Power grid operation data panoramic analysis method and system based on cloud scene digital platform

Abstract

The invention relates to the technical field of data analysis, and discloses a power grid operation data panoramic analysis method and system based on a cloud scene digital platform, and the method comprises the steps: obtaining first power grid operation data in real time; inputting the first power grid operation data into a hierarchical algorithm model for first judgment, second judgment and third judgment so as to obtain a capacity-increasing transformation suggestion, an automatic voltage control optimization suggestion and an equipment maintenance suggestion; and constructing a first comprehensive interface, and pushing the data wheel disc type suggestion list so as to realize panoramic analysis of the power grid operation data. According to the invention, automatic real-time acquisition and multi-dimensional mining analysis of data are realized, so that the power grid management efficiency is greatly improved, and the labor cost and time are saved; on the basis of dynamically generated capacity-increasing transformation, AVC optimization and other suggestion lists, decision scientization is promoted, and blind investment is effectively avoided; by means of equipment health degree model prediction and implementation of preventive maintenance measures, the power failure accident rate is successfully reduced, and the power supply reliability is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and particularly to a panoramic analysis method and system for power grid operation data based on a cloud view digital platform. Background Art

[0002] With the development of smart grids, the amount of data generated during the operation of power grids has increased explosively. However, the current power grid operation field mainly relies on traditional manual data extraction and analysis modes, and a series of technical defects have emerged in dealing with the massive data of modern power grids.

[0003] Firstly, in terms of data processing efficiency, traditional data analysis methods greatly limit work efficiency. For example, key power grid operation data such as heavy load data and AVC blocking data need to be manually extracted from the OMS system, and the generation process of daily reports, weekly reports, etc. takes a long time, usually several hours to complete, and involves a large amount of repetitive labor. A certain power supply bureau thus spends up to 350 + 4914 working hours every year, which not only increases labor costs but also delays the timely feedback of information and decision-making.

[0004] Secondly, the existing analysis technologies have a single dimension and can only perform basic data statistics work, such as heavy load frequency statistics, etc., lacking the ability to conduct multi-dimensional data correlation analysis and unable to deeply explore the value behind the data. For example, the data during heavy load periods cannot be correlated with the potential of electricity consumption load, nor can the trend of equipment failures be effectively predicted, thus affecting the safety and economy of power grid operation.

[0005] Furthermore, the existing result presentation forms (such as PPT / Excel) cannot provide a real-time visualization interface and are difficult to dynamically support decision-making. Optimization suggestions such as the priority of main transformer capacity increase and reactive power compensation schemes cannot be automatically generated based on real-time data, resulting in the lag of power grid optimization and adjustment behind actual needs.

[0006] Finally, although there is a large amount of historical power grid operation data, such as trip records and defect data, these data have not been fully explored and utilized. An effective equipment health assessment and power grid operation optimization strategy have not been formed, resulting in a large amount of valuable data resources being idle and unable to be transformed into practical application value.

[0007] Although the literature "The Development and Analysis of Power Big Data and Smart Grids" (2018) has discussed the application prospects of big data in smart grids, it has not given a specific implementation path of big data in the power grid system. Therefore, how to construct a panoramic analysis method that can efficiently process, deeply analyze, and fully utilize power grid operation data has become the key to improving power grid operation efficiency and ensuring the safe and stable operation of power grids. Summary of the Invention

[0008] In view of the above existing problems, the present invention is proposed.

[0009] Therefore, the present invention provides a panoramic analysis method and system for power grid operation data based on the cloud view digital platform to solve the problems of low data processing efficiency, single analysis dimension, and insufficient decision support in the prior art.

[0010] To solve the above technical problems, the present invention provides the following technical solutions:

[0011] In a first aspect, the present invention provides a panoramic analysis method for power grid operation data based on the cloud view digital platform, including:

[0012] Real-time acquisition of first power grid operation data;

[0013] Inputting the first power grid operation data into a hierarchical algorithm model for first determination, second determination, and third determination to obtain capacity expansion and renovation suggestions, automatic voltage control optimization suggestions, and equipment maintenance suggestions;

[0014] Constructing a first comprehensive interface and pushing a data roulette-style suggestion list to achieve panoramic analysis of power grid operation data.

