A method and system for adaptive control of intelligent switch equipment

Abstract

The present invention discloses an adaptive control method and system for intelligent switch equipment, and relates to the technical field of intelligent switch equipment control. The adaptive control method for intelligent switch equipment includes the following steps: load data collection and processing, fluctuation compliance evaluation and judgment, balanced optimization evaluation and judgment, and fault detection evaluation and feedback. The present invention obtains load-related data by preprocessing the initial load data, and then performs load evaluation on a preset industrial park area according to the load-related data to determine whether to perform load balancing optimization, and then evaluates the effect of the load balancing optimization and determines whether to perform equipment fault detection, and finally obtains the switch equipment fault evaluation index after performing equipment fault detection and performs a comprehensive evaluation to obtain a comprehensive evaluation result, thereby achieving the effect of improving the adaptive control efficiency of the intelligent switch equipment and solving the problem of low adaptive control efficiency of the intelligent switch equipment in the prior art.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent switch device control, and in particular to an intelligent switch device adaptive control method and system thereof. Background Art

[0002] With the development of industry and intelligent manufacturing, industrial parks have been encouraged to introduce technologies such as intelligent manufacturing, the Internet of Things, big data and artificial intelligence to achieve optimization and intelligent control of production processes. As the core support for industrial park production, the automation and intelligent upgrade of the power system is particularly important. The management mode of traditional power equipment has been unable to meet the increasingly complex production needs. The application of intelligent switching equipment and its adaptive control system is the key technology to meet this transformation demand. The adaptive control system of intelligent switching equipment can be linked with other intelligent devices and systems to achieve intelligent control and management of the entire process.

[0003] The existing adaptive control system of intelligent switching equipment monitors the operating status of the power grid and the power parameters of the equipment in real time. Its core lies in the adaptive control algorithm, which can dynamically adjust the operating status of the equipment according to the real-time collected data to ensure operation under the best working conditions. At the same time, through the organic combination of real-time monitoring, intelligent algorithms, adaptive control and remote monitoring technologies, it realizes efficient management of the complex power demand of industrial parks, providing strong technical guarantee for the efficient and stable operation of industrial parks.

[0004] For example, a patent application with publication number: CN116679741A discloses a method and system for identifying power equipment based on a satellite-based controlled UAV, including: obtaining a topographic map of the current area; parsing the topographic map to obtain a distribution map; determining the satellite connection area based on the satellite-based positioning parameters and distribution map of the current position of the UAV and patrolling the current area based on the topographic map and the satellite connection area; and obtaining power equipment information through the detection equipment carried by the UAV.

[0005] For example, the invention patent with announcement number: CN113849000B discloses a control method for an automatic detection system for the coating thickness of silver-plated parts of a high-voltage disconnector, which includes: automatically identifying the workpiece and accurately adjusting the workpiece to the detection position through image processing technology, workpiece position deviation adaptive algorithm, and visual servoing, judging the detection progress by automatically identifying button color changes, and controlling mouse movement and clicking, automatically identifying coating thickness information, and judging whether it is a defective product.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0007] The electricity demand of various enterprises in the industrial park varies greatly and the load fluctuates frequently, which requires the intelligent switching equipment to frequently adjust its working status. However, the existing adaptive control algorithm may not be able to quickly and accurately adjust the working status of the intelligent switching equipment according to the actual situation, resulting in slow response when the load surges or decreases, and unable to achieve precise adjustment.

[0008] In the prior art, when facing a fault, the intelligent switch equipment in the existing industrial park can often only respond passively without an early warning function, thereby affecting the overall response efficiency. There is a problem that the adaptive control efficiency of the intelligent switch equipment is not high. Summary of the invention

[0009] The embodiment of the present application solves the problem of low adaptive control efficiency of intelligent switching devices in the prior art by providing an adaptive control method and system for intelligent switching devices, thereby improving the adaptive control efficiency of intelligent switching devices.

[0010] The embodiment of the present application provides an adaptive control method for intelligent switching equipment, comprising the following steps: S1, obtaining initial load data in a preset industrial park area within a preset time interval, preprocessing the initial load data to obtain load-related data, wherein the initial load data includes an initial load-related compliance value and an initial load-related average value; S2, performing load evaluation on the preset industrial park area according to the load-related data to obtain a load fluctuation compliance evaluation index, judging whether to perform load balancing optimization based on the load fluctuation compliance evaluation index and a preset fluctuation compliance threshold range, wherein the load fluctuation compliance evaluation index is used to comprehensively quantify the load fluctuation compliance situation in the preset industrial park area; S3, after performing load balancing optimization, evaluating the effect of load balancing optimization to obtain a load balancing optimization evaluation index, judging whether to perform equipment fault detection based on the load balancing optimization evaluation index and a preset balancing optimization effect threshold range, wherein the load balancing optimization evaluation index is used to evaluate the effect of load balancing optimization; S4, obtaining a switch device fault evaluation index after performing equipment fault detection, and performing a comprehensive evaluation based on the load fluctuation compliance evaluation index and the load balancing optimization evaluation index to obtain a comprehensive evaluation result and provide feedback, wherein the switch device fault evaluation index is used to comprehensively quantify the compliance degree of equipment fault detection.

[0011] Furthermore, the specific steps of performing load evaluation on a preset industrial park area according to load-related data to obtain a load fluctuation compliance evaluation index are as follows: obtaining a load-related compliance value and a load-related average value at a preset time interval, wherein the load-related compliance value includes a load current value, a load voltage value, a load power value, a load frequency value, a maximum load power value, and a minimum load power value, and the load-related average value includes a load current average value, a load voltage average value, a real-time load power average value, and a load frequency average value; obtaining a fluctuation stability compliance value at a preset time interval according to the load-related compliance value and the load-related average value, and combining the fluctuation stability compliance value, the load-related compliance value, and the load-related average value with reference data obtained from a preset database to obtain a load fluctuation compliance evaluation index; the reference data includes reference values, evaluation weights, and influencing factors; the reference values ​​include reference load current fluctuation values, reference load voltage fluctuation values, reference load power fluctuation values, reference load fluctuation amplitude values, and reference load frequency fluctuation values; the influencing factors include current fluctuation influence factors, voltage fluctuation influence factors, and power fluctuation influence factors; the evaluation weights include fluctuation stability compliance weights, fluctuation amplitude compliance weights, and fluctuation discreteness weights.

[0012] Furthermore, the method for obtaining the load fluctuation compliance evaluation index is as follows:

[0013] ;

[0014] ;

[0015] In the formula, Indicates that the load fluctuation in the i-th preset time period within the preset time interval meets the evaluation index, Indicates the number of the preset time period within the preset time interval. , Indicates the total number of preset time periods. Indicates that the volatility is stable and meets the weight, Indicates that the fluctuation range is consistent with the weight, represents the volatility discreteness weight, Indicates the duration of each preset time period. It represents the fluctuation stability compliance value of the i-th preset time period, represents the current fluctuation influence factor, represents the voltage fluctuation influence factor, represents the power fluctuation influence factor, Indicates any moment within the preset time period. represents the load current value at time t in the i-th preset time period, represents the average value of the load current, Indicates the reference load current fluctuation value, represents the load voltage value at time t in the i-th preset time period, represents the average value of the load voltage, Indicates the reference load voltage fluctuation value, represents the load power value at time t in the i-th preset time period, Indicates the average load power. Indicates the reference load power fluctuation value, Indicates the maximum load power in the i-th preset time period, represents the minimum load power value in the i-th preset time period, Indicates the real-time average load power. Indicates the reference load fluctuation amplitude value, represents the load frequency value at time t in the i-th preset time period, represents the average value of load frequency, Indicates the reference load frequency fluctuation value.

[0016] Furthermore, the specific steps of determining whether to perform load balancing optimization based on the load fluctuation compliance evaluation index and the preset fluctuation compliance threshold range are as follows: compare the load fluctuation compliance evaluation index with the preset fluctuation compliance threshold range: if the load fluctuation compliance evaluation index is within the preset fluctuation compliance threshold range, load balancing optimization is not performed, and the load fluctuation compliance evaluation index is transmitted to the preset load analysis database and automatically backed up; if the load fluctuation compliance evaluation index exceeds the preset fluctuation compliance threshold range, load balancing optimization is performed.

