Adaptive frequency compensation method for intelligent, secure and energy-saving terminals using information perception

Abstract

The present invention relates to the field of terminal device frequency control, and specifically to a method for adaptive frequency compensation of an intelligent, safe, and energy-saving terminal using information perception. The method first obtains power data of an intelligent, safe, and energy-saving terminal device within a preset historical time period, periodically segments the power data within the preset historical time period, obtains multiple data segments, analyzes the frequency compensation degree and frequency compensation efficiency value of each data segment based on the degree of fluctuation of the power data in each data segment, clusters all the data segments, obtains multiple cluster clusters, and then analyzes the device operating status value of each data segment in combination with the overshoot risk degree of each data segment, and uses a model predictive control algorithm to perform real-time compensation adjustment on the operating frequency of the terminal device based on the objective function value of each data segment obtained. The present invention can avoid overcompensation during the frequency compensation process of the intelligent, safe, and energy-saving terminal and improve the effect of adaptive frequency compensation.

Description

Technical Field

[0001] The present invention relates to the field of terminal device frequency control, and in particular to an intelligent, safe and energy-saving terminal adaptive frequency compensation method using information perception. Background Art

[0002] Intelligent, safe and energy-saving terminal equipment, such as power-saving voltage stabilizers, achieves the goals of saving energy, extending equipment life, and improving equipment performance by stabilizing voltage and reducing current fluctuations when power equipment is running. In order to improve the response performance of the system under dynamically changing conditions, prevent oscillation or instability, and ensure signal output accuracy and system operation stability, frequency compensation is required. The frequency compensation method of traditional equipment terminals cannot respond to load fluctuations and external disturbances in real time, resulting in low system efficiency and safety hazards. Therefore, information perception technology, namely sensor networks, Internet of Things technology and big data analysis, can be used to accurately grasp the system's load changes, input disturbances and other information, and dynamically adjust the frequency compensation strategy, which can effectively improve the dynamic response performance of the equipment terminal, thereby optimizing energy use, improving safety, and reducing power loss while ensuring the long-term stable operation of the power system.

[0003] In related technologies, frequency compensation is generally performed adaptively based on the load changes of the intelligent, safe and energy-saving terminal monitored in real time to quickly eliminate voltage fluctuations caused by load mutations or input disturbances and maintain the stability of the output signal. However, when performing frequency compensation for transient responses caused by load mutations or input disturbances, the system may overshoot due to excessive adjustment of frequency compensation, resulting in the output voltage being unable to quickly reach a stable value after the load mutation, affecting the signal output quality of the terminal device, thereby reducing the effect of adaptive frequency compensation for the intelligent, safe and energy-saving terminal. Summary of the Invention

[0004] In order to solve the technical problem of poor adaptive frequency compensation effect for intelligent, safe and energy-saving terminals due to excessive frequency compensation, the present invention aims to provide an adaptive frequency compensation method for intelligent, safe and energy-saving terminals using information perception. The technical solution adopted is as follows:

[0005] The present invention proposes an adaptive frequency compensation method for an intelligent, secure and energy-saving terminal using information perception, the method comprising:

[0006] Obtain power data of smart, safe and energy-saving terminal devices at each moment in a preset historical time period;

[0007] The power data within a preset historical time period is periodically segmented to obtain multiple data segments within the preset historical time period; any data segment is used as a target data segment, and the degree of fluctuation of the target data segment is obtained based on the fluctuation of the power data at each moment in the target data segment; the frequency compensation amplitude of the target data segment is obtained based on the deviation of the degree of fluctuation of the target data segment relative to the overall level of the degree of fluctuation of all data segments; the frequency compensation efficiency value of the target data segment is obtained based on the difference in the frequency compensation amplitude and the difference in the degree of fluctuation between the target data segment and adjacent data segments; all data segments are clustered based on the difference in the frequency compensation amplitude and the difference in the frequency compensation efficiency value between the data segments to obtain multiple cluster clusters, wherein the multiple cluster clusters include a safe reference cluster cluster;

[0008] Obtaining the overshoot risk level of the target data segment based on a deviation of the frequency compensation efficiency value of the target data segment relative to the overall level of the frequency compensation efficiency values ​​of all data segments in the safety reference cluster; obtaining the equipment operating status value of the target data segment based on the correlation between the frequency compensation amplitudes of each data segment in the cluster where the target data segment is located and the overshoot risk level, as well as the frequency compensation amplitude of the target data segment; and obtaining the objective function value of the target data segment based on the operating status value of the target data segment and the power data at each moment;

[0009] The model predictive control algorithm is used to compensate the operating frequency of the terminal equipment in real time based on the objective function value of each data segment.

[0010] Furthermore, obtaining a plurality of data segments within a preset historical time period includes:

[0011] Performing Fourier transform processing on the power data at all times within a preset historical time period to obtain a spectrum graph, and analyzing the main frequency components in the spectrum graph to obtain the operating cycle of the terminal device;

[0012] The power data within the preset historical time period is segmented according to the operation cycle to obtain multiple data segments within the preset historical time period, wherein the duration of the target data segment is equal to the operation cycle.

