An alternating current intelligent electric energy meter with automatic analysis load characteristic

Abstract

The present application relates to the technical field of electric energy meter, especially to an AC intelligent electric energy meter with automatic load characteristic analysis, comprising: a data acquisition module, used to periodically acquire real-time current and voltage signals of a target power grid line and generate corresponding load characteristic data; a data storage module, used to store the load characteristic data of the target power grid line in a target time period and the corresponding acquisition time; a load characteristic analysis module, used to determine load sensitive characteristic data, load change trend in a future preset time period, and load type and operating state of the target power grid line based on the load characteristic data of the target power grid line in the target time period and the corresponding acquisition time; and a load adjustment module, used to determine whether to trigger load adjustment, and if so, determine a load adjustment strategy. The present application can automatically analyze load characteristics and improve the accuracy of load prediction.

Description

Technical Field

[0001] This invention relates to the field of electricity meter technology, and in particular to an AC smart electricity meter with automatic load analysis characteristics. Background Technology

[0002] With rapid economic development and the increasing complexity of power systems, accurate monitoring and management of power grid load have become crucial. The number of electricity users is constantly increasing, and various electrical devices are widely used, resulting in diverse and complex electricity consumption behaviors and load characteristics. At the same time, the large-scale integration of distributed energy sources, such as solar and wind power generation, further increases the uncertainty of power grid load.

[0003] Traditional electricity meters can only measure electrical energy and cannot perform in-depth analysis of load characteristics. While some existing smart meters possess certain data acquisition capabilities, their ability to identify load fluctuations, periodic patterns, and different load types of electrical equipment is insufficient. This makes it difficult to comprehensively and accurately grasp load changes and provide adequate information support for power management. In terms of load forecasting, existing technologies often rely on simple mathematical models or historical data averaging methods, failing to fully consider the complexity and uncertainty of load changes. This results in significant deviations between forecasts and actual loads, failing to meet the power system's requirements for accurate load forecasting. Therefore, there is an urgent need for an AC smart meter capable of automatically analyzing load characteristics to monitor grid load conditions in real time and provide strong support for power dispatching and demand-side management.

[0004] Chinese Patent Publication No. CN221465623U discloses a high-precision AC smart energy meter, including a base and a main body. The main body is slidably connected to the top of the base. A display is fixedly connected to the upper side of the front side wall of the main body. An indicator light is fixedly connected to the middle of the front side wall of the main body. Slider blocks are fixedly connected to the left and right side walls of the main body. A second socket is fixedly connected to the bottom of the main body. A connection port is fixedly connected to the top of the inner cavity of the second socket. A second connecting wire is fixedly connected to the top of the second socket. The end of the second connecting wire passes through the bottom of the main body and extends into the inner cavity of the main body, and an integrated board is fixedly connected to the end of the wire. A main control board is fixedly connected to the top of the integrated board.

[0005] Existing technologies have the following problems: they can only measure electrical energy and issue low energy alarms, but they cannot automatically analyze load characteristics and have relatively low accuracy in load forecasting. Summary of the Invention

[0006] Therefore, the present invention provides an AC smart energy meter with automatic load characteristic analysis to overcome the problem that the prior art cannot automatically analyze load characteristics and has relatively low load prediction accuracy.

[0007] To achieve the above objectives, the present invention provides an AC smart energy meter with automatic load characteristic analysis, comprising:

[0008] The data acquisition module is used to periodically acquire real-time current and voltage signals of the target power grid line and generate corresponding load characteristic data, including voltage, current, active power, reactive power, power factor, harmonic components and harmonic distortion rate.

[0009] The data storage module is connected to the data acquisition module and is used to store the load characteristic data of the target power grid line within the target time period and the corresponding acquisition time.

[0010] The load characteristic analysis module is connected to the data storage module. It is used to determine the load sensitive characteristic data corresponding to the target time period based on the load characteristic data of the target power grid line within the target time period and the corresponding acquisition time, and to determine the load change trend within the future preset time period, as well as to determine the load type and operating status of the target power grid line based on the load sensitive characteristic data.

[0011] A load adjustment module, connected to the load characteristic analysis module, is used to determine whether to trigger load adjustment based on the load type and operating status of the target power grid line. If load adjustment is triggered, a load adjustment strategy is determined based on the load change trend within a preset future time period, including:

[0012] If the load change trend is the first change trend, then based on the load sensitivity characteristic data corresponding to the target time period, a number of non-critical devices are identified, and the operating status of each of the non-critical devices is adjusted.

[0013] If the load change trend is the second trend, then high-load equipment is identified based on the current load characteristic data, and the operating status of the high-load equipment is adjusted.

[0014] Furthermore, the load characteristic analysis module includes:

[0015] A sensitive data determination unit, which is connected to the data storage module, is used to determine the load sensitive characteristic data corresponding to the target time period based on the load characteristic data of the target power grid line within the target time period and the corresponding acquisition time.

[0016] The trend determination unit is connected to the sensitive data determination unit and the data storage module respectively, and is used to determine the load change trend in a future preset time period based on the load sensitive characteristic data corresponding to the target time period and the corresponding collection time.

[0017] A load type determination unit, connected to the sensitive data determination unit, is used to determine load characteristic values ​​based on load sensitivity characteristic data corresponding to the target time period, and to determine the load type of the target power grid line based on the load characteristic values.

[0018] A line status determination unit, which is connected to the change trend determination unit and the load type determination unit respectively, is used to determine the operating status of the target power grid line based on the load change trend within a future preset time period and the load type of the target power grid line.

