AC intelligent electric energy meter capable of automatically analyzing load characteristics

Abstract

The invention relates to the technical field of electric energy meters, in particular to an alternating-current intelligent electric energy meter capable of automatically analyzing load characteristics, and the electric energy meter comprises a data collection module which is used for periodically collecting real-time current and voltage signals of a target power grid line, and generating corresponding load characteristic data; the data storage module is used for storing the load characteristic data of the target power grid line in the target time period and the corresponding acquisition time; the load characteristic analysis module is used for determining load sensitive characteristic data, a load change trend in a future preset time period and a load type and an operation 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 the load adjustment module is used for judging whether load adjustment is triggered or not, and determining a load adjustment strategy if the load adjustment is triggered. According to the method, the load characteristics can be automatically analyzed, and the accuracy of load prediction is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric energy meters, and particularly to an AC intelligent electric energy meter with automatic load characteristic analysis. Background Art

[0002] With the rapid economic development and the increasing complexity of the power system, accurate monitoring and management of the grid load have become crucial. The number of power users is increasing continuously, and various electrical equipment is widely popularized, with their electricity consumption behaviors and load characteristics showing diverse and complex features. At the same time, the large-scale access of distributed energy sources, such as solar and wind power generation, further increases the uncertainty of the grid load.

[0003] Traditional electric energy meters can only measure electric energy and cannot conduct in-depth analysis of load characteristics. Although some existing intelligent electric energy meters have certain data acquisition functions, they have insufficient capabilities in identifying the fluctuation characteristics, periodic laws of the load, and the load types of different electrical equipment, making it difficult to comprehensively and accurately grasp the load changes and unable to provide sufficient information support for power management. In terms of load forecasting, existing technologies often rely on simple mathematical models or historical data averaging methods, without fully considering the complexity and uncertainty factors of load changes, resulting in a large deviation between the forecasting results and the actual load and unable to meet the requirements of the power system for accurate load forecasting. Therefore, there is an urgent need for an AC intelligent electric energy meter that can automatically analyze load characteristics in order to timely master the grid load situation and provide strong support for power dispatching, demand-side management, etc.

[0004] Chinese Patent Publication No. CN221465623U discloses a high-precision AC intelligent electric energy meter, including a base and a main body. The main body is slidably connected to the top of the base. The upper side of the front side wall of the main body is fixedly connected with a display, the middle part of the front side wall of the main body is fixedly connected with a signal lamp, the left and right side walls of the main body are fixedly connected with sliders, the bottom of the main body is fixedly connected with a second socket, the top inner cavity of the second socket is fixedly connected with a connection socket, the top of the second socket is fixedly connected with a second connecting wire, the end of the second connecting wire penetrates through the bottom of the main body and extends into the inner cavity of the main body, and the end is fixedly connected with an integrated board, and the top of the integrated board is fixedly connected with a main control board.

[0005] The existing technology has the following problems: It can only measure electric energy and conduct low-electricity-degree alarm, cannot automatically analyze load characteristics, and has relatively low accuracy in load forecasting. Summary of the Invention

[0006] Therefore, the present invention provides an AC intelligent electric energy meter with automatic load characteristic analysis to overcome the problems in the existing technology that it cannot automatically analyze load characteristics and has relatively low accuracy in load forecasting.

[0007] To achieve the above object, the present invention provides an AC intelligent energy meter with automatic load characteristic analysis, including:

[0008] A data acquisition module, which is used to periodically collect the real-time current and voltage signals of the target power grid line and generate corresponding load characteristic data, and the load characteristics include voltage, current, active power, reactive power, power factor, harmonic components, and harmonic distortion rate;

[0009] A data storage module, which 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] A load characteristic analysis module, which is connected to the data storage module and 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 determine the load change trend within a future preset time period, and determine the load type and operating state of the target power grid line based on the load-sensitive characteristic data;

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

[0012] If the load change trend is the first change trend, a number of non-critical devices are determined based on the load-sensitive characteristic data corresponding to the target time period, and the operating states of the non-critical devices are adjusted;

[0013] If the load change trend is the second change trend, high-load devices are determined based on the current load characteristic data, and the operating states of the high-load devices are adjusted.