[0017] Furthermore, the specific steps for performing load balancing optimization are as follows: A1, obtaining the preset power equipment priority in the preset industrial park area, the preset power equipment priority includes a preset high priority power equipment, a preset medium priority power equipment and a preset low priority power equipment, and after shutting down the preset low priority power equipment one by one, determining whether the monitored load fluctuation compliance evaluation index is within the preset fluctuation compliance threshold range, if so, stopping the load balancing optimization, otherwise executing A2; A2, shutting down the preset medium priority power equipment one by one, and determining whether the load fluctuation compliance evaluation index is within the preset fluctuation compliance threshold range, if so, stopping the load balancing optimization, otherwise executing A3; A3, performing load uniform distribution processing on the preset high priority power equipment through the load scheduling algorithm, and at the same time evaluating the effect of the load balancing optimization to obtain the load balancing optimization evaluation index.

[0018] Furthermore, the specific steps of evaluating the effect of load balancing optimization to obtain the load balancing optimization evaluation index are as follows: obtaining initial evaluation related data of the intelligent switching device within the preset time period to be optimized, the initial evaluation related data including the initial energy consumption evaluation value, the initial carbon emission evaluation value, the initial load power evaluation value and the initial average load power, the preset time period to be optimized is used to describe the preset time period before the load balancing optimization is performed; obtaining optimization evaluation related data of the intelligent switching device within the corresponding optimization preset time period after the load balancing optimization is performed, the optimization evaluation related data including the optimized energy consumption evaluation value, the optimized carbon emission evaluation value, the optimized load power evaluation value and the optimized average load power, the optimized preset time period is used to describe the preset time period after the load balancing optimization is performed, which is the same length as the preset time period to be optimized; The initial load standard deviation is obtained according to the initial load power evaluation value and the initial average load power, and the optimized load standard deviation is obtained according to the optimized load power evaluation value and the optimized average load power; the difference between the initial energy consumption evaluation value and the optimized energy consumption evaluation value is ratioed with the initial energy consumption evaluation value to obtain the energy consumption calculation result, and the carbon emission calculation result is obtained by processing the initial carbon emission evaluation value and the optimized carbon emission evaluation value; the complement of the ratio of the initial load standard deviation to the optimized load standard deviation, the ratio of the energy consumption calculation result to the reference energy consumption change value, and the ratio of the carbon emission calculation result to the reference carbon emission change value are combined with the corresponding optimization evaluation weights obtained from the preset database and multiplied and then processed to obtain the load balancing optimization evaluation index, wherein the optimization evaluation weights include energy consumption optimization evaluation weights, carbon emission optimization evaluation weights and balancing efficiency optimization evaluation weights.

[0019] Furthermore, the specific steps for determining whether to perform equipment fault detection based on the load balancing optimization evaluation index and the preset balancing optimization effect threshold range are as follows: compare the load balancing optimization evaluation index with the preset balancing optimization effect threshold range: if the load balancing optimization evaluation index is within the preset balancing optimization effect threshold range, then do not perform equipment fault detection, and at the same time, back up the load balancing optimization evaluation index in real time and upload it to cloud storage; if the load balancing optimization evaluation index exceeds the preset balancing optimization effect threshold range, then obtain the switch device fault evaluation index after performing equipment fault detection.

[0020] Furthermore, the specific steps of obtaining the switching device fault assessment index after executing the equipment fault detection are as follows: obtaining the actual operating value of the intelligent switching device within a preset time interval to be detected, the actual operating value of the device including the actual operating current of the device, the actual operating voltage of the device, the actual operating power of the device and the actual operating temperature of the device; obtaining the reference operating value and the operating reference deviation of the intelligent switching device from a preset database, the reference operating value including the reference operating current of the device, the reference operating voltage of the device, the reference operating power of the device and the reference operating temperature of the device, the operating reference deviation including the operating current reference deviation, the operating voltage reference deviation, the operating power reference deviation and the operating temperature reference deviation; obtaining the corresponding operating ratio result by processing the actual operating value of the device and the reference operating value, dividing the operating ratio result by the corresponding reference operating value and then multiplying the result by the corresponding fault assessment weight obtained from the preset database, and then processing the complement of the addition operation to obtain the switching device fault assessment index, the fault assessment weight including the current detection assessment weight, the voltage detection assessment weight, the power detection assessment weight and the temperature detection assessment weight.

[0021] Furthermore, the comprehensive evaluation result includes load fluctuation results, load balancing optimization results and equipment fault detection results; the load fluctuation result is used to determine whether the obtained load fluctuation compliance evaluation index is within the preset fluctuation compliance threshold range. If the obtained load fluctuation compliance evaluation index is within the preset fluctuation compliance threshold range, the load fluctuation result is recorded as normal load fluctuation and no load balancing optimization is performed; the load balancing optimization result is used to determine whether the obtained load balancing optimization evaluation index is within the preset balanced optimization effect threshold range. If the load balancing optimization evaluation index is within the preset balanced optimization effect threshold range, the load balancing optimization result is recorded as abnormal load fluctuation and qualified balanced optimization; the equipment fault detection result is used to determine whether the obtained switching device fault evaluation index is within the preset fault threshold range. If the switching device fault evaluation index is within the preset fault threshold range, the equipment fault detection result is recorded as abnormal load fluctuation, unqualified balanced optimization and abnormal equipment detection.

[0022] The embodiment of the present application provides an adaptive control system for intelligent switching equipment, including: a load data acquisition and processing module, a fluctuation compliance evaluation and judgment module, a balanced optimization evaluation and judgment module, and a fault detection evaluation feedback module; wherein the load data acquisition and processing module is used to obtain initial load data in a preset industrial park area within a preset time interval, and pre-process the initial load data to obtain load-related data, wherein the initial load data includes an initial load-related compliance value and an initial load-related average value; the fluctuation compliance evaluation and judgment module is used to perform load evaluation on the preset industrial park area according to the load-related data to obtain a load fluctuation compliance evaluation index, and judge whether to perform load balancing optimization based on the load fluctuation compliance evaluation index and a preset fluctuation compliance threshold range, wherein the load fluctuation The compliance evaluation index is used to comprehensively quantify the compliance of load fluctuations within the preset industrial park area; the balanced optimization evaluation judgment module is used to evaluate the effect of load balancing optimization after executing load balancing optimization to obtain a load balancing optimization evaluation index, and judge whether to execute equipment fault detection based on the load balancing optimization evaluation index and the preset balanced optimization effect threshold range. The load balancing optimization evaluation index is used to evaluate the effect of load balancing optimization; the fault detection evaluation feedback module is used to obtain the switching device fault evaluation index after executing equipment fault detection, and conduct a comprehensive evaluation based on the load fluctuation compliance evaluation index and the load balancing optimization evaluation index to obtain a comprehensive evaluation result and provide feedback. The switching device fault evaluation index is used to comprehensively quantify the compliance degree of equipment fault detection.

[0023] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0024] 1. By performing load evaluation on the preset industrial park area to determine whether to perform load balancing optimization, and then after performing load balancing optimization, determine whether to perform equipment fault detection based on the load balancing optimization evaluation index, and finally obtain the switch device fault evaluation index after performing equipment fault detection and perform a comprehensive evaluation, thereby realizing the evaluation and processing of load fluctuations in the preset industrial park area, and then realizing the improvement of the adaptive control efficiency of the intelligent switching device, effectively solving the problem of low adaptive control efficiency of the intelligent switching device in the prior art.

[0025] 2. By obtaining the load-related compliance value and the load-related average value of the preset time interval, and then obtaining the fluctuation stability compliance value of the preset time interval according to the load-related compliance value and the load-related average value, and finally combining the fluctuation stability compliance value, the load-related compliance value and the load-related average value with the reference data obtained from the preset database to obtain the load fluctuation compliance evaluation index, thereby realizing the numerical evaluation of the load fluctuation compliance situation in the preset industrial park area, and then realizing a more accurate evaluation of the load fluctuation compliance situation in the preset industrial park area.