[0015] As a preferred solution of the panoramic analysis method for power grid operation data based on the cloud view digital platform of the present invention, wherein: the first determination includes:

[0016] Extracting second power grid operation data, where the second power grid operation data at least includes the heavy load frequency and heavy load time period data within a month of the substation;

[0017] When the heavy load frequency within the year is greater than the first threshold, it is marked as high-frequency heavy load;

[0018] When the heavy load frequency within the year is between the second threshold and the first threshold, it is marked as medium-frequency heavy load.

[0019] As a preferred solution of the panoramic analysis method for power grid operation data based on the cloud view digital platform of the present invention, wherein: the second determination includes:

[0020] Statistical third power grid operation data, where the third power grid operation data at least includes the number of AVC lockouts and the main transformer tap-changing frequency within a month of the substation;

[0021] When the number of AVC lockouts of the substation is greater than the third threshold and the main transformer tap-changing frequency exceeds the first condition limit, it is determined as "AVC parameters need to be optimized" and a capacitor optimization suggestion is generated.

[0022] As a preferred solution of the panoramic analysis method for power grid operation data based on the cloud view digital platform of the present invention, wherein: the third determination includes:

[0023] Integrate the operation data of the fourth power grid and calculate the equipment health value;

[0024] Output the equipment health status and suggestions for inspection cycles according to the equipment health value.

[0025] As a preferred solution of the power grid operation data panoramic analysis method based on the cloud view digital platform of the present invention, wherein: the obtaining of the capacity increase and transformation suggestions includes:

[0026] If it is marked as high-frequency heavy load, trigger the suggestion of "capacity increase is required within 3 months";

[0027] If it is marked as medium-frequency heavy load, trigger the suggestion of "capacity increase is required within 6 months";

[0028] Combine the heavy load period data to generate a main transformer heavy load peak data sheet for the network area and push corresponding alarms.

[0029] As a preferred solution of the power grid operation data panoramic analysis method based on the cloud view digital platform of the present invention, wherein: predict the voltage fluctuation trend of the area through the voltage deviation analysis model, and push the prediction result to the voltage deviation analysis interface. In the voltage deviation analysis model, the substation voltage deviation index is the sum of the number of tap-changing operations and the number of capacitor switching operations.

[0030] As a preferred solution of the power grid operation data panoramic analysis method based on the cloud view digital platform of the present invention, wherein: the calculation of the equipment health value is 0.5 * the number of faults during trip handling + 0.1 * the number of archived faults during trip + 1 * the number of urgent defects + 0.1 * the number of other defects + 1 * the number of voltage anomaly faults.

[0031] In a second aspect, the present invention provides a power grid operation data panoramic analysis system based on the cloud view digital platform, including:

[0032] A data acquisition module for real-time acquisition of the operation data of the first power grid;

[0033] An analysis and determination module for inputting the operation data of the first power grid into a hierarchical algorithm model for first determination, second determination, and third determination to obtain capacity increase and transformation suggestions, automatic voltage control optimization suggestions, and equipment maintenance suggestions;

[0034] A visualization output module for constructing a first comprehensive interface and pushing a data roulette-style suggestion list to achieve panoramic analysis of the power grid operation data.

[0035] In a third aspect, the present invention provides an electronic device, including:

[0036] A memory and a processor;

[0037] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the panoramic analysis method for power grid operation data based on the cloud view digital platform are realized.

[0038] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the panoramic analysis method for power grid operation data based on the cloud view digital platform.

