Power grid operation early warning system, method and program product

Abstract

The invention relates to the technical field of power grid operation management, and discloses a power grid operation early warning system and method and a program product, and the system comprises a data collection unit and a background processing early warning unit. The data acquisition unit comprises a power distribution network architecture acquisition subunit and a meteorological data acquisition subunit; the background processing early warning unit comprises a data processing subunit, a data storage subunit, a data analysis and evaluation subunit and an operation early warning subunit, and is used for processing the acquired operation state data and meteorological environment data to obtain state information of the operation state of the power distribution network in the target area; and performing target area power distribution network operation analysis and evaluation on the operation state data, evaluating the risk level of the operation state of the target area power distribution network according to an analysis and evaluation result, and simultaneously sending out a corresponding early warning signal. According to the method, analysis and evaluation can be carried out from the aspect of a power distribution network architecture, and analysis and evaluation can also be carried out from the aspect of a meteorological environment, so that the whole analysis and evaluation range is wider, and the accuracy of power grid operation early warning is higher.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid operation management, and in particular to a power grid operation early warning system, method and program product. Background Art

[0002] During the operation of the power system, the distribution network plays a vital role in whether it can provide stable power supply to users. However, in actual work, due to the influence of the distribution network equipment status and the external meteorological environment, it is inevitable that the distribution network operation failure will occur, causing power outages and other accidents, especially meteorological factors, which are difficult to predict. Therefore, it is particularly important to effectively predict the operation failure of the distribution network. Accurate judgment of the distribution network operation failure will also provide guidance to the operators, so that the operation and maintenance personnel can quickly solve the failure, ensure the safe operation of the distribution network system, and reduce the losses caused by power outages.

[0003] In recent years, the development speed of artificial intelligence technology has gradually accelerated. With the help of modern technologies such as big data and deep learning, it is possible to systematically analyze the statistical characteristics of the distribution network in a faulty or normal state, and provide support for distribution network situation awareness and fault warning. The distribution network structure mainly includes overhead lines, disconnectors, cables, reactive power compensators, distribution transformers and towers, which play the role of power distribution in the power grid.

[0004] As for the operation warning of the power grid, the overhead lines, disconnectors, cables, etc. in the general distribution network structure are better for early warning; the most difficult to warn is the judgment of meteorological factors. As more and more sources of energy are connected to the power grid, such as wind power generation, solar power generation, etc., these are greatly affected by the weather. In addition, the distribution range of wind power generation and solar power generation is also relatively wide, most of which are located in remote places such as deep mountains, and meteorological monitoring is also difficult. This has made the stable operation of the power grid increasingly difficult. Summary of the invention

[0005] In view of this, the purpose of the embodiments of the present application is to provide a power grid operation early warning system, method and program product, which can not only perform analysis and evaluation from the distribution network architecture, but also perform analysis and evaluation from the meteorological environment, so that the scope of the entire analysis and evaluation is wider and the accuracy of the power grid operation early warning is higher.

[0006] In order to achieve the above technical objectives, the technical solutions adopted in this application are as follows:

[0007] In a first aspect, the present invention provides a power grid operation early warning system, including a data acquisition unit and a background processing early warning unit, wherein the data acquisition unit is communicatively connected with the background processing early warning unit;

[0008] The data acquisition unit includes a distribution network architecture acquisition subunit and a meteorological data acquisition subunit, which are used to collect the operating status data and meteorological environment data of the distribution network in the target area in real time;

[0009] The background processing and early warning unit includes a data processing subunit, a data storage subunit, a data analysis and evaluation subunit and an operation early warning subunit, which are used to process the acquired operation status data and meteorological environment data to obtain status information used to describe the operation status of the target area distribution network, and then perform operation analysis and evaluation of the target area distribution network on the operation status data evaluated and analyzed by the data processing subunit, and then assess the risk level of the operation status of the target area distribution network based on the analysis and evaluation results of the data analysis and evaluation subunit, and issue a corresponding early warning signal based on the obtained risk level; the data storage subunit is used to store the data collected by the data collection unit at fixed times in the past, which is used for the retrieval and comparative evaluation of the data analysis and evaluation subunit.