[0013] Furthermore, obtaining the fluctuation degree of the target data segment includes:

[0014] Any two adjacent moments in the target data segment are taken as an adjacent moment group, and the absolute value of the difference between the power data of the two moments in each adjacent moment group is taken as the data variation of each adjacent moment group;

[0015] The average value of the data changes of all adjacent time groups is used as the first fluctuation parameter of the target data segment;

[0016] Analyze the discrete degree of the power data at all moments in the target data segment to obtain a second fluctuation parameter of the target data segment;

[0017] The first fluctuation parameter and the second fluctuation parameter are combined to obtain the fluctuation degree of the target data segment.

[0018] Furthermore, obtaining the frequency compensation amplitude of the target data segment includes:

[0019] The average value of the fluctuation levels of all data segments is used as the overall fluctuation level of the preset historical time period;

[0020] The absolute value of the difference between the fluctuation degree of the target data segment and the overall fluctuation degree is used as the fluctuation deviation value of the target data segment;

[0021] The fluctuation degree and the fluctuation deviation value of the target data segment are integrated to obtain the frequency compensation amplitude of the target data segment.

[0022] Furthermore, obtaining the frequency compensation efficiency value of the target data segment includes:

[0023] taking the absolute value of the difference between the frequency compensation amplitudes of the target data segment and the adjacent subsequent data segment as the first efficiency value of the target data segment;

[0024] using the absolute value of the difference in the degree of fluctuation between the target data segment and the adjacent previous data segment as the second efficiency value of the target data segment;

[0025] The first efficiency value and the second efficiency value are integrated to obtain a frequency compensation efficiency value of the target data segment.

[0026] Furthermore, obtaining a plurality of clusters includes:

[0027] Using the absolute value of the difference between the frequency compensation amplitudes of any two data segments as a first distance metric between the any two data segments;

[0028] Using a K-means clustering algorithm and based on the first distance metric between any two data segments, performing a first clustering on all data segments to obtain a plurality of first clusters, and taking an average value of the frequency compensation amplitudes of all data segments in each first cluster as a first cluster center value of each first cluster;

[0029] The first cluster with the largest first cluster center value is used as a high-load cluster, and the absolute value of the difference between the frequency compensation efficiency values ​​of any two data segments in the high-load cluster is used as a second distance metric between any two data segments in the high-load cluster;

[0030] Using a K-means clustering algorithm and based on the second distance metric between any two data segments in the high-load cluster, performing a second clustering on all data segments in the high-load cluster to obtain a plurality of second clusters, and taking an average of the frequency compensation efficiency values ​​of all data segments in each second cluster as a second cluster center value of each second cluster;

[0031] The second cluster with the largest second cluster center value is used as the safety reference cluster.

[0032] Furthermore, obtaining the overshoot risk level of the target data segment includes:

[0033] The absolute value of the difference between the frequency compensation efficiency value of the target data segment and the second cluster center value of the safety reference cluster is used as the overshoot risk degree of the target data segment.

[0034] Furthermore, obtaining the device operating status value of the target data segment includes:

[0035] Using a sequence consisting of the frequency compensation amplitudes of all data segments in the cluster where the target data segment is located as a first sequence, and using a sequence consisting of the overshoot risk levels of all data segments in the cluster where the target data segment is located as a second sequence;

[0036] Normalization is performed on the product value of the absolute value of the Pearson correlation coefficient between the first sequence and the second sequence and the frequency compensation amplitude of the target data segment to obtain the device operation status value of the target data segment.

[0037] Furthermore, obtaining the objective function value of the target data segment includes:

[0038] The objective function value of the target data segment is obtained based on the calculation formula of the objective function value, and the calculation formula of the objective function value is:

[0039]

[0040] Where J represents the objective function value of the target data segment; a n Indicates the power data at the nth moment in the target data segment; a ′ Indicates the expected output standard power data; U indicates the equipment operating status value of the target data segment; PB n represents the frequency compensation amount of the power data at the nth moment in the target data segment calculated by the model predictive control algorithm; N represents the number of all moments in the target data segment.

[0041] Furthermore, the real-time compensation of the operating frequency of the terminal device includes:

[0042] The maximum value of the objective function value of all data segments and the real-time power data of the terminal device are input into the model predictive control algorithm, and the model predictive control algorithm outputs a frequency compensation signal to the control unit of the terminal device, and the operating frequency of the terminal device is compensated and controlled in real time by the control unit.

[0043] The present invention has the following beneficial effects:

[0044] When performing adaptive frequency compensation on a device terminal, the terminal may enter an unstable operating state due to a sudden change in load, which may cause excessive adjustment of the frequency compensation and the terminal may be unable to quickly enter a stable state. Therefore, the present invention monitors the changes in power data of the power equipment terminal in real time, analyzes the dynamic characteristics of the load, understands the operating status of the terminal according to the load changes, limits the frequency compensation amplitude in stages, and avoids excessive frequency adjustment, so that the terminal can recover to a stable state more quickly and smoothly after being disturbed.

