Intelligent sensing power grid steady-state adaptive cruise control method and control device

Abstract

The invention discloses an intelligent sensing power grid steady-state adaptive cruise regulation and control method and control device, and relates to the technical field of power systems, the method comprises the following steps: establishing an equipment set including a plurality of power grid equipment with position identifiers; reading a predetermined key point location set, marking the predetermined key point location set and the equipment to the three-dimensional power grid model, and generating a target power grid model; performing dynamic cruise monitoring on the power grid according to a predetermined cruise strategy, recording real-time cruise information and rendering the real-time cruise information into the power grid model; judging whether the real-time cruise record meets a power grid constraint condition or not by combining a power grid model; if yes, a steady-state analysis instruction is sent out, and a real-time steady-state index is evaluated through a steady-state evaluation function; and when the steady-state index is not within the preset threshold value, sending a regulation and control instruction, and carrying out regulation and control execution on the power grid. Therefore, the technical effects of improving the monitoring dynamic adjustment capability and optimizing the monitoring response efficiency are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to an intelligently perceived power grid steady-state adaptive cruise control method and a control device. Background Art

[0002] In power grid management, steady-state control and cruise monitoring are key links to ensure safe and stable operation of the power grid. Existing steady-state monitoring and regulation of power grids usually rely on fixed monitoring points, monitoring routes and preset regulation strategies, lacking dynamic adaptability and making it difficult to cope with the complex and changing actual conditions in the power grid. There are technical problems such as single monitoring mode, delayed monitoring response and inability to adapt to dynamic changes. Summary of the invention

[0003] The present invention provides an intelligent-perception grid steady-state adaptive cruise control method and control device to solve the technical problems of the prior art such as single monitoring mode, delayed monitoring response, and inability to adapt to dynamic changes, and to achieve the technical effect of improving the dynamic adjustment capability of monitoring and optimizing the monitoring response efficiency.

[0004] In a first aspect, the present invention provides a method for intelligently sensing a steady-state adaptive cruise control of a power grid, wherein the method comprises:

[0005] A device set is formed, wherein the device set refers to a collection of devices in a target power grid, and the device set includes a plurality of devices with location identifiers. A predetermined key point set is read, and the predetermined key point set and the plurality of devices with location identifiers are marked in a three-dimensional power grid model to obtain a target power grid model, wherein the three-dimensional power grid model refers to a three-dimensional structural model of the target power grid. Dynamic cruise monitoring is performed on the target power grid based on a predetermined cruise strategy to obtain a real-time cruise record, and the real-time cruise record is rendered to the target power grid model. In combination with the target power grid model, it is determined whether the real-time cruise record meets the predetermined power grid constraints. If it meets the requirements, a steady-state analysis instruction is issued, and a steady-state evaluation function is introduced based on the steady-state analysis instruction to evaluate and analyze the real-time cruise record to obtain a real-time steady-state index. When the real-time steady-state index is not at a predetermined steady-state index threshold, a control processing instruction is issued, and the target power grid is controlled and executed based on the control processing instruction.

[0006] In a second aspect, the present invention further provides an intelligently perceived grid steady-state adaptive cruise control device, wherein the control device comprises:

[0007] A device set building module, wherein the device set building module is used to build a device set, wherein the device set refers to a collection of devices in a target power grid, and the device set includes a plurality of devices with location identifiers.

[0008] A power grid model marking module, the power grid model marking module is used to read a predetermined key point set, and mark the predetermined key point set and the multiple devices with location identifiers into a three-dimensional power grid model to obtain a target power grid model, wherein the three-dimensional power grid model refers to a three-dimensional structural model of the target power grid.

[0009] A dynamic cruise monitoring module is used to perform dynamic cruise monitoring on the target power grid based on a predetermined cruise strategy, obtain real-time cruise records, and render the real-time cruise records to the target power grid model.

[0010] A compliance determination module is used to determine whether the real-time cruise record complies with a predetermined grid constraint in combination with the target grid model.

[0011] A steady-state analysis module is used to issue a steady-state analysis instruction if it meets the requirements, and introduce a steady-state evaluation function based on the steady-state analysis instruction to evaluate and analyze the real-time cruise record to obtain a real-time steady-state index.

[0012] A determination and control module is used to issue a control processing instruction when the real-time steady-state index is not within a predetermined steady-state index threshold, and to perform control execution on the target power grid based on the control processing instruction.

[0013] The present invention discloses an intelligently perceived power grid steady-state adaptive cruise control method and control device, comprising: constructing a device set, the device set refers to a collection of devices in a target power grid, and includes a plurality of devices with position identifiers; reading a predetermined key point set, and marking it and a plurality of devices with position identifiers into a three-dimensional power grid model, generating a target power grid model, the three-dimensional power grid model being a three-dimensional structural representation of the target power grid; based on a predetermined cruise strategy, dynamically cruise monitoring the target power grid is performed, a real-time cruise record is generated, and the real-time cruise record is rendered into the target power grid model; combined with the target power grid model, it is determined whether the real-time cruise record meets the predetermined power grid constraint conditions; if it meets, a steady-state analysis instruction is issued, and a steady-state evaluation function is introduced based on the instruction, and the real-time cruise record is evaluated and analyzed to obtain a real-time steady-state index; when the real-time steady-state index is not within the predetermined steady-state index threshold range, a control processing instruction is issued, and the corresponding control operation is performed on the target power grid according to the instruction. The intelligently perceived power grid steady-state adaptive cruise control method and control device disclosed by the present invention solve the technical problems of single monitoring mode, delayed monitoring response, and inability to adapt to dynamic changes, and achieve the technical effect of improving the dynamic adjustment capability of monitoring and optimizing the monitoring response efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a flow chart of the method for intelligently sensing steady-state adaptive cruise control of power grid according to the present invention;