[0026] 3. By obtaining the initial evaluation related data of the intelligent switching device within the preset time period to be optimized, and at the same time obtaining the optimization evaluation related data of the intelligent switching device within the corresponding optimization preset time period after the load balancing optimization is executed, finally the complement of the ratio of the initial load standard deviation to the optimized load standard deviation, the ratio of the energy consumption calculation result to the reference energy consumption change value, and the ratio of the carbon emission calculation result to the reference carbon emission change value are combined with the corresponding optimization evaluation weights obtained from the preset database and multiplied and then processed to obtain the load balancing optimization evaluation index, thereby realizing the numerical evaluation of the load balancing optimization effect, and then realizing a more accurate evaluation of the load balancing optimization effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 A flow chart of an adaptive control method for an intelligent switch device provided in an embodiment of the present application;

[0028] Figure 2 A graph showing changes in the load balancing optimization evaluation index provided in an embodiment of the present application;

[0029] Figure 3 A schematic diagram of the structure of an adaptive control system for an intelligent switching device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0030] The embodiment of the present application solves the problem of low adaptive control efficiency of intelligent switching devices in the prior art by providing an intelligent switching device adaptive control method and system thereof. The method obtains initial load data in a preset industrial park area within a preset time interval, pre-processes the initial load data to obtain load-related data, and then performs load evaluation on the preset industrial park area based on the load-related data to obtain a load fluctuation compliance evaluation index and determine whether to perform load balancing optimization. After performing the load balancing optimization, the effect of the load balancing optimization is evaluated to obtain a load balancing optimization evaluation index. Based on the load balancing optimization evaluation index and a preset balanced optimization effect threshold range, it is determined whether to perform equipment fault detection. Finally, the switch device fault evaluation index after equipment fault detection is obtained. A comprehensive evaluation is performed based on the load fluctuation compliance evaluation index and the load balancing optimization evaluation index to obtain a comprehensive evaluation result and provide feedback, thereby improving the adaptive control efficiency of the intelligent switching device.

[0031] The technical solution in the embodiment of the present application is to solve the problem that the adaptive control efficiency of the above-mentioned intelligent switch device is low. The overall idea is as follows:

[0032] Through the load-related data, the load of the preset industrial park area is evaluated to determine whether to perform load balancing optimization. Then, the effect of the load balancing optimization is evaluated and it is determined whether to perform equipment fault detection. Finally, a comprehensive evaluation is performed to obtain the comprehensive evaluation results and provide feedback, thereby achieving the effect of improving the adaptive control efficiency of the intelligent switching equipment.

[0033] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0034] like Figure 1 As shown, it is a flow chart of an intelligent switch device adaptive control method provided by an embodiment of the present application, and the method includes the following steps: S1, obtaining initial load data in a preset industrial park area within a preset time interval, preprocessing the initial load data to obtain load-related data, and the initial load data includes an initial load-related compliance value and an initial load-related average value; S2, performing load evaluation on the preset industrial park area according to the load-related data to obtain a load fluctuation compliance evaluation index, judging whether to perform load balancing optimization based on the load fluctuation compliance evaluation index and the preset fluctuation compliance threshold range, and the load fluctuation compliance evaluation index is used to comprehensively quantify the load fluctuation compliance situation in the preset industrial park area; S3, after performing load balancing optimization, evaluating the effect of load balancing optimization to obtain a load balancing optimization evaluation index, judging whether to perform equipment fault detection based on the load balancing optimization evaluation index and the preset balancing optimization effect threshold range, and the load balancing optimization evaluation index is used to evaluate the effect of load balancing optimization; S4, obtaining the switch device fault evaluation index after performing equipment fault detection, and performing a comprehensive evaluation based on the load fluctuation compliance evaluation index and the load balancing optimization evaluation index to obtain a comprehensive evaluation result and provide feedback, and the switch device fault evaluation index is used to comprehensively quantify the compliance degree of equipment fault detection.

[0035] In this embodiment, initial load data is obtained through smart meters in a preset industrial park area. Specifically, since the collected initial load data may be affected by various factors (such as noise, network transmission delay, etc.), resulting in poor data quality, abnormal values ​​or incomplete data, in order to ensure the accuracy of the analysis, the initial load data needs to be preprocessed, where the preprocessing includes data cleaning, data smoothing, data normalization and de-unitization processing.

[0036] First, outliers are detected and eliminated through the Z-score method or interquartile range. Outliers are data points that exceed the range of the mean plus or minus three times the standard deviation. Missing values ​​are filled by the mean filling method; then, data smoothing is performed by the moving average method; then, data normalization is performed by minimum-maximum normalization; finally, de-unitization is performed by dimensionless technology; by collecting initial load data and preprocessing it, the load conditions in the preset industrial park area are comprehensively monitored. In the preset industrial park area, the intelligent switching equipment includes intelligent circuit breakers, intelligent lighting switches, intelligent power management switches, intelligent security switches, and intelligent sensor switches. Specifically, parameters such as current and voltage are monitored through intelligent distribution cabinets, thereby improving the quality of load data in the preset industrial park area.

[0037] Furthermore, the specific steps of performing load evaluation on a preset industrial park area according to load-related data to obtain a load fluctuation compliance evaluation index are as follows: obtain load-related compliance values ​​and load-related average values ​​at preset time intervals, the load-related compliance values ​​include load current value, load voltage value, load power value, load frequency value, load power maximum value and load power minimum value, and the load-related average values ​​include load current average value, load voltage average value, real-time load power average value and load frequency average value; obtain fluctuation stability compliance values ​​at preset time intervals according to the load-related compliance values ​​and load-related average values, and combine the fluctuation stability compliance values, load-related compliance values ​​and load-related average values ​​with reference data obtained from a preset database to obtain a load fluctuation compliance evaluation index; the reference data include reference values, evaluation weights and influencing factors; the reference values ​​include reference load current fluctuation values, reference load voltage fluctuation values, reference load power fluctuation values, reference load fluctuation amplitude values ​​and reference load frequency fluctuation values; the influencing factors include current fluctuation influencing factors, voltage fluctuation influencing factors and power fluctuation influencing factors; the evaluation weights include fluctuation stability compliance weights, fluctuation amplitude compliance weights and fluctuation discreteness weights.

[0038] The method for obtaining the load fluctuation compliance evaluation index is as follows:

[0039] ;

[0040] ;

[0041] In the formula, Indicates that the load fluctuation in the i-th preset time period within the preset time interval meets the evaluation index, Indicates the number of the preset time period within the preset time interval. , Indicates the total number of preset time periods. Indicates that the volatility is stable and meets the weight, Indicates that the fluctuation range is consistent with the weight, represents the volatility discreteness weight, Indicates the duration of each preset time period. It represents the fluctuation stability compliance value of the i-th preset time period, represents the current fluctuation influence factor, represents the voltage fluctuation influence factor, represents the power fluctuation influence factor, Indicates any moment within the preset time period. represents the load current value at time t in the i-th preset time period, represents the average value of the load current, Indicates the reference load current fluctuation value, represents the load voltage value at time t in the i-th preset time period, represents the average value of the load voltage, Indicates the reference load voltage fluctuation value, represents the load power value at time t in the i-th preset time period, Indicates the average load power. Indicates the reference load power fluctuation value, Indicates the maximum load power in the i-th preset time period, represents the minimum load power value in the i-th preset time period, Indicates the real-time average load power. Indicates the reference load fluctuation amplitude value, represents the load frequency value at time t in the i-th preset time period, represents the average value of load frequency, Indicates the reference load frequency fluctuation value.

[0042] In this embodiment, t represents any moment within a preset time period, and is obtained by randomly selecting a moment within the preset time period; the load-related compliance value and the load-related average value are obtained through the smart meters in the preset industrial park area. Specifically, the programmable logic controller (PLC) reads data from the smart meters through the Modbus protocol to obtain the load-related compliance value, and then accumulates and calculates according to the programming logic to obtain the load-related average value.

[0043] Among them, the current fluctuation influence factor is used to describe the influence degree of load current fluctuation on the fluctuation stability compliance value. Specifically, the current fluctuation influence factor is the influence factor corresponding to the current fluctuation preset in the preset database, which represents the numerical value of the influence degree of load current fluctuation on the fluctuation stability compliance value. When used, the preset current fluctuation influence factor can be directly obtained from the preset database, and the corresponding relationship can be a pre-set mapping relationship. For example, the load current fluctuation and the influence factor corresponding to the current fluctuation preset in the preset database form a mapping set, and the real-time load current fluctuation is input into the mapping set to obtain the corresponding influence factor. The mapping relationship can be one-to-one or many-to-one. In this example, the value range is [0, 1].

