A Smart Control Method for New Power Grid Systems

Abstract

This invention relates to the field of voltage regulation technology, specifically to an intelligent control method for new power grid systems. The method analyzes voltage data fluctuations in distribution boxes within each construction area to determine voltage jump moments; it identifies local power supply difficulties by analyzing fluctuations over local time periods, and analyzes the power supply control focus and determines the key control moments by considering the start-up and shutdown impact of operating equipment; it analyzes the operational stability of equipment over reference time periods to obtain operational stability, analyzes operational fluctuation consistency indicators by considering temporal synchronization, and determines key control moments by combining power supply control focus; and it adjusts the weights of key control moments for predictive regulation. This invention considers the impact of equipment start-up and shutdown as well as the superimposed impact of operating loads to progressively screen for more realistic and accurate key control moments, making control focus more reliable, improving the accuracy of subsequent load forecasting results, and ensuring the reliability of overall power grid system regulation.

Description

Technical Field

[0001] This invention relates to the field of voltage regulation technology, and more specifically to an intelligent control method for new power grid systems. Background Technology

[0002] Construction sites are characterized by drastic load fluctuations, a high proportion of temporary power consumption, complex working environments, and significant safety risks. With the integration of new grid energy sources and the superposition of multi-energy complementarity, the core challenge of regulation is to adapt to the dynamic load demands of construction sites while ensuring construction safety and progress.

[0003] Construction sites typically require substantial power for facilities such as construction equipment, lighting, and air conditioning. These facilities have high and fluctuating power demands. Furthermore, construction load demand is not fixed but fluctuates with construction phases, work duration, and work sequence adjustments. The instability of power demand at construction sites is common; rapid changes in load due to equipment switching, construction process variations, etc., can easily be confused with genuine anomalies such as equipment failures, thus reducing the accuracy of load forecasting. Existing load forecasting methods often predict the overall total load of the construction site, failing to reflect the impact of localized shocks on specific construction areas. This leads to a disconnect between control strategies and actual load impacts on site, reducing the responsiveness of power grid control. Summary of the Invention

[0004] To address the technical problem of unstable power demand caused by changes in equipment switching and construction processes at construction sites, leading to rapid changes in power load and confusion between localized impacts and actual abnormal fluctuations such as equipment failures, thereby reducing the accuracy of load forecasting and causing a disconnect between control strategies and actual load impacts, this invention aims to provide an intelligent control method for new power grid systems. The specific technical solution adopted is as follows:

[0005] The first aspect of this application provides an intelligent control method for a new type of power grid system, including:

[0006] For each construction area, obtain the voltage data of the distribution box in the time sequence;

[0007] Filter the voltage jump moments based on the degree of voltage fluctuation; obtain the local power supply difficulty at each moment by analyzing the distribution of voltage jump moments at previous moments;

[0008] Analyze the impact of equipment start-up and shutdown changes in the preceding reference period at each moment, and combine the synchronous distribution of voltage jump moments and the difficulty of local power supply to determine the power supply control focus at each moment and screen out the control focus moments.

[0009] The operational stability of each piece of equipment in the construction area is obtained by analyzing the duration of operation of each piece of equipment in the preceding reference period at each control focus time. Based on the synchronization between the control focus time and the voltage jump time in the preceding reference period at each control focus time, the changes and operational stability of the equipment under synchronization are analyzed to obtain the operational fluctuation consistency index at each control focus time.

[0010] Based on the power supply control attention and operational fluctuation consistency index at each control focus point, the actual power supply control coefficient for each control focus point is determined, and key control points are selected; the weight of key control points is adjusted according to the actual power supply control coefficient to carry out data prediction and control for each construction area.

[0011] Furthermore, the method for obtaining the voltage transition moment includes:

[0012] Based on the voltage data difference between each time step and the previous time step, the voltage change rate at each time step is obtained;

[0013] The moment when the voltage change exceeds a preset voltage change threshold is taken as the voltage jump moment.

[0014] Furthermore, the method for obtaining the difficulty of local power supply includes:

[0015] Based on the distribution of the number of voltage transition moments in the preceding reference period at each moment, the transition percentage at each moment is obtained.

[0016] The maximum difference in voltage data between each two adjacent moments in the preceding reference period is analyzed to obtain the maximum amplitude at that moment.

[0017] The percentage of jumps and the maximum / minimum amplitude at each moment are combined to determine the local power supply difficulty at each moment.

[0018] Furthermore, the method for obtaining the power supply control attention includes:

[0019] Obtain the set of operating equipment in operation in the corresponding construction area at each time point; for any given time point, take the intersection of the set of operating equipment at this time point and the previous time point as the operating intersection; take the complement of the operating intersection in the set of operating equipment at this time point as the start-up set; take the complement of the operating intersection in the set of operating equipment at the previous time point as the stop-up set; combine the number of equipment in the start-up set and the stop-up set with voltage data fluctuations to obtain the start-up and stop-down impact assessment degree at this time point; take the time when the start-up and stop-down impact assessment degree is not zero as the start-up and stop time point.

[0020] Each voltage transition moment in the preceding reference period is taken as the analysis moment. When there are start and stop moments in a local period of the analysis moment, the analysis moment is recorded as a synchronization moment. Based on the proportion of synchronization moments in the preceding reference period, the start and stop synchronization proportion of this moment is obtained.

[0021] By combining the start-stop impact assessment degree of all start-stop times in the preceding reference period and the start-stop synchronization ratio, the stability mismatch degree of this moment is obtained; the start-stop impact assessment degree and the start-stop synchronization ratio of all start-stop times are negatively correlated with the stability mismatch degree.