[0015] Figure 2 It is a structural schematic diagram of the intelligent sensing grid steady-state adaptive cruise control device of the present invention.

[0016] Explanation of the reference numerals: equipment set construction module 11, power grid model marking module 12, dynamic cruise monitoring module 13, compliance determination module 14, steady-state analysis module 15, determination and control module 16. DETAILED DESCRIPTION

[0017] The technical solution provided in the embodiments of the present invention is to solve the technical problems of the prior art such as single monitoring mode, delayed monitoring response, and inability to adapt to dynamic changes. The overall idea adopted is as follows:

[0018] First, a device set is formed, which refers to the collection of all devices in the target power grid, and each device has unique location information. Then, the predetermined key point set is read, these key points are combined with the device set, and marked in the three-dimensional power grid model to generate the target power grid model. The three-dimensional power grid model can intuitively display the three-dimensional structural layout of the power grid, including the locations of all key devices and nodes. After obtaining the target power grid model, dynamic cruise monitoring of the target power grid is performed based on the predetermined cruise strategy. The cruise information is recorded in real time, and these real-time cruise records are rendered into the three-dimensional power grid model. Next, it will be determined whether these real-time cruise records meet the predetermined power grid constraints according to the target power grid model. If the cruise record meets these constraints, a steady-state analysis instruction is issued, and a steady-state evaluation function is introduced to evaluate and analyze the real-time cruise record to obtain a real-time steady-state index. If the real-time steady-state index is not within the predetermined steady-state index threshold range, a control processing instruction is issued to perform the control operation on the target power grid to ensure the safe and stable operation of the power grid.

[0019] The above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods of the specification to better understand the above technical solution. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments of the present invention. It should be understood that the present invention is not limited to the example embodiments used only to explain the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. In addition, it should be noted that, for the convenience of description, only the parts related to the present invention are shown in the drawings, rather than all of them.

[0020] Embodiment 1

[0021] Figure 1 The figure is a flow chart of the method for controlling the steady-state adaptive cruise control of a power grid based on intelligent perception according to the present invention, wherein the method comprises:

[0022] A device set is formed, where the device set refers to a collection of devices in a target power grid, and the device set includes a plurality of devices with location identifiers.

[0023] Specifically, the target power grid management platform or the target power grid equipment database is first interacted with to obtain the equipment information of multiple devices in the target power grid, thereby forming an equipment set, which is a collection of multiple devices, and each device has its own location identifier, so as to quickly determine the location of multiple devices for power grid analysis, optimization or management. Among them, the equipment included in the equipment set includes substations, transmission lines, switchgear, etc.

[0024] Exemplarily, an API (application programming interface) or other types of interfaces are used to obtain device information from a management platform or device database of a power grid, including the type, status, location, energy consumption, production date, maintenance records, etc. of the device. Then, the location information corresponding to multiple devices is extracted, marked as a location identifier, and the above-mentioned device set is generated.

[0025] The predetermined key point set is read, and the predetermined key point set and the plurality of devices with location identifiers are marked in a three-dimensional power grid model to obtain a target power grid model, wherein the three-dimensional power grid model refers to a three-dimensional structural model of the target power grid.

[0026] Specifically, the predetermined key point set refers to a collection of multiple power grid equipment points that need to be monitored by adaptive cruise control, and the corresponding equipment set includes multiple key power equipment that need to be controlled by adaptive cruise control, which has a specific importance or function. Exemplary includes substations, important distribution equipment, key transmission lines, etc. The location information of these key points may be stored in the predetermined key point set and can be read through a programming interface or a file reading operation.

[0027] Specifically, after obtaining the above-mentioned predetermined key point set, multiple key points and multiple power equipment are marked in the three-dimensional power grid model to determine their positions in the three-dimensional model. The obtained three-dimensional structure model containing all key points and equipment will be used for power grid analysis, optimization or management.

[0028] In some embodiments, target multi-dimensional feature information of the target power grid is acquired, and the three-dimensional power grid model is constructed according to target structural feature information in the target multi-dimensional feature information.

[0029] The three-dimensional power grid model is a digital model of the target power grid constructed by using GIS software or special three-dimensional modeling software.