[0010] Furthermore, the distribution network architecture collection subunit includes an overhead line information collection module, an isolating switch information collection module, a cable information collection module, a reactive compensator information collection module, a distribution network transformer information collection module and a tower information collection module; and is used to respectively collect the overhead line information, isolating switch information, cable information, reactive compensator information, distribution network transformer information and tower information of the distribution network in the target area.

[0011] Furthermore, the meteorological data acquisition subunit includes a temperature information acquisition module, a humidity information acquisition module, a wind speed information acquisition module, a wind direction information acquisition module, an air pressure information acquisition module, and a solar radiation intensity information acquisition module; and is used to respectively collect temperature information, humidity information, wind speed information, wind direction information, air pressure information, and solar radiation intensity information of the target area distribution network.

[0012] Furthermore, the data analysis and evaluation subunit includes a distribution network architecture analysis module, a meteorological data analysis module, a past data comparison and analysis module and a comprehensive analysis module; the distribution network architecture analysis module is used to analyze and evaluate the risk level of the impact of the distribution network architecture on the distribution network, the meteorological data analysis module is used to analyze the risk level of the impact of the meteorological environment on the distribution network, the past data comparison and analysis module is used to retrieve and compare the current data with the data collected at fixed times in the past by the data storage subunit, and the comprehensive analysis module is used to comprehensively analyze and evaluate the analysis data of the distribution network architecture analysis module, the meteorological data analysis module, and the past data comparison and analysis module, and according to the comprehensive analysis and evaluation results, the risk level of the distribution network operation status in the target area is assessed, and corresponding warning signals are issued according to the obtained risk level.

[0013] In a second aspect, the present invention further discloses a power grid operation early warning method, using the above-mentioned power grid operation early warning system, comprising the following steps:

[0014] S1, the distribution network architecture acquisition subunit and the meteorological data acquisition subunit collect the operating status data and meteorological environment data of the distribution network in the target area in real time;

[0015] S2, the data processing subunit processes the acquired operation status data and meteorological environment data to obtain status information for describing the operation status of the distribution network in the target area;

[0016] S3, the data analysis and evaluation subunit analyzes and evaluates the operation status of the state information, and then evaluates the risk level of the operation status of the distribution network in the target area according to the analysis and evaluation results of the data analysis and evaluation subunit and the risk level of the impact of past meteorological data on the distribution network, and stores the state information and the risk level in the data storage subunit;

[0017] S4. The operation warning subunit issues a corresponding warning signal according to the risk level of the distribution network operation status in the target area.

[0018] Furthermore, the meteorological data collection subunit in step S1 collects meteorological environment data of the target area distribution network in real time, including the following steps:

[0019] S101, obtaining temperature information, humidity information, wind speed information, wind direction information, air pressure information, and solar radiation intensity information of the distribution network in the target area through a temperature information acquisition module, a humidity information acquisition module, a wind speed information acquisition module, a wind direction information acquisition module, an air pressure information acquisition module, and a solar radiation intensity information acquisition module;

[0020] S102, performing data processing on the acquired temperature information, humidity information, wind speed information, wind direction information, air pressure information, and solar radiation intensity information to obtain first sampling data of a plurality of target sub-areas;

[0021] S103: performing defect processing on the defect values ​​in the first sampling data to obtain second sampling data after defect processing, that is, final meteorological environment data.

[0022] Furthermore, the method for performing defect processing on the defect value in the first sampling data includes:

[0023] S1031, preprocessing the first sampling data to obtain preprocessing feature graphs of different scales;

[0024] S1032, performing a first convolution operation on the preprocessing feature map to obtain processing feature maps of different scales corresponding to the preprocessing feature map;

[0025] S1033, determining the influencing factors causing defects in the first sampling data, and assigning a weight to each influencing factor;

[0026] S1034. Perform a second convolution operation based on the influencing factors and the weight of each influencing factor in combination with the processing characteristic graph to obtain a defect processing value after the defect value is processed; the second convolution operation is:

[0027]

[0028] Wherein, y(t) is the first convolution result obtained after the first convolution operation is performed on each first sampling data in the processing feature graph, and the second convolution result obtained after the second convolution operation is performed, t is one of the first sampling data, x(t) is the influence factor of the first sampling data, h(t) is the weight of the first sampling data, and p is the first sampling data to be operated next after the first sampling data of the previous operation is operated;

[0029] S1035 . Replace the defect value in the first sampling data with the defect processing value to obtain a second sampling data value.

