For the first The power grid operation risk weights corresponding to each basic node of the power grid This is the correction factor for the node risk assessment value.

[0110] By integrating multiple factors such as the mean frequency of power grid operation, frequency standard deviation, kurtosis of frequency fluctuations, and the degree of power grid operational losses, this approach comprehensively considers various factors affecting the safe operation of the power grid. This ensures that the assessment results are not based on a single parameter but rather on multi-dimensional information fusion, helping to more accurately reflect the complexity of power grid operation. The kurtosis of power grid operating frequency fluctuations in the formula measures the sharpness of frequency fluctuations. Kurtosis reflects the prominence of peaks in the frequency distribution, and these peaks are often precursors to system instability or potential faults. Therefore, introducing kurtosis as a parameter allows for better identification of potential abnormal fluctuations in the power grid, enhancing the sensitivity of risk assessment. The degree of power grid operational losses is a crucial parameter in risk assessment, reflecting the losses caused by faults or anomalies in power grid operation. Introducing this factor helps quantify the potential impact of faults on different nodes during risk assessment, ensuring that the final risk value not only depends on frequency characteristics but also considers the possibility of economic losses or safety damage. Secondly, the power grid operation risk weights in this formula allow for differentiated treatment of the importance of different basic nodes in the power grid. By dynamically adjusting the weights, different risk assessment weights can be assigned to different nodes based on their characteristics, location, or criticality within the power grid. For example, the risk weights of critical nodes can be appropriately increased, thereby enhancing their influence in risk assessment and ensuring focused monitoring of areas crucial to power grid security. The exponential decay function in the formula is a modeling method for the relationship between the power grid frequency mean and standard deviation. Exponential decay reflects the non-linear impact of the power grid frequency mean and standard deviation on risk level. When the frequency mean is closer to the normal value, the risk level decreases rapidly, while when the frequency fluctuates significantly, the risk level increases significantly. This sensitivity design helps to highlight nodes with large fluctuations and provide early warning of potential risks. In addition, the correction coefficient in the formula provides an adaptive adjustment space for the risk level value. This coefficient can be adjusted according to factors such as the operating environment of different power grids, historical data, and seasonal changes, ensuring that the assessment model has good flexibility and adaptability. Through the correction coefficient, the accuracy of risk assessment can be further improved, making the calculation formula more consistent with actual operating conditions. Meanwhile, by introducing the correction coefficient ξ of the power grid operation node risk assessment value, adjustments can be made based on the errors that occur during the calculation process, thereby improving the accuracy and applicability of the power grid operation risk assessment calculation formula.

[0111] Step 104: Based on the safety risk area map and the historical risk response decisions of each power grid grassroots node, generate risk management authority allocation decisions to adjust the risk management authority corresponding to each power grid grassroots node.

[0112] In this embodiment, a safety risk area map clearly defines the scope of impact of power grid risks. This scope considers factors such as the power linkage between power grid nodes, current transmission paths, and load distribution. Based on a preset impact assessment model, the impact scope of each risk area is calculated. Then, by comprehensively considering the emergency response capabilities of the basic power grid nodes (such as the availability of maintenance teams and the accessibility of communication facilities), the emergency response capabilities of the area are further assessed. Based on these assessment results, historical risk response decisions are generated to determine whether it is necessary to delegate some risk management authority to local power grid management departments. The generation and delegation of dynamic management authority ensures that in the event of a sudden power grid safety risk, each region can respond quickly and handle it in a timely manner, preventing the expansion and spread of safety risks.

[0113] In one example of the present invention, step 104 may include the following sub-steps S11-S14:

[0114] S11. Obtain data on the distribution of power grid lines, the location of substations, and the land cover resistivity within the safety risk area map, and perform risk prediction to determine the scope of safety risk impact.

