Intelligent safe energy-saving terminal self-adaptive frequency compensation method using information perception

Abstract

The invention relates to the field of frequency control of terminal equipment, in particular to an intelligent safe energy-saving terminal self-adaptive frequency compensation method utilizing information perception. The method comprises the following steps: firstly, acquiring electric power data of the intelligent safe energy-saving terminal equipment in a preset historical time period, periodically segmenting the electric power data in the preset historical time period to obtain a plurality of data segments, analyzing the frequency compensation degree and the frequency compensation efficiency value of each data segment based on the fluctuation degree of the electric power data in each data segment, and calculating the frequency compensation efficiency value of the intelligent safe energy-saving terminal equipment according to the frequency compensation degree and the frequency compensation efficiency value. The method comprises the following steps: acquiring a plurality of data segments, clustering all the data segments to obtain a plurality of clusters, further analyzing an equipment operation state value of each data segment in combination with the overshoot risk degree of each data segment, and performing real-time compensation adjustment on the working frequency of the terminal equipment by using a model prediction control algorithm based on the acquired target function value of each data segment. According to the invention, excessive compensation can be avoided in the frequency compensation process of the intelligent safe energy-saving terminal, and the effect of adaptive frequency compensation is improved.

Description

Technical Field

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

[0002] Intelligent security energy-saving terminal devices, such as power-saving voltage regulators, aim to save energy, extend the device life, and improve the device performance by stabilizing the voltage and reducing current fluctuations during the operation of power equipment. To improve the response performance of the system under dynamic change conditions, prevent oscillation or instability, and ensure signal output accuracy and system operation stability, frequency compensation is required. The frequency compensation methods of traditional device terminals cannot respond to load fluctuations and external disturbances in real time, resulting in low system efficiency and potential safety hazards. Therefore, information perception technologies, namely sensor networks, Internet of Things technologies, and big data analysis, can be used to accurately grasp information such as system load changes and input disturbances, dynamically adjust the frequency compensation strategy, effectively improve the dynamic response performance of device terminals, thereby optimizing energy use, enhancing safety, and ensuring the long-term stable operation of the power system while reducing power losses.

[0003] In related technologies, generally, frequency compensation is adaptively performed according to the load changes of intelligent security energy-saving terminals monitored in real time to quickly eliminate voltage fluctuations caused by load mutations or input disturbances and maintain the stability of output signals. However, during the frequency compensation of transient responses caused by load mutations or input disturbances, over-adjustment of frequency compensation may lead to system overshoot, resulting in the output voltage being unable to quickly reach a stable value after a load mutation, affecting the signal output quality of terminal devices, and thus reducing the effect of adaptive frequency compensation for intelligent security energy-saving terminals. Summary of the Invention

[0004] In order to solve the technical problem of poor adaptive frequency compensation effect for intelligent security energy-saving terminals due to excessive frequency compensation, the purpose of the present invention is to provide an intelligent security energy-saving terminal adaptive frequency compensation method using information perception, and the specific technical solutions adopted are as follows:

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

[0006] Obtain the power data of the intelligent security energy-saving terminal device at each moment within a preset historical time period;

[0007] Periodically segment the power data within a preset historical time period to obtain multiple data segments within the preset historical time period; take any one of the data segments as the target data segment, and obtain the degree of fluctuation of the target data segment according to the fluctuations of the power data at each moment in the target data segment; obtain the frequency compensation amplitude of the target data segment according to the deviation of the degree of fluctuation of the target data segment from the overall level of the degree of fluctuation of all data segments; obtain the frequency compensation efficiency value of the target data segment according to the difference in the frequency compensation amplitude and the difference in the degree of fluctuation between the target data segment and the adjacent data segments; cluster all data segments 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 clustering clusters, where one of the multiple clustering clusters contains a safety reference clustering cluster;

[0008] Obtain the overshoot risk degree of the target data segment according to the deviation of the frequency compensation efficiency value of the target data segment from the overall level of the frequency compensation efficiency values of all data segments in the safety reference clustering cluster; obtain the device operating state value of the target data segment according to the correlation between the frequency compensation amplitude and the overshoot risk degree of each data segment in the clustering cluster where the target data segment is located, and the frequency compensation amplitude of the target data segment; obtain the objective function value of the target data segment according to the operating state value of the target data segment and the power data at each moment;

[0009] Use the model predictive control algorithm to perform real-time compensation on the operating frequency of the terminal device based on the objective function values of each data segment.