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

[0015] A sensitive data determination unit, which is connected to the data storage module and 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] A change trend determination unit, which is respectively connected to the sensitive data determination unit and the data storage module and is used 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;

[0017] A load type determination unit, which is connected to the sensitive data determination unit, is used to determine a load characteristic value based on the load sensitive characteristic data corresponding to a target time period, and determine the load type of the target power grid line based on the load characteristic value;

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

[0019] Further, the load adjustment module includes:

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

[0021] An adjustment strategy determination unit, which is connected to the data storage module, is used to determine a load adjustment strategy based on the load sensitive characteristic data corresponding to a target time period, including determining a number of non-critical devices based on the load sensitive characteristic data corresponding to the target time period, and adjusting the operating states of the non-critical devices, or determining high-load devices based on the current load characteristic data and adjusting the operating states of the high-load devices;

[0022] A load adjustment unit, which is respectively connected to the trigger determination unit and the adjustment strategy determination unit, is used to perform load adjustment based on the determination result of the trigger determination unit for triggering load adjustment and the load adjustment strategy determined by the adjustment strategy determination unit.

[0023] Further, the adjustment strategy determination unit determines the association relationship of each electrical device corresponding to the target power grid line based on the load sensitive 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 association relationship and the priority of each electrical device.

[0024] Further, the adjustment strategy determination unit determines high-load devices based on the comparison result between the current load characteristic data and the preset load characteristic data.

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

[0026] Wherein, if the number of non-critical devices is greater than a preset number, the operating states of the non-critical devices are adjusted to a temporarily stopped state;

[0027] If the number of the non-critical devices is less than or equal to a preset number, adjust the operating states of the non-critical devices to the load reduction states.

[0028] Further, the adjustment strategy determination unit determines the operating state of the high-load device based on the device type of the high-load device;

[0029] Among them, if the device type of the high-load device is a critical type, adjust the operating state of the high-load device to the load reduction state;

[0030] If the device type of the high-load device is a non-critical type, adjust the operating state of the high-load device to the temporary stop state.

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

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

[0033] Among them, if the load deviation is less than the preset deviation, input the load sensitivity characteristic data corresponding to the target time period and the corresponding acquisition time into a load trend prediction model to obtain a load change trend within 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, correct the load sensitivity characteristic data corresponding to the target time period based on the load deviation to obtain corrected load sensitivity characteristic data, and input the corrected load sensitivity characteristic data and the corresponding acquisition time into the load trend prediction model to obtain a load change trend within the future preset time period output by the load trend prediction model.

[0035] Further, it further includes:

[0036] A load warning module, which is connected to the load characteristic analysis module, is used to determine whether to trigger a load warning based on the load change trend within the future preset time period. If a load warning is triggered, adjust the electrical equipment corresponding to the target power grid line to the temporary stop state;

[0037] An acquisition adjustment module, which is connected to the change trend determination unit, is used to adjust the acquisition period based on a comparison result between the load deviation and the preset deviation.

[0038] Compared with the prior art, the beneficial effects of the present invention are as follows. The present invention sets up a data acquisition module to periodically collect the real-time current and voltage signals of the target power grid line, generate load characteristic data, and improve the accuracy of subsequent load analysis through multi-dimensional data analysis. By setting up a data storage module to store the load characteristic data of the target power grid line within the target time period, the data storage volume can be reduced, providing a data basis for subsequent load analysis. By setting up a load characteristic analysis module to determine the load-sensitive characteristic data corresponding to the target time period, the data that significantly affects the load analysis of the target power grid line within the target time period can be determined, 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 prediction can be improved. By analyzing the load type and operating status of the target power grid line, the dynamic status assessment and automatic load analysis of the target power grid line can be realized. By setting up a load adjustment module to determine whether to trigger load adjustment according to the load type and operating status of the target power grid line, the accuracy of determination can be improved. The load adjustment strategy includes adjusting the operating status of each non-critical device and adjusting the operating status of high-load devices. If the load change trend is the first change trend and the load gradually increases, by adjusting non-critical devices in advance, peak shaving and valley filling of the power grid can be achieved. If the load change trend is the second change trend and the load tends to be overloaded, by real-time processing of high-load devices, power grid overload tripping can be avoided. By proposing different load adjustment strategies for different load change trends, the energy-saving effect can be improved and intelligent load adjustment of the power grid line can be realized.