[0030] Further, the first convolution operation is:

[0031] h j =gat⊙m j

[0032]

[0033] In the formula, h j represents the first convolution result obtained after the first convolution operation of each first sampling data in the preprocessing feature map, gat represents gated convolution, Represents the first sample data in the convolution area of ​​the i-th convolution kernel of the k-1th layer, represents the weight corresponding to the i-th convolution kernel of the k-th layer, represents the bias term corresponding to the i-th convolution kernel of the k-th layer, represents convolution, R represents the convolution region, sig represents the sigmoid activation function, and f(·) represents any activation function.

[0034] Further, in the previous data comparison and analysis module, the risk level of the impact of previous meteorological data on the distribution network is analyzed, and the risk level of the impact of the current meteorological environment on the distribution network is calculated, including the following steps:

[0035] S201, collecting temperature information, humidity information, wind speed information, wind direction information, air pressure information, and solar radiation intensity information from the target area distribution network. If the variation of the collected information value within a unit time is greater than a set value, the factor is recorded as a key factor affecting the weather;

[0036] S202, let the eigenvector of the jth day be T j =(t j1 , t j2 , t j3 ,…,t jn ), the feature of the day to be predicted is T 0 =(t 01 , t 02 , t 03 ,…,t 0n ), where n represents the number of meteorological factors collected by the meteorological data analysis module;

[0037] S203, based on the feature T of the prediction day 0 As the parent sequence, T j As a subsequence, calculate the relationship coefficient F between the parent sequence and the subsequence 0j , construct the association discriminant matrix F, specifically: Where m is the feasibility of the impact of meteorological factors in the past year;

[0038] S204, weighting is performed according to the weights of temperature, humidity, wind speed, information, information, and solar radiation intensity, and a weight matrix is ​​constructed, which is W=(w 1 , w 2 , w 3 ,…,w n ), and obtain the weighted correlation matrix

[0039] S205. Calculate the risk level coefficient of the current period's meteorological environment on the distribution network based on the weighted correlation matrix F'

[0040] Where D j It represents the risk level coefficient of a certain meteorological factor on the distribution network, n represents the number of meteorological factors collected by the meteorological data analysis module, and m is the feasibility of the impact of meteorological factors in the past year.

[0041] In a third aspect, the present invention further discloses a computer program product, including a computer program, which, when executed by a processor, implements the method described in any one of the second aspects of the present invention.

[0042] The invention adopting the above technical solution has the following advantages:

[0043] 1. The power grid operation early warning system of the present invention collects temperature information, humidity information, wind speed information, wind direction information, air pressure information, and solar radiation intensity information of the target area distribution network; as well as overhead line information, disconnector information, cable information, reactive power compensator information, distribution network transformer information, and tower information of the target area distribution network. It collects more factors and can analyze and evaluate not only from the distribution network architecture but also from the meteorological environment, so that the scope of the entire analysis and evaluation is wider and the accuracy of the power grid operation early warning is higher.

[0044] 2. After collecting meteorological data, the present invention can perform defect processing on the defect values ​​in the first sampling data, so as to avoid inaccurate final analysis due to missing or abnormal data at certain collection points; when performing defect processing, the first convolution operation is used to pre-process the collected first sampling data, and then its influencing factors and weights are determined, and finally the second convolution operation is used to obtain the defect processing value, and the defect processing value replaces the defect value, so that the final second adopted data value is more accurate, thereby further improving the accuracy of the power grid operation warning.