[0022] By combining the local power supply difficulties and start-stop stability mismatch at that moment, the power supply control focus at that moment can be obtained.

[0023] Furthermore, the method for obtaining the control focus moment includes:

[0024] The moment when the power supply control attention level exceeds the preset control attention threshold is taken as the control attention moment.

[0025] Furthermore, the method for obtaining operational stability includes:

[0026] For any operating equipment in the construction area, the moment when the equipment is in an operating state in terms of time sequence is taken as the operating moment of the equipment; and the operating time segments are obtained by combining consecutively adjacent operating moments.

[0027] For any given control and attention moment, the operational stability of the equipment at that control and attention moment is obtained by combining the longest operating period of the equipment and the number of operating periods in the preceding reference period. The number of operating periods is negatively correlated with operational stability.

[0028] Furthermore, the method for obtaining the operational volatility consistency index includes:

[0029] For any given moment of regulatory focus, the moment that is simultaneously recorded as the voltage jump moment and the moment of regulatory focus in the preceding reference period will be used as the high fluctuation synchronization moment.

[0030] Based on the proportion of high-volatility synchronous moments in the preceding reference period of the time of focus for regulation, the volatility synchronization degree of the time of focus for regulation is obtained.

[0031] Obtain the intersection of the sets of operating equipment at all times in the preceding reference period of the time of control and interest, and use it as the stable intersection; based on the number of operating equipment in the difference set between the set of operating equipment at each time in the preceding reference period of the time of control and interest and the stable intersection, obtain the degree of equipment change at each time in the preceding reference period of the time of control and interest.

[0032] By combining the operational stability and equipment variability of all operating equipment at each high-fluctuation synchronization moment in the preceding reference period of the control focus moment, the superimposed instability of the control focus moment is obtained. Operational stability and superimposed instability are negatively correlated.

[0033] By combining the degree of fluctuation synchronization and the degree of superimposed instability at the time of focus of the regulation, an operational fluctuation consistency index is obtained at the time of focus of the regulation.

[0034] Furthermore, the method for obtaining the actual power supply regulation coefficient includes:

[0035] By combining the consistent operational fluctuation index with the power supply control focus at each control focus moment, the actual power supply control coefficient at each control focus moment is obtained. The consistent operational fluctuation index and the actual power supply control coefficient are negatively correlated.

[0036] Furthermore, the method for obtaining the key moments of regulation includes:

[0037] The moment when the actual power supply regulation coefficient is greater than the preset key threshold is taken as the key moment for regulation.

[0038] Furthermore, the method of adjusting the weights of key control moments based on the actual power supply control coefficient for data prediction and control of each construction area includes:

[0039] For each construction area, the weights of all non-critical control moments are set to preset weight values; the sum of the actual power supply control coefficient and the preset weight value at each critical control moment is used as the weight of each critical control moment.

[0040] The voltage data in the construction area is predicted using a weighted ARIMA model based on the weight of each time point. Based on the predicted data in all construction areas, the power grid system of the construction site is regulated.

[0041] Secondly, this application provides an intelligent control system for a new type of power grid system, the system comprising:

[0042] The data acquisition module is used to acquire voltage data from the distribution box in time sequence for each construction area;

[0043] The stability analysis module is used to filter voltage jump moments based on the degree of voltage fluctuation; by analyzing the distribution of voltage jump moments at each moment in the preceding moments, the local power supply difficulty at each moment is obtained.

[0044] The control focus time analysis module is used to analyze the impact of equipment start-up and shutdown changes in the preceding reference period of each moment. Combined with the synchronous distribution of voltage jump moments and the difficulty of local power supply, it determines the power supply control focus of each moment and filters out the control focus moments.

[0045] The critical moment analysis module is used to analyze the duration of operation of each piece of equipment in the construction area during the preceding reference period at each critical moment of control, and to obtain the operational stability. Based on the synchronization between the critical moment of control and the voltage jump moment during the preceding reference period of each critical moment of control, the module analyzes the changes and operational stability of the equipment under synchronization, and obtains the operational fluctuation consistency index for each critical moment of control.

[0046] Based on the power supply control attention and operational fluctuation consistency index at each control attention moment, the actual power supply control coefficient at each control attention moment is determined and key control moments are screened.

[0047] The predictive control module is used to adjust the weights of key control moments based on the actual power supply control coefficient to predict and control data for each construction area.

[0048] Thirdly, this application provides a computer device including a memory and a processor. The memory is used to store computer program code, and the processor is used to call and run the computer program code from the memory to perform the method as described in the first aspect of this application or any embodiment of the first aspect.

[0049] Fourthly, this application provides a computer program product comprising computer program code, which, when executed, performs the method as described in the first aspect of this application or any embodiment thereof.

[0050] Fifthly, this application provides a computer-readable storage medium that stores computer program code, which, when executed, performs the method as described in the first aspect of this application or any embodiment thereof.