[0039] Furthermore, the present invention determines the load-sensitive characteristic data corresponding to the target time period by setting up a sensitive data determination unit, which can accurately identify the sensitive load characteristics in the load characteristics, reducing the data processing volume of subsequent load analysis and the accuracy of load prediction. By setting up a change 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 collection time, the accuracy of load prediction 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 determine the load type of the target power grid line based on the load characteristic value, accurate classification can be achieved. Different load types adapt 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. By accurately identifying the operating status, the accuracy of load analysis can be improved.

[0040] Furthermore, by setting a trigger determination unit, the present invention determines whether to trigger load adjustment according to the load type and operation status of the target power grid line and the comparison result with the preset standard, which can improve the accuracy and efficiency of determination. By setting an adjustment strategy determination unit, based on the load sensitivity characteristic data corresponding to the target time period, the load adjustment strategy is determined, which can improve the accuracy of load adjustment, adapt to different load change trends, and improve the flexibility of load adjustment.

[0041] Furthermore, the adjustment strategy determination unit of the present invention determines the association relationship of each electrical equipment 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 equipment corresponding to the target power grid line, and determines a number of non-critical equipment based on the association relationship and priority of each electrical equipment, which can avoid misjudgment by a single index, accurately identify non-critical equipment, and reduce the impact on the normal production and life of users.

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

[0043] Furthermore, the adjustment strategy determination unit of the present invention dynamically adjusts the operation status of non-critical equipment based on the number of non-critical equipment, which can improve the flexibility of load adjustment. The number of non-critical equipment is greater than the preset number, indicating that the load is gradually rising, and the rising speed is relatively fast, and the overload is relatively serious. By adjusting the operation status of each non-critical equipment to the temporarily stopped state, the peak load can be quickly reduced; the number of non-critical equipment is less than or equal to the preset number, indicating that the load is gradually rising, but the rise is relatively slow. The operation status of each non-critical equipment can be adjusted to the load-reducing state to balance the load and the user experience and reduce the equipment start-stop loss.

[0044] Furthermore, the adjustment strategy determination unit of the present invention determines the operation status of high-load equipment based on the equipment type of high-load equipment, which can improve the flexibility of load adjustment. For high-load equipment of critical types, if directly shut down, it will affect the use of users. By adopting the load-reducing state, energy consumption can be reduced while ensuring basic functions, and production interruption or safety risks caused by direct shutdown can be avoided; for high-load equipment of non-critical types, the temporarily stopped state is adopted to quickly reduce the load, and the impact on users is relatively small, which can improve the accuracy of load regulation.

[0045] Further, the load change trend determination unit of the present invention can improve the accuracy of load prediction and reduce prediction deviation by determining the load change trend within a preset future time period based on the comparison result between the load deviation and the preset deviation. If the load deviation is less than the preset deviation, it indicates that the error of the load sensitive characteristic data in the target time period is small. By directly using the original load sensitive characteristic data to input the load trend prediction model and avoiding information loss caused by excessive correction, the data processing volume can be reduced and the prediction accuracy can be improved. If the load deviation is greater than or equal to the preset deviation, it indicates that the error of the load sensitive characteristic data in the target time period is large and special situations may occur. By correcting the load sensitive characteristic data corresponding to the target time period, sudden load changes can be reasonably processed, prediction deviation can be avoided, and the accuracy of load prediction can be improved.

[0046] Further, by setting up a load warning module in the present invention, early warning can be given in time before the load appears abnormal, the stability of the target power grid line can be maintained, and equipment loss can be reduced. By setting up an acquisition adjustment module, it can adapt to load changes. When the load deviation is large, it indicates that the power grid load is in an unstable state. At this time, shortening the acquisition period can obtain data more frequently, capture the change trend of the load in time, and provide more accurate data support for subsequent analysis and decision-making. When the load deviation is small, it indicates that the power grid load is relatively stable. At this time, extending the acquisition period can reduce unnecessary data acquisition, avoid data redundancy, reduce the cost of data storage and processing, and further improve the accuracy of load analysis and prediction. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

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

[0050] Figure 4 It is a logical decision diagram for determining the operating state of non-critical equipment according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0051] In order to make the objectives and advantages of the present invention clearer and more understandable, the present invention will be further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0052] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principle of the present invention and do not limit the protection scope of the present invention.