[0045] 3. When analyzing and evaluating the power grid operation data, the present invention not only analyzes the first sampled data collected, but also analyzes the risk level of the impact of past meteorological data on the distribution network, and calculates the risk level of the impact of the current meteorological environment on the distribution network; therefore, the present invention not only combines the current analysis data, but also compares it with past empirical values ​​to reduce misjudgment of power grid operation warnings. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The present application may be further described by the non-limiting embodiments given in the accompanying drawings. It should be understood that the following drawings only illustrate certain embodiments of the present application and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, other relevant drawings may be obtained based on these drawings without creative effort.

[0047] Figure 1 The present invention is a block diagram of a power grid operation early warning system.

[0048] Figure 2 The present invention is a flow chart of a power grid operation early warning method. DETAILED DESCRIPTION

[0049] The present application will be described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that in the drawings or descriptions, similar or identical parts use the same figure numbers, and the implementation methods not shown or described in the drawings are forms known to ordinary technicians in the relevant technical field. In the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0050] Embodiment 1,

[0051] This embodiment is a power grid operation early warning system and early warning method thereof, such as Figure 1 As shown, it includes a data acquisition unit and a background processing warning unit, and the data acquisition unit is communicatively connected with the background processing warning unit;

[0052] The data acquisition unit includes a distribution network architecture acquisition subunit and a meteorological data acquisition subunit, which are used to collect the operating status data and meteorological environment data of the target area distribution network in real time; the background processing and early warning unit includes a data processing subunit, a data storage subunit, a data analysis and evaluation subunit and an operation early warning subunit, which are used to process the acquired operating status data and meteorological environment data to obtain status information used to describe the operating status of the target area distribution network, and then perform target area distribution network operation analysis and evaluation on the operating status data evaluated and analyzed by the data processing subunit, and then assess the risk level of the target area distribution network operation status based on the analysis and evaluation results of the data analysis and evaluation subunit, and issue a corresponding early warning signal based on the obtained risk level; the data storage subunit is used to store the data collected by the data acquisition unit at fixed times in the past, which is used for the retrieval and comparative evaluation of the data analysis and evaluation subunit.

[0053] The distribution network architecture acquisition subunit includes an overhead line information acquisition module, an isolating switch information acquisition module, a cable information acquisition module, a reactive compensator information acquisition module, a distribution network transformer information acquisition module and a pole tower information acquisition module; it is used to respectively acquire the overhead line information, isolating switch information, cable information, reactive compensator information, distribution network transformer information and pole tower information of the distribution network in the target area. The meteorological data acquisition subunit includes a temperature information acquisition module, a humidity information acquisition module, a wind speed information acquisition module, a wind direction information acquisition module, an air pressure information acquisition module and a solar radiation intensity information acquisition module; it is used to respectively acquire the temperature information, humidity information, wind speed information, wind direction information, air pressure information and solar radiation intensity information of the distribution network in the target area.

[0054] The data analysis and evaluation subunit includes a distribution network architecture analysis module, a meteorological data analysis module, a previous data comparison and analysis module and a comprehensive analysis module; the distribution network architecture analysis module is used to analyze and evaluate the risk level of the impact of the distribution network architecture on the distribution network, the meteorological data analysis module is used to analyze the risk level of the impact of the meteorological environment on the distribution network, the previous data comparison and analysis module is used to retrieve and compare the current data with the data collected at fixed times in the past by the data storage subunit, and the comprehensive analysis module is used to conduct a comprehensive analysis and evaluation on the analysis data of the distribution network architecture analysis module, the meteorological data analysis module, and the previous data comparison and analysis module, and according to the comprehensive analysis and evaluation results, the risk level of the distribution network operation status in the target area is assessed, and a corresponding warning signal is issued according to the obtained risk level.

[0055] The embodiment of the present application provides a power grid operation early warning system, method and program product. The temperature information, humidity information, wind speed information, wind direction information, air pressure information, solar radiation intensity information of the target area distribution network, as well as the overhead line information, disconnector information, cable information, reactive power compensator information, distribution network transformer information and tower information of the target area distribution network are collected respectively. The collected factors are more, and the analysis and evaluation can be performed not only from the distribution network architecture, but also from the meteorological environment, so that the scope of the entire analysis and evaluation is wider, and the accuracy of the power grid operation early warning is higher.