[0019] Furthermore, the load adjustment module includes:

[0020] A trigger determination unit, which is connected to the load type determination unit and the line status determination unit respectively, is used to determine whether to trigger load adjustment based on the comparison result between the load type and operating status of the target power grid line and the preset standard.

[0021] The adjustment strategy determination unit is connected to the data storage module and is used to determine the load adjustment strategy based on the load sensitivity characteristic data corresponding to the target time period. This includes determining a number of non-critical devices based on the load sensitivity characteristic data corresponding to the target time period and adjusting the operating status of each of the non-critical devices, or determining high-load devices based on the current load characteristic data and adjusting the operating status of the high-load devices.

[0022] The load adjustment unit is connected to both the trigger determination unit and the adjustment strategy determination unit, and is used to determine the result of triggering load adjustment based on the trigger determination unit, and to perform load adjustment based on the load adjustment strategy determined by the adjustment strategy determination unit.

[0023] Furthermore, the adjustment strategy determination unit determines the correlation between each electrical device corresponding to the target power grid line based on the load sensitivity characteristic data corresponding to the target time period, and determines the priority of each electrical device corresponding to the target power grid line, and determines a number of non-critical devices based on the correlation and priority of each electrical device.

[0024] Furthermore, the adjustment strategy determination unit determines high-load equipment based on the comparison results between the current load characteristic data and the preset load characteristic data.

[0025] Furthermore, the adjustment strategy determination unit determines the operating status of each of the non-critical devices based on the number of the non-critical devices;

[0026] If the number of non-critical devices is greater than the preset number, the operating status of each non-critical device will be adjusted to a temporary stop state.

[0027] If the number of non-critical devices is less than or equal to the preset number, the operating status of each non-critical device will be adjusted to a reduced load state.

[0028] Furthermore, the adjustment strategy determination unit determines the operating status of the high-load equipment based on the equipment type of the high-load equipment;

[0029] If the high-load equipment is a critical type, then the operating status of the high-load equipment will be adjusted to a load reduction state.

[0030] If the high-load equipment is of a non-critical type, then the operating status of the high-load equipment will be adjusted to a temporarily stopped state.

[0031] Furthermore, the trend determination unit determines the load deviation based on the load sensitivity characteristic data corresponding to the target time period, and determines the load change trend within a future preset time period based on the load deviation.

[0032] Furthermore, the trend determination unit determines the method for determining the load change trend within a future preset time period based on the comparison result between the load deviation and the preset deviation;

[0033] If the load deviation is less than the preset deviation, the load sensitivity characteristic data corresponding to the target time period and the corresponding collection time are input into the load trend prediction model to obtain the load change trend in the future preset time period output by the load trend prediction model.

[0034] If the load deviation is greater than or equal to the preset deviation, the load sensitivity data corresponding to the target time period is corrected based on the load deviation to obtain the corrected load sensitivity data. The corrected load sensitivity data and the corresponding acquisition time are then input into the load trend prediction model to obtain the load change trend within the future preset time period output by the load trend prediction model.

[0035] Furthermore, it also includes:

[0036] The load warning module is connected to the load characteristic analysis module and is used to determine whether a load warning is triggered based on the load change trend within the future preset time period. If a load warning is triggered, the electrical equipment corresponding to the target power grid line is adjusted to a temporary shutdown state.

[0037] The data acquisition and adjustment module, which is connected to the trend determination unit, is used to adjust the acquisition cycle based on the comparison result between the load deviation and the preset deviation.

[0038] Compared with existing technologies, the advantages of this invention are as follows: It includes a data acquisition module that periodically collects real-time current and voltage signals of the target power grid line to generate load characteristic data. Through multi-dimensional data analysis, the accuracy of subsequent load analysis is improved. A data storage module stores the load characteristic data of the target power grid line within a target time period, reducing data storage volume and providing a data foundation for subsequent load analysis. A load characteristic analysis module identifies the load-sensitive characteristic data corresponding to the target time period, determining data that significantly impacts the load analysis of the target power grid line within that period, thus improving the accuracy and efficiency of load analysis. Determining the load change trend within a future preset time period based on the load characteristic data corresponding to the target time period improves the accuracy of load forecasting. Dynamic status assessment and automatic load analysis of the target power grid line are achieved by considering its load type and operating status. Finally, a load adjustment module determines whether to trigger load adjustment based on the load type and operating status of the target power grid line, improving the accuracy of the determination. Load adjustment strategies include adjusting the operating status of non-critical equipment and high-load equipment. If the load change trend is the first trend, with the load gradually increasing, peak shaving and valley filling of the power grid can be achieved by adjusting non-critical equipment in advance. If the load change trend is the second trend, with the load tending to be overloaded, overload tripping of the power grid can be avoided by processing high-load equipment in real time. By proposing different load adjustment strategies for different load change trends, energy saving effect can be improved and intelligent load adjustment of power grid lines can be realized.

[0039] Furthermore, this invention, by setting up a sensitive data determination unit to determine the load sensitive characteristic data corresponding to the target time period, can accurately identify sensitive load characteristics in the load characteristics, thereby reducing the data processing volume of subsequent load analysis and improving the accuracy of load forecasting. By setting up a trend determination unit to determine the load change trend within a future preset time period based on the load sensitive characteristic data corresponding to the target time period and the corresponding acquisition time, the accuracy of load forecasting can be improved. By setting up a load type determination unit to determine the load characteristic value based on the load sensitive characteristic data corresponding to the target time period, and to determine the load type of the target power grid line based on the load characteristic value, accurate classification is possible. Different load types are adapted to different load adjustment strategies and operating status standards, providing support for load analysis. By setting up a line status determination unit to determine the operating status of the target power grid line based on the load change trend within a future preset time period and the load type of the target power grid line, the operating status of the power grid line can be accurately identified. Through accurate identification of the operating status, the accuracy of load analysis can be improved.