[0039] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention provides a panoramic analysis method and system for power grid operation data based on the cloud view digital platform. By realizing the automated real-time collection and multi-dimensional mining analysis of data, the power grid management efficiency is greatly improved, the labor cost and time are saved, and the effect of significant annual cost savings is achieved. At the same time, based on the dynamically generated list of suggestions such as capacity expansion transformation and AVC optimization, the decision-making is promoted to be scientific, and blind investment is effectively avoided. The present invention predicts and implements preventive maintenance measures with the help of the equipment health model, successfully reduces the power outage accident rate, and improves the power supply reliability. In addition, the system has good scalability and compatibility, supports docking with the OMS systems of multiple local power supply bureaus, and meets the usage requirements of different regions. Generally speaking, the present invention not only improves the operation efficiency and service quality of the power system, but also provides strong technical support for the development of the smart grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts. [[ID=~12]] ...

[0041] Figure 1 It is a schematic diagram of the overall process logic of the panoramic analysis method for power grid operation data based on the cloud view digital platform according to an embodiment of the present invention;

[0042] Figure 2 It is a schematic diagram of the overload analysis comprehensive interface of the panoramic analysis method for power grid operation data based on the cloud view digital platform according to an embodiment of the present invention (showing the overload frequency, frequency analysis, capacity expansion suggestions, etc. of the substation);

[0043] Figure 3 It is a schematic diagram of the voltage deviation analysis comprehensive interface of the panoramic analysis method for power grid operation data based on the cloud view digital platform according to an embodiment of the present invention (showing the ranking, time period, capacity expansion suggestions, etc. of the substation);

[0044] Figure 4 The equipment health value display diagram of the power grid operation data panoramic analysis method based on the cloud view digital platform described in an embodiment of the present invention (including the judgment of the health degree of power transmission and transformation equipment, the output of differential operation and maintenance suggestions, etc.). Detailed implementation manners

[0045] To make the above objects, features and advantages of the present invention more obvious and understandable, the following will describe the detailed implementation manners of the present invention with reference to the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0046] Embodiment 1, refer to Figure 1 An embodiment of the present invention provides a panoramic analysis method for power grid operation data based on the cloud view digital platform, specifically solving: 1. How to automatically associate multi-source data of OMS (such as heavy load data, AVC blocking data) through algorithms and generate decision-making suggestions such as substation capacity expansion priorities and reactive power optimization schemes in real time; 2. How to build a dynamic visualization interface to realize the panoramic display and risk warning of the power grid operation status (such as voltage deviation, equipment health value); 3. How to build an equipment failure prediction model based on historical data to optimize the inspection cycle and maintenance strategy of power transmission and transformation equipment. As Figure 1 shown, it specifically includes the following steps:

[0047] S100: Obtain the first power grid operation data in real time;

[0048] S200: Input the first power grid operation data into a hierarchical algorithm model for the first judgment, the second judgment and the third judgment to obtain capacity expansion and transformation suggestions, automatic voltage control optimization suggestions and equipment maintenance suggestions;

[0049] S300: Build a first comprehensive interface and push a data roulette-style suggestion list to realize the panoramic analysis of the power grid operation data.

[0050] It should be noted that the present invention provides a panoramic analysis method and system for power grid operation data based on a cloud view digital platform. By realizing the automatic real-time collection and multi-dimensional mining analysis of data, the power grid management efficiency is greatly improved, labor costs and time are saved, and significant annual cost savings are achieved. At the same time, based on the dynamically generated list of suggestions such as capacity expansion and transformation and AVC optimization, the decision-making is promoted to be scientific, and blind investment is effectively avoided. The present invention predicts and implements preventive maintenance measures with the help of an equipment health model, successfully reduces the power outage accident rate, and improves the power supply reliability. In addition, the system has good scalability and compatibility, supports docking with the OMS systems of multiple local power supply bureaus, and meets the usage requirements of different regions. Generally speaking, the present invention not only improves the operation efficiency and service quality of the power system, but also provides strong technical support for the development of the smart grid.

[0051] Example 2, referring to Figures 2 to 4 This is an embodiment of the present invention, which provides a specific implementation manner of the panoramic analysis method for power grid operation data based on a cloud view digital platform to illustrate the technical solution of the present invention.