[0030] Specifically, first, the management platform or device database of the interactive power grid obtains the multi-dimensional feature information of the equipment, including the location, type, status, energy consumption, etc. of the equipment; the topological structure of the power grid, power flow information, the operating status of the power grid, etc.; the geographic information of the area covered by the power grid (such as terrain, landform, building distribution, etc.). Then, a three-dimensional power grid model is constructed based on the structural feature information in the multi-dimensional feature information. The structural feature information includes the location information of the equipment, the topological structure of the power grid, etc. Exemplarily, it includes determining the specific location of each device in three-dimensional space based on the device location information (including longitude, latitude and altitude information). Then, based on the topological structure and topological connection path of the power grid, the digital models of multiple devices obtained by positioning are connected. Finally, the power grid model is integrated with the geographic information to ensure the accurate positioning of the power grid model in the geographic space, and then the above-mentioned three-dimensional power grid model is generated.

[0031] The three-dimensional power grid model created through the above method steps helps to better understand the structure and operation status of the power grid by marking the location of the equipment and the topological structure of the power grid in the model, so as to make better decisions.

[0032] In some embodiments, the target multidimensional feature information also includes target line feature information; the target line feature information is analyzed to obtain first line feature information of a first line intersection; the first line feature information is normalized and weighted to obtain a first weight of the first line intersection; when the first weight reaches a predetermined weight threshold, the first line intersection is recorded as a first key point, and the predetermined key point set is formed.

[0033] Specifically, the above-mentioned target multi-dimensional feature information also includes target line feature information, wherein the line feature information refers to the detailed information of each line in the power grid, including line length, line type, electrical characteristics of the line, operating status of the line, etc.

[0034] Specifically, the target line characteristic information is analyzed to obtain the first line characteristic information of the first line intersection, including analyzing the geometric information and topological structure of the line, identifying the intersections of each line in the power grid, and extracting relevant characteristic information from the identified line intersections, such as the geographical location of the intersection, the number of connected lines, and the electrical characteristics of the intersection (such as load capacity, voltage level, etc.). Then, the extracted line characteristic information is normalized to eliminate the dimensional differences between different features, and the normalized characteristic information is weighted according to the importance of different features, and the comprehensive weight of each line intersection is calculated and output as the first weight. The first weight is used to quantitatively reflect the importance of the line intersection and enable the importance of different intersections to be quantitatively represented.

[0035] Optionally, a predetermined weight threshold is set to determine whether a line intersection is a key point. When the weight of a line intersection reaches or exceeds the predetermined weight threshold, the line intersection is recorded as a key point. Then, all identified key points are stored as a predetermined key point set, which provides a target reference for the subsequent construction of the three-dimensional power grid model.

[0036] Dynamic cruise monitoring is performed on the target power grid based on a predetermined cruise strategy to obtain a real-time cruise record, and the real-time cruise record is rendered to the target power grid model.

[0037] Specifically, the dynamic cruise monitoring of the target power grid is continued according to a predetermined cruise strategy, wherein the predetermined cruise strategy refers to a cruise plan and rules pre-established in power grid management, which is used to regularly or irregularly inspect and monitor the equipment and key points in the power grid. Exemplarily, the predetermined cruise strategy is used to control the power grid inspection equipment or power grid inspection personnel to perform dynamic cruise monitoring.

[0038] In some embodiments, the predetermined cruise strategy includes a predetermined equipment cruise strategy and a predetermined key point cruise strategy, wherein the predetermined equipment cruise strategy includes a predetermined equipment cruise path and a predetermined equipment cruise cycle, and the predetermined key point cruise strategy includes a predetermined key point cruise path and a predetermined key point cruise cycle;

[0039] Performing dynamic cruise monitoring on the device set in the target power grid according to the predetermined device cruise path to obtain a real-time device cruise record;

[0040] Specifically, the predetermined equipment cruise path refers to one or more cruise routes pre-set according to the distribution of power grid equipment. The path covers all key equipment in the power grid to ensure that the operating status of the equipment can be fully monitored during the cruise process.

[0041] Specifically, the scheduled equipment cruise cycle refers to the cruise frequency pre-set according to the importance and operating status of the equipment. Different equipment requires different cruise cycles to ensure timely detection and handling of problems. Exemplarily, the equipment is classified and prioritized according to its importance, failure rate and historical data to determine the cruise cycles of different equipment. In addition, big data analysis and machine learning technology are combined to predict the potential failure risk of equipment and dynamically adjust the cruise cycle. Through a reasonable cruise cycle, the utilization of cruise resources can be optimized and maintenance costs can be reduced while ensuring equipment safety.

[0042] Specifically, the predetermined key point cruise path refers to one or more cruise routes that are pre-set according to the distribution of key points in the power grid (such as substations, switch stations, etc.). These routes cover all key points to ensure that the operating status of key points can be fully monitored during the cruise process. In other words, the predetermined key point cruise path is a route for cruise monitoring of lines outside the equipment or other power grid components.

[0043] Specifically, the cruise cycle of the predetermined key points refers to the cruise frequency pre-set according to the importance and operating status of the key points. Different key points may require different cruise cycles to ensure timely detection and handling of problems. Exemplary methods for determining the cruise cycle of the predetermined key points include: using risk assessment models to conduct risk analysis on key points and determine the cruise cycle; through real-time monitoring of the operating status of key points, combined with historical data and prediction models, dynamically adjusting the cruise cycle, etc. By reasonably setting the cruise cycle of key points, the utilization of cruise resources can be optimized and the effectiveness of cruise can be improved while ensuring the safety of key points.