[0056] It should be noted that the temperature information collection module, humidity information collection module, wind speed information collection module, wind direction information collection module, air pressure information collection module, solar radiation intensity information collection module, overhead line information collection module, disconnector information collection module, cable information collection module, reactive power compensator information collection module, distribution network transformer information collection module and pole tower information collection module in this application are all prior art and will not be repeated here in this application.

[0057] The power grid operation early warning method of embodiment 1 uses the power grid operation early warning system of embodiment 1 above, such as Figure 2 As shown, the following steps are included:

[0058] S1, the distribution network architecture collection subunit and the meteorological data collection subunit collect the operating status data and meteorological environment data of the distribution network in the target area in real time; wherein the meteorological data collection subunit collects the meteorological environment data of the distribution network in the target area in real time, including the following steps:

[0059] S101, obtaining temperature information, humidity information, wind speed information, wind direction information, air pressure information, and solar radiation intensity information of the distribution network in the target area through a temperature information acquisition module, a humidity information acquisition module, a wind speed information acquisition module, a wind direction information acquisition module, an air pressure information acquisition module, and a solar radiation intensity information acquisition module;

[0060] S102, performing data processing on the acquired temperature information, humidity information, wind speed information, wind direction information, air pressure information, and solar radiation intensity information to obtain first sampling data of a plurality of target sub-areas;

[0061] S103, performing defect processing on the defect value in the first sampling data to obtain the second sampling data after defect processing, that is, the final meteorological environment data; the method for performing defect processing on the defect value in the first sampling data includes:

[0062] S1031, preprocessing the first sampling data to obtain preprocessing feature graphs of different scales;

[0063] S1032, performing a first convolution operation on the preprocessing feature map to obtain processing feature maps of different scales corresponding to the preprocessing feature map; the first convolution operation is:

[0064] h j =gat⊙m j

[0065]

[0066] In the formula, h j represents the first convolution result obtained after the first convolution operation of each first sampling data in the preprocessing feature map, gat represents gated convolution, Represents the first sample data in the convolution area of ​​the i-th convolution kernel of the k-1th layer, represents the weight corresponding to the i-th convolution kernel of the k-th layer, represents the bias term corresponding to the i-th convolution kernel of the k-th layer, represents convolution, R represents the convolution region, sig represents the sigmoid activation function, and f(·) represents any activation function.

[0067] S1033, determining the influencing factors causing defects in the first sampling data, and assigning a weight to each influencing factor;

[0068] S1034. Perform a second convolution operation based on the influencing factors and the weight of each influencing factor in combination with the processing characteristic graph to obtain a defect processing value after the defect value is processed; the second convolution operation is:

[0069]

[0070] Wherein, y(t) is the first convolution result obtained after the first convolution operation is performed on each first sampling data in the processing feature graph, and the second convolution result obtained after the second convolution operation is performed, t is one of the first sampling data, x(t) is the influence factor of the first sampling data, h(t) is the weight of the first sampling data, and p is the first sampling data to be operated next after the first sampling data of the previous operation is operated;

[0071] S1035 . Replace the defect value in the first sampling data with the defect processing value to obtain a second sampling data value.

[0072] After collecting meteorological data, defect processing can be performed on the defect values ​​in the first sampling data, so as to avoid inaccurate final analysis due to missing or abnormal data at certain collection points; when performing defect processing, the first convolution operation is used to pre-process the collected first sampling data, and then its influencing factors and weights are determined, and finally the second convolution operation is used to obtain the defect processing value, and the defect processing value replaces the defect value, so that the final second adopted data value is more accurate, thereby further improving the accuracy of power grid operation warning.