[0052] S100: Obtain the first power grid operation data in real time;

[0053] In an optional embodiment, the first power grid operation data may include information such as the real-time load condition of the power grid, voltage and current parameters, power factor, frequency fluctuation, equipment temperature, and environmental conditions.

[0054] In the embodiment of the present application, the first power grid operation data includes, but is not limited to, operation data such as tap changing, capacitor switching, tripping, and defects.

[0055] It should be noted that the above step S100 can dynamically and accurately capture the instant state of the power grid, provide reliable data support for subsequent analysis, thereby greatly improving the response speed and decision-making accuracy of power grid management, and effectively saving labor costs and time.

[0056] S200: Input the first power grid operation data into a hierarchical algorithm model for the first determination, the second determination, and the third determination to obtain suggestions for capacity expansion and transformation, automatic voltage control optimization suggestions, and equipment maintenance suggestions;

[0057] In an alternative embodiment, the first determination may be a load capacity comparison analysis, which compares the load values in the real-time power grid operation data with the design capacity of the equipment or line. If it is found that the load in certain areas or of certain equipment is close to or exceeds the design capacity for a long time, it is determined that capacity expansion and renovation are required to avoid equipment damage or power supply interruption caused by overload. The first determination may also be a trend prediction and load growth analysis. Based on historical power grid operation data and current real-time data, time series analysis or machine learning algorithms are used to predict the future load growth trend. If the prediction results show that the power demand in a certain area will increase significantly in the future and may exceed the carrying capacity of the existing equipment, a suggestion for capacity expansion and renovation is put forward to cope with potential power supply pressure in advance.

[0058] In an alternative embodiment, the first determination may also be a thermal stability and loss assessment. By analyzing parameters such as line current, equipment temperature, and power loss in the power grid operation data, the thermal stability and operation efficiency of the power grid equipment are evaluated. If it is found that the temperature rise of certain lines or equipment is abnormally high, or the loss is too large, indicating that its operation state has approached the limit, it is determined that capacity expansion and renovation are required, such as replacing conductors with a larger capacity or upgrading transformers.

[0059] In the embodiment of the present application, the first determination is the determination of the priority of substation area capacity expansion and renovation. The specific steps include:

[0060] Extract the second power grid operation data from the power grid dispatching management system, where the second power grid operation data at least includes the heavy load frequency and heavy load time period data of the substation within a month;

[0061] When the heavy load frequency within the year is greater than the first threshold, it is marked as high-frequency heavy load;

[0062] When the heavy load frequency within the year is between the second threshold and the first threshold, it is marked as medium-frequency heavy load.

[0063] It should be noted that the first threshold may be 30 times, and the threshold is set based on the average heavy load times of the top 10 main transformers with heavy loads in the Guigang network area in a year. The second threshold should be less than the first threshold. When a substation is marked as "high-frequency heavy load", it indicates that the substation has had a relatively large number of times of load exceeding its design capacity within the year, and further analysis may be required to determine whether capacity expansion and renovation or other measures are needed to relieve the load pressure. When a substation is marked as "medium-frequency heavy load", it means that although the heavy load situation of the substation does not reach the level of high-frequency heavy load, it still shows a certain degree of overload trend, and attention may need to be paid and preventive maintenance or optimization strategies may be considered.

[0064] Specifically, such as Figure 2As shown, if a substation is marked as high-frequency heavy load, the suggestion of "capacity expansion is required within 3 months" is triggered; if a substation is marked as medium-frequency heavy load, the suggestion of "capacity expansion is required within 6 months" is triggered; combined with the heavy load time period data, a main transformer heavy load peak data sheet for the network area is generated and corresponding alarms are pushed.

[0065] In an optional embodiment, the second determination may be to continuously monitor and analyze the power factor in the power grid operation data to evaluate whether the power factor of each node in the power grid remains within the ideal range. When it is found that the power factor in some areas or nodes is lower than the set threshold, it indicates that there may be problems of insufficient or excessive reactive power in this area, resulting in voltage instability. Based on this analysis result, suggestions for adding or adjusting reactive power compensation equipment are proposed to improve the power factor and thus optimize the voltage quality.