[0115] In this embodiment of the invention, to obtain the distribution of power grid lines, substation locations, and land cover resistivity in the basic safety risk areas of the power grid, detailed data collection and processing are required. First, the distribution data of power grid lines is obtained through a geographic information system (GIS). This data includes the precise location, operating status, load conditions, and connection relationships with other lines of each power grid line. Next, the specific locations of power grid substations are obtained using satellite remote sensing technology or ground measurement methods. The coordinates of these substations are combined with the power supply situation in their respective areas. In addition, the acquisition of land cover resistivity is usually accomplished through electromagnetic wave detection, geological exploration, etc. This data can be obtained through remote sensing technology or on-site sampling and analysis methods, thereby obtaining resistivity information of different land types within the power grid area. Finally, the distribution of power grid lines, substation locations, and land cover resistivity corresponding to the basic nodes of the power grid are obtained.

[0116] To assess the impact range of safety risks in basic power grid areas based on power grid line distribution, substation locations, and land resistivity, the first step is to establish a risk impact model that integrates data from power grid lines, substations, and land resistivity. In this step, methods such as finite element analysis and Monte Carlo simulation can be used to predict power grid safety risks and simulate the impact of different types of safety risks (such as short circuits, equipment failures, and natural disasters) on the power grid system. These simulation analyses can estimate the propagation range and impact area of ​​different types of risks within the power grid. Based on the distribution of power grid lines and the layout of substations, the expansion trend within the power grid can be determined. Combined with the influence of land resistivity, the specific affected areas can be assessed, ultimately yielding the scope of safety risk impact.

[0117] S12. Identify risk propagation information within the safety risk area map, and predict emergency deployment based on the risk propagation information to determine risk emergency deployment response information;

[0118] In this embodiment, information on emergency facilities, personnel distribution, and response procedures in various areas of the power grid is acquired. Specifically, the distribution of emergency response resources, including emergency personnel, emergency supplies, and emergency equipment, in each grassroots power grid area must first be clarified. Secondly, by simulating different risk scenarios (such as natural disasters and equipment failures), the effectiveness of existing emergency response procedures can be assessed to ensure power grid safety within a specified time. During this process, the effectiveness of emergency response capabilities can be verified through simulation and emergency drills. For example, by simulating a fire at a substation, the ability of emergency response personnel at the grassroots nodes of the power grid to arrive at the scene in a timely manner and effectively handle the fault is assessed. By combining factors such as response time and the efficiency of response personnel, risk emergency deployment and response information is obtained to quantify the risk emergency response capabilities of the grassroots power grid.

[0119] Furthermore, S12 may include the following sub-steps:

[0120] Obtain the region topology map corresponding to the security risk region map;

[0121] Risk propagation information is identified by combining the regional topology map with the corresponding power information; the risk propagation information includes the propagation path and propagation speed.

[0122] Simulate emergency response paths based on transmission routes and speeds, and calculate the duration of emergency response deployment based on these paths.

[0123] Based on the acquired emergency reserve resources and the duration of emergency response, emergency allocation forecasts are made to determine emergency response information.