[0073] S2, the data processing subunit processes the acquired operation status data and meteorological environment data to obtain status information for describing the operation status of the distribution network in the target area;

[0074] S3, the data analysis and evaluation subunit analyzes and evaluates the operation status of the state information, and then evaluates the risk level of the operation status of the distribution network in the target area according to the analysis and evaluation results of the data analysis and evaluation subunit and the risk level of the impact of past meteorological data on the distribution network, and stores the state information and the risk level in the data storage subunit;

[0075] It should be noted that there are existing technologies for risk assessment of the impact of meteorological environment on power grid operation in this application, such as random forest algorithm, deep learning algorithm, etc., which can be used in conjunction with this application and will not be repeated here.

[0076] In the previous data comparison and analysis module, the risk level of the impact of previous meteorological data on the distribution network is analyzed, and the risk level of the impact of the current meteorological environment on the distribution network is calculated, including the following steps:

[0077] S201, collecting temperature information, humidity information, wind speed information, wind direction information, air pressure information, and solar radiation intensity information from the target area distribution network. If the variation of the collected information value within a unit time is greater than a set value, the factor is recorded as a key factor affecting the weather;

[0078] S202, let the eigenvector of the jth day be T j =(tj1 , t j2 , t j3 ,…,t jn ), the feature of the day to be predicted is T 0 =(t 01 , t 02 , t 03 ,…,t 0n ), where n represents the number of meteorological factors collected by the meteorological data analysis module;

[0079] S203, based on the feature T of the prediction day 0 As the parent sequence, T j As a subsequence, calculate the relationship coefficient F between the parent sequence and the subsequence 0j , construct the association discriminant matrix F, specifically: Where m is the feasibility of the impact of meteorological factors in the past year;

[0080] S204, weighting is performed according to the weights of temperature, humidity, wind speed, information, information, and solar radiation intensity, and a weight matrix is ​​constructed, which is W=(w 1 , w 2 , w 3 ,…,w n ), and obtain the weighted correlation matrix

[0081] S205. Calculate the risk level coefficient of the current period's meteorological environment on the distribution network based on the weighted correlation matrix F'

[0082] Where D j It represents the risk level coefficient of a certain meteorological factor on the distribution network, n represents the number of meteorological factors collected by the meteorological data analysis module, and m is the feasibility of the impact of meteorological factors in the past year.

[0083] When performing power grid operation data analysis and evaluation, the present invention not only analyzes the collected first sampling data, but also analyzes the risk level of the impact of past meteorological data on the distribution network, and calculates the risk level of the impact of the current meteorological environment on the distribution network; therefore, the present invention not only combines the current analysis data, but also compares it with past empirical values ​​to reduce misjudgment of power grid operation warnings.

[0084] S4, the operation warning subunit sends out a corresponding warning signal according to the risk level of the target area distribution network operation status. The warning level of this embodiment is divided into four levels according to the risk level of power supply of the power grid: level 1 warning (red), level 2 warning (orange), level 3 warning (yellow) and level 4 warning (blue). Level 1 warning is the highest level, indicating that the power supply of the power grid is facing a serious threat, the distribution network architecture has a greater risk, and meteorological factors also have a greater impact risk. Large-scale failure problems may occur, and comprehensive inspections and emergency preparations are required; Level 2 is that the meteorological environment will change significantly in the next 24 hours, which may have a greater impact on the power generation of the regional power grid and may have an impact on normal power supply; Level 3 is that there are more than two risk problems in the distribution network architecture, and corresponding inspections and emergency preparations need to be made; Level 4 risk is that there is a risk problem in the distribution network architecture or meteorological environment, which requires real-time attention.

[0085] Embodiment 2,

[0086] This embodiment is a computer program product, including a computer program. When the computer program is executed by a processor, the computer program implements the method described in any one of the second aspects of the present invention.

[0087] Through the description of the above implementation methods, technical personnel in this field can clearly understand that the present application can be implemented by hardware, and can also be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each implementation scenario of the present application.

[0088] In the embodiments provided by the present application, it should be understood that the disclosed method can also be implemented in other ways. The method embodiments described above are merely schematic, for example, the flow charts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the methods and computer program products according to the multiple embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a part of a module, a program segment or a code, and a part of the module, a program segment or a code includes one or more executable instructions for implementing the specified logical function. It should also be noted that each box in the block diagram and / or the flow chart, and the combination of the boxes in the block diagram and / or the flow chart can be implemented by a dedicated hardware-based system that performs the specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions. In addition, each functional module in each embodiment of the present application can be integrated together to form an independent part, or each module can exist separately, or two or more modules can be integrated to form an independent part.