[0044] Furthermore, according to the predetermined equipment cruise path, dynamic cruise monitoring is performed on the equipment set in the target power grid to obtain real-time equipment cruise records. Among them, dynamic cruise monitoring refers to real-time monitoring of power grid equipment according to the predetermined cruise path and cycle, recording the operating status and environmental parameters of the equipment, so as to timely discover and handle abnormal situations. Specifically, it includes real-time collection of equipment operation data through various sensors installed on the equipment (such as temperature sensors, vibration sensors, current sensors, etc.). Through wireless communication technology (such as LoRa, NB-IoT, etc.), the collected data is transmitted to the monitoring center in real time, and the real-time data is processed and analyzed to generate real-time equipment cruise records.

[0045] In combination with the target power grid model, it is determined whether the real-time cruise record complies with a predetermined power grid constraint.

[0046] Specifically, the predetermined grid constraint is a grid constraint determined in combination with the above-mentioned predetermined cruise strategy, and the predetermined grid constraint is used to preliminarily determine whether the real-time cruise record is usable record data to ensure the validity and reliability of the real-time cruise record and meet the standards for grid monitoring and analysis. Exemplarily, the predetermined grid constraint includes grid route constraint, collection accuracy constraint, collection time constraint, collection cycle constraint, etc.

[0047] Among them, the power grid route constraint means that during the cruise monitoring process, data collection must be carried out according to the predetermined power grid route to ensure that all key nodes and lines are covered. The collection accuracy constraint refers to the accuracy requirements of the cruise equipment to collect data to ensure the accuracy and reliability of the data. The collection time constraint refers to the requirement that the cruise equipment completes data collection within the predetermined time period to ensure the timeliness of the data. The collection cycle constraint refers to the requirement that the cruise equipment collects data according to the predetermined cycle to ensure the continuity and comparability of the data.

[0048] If it is in compliance, a steady-state analysis instruction is issued, and a steady-state evaluation function is introduced based on the steady-state analysis instruction to evaluate and analyze the real-time cruise record to obtain a real-time steady-state index.

[0049] Specifically, if all parameters in the real-time cruise record meet the predetermined grid constraints, the recorded data is preliminarily determined to be usable data. Correspondingly, if any parameter does not meet the predetermined grid constraints, the recorded data is preliminarily determined to be unusable data, and an early warning signal is issued.

[0050] Specifically, the steady-state analysis instruction is an instruction issued to evaluate the steady-state operation status of the power grid when the real-time cruise record meets the predetermined power grid constraints. The steady-state evaluation function is a mathematical function used to evaluate the steady-state operation status of the power grid. The function combines various parameters in the real-time cruise record to calculate the real-time steady-state index of the power grid, and then quantitatively evaluates the stability performance of the target power grid.

[0051] In some embodiments, the real-time cruise record is evaluated and analyzed to obtain a real-time steady-state index, including:

[0052] The real-time device cruise record is analyzed in combination with the predetermined device cruise cycle to obtain first device operation status information of the first device, where the first device refers to any one device in the device set; the first device operation status information is analyzed to obtain a first device status index; dynamic cruise monitoring is performed on the predetermined key point set in the target power grid according to the predetermined key point cruise path to obtain real-time key point cruise records; the real-time key point cruise records are analyzed in combination with the predetermined key point cruise cycle to obtain a first load of the first key point; a target unit duration is determined according to the predetermined device cruise cycle and the predetermined key point cruise cycle; a steady-state analysis is performed on the first device status index and the first load according to the steady-state evaluation function to obtain the real-time steady-state index under the target unit duration.

[0053] Specifically, first, select any one device in the device set, extract its operating status information, and obtain the first device operating status information of the first device. Among them, the first device operating status information may include voltage, current, temperature, load, power factor, harmonics and other parameter information. Then, according to the state evaluation operator preset by the target power grid, combined with the operating status information of the first device, its state index is calculated and output as the first device state index, which quantifies the operating status of the first device and represents the overall health status of the device. Among them, the state evaluation operator is a weighted evaluation operator determined by the professional and technical personnel of the target power grid or the expert system according to the actual control needs of the target power grid, and different power grid equipment may correspond to different state evaluation operators.

[0054] Specifically, according to the pre-set cruise monitoring path of key points, a predetermined set of key points in the target power grid is monitored in real time, and the operation data of the key points are collected. Then, according to the periodicity of cruise monitoring at different points, the cruise data of the key points are processed and the first load is extracted therefrom. The load of the key point represents the power demand or transmission capacity of the point.

[0055] Specifically, the cruise cycles of equipment and key points are usually different, so in this step, the target unit duration is determined by calculating the average of the two or setting a fixed monitoring cycle. The target unit duration will be used as the basic time period for analyzing the steady-state index.

[0056] Among them, a longer target unit duration helps to reduce the impact of short-term fluctuations, that is, short-term load fluctuations or equipment status changes can be averaged over a longer time frame, avoiding short-term anomalies from affecting the overall evaluation results, thereby obtaining a more stable operating status index and improving the representativeness and accuracy of the data. A shorter target unit duration helps to obtain more timely and sensitive operating status monitoring. In other words, a short target unit duration can capture the operating status changes of equipment or key points in the power grid in a timely manner, especially in emergencies or abnormal equipment status, which helps to discover problems in real time, take countermeasures quickly, and avoid the expansion of problems.