[0045] By real-time monitoring of changes in power data and analysis of load dynamic characteristics, we can timely grasp the trend of dynamic load changes, provide accurate system operating status data, cluster different load changes into several terminal operating states, implement phased optimization control strategies, and design different frequency compensation amplitudes for different load states to improve the pertinence and efficiency of frequency compensation, prevent excessive adjustment of frequency compensation from causing system oscillation or instability, and avoid drastic changes in output signals causing losses to terminal equipment. By limiting overcompensation and dynamic balancing performance, terminal equipment can be enabled to operate quickly, smoothly and efficiently under complex load conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0047] Figure 1 A flow chart of an adaptive frequency compensation method for an intelligent, secure and energy-saving terminal using information perception is provided in accordance with an embodiment of the present invention. DETAILED DESCRIPTION

[0048] To further illustrate the technical means and effectiveness of the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail the specific implementation, structure, features, and effectiveness of an adaptive frequency compensation method for a smart, secure, and energy-saving terminal utilizing information perception proposed by the present invention. In the following description, different references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0049] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0050] The following describes in detail a specific scheme of an adaptive frequency compensation method for an intelligent, secure and energy-saving terminal using information perception provided by the present invention with reference to the accompanying drawings.

[0051] See also Figure 1 , which shows a flow chart of a method for adaptive frequency compensation of an intelligent, secure and energy-saving terminal using information perception provided by one embodiment of the present invention, the method comprising:

[0052] Step S1: Obtain the power data of the intelligent security energy-saving terminal device at each moment in a preset historical time period.

[0053] In an embodiment of the present invention, relevant power sensors, such as Hall sensors or current and voltage sensors, are first installed at the input end of the intelligent, safe and energy-saving terminal device, and then the sensors are used to collect power data of the intelligent, safe and energy-saving terminal device at each moment within a preset historical time period. The power data can be current data, voltage data or electric power data, etc. In one embodiment of the present invention, the collection of current data is taken as an example, wherein the preset historical time period is set to the most recent 1 hour, and the time interval for data collection is set to 1 second. The specific values ​​of the preset historical time period and the time interval for data collection can also be set by the implementer according to the specific implementation scenario, and are not limited here.

[0054] Step S2: periodically segment the power data within a preset historical time period to obtain multiple data segments within the preset historical time period; take any data segment as the target data segment, and obtain the degree of fluctuation of the target data segment based on the fluctuation of the power data at each moment in the target data segment; obtain the frequency compensation amplitude of the target data segment based on the deviation of the degree of fluctuation of the target data segment relative to the overall level of the degree of fluctuation of all data segments; obtain the frequency compensation efficiency value of the target data segment based on the difference in frequency compensation amplitude and the difference in fluctuation degree between the target data segment and the adjacent data segments; cluster all data segments based on the difference in frequency compensation amplitude and the difference in frequency compensation efficiency value between each data segment to obtain multiple cluster clusters, wherein the multiple cluster clusters include a safety reference cluster cluster.

[0055] Intelligent, safe and energy-saving terminals achieve the goals of saving energy, extending equipment life, and improving equipment performance by stabilizing voltage and reducing current fluctuations when power equipment is running. The operating frequency of the terminal determines the efficiency and stability of the equipment's energy conversion. In actual operation, power equipment terminals are often affected by dynamic changes such as sudden increases or decreases in load or external disturbances, resulting in unstable operating frequency. Therefore, it is necessary to automatically adjust the compensation signal according to real-time load changes to ensure that the terminal's output quality remains stable within the target range.

[0056] In order to improve the accuracy and efficiency of frequency compensation for intelligent, safe and energy-saving terminal equipment, adapt to changes in load dynamic characteristics and external disturbances, and ensure that terminal equipment can achieve optimal performance under different operating conditions, the embodiment of the present invention first periodically segments the power data within a preset historical time period to obtain multiple data segments within the preset historical time period. Subsequently, different data segments can be analyzed to more accurately adjust the frequency compensation strategy to adapt to the operating conditions of different time periods.

[0057] Preferably, in one embodiment of the present invention, the method for obtaining multiple data segments within a preset historical time period specifically includes:

[0058] The power data at all times within the preset historical time period is subjected to Fourier transform processing to obtain a spectrum diagram, and the main frequency components in the spectrum diagram are analyzed to obtain the operating cycle of the terminal equipment, wherein the operating cycle can also be considered as the cycle of power data changes. The power data within the preset historical time period is then segmented according to the operating cycle to obtain multiple data segments within the preset historical time period, wherein the duration of the target data segment is equal to the operating cycle. It should be noted that using Fourier transform to identify the main frequency components and obtain the data cycle is a technical means well known to those skilled in the art and will not be elaborated here.