[0066] In an optional embodiment, the second determination may also be to combine multi-source data such as historical load data and meteorological information, use machine learning algorithms to accurately predict future loads, and analyze the voltage change trends under different load patterns. For areas where the prediction shows that high loads may occur in the future and there is a risk of voltage drop, corresponding voltage regulation strategies are formulated in advance, such as pre-investing capacitor banks or adjusting the tap position of transformers, to maintain voltage stability. In addition, these strategies can be dynamically adjusted according to real-time monitoring data to ensure effective control of the voltage level even in the case of rapid load changes.

[0067] In the embodiment of the present application, the second determination is a regional AVC optimization determination, combined with Figure 3 The specific steps are as follows:

[0068] Statistical third power grid operation data, where the third power grid operation data at least includes the number of AVC lockouts of the substation within a month and the main transformer tap-changing frequency.

[0069] When the number of AVC lockouts of the substation is greater than the third threshold and the main transformer tap-changing frequency exceeds the first condition limit, it is determined as "AVC parameters need to be optimized" and a capacitor optimization suggestion is generated.

[0070] Predict the voltage fluctuation trend of the area through the voltage deviation analysis model, and push the prediction result to the voltage deviation analysis interface. In the voltage deviation analysis model, the substation voltage deviation index is the sum of the tap-changing times and the capacitor switching times.

[0071] It should be noted that the third threshold can be set by analyzing the locking records of the substation AVC system over a past period of time to find a reasonable threshold that can distinguish normal operations from abnormal situations. For example, if the average number of AVC lockings per month at a certain substation was 5 times in the past 12 months, then the third threshold can be set higher than this value, such as 8 times or 10 times; the design documents of the substation AVC system and the technical specifications provided by the manufacturer can also be referred to to understand the maximum allowable number of lockings during equipment design, and this can be used as a reference to set the threshold; a threshold that can ensure grid safety and effectively reflect problems can also be set by considering the secure and stable operation of the power grid.

[0072] It should be noted that the first conditional limit can be considered based on the service life of the equipment, that is, it can be set according to the upper limit of the annual tap-changing times recommended by the transformer manufacturer. For example, if the manufacturer recommends no more than 300 times per year, then the first conditional limit for the monthly tap-changing frequency can be set to approximately 25 times (300 times / 12 months). Of course, appropriate adjustments need to be made considering factors such as seasonal load changes; the actual tap-changing frequency of the transformer under different load conditions can also be evaluated through historical operation data to find a reasonable limit that neither affects the equipment life nor fails to meet the voltage regulation requirements.

[0073] In an alternative embodiment, the third determination can be an equipment status assessment based on temperature monitoring, that is, setting a corresponding safe operating temperature range according to the equipment type and performing trend analysis in combination with historical temperature data. If it is detected that the temperature of some equipment abnormally rises or continuously exceeds the set safe threshold, it is determined that the equipment has a potential failure risk and special inspection or maintenance is required; the third determination can also be an analysis of the equipment operation years and maintenance records, that is, by analyzing the installation date, operation years, and previous maintenance records of the equipment, evaluating the aging degree and possible problems of the equipment. For those equipment that are close to or exceed the designed service life and whose maintenance records show frequent failures, it is recommended to plan for replacement or major overhaul in advance.

[0074] In an alternative embodiment, the third determination can also be a load rate and efficiency analysis, that is, if it is found that some equipment is in a high-load state for a long time or its efficiency significantly decreases, it indicates that these equipment may have an overload risk or increased internal component losses. Based on this analysis result, targeted optimization suggestions can be put forward, such as adjusting the load distribution, increasing cooling measures, or considering upgrading and replacement.