[0057] Specifically, the steady-state evaluation function is an empirical function determined based on historical steady-state analysis data, which is used to analyze the equipment status index and load and evaluate the stability of the equipment or system within the target time period. The steady-state evaluation function takes the first equipment status index and the first load as input parameters, evaluates the working state of the equipment under the current load, and determines whether the equipment is operating stably and efficiently.

[0058] Optionally, the steady-state evaluation function is constructed based on mathematical analysis or machine learning methods. Exemplarily, the mathematical analysis method uses mathematical methods such as statistical analysis and regression models to analyze and model historical data, extract the core parameters that affect the steady-state of the equipment (such as equipment load, temperature, etc.), and construct a steady-state evaluation function based on the weights and correlations of these parameters. Exemplarily, the machine learning method trains the machine learning model through a large amount of historical data, automatically extracts the complex correlations in the steady-state analysis, and constructs a more accurate steady-state evaluation model. Specifically, it includes decision trees, support vector machines (SVM) or neural networks.

[0059] Specifically, the real-time steady-state index is a measure of the stability of the system within the target unit time. It reflects the balance between the equipment and the load at key points, whether there is overload, poor equipment operation or other unstable factors. For example, the higher the value of the steady-state index, the more stable the equipment state and the better the load matching.

[0060] Through the above-mentioned method steps, combined with the steady-state evaluation function (whether based on mathematical analysis or machine learning methods) for real-time analysis, the current steady-state operation of the equipment or system can be quickly evaluated, thereby improving the accuracy of the analysis results.

[0061] In some implementations, if the predetermined device cruise cycle is greater than the predetermined key point cruise cycle, the first cycle duration of the predetermined device cruise cycle is obtained, and the first cycle duration is recorded as the target unit duration; if the predetermined device cruise cycle is less than the predetermined key point cruise cycle, the second cycle duration of the predetermined key point cruise cycle is obtained, and the second cycle duration is recorded as the target unit duration.

[0062] Optionally, the longer cycle length between the predetermined equipment cruise cycle and the predetermined key point cruise cycle is used as the target unit duration, so that the analysis process uses all equipment status indexes and load information, which helps to improve the accuracy of the analysis.

[0063] In some implementations, the method further includes:

[0064] The target external feature information in the dynamic external feature record is extracted based on the target unit time length; the target external temperature feature information and the target external radiation feature information in the target external feature information are analyzed to obtain the target external interference coefficient; and the real-time steady-state index is adjusted based on the target external interference coefficient.

[0065] Optionally, the dynamic external characteristic record includes various characteristic information of the external environment, such as temperature, radiation, humidity, wind speed, etc. The external temperature characteristic information and external radiation characteristic information within the target unit time are extracted from the dynamic external characteristic record to reflect the external environment level of the target power grid under the target unit time. Specifically, extreme temperature or high radiation level will affect the accuracy of monitoring data and the stability of the target power grid.

[0066] Specifically, by analyzing historical data or conducting interference experiments, the influence characteristics of external characteristic information on the target power grid are determined, and the external interference evaluation function is determined. Then, the acquired external temperature characteristic information and external radiation characteristic information are brought into the external interference evaluation function for interference analysis, and the corresponding target external interference coefficient is output. Exemplarily, the greater the external interference, the greater the corresponding external interference coefficient, and the external interference coefficient is greater than or equal to 1.

[0067] Optionally, an adjustment formula is used to adjust the real-time steady-state index based on the target external interference coefficient. The adjusted real-time steady-state index is the product of the real-time steady-state index and the target external interference coefficient. By extracting external characteristic information within the target unit time, analyzing characteristics such as temperature and radiation, calculating the target external interference coefficient, and adjusting the real-time steady-state index based on the interference coefficient, the impact of the external environment on the steady-state operation of the power grid can be more accurately reflected. This helps to improve the accuracy and reliability of power grid operation monitoring and ensure the safe and stable operation of the power grid.

[0068] When the real-time steady-state index is not within a predetermined steady-state index threshold, a control processing instruction is issued, and the target power grid is controlled and executed based on the control processing instruction.

[0069] Specifically, when the real-time steady-state index does not meet the predetermined steady-state index threshold, a control processing instruction is generated, and based on the control processing instruction, the real-time cruise record is called to locate the control target and match the control scheme, and the target power grid is adaptively controlled to ensure that the power grid returns to steady-state operation. The control scheme includes adjusting load distribution, enabling backup power supply, adjusting generator output, etc.

[0070] In some embodiments, the method further comprises:

[0071] Obtain a first cruise time of the first device operation status information; generate a first device status time series of the first device according to a first corresponding relationship between the first cruise time and the first device status index; obtain a second cruise time of the first load; generate a first load time series of a first key point according to a second corresponding relationship between the second cruise time and the first load; perform trend forecasting analysis on the first device status time series and the first load time series in turn to obtain a first forecast state index and a first forecast load, respectively; perform steady-state analysis on the first forecast state index and the first forecast load according to the steady-state evaluation function to obtain a forecast steady-state index; when the forecast steady-state index is not within the predetermined steady-state index threshold, issue a feedforward control processing instruction; and perform predictive control on the target power grid based on the feedforward control processing instruction.