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

[0054] In addition, it should also be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installation", "connection", and "connection" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0055] Please refer to Figure 1 as shown, which is the structural block diagram of an AC intelligent electricity meter with automatic load characteristic analysis according to an embodiment of the present invention; an embodiment of the present invention provides an AC intelligent electricity meter with automatic load characteristic analysis, including:

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

[0057] In implementation, the method of periodically acquiring the current and voltage signals is not specifically limited. This is prior art and will not be elaborated here.

[0058] It can be understood that the actual implementer can set the acquisition period according to the actual situation. Preferably, the value range of the acquisition period is set to 3 min to 5 min.

[0059] A data storage module, which 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 implementation, the actual implementer can set the target time period according to the actual situation. If the target time period is set too long, it will lead to excessive data processing volume and storage volume, reducing the data processing efficiency; if the target time period is set too short, it will lead to too little data volume, reducing the subsequent prediction accuracy. Preferably, the value range of the target time period is set to 2 days to 5 days.

[0061] A load characteristic analysis module, 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 collection time, and determine the load change trend within a preset future time period, and determine the load type and operating state of the target power grid line based on the load-sensitive characteristic data;

[0062] Please refer to Figure 2 as shown, which is the structural block diagram of the load characteristic analysis module in 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 collection time;

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

[0065] It can be understood that the actual implementer can set the preset fluctuation value according to the actual situation or based on the fluctuation values of the load characteristics that pass the qualification test in the historical data. Preferably, the value range of the preset fluctuation value is set to 3 to 8.

[0066] A change trend determination unit, which is respectively connected to the sensitive data determination unit and the data storage module, 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 acquisition time;

[0067] Specifically, the change trend determination unit determines the load deviation based on the load sensitive characteristic data corresponding to the target time period, and determines the determination method of the load change trend in the 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 the active power is the load sensitive characteristic, then the active powers Y1, Y2,..., Y j ,…, Y m and the standard active powers E1, E2,..., E j ,…, E m The 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 is the standard active power corresponding to the jth acquisition, and P = sqrt() is a preset square root determination function. It can be understood that the load deviation is determined based on the mean value of the load deviations of each load sensitive characteristic.

[0069] It can be understood that the actual implementer can determine the corresponding standard load characteristic data according to the actual situation or based on the load characteristic data that has passed the qualification test in the historical data during the same period as the target time period, or set the standard load characteristic data based on the load expected value of the target time period output by the load prediction model.

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

[0071] Among them, if the load deviation is less than the preset deviation, the load sensitive characteristic data corresponding to the target time period and the corresponding acquisition 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 characteristic data corresponding to the target time period is corrected based on the load deviation to obtain the corrected load sensitivity characteristic data, and the corrected load sensitivity characteristic data and the corresponding acquisition time are input into the load trend prediction model to obtain the load change trend within a future preset time period output by the load trend prediction model.

[0073] In implementation, the actual implementer can set the preset deviation according to the actual situation or based on the mean value of the load deviations that pass the qualification test in the historical data. Preferably, the value range of the preset deviation is set to 0.7 - 0.8.

[0074] It can be understood that data preprocessing can be performed on the load sensitivity characteristic data and the corresponding acquisition time in the historical data to generate a data set. The data preprocessing includes missing value filling, normalization, sequence construction, etc., and the initial model is trained based on the data set to obtain the load trend prediction model. It should be noted that those skilled in the art know that any prediction model capable of predicting the load change trend in the prior art falls within the protection scope of the present invention. For example, traditional time series models, machine learning models, deep learning models, etc. will not be elaborated here. It can be understood that the actual implementer can set the preset time period according to the actual situation. Preferably, the preset time period is set to 2 hours - 5 hours.

[0075] It can be understood that if there is a load deviation, it indicates that the corresponding load sensitivity characteristic may be abnormal. Predicting the load change trend in this way will reduce the prediction accuracy. Therefore, correction is required. The correction parameter can be determined according to 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 parameter, and the load sensitivity characteristic data corresponding to the target time period is corrected accordingly.