[0124] In this embodiment, by analyzing the safety risk areas at the grassroots level of the power grid, network analysis tools are used to identify the propagation paths of risks through the topology of the power grid system. Specifically, based on the connection relationships between power grid lines, substations, and load centers, the propagation path starting from a certain point of occurrence is calculated. The identification of this propagation path needs to consider the load, fault switching capability, and line carrying capacity of each node in the power grid. The propagation speed is determined based on the current and voltage change rates of each area in the power grid, as well as the electrical characteristics of the lines (such as resistance and inductance), combined with the response speed of the power grid dispatching system for estimation. This analysis uses a dynamic simulation model to simulate the propagation in the power grid through time and space variables, ensuring that the obtained propagation paths and propagation speeds conform to the actual operation and dynamic response characteristics of the power grid, and finally obtains the propagation path and propagation speed corresponding to the grassroots risks in the power grid. Based on the previously obtained propagation paths and speeds, the analysis begins with the emergency dispatch response at the grassroots level of the power grid. The calculation of emergency dispatch response time requires comprehensive consideration of factors such as the workflow of the power grid emergency dispatch center, personnel deployment efficiency, material reserves, type of emergency response, and priority of emergency response. Specifically, an optimization algorithm can be used to solve the time required for emergency resources to arrive from the point of occurrence. First, the emergency dispatch system is used to input the resource reserves and emergency response capabilities of each block in the power grid, combined with real-time load information of the power grid, to conduct emergency dispatch simulation. Next, by simulating the power grid response path, the optimal dispatch path is determined, and the actual time from issuing the dispatch order to the resources reaching the designated location is further calculated. This analysis process also needs to consider possible power grid node switching, line fault isolation, and other operations to optimize the timeliness and resource utilization of the dispatch path. Through this step, the emergency dispatch response time of the risk area at the grassroots level of the power grid is obtained, ultimately yielding the risk emergency dispatch response time. By utilizing the power grid resource management system, the emergency reserves (including backup power, equipment, materials, and personnel) of each region of the power grid are quantified. The values ​​of these emergency reserves need to be determined based on historical data of the power grid, different levels of demand, and regional resource allocation. In the process of analyzing the emergency reserve volume, the handling requirements of different types of risks also need to be considered to determine the minimum resource allocation required, thereby obtaining the corresponding risk emergency reserve volume. Then, combined with the previously obtained emergency dispatch response time, it is analyzed whether the reserve volume can be effectively allocated and reach the region within the specified time. Through a dynamic evaluation model, the dispatch and response time of emergency resources under different scenarios are simulated to calculate the emergency response capability of the power grid grassroots. If the analysis results show that there is a shortage of resources in a certain region or the response time exceeds expectations, it is necessary to optimize and adjust the region to improve resource reserves or strengthen the efficiency of dispatch paths. This process can accurately assess the emergency response capability of the power grid grassroots in the face of different risks, ensuring that the power grid can restore stable operation in a timely manner after an event, and finally obtain the risk emergency response capability of the power grid grassroots.

[0125] S13. Extract key influencing factors from the historical risk response decisions of each power grid grassroots node;

[0126] S14. Based on key influencing factors, the scope of safety risk impact, and risk emergency response information, a hierarchical structure model is used to assign weights to the risk management authority corresponding to each power grid grassroots node, thus obtaining the risk management authority allocation decision.

[0127] In this embodiment of the invention, by acquiring experience in power grid risk emergency management and allocating risk management authority based on this experience to the scope of risk impact and emergency response capabilities of the power grid's grassroots nodes, the following steps are taken: First, historical experience in power grid risk emergency management, including successful and unsuccessful cases, is collected and analyzed. Through in-depth analysis of these experiences, key influencing factors in the emergency response process are identified, such as resource allocation, coordination of emergency personnel, and efficiency of information transmission. Based on this, the Analytic Hierarchy Process (AHP) is used to allocate the management authority of the power grid's grassroots nodes. In specific implementation, the roles and authority of different power grid nodes in risk management can be divided according to the risk assessment results, emergency response capabilities, and historical management experience of each grassroots node. For example, higher risk management authority can be allocated to areas with a larger impact range to enable rapid response and handling of emergencies. The weight values ​​obtained through the AHP will determine the final allocation of management authority, ensuring that the power grid's grassroots nodes can operate efficiently in risk management, and ultimately generating a risk management authority allocation decision.

[0128] In another example of the present invention, the method further includes the following steps S21-S22:

[0129] S21. After the risk management authority allocation decision is executed, obtain the power change information of each power grid grassroots node in real time.

[0130] S22. Perform fuzzy logic control on power change information according to the preset fuzzy rule base to generate new risk management permission allocation decisions.

[0131] In this embodiment, the delegation of risk management authority will be adjusted based on the specific risk level of the region, emergency response capabilities, and the potential severity of the event. This adjustment includes assigning specific responsibility areas and emergency resources to the local management team and granting corresponding decision-making authority. This process is controlled and optimized through an intelligent scheduling system to ensure the timeliness and effectiveness of power grid safety management.