[0072] Specifically, the first device state time series and the first load time series refer to serialized device state information and device load information, which reflect the state change of the target device in the time dimension. By performing trend analysis on the first device state time series and the first load time series, the state change pattern or law of the target device can be obtained, thereby predicting the device state at a certain point in the future, which is convenient for forward-looking feedforward control of the target power grid. Among them, the prediction method includes time series analysis, machine learning, etc.

[0073] Furthermore, based on the same method principle as the above-mentioned steady-state analysis, according to the obtained predicted state index and the first predicted load, combined with the steady-state evaluation function, analysis is performed to obtain a predicted steady-state index, which reflects the predicted stability level of the equipment at a certain time node in the future. If the predicted steady-state index is not within the predetermined steady-state index threshold range, a control instruction is issued in advance to prevent potential grid instability and ensure the steady-state operation of the grid in the future time period.

[0074] By acquiring equipment operating status information and load information, generating corresponding time series data, and performing trend forecasting analysis on these time series data, potential problems in power grid operation can be identified in advance. When the predicted steady-state index is not within the predetermined steady-state index threshold, a feedforward control processing instruction is issued, and predictive control is performed based on the instruction, thereby ensuring the safe and stable operation of the power grid and improving the operational reliability and response capability of the power grid.

[0075] In summary, the intelligent perception grid steady-state adaptive cruise control method provided by the present invention has the following technical effects:

[0076] By constructing a device set, which refers to a collection of devices in the target power grid, and including multiple devices with location identifiers; reading a predetermined key point set, and marking it and multiple devices with location identifiers into a three-dimensional power grid model, generating a target power grid model, which is a three-dimensional structural representation of the target power grid; based on a predetermined cruise strategy, dynamic cruise monitoring is performed on the target power grid, real-time cruise records are generated, and the real-time cruise records are rendered into the target power grid model; combined with the target power grid model, it is determined whether the real-time cruise records meet the predetermined power grid constraints; if they meet, a steady-state analysis instruction is issued, and a steady-state evaluation function is introduced based on the instruction, and the real-time cruise records are evaluated and analyzed to obtain a real-time steady-state index; when the real-time steady-state index is not within the predetermined steady-state index threshold range, a control processing instruction is issued, and the corresponding control operation is performed on the target power grid according to the instruction. Thus, the technical effect of improving the dynamic adjustment capability of monitoring and optimizing the monitoring response efficiency is achieved.

[0077] Embodiment 2

[0078] Figure 2 Schematic diagram of the structure of the intelligent sensing grid steady-state adaptive cruise control device of the present invention. For example, Figure 1 The flowchart of the method for intelligently sensing the steady-state adaptive cruise control of the power grid of the present invention can be shown as follows: Figure 2 The structure shown is implemented.

[0079] Based on the same concept as the intelligent sensing grid steady-state adaptive cruise control method in the above embodiment, the present invention also provides an intelligent sensing grid steady-state adaptive cruise control device comprising:

[0080] The device set building module 11 is used to build a device set, where the device set refers to a collection of devices in a target power grid, and the device set includes a plurality of devices with location identifiers.

[0081] The power grid model marking module 12 is used to read a predetermined key point set and mark the predetermined key point set and the multiple devices with location identifiers into a three-dimensional power grid model to obtain a target power grid model, wherein the three-dimensional power grid model refers to a three-dimensional structural model of the target power grid.

[0082] The dynamic cruise monitoring module 13 is used to perform dynamic cruise monitoring on the target power grid based on a predetermined cruise strategy, obtain real-time cruise records, and render the real-time cruise records to the target power grid model.

[0083] The compliance determination module 14 is used to determine whether the real-time cruise record complies with the predetermined grid constraint in combination with the target grid model.

[0084] The steady-state analysis module 15 is used to issue a steady-state analysis instruction if it meets the requirements, and introduce a steady-state evaluation function based on the steady-state analysis instruction to evaluate and analyze the real-time cruise record to obtain a real-time steady-state index.

[0085] The determining and regulating module 16 is used to issue a regulation processing instruction when the real-time steady-state index is not within a predetermined steady-state index threshold, and to perform regulation on the target power grid based on the regulation processing instruction.

[0086] The power grid model marking module 12 includes:

[0087] The line characteristic information analysis unit is used to analyze the target line characteristic information to obtain the first line characteristic information of the first line intersection.

[0088] The line characteristic information normalization weighted processing unit is used to perform normalization weighted processing on the first line characteristic information to obtain a first weight of the first line intersection.

[0089] The weight threshold judgment and key point formation unit is used to record the first line intersection as the first key point and form the predetermined key point set when the first weight reaches a predetermined weight threshold.

[0090] In some embodiments, the dynamic cruise monitoring module 13 includes:

[0091] The device cruise monitoring and record acquisition unit is used to perform dynamic cruise monitoring on the device set in the target power grid according to the predetermined device cruise path to obtain real-time device cruise records.