[0076] The change trend determination unit of the present invention can improve the accuracy of load prediction and reduce the prediction deviation by determining the load change trend within a future preset time period based on the comparison result between the load deviation and the preset deviation. If the load deviation is less than the preset deviation, it indicates that the error of the load sensitivity characteristic data in the target time period is small. By directly using the original load sensitivity characteristic data and inputting it into the load trend prediction model, the information loss caused by excessive correction can be avoided, the data processing volume can be reduced, and the prediction accuracy can be improved. If the load deviation is greater than or equal to the preset deviation, it indicates that the error of the load sensitivity characteristic data in the target time period is large, and special situations may occur. By correcting the load sensitivity characteristic data corresponding to the target time period, sudden load changes can be reasonably handled, prediction deviation can be avoided, and the accuracy of load prediction can be improved.

[0077] A load type determination unit, which is connected to the sensitive data determination unit, is configured to determine a load characteristic value based on the load sensitive characteristic data corresponding to a target time period, and determine the load type of the target power grid line based on the load characteristic value;

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

[0079] It can be understood that the load type of the target power grid line can be determined based on the comparison result between the load characteristic value and a first preset characteristic value and a second preset characteristic value, where 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 an 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 a 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 a commercial load.

[0080] It can be understood that the actual implementer 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 pass the qualification test in the historical data. Preferably, the value range of the first preset characteristic value is set to 3000kW - 5000kW, and the value range of the second preset characteristic value is set to 500kW - 1000kW.

[0081] A line status determination unit, which is respectively connected to the change trend determination unit and the load type determination unit, is configured 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 a normal status, a warning status, a light overload status, and an over overload status. For the normal status, the load is within the safe range, without overload or abnormal fluctuations; for the warning status, the load is close to the safety threshold (the load is greater than 80% - 90% of the safety threshold and less than the safety threshold), or the fluctuations are abnormal; for the light overload status, the load exceeds the safety threshold, but the amplitude of exceeding the safety threshold is less than 20% - 30% of the safety threshold; for the over overload status, the load exceeds the safety threshold, and the amplitude of exceeding the safety threshold is greater than 20% - 30% of the safety threshold.

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

[0084] In the present invention, by setting a sensitive data determination unit to determine the load sensitive characteristic data corresponding to the target time period, the sensitive load characteristics in the load characteristics can be accurately identified, which can reduce the data processing amount of subsequent load analysis and the accuracy of load prediction. By setting a change trend determination unit to determine the load change trend within a preset future time period based on the load sensitive characteristic data corresponding to the target time period and the corresponding collection time, the accuracy of load prediction can be improved. By setting 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 determine the load type of the target power grid line based on the load characteristic value, accurate classification can be achieved. Different load types adapt to different load adjustment strategies and operation status standards, providing support for load analysis. By setting a line status determination unit to determine the operation status of the target power grid line based on the load change trend within a preset future time period and the load type of the target power grid line, the operation status of the power grid line can be accurately identified. Through the accurate identification of the operation status, the accuracy of load analysis can be improved.

[0085] A load adjustment module, which is connected to the load characteristic analysis module, is used to determine whether to trigger load adjustment according to the load type and operation 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 the preset future time period, including:

[0086] If the load change trend is the first change trend, a number of non-critical devices are determined based on the load sensitive characteristic data corresponding to the target time period, and the operation status of each non-critical device is adjusted;

[0087] If the load change trend is the second change trend, high-load devices are determined based on the current load characteristic data, and the operation status of the high-load devices is adjusted.

[0088] Please refer to Figure 3 - Figure 4 shown in Figure 3 which is the structural block diagram of the load adjustment module according to the embodiment of the present invention; Figure 4 which is the logical determination diagram for determining the operation status of non-critical devices according to the embodiment of the present invention; specifically, the load adjustment module includes:

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

[0090] In implementation, the preset standard is as follows: for residential loads, the load is small and the operating duration of electrical equipment is not long. In the normal state, warning state, and light overload state, it can be used normally. Then, when the operating state is the overload state, it is determined to trigger load adjustment; for industrial loads, the load is large and the electrical equipment works continuously for production. In order to avoid affecting production activities, it can work normally in the normal state, and load adjustment is required in the warning state. Then, when the operating state is the warning state, light overload state, and overload state, it is determined to trigger load adjustment; for commercial loads, the load is relatively large and the electrical equipment works in different time periods. It can work normally in the normal state and warning state. Then, when the operating state is the light overload state and overload state, it is determined to trigger load adjustment.