[0089] The above description is only an embodiment of the present application and is not intended to limit the protection scope of the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A power grid operation early warning system, characterized in that: It includes a data collection unit and a background processing warning unit, wherein the data collection unit is communicatively connected with the background processing warning unit; The data acquisition unit includes a distribution network architecture acquisition subunit and a meteorological data acquisition subunit, which are used to collect the operating status data and meteorological environment data of the distribution network in the target area in real time; The background processing and early warning unit includes a data processing subunit, a data storage subunit, a data analysis and evaluation subunit and an operation early warning subunit, which are used to process the acquired operation status data and meteorological environment data to obtain status information used to describe the operation status of the target area distribution network, and then perform operation analysis and evaluation of the target area distribution network on the operation status data evaluated and analyzed by the data processing subunit, and then assess the risk level of the operation status of the target area distribution network based on the analysis and evaluation results of the data analysis and evaluation subunit, and issue a corresponding early warning signal based on the obtained risk level; the data storage subunit is used to store the data collected by the data collection unit at fixed times in the past, which is used for the retrieval and comparative evaluation of the data analysis and evaluation subunit.

2. The power grid operation early warning system according to claim 1, characterized in that: The distribution network architecture acquisition subunit includes an overhead line information acquisition module, an isolating switch information acquisition module, a cable information acquisition module, a reactive compensator information acquisition module, a distribution network transformer information acquisition module and a tower information acquisition module; It is used to collect overhead line information, disconnector information, cable information, reactive compensator information, distribution network transformer information and tower information of the distribution network in the target area.

3. The power grid operation early warning system according to claim 2, characterized in that: The meteorological data acquisition subunit includes a temperature information acquisition module, a humidity information acquisition module, a wind speed information acquisition module, a wind direction information acquisition module, an air pressure information acquisition module, and a solar radiation intensity information acquisition module; It is used to collect temperature information, humidity information, wind speed information, wind direction information, air pressure information, and solar radiation intensity information of the distribution network in the target area.

4. The power grid operation early warning system according to claim 3 is characterized in that: The data analysis and evaluation subunit includes a distribution network architecture analysis module, a meteorological data analysis module, a previous data comparison and analysis module and a comprehensive analysis module; the distribution network architecture analysis module is used to analyze and evaluate the risk level of the impact of the distribution network architecture on the distribution network, the meteorological data analysis module is used to analyze the risk level of the impact of the meteorological environment on the distribution network, the previous data comparison and analysis module is used to retrieve and compare the current data with the data collected at fixed times in the past by the data storage subunit, and the comprehensive analysis module is used to conduct a comprehensive analysis and evaluation on the analysis data of the distribution network architecture analysis module, the meteorological data analysis module, and the previous data comparison and analysis module, and according to the comprehensive analysis and evaluation results, the risk level of the distribution network operation status in the target area is assessed, and a corresponding warning signal is issued according to the obtained risk level.

5. A power grid operation early warning method, characterized in that: Using the power grid operation early warning system according to any one of claims 1 to 4, comprising the following steps: S1, the distribution network architecture acquisition subunit and the meteorological data acquisition subunit collect the operating status data and meteorological environment data of the distribution network in the target area in real time; S2, the data processing subunit processes the acquired operation status data and meteorological environment data to obtain status information for describing the operation status of the distribution network in the target area; S3, the data analysis and evaluation subunit analyzes and evaluates the operation status of the state information, and then evaluates the risk level of the operation status of the distribution network in the target area according to the analysis and evaluation results of the data analysis and evaluation subunit and the risk level of the impact of past meteorological data on the distribution network, and stores the state information and the risk level in the data storage subunit; S4. The operation warning subunit issues a corresponding warning signal according to the risk level of the distribution network operation status in the target area.