[0092] The device operation status analysis and status index calculation unit is used to analyze the real-time device cruise record in combination with the predetermined device cruise cycle to obtain first device operation status information of a first device, wherein the first device refers to any device in the device set. The first device operation status information is analyzed to obtain a first device status index.

[0093] The key point cruise monitoring and record acquisition unit is used to perform dynamic cruise monitoring on the predetermined key point set in the target power grid according to the predetermined key point cruise path to obtain real-time key point cruise records.

[0094] The key point load analysis and calculation unit is used to analyze the real-time key point cruise record in combination with the predetermined key point cruise cycle to obtain the first load of the first key point.

[0095] The unit duration determination unit is used to determine the target unit duration according to the predetermined device cruise cycle and the predetermined key point cruise cycle.

[0096] A steady-state index calculation unit is used to perform a steady-state analysis on the first device state index and the first load according to the steady-state evaluation function to obtain the real-time steady-state index under the target unit time length.

[0097] In some embodiments, the steady-state analysis module 15 includes:

[0098] In some embodiments, the key point load analysis and calculation unit in the dynamic cruise monitoring module 13 is also used for:

[0099] If the predetermined device cruise cycle is greater than the predetermined key point cruise cycle, the first cycle duration of the predetermined device cruise cycle is obtained, and the first cycle duration is recorded as the target unit duration; if the predetermined device cruise cycle is less than the predetermined key point cruise cycle, the second cycle duration of the predetermined key point cruise cycle is obtained, and the second cycle duration is recorded as the target unit duration.

[0100] In some embodiments, the steady-state index calculation unit in the dynamic cruise monitoring module 13 includes:

[0101] The external feature information extraction unit is used to extract the target external feature information in the dynamic external feature record based on the target unit duration.

[0102] The external characteristic information analysis unit is used to analyze the target external temperature characteristic information and the target external radiation characteristic information in the target external characteristic information to obtain the target external interference coefficient.

[0103] A steady-state index adjustment unit is used to adjust the real-time steady-state index based on the target external interference coefficient.

[0104] In some embodiments, the control device further comprises:

[0105] The device status information and cruise time association unit is used to obtain the first cruise time of the first device operation status information.

[0106] The device state sequence generating unit is configured to generate a first device state sequence of the first device according to a first corresponding relationship between the first cruise time and the first device state index.

[0107] The load information and cruising time associating unit is used to obtain the second cruising time of the first load.

[0108] A load time sequence generating unit is used to generate a first load time sequence of a first key point according to a second corresponding relationship between the second cruising time and the first load.

[0109] The time series prediction and analysis unit is used to perform trend prediction and analysis on the first device state time series and the first load time series in sequence to obtain a first predicted state index and a first predicted load respectively.

[0110] A predicted steady-state index analysis unit is used to analyze the first predicted state index and the first predicted load according to the steady-state evaluation function to perform steady-state analysis to obtain a predicted steady-state index.

[0111] The feedforward control processing instruction issuing unit is used to issue a feedforward control processing instruction when the predicted steady-state index is not within the predetermined steady-state index threshold.

[0112] A predictive control execution unit is used to perform predictive control execution on the target power grid based on the feedforward control processing instruction.

[0113] In some embodiments, the control device further comprises:

[0114] The three-dimensional model building unit is used to obtain target multi-dimensional feature information of the target power grid, and build the three-dimensional power grid model according to target structural feature information in the target multi-dimensional feature information.

[0115] It should be understood that the embodiments mentioned in this specification focus on their differences from other embodiments. The specific embodiments in the aforementioned embodiment one are also applicable to the intelligent sensing grid steady-state adaptive cruise control device described in embodiment two. For the sake of brevity of the specification, they will not be further elaborated here.

[0116] It should be understood that the embodiments disclosed in the present invention and the above description can enable those skilled in the art to use the present invention to implement the present invention. At the same time, the present invention is not limited to the above-mentioned embodiments. It should be understood that those skilled in the art can still modify the technical solutions recorded in the above-mentioned embodiments, or replace some of the technical features therein by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. An intelligent sensing grid steady-state adaptive cruise control method, characterized in that: include: Establishing a device set, wherein the device set refers to a collection of devices in a target power grid, and the device set includes a plurality of devices with location identifiers; Reading a predetermined key point set, and marking the predetermined key point set and the plurality of devices with location identifiers into a three-dimensional power grid model to obtain a target power grid model, wherein the three-dimensional power grid model refers to a three-dimensional structural model of the target power grid; Performing dynamic cruise monitoring on the target power grid based on a predetermined cruise strategy to obtain real-time cruise records, and rendering the real-time cruise records to the target power grid model; determining whether the real-time cruise record complies with a predetermined grid constraint in combination with the target grid model; If it is in compliance, a steady-state analysis instruction is issued, and a steady-state evaluation function is introduced based on the steady-state analysis instruction to evaluate and analyze the real-time cruise record to obtain a real-time steady-state index; When the real-time steady-state index is not within a predetermined steady-state index threshold, a control processing instruction is issued, and the target power grid is controlled and executed based on the control processing instruction.