[0044] The voltage fluctuation influence factor is used to describe the influence of load voltage fluctuation on the fluctuation stability compliance value. Specifically, the voltage fluctuation influence factor is the influence factor corresponding to the voltage fluctuation preset in the preset database, which represents the numerical value of the influence of the load voltage fluctuation on the fluctuation stability compliance value. When used, the preset voltage fluctuation influence factor can be directly obtained from the preset database, and the corresponding relationship can be a pre-set mapping relationship. For example, the load voltage fluctuation and the influence factor corresponding to the voltage fluctuation preset in the preset database form a mapping set, and the real-time load voltage fluctuation is input into the mapping set to obtain the corresponding influence factor. The mapping relationship can be one-to-one or many-to-one. In this example, the value range is [0, 1].

[0045] In this example, the sum of the current fluctuation influence factor, the voltage fluctuation influence factor, and the power fluctuation influence factor is 1. The power fluctuation influence factor is used to describe the influence of load power fluctuation on the fluctuation stability compliance value.

[0046] Among them, the fluctuation stability compliance weight is used to describe the influence of the stability compliance of load fluctuations on the load fluctuation compliance evaluation index. Specifically, the fluctuation stability compliance weight is the weight corresponding to the fluctuation stability compliance preset in the preset database, which represents the numerical value of the influence of the stability compliance of load fluctuations on the load fluctuation compliance evaluation index. When used, the corresponding weight of the preset fluctuation stability compliance can be directly obtained from the preset database, and the corresponding relationship can be a pre-set mapping relationship. For example, the stability compliance of load fluctuations and the weight corresponding to the fluctuation stability compliance preset in the preset database form a mapping set, and the stability compliance of real-time load fluctuations is input into the mapping set to obtain the corresponding weight. The mapping relationship can be one-to-one or many-to-one. In this example, the value range is [0, 1].

[0047] The fluctuation amplitude compliance weight is used to describe the influence of the load fluctuation amplitude compliance on the load fluctuation compliance evaluation index. Specifically, the fluctuation amplitude compliance weight is the corresponding weight of the fluctuation amplitude compliance preset in the preset database, which represents the numerical value of the influence of the load fluctuation amplitude compliance on the load fluctuation compliance evaluation index. When used, the corresponding weight of the preset fluctuation amplitude compliance can be directly obtained from the preset database, and the corresponding relationship can be a pre-set mapping relationship. For example, the load fluctuation amplitude compliance and the corresponding weight of the fluctuation amplitude compliance preset in the preset database form a mapping set, and the real-time load fluctuation amplitude compliance is input into the mapping set to obtain the corresponding weight. The mapping relationship can be one-to-one or many-to-one. In this example, the value range is [0, 1].

[0048] In this example, the sum of the fluctuation stability compliance weight, the fluctuation amplitude compliance weight and the fluctuation discreteness weight is 1. The fluctuation discreteness weight is used to describe the influence of the discreteness of load fluctuation on the load fluctuation compliance evaluation index.

[0049] The load fluctuation compliance assessment index is used to comprehensively quantify the load fluctuation compliance situation in the preset industrial park area; among them, the load fluctuation compliance assessment index includes parameters in multiple aspects, and the various parameters are related, not independent, and not obtained by simple combination and addition; for example, in the preset industrial park area, when high-power equipment (such as motors, etc.) is started, a large load current value may be generated, accompanied by changes in the load voltage value, thereby affecting the load power value; in addition, when multiple devices are running together, the fluctuation of the load voltage value of different devices will produce interference such as harmonics, thereby affecting the load current value and load power value; in addition, when the load power value increases, it indicates that more current needs to be provided to Maintaining power supply means that the load frequency value decreases accordingly; when the load frequency value decreases, the load voltage value may decrease, and the load current value also changes accordingly; at the same time, as the fluctuation stability compliance value increases, the load fluctuation compliance evaluation index decreases accordingly; in summary, multiple factors have an impact on the load fluctuation compliance evaluation index. Therefore, through a quantitative approach, the correlation and mutual influence between various factors are considered, and the load fluctuation compliance evaluation index is obtained through comprehensive analysis; a numerical evaluation of the load fluctuation compliance situation in the preset industrial park area is realized, and the load fluctuation compliance situation in the preset industrial park area is judged through numerical evaluation, thereby improving the reliability of the load fluctuation compliance judgment in the preset industrial park area.

[0050] Furthermore, the specific steps for determining whether to perform load balancing optimization based on the load fluctuation compliance evaluation index and the preset fluctuation compliance threshold range are as follows: Compare the load fluctuation compliance evaluation index with the preset fluctuation compliance threshold range: If the load fluctuation compliance evaluation index is within the preset fluctuation compliance threshold range, load balancing optimization is not performed, and the load fluctuation compliance evaluation index is transmitted to the preset load analysis database and automatically backed up; if the load fluctuation compliance evaluation index exceeds the preset fluctuation compliance threshold range, load balancing optimization is performed.

[0051] In this embodiment, the preset fluctuation compliance threshold range is set by professionals according to standards in the field. For example, the preset fluctuation compliance threshold range is generally set to 0.1 to 0.4; wherein, a scheduled task (such as Cron Job or TaskScheduler) is used to transmit the load fluctuation compliance evaluation index to a preset load analysis database, wherein the preset load analysis database is used to store and monitor the load fluctuation compliance evaluation index; and it is backed up to a local server through cloud storage (such as Google Cloud Storage), thereby realizing an accurate evaluation of the load fluctuation compliance situation in the preset industrial park area.

[0052] Furthermore, the specific steps for performing load balancing optimization are as follows: A1, obtain the preset power equipment priority in the preset industrial park area, the preset power equipment priority includes preset high priority power equipment, preset medium priority power equipment and preset low priority power equipment, and after shutting down the preset low priority power equipment one by one, determine whether the monitored load fluctuation compliance evaluation index is within the preset fluctuation compliance threshold range. If so, stop the load balancing optimization, otherwise execute A2; A2, shut down the preset medium priority power equipment one by one, and determine whether the load fluctuation compliance evaluation index is within the preset fluctuation compliance threshold range. If so, stop the load balancing optimization, otherwise execute A3; A3, use the load scheduling algorithm to evenly distribute the load of the preset high priority power equipment, and at the same time evaluate the effect of the load balancing optimization to obtain the load balancing optimization evaluation index.

[0053] In this embodiment, shutting down preset power equipment during the execution of load balancing optimization can reduce the occurrence of power supply quality problems, thereby effectively reducing the burden on the power system and maintaining the relative stability of the power system; wherein, the preset power equipment priority is divided by professionals according to the standard settings in the field, for example, the preset high-priority power equipment is the machines directly involved in manufacturing or processing in the preset industrial park area (such as mechanical equipment of the automated production line) and safety equipment (such as fire pumps), the preset medium-priority power equipment is auxiliary to the production process (such as material handling equipment) and auxiliary power equipment (such as ventilation equipment, exhaust equipment), and the preset low-priority power equipment is auxiliary equipment that has no direct impact on production (such as office air conditioners, landscape lights); the load scheduling algorithm includes a polling scheduling algorithm, a minimum load priority algorithm, a weighted polling scheduling algorithm and a dynamic load scheduling algorithm. For example, the minimum load priority algorithm calculates the load value of each power device and preferentially allocates the load to the power device with the lowest load until the load of all power devices tends to be balanced; by performing load balancing optimization, load regulation and distribution are achieved, thereby achieving the improvement of the adaptive control stability of the intelligent switching device.