[0132] Specifically, through the allocation of power grid risk incident management authority, the system monitors changes in the scope of risk incident impact and the effectiveness of emergency response in real time, and dynamically adjusts accordingly. The key to this process lies in the flexible adjustment of risk management authority to address potential changes in power grid risk management. First, the system utilizes power grid monitoring systems and sensor networks to monitor the power grid's operational status in real time, acquiring data on the operation of power grid lines and changes in the incident's scope after an incident. Simultaneously, fuzzy logic control methods are used to analyze and make decisions on this data in real time. By fuzzy processing parameters such as the incident's scope and the effectiveness of the emergency response, fuzzy logic control can flexibly adjust the risk management authority of power grid grassroots nodes in a dynamically changing environment. If the risk in a certain power grid area expands and emergency response needs to be strengthened, more management authority can be delegated to the grassroots nodes in that area; conversely, if the risk is controlled or the emergency response is effective, some management authority can be revoked. Furthermore, the combination of real-time monitoring and fuzzy logic allows for the optimization and adjustment of emergency measures based on data feedback to ensure power grid safety. In this process, the risk management work at the power grid grassroots level is dynamically adjusted and responds promptly, ultimately generating dynamic management authority for power grid grassroots risk incidents to execute corresponding power grid grassroots safety risk incident management work.

[0133] In this embodiment of the invention, by deploying voltage transformers, current transformers, and load sensors at various power grid grassroots nodes, key voltage, current, and load data in the power grid can be comprehensively collected. Voltage transformers can accurately monitor voltage changes in the power grid, current transformers can capture current flow in real time, and load sensors can help measure load changes at each node. These data reflect the operating status of the power grid and can help maintenance personnel to promptly identify potential problems in the power grid. Using edge computing, the raw data collected by the sensors is first processed locally, which can greatly reduce data transmission latency, improve system response speed, and reduce the burden on the central data processing unit. Edge computing not only enables rapid response to real-time power grid data, but this step also provides a reliable data foundation and efficient data processing path for subsequent power grid operation analysis and fault location, thereby improving the intelligence level and operating efficiency of the power grid system. Secondly, by using data processing units to perform frequency analysis on voltage and current data at various grid nodes, it is possible to reveal the operating patterns of the grid, especially the frequency fluctuation patterns. Grid frequency fluctuations are often a significant indicator of grid instability; frequency deviations from rated values ​​signify load imbalances, equipment failures, or abnormal grid operation. Systematic analysis of the grid frequency at each node yields a grid operating frequency distribution map, clarifying the grid's operating status and identifying potential risk areas. Furthermore, based on the grid's frequency distribution, operational losses can be further assessed, helping to identify potential problems in grid operation and providing data support for grid scheduling and optimization. Then, by analyzing the frequency distribution and operational loss levels of the power grid at each basic node, the data processing unit can comprehensively assess the operational safety of the power grid. By combining frequency fluctuations with operational losses, it can identify high-risk areas in the power grid, thereby quantifying the operational safety of the power grid. By analyzing the safety risk values ​​of each node, it can determine which areas (i.e., which basic nodes of the power grid) face greater risks of power grid failure or overload. By combining the real-time operating status of the power grid, it can automatically locate potential safety risk areas, providing support for timely fault elimination and rapid response to accidents. This process can also effectively improve the resilience of the power grid, enabling it to cope with changes in the external environment and internal equipment failures, thereby ensuring the accuracy and stability of the power grid safety risk accident assessment.Finally, by acquiring the impact range and emergency response capabilities of power grid-level safety risk incident areas, targeted emergency response measures can be taken for different safety risk incident areas. These measures are not limited to rapid technical response, but also include optimizing power grid management strategies, dynamically adjusting risk management authority, and developing more precise emergency plans. By assessing the risk impact range of each power grid-level node, it is possible to determine which areas are significantly affected, which areas have strong emergency response capabilities, and which areas need further strengthening of their emergency response capabilities. This provides an important basis for power grid resource allocation, avoiding resource waste and delayed emergency response. Based on this, the risk management authority at the power grid level can be adjusted more flexibly and precisely, ensuring that resources can be mobilized for handling in the shortest possible time when a safety incident occurs. This enables the timely detection and response to potential safety hazards during power grid operation, solving the problems of insufficient decision-making information and untimely response measures, thereby improving the handling effect of power grid operation safety risk incidents and ensuring the stable and safe operation of the power grid.