[0091] An adjustment strategy determination unit, which 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, including determining a number of non-critical devices based on the load sensitivity characteristic data corresponding to the target time period and adjusting the operating states of the non-critical devices, or determining high-load devices based on the current load characteristic data and adjusting the operating states of the high-load devices;

[0092] Specifically, the adjustment strategy determination unit determines the association 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, and determines a number of non-critical devices based on the association relationship and priority of each electrical device.

[0093] In implementation, the usage of electrical equipment corresponding to the target power grid line is statistically analyzed, and based on the load sensitivity characteristic data corresponding to the target time period, the load sensitivity characteristic curves of each electrical equipment and the load sensitivity characteristic curves of each branch of the target power grid line are constructed. Cluster analysis is performed on the load sensitivity characteristic curves of each electrical equipment and the load sensitivity characteristic curves of each branch to determine several clustering groups. The load patterns of the electrical equipment in the same clustering group are similar, have an association relationship, and are connected to the branches in the same clustering group. Based on a preset priority evaluation index, the priorities of the electrical equipment in each clustering group are evaluated. The preset priority evaluation index includes safety (whether it is fire-fighting or medical equipment), economy (production losses caused by equipment downtime), technology (whether the equipment can interrupt the process), etc. Each priority evaluation index is assigned a value. For example, 10 levels are set, with level 1 assigned 1, corresponding to the lowest priority of the priority evaluation index, and level 10 assigned 10, corresponding to the highest priority of the priority evaluation index. And the priorities of each electrical equipment are calculated (the sum of the priority assignments corresponding to each priority evaluation index), and the priorities of each clustering group are calculated (the sum / mean of the priorities of the electrical equipment in each clustering group). The priorities of each clustering group are compared, and the electrical equipment in the clustering group with the lowest priority is determined as non-critical equipment.

[0094] The adjustment strategy determination unit of the present invention determines the association relationship of each electrical equipment corresponding to the target power grid line based on the load sensitivity characteristic data corresponding to the target time period, determines the priorities of each electrical equipment corresponding to the target power grid line, and determines several non-critical equipment based on the association relationship and priorities of each electrical equipment, which can avoid misjudgment by a single index, accurately identify non-critical equipment, and reduce the impact on the normal production and life of users.

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

[0096] In implementation, the actual implementer can set the preset load characteristic data according to the actual situation, including the preset load characteristic data corresponding to each electrical equipment connected to the target power grid line, which can be set based on the mean value of the load characteristic data of the electrical equipment that has passed the qualification test in the historical data.

[0097] It can be understood that according to the current load characteristic data YB1, YB2,..., YB i ,..., YB n and the preset load characteristic data EB1, EB2,..., EB i ,..., EB n corresponding to a certain electrical equipment, 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 is the current load characteristic data corresponding to the i-th load characteristic, EB i is 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 determined as a high-load device. It can be understood that the actual implementer can set the preset matching degree based on the actual situation. Preferably, the value range of the preset matching degree is set to 0.8 to 0.9.

[0098] The adjustment strategy determination unit of the present invention can accurately identify high-load devices through data comparison and reduce misjudgment.

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

[0100] Among them, if the number of the non-critical devices is greater than the preset number, the operating states of the non-critical devices are adjusted to the temporarily stopped state;

[0101] If the number of the non-critical devices is less than or equal to the preset number, the operating states of the non-critical devices are adjusted to the load reduction state.

[0102] In implementation, the actual implementer can set the preset number according to the actual situation or according to 1 / 3 to 1 / 5 of the number of actual electrical equipment. Preferably, the value range of the preset number is set to 4 to 6.

[0103] It can be understood that the load reduction amplitude 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 amplitude.

[0104] The adjustment strategy determination unit of the present invention dynamically adjusts the operating states of the non-critical devices based on the number of non-critical devices, which can improve the flexibility of load adjustment. The number of non-critical devices being greater than the preset number indicates that the load is gradually rising, and the rising speed is relatively fast, with serious overload. By adjusting the operating states of the non-critical devices to the temporarily stopped state, the peak load can be quickly reduced; the number of non-critical devices being less than or equal to the preset number indicates that the load is gradually rising, but the rising is relatively slow. The operating states of the non-critical devices can be adjusted to the load reduction state to balance the load and the user experience and reduce the start-stop loss of the devices.