[0040] Furthermore, by setting up a trigger determination unit, this invention determines whether to trigger load adjustment based on a comparison of the load type and operating status of the target power grid line with a preset standard, thereby improving the accuracy and efficiency of the determination. By setting up an adjustment strategy determination unit, the load adjustment strategy is determined based on the load sensitivity characteristic data corresponding to the target time period, which improves the accuracy of load adjustment, adapts to different load change trends, and enhances the flexibility of load adjustment.

[0041] Furthermore, the adjustment strategy determination unit of the present invention determines the correlation relationship of each electrical device corresponding to the target power grid line based on the load sensitivity characteristic data corresponding to the target time period, and determines the priority of each electrical device corresponding to the target power grid line. Based on the correlation relationship and priority of each electrical device, it determines a number of non-critical devices, which can avoid misjudgment by a single indicator, accurately identify non-critical devices, and reduce the impact on users' normal production and life.

[0042] Furthermore, the adjustment strategy determination unit of the present invention can accurately identify high-load equipment through data comparison, thereby reducing misjudgments.

[0043] Furthermore, the adjustment strategy determination unit of this invention dynamically adjusts the operating status of non-critical equipment based on the number of non-critical equipment, which can improve the flexibility of load adjustment. When the number of non-critical equipment is greater than the preset number, it indicates that the load is gradually increasing and the rate of increase is relatively fast, and the overload is relatively serious. By adjusting the operating status of each non-critical equipment to a temporary stop state, the peak load can be quickly reduced. When the number of non-critical equipment is less than or equal to the preset number, it indicates that the load is gradually increasing, but the increase is relatively slow. By adjusting the operating status of each non-critical equipment to a load reduction state, the load and user experience can be balanced, and equipment start-up and shutdown losses can be reduced.

[0044] Furthermore, the adjustment strategy determination unit of this invention determines the operating status of high-load equipment based on the equipment type of the high-load equipment, which can improve the flexibility of load adjustment. For critical types of high-load equipment, if it is shut down directly, it will affect the user's use. By adopting a load reduction state, energy consumption can be reduced while ensuring basic functions, and production interruption or safety risks caused by direct shutdown can be avoided. For non-critical types of high-load equipment, a temporary stop state is adopted to quickly reduce the load, and the impact on users is small, which can improve the accuracy of load control.

[0045] Furthermore, the load trend determination unit of this invention determines the load change trend within a preset time period based on a comparison between the load deviation and the preset deviation. This improves the accuracy of load forecasting and reduces forecast deviation. If the load deviation is less than the preset deviation, it indicates that the error in the load-sensitive characteristic data for the target time period is small. By directly inputting the original load-sensitive characteristic data into the load trend forecasting model, information loss due to over-correction is avoided, reducing data processing volume and improving forecast accuracy. If the load deviation is greater than or equal to the preset deviation, it indicates that the error in the load-sensitive characteristic data for the target time period is large, and special circumstances may occur. By correcting the load-sensitive characteristic data corresponding to the target time period, sudden load changes can be reasonably handled, forecast deviation can be avoided, and the accuracy of load forecasting can be improved.

[0046] Furthermore, by incorporating a load early warning module, this invention can provide timely warnings before load anomalies occur, maintaining the stability of the target power grid lines and reducing equipment losses. By setting up a data acquisition and adjustment module, it can adapt to load changes. When the load deviation is large, indicating an unstable power grid load, shortening the acquisition cycle allows for more frequent data acquisition, timely capturing of load change trends, and providing more accurate data support for subsequent analysis and decision-making. When the load deviation is small, indicating a relatively stable power grid load, extending the acquisition cycle reduces unnecessary data collection, avoids data redundancy, lowers data storage and processing costs, and further improves the accuracy of load analysis and forecasting. Attached Figure Description

[0047] Figure 1 This is a structural block diagram of an AC smart energy meter with automatic load characteristic analysis according to an embodiment of the present invention;

[0048] Figure 2 This is a structural block diagram of the load characteristic analysis module according to an embodiment of the present invention;

[0049] Figure 3 This is a structural block diagram of the load adjustment module according to an embodiment of the present invention;

[0050] Figure 4 This is a logic decision diagram for determining the operating status of non-critical equipment in an embodiment of the present invention. Detailed Implementation

[0051] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0052] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0053] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0054] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0055] Please see Figure 1 The diagram shown is a structural block diagram of an AC smart energy meter with automatic load characteristic analysis according to an embodiment of the present invention. The present invention provides an AC smart energy meter with automatic load characteristic analysis, comprising:

[0056] The data acquisition module is used to periodically acquire real-time current and voltage signals of the target power grid line and generate corresponding load characteristic data, including voltage, current, active power, reactive power, power factor, harmonic components and harmonic distortion rate.

[0057] In practice, there are no specific limitations on the method of periodically acquiring current and voltage signals, as this is existing technology and will not be elaborated here.

[0058] It is understandable that implementers can set the data collection period according to the actual situation. Preferably, the data collection period is set to a range of 3 minutes to 5 minutes.