[0059] The purpose of the intelligent, safe and energy-saving terminal is to maintain stable output quality. When the power data passing through the terminal device is relatively stable, the frequency compensation amplitude should be small. When the load suddenly changes or there is an external disturbance, the power data will fluctuate greatly. At this time, frequency compensation is required to ensure stable output quality. At the same time, the fluctuation of power data in different data segments is different. Therefore, any data segment can be analyzed first, and any data segment can be used as the target data segment. The fluctuation of power data at each moment in the target data segment is analyzed. The degree of fluctuation obtained can reflect the degree of fluctuation of power data in the target data segment. Subsequently, the compensation amplitude of the terminal device operating frequency can be adaptively adjusted for data segments with different fluctuation degrees to avoid excessive frequency adjustment.

[0060] Preferably, in one embodiment of the present invention, the method for obtaining the fluctuation degree of the target data segment specifically includes:

[0061] Take any two adjacent moments in the target data segment as an adjacent moment group, and take the absolute value of the difference between the power data of the two moments in each adjacent moment group as the data change of each adjacent moment group. The larger the data change, the greater the degree of fluctuation of the power data of the two adjacent moments. Then take the average value of the data change of all adjacent moment groups as the first fluctuation parameter of the target data segment.

[0062] The discrete degree of the power data at all moments in the target data segment is analyzed to obtain a second fluctuation parameter of the target data segment.

[0063] In an embodiment of the present invention, the statistical quantities such as the range, variance or standard deviation of the power data at all moments in the target data segment may be used as the second fluctuation parameter of the target data segment, which is not limited here.

[0064] The first fluctuation parameter and the second fluctuation parameter are then combined to obtain the fluctuation degree of the target data segment.

[0065] In the embodiment of the present invention, the sum or product of the first fluctuation parameter and the second fluctuation parameter may be used as the fluctuation degree of the target data segment to achieve a combination of the two, which is not limited here.

[0066] As an example, in one embodiment of the present invention, the expression for the fluctuation degree of the target data segment may be specifically, for example, as follows:

[0067]

[0068] Among them, A represents the degree of fluctuation of the target data segment; a max Indicates the maximum value of the power data at all times in the target data segment; a minrepresents the minimum value of the power data at all times in the target data segment; a max -a min represents the range of the power data at all moments in the target data segment, i.e., the second fluctuation parameter of the target data segment; a (1,m) and a (2,m) Respectively represent the power data of two moments in the mth adjacent moment group; |a (1,m) -a (2,m) | represents the data change of the mth adjacent time group; M represents the number of adjacent time groups in the target data segment; Indicates the first fluctuation parameter of the target data segment.

[0069] The fluctuation degree of each data segment can be obtained by the same method as above. During the operation of the terminal equipment, the greater the fluctuation degree of the target data segment and the greater the deviation of the fluctuation degree of the target data segment relative to the overall level of the fluctuation degree of all data segments, it means that the power system may have a sudden load change in the target data segment. At this time, it is necessary to compensate the frequency of the terminal equipment with a larger compensation amplitude to ensure stable operation of the terminal. Therefore, the deviation of the fluctuation degree of the target data segment relative to the overall level of the fluctuation degree of all data segments can be analyzed to obtain the frequency compensation amplitude of the target data segment. Subsequently, based on the frequency compensation amplitude of each data segment, each data segment can be clustered and analyzed, and the operating status of the terminal equipment in each data segment can be analyzed, thereby realizing compensation adjustment of the operating frequency of the terminal equipment.

[0070] Preferably, in one embodiment of the present invention, the method for obtaining the frequency compensation amplitude of the target data segment specifically includes:

[0071] The average value of the fluctuation degree of all data segments is used as the overall fluctuation degree of the preset historical time period, and the overall level of the fluctuation degree of all data segments is reflected by the overall fluctuation degree.

[0072] The absolute value of the difference between the fluctuation degree of the target data segment and the overall fluctuation degree is taken as the fluctuation deviation value of the target data segment. The larger the fluctuation deviation value, the greater the difference between the fluctuation degree of the target data segment and the overall level of fluctuation degree of all data segments, which further indicates that the amplitude of frequency compensation required for the terminal device within the target data segment is greater.

[0073] At the same time, the greater the fluctuation degree of the target data segment, the greater the frequency compensation of the terminal device needs to be within the target data segment. Therefore, the fluctuation degree and fluctuation deviation value of the target data segment can be combined to obtain the frequency compensation amplitude of the target data segment.

[0074] In an embodiment of the present invention, the sum or product of the fluctuation degree and the fluctuation deviation value of the target data segment can be used as the frequency compensation amplitude of the target data segment to achieve a combination of the two, which is not limited here.