[0051] The present invention has the following beneficial effects:

[0052] This invention analyzes voltage fluctuations in distribution boxes within each construction area to determine voltage jump moments, filters out moments of severe fluctuation, and assesses local power supply difficulties by considering unstable fluctuations within specific time periods, thus quantifying local power supply needs. It also considers the impact of equipment start-up and shutdown changes, analyzing the load impact caused by equipment start-up and shutdown operations to filter out key control moments based on power supply control focus. This considers normal fluctuations and suspected anomalies during equipment start-up and shutdown, reducing the impact of local impacts on actual control needs and improving the accuracy of key control location selection. Further analysis of equipment operation continuity during the reference time period for control focus moments yields operational stability, and analysis of fluctuation synchronization in time series reveals operational fluctuation consistency indicators. It considers normal fluctuations caused by the superposition of stable loads from multiple continuously operating devices, reducing misjudgments of interference. By combining power supply control focus, it identifies critical moments for abnormal control within the control focus moments, thereby adjusting the weight of key control moments for predictive control. This invention progressively filters more realistic and accurate key control moments by considering the impact of equipment start-up and shutdown as well as the impact of superimposed operating loads, making control focus more reliable, improving the accuracy of subsequent load prediction results, and ensuring the reliability of overall power grid system control. Attached Figure Description

[0053] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0054] Figure 1 A flowchart of an intelligent control method for a novel power grid system provided in one embodiment of the present invention;

[0055] Figure 2 This is a structural diagram of an intelligent control system for a novel power grid system provided in one embodiment of the present invention;

[0056] Figure 3 The diagram illustrates a computer device structure according to an embodiment of the present invention. Detailed Implementation

[0057] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of an intelligent control method for a novel power grid system proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0058] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0059] The following description, in conjunction with the accompanying drawings, details a specific scheme for an intelligent control method for a new type of power grid system provided by the present invention.

[0060] Please see Figure 1 The diagram illustrates a flowchart of an intelligent control method for a novel power grid system according to an embodiment of the present invention. The method includes the following steps:

[0061] S1: For each construction area, obtain the voltage data of the distribution box in the timing sequence.

[0062] Temporary power grids at construction sites lack fixed pipelines and rely entirely on distribution boxes and temporary cables for power supply. The location of the distribution boxes directly determines the power supply boundary. By ensuring that all equipment within the area is covered by the same power supply circuit, each construction zone corresponding to each distribution box is defined, and each piece of equipment within the construction zone is marked and monitored. Power grid control in construction scenarios requires commands to be sent to specific circuits and executed on specific equipment, and the distribution box is the final execution node for these control commands.

[0063] In this embodiment of the invention, sensors monitor the voltage data of each distribution box on the construction site. The voltage data of the distribution boxes directly reflects the electrical load characteristics of the construction area. Load fluctuations usually cause voltage fluctuations. Therefore, monitoring the voltage changes of the distribution boxes helps to identify load change trends, thereby improving the accuracy of load forecasting. The acquisition frequency can be set to once per second. The specific acquisition settings can be adjusted by the implementer according to the specific implementation scenario, and are not limited here.

[0064] Since each distribution box corresponds to a fixed construction area, the monitored voltage data can directly reflect the electrical load characteristics of the area. Therefore, this embodiment of the invention performs separate analysis and load prediction for any construction area. Simultaneously, it statistically analyzes the equipment in operation at each time point, facilitating the analysis of the impact of different types of equipment.

[0065] S2: Filter voltage jump moments based on the degree of voltage fluctuation; obtain the local power supply difficulty at each moment by analyzing the distribution of voltage jump moments at previous moments.

[0066] Construction site equipment starts and stops entirely in sync with construction procedures, such as piling, pouring concrete, or welding. Different equipment has vastly different load characteristics, so load fluctuations can be extremely drastic within the construction area as equipment starts and stops, leading to voltage instability. Excessive voltage fluctuations indicate an unstable power supply system, potentially causing equipment damage or power outages. Therefore, to help identify potential overload or voltage anomalies in the power grid, it is necessary to first identify the moments of voltage jumps in the power supply.

[0067] In this embodiment of the invention, the process of obtaining the voltage jump moment includes: obtaining the voltage change degree at each moment based on the voltage data difference between each moment and the previous moment; the greater the voltage data change between moments, the greater the fluctuation. In a specific embodiment of the invention, for any given moment, the voltage data difference between that moment and the previous moment is calculated and normalized to obtain the voltage change degree at that moment. The difference is obtained through the absolute value of the difference between voltage data. Therefore, when the voltage change degree is greater than a preset voltage change threshold, the corresponding moment can be taken as the voltage jump moment. The preset voltage change threshold can be set to 0.8, and the specific value can be adjusted by the implementer and is not limited here.

[0068] It should be noted that normalization is a technique well known to those skilled in the art. The choice of normalization can be linear normalization or standard normalization, such as through the hyperbolic tangent function. The specific normalization method is not limited here.

[0069] Data from voltage jump moments allows for more accurate accounting of potential voltage instability factors when forecasting load, thus preventing severe future voltage fluctuations. For example, frequent small fluctuations in voltage data over a certain period may be due to significant changes in equipment load during that time, leading to untimely adjustments in the distribution system. Therefore, by analyzing data from these voltage jump moments during load forecasting, the power system can anticipate potential future load anomalies, adjust the forecasting model in a timely manner, and improve the accuracy of load forecasting.

[0070] Therefore, by further reflecting the degree of local instability at each moment in the preceding local time period, the method for obtaining the local power supply difficulty in this embodiment of the invention includes:

[0071] Within the preceding reference period at each moment, the transition ratio for each moment is obtained based on the distribution of the number of voltage transition moments. That is, for any given moment, the ratio of the total number of voltage transition moments to the total number of moments in the preceding reference period is taken as the transition ratio for that moment. A higher voltage fluctuation frequency within the reference period, i.e., a larger ratio, indicates a potential high-frequency impact state and a higher degree of instability. In this embodiment of the invention, the preceding reference period can be set to the 60 moments preceding the current moment; the implementer can adjust this as needed, and no limitation is imposed here.

[0072] Further analysis of the maximum voltage data difference between each two adjacent moments in the preceding reference period at each moment yields the maximum amplitude of the voltage data at that moment. In a specific embodiment of the present invention, after calculating the voltage data difference between each two adjacent moments in the preceding reference period, the maximum voltage data difference is taken as the maximum amplitude of the voltage data at each moment. The maximum amplitude reflects the maximum fluctuation of the voltage data in the reference period. The larger the maximum amplitude, the more severe the load fluctuation in the local area and the higher the degree of instability.