[0075] In the embodiment of the present application, the third determination is an equipment health assessment, combined with Figure 4 The specific steps shown include:

[0076] Integrate the fourth power grid operation data, where the fourth power grid operation data at least includes tripping data, defect data, etc.;

[0077] The device health value is calculated using a failure rate model, and the calculation of the device health value is 0.5 * the number of faults being processed for tripping + 0.1 * the number of archived faults for tripping + 1 * the number of urgent defects + 0.1 * the number of other defects + 1 * the number of voltage anomaly faults;

[0078] Output the device health status and the recommended inspection cycle based on the device health value;

[0079] Specifically, when the device health value is less than or equal to 0.5, it is determined that the device health status is unhealthy, and it is recommended to conduct an inspection once every three days; when the device health value is greater than 0.5 and less than 1, it is determined that the device health status is sub-healthy, and it is recommended to conduct an inspection once a week; when the device health value is greater than or equal to 1, it is determined that the device health status is good, and it is recommended to conduct an inspection once within the inspection cycle.

[0080] It should be noted that the thresholds for determining the device health status are dynamically adjusted based on the comprehensive health values of the top 20%, 50%, and 30% of the substations in the Guigang network area.

[0081] It should be noted that the above step S200 can deeply analyze the grid operation status, accurately generate suggestions for capacity expansion and transformation, automatic voltage control optimization, and equipment maintenance, thereby improving the scientificity and pertinence of decision-making, effectively avoiding blind investment of resources, and enhancing the grid operation efficiency and stability.

[0082] S300: Construct a first comprehensive interface and push a data roulette-style list of suggestions to achieve a panoramic analysis of grid operation data.

[0083] It should be noted that step S300 provides an intuitive and comprehensive grid operation data analysis platform, enabling operation personnel to quickly obtain and understand complex grid states and optimization suggestions, thereby improving decision-making efficiency and operation convenience, and significantly enhancing the overall efficiency and response speed of grid management.

[0084] Embodiment 3, in this embodiment, a panoramic analysis system for grid operation data based on the Yunjing digital platform is provided, including a data acquisition module, an analysis and determination module, and a visualization output module;

[0085] Specifically, the data acquisition module is used to obtain the first grid operation data in real time;

[0086] Specifically, the analysis and determination module is used to input the first grid operation data into a hierarchical algorithm model for the first determination, the second determination, and the third determination to obtain suggestions for capacity expansion and transformation, automatic voltage control optimization, and equipment maintenance;

[0087] Specifically, the visualization output module is used to construct a first integrated interface and push a data roulette-style suggestion list to achieve panoramic analysis of power grid operation data.

[0088] It should be noted that the technical solution of the power grid operation data panoramic analysis system based on the cloud view digital platform and the technical solution of the above-mentioned power grid operation data panoramic analysis method based on the cloud view digital platform belong to the same concept. For the details not described in detail in the technical solution of the power grid operation data panoramic analysis system based on the cloud view digital platform in this embodiment, reference can be made to the description of the technical solution of the power grid operation data panoramic analysis method based on the cloud view digital platform.

[0089] The above-mentioned unit modules can be embedded in the processor of the computer device in hardware form or be independent of it, or be stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above modules.

[0090] This embodiment also provides an electronic device, which includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a carrier network, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it realizes the power grid operation data panoramic analysis method based on the cloud view digital platform. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a button, a trackball, or a touchpad set on the shell of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0091] This embodiment also provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by the processor, it realizes the method proposed in the above embodiment.

[0092] The storage medium proposed in this embodiment and the method proposed in the above embodiment belong to the same inventive concept. The technical details not described in detail in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0093] From the above description of the embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software and the necessary general-purpose hardware. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disc of a computer, etc., including several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the method of the embodiments of the present invention.

[0094] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

[0095] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present application can be implemented in various computer languages.

[0096] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of flows and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0097] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to operate in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction device that implements the functions specified in one or more processes and / or blocks Figure 1 in the flowchart(s) Figure 1 and / or block(s).

[0098] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in one or more processes Figure 1 and / or block(s). Figure 1 in the flowchart(s) and / or block(s).