[0054] Furthermore, the specific steps of evaluating the effect of load balancing optimization to obtain the load balancing optimization evaluation index are as follows: obtaining initial evaluation related data of the intelligent switching device within the preset time period to be optimized, the initial evaluation related data including the initial energy consumption evaluation value, the initial carbon emission evaluation value, the initial load power evaluation value and the initial average load power, the preset time period to be optimized is used to describe the preset time period before the load balancing optimization is performed; obtaining optimization evaluation related data of the intelligent switching device within the corresponding optimization preset time period after the load balancing optimization is performed, the optimization evaluation related data including the optimized energy consumption evaluation value, the optimized carbon emission evaluation value, the optimized load power evaluation value and the optimized average load power, the optimized preset time period is used to describe the preset time period after the load balancing optimization is performed, which is the same length as the preset time period to be optimized; according to the initial The load power evaluation value and the initial average load power are used to obtain the initial load standard deviation, and the optimized load standard deviation is obtained according to the optimized load power evaluation value and the optimized average load power. The difference between the initial energy consumption evaluation value and the optimized energy consumption evaluation value is ratioed with the initial energy consumption evaluation value to obtain the energy consumption calculation result, and the carbon emission calculation result is obtained by processing the initial carbon emission evaluation value and the optimized carbon emission evaluation value. The complement of the ratio of the initial load standard deviation to the optimized load standard deviation, the ratio of the energy consumption calculation result to the reference energy consumption change value, and the ratio of the carbon emission calculation result to the reference carbon emission change value are combined with the corresponding optimization evaluation weights obtained from the preset database and multiplied and then processed to obtain the load balancing optimization evaluation index. The optimization evaluation weights include energy consumption optimization evaluation weights, carbon emission optimization evaluation weights and balancing efficiency optimization evaluation weights.

[0055] The method for obtaining the load balancing optimization evaluation index is as follows:

[0056] ;

[0057] ;

[0058] In the formula, It represents the load balancing optimization evaluation index of the dth preset time period to be optimized. Indicates the number of preset time periods to be optimized. , Indicates the total number of preset time periods to be optimized. Indicates the number of preset time periods for optimization. , Indicates the total number of optimized preset time periods. represents the energy consumption optimization evaluation weight, represents the carbon emission optimization assessment weight, represents the equilibrium efficiency optimization evaluation weight, represents the initial energy consumption evaluation value of the dth preset time period to be optimized, represents the optimized energy consumption evaluation value of the yth optimization preset time period, Indicates the reference energy consumption change value, represents the initial carbon emission assessment value of the dth preset time period to be optimized, represents the optimized carbon emission assessment value of the yth optimized preset time period, represents the reference carbon emission change value, represents the standard deviation of the optimized load in the yth optimization preset time period, represents the initial load standard deviation of the dth preset time period to be optimized, Indicates the number of preset high-priority power devices. , Indicates the total number of preset high-priority power devices. represents the optimized load power evaluation value of the yth optimization preset time period, represents the initial load power evaluation value of the dth preset time period to be optimized, represents the optimized average load power of the yth optimization preset time period, represents the initial average load power of the dth preset time period to be optimized, Indicates that the preset fluctuation meets the threshold range.

[0059] Specifically, the initial energy consumption evaluation value is used to describe the total energy consumption of the preset time period to be optimized, the optimized energy consumption evaluation value is used to describe the total energy consumption of the optimized preset time period, the initial carbon emission evaluation value is used to describe the total carbon emissions of the preset time period to be optimized, the optimized carbon emission evaluation value is used to describe the total carbon emissions of the optimized preset time period, the optimized load power evaluation value is used to describe the maximum load power of the optimized preset time period, and the initial load power evaluation value is used to describe the maximum load power of the preset time period to be optimized; wherein, the initial evaluation related data and the optimization evaluation related data are obtained through the smart meter.

[0060] Among them, the energy consumption optimization evaluation weight is used to describe the influence of energy consumption optimization on the load balancing optimization evaluation index during the load balancing optimization process. Specifically, the energy consumption optimization evaluation weight is the weight corresponding to the energy consumption optimization evaluation preset in the preset database, which represents the numerical value of the influence of energy consumption optimization on the load balancing optimization evaluation index during the load balancing optimization process. When used, the weight corresponding to the preset energy consumption optimization evaluation can be directly obtained from the preset database, and the corresponding relationship can be a pre-set mapping relationship. For example, the energy consumption optimization situation in the load balancing optimization process and the weight corresponding to the energy consumption optimization evaluation preset in the preset database form a mapping set, and the energy consumption optimization situation in the real-time load balancing optimization process is input into the mapping set to obtain the corresponding weight. The mapping relationship can be one-to-one or many-to-one. In this example, the value range is [0, 1].

[0061] The carbon emission optimization evaluation weight is used to describe the influence of carbon emission optimization on the load balancing optimization evaluation index during the load balancing optimization process. Specifically, the carbon emission optimization evaluation weight is the weight corresponding to the carbon emission optimization evaluation preset in the preset database, which represents the numerical value of the influence of carbon emission optimization on the load balancing optimization evaluation index during the load balancing optimization process. When used, the weight corresponding to the preset carbon emission optimization evaluation can be directly obtained from the preset database, and the corresponding relationship can be a preset mapping relationship. For example, the carbon emission optimization situation in the load balancing optimization process and the weight corresponding to the carbon emission optimization evaluation preset in the preset database form a mapping set, and the carbon emission optimization situation in the real-time load balancing optimization process is input into the mapping set to obtain the corresponding weight. The mapping relationship can be one-to-one or many-to-one. In this example, the value range is [0, 1].

[0062] In this example, the sum of the energy consumption optimization evaluation weight, the carbon emission optimization evaluation weight, and the balancing efficiency optimization evaluation weight is 1. The balancing efficiency optimization evaluation weight is used to describe the influence of the load balancing optimization efficiency on the load balancing optimization evaluation index.

[0063] like Figure 2As shown, it is a change graph of the load balancing optimization evaluation index provided in an embodiment of the present application, wherein it is set that: the total number of preset time periods to be optimized is 1, the total number of preset time periods to be optimized is 1, the energy consumption optimization evaluation weight is 0.5, the carbon emission optimization evaluation weight is 0.3, the balancing efficiency optimization evaluation weight is 0.2, the initial energy consumption evaluation value is 100kWh, the initial carbon emission evaluation value is 80 tons of carbon dioxide, the reference energy consumption change value is 20, the reference carbon emission change value is 15, the initial load standard deviation is 10, the optimized load standard deviation is 8, the total number of preset high-priority power equipment is 5, the initial load power evaluation values ​​are 12kW, 14kW, 10kW, 13kW, and 11kW respectively, and the optimized load power evaluation values ​​are 11kW, 13kW, 9kW, 12kW, and 10kW respectively; Figure 2 It can be seen that when the optimized carbon emission assessment value is a fixed value (i.e. 60 tons of carbon dioxide), as the optimized energy consumption assessment value decreases, the load balancing optimization assessment index increases accordingly; at the same time, when the optimized energy consumption assessment value is a fixed value (i.e. 70kWh), as the optimized carbon emission assessment value decreases, the load balancing optimization assessment index also increases accordingly.

[0064] In addition, the load balancing optimization evaluation index includes parameters from multiple aspects, and each parameter is related and does not exist independently. For example, in industrial parks, the reduction of energy consumption is usually accompanied by the reduction of carbon emissions, because carbon emissions mainly come from energy consumption, especially fossil fuels. By reducing energy consumption, carbon emissions will also be reduced; therefore, energy consumption optimization directly promotes the improvement of carbon emissions, that is, the change of the optimized energy consumption evaluation value leads to the change of the optimized carbon emission evaluation value; at the same time, in the case of over-optimization of carbon emissions (such as over-reliance on intermittent renewable energy), it may face instability (such as photovoltaic power generation affected by weather), which leads to an increase in the standard deviation of the optimized load. Therefore, the load balancing optimization evaluation index is obtained through comprehensive analysis; the numerical evaluation of the load balancing optimization effect is realized, and the evaluation and judgment of the load balancing optimization effect are carried out through numerical evaluation, thereby realizing the improvement of the accuracy of the load balancing optimization effect evaluation under the abnormal conditions of load fluctuation in the preset industrial park area.

[0065] Furthermore, the specific steps for determining whether to perform equipment fault detection based on the load balancing optimization evaluation index and the preset balancing optimization effect threshold range are as follows: Compare the load balancing optimization evaluation index with the preset balancing optimization effect threshold range: If the load balancing optimization evaluation index is within the preset balancing optimization effect threshold range, then do not perform equipment fault detection, and at the same time, back up the load balancing optimization evaluation index in real time and upload it to cloud storage; if the load balancing optimization evaluation index exceeds the preset balancing optimization effect threshold range, then obtain the switch device fault evaluation index after performing equipment fault detection.

[0066] In this embodiment, the preset balanced optimization effect threshold range is set by professionals according to standards in the field. For example, the optimized energy consumption assessment value range (generally 5% to 30% of the initial energy consumption assessment value), the optimized carbon emission assessment value range (generally 5% to 30% of the initial carbon emission assessment value), and the optimized load standard deviation to initial load standard deviation ratio range (generally 0.5 to 0.9) are set to determine the preset balanced optimization effect threshold range; accurate evaluation of the load balancing optimization effect is achieved.