[0073] Finally, the percentage of jumps and the maximum / minimum amplitude at each moment are combined to determine the local power supply difficulty at each moment. A higher percentage of jumps and a larger unstable amplitude indicate that the power supply system is more likely to be in a high-frequency, high-impact state, with severe load fluctuations and greater difficulty in achieving stable local power supply. In this embodiment of the invention, the product of the percentage of jumps and the maximum / minimum amplitude at each moment is used as the local power supply difficulty at each moment.

[0074] S3: Analyze the impact of equipment start-up and shutdown changes in the preceding reference period at each moment, and combine the synchronous distribution of voltage jump moments and the difficulty of local power supply to determine the power supply control focus at each moment and screen out the control focus moments.

[0075] When voltage jumps occur, they may be caused not only by load fluctuations in the power grid itself, but also by load changes in equipment within the construction area. For example, the start-up and shutdown of equipment during construction may generate instantaneous voltage, and the start-up of a certain piece of equipment may instantly increase the power demand, thereby causing a sudden increase in the load of the power grid, which in turn affects voltage fluctuations and system stability.

[0076] Since the voltage will fluctuate within 0.5-2 seconds after the equipment start-up or shutdown command is issued, the voltage instability caused by the equipment start-up and shutdown must have strong time synchronization. If the voltage jump time is not synchronized with the equipment start-up and shutdown time, for example, the equipment is running stably, but a sudden voltage drop occurs, it is very likely to be an abnormal fluctuation. This abnormal fluctuation is the moment that needs more attention and control.

[0077] Therefore, by combining the changes in the number of operating devices to characterize the start-up and shutdown changes of the devices, and by analyzing the synchronicity to preliminarily determine the degree of abnormal fluctuations at each moment, preferably, in this embodiment of the invention, the method for obtaining the power supply control focus includes:

[0078] First, we obtain the set of operating equipment in operation within the corresponding construction area at each time point. In a construction scenario, the impact of starting and stopping different equipment on voltage fluctuations varies greatly. To more accurately correlate voltage fluctuations, we analyze the impact of starting and stopping changes among the sets of operating equipment, and combine this with load changes caused by high-power and low-power equipment. This makes the correlation analysis between impact changes and stability more reliable.

[0079] Furthermore, for any given moment, the intersection of the set of operating equipment at that moment and the set of operating equipment at the previous moment is taken as the operating intersection, reflecting the continuous operation of the equipment between moments. The complement of the operating intersection in the set of operating equipment at that moment is taken as the start set, and the complement of the operating intersection in the set of operating equipment at the previous moment is taken as the stop set. Here, let set U contain a subset A. A set U that does not contain any set consisting of elements from set A is called the complement of set A in set U.

[0080] The degree of conflict change is represented by the start-up set and the shutdown set. The more start-up and shutdown changes there are, the greater the impact. Combining the number of devices in the start-up set and the shutdown set, as well as voltage data fluctuations, the start-up and shutdown impact assessment degree at that moment is obtained. In a specific embodiment of the present invention, the sum of the number of devices in the start-up set and the shutdown set is used as the start-up and shutdown impact degree. The product of the start-up and shutdown impact degree and the maximum and minimum amplitude at that moment is normalized and used as the start-up and shutdown impact assessment degree. When the total number of start-ups and shutdowns is larger, and the voltage fluctuation amplitude is also higher over a period of time, it reflects that the power system state is more significantly affected by sudden shocks, and the possibility of strong load shocks is higher.

[0081] Therefore, the moment when the start-stop impact assessment degree is higher than the preset start-stop impact threshold is taken as the start-stop moment. The higher the start-stop impact assessment degree, the more likely it is that there is a start-stop impact on the running equipment, that is, the voltage instability caused by the start-stop change of the equipment. Therefore, the start-stop moment is determined. In this embodiment of the invention, the preset start-stop impact threshold can be set to 0.3. The specific value can be adjusted by the implementer and is not limited here.

[0082] Then, each voltage jump moment in the preceding reference period is taken as the analysis moment. When there is a start-stop moment in the local period of the analysis moment, the analysis moment is recorded as the synchronization moment. In this embodiment of the invention, the local period can be set as the period 2 seconds before the voltage jump moment. That is, when the start-stop moment occurs within 2 seconds before the voltage jump moment, this voltage jump moment is very likely caused by the start-stop of the equipment, which has the possibility of synchronization and is less likely to be an abnormal fluctuation.

[0083] Furthermore, based on the proportion of synchronous moments in the preceding reference period, the start-stop synchronization ratio of that moment is obtained. That is, the ratio of the total number of synchronous moments in the preceding reference period to the total number of voltage jump moments is used as the start-stop synchronization ratio of that moment, reflecting the overall proportion of synchronization during voltage jump moments. The higher the start-stop synchronization ratio, the higher the matching degree between unstable fluctuations and equipment start-stop.

[0084] Furthermore, by combining the start-stop impact assessment degree of all start-stop times in the preceding reference period and the start-stop synchronization ratio, the stability mismatch degree of that moment is obtained. By comprehensively considering the start-stop impact and synchronization ratio in the reference period, the degree of mismatch is assessed from the fluctuation synchronization feedback and impact feedback, and the probability of abnormal fluctuations is more accurately quantified.