[0010] Further, the obtaining of multiple data segments within the preset historical time period includes:

[0011] Perform Fourier transform processing on the power data at all moments within the preset historical time period to obtain a spectrogram, and analyze the main frequency components in the spectrogram to obtain the operating period of the terminal device;

[0012] Segment the power data within the preset historical time period according to the operating period to obtain multiple data segments within the preset historical time period, where the duration of the target data segment is equal to the operating period.

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

[0014] 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 amount of each adjacent moment group;

[0015] Take the average value of the data change amounts of all adjacent moment groups as the first fluctuation parameter of the target data segment;

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

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

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

[0019] Take the average value of the degrees of fluctuation of all data segments as the overall degree of fluctuation of the preset historical time period;

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

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

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

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

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

[0025] Integrate the first efficiency value and the second efficiency value to obtain the frequency compensation efficiency value of the target data segment.

[0026] Further, the obtaining of multiple clustering clusters includes:

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

[0028] Use the K-means clustering algorithm and, based on the first distance metric between any two data segments, perform the first clustering on all data segments to obtain multiple first clustering clusters, and take the average value of the frequency compensation amplitudes of all data segments in each first clustering cluster as the first clustering center value of each first clustering cluster;

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

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

[0031] Take the second clustering cluster with the largest second clustering center value as the safety reference clustering cluster.

[0032] Further, the obtaining of the overshoot risk degree of the target data segment includes:

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

[0034] Further, the obtaining of the device operating state value of the target data segment includes:

[0035] Take the sequence composed of the frequency compensation amplitudes of all data segments in the clustering cluster where the target data segment is located as the first sequence, and take the sequence composed of the overshoot risk degrees of all data segments in the clustering cluster where the target data segment is located as the second sequence;

[0036] Perform normalization processing 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 operating state value of the target data segment.

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

[0038] Based on the calculation formula of the objective function value, obtain the objective function value of the target data segment. 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 represents the power data at the nth moment in the target data segment; a ′ represents the standard power data of the expected output; U represents the device operating state 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] Further, the real-time compensation of the operating frequency of the terminal device includes:

[0042] The maximum value of the objective function values 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 control unit performs real-time compensation control on the operating frequency of the terminal device.

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

[0044] When performing adaptive frequency compensation on the device terminal, due to sudden load changes causing the terminal to enter an unstable operating state, it may cause over-adjustment of frequency compensation, and the terminal cannot quickly enter a stable state. Therefore, the present invention analyzes the dynamic characteristics of the load by monitoring the changes in the power data of the power device terminal in real time, understands the operating state of the terminal according to the load changes, and limits the frequency compensation amplitude in stages to avoid over-adjustment of the frequency, so that the terminal can recover to a stable state more quickly and smoothly after being disturbed.

[0045] By monitoring the changes in power data in real time and analyzing the dynamic characteristics of the load, the trend of dynamic changes in the load can be grasped in time, accurate system operating state data can be provided, different load change situations are clustered and divided into several operating states of the terminal, a staged optimization control strategy is realized, different frequency compensation amplitudes are designed for different load states, the pertinence and efficiency of frequency compensation are improved, over-adjustment of frequency compensation leading to system oscillation or instability is prevented, and damage to the terminal device caused by drastic changes in the output signal is avoided. By limiting over-compensation and dynamic balance performance, the terminal device can 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 in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0047] Figure 1 It is a flowchart of an adaptive frequency compensation method for an intelligent safety and energy-saving terminal using information perception provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0048] To further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following specifically describes, in conjunction with the accompanying drawings and preferred embodiments, a method for adaptive frequency compensation of an intelligent security and energy-saving terminal using information perception according to the present invention, including its specific implementation manner, structure, features, and effects. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0049] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0050] The following specifically describes, in conjunction with the accompanying drawings, the specific solution of a method for adaptive frequency compensation of an intelligent security and energy-saving terminal using information perception provided by the present invention.

[0051] Please refer to Figure 1 , which shows a flowchart of a method for adaptive frequency compensation of an intelligent security and energy-saving terminal using information perception provided by an embodiment of the present invention. The method includes:

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

[0053] In the embodiment of the present invention, relevant power sensors, such as Hall sensors or current-voltage sensors, are first installed at the input end of the intelligent security and energy-saving terminal device, and then the sensors are used to collect the power data of the intelligent security and energy-saving terminal device at each moment within a preset historical time period. The power data can be current data, voltage data, electric power data, etc. In an embodiment of the present invention, the collection of current data is taken as an example. Among them, 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 herein.