[0134] Please see Figure 2 , Figure 2 This is a structural block diagram of a dynamic allocation system for power grid risk management permissions provided in an embodiment of the present invention.

[0135] This invention provides a device for dynamically allocating power grid risk management permissions, comprising:

[0136] The data acquisition module 201 is used to periodically acquire power data corresponding to each basic node of the power grid;

[0137] The node operation loss calculation module 202 is used to calculate the operating frequency distribution corresponding to the power data, and to evaluate the loss according to the operating frequency distribution to obtain the node operation loss degree.

[0138] The safety risk area map generation module 203 is used to conduct risk assessments on each basic node of the power grid based on the operating frequency distribution and the degree of node operating loss, and generate a safety risk area map according to the node risk assessment value.

[0139] The decision generation module 204 is used to generate risk management authority allocation decisions based on the safety risk area map and the historical risk response decisions of each power grid grassroots node, so as to adjust the risk management authority corresponding to each power grid grassroots node.

[0140] Optionally, the node operation loss calculation module 202 is specifically used for:

[0141] Perform Fourier transform on the power data to obtain power frequency domain data and extract the corresponding harmonic frequency amplitude features;

[0142] Wavelet transform decomposition is performed on power data to determine power variation characteristics;

[0143] The harmonic frequency amplitude characteristics and power variation characteristics are fused together to generate power spectrum characteristics;

[0144] Frequency statistics are performed on the power spectrum characteristics according to the preset frequency grouping intervals to generate a frequency distribution map;

[0145] Extract the frequency fluctuation periods and phases from the frequency distribution map to generate the operating frequency distribution;

[0146] The degree of node operation loss is determined by combining the operating frequency distribution with the power grid operation loss formula.

[0147] Optionally, the formula for power grid operation loss is:

[0148] ;

[0149] in, For the first The degree of operational loss corresponding to each basic node of the power grid This represents the total number of basic nodes in the power grid. ; For the range of runtime loss, For time-varying parameters, For the first Each power grid grassroots node in time Instantaneous voltage at the point, For the first Each power grid grassroots node in time Instantaneous current at the point, For the first Each power grid grassroots node in time The power grid load at the location, For the first Each power grid grassroots node in time The operating frequency of the power grid at that location, For the first The attenuation coefficient of operating state loss for each basic node pair of the power grid For the first Each power grid grassroots node in time Instantaneous active power of the power grid at the location, For the first Each power grid grassroots node in time Instantaneous reactive power of the power grid at the location, This is a correction factor for the degree of power grid operation loss.

[0150] Optionally, the security risk area map generation module 203 is specifically used for:

[0151] Calculate the mean frequency and standard deviation of each power grid node based on the operating frequency distribution;

[0152] Calculate the frequency kurtosis corresponding to the operating frequency distribution based on the frequency mean and frequency standard deviation;

[0153] By substituting the frequency mean, frequency standard deviation, frequency kurtosis, and node operational loss into the risk assessment formula, the node risk assessment value is obtained.

[0154] Grid-level nodes whose node risk assessment value is greater than the risk threshold are selected as nodes to be determined.

[0155] Based on the historical power data of each undetermined node and the associated power grid topology, a corresponding security risk area map is generated.

[0156] Optionally, the risk assessment formula is:

[0157] ;

[0158] in, For the first The node risk assessment value corresponding to each basic node of the power grid. For the first The average frequency corresponding to each basic node of the power grid For the first The frequency standard deviation corresponding to each basic node of the power grid For the first Frequency fluctuation kurtosis corresponding to each basic node of the power grid For the first The degree of operational loss corresponding to each basic node of the power grid For the first The power grid operation risk weights corresponding to each basic node of the power grid This is the correction factor for the node risk assessment value.

[0159] Optionally, the decision generation module 204 includes:

[0160] The impact range determination submodule is used to obtain power grid line distribution data, substation location data and land cover resistivity within the safety risk area map and perform risk prediction to determine the impact range of safety risks.