[0059] The data storage module is connected to the data acquisition module and is used to store the load characteristic data of the target power grid line within the target time period and the corresponding acquisition time.

[0060] In practice, implementers can set the target time period according to the actual situation. Setting the target time period too long will result in excessive data processing and storage, reducing data processing efficiency; setting the target time period too short will result in insufficient data, reducing the accuracy of subsequent predictions. Preferably, the target time period is set to a range of 2 to 5 days.

[0061] The load characteristic analysis module is connected to the data storage module. It is used to determine the load sensitive characteristic data corresponding to the target time period based on the load characteristic data of the target power grid line within the target time period and the corresponding acquisition time, and to determine the load change trend within the future preset time period, as well as to determine the load type and operating status of the target power grid line based on the load sensitive characteristic data.

[0062] Please see Figure 2 The diagram shown is a structural block diagram of the load characteristic analysis module according to an embodiment of the present invention; specifically, the load characteristic analysis module includes:

[0063] A sensitive data determination unit, which is connected to the data storage module, is used to determine the load sensitive characteristic data corresponding to the target time period based on the load characteristic data of the target power grid line within the target time period and the corresponding acquisition time.

[0064] In implementation, the fluctuation value corresponding to each load characteristic is determined based on the load characteristic data of the target power grid line within the target time period. For example, the active power corresponding to the target time period: Y1, Y2, ..., Y j , ..., Y m Y j Let m be the active power generated by the j-th data collection within the target time period, and m be the number of data collections. Then, the fluctuation value S corresponding to the active power is S = (∑ m j＝1 (Y j -((∑ m j＝1 Y j ) / m)) 2 ) / m, and calculate the fluctuation values ​​corresponding to load characteristics such as voltage, current, reactive power, power factor, harmonic components and harmonic distortion rate respectively. If the fluctuation value corresponding to each load characteristic is greater than the preset fluctuation value, it indicates that the corresponding load characteristic is highly sensitive to load changes and is easily affected by load changes. Then, the corresponding load characteristic is determined as a load sensitive characteristic.

[0065] It is understandable that implementers can set preset fluctuation values ​​based on actual conditions or on the fluctuation values ​​of load characteristics that have passed the qualification test in historical data. Preferably, the preset fluctuation value is set to a range of 3 to 8.

[0066] The trend determination unit is connected to the sensitive data determination unit and the data storage module respectively, and is used to determine the load change trend in a future preset time period based on the load sensitive characteristic data corresponding to the target time period and the corresponding collection time.

[0067] Specifically, the trend determination unit determines the load deviation based on the load sensitivity characteristic data corresponding to the target time period, and determines the load change trend within a future preset time period based on the load deviation.

[0068] In implementation, the load deviation is determined based on the difference between the load-sensitive characteristic data corresponding to the target time period and the standard load characteristic data. For example, if active power is a load-sensitive characteristic, then the active power Y1, Y2, ..., Y j , ..., Y m Compared with standard active power E1, E2, ..., E j , ..., E m Load deviation P = (∑ m j＝1 Y j ×E j ) / (sqrt(∑ m j＝1 (Y j ) 2 )×sqrt(∑ m j＝1 (E j ) 2 ), where E j Let P = sqrt() be the standard active power corresponding to the j-th data collection, and let P = sqrt() be the preset square root determination function. It can be understood that the load deviation is determined based on the mean of the load deviations of each load sensitivity characteristic.

[0069] Understandably, implementers can determine the corresponding standard load characteristic data based on the actual situation or on the load characteristic data that passed the qualification test during the same period of the target time period from historical data, or set the standard load characteristic data based on the expected load value of the target time period output by the load forecasting model.

[0070] Specifically, the trend determination unit determines the method for determining the load change trend within a future preset time period based on the comparison result between the load deviation and the preset deviation;

[0071] If the load deviation is less than the preset deviation, the load sensitivity characteristic data corresponding to the target time period and the corresponding collection time are input into the load trend prediction model to obtain the load change trend in the future preset time period output by the load trend prediction model.

[0072] If the load deviation is greater than or equal to the preset deviation, the load sensitivity data corresponding to the target time period is corrected based on the load deviation to obtain the corrected load sensitivity data. The corrected load sensitivity data and the corresponding acquisition time are then input into the load trend prediction model to obtain the load change trend within the future preset time period output by the load trend prediction model.

[0073] In practice, the implementers can set a preset deviation based on the actual situation or the average value of load deviations that have passed the qualification test in historical data. Preferably, the preset deviation is set to a range of 0.7 to 0.8.

[0074] It is understood that data preprocessing can be performed based on load-sensitivity characteristics and corresponding collection times in historical data to generate a dataset. Data preprocessing includes missing value imputation, normalization, sequence construction, etc., and the initial model is trained based on the dataset to obtain a load trend prediction model. It should be noted that those skilled in the art will understand that any prediction model in the prior art capable of predicting load change trends falls within the protection scope of this invention, such as traditional time series models, machine learning models, deep learning models, etc., which will not be elaborated here. It is understood that practitioners can set a preset time period according to actual conditions; preferably, the preset time period is set to 2 to 5 hours.

[0075] Understandably, the existence of load deviation indicates that the corresponding load sensitivity characteristics may be abnormal. Using this to predict load change trends would reduce prediction accuracy, thus requiring correction. Correction parameters can be determined based on the difference or ratio between the load deviation and the preset deviation. The difference or ratio between the load deviation and the preset deviation is positively correlated with the correction parameters, thereby correcting the load sensitivity data corresponding to the target time period.