[0067] Furthermore, the specific steps for obtaining the fault assessment index of the switching device after executing the equipment fault detection are as follows: obtaining the actual operating value of the intelligent switching device within the preset time interval to be detected, the actual operating value of the equipment includes the actual operating current of the equipment, the actual operating voltage of the equipment, the actual operating power of the equipment and the actual operating temperature of the equipment; obtaining the reference operating value and the operating reference deviation of the intelligent switching device from the preset database, the reference operating value includes the reference operating current of the equipment, the reference operating voltage of the equipment, the reference operating power of the equipment and the reference operating temperature of the equipment, the operating reference deviation includes the operating current reference deviation, the operating voltage reference deviation, the operating power reference deviation and the operating temperature reference deviation; obtaining the corresponding operating ratio result by processing the actual operating value of the equipment and the reference operating value, dividing the operating ratio result by the corresponding reference operating value and then multiplying it with the corresponding fault assessment weight obtained from the preset database, and then processing it by performing the complement of the addition operation to obtain the switching device fault assessment index, the fault assessment weight includes the current detection assessment weight, the voltage detection assessment weight, the power detection assessment weight and the temperature detection assessment weight.

[0068] The method for obtaining the switchgear fault assessment index is as follows:

[0069] ;

[0070] In the formula, represents the switchgear fault assessment index, represents the current detection evaluation weight, represents the voltage detection evaluation weight, represents the power detection evaluation weight, represents the temperature detection evaluation weight, Indicates the actual operating current of the device. Indicates the reference operating current of the device, Indicates the operating current reference deviation, Indicates the actual operating voltage of the device. Indicates the reference operating voltage of the device. Indicates the operating voltage reference deviation, Indicates the actual operating power of the device. Indicates the reference operating power of the device. Indicates the operating power reference deviation, Indicates the actual operating temperature of the device. Indicates the reference operating temperature of the device. Indicates the operating temperature reference deviation, Indicates the preset balance optimization effect threshold range.

[0071] In this embodiment, specifically, the device fault detection process is executed for a single preset high-priority power device; the actual operating current of the device is obtained by a current sensor deployed at both ends of the circuit power supply of the intelligent switching device, the actual operating voltage of the device is obtained by a voltage sensor deployed at both ends of the circuit power supply of the intelligent switching device, the actual operating power of the device is obtained by a power sensor deployed at both ends of the circuit power supply of the intelligent switching device, and the actual operating temperature of the device is obtained by a temperature sensor deployed on a power element (such as a relay) of the intelligent switching device.

[0072] The current detection evaluation weight is used to describe the degree of influence of the current detection situation in equipment fault detection on the switching device fault evaluation index. Specifically, the current detection evaluation weight is the weight corresponding to the current detection evaluation preset in the preset database, which represents the numerical value of the influence of the current detection situation in equipment fault detection on the switching device fault evaluation index. When used, the weight corresponding to the preset current detection evaluation can be directly obtained from the preset database, and the corresponding relationship can be a pre-set mapping relationship. For example, the current detection situation in equipment fault detection and the weight corresponding to the current detection evaluation preset in the preset database form a mapping set, and the current detection situation in real-time equipment fault detection is input into the mapping set to obtain the corresponding weight. The mapping relationship can be one-to-one or many-to-one. In this example, the value range is [0, 1].

[0073] The voltage detection evaluation weight is used to describe the degree of influence of the voltage detection situation in equipment fault detection on the switching device fault evaluation index. Specifically, the voltage detection evaluation weight is the weight corresponding to the voltage detection evaluation preset in the preset database, which represents the numerical value of the influence of the voltage detection situation in equipment fault detection on the switching device fault evaluation index. When used, the weight corresponding to the preset voltage detection evaluation can be directly obtained from the preset database, and the corresponding relationship can be a pre-set mapping relationship. For example, the voltage detection situation in equipment fault detection and the weight corresponding to the voltage detection evaluation preset in the preset database form a mapping set, and the voltage detection situation in real-time equipment fault detection is input into the mapping set to obtain the corresponding weight. The mapping relationship can be one-to-one or many-to-one. In this example, the value range is [0, 1].

[0074] The power detection evaluation weight is used to describe the degree of influence of the power detection situation in equipment fault detection on the switching device fault evaluation index. Specifically, the power detection evaluation weight is the weight corresponding to the power detection evaluation preset in the preset database, which represents the numerical value of the influence of the power detection situation in equipment fault detection on the switching device fault evaluation index. When used, the weight corresponding to the preset power detection evaluation can be directly obtained from the preset database, and the corresponding relationship can be a pre-set mapping relationship. For example, the power detection situation in equipment fault detection and the weight corresponding to the power detection evaluation preset in the preset database form a mapping set, and the power detection situation in real-time equipment fault detection is input into the mapping set to obtain the corresponding weight. The mapping relationship can be one-to-one or many-to-one. In this example, the value range is [0, 1].

[0075] In this example, the sum of the current detection evaluation weight, voltage detection evaluation weight, power detection evaluation weight and temperature detection evaluation weight is 1. The temperature detection evaluation weight is used to describe the influence of temperature detection in equipment fault detection on the switching device fault evaluation index.

[0076] Specific assumptions: the current detection evaluation weight is 0.25, the voltage detection evaluation weight is 0.25, the power detection evaluation weight is 0.25, the temperature detection evaluation weight is 0.25, the equipment reference operating current is 100A, the operating current reference deviation is 5, the equipment reference operating voltage is 220V, the operating voltage reference deviation is 10, the equipment reference operating power is 5000W, the operating power reference deviation is 300, the equipment reference operating temperature is 30℃, and the operating temperature reference deviation is 2; the above method can be used to calculate the switch device fault evaluation index, and the change statistics of the switch device fault evaluation index are shown in Table 1:

[0077] Table 1 Statistics of changes in switchgear fault assessment index

[0078] <![CDATA[Actual operating current I of the device s (A)]]> <![CDATA[Actual operating voltage V of the device s (V)]]> <![CDATA[Actual operating power P of the device s (W)]]> <![CDATA[Actual operating temperature of the device W s (°C)]]> Switchgear Fault Assessment Index Sf 100 220 5000 30 1.3130 100 220 5000 35 1.3285 105 228 5000 30 1.3155 99 210 4800 28 1.3204 101 218 5200 32 1.3197 ... ... ... ... ...

[0079] It can be seen from Table 1 that when the actual operating current, the actual operating voltage and the actual operating power of the equipment are fixed values, if the actual operating temperature of the equipment is the same as the reference operating temperature of the equipment, the operating state of the switch device reaches the optimal state (equivalent to the minimum value of the switch device fault assessment index is 1.3130). As the actual operating temperature of the equipment gradually deviates from the reference operating temperature of the equipment, the fault assessment index of the switch device increases accordingly, that is, the compliance degree of the equipment fault detection increases, such as the first and second groups of data in the table; as the actual operating current, the actual operating voltage, the actual operating power and the actual operating temperature of the equipment change, the fault assessment index of the switch device changes directly.

[0080] In addition, the switchgear fault assessment index includes parameters from multiple aspects, and the various parameters are related to each other. They do not exist independently and are not obtained by simple combination and addition. For example, the increase in the actual operating temperature of the equipment will increase the internal resistance, resulting in the need to increase the current to maintain the same power output at the same voltage. As the current increases, the equipment may be further heated, which in turn causes the actual operating temperature of the equipment to further increase. At the same time, temperature changes have a greater impact on the insulation performance of the equipment. When the equipment is overheated, the degradation of the insulating material causes leakage or fluctuations in the actual operating voltage of the equipment. At too high or too low temperatures, the accuracy of the voltage control system decreases, resulting in instability in the actual operating voltage of the equipment. Therefore, through a quantitative approach, the correlation and mutual influence between various factors are taken into account, and a comprehensive analysis is performed to obtain the switchgear fault assessment index. The numerical evaluation of the degree of compliance of equipment fault detection is realized, and the degree of compliance of equipment fault detection is judged through numerical evaluation, thereby improving the accuracy of equipment fault detection in the event of abnormal load fluctuation compliance in the preset industrial park area.