[0085] In this embodiment of the invention, the average value of the start-stop impact assessment of all start-stop times in the preceding reference period is calculated as the start-stop instability at that time, quantifying the overall start-stop impact. The higher the start-stop instability, the higher the impact feedback, and the higher the reliability of the voltage jump time corresponding to the start-stop effect.

[0086] Therefore, the product of the start-stop synchronization ratio and the start-stop instability at that moment is negatively correlated and normalized to obtain the start-stop stability mismatch degree at that moment. The start-stop impact assessment degree and the start-stop synchronization ratio at all start-stop moments are negatively correlated with the stability mismatch degree. The start-stop instability is used to measure the credibility of the start-stop impact, and the start-stop synchronization ratio reflects the matching credibility between the instability and the start-stop. The smaller both the start-stop instability and the start-stop synchronization ratio are, the higher the degree of mismatch.

[0087] It should be noted that negative correlation mapping is a technique well known to those skilled in the art, such as using inverse proportional values ​​or negative exponential forms with the natural constant as the base, etc., and will not be limited or elaborated here.

[0088] Finally, by combining the local power supply difficulties and the start-stop stability mismatch at that moment, the power supply control attention at that moment is obtained. In this embodiment of the invention, the product of the local power supply difficulties and the start-stop stability mismatch at that moment is normalized to obtain the power supply control attention at that moment. The higher the degree of mismatch between the instability and the equipment start-stop, and the greater the local power supply instability, the weaker the correlation between the regional voltage fluctuation and the equipment start-stop at that moment, the higher the risk of complex fluctuations, and the more attention is needed for subsequent regulation.

[0089] Understandably, when there are no voltage jump moments in the reference time period, it indicates that the local area is in a stable state at that moment, and no anomaly analysis is required.

[0090] By monitoring the power supply control level, moments that may exhibit abnormal fluctuations can be preliminarily identified. In this embodiment of the invention, moments when the power supply control level is greater than a preset control attention threshold are taken as control attention moments. The preset control attention threshold can be set to 0.7, and the implementer can adjust the specific value as needed.

[0091] S4: Analyze the duration of operation of each piece of equipment in the construction area during the preceding reference period at each control focus time to obtain operational stability; based on the synchronization between the control focus time and the voltage jump time during the preceding reference period at each control focus time, analyze the changes and operational stability of the equipment under synchronization conditions to obtain the operational fluctuation consistency index at each control focus time.

[0092] Due to the influence of equipment operating conditions and process connections in the actual construction environment, the load will change dynamically. After the equipment starts up and enters the operating state, it does not always maintain a constant load. Each change in load will ultimately manifest as voltage fluctuations in the distribution box. Therefore, the voltage fluctuations at the time of monitoring may also be affected by the equipment's operating load status. For example, during welding, changes in the distance between the welding rod and the workpiece will cause the welding current to fluctuate from 80A to 120A, the line voltage drop to fluctuate from 16V to 24V, and the distribution box voltage to fluctuate from 364V to 356V.

[0093] Therefore, in order to accurately assess the timing of actual fluctuations in the power system, changes in equipment operating status are also considered. Preferably, in this embodiment of the invention, the method for obtaining operational stability includes:

[0094] For any operating equipment in the construction area, the moment when the equipment is in the operating state in terms of time sequence is taken as the operating time of the equipment. The operating time is obtained by combining consecutive adjacent operating times, and the operating time of the equipment is initially recorded.

[0095] Furthermore, for any given control and attention moment, the operational stability of the operating equipment at that control and attention moment is obtained by combining the longest operating segment and the number of operating segments in the preceding reference period. The longer the longest operating segment of each equipment in the reference period of the control and attention moment, the higher the stability of the equipment's operating state at that time. And when the number of operating segments is smaller, it indicates that the equipment is not in a state of frequent start-stop, resulting in lower volatility and a more stable operating state.

[0096] Therefore, by combining the runtime and distribution, a stable assessment of continuous operation is obtained. In one specific embodiment of the present invention, the longest runtime of the operating device is taken as the duration of the control focus moment. The number of runtime segments is negatively correlated with the continuity of operation. The number of runtime segments of the operating device in the preceding reference period is negatively correlated and normalized to obtain the continuous distribution value of the control focus moment. The product of the duration of the control focus moment and the continuous distribution value is normalized to obtain the operational stability of the operating device at the control focus moment. This measures the degree of stability of each operating device at the control focus moment. The higher the operational stability, the smaller the fluctuation impact of this device.

[0097] Considering that voltage fluctuations in distribution boxes within the same construction area of ​​a construction site are usually caused by the superposition of loads from multiple devices operating in parallel, rather than by the independent influence of a single device, such as tower cranes, rebar cutters, and rebar bending machines, which are usually operating simultaneously, further analysis is needed to determine whether voltage fluctuations are affected by the operating status of the equipment in order to improve the acquisition of the true impact of the fluctuations.

[0098] By identifying a match between operational status and fluctuations, if the changes in the operational status of the equipment at a given time are highly synchronized with the fluctuations, it indicates that the equipment load change at that time is caused by normal fluctuations, consistent with normal disturbances driven by construction needs. Preferably, in this embodiment of the invention, the method for obtaining the operational fluctuation consistency index includes:

[0099] First, for any moment of control focus, in the preceding reference period, the moment that is simultaneously recorded as the voltage jump moment and the control focus moment is taken as the high fluctuation synchronization moment. In the reference period, when the voltage jump moment is synchronized with the control focus moment, it reflects that while fluctuations occur at this moment, there is also the possibility that the actual impact of local fluctuations is higher, and the control demand for fluctuations caused by equipment is greater.