[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 one of the data segments as the target data segment, and obtain the fluctuation degree of the target data segment according to the fluctuations of the power data at each moment in the target data segment; Obtain the frequency compensation amplitude of the target data segment according to the deviation of the fluctuation degree of the target data segment from the overall level of the fluctuation degrees of all data segments; Obtain the frequency compensation efficiency value of the target data segment according to the differences in the frequency compensation amplitudes and the differences in the fluctuation degrees between the target data segment and the adjacent data segments; Cluster all data segments based on the differences in the frequency compensation amplitudes and the differences in the frequency compensation efficiency values between the data segments to obtain multiple clusters, where one of the multiple clusters is a safety reference cluster.

[0055] The intelligent safety and energy-saving terminal aims to save energy, extend the equipment life, and improve the equipment performance by stabilizing the voltage and reducing the current fluctuation when the power equipment is operating. The working frequency of the terminal determines the efficiency and stability of the equipment energy conversion. In actual operation, the power equipment terminal is often affected by dynamic changes such as sudden increase or decrease in load or external disturbances, resulting in unstable working frequency. Therefore, it is necessary to automatically adjust the compensation signal according to the real-time load change to ensure that the output quality of the terminal is stably maintained within the target range.

[0056] To improve the accuracy and efficiency of frequency compensation for the intelligent safety and energy-saving terminal equipment, adapt to the dynamic characteristics of the load and the changes of external disturbances, and ensure that the terminal equipment can achieve the best performance under different working 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, and then can analyze different data segments to more precisely adjust the frequency compensation strategy to adapt to the operating states of different time periods.

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

[0058] Perform Fourier transform processing on the power data at all moments within the preset historical time period to obtain a spectrogram, and analyze the main frequency components in the spectrogram to obtain the operating period of the terminal equipment. The operating period can also be considered as the period of power data change. Then, segment the power data within the preset historical time period according to the operating period to obtain multiple data segments within the preset historical time period. The duration of the target data segment is equal to the operating period. It should be noted that using Fourier transform to identify the main frequency components and obtain the period of the data is a well-known technical means for those skilled in the art and will not be elaborated here.

[0059] The purpose of the intelligent safety 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 are external disturbances, the power data will show large fluctuations. At this time, to ensure stable output quality, frequency compensation is required. At the same time, the fluctuation conditions of the power data in different data segments are different. Therefore, any data segment can be analyzed first. Any data segment is used as the target data segment, and the fluctuation conditions of the power data at each moment in the target data segment are analyzed. The degree of fluctuation of the power data in the target data segment is reflected by the obtained degree of fluctuation. Subsequently, for data segments with different degrees of fluctuation, the compensation amplitude for the operating frequency of the terminal device can be adaptively adjusted to avoid excessive frequency adjustment.

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

[0061] Any two adjacent moments in the target data segment are used as an adjacent moment group. The absolute value of the difference between the power data of the two moments in each adjacent moment group is used as the data change amount of each adjacent moment group. The larger the data change amount, the greater the degree of fluctuation of the power data between the two adjacent moments. Then, the average value of the data change amounts of all adjacent moment groups is used as the first fluctuation parameter of the target data segment.

[0062] Analyze the degree of dispersion of the power data of all moments in the target data segment to obtain the second fluctuation parameter of the target data segment.

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

[0064] Furthermore, the first fluctuation parameter and the second fluctuation parameter are synthesized to obtain the degree of fluctuation of the target data segment.

[0065] In an embodiment of the present invention, the sum value or product value of the first fluctuation parameter and the second fluctuation parameter can be used as the degree of fluctuation of the target data segment to achieve the synthesis of the two, which is not limited herein.

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

[0067]

[0068] where A represents the degree of fluctuation of the target data segment; a max represents the maximum value of the power data of all moments in the target data segment; a minrepresents the minimum value of the power data at all moments in the target data segment; a max -a min represents the range of the power data at all moments in the target data segment, that is, the second fluctuation parameter of the target data segment; a (1,m) and a (2,m) respectively represent the power data at two moments in the m-th adjacent moment group; |a (1,m) -a (2,m) | represents the data change amount of the m-th adjacent moment group; M represents the number of adjacent moment groups in the target data segment; represents the first fluctuation parameter of the target data segment.

[0069] By the same method as above, the fluctuation degree of each data segment can be obtained. During the operation of the terminal device, when the fluctuation degree of the target data segment is larger and the deviation of the fluctuation degree of the target data segment from the overall level of the fluctuation degrees of all data segments is larger, it indicates that a load mutation may occur in the power system in the target data segment. At this time, it is necessary to compensate the frequency of the terminal device with a larger compensation amplitude to ensure the stable operation of the terminal. Therefore, the deviation of the fluctuation degree of the target data segment from the overall level of the fluctuation degrees of all data segments can be analyzed to obtain the frequency compensation amplitude of the target data segment. Subsequently, based on the frequency compensation amplitudes of each data segment, clustering analysis can be performed on each data segment, and the operating state of the terminal device in each data segment can be analyzed, so as to realize the compensation adjustment of the operating frequency of the terminal device.