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

[0106] Among them, if the device type of the high-load device is a critical type, the operating state of the high-load device is adjusted to a load reduction state;

[0107] If the device type of the high-load device is a non-critical type, the operating state of the high-load device is adjusted to a temporary stop state.

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

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

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

[0111] By setting the trigger determination unit in the present invention, it can determine whether to trigger load adjustment according to the load type and operating state of the target power grid line and the comparison result with the preset standard, which can improve the accuracy and efficiency of the determination. By setting the adjustment strategy determination unit to determine the load adjustment strategy based on the load sensitive characteristic data corresponding to the target time period, it can improve the accuracy of load adjustment, adapt to different load change trends, and improve the flexibility of load adjustment.

[0112] The present invention provides a data acquisition module to periodically collect real-time current and voltage signals of the target power grid line, generate load characteristic data, and improve the accuracy of subsequent load analysis through multi-dimensional data analysis. By providing a data storage module to store the load characteristic data of the target power grid line within a target time period, the data storage volume can be reduced, providing a data basis for subsequent load analysis. By providing a load characteristic analysis module to determine the load-sensitive characteristic data corresponding to the target time period, the data that significantly affects the load analysis of the target power grid line within the target time period can be determined, 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 prediction can be improved. Through the load type and operating status of the target power grid line, dynamic status assessment and automatic load analysis of the target power grid line can be achieved. By providing a load adjustment module to determine whether to trigger load adjustment according to the load type and operating status of the target power grid line, the accuracy of determination can be improved. The load adjustment strategy includes adjusting the operating status of each non-critical device and adjusting the operating status of high-load devices. If the load change trend is the first change trend and the load gradually increases, by adjusting non-critical devices in advance, peak shaving and valley filling of the power grid can be achieved. If the load change trend is the second change trend and the load tends to be overloaded, by processing high-load devices in real time, power grid overload tripping can be avoided. By proposing different load adjustment strategies for different load change trends, the energy-saving effect can be improved and intelligent load adjustment of the power grid line can be realized.

[0113] Specifically, it further includes:

[0114] A load warning module, which is connected to the load characteristic analysis module, is used to determine whether to trigger a load warning 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 temporarily stopped state;

[0115] In implementation, the load rate and the load change rate are determined based on the load change trend within the future preset time period. Whether to trigger a load warning is determined based on the comparison result between the load rate and the preset load rate and the comparison result between the load change rate and the preset change rate. 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 within the future preset time period has an abnormal situation, such as a sharp increase or decrease in load, then it is determined to trigger a load warning. Preferably, the value range of the preset load rate is set to 90% - 95%, and the value range of the preset change rate is set to 30% - 45%.

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

[0117] In implementation, the adjustment amount of the acquisition period 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 and positively correlated with the acquisition period.

[0118] By setting up a load warning module, the present invention can give a timely warning before the load becomes abnormal, maintain the stability of the target power grid line, and reduce equipment loss. By setting up an acquisition adjustment module, it can adapt to the load change. When the load deviation is large, it indicates that the power grid load is in an unstable state. At this time, shortening the acquisition period can obtain data more frequently, capture the change trend of the load in time, and provide more accurate data support for subsequent analysis and decision-making. When the load deviation is small, it indicates that the power grid load is relatively stable. At this time, extending the acquisition period can reduce unnecessary data acquisition, avoid data redundancy, reduce the cost of data storage and processing, and further improve the accuracy of load analysis and prediction.

[0119] So far, the technical solution of the present invention has been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or replacements to the relevant technical features, and the technical solutions after these changes or replacements will all fall within the protection scope of the present invention.

Claims

1. An AC intelligent electric energy meter with automatic load characteristic analysis, characterized in that, Including: A data acquisition module, which is used to periodically acquire the real-time current and voltage signals of the target power grid line and generate corresponding load characteristic data. The load characteristics include voltage, current, active power, reactive power, power factor, harmonic components, and harmonic distortion rate; A data storage module, which is connected to the data acquisition module and is used to store the load characteristic data of the target power grid line and the corresponding acquisition time within a target time period; A load characteristic analysis module, which is connected to the data storage module and 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 and the corresponding acquisition time within the target time period, determine the load change trend within a future preset time period, and determine the load type and operating state of the target power grid line based on the load-sensitive characteristic data; A load adjustment module, which is connected to the load characteristic analysis module and is used to determine whether to trigger load adjustment according to the load type and operating state of the target power grid line. If load adjustment is triggered, a load adjustment strategy is determined based on the load change trend within the future preset time period, including If the load change trend is the first change trend, several non-critical devices are determined based on the load-sensitive characteristic data corresponding to the target time period, and the operating states of each non-critical device are adjusted; If the load change trend is the second change trend, high-load devices are determined based on the current load characteristic data, and the operating states of the high-load devices are adjusted.