[0081] Furthermore, the comprehensive evaluation results include load fluctuation results, load balancing optimization results and equipment fault detection results; the load fluctuation results are used to determine whether the obtained load fluctuation compliance evaluation index is within the preset fluctuation compliance threshold range. If the obtained load fluctuation compliance evaluation index is within the preset fluctuation compliance threshold range, the load fluctuation result is recorded as normal load fluctuation and no load balancing optimization is performed; the load balancing optimization result is used to determine whether the obtained load balancing optimization evaluation index is within the preset balanced optimization effect threshold range. If the load balancing optimization evaluation index is within the preset balanced optimization effect threshold range, the load balancing optimization result is recorded as abnormal load fluctuation and qualified balanced optimization; the equipment fault detection result is used to determine whether the obtained switch device fault evaluation index is within the preset fault threshold range. If the switch device fault evaluation index is within the preset fault threshold range, the equipment fault detection result is recorded as abnormal load fluctuation, unqualified balanced optimization and abnormal equipment detection.

[0082] In this embodiment, specifically, if the obtained load fluctuation compliance evaluation index is within the preset fluctuation compliance threshold range, the load fluctuation result does not perform fault detection, otherwise the load fluctuation result is recorded as load fluctuation abnormality and load balancing optimization is performed; wherein, if the load balancing optimization evaluation index is within the preset balancing optimization effect threshold range, the load balancing optimization result is not to perform fault detection, otherwise the load balancing optimization result is recorded as load fluctuation abnormality, balancing optimization failure and fault detection is performed; wherein, if the switch device fault evaluation index is within the preset fault threshold range, the preset personnel is reminded to perform equipment fault aging detection, otherwise the equipment fault detection result is recorded as load fluctuation abnormality, balancing optimization failure and equipment detection normal, and the preset personnel is reminded to re-acquire the initial load data.

[0083] Among them, the preset fault threshold range is set by professionals according to the standards in the field. For example, the actual operating current range of the equipment (generally 50A to 300A), the actual operating voltage range of the equipment (generally 220V to 400V), the actual operating power range of the equipment (generally 5000W to 50000W), and the actual operating temperature range of the equipment (generally 30℃ to 70℃) are set to determine the preset fault threshold range; accurate assessment of the compliance degree of equipment fault detection is achieved.

[0084] like Figure 3 As shown, it is a structural schematic diagram of an intelligent switch device adaptive control system provided by an embodiment of the present application. An intelligent switch device adaptive control system provided by an embodiment of the present application includes: a load data acquisition and processing module, a fluctuation compliance evaluation and judgment module, a balanced optimization evaluation and judgment module, and a fault detection evaluation feedback module; wherein the load data acquisition and processing module is used to obtain initial load data in a preset industrial park area within a preset time interval, and pre-process the initial load data to obtain load-related data, wherein the initial load data includes an initial load-related compliance value and an initial load-related average value; the fluctuation compliance evaluation and judgment module is used to perform load evaluation on a preset industrial park area according to the load-related data to obtain a load fluctuation compliance evaluation index, and based on the load fluctuation compliance evaluation index and the preset fluctuation compliance threshold range The load fluctuation compliance evaluation index is used to comprehensively quantify the load fluctuation compliance situation in the preset industrial park area; the balanced optimization evaluation judgment module is used to evaluate the effect of load balancing optimization after executing load balancing optimization to obtain the load balancing optimization evaluation index, and judge whether to execute equipment fault detection based on the load balancing optimization evaluation index and the preset balanced optimization effect threshold range, and the load balancing optimization evaluation index is used to evaluate the effect of load balancing optimization; the fault detection evaluation feedback module is used to obtain the switching device fault evaluation index after executing equipment fault detection, and conduct a comprehensive evaluation based on the load fluctuation compliance evaluation index and the load balancing optimization evaluation index to obtain a comprehensive evaluation result and provide feedback, and the switching device fault evaluation index is used to comprehensively quantify the compliance degree of equipment fault detection.

[0085] In this embodiment, the comprehensive evaluation results can be fed back via SMS or email, and displayed in a pop-up window through a visual interface (such as a dashboard); by performing load evaluation on the preset industrial park area, load balancing optimization and equipment fault detection under abnormal load evaluation conditions can be achieved, thereby improving the adaptive control stability of the intelligent switching equipment.

[0086] To summarize, the embodiment of the present application performs a load evaluation on a preset industrial park area to determine whether to perform load balancing optimization, and then after performing load balancing optimization, determines whether to perform equipment fault detection based on the load balancing optimization evaluation index, and finally obtains the switch device fault evaluation index after performing equipment fault detection and performs a comprehensive evaluation, thereby achieving the evaluation and processing of load fluctuations in the preset industrial park area, and further achieving the improvement of the adaptive control efficiency of the intelligent switching device, effectively solving the problem of low adaptive control efficiency of the intelligent switching device in the prior art.

[0087] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may 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.

[0088] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0089] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0090] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1The steps for the functions specified in one or more boxes.

[0091] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0092] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. An adaptive control method for an intelligent switch device, characterized in that: The following steps are involved: S1, obtaining initial load data in a preset industrial park area within a preset time interval, and preprocessing the initial load data to obtain load-related data, wherein the initial load data includes an initial load-related compliance value and an initial load-related average value; S2, performing load evaluation on the preset industrial park area according to the load-related data to obtain a load fluctuation compliance evaluation index, and judging whether to perform load balancing optimization based on the load fluctuation compliance evaluation index and a preset fluctuation compliance threshold range, wherein the load fluctuation compliance evaluation index is used to comprehensively quantify the load fluctuation compliance situation in the preset industrial park area; S3, after performing the load balancing optimization, evaluating the effect of the load balancing optimization to obtain a load balancing optimization evaluation index, and judging whether to perform device fault detection based on the load balancing optimization evaluation index and a preset load balancing optimization effect threshold range, wherein the load balancing optimization evaluation index is used to evaluate the effect of the load balancing optimization; S4, obtaining a switchgear fault evaluation index after executing the equipment fault detection, and performing a comprehensive evaluation according to the load fluctuation compliance evaluation index and the load balancing optimization evaluation index to obtain a comprehensive evaluation result and provide feedback, wherein the switchgear fault evaluation index is used to comprehensively quantify the compliance degree of the equipment fault detection; The specific steps of performing load evaluation on a preset industrial park area according to load-related data to obtain a load fluctuation compliance evaluation index are as follows: Obtaining load-related compliance values ​​and load-related average values ​​at preset time intervals, wherein the load-related compliance values ​​include load current values, load voltage values, load power values, load frequency values, load power maximum values, and load power minimum values, and the load-related average values ​​include load current average values, load voltage average values, real-time load power average values, and load frequency average values; Obtaining a fluctuation stability compliance value at a preset time interval according to the load-related compliance value and the load-related average value, and combining the fluctuation stability compliance value, the load-related compliance value and the load-related average value with reference data obtained from a preset database to obtain a load fluctuation compliance evaluation index; The reference data includes reference values, evaluation weights and impact factors; The reference values ​​include a reference load current fluctuation value, a reference load voltage fluctuation value, a reference load power fluctuation value, a reference load fluctuation amplitude value, and a reference load frequency fluctuation value; The influencing factors include current fluctuation influencing factors, voltage fluctuation influencing factors and power fluctuation influencing factors; The evaluation weights include fluctuation stability compliance weights, fluctuation amplitude compliance weights and fluctuation discreteness weights; The method for obtaining the load fluctuation compliance evaluation index is as follows: ； ； In the formula, Indicates that the load fluctuation in the i-th preset time period within the preset time interval meets the evaluation index, Indicates the number of the preset time period within the preset time interval. , Indicates the total number of preset time periods. Indicates that the volatility is stable and meets the weight, Indicates that the fluctuation range is consistent with the weight, represents the volatility discreteness weight, Indicates the duration of each preset time period. It represents the fluctuation stability compliance value of the i-th preset time period, represents the current fluctuation influence factor, represents the voltage fluctuation influence factor, represents the power fluctuation influence factor, Indicates any moment within the preset time period. represents the load current value at time t in the i-th preset time period, represents the average value of the load current, Indicates the reference load current fluctuation value, represents the load voltage value at time t in the i-th preset time period, represents the average value of the load voltage, Indicates the reference load voltage fluctuation value, represents the load power value at time t in the i-th preset time period, Indicates the average load power. Indicates the reference load power fluctuation value, Indicates the maximum load power in the i-th preset time period, represents the minimum load power value in the i-th preset time period, Indicates the real-time average load power. Indicates the reference load fluctuation amplitude value, represents the load frequency value at time t in the i-th preset time period, represents the average value of load frequency, Indicates the reference load frequency fluctuation value.