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

[0071] Taking the average value of the fluctuation degrees of all data segments as the overall fluctuation degree of the preset historical time period, and reflecting the overall level of the fluctuation degrees of all data segments through the overall fluctuation degree.

[0072] Taking the absolute value of the difference between the fluctuation degree of the target data segment and the overall fluctuation degree 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 the fluctuation degrees of all data segments, and further the greater the amplitude of the frequency compensation required for the terminal device in the target data segment.

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

[0074] In an embodiment of the present invention, the sum value or product value 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 the integration of the two, and no limitation is made here.

[0075] As an example, in an embodiment of the present invention, the expression of the frequency compensation amplitude of the target data segment can be specifically, for example:

[0076]

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

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

[0079] During the operation of the intelligent safety and energy-saving terminal, if there is a load fluctuation, frequency compensation is required to make the terminal output stable. The mutation degree of 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 faster compensation. In order to achieve fast transient response, the control loop adjustment of the terminal will be more sensitive, making the frequency compensation strategy unable to adapt in time, resulting in short-term instability or oscillation. To avoid excessive adjustment of frequency compensation, in an embodiment of the present invention, first, the differences in the frequency compensation amplitude and the fluctuation degree between the target data segment and the adjacent data segment are analyzed to obtain the frequency compensation efficiency value of the target data segment, so as to evaluate the compensation result based on the frequency compensation efficiency value, which is convenient for subsequent adjustment of the frequency compensation strategy. And subsequently, the frequency compensation efficiency value and the frequency compensation amplitude of each data segment can be combined to cluster the data segments, so as to analyze the operating state of the terminal device in each data segment.

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

[0081] Taking the absolute value of the difference in the frequency compensation amplitude between the target data segment and the adjacent next data segment as the first efficiency value of the target data segment; taking the absolute value of the difference in the fluctuation degree between the target data segment and the adjacent previous data segment as the second efficiency value of the target data segment. The larger the first efficiency value and the second efficiency value, the better the frequency compensation effect and the faster the efficiency of the target data segment. Therefore, the first efficiency value and the second efficiency value can be integrated to obtain the frequency compensation efficiency value of the target data segment.

[0082] In an embodiment of the present invention, the sum value or product value of the first efficiency value and the second efficiency value can be used as the frequency compensation efficiency value of the target data segment to achieve the integration of the two, and no limitation is made 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 at this time, 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 at this time, 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 an embodiment of the present invention, the expression of the frequency compensation efficiency value of the target data segment can be specifically, for example:

[0085] E = E 1 × E 2

[0086] Wherein, E represents the frequency compensation efficiency value of the target data segment; E 1 represents the first efficiency value of the target data segment; E 2 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 above same method.

[0088] Different load states have different compensation requirements. Under light load conditions, the terminal has a higher demand for frequency compensation efficiency, while under heavy load conditions, more attention is paid to stability and fast response. Cluster analysis helps to classify the complex load dynamic characteristics into limited terminal operating states, so as to design targeted frequency compensation strategies. Therefore, in the embodiment of the present invention, based on the differences in the frequency compensation amplitude and the frequency compensation efficiency value between each data segment, all data segments are clustered to obtain multiple clustering clusters, and a safe reference clustering cluster is selected from the multiple clustering clusters. Subsequently, based on the frequency compensation efficiency values of the data segments in the safe reference clustering 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 an embodiment of the present invention, the method for obtaining multiple clustering clusters specifically includes:

[0090] First, the absolute value of the difference in frequency compensation amplitudes 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 clustering clusters. The average value of the frequency compensation amplitudes of all data segments in each first clustering cluster is used as the first clustering center value of each first clustering cluster. In one embodiment of the present invention, the number of first clustering clusters is set to three, corresponding to clusters with three load levels. The larger the first clustering center value, the higher the load level in the data segments in the first clustering cluster. In other embodiments of the present invention, the specific number of first clustering clusters may 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 between the frequency compensation efficiency values ​​of 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 may 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 may 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 of each data segment in the cluster where the target data segment is located and the overshoot risk degree, and 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 compensation amount of the frequency is related to the output fluctuation of the corresponding data segment. The more the compensation amount of the frequency, the faster the output reaches stability within a short period of time, and the better the frequency compensation efficiency. However, if the degree of load mutation is large and there is overshoot in frequency compensation, the compensation amount of the frequency is large, but the output of the terminal device cannot stabilize quickly within a short period of time, but instead shows greater fluctuations, and the frequency compensation efficiency is poor. Therefore, the overshoot risk degree of the target data segment can be obtained according to the deviation of the frequency compensation efficiency value of the target data segment from the overall level of the frequency compensation efficiency values of all data segments in the safety reference clustering cluster, providing a data basis for subsequent analysis of the operating state of the terminal device in each data segment.