[0099] Although the preferred embodiments of the present application have been described, additional changes and modifications can be made by those skilled in the art once they learn of the basic inventive concept. Therefore, the appended claims are intended to be construed to cover the preferred embodiments as well as all changes and modifications that fall within the scope of the present application.

[0100] It is obvious that those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to include these modifications and variations.

Claims

1. A panoramic analysis method for power grid operation data based on a cloud view digital platform, characterized in that, Including: Obtaining the first power grid operation data in real time; Inputting the first power grid operation data into a hierarchical algorithm model for the first determination, the second determination, and the third determination to obtain capacity expansion and renovation suggestions, automatic voltage control optimization suggestions, and equipment maintenance suggestions; Constructing a first integrated interface and pushing a data roulette-style suggestion list to achieve a panoramic analysis of the power grid operation data.

2. The panoramic analysis method of power grid operation data based on the cloud view digital platform according to claim 1, characterized in that The first determination includes: Extracting the second power grid operation data, where the second power grid operation data at least includes the monthly heavy load frequency and heavy load time period data of the substation; When the annual heavy load frequency is greater than the first threshold, it is marked as high-frequency heavy load; When the annual heavy load frequency is between the second threshold and the first threshold, it is marked as medium-frequency heavy load.

3. The panoramic analysis method of power grid operation data based on the cloud view digital platform according to claim 2, characterized in that, The second determination includes: Statistical analysis of the third power grid operation data, where the third power grid operation data at least includes the monthly AVC locking times and main transformer tap-changing frequencies of the substation; When the AVC locking times of the substation are greater than the third threshold and the main transformer tap-changing frequency exceeds the first condition limit, it is determined as "AVC parameters need to be optimized" and a capacitor optimization suggestion is generated.

4. The panoramic analysis method of power grid operation data based on the cloud view digital platform according to claim 3, wherein, The third determination includes: Integrating the fourth power grid operation data and calculating the equipment health value; Outputting the equipment health status and inspection cycle suggestions based on the equipment health value.

5. The panoramic analysis method for power grid operation data based on the cloud view digital platform according to claim 2, wherein The obtaining of the capacity expansion and renovation suggestions includes: If it is marked as high-frequency heavy load, trigger the suggestion of "capacity expansion is required within 3 months"; If it is marked as medium-frequency heavy load, trigger the suggestion of "capacity expansion is required within 6 months"; Combining the heavy load time period data, generating a main transformer heavy load peak data sheet for the network area and pushing the corresponding alarm.

6. The panoramic analysis method of power grid operation data based on the cloud view digital platform according to claim 3, characterized in that Predicting the voltage fluctuation trend of the area through a voltage deviation analysis model and pushing the prediction result to the voltage deviation analysis interface, where the substation voltage deviation index in the voltage deviation analysis model is the sum of the tap-changing times and capacitor switching times.

7. The panoramic analysis method of power grid operation data based on the cloud view digital platform according to claim 4, wherein The calculation of the equipment health value is 0.5 * the number of faults being processed for tripping + 0.1 * the number of archived faults for tripping + 1 * the number of emergency defects + 0.1 * the number of other defects + 1 * the number of voltage anomaly faults.

8. A panoramic analysis system for power grid operation data based on a cloud view digital platform, applying the method according to any one of claims 1 to 7, characterized in that, Including: A data acquisition module for obtaining the first power grid operation data in real time; An analysis and determination module for inputting the first power grid operation data into a hierarchical algorithm model for the first determination, the second determination, and the third determination to obtain capacity expansion and renovation suggestions, automatic voltage control optimization suggestions, and equipment maintenance suggestions; A visualization output module for constructing a first integrated interface and pushing a data roulette-style suggestion list to achieve a panoramic analysis of the power grid operation data.

9. An electronic device, comprising a memory and a processor, characterized in that: The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having computer-executable instructions stored thereon, characterized in that: When the computer-executable instructions are executed by the processor, the steps of the method according to any one of claims 1 to 7 are implemented.

Images