[0100] Therefore, based on the proportion of high-fluctuation synchronous moments in the preceding reference period of the time of control, the fluctuation synchronization degree of the time of control is obtained. In this embodiment of the invention, the ratio of the total number of high-fluctuation synchronous moments to the total number of moments in the preceding reference period is used as the fluctuation synchronization degree of the time of control. When the proportion of high-fluctuation synchronous moments is higher, it indicates that there is a higher probability of synchronization.

[0101] Furthermore, the intersection of the sets of operating equipment across all time points in the preceding reference period of the time of interest is obtained as a stable intersection, with equipment that has been continuously operating throughout the reference period considered as equipment types whose operating status has not changed. Then, based on the number of operating equipment in the difference set between the set of operating equipment at each time point in the preceding reference period of the time of interest and the stable intersection, the degree of equipment variability at each time point in the preceding reference period of the time of interest is obtained. The difference set reflects the situation where equipment type changes at each time point, and the differences in equipment variability are analyzed in set form. Further analysis is conducted on the potential cumulative effects caused by changes in equipment type despite a constant number of equipment. For example, the operating equipment changes from 3 cranes to 2 cranes and 1 pump truck.

[0102] The superimposed stability impact of operating equipment is comprehensively assessed by considering changes in operating equipment and its operational status. The superimposed instability at the time of control is obtained by combining the operational stability and variability of all operating equipment at each high-fluctuation synchronization point in the preceding reference period. Operational stability and superimposed instability are negatively correlated. The more significant the changes in operating equipment and the lower the operational stability, and the more significant the changes in operating equipment and the worse the overall stable operation at the high-fluctuation synchronization point, the greater the superimposed load impact of the equipment.

[0103] In one specific embodiment of the present invention, the average operational stability of all operating equipment at each high-fluctuation synchronization moment is negatively correlated and mapped to serve as the average instability degree for each high-fluctuation synchronization moment. The product of the equipment variability and the average instability degree at each high-fluctuation synchronization moment is normalized and used as the instability impact index for each high-fluctuation synchronization moment, reflecting the degree of load impact under each synchronization fluctuation condition. The sum of the instability impact indices of all high-fluctuation synchronization moments in the preceding reference period is used as the superimposed instability degree. The greater the superimposed degree of unstable operational impact, the higher the probability of equipment operation fluctuation.

[0104] Finally, by combining the fluctuation synchronization degree and superimposed instability degree at the time of regulatory focus, an operational fluctuation consistency index is obtained for that time. Higher fluctuation synchronization degree and higher degree of unstable operational superposition indicate a higher degree of matching between equipment operational changes and suspected abnormal fluctuations during the suspected abnormal reference period. In one specific embodiment of the invention, the product of the fluctuation synchronization degree and superimposed instability degree at the time of regulatory focus is normalized to obtain the operational fluctuation consistency index. A larger operational fluctuation consistency index indicates a higher probability that the abnormal fluctuation is caused by the superposition of loads from multiple operating devices, and a lower demand for regulatory focus.

[0105] S5: Based on the power supply control attention and operational fluctuation consistency index at each control focus moment, determine the actual power supply control coefficient for each control focus moment and screen key control moments; adjust the weight of key control moments based on the actual power supply control coefficient to perform data prediction control for each construction area.

[0106] By further assessing the potential impact of voltage fluctuations caused by normal equipment interference through the operation of the fluctuation consistency index, and adjusting the focus of power supply control, a more accurate true power supply fluctuation is determined. In this embodiment of the invention, the method for obtaining the true power supply control coefficient includes:

[0107] By combining the operational fluctuation consistency index and the power supply control attention at each control focus moment, the actual power supply control coefficient for each control focus moment is obtained. The operational fluctuation consistency index and the actual power supply control coefficient are negatively correlated. In a specific embodiment of the present invention, for any control focus moment, the product of the negatively correlated value of the operational fluctuation consistency index for that control focus moment and the power supply control attention is normalized to obtain the actual power supply control coefficient for that control focus moment. The smaller the operational fluctuation consistency index, the less the fluctuation conforms to the construction scenario pattern, the lower the possibility of normal interference, the higher the possibility of real abnormal fluctuation, and the higher the control focus demand; therefore, the larger the actual power supply control coefficient.

[0108] The critical moments for regulation are selected by the magnitude of the actual power supply regulation coefficient. In this embodiment of the invention, the moment when the actual power supply regulation coefficient is greater than a preset critical threshold is considered the critical moment for regulation, and the other non-critical moments are considered normal operation moments. The preset critical threshold can be set to 0.5, and the specific value can be adjusted by the implementer according to the specific implementation scenario, without limitation.

[0109] By adjusting the weights, the system focuses more on voltage fluctuations at the moments when they truly impact the system, increasing the weight of data from critical control moments. This allows subsequent prediction processes to enhance the training value of high-fluctuation data on the power grid control model. Because voltage fluctuations at critical control moments often reveal characteristics of power grid vulnerability, such as local load-bearing limits and risk thresholds for equipment coordination, increasing their weight allows the intelligent control system to more accurately capture abnormal patterns in the power grid during construction, ultimately optimizing targeted control strategies.

[0110] In this embodiment of the invention, for each construction area, the weights of all non-control critical moments are set to preset weight values. The sum of the actual power supply control coefficient and the preset weight value at each control critical moment is used as the weight of each control critical moment, where the preset weight value is set to 1. Based on the weight of each moment, the voltage data in the construction area is predicted using a weighted ARIMA model to obtain predicted data. It should be noted that the method of using the weighted ARIMA model for prediction is a well-known technique familiar to those skilled in the art and will not be described in detail here.