[0076] The load trend determination unit of this invention determines the load change trend within a preset time period based on a comparison between the load deviation and a preset deviation. This method improves the accuracy of load forecasting and reduces forecast deviation. If the load deviation is less than the preset deviation, it indicates that the error in the load-sensitive characteristic data for the target time period is small. By directly inputting the original load-sensitive characteristic data into the load trend forecasting model, information loss due to over-correction is avoided, reducing data processing volume and improving forecast accuracy. If the load deviation is greater than or equal to the preset deviation, it indicates that the error in the load-sensitive characteristic data for the target time period is large, and special circumstances may occur. By correcting the load-sensitive characteristic data corresponding to the target time period, sudden load changes can be reasonably handled, forecast deviation can be avoided, and the accuracy of load forecasting can be improved.

[0077] A load type determination unit, connected to the sensitive data determination unit, is used to determine load characteristic values ​​based on load sensitivity characteristic data corresponding to the target time period, and to determine the load type of the target power grid line based on the load characteristic values.

[0078] In implementation, a load change curve is generated based on the load sensitivity characteristic data corresponding to the target time period. Peak load and valley load are extracted based on the load change curve. The load characteristic value is determined based on the difference between the peak load and the valley load.

[0079] Understandably, the load type of the target power grid line can be determined based on the comparison results between the load characteristic value and the first preset characteristic value and the second preset characteristic value. The first preset characteristic value is greater than the second preset characteristic value. If the load characteristic value is greater than the first preset characteristic value, the load type of the target power grid line is industrial load. If the load characteristic value is less than the second preset characteristic value, the load type of the target power grid line is residential load. If the load characteristic value is not less than the second preset characteristic value and not greater than the first preset characteristic value, the load type of the target power grid line is commercial load.

[0080] It is understandable that the implementers can set the first preset characteristic value and the second preset characteristic value according to the actual situation or based on the maximum and minimum values ​​of the load characteristic values ​​that have passed the qualification test in historical data. Preferably, the range of the first preset characteristic value is set to 3000kW to 5000kW, and the range of the second preset characteristic value is set to 500kW to 1000kW.

[0081] A line status determination unit, which is connected to the change trend determination unit and the load type determination unit respectively, is used to determine the operating status of the target power grid line based on the load change trend within a future preset time period and the load type of the target power grid line.

[0082] In implementation, the operating status of the target power grid line includes normal status, warning status, light overload status, and overload status. In the normal status, the load is within the safe range, with no overload or abnormal fluctuations. In the warning status, the load is close to the safe threshold (the load is greater than 80% to 90% of the safe threshold but less than the safe threshold), or the fluctuations are abnormal. In the light overload status, the load exceeds the safe threshold, but the extent of the exceedance is less than 20% to 30% of the safe threshold. In the overload status, the load exceeds the safe threshold, and the extent of the exceedance is greater than 20% to 30% of the safe threshold.

[0083] Understandably, an operational status classification decision tree or operational status model can be constructed, and the load change trend and load type of the target power grid line within a preset time period can be input into the operational status classification decision tree or operational status model to obtain the operational status of the target power grid line output by the operational status classification decision tree or operational status model.

[0084] This invention, by setting up a sensitive data determination unit, determines the load sensitive characteristic data corresponding to the target time period, enabling accurate identification of sensitive load characteristics and reducing the data processing volume for subsequent load analysis and improving the accuracy of load forecasting. By setting up a trend determination unit, it determines the load change trend within a future preset time period based on the load sensitive characteristic data corresponding to the target time period and the corresponding acquisition time, improving the accuracy of load forecasting. By setting up a load type determination unit, it determines the load characteristic value based on the load sensitive characteristic data corresponding to the target time period, and then determines the load type of the target power grid line based on the load characteristic value, enabling accurate classification. Different load types are adapted to different load adjustment strategies and operating status standards, providing support for load analysis. By setting up a line status determination unit, it determines the operating status of the target power grid line based on the load change trend within the future preset time period and the load type of the target power grid line, enabling accurate identification of the power grid line's operating status. Accurate identification of the operating status improves the accuracy of load analysis.

[0085] A load adjustment module, connected to the load characteristic analysis module, is used to determine whether to trigger load adjustment based on the load type and operating status of the target power grid line. If load adjustment is triggered, a load adjustment strategy is determined based on the load change trend within a preset future time period, including:

[0086] If the load change trend is the first change trend, then based on the load sensitivity characteristic data corresponding to the target time period, a number of non-critical devices are identified, and the operating status of each of the non-critical devices is adjusted.

[0087] If the load change trend is the second trend, then high-load equipment is identified based on the current load characteristic data, and the operating status of the high-load equipment is adjusted.

[0088] Please see Figures 3-4 As shown, Figure 3 This is a structural block diagram of the load adjustment module according to an embodiment of the present invention; Figure 4 This invention provides a logic diagram for determining the operating status of non-critical equipment in an embodiment of the invention; specifically, the load adjustment module includes:

[0089] A trigger determination unit, which is connected to the load type determination unit and the line status determination unit respectively, is used to determine whether to trigger load adjustment based on the comparison result between the load type and operating status of the target power grid line and the preset standard.