2. The AC intelligent electric energy meter with automatic analysis of load characteristics 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 and 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 and the corresponding acquisition time within the target time period; A change trend determination unit, which is respectively connected to the sensitive data determination unit and the data storage module and is used 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; A load type determination unit, which is connected to the sensitive data determination unit and is used to determine a load characteristic value based on the load-sensitive characteristic data corresponding to the target time period and determine the load type of the target power grid line based on the load characteristic value; A line state determination unit, which is respectively connected to the change trend determination unit and the load type determination unit and is used to determine the operating state 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.

3. The AC intelligent electric 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 respectively connected to the load type determination unit and the line state determination unit and is used to determine whether to trigger load adjustment according to the comparison result between the load type and operating state of the target power grid line and a preset standard; An adjustment strategy determination unit, connected to the data storage module, for determining a load adjustment strategy based on the load sensitivity characteristic data corresponding to the target time period, including determining a number of non-critical devices based on the load sensitivity characteristic data corresponding to the target time period, and adjusting the operating states of the non-critical devices, or determining high-load devices based on the current load characteristic data, and adjusting the operating states of the high-load devices; A load adjustment unit, respectively connected to the trigger determination unit and the adjustment strategy determination unit, for performing load adjustment based on the determination result of the trigger determination unit determining the trigger of load adjustment and based on the load adjustment strategy determined by the adjustment strategy determination unit.

4. The AC intelligent watt-hour meter with automatic load characteristic analysis according to claim 3, characterized in that, The adjustment strategy determination unit determines the association relationships of the 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 priorities of the electrical devices corresponding to the target power grid line, and determines a number of non-critical devices based on the association relationships and priorities of the electrical devices.

5. The AC intelligent electric energy meter with automatic load characteristic analysis according to claim 4, characterized in that, The adjustment strategy determination unit determines high-load devices based on the comparison result between the current load characteristic data and the preset load characteristic data.

6. The AC intelligent electric energy meter with automatic load characteristic analysis according to claim 5, characterized in that, The adjustment strategy determination unit determines the operating states of the non-critical devices based on the number of the non-critical devices; Wherein, if the number of the non-critical devices is greater than the preset number, the operating states of the non-critical devices are adjusted to the temporarily stopped state; If the number of the non-critical devices is less than or equal to the preset number, the operating states of the non-critical devices are adjusted to the load reduction state.

7. The AC intelligent energy meter with automatic load characteristic analysis according to claim 6, characterized in that The adjustment strategy determination unit determines the operating state of the high-load device based on the device type of the high-load device; Wherein, if the device type of the high-load device is the critical type, the operating state of the high-load device is adjusted to the load reduction state; If the device type of the high-load device is the non-critical type, the operating state of the high-load device is adjusted to the temporarily stopped state.

8. The AC intelligent watt-hour meter with automatic load characteristic analysis according to claim 7, characterized in that The change trend determination unit determines a load deviation based on the load sensitivity characteristic data corresponding to the target time period, and determines a determination method for the load change trend within a future preset time period based on the load deviation.

9. The AC intelligent electric energy meter with automatic load characteristic analysis according to claim 8, characterized in that, The change trend determination unit determines a determination method for the load change trend within a future preset time period based on the comparison result between the load deviation and the preset deviation; Wherein, if the load deviation is less than the preset deviation, the load sensitivity characteristic data corresponding to the target time period and the corresponding acquisition time are 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; 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, and the corrected load sensitivity characteristic data and the corresponding acquisition time are 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.

10. The AC intelligent electric energy meter with automatic load characteristic analysis according to claim 9, characterized in that, Further comprising: A load warning module, which is connected to the load characteristic analysis module, is used to determine whether to trigger a load warning based on the load change trend in 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 temporarily stopped state; A collection adjustment module, which is connected to the change trend determination unit, is used to adjust the collection period based on the comparison result between the load deviation and the preset deviation.

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