[0111] Based on predicted data from all construction areas, the power grid system of the construction site is regulated. In one specific embodiment of this invention, the predicted data of each construction area is mapped to the distribution network topology of the site according to its spatial location, resulting in the predicted voltage curve for each distribution circuit. Based on the predicted voltage and the corresponding predicted power demand, the power-voltage sensitivity coefficient of each circuit is calculated to determine whether it is approaching the rated voltage or load limit. A lower or upper voltage threshold is set for each area. If the predicted voltage exceeds the threshold in the future, an early warning is triggered. Finally, a regulation command is generated, the regulation results of each area are summarized, and multi-objective optimization is used, such as minimizing voltage fluctuations, minimizing energy consumption, and maximizing equipment utilization, to generate a globally optimal power allocation scheme at the dispatch center, which continues to iterate in subsequent periods.

[0112] In summary, this invention analyzes voltage fluctuations in distribution boxes within each construction area to determine voltage jump moments, filters out points of severe fluctuation, and assesses local power supply difficulties by considering unstable fluctuations within specific time periods, thus quantifying local power supply needs. It also considers the impact of equipment start-up and shutdown changes, analyzing the load impact caused by equipment start-up and shutdown operations to filter out key control moments based on power supply control focus. This considers normal fluctuations and suspected anomalies during equipment start-up and shutdown, reducing the impact of local impacts on actual control needs and improving the accuracy of key control location selection. Furthermore, it analyzes the operational stability of equipment during reference periods of control focus moments, analyzes operational fluctuation consistency indicators based on temporal fluctuation synchronization, and considers normal fluctuations caused by the superposition of stable loads from multiple continuously operating devices, reducing misjudgments of interference. Finally, it identifies critical moments for abnormal control within the control focus moments based on power supply control focus, adjusting the weight of key control moments for predictive control. This invention progressively filters more realistic and accurate key control moments by considering the impact of equipment start-up and shutdown as well as the impact of superimposed operating loads, making control focus more reliable, improving the accuracy of subsequent load prediction results, and ensuring the reliability of overall power grid system control.

[0113] This application also provides an intelligent control system for a new type of power grid system; please refer to [link / reference]. Figure 2The diagram illustrates a structural diagram of an intelligent control system for a novel power grid system according to an embodiment of the present invention. The system includes: a data acquisition module 201, a stability analysis module 202, a control attention moment analysis module 203, a control critical moment analysis module 204, and a predictive control module 205.

[0114] The data acquisition module 201 is used to acquire voltage data of the distribution box in time sequence for each construction area;

[0115] The stability analysis module 202 is used to filter voltage jump moments based on the degree of voltage fluctuation; by analyzing the distribution of voltage jump moments at each moment in the preceding moments, the local power supply difficulty at each moment is obtained.

[0116] The control attention moment analysis module 203 is used to analyze the impact of equipment operation start-up and shutdown changes in the preceding reference period of each moment. Combined with the synchronous distribution of voltage jump moments and the difficulty of local power supply, it determines the power supply control attention of each moment and filters out the control attention moments.

[0117] The critical moment analysis module 204 is used to analyze the duration of operation of each operating equipment in the construction area during the preceding reference period at each critical moment of control, and to obtain the operation stability. Based on the synchronization between the critical moment of control and the voltage jump moment during the preceding reference period of each critical moment of control, the module analyzes the changes and operation stability of the operating equipment under the synchronization condition, and obtains the operation fluctuation consistency index at each critical moment of control.

[0118] Based on the power supply control attention and operational fluctuation consistency index at each control attention moment, the actual power supply control coefficient at each control attention moment is determined and key control moments are screened.

[0119] The prediction and control module 205 is used to adjust the weight of key moments in the control based on the actual power supply control coefficient to perform data prediction and control for each construction area.

[0120] It should be noted that the system provided in the above embodiments is only an example of the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device can be divided into different functional modules to complete all or part of the functions described above. In addition, the intelligent control system for a new type of power grid system and the intelligent control method for a new type of power grid system provided in the above embodiments belong to the same concept. The specific implementation process is detailed in the method embodiments and will not be repeated here.

[0121] This application also provides a computer device; please refer to [link / reference]. Figure 3The illustration shows a schematic diagram of a computer device structure according to an embodiment of the present invention. The computer device includes a memory 301, a processor 302, and a computer program 303 stored in the memory 301 and running on the processor 302. When the processor 302 executes the computer program 303, the computer device can execute any of the intelligent control methods for new power grid systems described above.

[0122] This application also provides a computer program product that, when run on a computer device, enables the computer device to execute any of the aforementioned intelligent control methods for new power grid systems.

[0123] This application also provides a computer-readable storage medium storing computer program code. When the computer program code is run on a computer device, the computer device can execute any of the aforementioned intelligent control methods for new power grid systems.

[0124] In the embodiments provided in this application, it should be understood that the computer device, computer program product and computer-readable storage medium provided are all used to perform the corresponding methods provided above, and therefore the beneficial effects they can achieve can be referred to the beneficial effects of the methods provided above, which will not be repeated here.

[0125] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0126] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for intelligent control of a new power grid system, characterized in that, The method comprises: For each construction area, obtain voltage data on the time sequence of the distribution box; Screen the voltage jump time of the voltage fluctuation degree; obtain the local power supply difficulty of each time point through the voltage jump time distribution of each time point on the previous sequence time point; Analyze the impact degree of the start-stop change of the equipment operation in the previous reference period of each time point, combine the synchronous distribution of the voltage jump time and the local power supply difficulty, determine the power supply control attention degree of each time point, and screen out the regulation and control attention time; Analyze the running duration of each running equipment in the previous reference period of each regulation and control attention time in the construction area, obtain the running stability, and analyze the change of the running equipment and the running stability under the synchronization according to the synchronization of the regulation and control attention time and the voltage jump time on the previous reference period of each regulation and control attention time, obtain the running fluctuation consistency index of each regulation and control attention time; According to the power supply control attention degree and the running fluctuation consistency index of each regulation and control attention time, determine the power supply real regulation and control coefficient of each regulation and control attention time and screen the regulation and control key time; adjust the weight of the regulation and control key time according to the power supply real regulation and control coefficient to carry out data prediction regulation of each construction area.