[0090] In implementation, the preset standards are as follows: For residential loads, the load is relatively small, and the electrical equipment operates for a short period of time. It can be used normally under normal, warning, and light overload conditions. Therefore, the operating status is determined to be overload, triggering load adjustment. For industrial loads, the load is large, and the electrical equipment operates continuously for production. In order to avoid affecting production activities, it can operate normally under normal conditions. Load adjustment is required under warning conditions. Therefore, the operating status is determined to be warning, light overload, and overload, triggering load adjustment. For commercial loads, the load is relatively large, and the electrical equipment operates in different time periods. It can operate normally under normal and warning conditions. Therefore, the operating status is determined to be light overload and overload, triggering load adjustment.

[0091] The adjustment strategy determination unit is connected to the data storage module and is used to determine the load adjustment strategy based on the load sensitivity characteristic data corresponding to the target time period. This includes determining a number of non-critical devices based on the load sensitivity characteristic data corresponding to the target time period and adjusting the operating status of each of the non-critical devices, or determining high-load devices based on the current load characteristic data and adjusting the operating status of the high-load devices.

[0092] Specifically, the adjustment strategy determination unit determines the correlation between each electrical device corresponding to the target power grid line based on the load sensitivity characteristic data corresponding to the target time period, and determines the priority of each electrical device corresponding to the target power grid line, and determines a number of non-critical devices based on the correlation and priority of each electrical device.

[0093] In implementation, the usage of electrical equipment corresponding to the target power grid line is statistically analyzed. Based on load sensitivity characteristic data for the target time period, load sensitivity characteristic curves for each electrical device and each branch of the target power grid line are constructed. Cluster analysis is then performed on these curves to determine several cluster groups. Electrical devices within the same cluster group exhibit similar load patterns, are correlated, and are connected to branches within the same cluster group. The priority of electrical devices in each cluster group is evaluated based on preset priority evaluation indicators, including safety (whether...). For each priority evaluation indicator, a value is assigned based on factors such as fire protection and medical equipment, economic efficiency (production losses caused by equipment downtime), and technical efficiency (whether the equipment can interrupt the process). For example, 10 levels are set, with level 1 assigned a value of 1, corresponding to the lowest priority evaluation indicator, and level 10 assigned a value of 10, corresponding to the highest priority evaluation indicator. The priority of each electrical device is calculated (the sum of the priority values ​​corresponding to each priority evaluation indicator), and the priority of each cluster group is calculated (the sum / mean of the priorities of electrical devices in each cluster group). The priorities of each cluster group are compared, and the electrical devices in the cluster group with the lowest priority are identified as non-critical equipment.

[0094] The adjustment strategy determination unit of this invention determines the correlation between various electrical devices corresponding to the target power grid line based on the load sensitivity characteristic data corresponding to the target time period, and determines the priority of each electrical device corresponding to the target power grid line. Based on the correlation and priority of each electrical device, it identifies a number of non-critical devices, which can avoid misjudgment based on a single indicator, accurately identify non-critical devices, and reduce the impact on users' normal production and life.

[0095] Specifically, the adjustment strategy determination unit determines high-load equipment based on the comparison results between the current load characteristic data and the preset load characteristic data.

[0096] In practice, implementers can set preset load characteristic data according to the actual situation, including preset load characteristic data for each electrical device connected to the target power grid line, which can be set based on the average load characteristic data of electrical devices that have passed the qualification test in historical data.

[0097] It is understandable that, based on the current load characteristic data YB1, YB2, ..., YB i , ..., YB n Preset load characteristic data EB1, EB2, ..., EB corresponding to a certain electrical device i , ..., EB n Determine the matching degree M = (∑ n i＝1 YB i ×EBi ) / (sqrt(∑ n i＝1 (YB i ) 2 )×sqrt(∑ n i＝1 (EB i ) 2 YB i EB represents the current load characteristic data corresponding to the i-th load characteristic. i Here, n represents the preset load characteristic data corresponding to the i-th load characteristic, and n is the number of load characteristics. If the matching degree is greater than the preset matching degree, the corresponding electrical equipment is identified as a high-load equipment. It is understood that the implementers can set the preset matching degree based on the actual situation. Preferably, the preset matching degree is set to a value range of 0.8 to 0.9.

[0098] The adjustment strategy determination unit of this invention can accurately identify high-load equipment through data comparison, thereby reducing misjudgments.

[0099] Specifically, the adjustment strategy determination unit determines the operating status of each non-critical device based on the number of non-critical devices;

[0100] If the number of non-critical devices is greater than the preset number, the operating status of each non-critical device will be adjusted to a temporary stop state.

[0101] If the number of non-critical devices is less than or equal to the preset number, the operating status of each non-critical device will be adjusted to a reduced load state.

[0102] In practice, the implementers can set a preset quantity based on the actual situation or 1 / 3 to 1 / 5 of the actual number of electrical devices. Preferably, the preset quantity is set to a range of 4 to 6.

[0103] It is understandable that the load reduction rate is determined based on the ratio of the number of non-critical devices to the preset number, and the ratio of the number of non-critical devices to the preset number is positively correlated with the load reduction rate.

[0104] The adjustment strategy determination unit of this invention dynamically adjusts the operating status of non-critical equipment based on the number of non-critical equipment, which can improve the flexibility of load adjustment. When the number of non-critical equipment is greater than the preset number, it indicates that the load is gradually increasing and the rate of increase is relatively fast, and the overload is relatively serious. By adjusting the operating status of each non-critical equipment to a temporary stop state, the peak load can be quickly reduced. When the number of non-critical equipment is less than or equal to the preset number, it indicates that the load is gradually increasing, but the increase is relatively slow. By adjusting the operating status of each non-critical equipment to a load reduction state, the load and user experience can be balanced, and equipment start-up and shutdown losses can be reduced.