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

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

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

[0097]

[0098] Wherein, G represents the overshoot risk degree of the target data segment; E represents the frequency compensation efficiency value of the target data segment; represents the second clustering center value of the safety reference clustering cluster.

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

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

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

[0102] The sequence formed by the frequency compensation amplitudes of all data segments in the cluster where the target data segment is located is used as the first sequence, and the sequence formed by the 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 greater the frequency compensation amplitude of the target data segment, the more likely it is that the frequency compensation of the target data segment will have an overshoot risk. Therefore, 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 can be normalized, and the calculation result is limited within the range of [0, 1], so as to obtain the device operating state value of the target data segment.

[0104] In one embodiment of the present invention, the normalization process can be, for example, the maximum-minimum normalization process. In other embodiments of the present invention, other normalization methods can be selected according to the specific range of values, which will not be elaborated here.

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

[0106] U = norm(|ρ| × D)

[0107] Wherein, U represents the device operating state 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; norm() represents the normalization function.

[0108] The device operating state values 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 state, 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, in the embodiment of the present invention, according to the operating state value of the target data segment and the power data at each moment, the objective function value of the target data segment is obtained. Subsequently, the model predictive control algorithm can be used, and based on the calculated objective function values of each data segment, the working 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] Among them, J represents the objective function value of the target data segment; a n represents the power data at the nth moment in the target data segment; a ′ represents the standard power data of the expected output, which is determined by the actual power system where the intelligent safety and energy-saving terminal device is located; U represents the device operating state 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 represents the deviation between the actual power data and the expected standard power data, which 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, ensure that the output value of the terminal under steady state is as close as possible to the expected value, and thus ensure the steady-state accuracy. U×PB n represents restricting the magnitude of the frequency compensation amount through the device operating state value to ensure that the system does not over-adjust the frequency compensation to avoid overshoot or oscillation. The device operating state 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 values of each data segment, perform real-time compensation on the operating frequency of the terminal device.

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

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

[0119] Input the maximum value of the objective function values of all data segments and the real-time power data of the terminal device 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 control unit performs real-time compensation control on the operating frequency of the terminal device.

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

[0121] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. The key point of each embodiment is to illustrate the differences from other embodiments.

Claims

1. An adaptive frequency compensation method for an intelligent, safe and energy-saving terminal using information perception, characterized in that: The method comprises: Obtain the 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 according to 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 according to 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 according to the difference in the frequency compensation amplitude and the difference in the degree of fluctuation between the target data segment and the adjacent data segments; based on the difference in the frequency compensation amplitude and the difference in the frequency compensation efficiency value between the data segments, all data segments are clustered to obtain multiple cluster clusters, wherein the multiple cluster clusters include a safety reference cluster cluster; According to 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, the overshoot risk degree of the target data segment is obtained; according to 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, and the frequency compensation amplitude of the target data segment, the equipment operation status value of the target data segment is obtained; according to the operation status value of the target data segment and the power data at each moment, the objective function value of the target data segment is obtained; 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, safe 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 in a preset historical time period to obtain a spectrum diagram, and analyzing the main frequency components in the spectrum diagram to obtain the operation cycle of the terminal equipment; The power data within a 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, safe 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 at 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 integrated to obtain the fluctuation degree of the target data segment.

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

7. The method for adaptive frequency compensation of an intelligent, safe 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 by: 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 cluster is used as the overshoot risk degree of the target data segment.

8. The method for adaptive frequency compensation of an intelligent, safe and energy-saving terminal using information perception according to claim 1, characterized in that: The obtaining of the device operation status value of the target data segment comprises: 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 degrees of all data segments in the cluster where the target data segment is located as a second sequence; 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 are normalized to obtain the device operation status value of the target data segment.

9. The method for adaptive frequency compensation of an intelligent, safe and energy-saving terminal using information perception according to claim 1, characterized in that: The objective function value of obtaining the target data segment comprises: Based on the calculation formula of the objective function value, the objective function value of the target data segment is obtained, and the calculation formula of the objective function value is: Where J represents the objective function value of the target data segment; a n represents the power data at the nth moment in the target data segment; a ′ Indicates the standard power data of the expected output; 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, safe 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 control unit performs real-time compensation control on the operating frequency of the terminal device.

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