[0075] As an example, in one embodiment of the present invention, the frequency compensation amplitude of the target data segment may be expressed as follows:

[0076]

[0077] Where D represents the frequency compensation amplitude of the target data segment; A represents the fluctuation degree of the target data segment; Indicates the overall volatility of the preset historical time period; Indicates the fluctuation deviation value of the target data segment.

[0078] The frequency compensation amplitude of each data segment can be obtained by the same method as above.

[0079] During the operation of the intelligent, safe and energy-saving terminal, if load fluctuations occur, frequency compensation is required to stabilize the terminal output. The degree of sudden change in the load determines the intensity of the frequency compensation that the terminal needs to make. Slight fluctuations only require small adjustments, while sudden large fluctuations require more rapid compensation. In order to achieve fast transient response, the terminal's control loop adjustment will be more sensitive, making the frequency compensation strategy unable to adapt in time, causing short-term instability or oscillation. To avoid excessive adjustment of frequency compensation, the embodiment of the present invention first analyzes the difference in frequency compensation amplitude and fluctuation degree between the target data segment and the adjacent data segments, obtains the frequency compensation efficiency value of the target data segment, and then evaluates the compensation result based on the frequency compensation efficiency value, which facilitates subsequent adjustment of the frequency compensation strategy. Subsequently, the data segments can be clustered based on the frequency compensation efficiency value and frequency compensation amplitude of each data segment, thereby analyzing the operating status of the terminal device in each data segment.

[0080] Preferably, in one embodiment of the present invention, the method for obtaining the frequency compensation efficiency value of the target data segment specifically includes:

[0081] The absolute value of the difference in frequency compensation amplitude between the target data segment and the adjacent subsequent data segment is used as the first efficiency value of the target data segment; the absolute value of the difference in fluctuation degree between the target data segment and the adjacent previous data segment is used as the second efficiency value of the target data segment. The larger the first efficiency value and the second efficiency value are, the better the frequency compensation effect of the target data segment and the higher the efficiency. Therefore, the first efficiency value and the second efficiency value can be combined to obtain the frequency compensation efficiency value of the target data segment.

[0082] In the embodiment of the present invention, the sum or product of the first efficiency value and the second efficiency value may be used as the frequency compensation efficiency value of the target data segment to achieve a combination of the two, which is not limited here.

[0083] It should be noted that if the target data segment is the last data segment and there is no adjacent subsequent data segment for the target data segment, the calculated second efficiency value of the target data segment can be used as the frequency compensation efficiency value of the target data segment; if the target data segment is the first data segment and there is no adjacent previous data segment for the target data segment, the calculated first efficiency value of the target data segment can be used as the frequency compensation efficiency value of the target data segment.

[0084] As an example, in one embodiment of the present invention, the expression of the frequency compensation efficiency value of the target data segment may be specifically, for example, as follows:

[0085] E=E1×E2

[0086] Wherein, E represents the frequency compensation efficiency value of the target data segment; E1 represents the first efficiency value of the target data segment; and E2 represents the second efficiency value of the target data segment.

[0087] The frequency compensation efficiency value of each data segment can be obtained by the same method as above.

[0088] Different load states have different compensation requirements. Under light load conditions, the terminal has higher requirements for frequency compensation efficiency, while under heavy load conditions, it pays more attention to stability and rapid response. Cluster analysis helps to classify complex load dynamic characteristics into limited terminal operating states, so as to design targeted frequency compensation strategies. Therefore, the embodiment of the present invention clusters all data segments based on the differences in frequency compensation amplitudes and frequency compensation efficiency values ​​between each data segment to obtain multiple clusters, and selects a safe reference cluster from the multiple clusters. Subsequently, based on the frequency compensation efficiency values ​​of each data segment in the safe reference cluster, the overshoot risk degree of frequency compensation for each data segment can be analyzed to achieve phased optimization of frequency compensation.

[0089] Preferably, in one embodiment of the present invention, the method for obtaining multiple clusters specifically includes:

[0090] First, the absolute value of the difference in frequency compensation amplitude between any two data segments is used as the first distance metric between any two data segments. The K-means clustering algorithm is used, and based on the first distance metric between any two data segments, all data segments are clustered for the first time to obtain multiple first clusters. The average value of the frequency compensation amplitude of all data segments in each first cluster is used as the first cluster center value of each first cluster. In one embodiment of the present invention, the number of first clusters is set to three, corresponding to clusters with three load levels. The larger the first cluster center value, the higher the load level in the data segments in the first cluster. In other embodiments of the present invention, the specific number of first clusters can also be set to other numbers, which are not limited here.