[0161] The response information generation submodule is used to identify risk propagation information within the security risk area map, and to perform emergency deployment prediction based on the risk propagation information to determine risk emergency deployment response information;

[0162] The influencing factors identification submodule is used to extract key influencing factors from the historical risk response decisions of each power grid grassroots node;

[0163] The decision generation submodule is used to assign weights to the risk management permissions of each power grid grassroots node according to key influencing factors, the scope of safety risk impact, and risk emergency dispatch response information, and to obtain the risk management permission allocation decision.

[0164] Optionally, the response information generation submodule is specifically used for:

[0165] Obtain the region topology map corresponding to the security risk region map;

[0166] Risk propagation information is identified by combining the regional topology map with the corresponding power information; the risk propagation information includes the propagation path and propagation speed.

[0167] Simulate emergency response paths based on transmission routes and speeds, and calculate the duration of emergency response deployment based on these paths.

[0168] Based on the acquired emergency reserve resources and the duration of emergency response, emergency allocation forecasts are made to determine emergency response information.

[0169] Optionally, the system also includes a permission update module, which is specifically used for:

[0170] Once the risk management authority allocation decision is executed, real-time power change information of each power grid grassroots node is obtained; fuzzy logic control is applied to the power change information according to the preset fuzzy rule base to generate a new risk management authority allocation decision.

[0171] Optionally, before periodically acquiring power data corresponding to each basic node of the power grid, the system also includes a deployment module, which is specifically used for:

[0172] Various data acquisition devices are deployed at each grassroots node of the power grid.

[0173] Based on the geographical location and terrain of each power grid grassroots node, construct the node transmission framework;

[0174] Power data from the basic nodes of the power grid is collected in real time by various data acquisition devices and uploaded according to the node transmission framework.

[0175] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the system and modules described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0176] In the embodiments provided by this invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between systems or modules may be electrical, mechanical, or other forms.

[0177] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for dynamically allocating power grid risk management authority, characterized in that, include: Periodically acquire power data corresponding to each basic node of the power grid; Calculate the operating frequency distribution corresponding to the power data, and perform loss assessment according to the operating frequency distribution to obtain the degree of node operating loss; Based on the operating frequency distribution and the degree of node operating loss, a risk assessment is performed on each of the power grid basic nodes, and a safety risk area map is generated according to the node risk assessment value. Based on the security risk area map and the historical risk response decisions of each of the power grid grassroots nodes, a risk management authority allocation decision is generated to adjust the risk management authority corresponding to each of the power grid grassroots nodes.

2. The method according to claim 1, characterized in that, The calculation of the operating frequency distribution corresponding to the power data, and the loss assessment based on the operating frequency distribution to obtain the degree of node operating loss, includes: Perform a Fourier transform on the power data to obtain power frequency domain data and extract the corresponding harmonic frequency amplitude features; The power data is decomposed using wavelet transform to determine the characteristics of power variation; The harmonic frequency amplitude characteristics and the power variation characteristics are fused together to generate power spectrum characteristics; The power spectrum characteristics are statistically analyzed according to preset frequency grouping intervals to generate a frequency distribution map; Extract the frequency fluctuation time periods and frequency fluctuation phases from the frequency distribution map to generate the operating frequency distribution; Based on the operating frequency distribution and the power grid operating loss formula, the degree of node operating loss is determined.

3. The method according to claim 2, characterized in that, The formula for power grid operation loss is: ； in, For the first The degree of operational loss corresponding to each basic node of the power grid This represents the total number of basic nodes in the power grid. ; For the range of runtime loss, For time-varying parameters, For the first Each power grid grassroots node in time Instantaneous voltage at the point, For the first Each power grid grassroots node in time Instantaneous current at the point, For the first Each power grid grassroots node in time The power grid load at the location, For the first Each power grid grassroots node in time The operating frequency of the power grid at that location, For the first The attenuation coefficient of operating state loss for each basic node pair of the power grid For the first Each power grid grassroots node in time Instantaneous active power of the power grid at the location, For the first Each power grid grassroots node in time Instantaneous reactive power of the power grid at the location, This is a correction factor for the degree of power grid operation loss.