2. The adaptive control method of an intelligent switch device according to claim 1, characterized in that: The specific steps of determining whether to perform load balancing optimization based on the load fluctuation compliance evaluation index and the preset fluctuation compliance threshold range are as follows: Compare the load fluctuation compliance evaluation index with the preset fluctuation compliance threshold range: If the load fluctuation compliance evaluation index is within the preset fluctuation compliance threshold range, the load balancing optimization is not performed, and the load fluctuation compliance evaluation index is transmitted to the preset load analysis database and automatically backed up; If the load fluctuation compliance evaluation index exceeds the preset fluctuation compliance threshold range, load balancing optimization is performed.

3. The adaptive control method of an intelligent switch device as claimed in claim 2, characterized in that: The specific steps of performing load balancing optimization are as follows: A1, obtaining the preset power equipment priority in the preset industrial park area, wherein the preset power equipment priority includes preset high priority power equipment, preset medium priority power equipment and preset low priority power equipment, shutting down the preset low priority power equipment one by one and judging whether the monitored load fluctuation compliance evaluation index is within the preset fluctuation compliance threshold range, if so, stopping the load balancing optimization, otherwise executing A2; A2, shut down the preset medium-priority power equipment one by one, and determine whether the load fluctuation compliance evaluation index is within the preset fluctuation compliance threshold range. If so, stop the load balancing optimization, otherwise execute A3; A3, evenly distribute the load of preset high-priority power equipment through load scheduling algorithm, and evaluate the effect of load balancing optimization to obtain the load balancing optimization evaluation index.

4. The adaptive control method of an intelligent switch device as claimed in claim 3, characterized in that: The specific steps of evaluating the effect of load balancing optimization to obtain the load balancing optimization evaluation index are as follows: Acquire initial evaluation related data of the intelligent switch device within a preset time period to be optimized, wherein the initial evaluation related data includes an initial energy consumption evaluation value, an initial carbon emission evaluation value, an initial load power evaluation value, and an initial average load power, and the preset time period to be optimized is used to describe a preset time period before performing load balancing optimization; Obtain optimization evaluation related data of the intelligent switch device within the corresponding optimization preset time period after the load balancing optimization is performed, wherein the optimization evaluation related data includes an optimized energy consumption evaluation value, an optimized carbon emission evaluation value, an optimized load power evaluation value, and an optimized average load power, and the optimized preset time period is used to describe a preset time period of the same length as the preset time period to be optimized after the load balancing optimization is performed; An initial load standard deviation is obtained according to an initial load power evaluation value and an initial average load power, and an optimized load standard deviation is obtained according to an optimized load power evaluation value and an optimized average load power; The energy consumption calculation result is obtained by performing a ratio operation on the difference between the initial energy consumption evaluation value and the optimized energy consumption evaluation value and the initial energy consumption evaluation value, and the carbon emission calculation result is obtained by processing the initial carbon emission evaluation value and the optimized carbon emission evaluation value; The complement of the ratio of the initial load standard deviation to the optimized load standard deviation, the ratio of the energy consumption calculation result to the reference energy consumption change value, and the ratio of the carbon emission calculation result to the reference carbon emission change value are combined with the corresponding optimization evaluation weights obtained from the preset database and multiplied and then processed to obtain the load balancing optimization evaluation index, wherein the optimization evaluation weights include energy consumption optimization evaluation weights, carbon emission optimization evaluation weights, and balancing efficiency optimization evaluation weights.

5. The adaptive control method of an intelligent switch device as claimed in claim 4, characterized in that: The specific steps of determining whether to perform device fault detection based on the load balancing optimization evaluation index and the preset load balancing optimization effect threshold range are as follows: Compare the load balancing optimization evaluation index with the preset load balancing optimization effect threshold range: If the load balancing optimization evaluation index is within the preset balancing optimization effect threshold range, the device fault detection is not performed, and the load balancing optimization evaluation index is backed up in real time and uploaded to the cloud storage; If the load balancing optimization evaluation index exceeds the preset balancing optimization effect threshold range, the switch device fault evaluation index after performing device fault detection is obtained.

6. The adaptive control method of an intelligent switch device as claimed in claim 5, characterized in that: The specific steps of obtaining the switch device fault assessment index after performing device fault detection are as follows: Acquire the actual operation value of the intelligent switch device within a preset detection time interval, wherein the actual operation value of the device includes the actual operation current of the device, the actual operation voltage of the device, the actual operation power of the device, and the actual operation temperature of the device; Acquire a reference operating value and an operating reference deviation of the intelligent switching device from a preset database, wherein the reference operating value includes a reference operating current of the device, a reference operating voltage of the device, a reference operating power of the device, and a reference operating temperature of the device, and the operating reference deviation includes an operating current reference deviation, an operating voltage reference deviation, an operating power reference deviation, and an operating temperature reference deviation; The corresponding operation ratio result is obtained by processing the actual operation value and the reference operation value of the equipment. The operation ratio result is divided by the corresponding reference operation value and then multiplied by the corresponding fault assessment weight obtained from the preset database. The fault assessment index of the switching device is obtained by processing the complement of the addition operation. The fault assessment weight includes the current detection assessment weight, the voltage detection assessment weight, the power detection assessment weight and the temperature detection assessment weight.

7. The adaptive control method of an intelligent switch device according to claim 1, characterized in that: The comprehensive evaluation results include load fluctuation results, load balancing optimization results and equipment fault detection results; The load fluctuation result is used to determine whether the obtained load fluctuation compliance evaluation index is within a preset fluctuation compliance threshold range. If the obtained load fluctuation compliance evaluation index is within the preset fluctuation compliance threshold range, the load fluctuation result is recorded as normal load fluctuation and load balancing optimization is not performed; The load balancing optimization result is used to determine whether the obtained load balancing optimization evaluation index is within the preset balancing optimization effect threshold range. If the load balancing optimization evaluation index is within the preset balancing optimization effect threshold range, the load balancing optimization result is recorded as abnormal load fluctuation and qualified balancing optimization; The equipment fault detection result is used to determine whether the acquired switching equipment fault assessment index is within the preset fault threshold range. If the switching equipment fault assessment index is within the preset fault threshold range, the equipment fault detection result is recorded as abnormal load fluctuation, unqualified balancing optimization and abnormal equipment detection.

8. A system using the adaptive control method for intelligent switchgear according to any one of claims 1 to 7, characterized in that: include: Load data acquisition and processing module, fluctuation compliance assessment and judgment module, balance optimization assessment and judgment module, fault detection assessment and feedback module; The load data acquisition and processing module is used to obtain the initial load data in the preset industrial park area within a preset time interval, and pre-process the initial load data to obtain load-related data, wherein the initial load data includes an initial load-related compliance value and an initial load-related average value; The fluctuation compliance evaluation judgment module is used to perform load evaluation on the preset industrial park area according to the load-related data to obtain a load fluctuation compliance evaluation index, and judge whether to perform load balancing optimization based on the load fluctuation compliance evaluation index and the preset fluctuation compliance threshold range. The load fluctuation compliance evaluation index is used to comprehensively quantify the load fluctuation compliance situation in the preset industrial park area; The load balancing optimization evaluation and judgment module is used to evaluate the effect of the load balancing optimization after performing the load balancing optimization to obtain a load balancing optimization evaluation index, and to determine whether to perform device fault detection based on the load balancing optimization evaluation index and a preset load balancing optimization effect threshold range, wherein the load balancing optimization evaluation index is used to evaluate the effect of the load balancing optimization; The fault detection evaluation feedback module is used to obtain the switching device fault evaluation index after executing the equipment fault detection, and to perform a comprehensive evaluation based on the load fluctuation compliance evaluation index and the load balancing optimization evaluation index to obtain a comprehensive evaluation result and provide feedback. The switching device fault evaluation index is used to comprehensively quantify the compliance degree of the equipment fault detection.

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