[0091] Then, the first cluster with the largest first cluster center value is taken as the high-load cluster. In the high-load cluster, it is easy to have the phenomenon of frequency compensation overshoot due to excessive load fluctuation, so that the output of the terminal device becomes more unstable. Therefore, the absolute value of the difference in frequency compensation efficiency value between any two data segments in the high-load cluster can be used as the second distance metric between any two data segments in the high-load cluster, and the K-means clustering algorithm is used. Based on the second distance metric between any two data segments in the high-load cluster, all data segments in the high-load cluster are clustered. Secondary clustering is performed to obtain multiple second clusters. In one embodiment of the present invention, the number of second clusters is set to two. In other embodiments of the present invention, the specific number of second clusters can also be set to other numbers, which are not limited here. Then, the average value of the frequency compensation efficiency values ​​of all data segments in each second cluster is used as the second cluster center value of each second cluster, and the second cluster with the largest second cluster center value is used as the safety reference cluster. In one embodiment of the present invention, four clusters are finally obtained. In other embodiments of the present invention, other clustering algorithms can also be used, which are not limited here.

[0092] Step S3: Obtain the overshoot risk degree of the target data segment based on the deviation of the frequency compensation efficiency value of the target data segment relative to the overall level of the frequency compensation efficiency values ​​of all data segments in the safety reference cluster; obtain the equipment operating status value of the target data segment based on the correlation between the frequency compensation amplitude and the overshoot risk degree of each data segment in the cluster where the target data segment is located, as well as the frequency compensation amplitude of the target data segment; obtain the objective function value of the target data segment based on the operating status value of the target data segment and the power data at each moment.

[0093] Under normal circumstances, the amount of frequency compensation is related to the output fluctuation of the corresponding data segment. The more the frequency compensation is, the faster the output reaches stability in a short period of time, and the better the frequency compensation efficiency. However, if the load mutation is large and the frequency compensation is over-adjusted, the frequency compensation is large, but the output of the terminal device cannot stabilize quickly in a short period of time, but instead experiences greater fluctuations, and the frequency compensation efficiency is poor. Therefore, the overshoot risk level of the target data segment can be obtained based on the deviation of the frequency compensation efficiency value of the target data segment relative to the overall level of the frequency compensation efficiency values ​​of all data segments in the safety reference cluster, providing a data basis for subsequent analysis of the operating status of the terminal device in each data segment.

[0094] Preferably, in one embodiment of the present invention, the method for obtaining the overshoot risk level of the target data segment specifically includes:

[0095] The second cluster center of the safety reference cluster can be used to reflect the overall level of frequency compensation efficiency values ​​of all data segments in the safety reference cluster. The greater the difference between the frequency compensation efficiency value of the target data segment and the second cluster center value of the safety reference cluster, the greater the risk of overshoot when frequency compensation is performed on the target data segment. Therefore, the absolute value of the difference between the frequency compensation efficiency value of the target data segment and the second cluster center value of the safety reference cluster can be used as the overshoot risk degree of the target data segment.

[0096] As an example, in one embodiment of the present invention, the expression for the overshoot risk degree of the target data segment may be specifically, for example, as follows:

[0097]

[0098] Where G represents the overshoot risk level of the target data segment; E represents the frequency compensation efficiency value of the target data segment; Indicates the second cluster center value of the safety reference cluster.

[0099] The overshoot risk level of each data segment can be obtained by the same method as above.

[0100] Applying different frequency compensation strategies in data segments with different overshoot risk levels can achieve higher compensation efficiency. Therefore, the device operating status value of the target data segment can be obtained based on the correlation between the frequency compensation amplitude of each data segment in the cluster where the target data segment is located and the overshoot risk level, as well as the frequency compensation amplitude of the target data segment. Subsequently, based on the device operating status value, the weight coefficient in the objective function in the model predictive control algorithm can be appropriately adjusted to achieve a balance between transient response and steady-state accuracy.

[0101] Preferably, in one embodiment of the present invention, the method for obtaining the device operating status value of the target data segment specifically includes:

[0102] A sequence consisting of frequency compensation amplitudes of all data segments in the cluster where the target data segment is located is used as the first sequence, and a sequence consisting of overshoot risk degrees of all data segments in the cluster where the target data segment is located is used as the second sequence.

[0103] The stronger the correlation between the first sequence and the second sequence, and the larger the frequency compensation amplitude of the target data segment, the more likely the frequency compensation of the target data segment is to have an overshoot risk. Therefore, the absolute value of the Pearson correlation coefficient between the first sequence and the second sequence and the product value of the frequency compensation amplitude of the target data segment can be normalized, and the calculation result can be limited to the range of [0,1] to obtain the equipment operation status value of the target data segment.

[0104] In one embodiment of the present invention, the normalization process may specifically be, for example, maximum and minimum value normalization process. In other embodiments of the present invention, other normalization methods may be selected according to a specific range of numerical values, which will not be described in detail.

[0105] As an example, in one embodiment of the present invention, the expression of the device operating status value of the target data segment may be specifically, for example:

[0106] U=norm(|ρ|×D)

[0107] Where U represents the device operating status value of the target data segment; ρ represents the Pearson correlation coefficient between the first sequence and the second sequence; D represents the frequency compensation amplitude of the target data segment; and norm() represents the normalization function.

[0108] The device operating status value of each data segment can be obtained by the same method as above.