6. The power grid operation early warning method according to claim 5, characterized in that: In step S1, the meteorological data collection subunit collects the meteorological environment data of the target area distribution network in real time, including the following steps: S101, obtaining temperature information, humidity information, wind speed information, wind direction information, air pressure information, and solar radiation intensity information of the distribution network in the target area through a temperature information acquisition module, a humidity information acquisition module, a wind speed information acquisition module, a wind direction information acquisition module, an air pressure information acquisition module, and a solar radiation intensity information acquisition module; S102, performing data processing on the acquired temperature information, humidity information, wind speed information, wind direction information, air pressure information, and solar radiation intensity information to obtain first sampling data of a plurality of target sub-areas; S103: performing defect processing on the defect values ​​in the first sampling data to obtain second sampling data after defect processing, that is, final meteorological environment data.

7. The power grid operation early warning method according to claim 6, characterized in that: The method for performing defect processing on the defect value in the first sampling data includes: S1031, preprocessing the first sampling data to obtain preprocessing feature graphs of different scales; S1032, performing a first convolution operation on the preprocessing feature map to obtain processing feature maps of different scales corresponding to the preprocessing feature map; S1033, determining the influencing factors causing defects in the first sampling data, and assigning a weight to each influencing factor; S1034. Perform a second convolution operation based on the influencing factors and the weight of each influencing factor in combination with the processing characteristic graph to obtain a defect processing value after the defect value is processed; the second convolution operation is: Wherein, y(t) is the first convolution result obtained after the first convolution operation is performed on each first sampling data in the processing feature graph, and the second convolution result obtained after the second convolution operation is performed, t is one of the first sampling data, x(t) is the influence factor of the first sampling data, h(t) is the weight of the first sampling data, and p is the first sampling data to be operated next after the first sampling data of the previous operation is operated; S1035 . Replace the defect value in the first sampling data with the defect processing value to obtain a second sampling data value.

8. The power grid operation early warning method according to claim 7, characterized in that: The first convolution operation is: h j =gat⊙m j In the formula, hj 表 represents the first convolution result obtained after the first convolution operation of each first sampling data in the preprocessing feature map, gat represents gated convolution, Represents the first sample data in the convolution area of ​​the i-th convolution kernel of the k-1th layer, represents the weight corresponding to the i-th convolution kernel of the k-th layer, represents the bias term corresponding to the i-th convolution kernel of the k-th layer, represents convolution, R represents the convolution region, sig represents the sigmoid activation function, and f(·) represents any activation function.

9. The power grid operation early warning method according to claim 5, characterized in that: Also included in the previous data comparison and analysis module is analyzing the risk level of the impact of previous meteorological data on the distribution network, including the following steps: S201, collecting temperature information, humidity information, wind speed information, wind direction information, air pressure information, and solar radiation intensity information from the target area distribution network. If the variation of the collected information value within a unit time is greater than a set value, the factor is recorded as a key factor affecting the weather; S202, let the eigenvector of the jth day be T j =(t j1 , t j2 , t j3 , ..., t jn ), the characteristic of the day to be predicted is T0=(t 01 , t 02 , t 03 , ..., t 0n ), where n represents the number of meteorological factors collected by the meteorological data analysis module; S203, taking the feature T0 of the predicted day as the parent sequence, j As a subsequence, calculate the relationship coefficient F between the parent sequence and the subsequence 0j , construct the association discriminant matrix F, specifically: Where m is the feasibility of the impact of meteorological factors in the past year; S204, weighting is performed according to the weights of temperature, humidity, wind speed, information, information, and solar radiation intensity, and a weight matrix is ​​constructed, which is W=(w1, w2, w3, ..., w n ), and obtain the weighted correlation matrix S205. Calculate the risk level coefficient of the impact of the current meteorological environment on the distribution network based on the weighted correlation matrix F′ Where D j It represents the risk level coefficient of a certain meteorological factor on the distribution network, n represents the number of meteorological factors collected by the meteorological data analysis module, and m is the feasibility of the impact of meteorological factors in the past year.

10. A computer program product, comprising a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 5 to 9 is implemented.

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