2. The method for controlling the steady-state adaptive cruise control of a power grid based on intelligent perception according to claim 1 is characterized in that: Target multi-dimensional feature information of the target power grid is acquired, and the three-dimensional power grid model is constructed according to target structural feature information in the target multi-dimensional feature information.

3. The method for controlling the steady-state adaptive cruise control of a power grid based on intelligent perception according to claim 2 is characterized in that: include: The target multi-dimensional feature information also includes target line feature information; Analyzing the target line characteristic information to obtain first line characteristic information of a first line intersection; Performing normalized weighted processing on the first line characteristic information to obtain a first weight of the first line intersection; When the first weight reaches a predetermined weight threshold, the first line intersection is recorded as a first key point, and the predetermined key point set is formed.

4. The method for controlling steady-state adaptive cruise control of a power grid based on intelligent perception according to claim 1 is characterized in that: include: The predetermined cruise strategy includes a predetermined equipment cruise strategy and a predetermined key point cruise strategy, wherein the predetermined equipment cruise strategy includes a predetermined equipment cruise path and a predetermined equipment cruise cycle, and the predetermined key point cruise strategy includes a predetermined key point cruise path and a predetermined key point cruise cycle; Performing dynamic cruise monitoring on the device set in the target power grid according to the predetermined device cruise path to obtain a real-time device cruise record; Analyzing the real-time device cruise record in combination with the predetermined device cruise cycle to obtain first device operation status information of a first device, where the first device refers to any one device in the device set; Analyze the first device operation status information to obtain a first device status index; Performing dynamic cruise monitoring on the predetermined key point set in the target power grid according to the predetermined key point cruise path to obtain real-time key point cruise records; Analyze the real-time key point cruise record in combination with the predetermined key point cruise cycle to obtain a first load of a first key point; Determine the target unit duration according to the predetermined equipment cruise cycle and the predetermined key point cruise cycle; A steady-state analysis is performed on the first device state index and the first load according to the steady-state evaluation function to obtain the real-time steady-state index under the target unit time length.

5. The method for controlling steady-state adaptive cruise control of a power grid based on intelligent perception according to claim 4 is characterized in that: If the predetermined device cruise cycle is greater than the predetermined key point cruise cycle, the first cycle duration of the predetermined device cruise cycle is obtained, and the first cycle duration is recorded as the target unit duration; if the predetermined device cruise cycle is less than the predetermined key point cruise cycle, the second cycle duration of the predetermined key point cruise cycle is obtained, and the second cycle duration is recorded as the target unit duration.

6. The method for controlling steady-state adaptive cruise control of a power grid based on intelligent perception according to claim 4 is characterized in that: Also includes: Extracting target external feature information in the dynamic external feature record based on the target unit duration; Analyzing target external temperature characteristic information and target external radiation characteristic information in the target external characteristic information to obtain a target external interference coefficient; The real-time steady-state index is adjusted based on the target external interference coefficient.

7. The method for controlling steady-state adaptive cruise control of a power grid based on intelligent perception according to claim 4 is characterized in that: Also includes: Acquire a first cruising time of the operating status information of the first device; generating a first device state time sequence of the first device according to a first corresponding relationship between the first cruise time and the first device state index; Obtaining a second cruising time of the first load; Generate a first load time sequence at a first key point according to a second corresponding relationship between the second cruising time and the first load; Performing trend forecasting analysis on the first device state time series and the first load time series in sequence to obtain a first forecast state index and a first forecast load respectively; Analyze the first predicted state index and the first predicted load according to the steady-state evaluation function to perform steady-state analysis to obtain a predicted steady-state index; When the predicted steady-state index is not within the predetermined steady-state index threshold, issuing a feedforward control processing instruction; Predictive regulation is performed on the target power grid based on the feedforward regulation processing instruction.

8. An intelligent sensing grid steady-state adaptive cruise control device, characterized in that: The control device is used to execute the intelligent perception grid steady-state adaptive cruise control method according to any one of claims 1 to 7, and the control device includes: A device set building module, the device set building module is used to build a device set, the device set refers to a collection of devices in the target power grid, and the device set includes a plurality of devices with location identifiers; A power grid model marking module, the power grid model marking module is used to read a predetermined key point set, and mark the predetermined key point set and the plurality of devices with location identifiers into a three-dimensional power grid model to obtain a target power grid model, wherein the three-dimensional power grid model refers to a three-dimensional structural model of the target power grid; A dynamic cruise monitoring module, the dynamic cruise monitoring module is used to perform dynamic cruise monitoring on the target power grid based on a predetermined cruise strategy, obtain real-time cruise records, and render the real-time cruise records to the target power grid model; A compliance determination module, the compliance determination module is used to determine whether the real-time cruise record complies with a predetermined grid constraint in combination with the target grid model; A steady-state analysis module, wherein the steady-state analysis module is used to issue a steady-state analysis instruction if the conditions are met, and introduce a steady-state evaluation function based on the steady-state analysis instruction to evaluate and analyze the real-time cruise record to obtain a real-time steady-state index; A determination and control module is used to issue a control processing instruction when the real-time steady-state index is not within a predetermined steady-state index threshold, and to perform control execution on the target power grid based on the control processing instruction.

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