[0109] The model predictive control algorithm can use the historical power data of the terminal device to predict the future operating status, combine the real-time load data and the objective function, select the optimal frequency compensation strategy, and reduce the problem of excessive frequency adjustment. Therefore, the embodiment of the present invention obtains the objective function value of the target data segment based on the operating status value of the target data segment and the power data at each moment. The model predictive control algorithm can be used subsequently, and based on the calculated objective function value of each data segment, the operating frequency of the terminal device can be compensated and adjusted in real time to avoid the occurrence of overcompensation and improve the effect of frequency compensation.

[0110] Preferably, in one embodiment of the present invention, the method for obtaining the objective function value of the target data segment specifically includes:

[0111] Based on the calculation formula of the objective function value, the objective function value of the target data segment is obtained. The calculation formula of the objective function value is:

[0112]

[0113] Where J represents the objective function value of the target data segment; a n Indicates the power data at the nth moment in the target data segment; a ′ Indicates the expected output standard power data, which is determined by the actual power system where the intelligent safety and energy-saving terminal device is located; U represents the device operating status value of the target data segment; PB n represents the frequency compensation amount of the power data at the nth moment in the target data segment calculated by the model predictive control algorithm; N represents the number of all moments in the target data segment.

[0114] Among them, |a n -a ′ | 2 Indicates the deviation between the actual power data and the expected standard power data. It is used to measure the error between the actual output value of the terminal and the expected value. The purpose is to minimize the deviation between the system output value and the expected value, ensuring that the output value of the terminal in steady state is as close to the expected value as possible, thereby ensuring steady-state accuracy. U×PB n This parameter limits the frequency compensation amount by using the device operating status value to ensure that the system does not over-adjust the frequency compensation to avoid overshoot or oscillation. The device operating status value U is used as a weight here.

[0115] The objective function value of each data segment can be obtained by the same method as above.

[0116] Step S4: Using the model predictive control algorithm, based on the objective function value of each data segment, the operating frequency of the terminal device is compensated in real time.

[0117] After obtaining the objective function value of each data segment, the model predictive control algorithm can be used to compensate the operating frequency of the terminal device in real time based on the objective function value of each data segment, thereby avoiding the occurrence of overcompensation and enabling the terminal device to quickly adaptively adjust and compensate the operating frequency in the event of load changes or disturbances.

[0118] Preferably, in one embodiment of the present invention, the method for real-time compensation of the operating frequency of the terminal device specifically includes:

[0119] The maximum value of the objective function value of all data segments and the real-time power data of the terminal device are input into the model predictive control algorithm, and the model predictive control algorithm outputs a frequency compensation signal to the control unit of the terminal device, and the operating frequency of the terminal device is compensated and controlled in real time by the control unit.

[0120] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0121] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. A method for adaptive frequency compensation of an intelligent, secure and energy-saving terminal using information perception, characterized in that: The method comprises: Obtain power data of smart, safe and energy-saving terminal devices at each moment in a preset historical time period; The power data within a preset historical time period is periodically segmented to obtain multiple data segments within the preset historical time period; any data segment is used as a target data segment, and the degree of fluctuation of the target data segment is obtained based on the fluctuation of the power data at each moment in the target data segment; the frequency compensation amplitude of the target data segment is obtained based on the deviation of the degree of fluctuation of the target data segment relative to the overall level of the degree of fluctuation of all data segments; the frequency compensation efficiency value of the target data segment is obtained based on the difference in the frequency compensation amplitude and the difference in the degree of fluctuation between the target data segment and adjacent data segments; all data segments are clustered based on the difference in the frequency compensation amplitude and the difference in the frequency compensation efficiency value between the data segments to obtain multiple cluster clusters, wherein the multiple cluster clusters include a safe reference cluster cluster; Obtaining the overshoot risk level of the target data segment based on a deviation of the frequency compensation efficiency value of the target data segment relative to the overall level of the frequency compensation efficiency values ​​of all data segments in the safety reference cluster; obtaining the equipment operating status value of the target data segment based on the correlation between the frequency compensation amplitudes of each data segment in the cluster where the target data segment is located and the overshoot risk level, as well as the frequency compensation amplitude of the target data segment; and obtaining the objective function value of the target data segment based on the operating status value of the target data segment and the power data at each moment; The model predictive control algorithm is used to compensate the operating frequency of the terminal equipment in real time based on the objective function value of each data segment.

2. The method for adaptive frequency compensation of an intelligent, secure and energy-saving terminal using information perception according to claim 1, characterized in that: The obtaining of multiple data segments within a preset historical time period includes: Performing Fourier transform processing on the power data at all times within a preset historical time period to obtain a spectrum graph, and analyzing the main frequency components in the spectrum graph to obtain the operating cycle of the terminal device; The power data within the preset historical time period is segmented according to the operation cycle to obtain multiple data segments within the preset historical time period, wherein the duration of the target data segment is equal to the operation cycle.