4. The method according to claim 1, characterized in that, The step of conducting risk assessments on each of the power grid's basic nodes based on the operating frequency distribution and the degree of node operating losses, and generating a safety risk area map according to the node risk assessment values, includes: Calculate the mean frequency and standard deviation of each of the power grid basic nodes according to the operating frequency distribution; Calculate the frequency kurtosis corresponding to the operating frequency distribution based on the frequency mean and the frequency standard deviation; The node risk assessment value is obtained by substituting the frequency mean, frequency standard deviation, frequency kurtosis, and node operational loss degree into the risk assessment formula. Power grid grassroots nodes whose node risk assessment values ​​are greater than the risk threshold are selected as nodes to be determined; Based on the historical power data of each node to be determined and the associated power grid topology, a corresponding security risk area map is generated.

5. The method according to claim 4, characterized in that, The risk assessment formula is as follows: ； in, For the first The node risk assessment value corresponding to each basic node of the power grid. For the first The average frequency corresponding to each basic node of the power grid For the first The frequency standard deviation corresponding to each basic node of the power grid For the first Frequency fluctuation kurtosis corresponding to each basic node of the power grid For the first The degree of operational loss corresponding to each basic node of the power grid For the first The power grid operation risk weights corresponding to each basic node of the power grid This is the correction factor for the node risk assessment value.

6. The method according to claim 1, characterized in that, The step of generating risk management authority allocation decisions based on the security risk area map and the historical risk response decisions of each of the power grid grassroots nodes includes: Acquire power grid line distribution data, substation location data, and land cover resistivity within the safety risk area map, and perform risk prediction to determine the scope of safety risk impact. Identify risk propagation information within the security risk area map, and perform emergency deployment prediction based on the risk propagation information to determine risk emergency deployment response information; Key influencing factors were extracted from the historical risk response decisions of each of the aforementioned power grid grassroots nodes; Based on the key influencing factors, the scope of the safety risks, and the emergency response information, a hierarchical model is used to assign weights to the risk management authority corresponding to each of the power grid's basic nodes, resulting in a risk management authority allocation decision.

7. The method according to claim 6, characterized in that, The process of identifying risk propagation information within the security risk area map and performing emergency deployment prediction based on the risk propagation information to determine risk emergency deployment response information includes: Obtain the region topology map corresponding to the security risk region map; Risk propagation information is identified by combining the regional topology map with the corresponding power information; wherein, the risk propagation information includes the propagation path and propagation speed; Simulate the emergency response path according to the propagation path and the propagation speed, and calculate the risk emergency deployment duration according to the emergency response path; Based on the obtained emergency reserve resource quantity and the aforementioned risk emergency allocation duration, emergency allocation prediction is performed to determine risk emergency allocation response information.

8. The method according to any one of claims 1-7, characterized in that, The method further includes: Once the risk management authority allocation decision is executed, the power change information of each of the power grid grassroots nodes is obtained in real time. The power change information is subjected to fuzzy logic control according to a preset fuzzy rule base to generate new risk management permission allocation decisions.

9. The method according to claim 1, characterized in that, Before performing the periodic acquisition of power data corresponding to each basic node of the power grid, the method further includes: Various data acquisition devices are deployed at each grassroots node of the power grid. Based on the geographical location and terrain of each of the aforementioned power grid grassroots nodes, a node transmission framework is constructed; The power data of the power grid's basic nodes are collected in real time by each of the aforementioned data acquisition devices and uploaded according to the node transmission framework.

10. A dynamic allocation device for power grid risk management authority, characterized in that, include: The data acquisition module is used to periodically acquire power data corresponding to each basic node of the power grid; The node operation loss calculation module is used to calculate the operating frequency distribution corresponding to the power data, and to perform loss assessment according to the operating frequency distribution to obtain the node operation loss degree. The safety risk area map generation module is used to perform risk assessment on each of the power grid basic nodes based on the operating frequency distribution and the degree of node operating loss, and generate a safety risk area map according to the node risk assessment value. The decision generation module is used to generate risk management permission allocation decisions based on the security risk area map and the historical risk response decisions of each of the power grid grassroots nodes, so as to adjust the risk management permissions corresponding to each of the power grid grassroots nodes.

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