2. The intelligent control method for the new power grid system according to claim 1, characterized in that, The voltage jump time acquisition method comprises: According to the voltage data difference between each time point and the previous time point, obtain the voltage change degree of each time point; The time point with the voltage change degree greater than the preset voltage change threshold value is regarded as the voltage jump time.

3. The intelligent control method for the new power grid system according to claim 1, characterized in that, The local power supply difficulty acquisition method comprises: In the previous reference period of each time point, the jump proportion of each time point is obtained based on the time point number distribution proportion of the voltage jump time; Analyze the maximum value of the voltage data difference between each adjacent two time points in the previous reference period of each time point, obtain the maximum value amplitude of the time point; Combine the jump proportion and the maximum value amplitude of each time point as the local power supply difficulty of each time point.

4. The intelligent control method for the new power grid system according to claim 1, characterized in that, The power supply control attention degree acquisition method comprises: Obtain the running equipment set in the corresponding construction area at each time point; for any one time point, the intersection of the running equipment set between the time point and the previous time point is regarded as the running intersection; the complement of the running intersection in the running equipment set at the time point is regarded as the start-up set; the complement of the running intersection in the running equipment set at the previous time point of the time point is regarded as the shutdown set; combine the number of equipment in the start-up set and the shutdown set, and the voltage data fluctuation, to obtain the start-stop impact evaluation degree of the time point; the time point with the start-stop impact evaluation degree not being zero is regarded as the start-stop time point; In turn, each voltage jump time in the previous reference period of the time point is regarded as the analysis time point; when there is a start-stop time point in the local period of the analysis time point, the analysis time point is recorded as the synchronization time point; according to the time point number proportion of the synchronization time point in the previous reference period, the start-stop synchronization proportion of the time point is obtained; Combine the start-stop impact evaluation degree of all start-stop time points in the previous reference period of the time point, and the start-stop synchronization proportion, to obtain the stable mismatch degree of the time point; the start-stop impact evaluation degree and the start-stop synchronization proportion of all start-stop time points are negatively correlated with the stable mismatch degree. Combine the local power supply difficulty and the start-stop stability mismatch at this moment to obtain the power supply control attention degree at this moment.

5. The intelligent control method for the new power grid system according to claim 1, characterized in that, The method for obtaining the regulation attention moment comprises: The moment with the power supply control attention degree greater than the preset control attention threshold is taken as the regulation attention moment.

6. The intelligent control method for the new power grid system according to claim 1, characterized in that, The method for obtaining the operation stability comprises: For any one operation device in the construction area, the moment when the operation device is in the operation state in the time sequence is taken as the operation moment of the operation device; the operation moments are combined to obtain the operation period; For any one regulation attention moment, in the previous reference period of the regulation attention moment, the operation stability of the operation device at the regulation attention moment is obtained in combination with the longest operation period of the operation device and the distributed number of operation periods, and the distributed number of operation periods is negatively correlated with the operation stability.

7. The intelligent control method for the new power grid system according to claim 4, characterized in that, The method for obtaining the operation fluctuation consistency index comprises: For any one regulation attention moment, in the previous reference period of the regulation attention moment, the moment that is recorded as the voltage jump moment and the regulation attention moment at the same time is taken as the high fluctuation synchronization moment; Based on the proportion of the number of high fluctuation synchronization moments in the previous reference period of the regulation attention moment, the fluctuation synchronization degree of the regulation attention moment is obtained; The intersection of the operation device set between all moments in the previous reference period of the regulation attention moment is taken as the stable intersection; the device change degree of each moment in the previous reference period of the regulation attention moment is obtained according to the number of operation devices in the difference set between the operation device set of each moment and the stable intersection in the previous reference period of the regulation attention moment; In combination with the operation stability of all operation devices at each high fluctuation synchronization moment in the previous reference period of the regulation attention moment and the device change degree, the superimposed instability degree of the regulation attention moment is obtained, and the operation stability is negatively correlated with the superimposed instability degree. The method for obtaining the power supply real regulation coefficient comprises:

8. The intelligent control method for the new power grid system according to claim 1, characterized in that, In combination with the operation fluctuation consistency index and the power supply control attention degree of each regulation attention moment, the power supply real regulation coefficient of each regulation attention moment is obtained, and the operation fluctuation consistency index is negatively correlated with the power supply real regulation coefficient. The method for obtaining the regulation key moment comprises:

9. The intelligent control method for the new power grid system according to claim 1, characterized in that, The moment with the power supply real regulation coefficient greater than the preset key threshold is taken as the regulation key moment. The data prediction regulation of each construction area according to the weight of the regulation key moment adjusted according to the power supply real regulation coefficient comprises:

10. The intelligent control method for the new power grid system according to claim 1, characterized in that, For each construction area, the weight of all non-regulation key moments is set to a preset weight value; the sum of the power supply real regulation coefficient of each regulation key moment and the preset weight value is taken as the weight of each regulation key moment; Based on the weight of each moment, the voltage data in the construction area is predicted by using the weighted ARIMA model to obtain the prediction data; the prediction data in all construction areas is used to complete the regulation of the construction site power grid system. ​

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