3. The method for adaptive frequency compensation of an intelligent, secure and energy-saving terminal using information perception according to claim 1, characterized in that: The obtaining of the fluctuation degree of the target data segment includes: Any two adjacent moments in the target data segment are taken as an adjacent moment group, and the absolute value of the difference between the power data of the two moments in each adjacent moment group is taken as the data variation of each adjacent moment group; The average value of the data changes of all adjacent time groups is used as the first fluctuation parameter of the target data segment; Analyze the discrete degree of the power data at all moments in the target data segment to obtain a second fluctuation parameter of the target data segment; The first fluctuation parameter and the second fluctuation parameter are combined to obtain the fluctuation degree of the target data segment.

4. The method for adaptive frequency compensation of an intelligent, secure and energy-saving terminal using information perception according to claim 1, characterized in that: Obtaining the frequency compensation amplitude of the target data segment includes: The average value of the fluctuation levels of all data segments is used as the overall fluctuation level of the preset historical time period; The absolute value of the difference between the fluctuation degree of the target data segment and the overall fluctuation degree is used as the fluctuation deviation value of the target data segment; The fluctuation degree and the fluctuation deviation value of the target data segment are integrated to obtain the frequency compensation amplitude of the target data segment.

5. The method for adaptive frequency compensation of an intelligent, secure and energy-saving terminal using information perception according to claim 1, characterized in that: Obtaining the frequency compensation efficiency value of the target data segment includes: taking the absolute value of the difference between the frequency compensation amplitudes of the target data segment and the adjacent subsequent data segment as the first efficiency value of the target data segment; using the absolute value of the difference in the degree of fluctuation between the target data segment and the adjacent previous data segment as the second efficiency value of the target data segment; The first efficiency value and the second efficiency value are integrated to obtain a frequency compensation efficiency value of the target data segment.

6. The method for adaptive frequency compensation of an intelligent, secure and energy-saving terminal using information perception according to claim 1, characterized in that: The obtaining of a plurality of clusters comprises: Using the absolute value of the difference between the frequency compensation amplitudes of any two data segments as a first distance metric between the any two data segments; Using a K-means clustering algorithm and based on the first distance metric between any two data segments, performing a first clustering on all data segments to obtain a plurality of first clusters, and taking an average value of the frequency compensation amplitudes of all data segments in each first cluster as a first cluster center value of each first cluster; The first cluster with the largest first cluster center value is used as a high-load cluster, and the absolute value of the difference between the frequency compensation efficiency values ​​of any two data segments in the high-load cluster is used as a second distance metric between any two data segments in the high-load cluster; Using a K-means clustering algorithm and based on the second distance metric between any two data segments in the high-load cluster, performing a second clustering on all data segments in the high-load cluster to obtain a plurality of second clusters, and taking an average of the frequency compensation efficiency values ​​of all data segments in each second cluster as a second cluster center value of each second cluster; The second cluster with the largest second cluster center value is used as the safety reference cluster.

7. The method for adaptive frequency compensation of an intelligent, secure and energy-saving terminal using information perception according to claim 6, characterized in that: The overshoot risk level of the target data segment is obtained as follows: The absolute value of the difference between the frequency compensation efficiency value of the target data segment and the second cluster center value of the safety reference cluster is used as the overshoot risk degree of the target data segment.

8. The method for adaptive frequency compensation of an intelligent, secure and energy-saving terminal using information perception according to claim 1, characterized in that: The obtaining of the device operating status value of the target data segment includes: Using a sequence consisting of the frequency compensation amplitudes of all data segments in the cluster where the target data segment is located as a first sequence, and using a sequence consisting of the overshoot risk levels of all data segments in the cluster where the target data segment is located as a second sequence; Normalization is performed on the product value of the absolute value of the Pearson correlation coefficient between the first sequence and the second sequence and the frequency compensation amplitude of the target data segment to obtain the device operation status value of the target data segment.

9. The method for adaptive frequency compensation of an intelligent, secure and energy-saving terminal using information perception according to claim 1, characterized in that: The objective function value of obtaining the target data segment includes: The objective function value of the target data segment is obtained based on the calculation formula of the objective function value, and the calculation formula of the objective function value is: Where J represents the objective function value of the target data segment; a n Indicates the power data at the nth moment in the target data segment; a ′ Indicates the expected output standard power data; U indicates the equipment operating status value of the target data segment; PB n represents the frequency compensation amount of the power data at the nth moment in the target data segment calculated by the model predictive control algorithm; N represents the number of all moments in the target data segment.

10. The method for adaptive frequency compensation of an intelligent, secure and energy-saving terminal using information perception according to claim 1, characterized in that: The real-time compensation of the operating frequency of the terminal device includes: The maximum value of the objective function value of all data segments and the real-time power data of the terminal device are input into the model predictive control algorithm, and the model predictive control algorithm outputs a frequency compensation signal to the control unit of the terminal device, and the operating frequency of the terminal device is compensated and controlled in real time by the control unit.

Images