[0105] Specifically, the adjustment strategy determination unit determines the operating status of the high-load equipment based on the equipment type of the high-load equipment;

[0106] If the high-load equipment is a critical type, then the operating status of the high-load equipment will be adjusted to a load reduction state.

[0107] If the high-load equipment is of a non-critical type, then the operating status of the high-load equipment will be adjusted to a temporarily stopped state.

[0108] In implementation, the equipment type of high-load equipment is determined based on a preset equipment type comparison table. Implementers can set up the preset equipment type comparison table according to the actual situation. Preferably, the load corresponding to the load reduction state is 70% to 80% of the original load.

[0109] The adjustment strategy determination unit of this invention determines the operating status of high-load equipment based on the equipment type of the high-load equipment, which can improve the flexibility of load adjustment. For critical types of high-load equipment, if it is shut down directly, it will affect the user's use. By adopting a load reduction state, energy consumption can be reduced while ensuring basic functions, and production interruption or safety risks caused by direct shutdown can be avoided. For non-critical types of high-load equipment, a temporary stop state is adopted to quickly reduce the load, with less impact on users, which can improve the accuracy of load control.

[0110] The load adjustment unit is connected to both the trigger determination unit and the adjustment strategy determination unit, and is used to determine the result of triggering load adjustment based on the trigger determination unit, and to perform load adjustment based on the load adjustment strategy determined by the adjustment strategy determination unit.

[0111] This invention improves the accuracy and efficiency of load adjustment by setting up a trigger determination unit to determine whether to trigger load adjustment based on a comparison between the load type and operating status of the target power grid line and a preset standard. Furthermore, by setting up an adjustment strategy determination unit, the invention determines the load adjustment strategy based on load sensitivity data corresponding to the target time period, thereby improving the accuracy of load adjustment, adapting to different load change trends, and enhancing the flexibility of load adjustment.

[0112] This invention includes a data acquisition module that periodically collects real-time current and voltage signals from the target power grid lines to generate load characteristic data. Through multi-dimensional data analysis, the accuracy of subsequent load analysis is improved. A data storage module stores the load characteristic data of the target power grid lines within a target time period, reducing data storage volume and providing a data foundation for subsequent load analysis. A load characteristic analysis module identifies load-sensitive characteristic data corresponding to the target time period, allowing for the identification of data that significantly impacts the load analysis of the target power grid lines, thus improving the accuracy and efficiency of load analysis. By determining the load change trend within a future preset time period based on the load characteristic data corresponding to the target time period, the accuracy of load forecasting is improved. Dynamic status assessment and automatic load analysis of the target power grid lines are achieved by considering their load type and operating status. Finally, a load adjustment module determines whether to trigger load adjustment based on the load type and operating status of the target power grid lines, improving the accuracy of the determination. Load adjustment strategies include adjusting the operating status of non-critical equipment and high-load equipment. If the load change trend is the first trend, with the load gradually increasing, peak shaving and valley filling of the power grid can be achieved by adjusting non-critical equipment in advance. If the load change trend is the second trend, with the load tending to be overloaded, overload tripping of the power grid can be avoided by processing high-load equipment in real time. By proposing different load adjustment strategies for different load change trends, energy saving effect can be improved and intelligent load adjustment of power grid lines can be realized.

[0113] Specifically, it also includes:

[0114] The load warning module is connected to the load characteristic analysis module and is used to determine whether a load warning is triggered based on the load change trend within the future preset time period. If a load warning is triggered, the electrical equipment corresponding to the target power grid line is adjusted to a temporary shutdown state.

[0115] In implementation, the load rate and load change rate are determined based on the load change trend within the future preset time period. The load rate is compared with the preset load rate and the load change rate is compared with the preset change rate to determine whether to trigger a load warning. If the load rate is greater than the preset load rate and the load change rate is greater than the preset change rate, it indicates that the load of the target power grid line is abnormal within the future preset time period, with a surge or drop in load. In this case, a load warning is triggered. Preferably, the preset load rate is set to a range of 90% to 95%, and the preset change rate is set to a range of 30% to 45%.

[0116] The data acquisition and adjustment module, which is connected to the trend determination unit, is used to adjust the acquisition cycle based on the comparison result between the load deviation and the preset deviation.

[0117] In practice, the adjustment amount of the acquisition cycle is determined based on the ratio of the load deviation to the preset deviation, and the ratio of the load deviation to the preset deviation is negatively correlated with the acquisition cycle.

[0118] This invention, by incorporating a load early warning module, can provide timely warnings before load anomalies occur, maintaining the stability of the target power grid lines and reducing equipment wear. By setting up a data acquisition and adjustment module, it can adapt to load changes. When the load deviation is large, indicating an unstable power grid load, shortening the acquisition cycle allows for more frequent data collection, timely capturing of load change trends, and providing more accurate data support for subsequent analysis and decision-making. When the load deviation is small, indicating a relatively stable power grid load, extending the acquisition cycle reduces unnecessary data collection, avoids data redundancy, lowers data storage and processing costs, and further improves the accuracy of load analysis and forecasting.

[0119] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. An AC smart energy meter with automatic load characteristic analysis, characterized in that, include: The data acquisition module is used to periodically acquire real-time current and voltage signals of the target power grid line and generate corresponding load characteristic data, including voltage, current, active power, reactive power, power factor, harmonic components and harmonic distortion rate. The data storage module is connected to the data acquisition module and is used to store the load characteristic data of the target power grid line within the target time period and the corresponding acquisition time. The load characteristic analysis module is connected to the data storage module. It is used to determine the load sensitive characteristic data corresponding to the target time period based on the load characteristic data of the target power grid line within the target time period and the corresponding acquisition time, and to determine the load change trend within the future preset time period, as well as to determine the load type and operating status of the target power grid line based on the load sensitive characteristic data. The method for determining the load deviation based on load sensitivity data corresponding to the target time period, and determining the load change trend within the future preset time period based on the comparison result of the load deviation and the preset deviation, includes: If the load deviation is less than the preset deviation, the load sensitivity characteristic data corresponding to the target time period and the corresponding collection time are input into the load trend prediction model to obtain the load change trend in the future preset time period output by the load trend prediction model. If the load deviation is greater than or equal to the preset deviation, the load sensitivity characteristic data corresponding to the target time period is corrected based on the load deviation to obtain the corrected load sensitivity characteristic data. The corrected load sensitivity characteristic data and the corresponding collection time are then input into the load trend prediction model to obtain the load change trend in the future preset time period output by the load trend prediction model. Based on the load sensitivity characteristic data corresponding to the target time period, the load characteristic value is determined to determine the load type of the target power grid line; A load adjustment module, connected to the load characteristic analysis module, is used to determine whether to trigger load adjustment based on the load type and operating status of the target power grid line. If load adjustment is triggered, a load adjustment strategy is determined based on the load change trend within a preset future time period, including: If the load change trend is the first change trend, then based on the load sensitivity characteristic data corresponding to the target time period, a number of non-critical devices are identified, and the operating status of each of the non-critical devices is adjusted. The operating status of each non-critical device is determined based on the number of such devices. If the number of non-critical devices exceeds the preset number, the operating status of each non-critical device will be adjusted to a temporary stop state. If the number of non-critical devices is less than or equal to the preset number, the operating status of each non-critical device will be adjusted to a reduced load state. Based on the load sensitivity characteristic data corresponding to the target time period, the load sensitivity characteristic curves of each electrical device and the load sensitivity characteristic curves of each branch of the target power grid are constructed. Cluster analysis is performed on the load sensitivity characteristic curves of each electrical device and the load sensitivity characteristic curves of each branch to determine several cluster groups. Based on the preset priority evaluation index, the priority of the electrical devices in each cluster group is evaluated to determine the priority of each cluster group. The electrical devices in the cluster group with the lowest priority are identified as non-critical devices. If the load change trend is the second trend, then high-load equipment is identified based on the current load characteristic data, and the operating status of the high-load equipment is adjusted. The operating status of the high-load equipment is determined based on its equipment type. If the high-load equipment is a critical type, then the operating status of the high-load equipment will be adjusted to a load reduction state. If the high-load equipment is of a non-critical type, then the operating status of the high-load equipment will be adjusted to a temporarily stopped state.

2. The AC smart energy meter with automatic load characteristic analysis according to claim 1, characterized in that, The load characteristic analysis module includes: A sensitive data determination unit, which is connected to the data storage module, is used to determine the load sensitive characteristic data corresponding to the target time period based on the load characteristic data of the target power grid line within the target time period and the corresponding acquisition time. The trend determination unit is connected to the sensitive data determination unit and the data storage module respectively, and is used to determine the load change trend in a future preset time period based on the load sensitive characteristic data corresponding to the target time period and the corresponding collection time. A load type determination unit, connected to the sensitive data determination unit, is used to determine load characteristic values ​​based on load sensitivity characteristic data corresponding to the target time period, and to determine the load type of the target power grid line based on the load characteristic values. A line status determination unit, which is connected to the change trend determination unit and the load type determination unit respectively, is used to determine the operating status of the target power grid line based on the load change trend within a future preset time period and the load type of the target power grid line.

3. The AC smart energy meter with automatic load characteristic analysis according to claim 2, characterized in that, The load adjustment module includes: A trigger determination unit, which is connected to the load type determination unit and the line status determination unit respectively, is used to determine whether to trigger load adjustment based on the comparison result between the load type and operating status of the target power grid line and the preset standard. The adjustment strategy determination unit is connected to the data storage module and is used to determine the load adjustment strategy based on the load sensitivity characteristic data corresponding to the target time period. This includes determining a number of non-critical devices based on the load sensitivity characteristic data corresponding to the target time period and adjusting the operating status of each of the non-critical devices, or determining high-load devices based on the current load characteristic data and adjusting the operating status of the high-load devices. The load adjustment unit is connected to both the trigger determination unit and the adjustment strategy determination unit, and is used to determine the result of triggering load adjustment based on the trigger determination unit, and to perform load adjustment based on the load adjustment strategy determined by the adjustment strategy determination unit.

4. The AC smart energy meter with automatic load characteristic analysis according to claim 3, characterized in that, The adjustment strategy determination unit determines high-load equipment based on the comparison results between the current load characteristic data and the preset load characteristic data.

5. The AC smart energy meter with automatic load characteristic analysis according to claim 4, characterized in that, Also includes: The load warning module is connected to the load characteristic analysis module and is used to determine whether a load warning is triggered based on the load change trend within the future preset time period. If a load warning is triggered, the electrical equipment corresponding to the target power grid line is adjusted to a temporary shutdown state. The data acquisition and adjustment module, which is connected to the trend determination unit, is used to adjust the acquisition cycle based on the comparison result between the load deviation and the preset deviation.

Images