CN118170004A - Control method and system based on Internet of things - Google Patents
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Abstract
The invention relates to the technical field of PID control and regulation, in particular to a control method and system based on the Internet of things. According to the method, the change distribution correlation among the partial change segments of the voltage time sequence data is analyzed through each PID control, the correlation between the voltage time sequence data and the environment time sequence data is combined, and the steady-state error index is determined by combining the deviation of the output voltage after each PID control and the expected voltage; and obtaining an adjusting integral gain value of the current PID control to carry out PID control on the voltage time sequence data according to the trend of all steady state error indexes before the current PID control. According to the invention, the stability condition and the environmental influence of the voltage data in the control and regulation process are comprehensively analyzed, the parameters of the current PID control are adaptively regulated by combining the history control and regulation condition, and the regulation and control efficiency is improved, so that the control performance of the power system is more stable.
Description
Technical Field
The invention relates to the technical field of PID control and regulation, in particular to a control method and system based on the Internet of things.
Background
The internet of things is a network technology for connecting various objects in the physical world with a network through a contract protocol to realize information interaction and remote control, the core and the foundation are the internet, and the internet is used for extending to integrate the objects in the physical world with the information in the virtual world, so that intelligent management and control are realized. The internet of things technology is also beneficial to integrating sustainable energy sources, such as a renewable energy source power system, a power supply system, a power distribution system and an electric energy storage system which are used as core components of the power system, and has important significance for guaranteeing power supply and improving energy utilization efficiency.
The PID controller is a common controller, can control in the power system to ensure the stable operation of the power system, and has the characteristics of simple structure, easy realization and the like. An electrical power system is typically composed of a plurality of subsystems and devices, involving a plurality of variables and parameters, which complicates the process of designing and adjusting the parameters of a PID controller, and during this control, the electrical power system is time-stationary, i.e. the response time of the system may vary over time, due to the dynamic changing characteristics of the electrical power system. Therefore, when the traditional PID controller processes the problems of dynamic change data and time lag, the problems of excessive regulation, long regulation time and the like easily occur, and the control performance of the power system is reduced or unstable.
Disclosure of Invention
In order to solve the technical problems of reduced control performance or instability of a power system caused by excessive adjustment, longer adjustment time and the like when a PID controller processes dynamic change data in time and stagnation in the prior art, the invention aims to provide a control method and a control system based on the Internet of things, and the adopted technical scheme is as follows:
The invention provides a control method based on the Internet of things, which comprises the following steps:
Acquiring voltage time sequence data before each PID control, different types of environment time sequence data and output voltage after each PID control in power operation; dividing voltage time sequence data before PID control into more than two local variation sections;
In the voltage time sequence data before each PID control, according to the numerical distribution change difference condition and the data distribution association condition between every two local change segments, obtaining a system stability index of each PID control; in the voltage time sequence data before each PID control, according to the change relativity between each type of environment time sequence data and the voltage time sequence data, obtaining the environment influence index of each control process;
Obtaining a steady-state error index of each PID control according to the deviation degree of the output voltage after each PID control and a preset voltage expected value, and an environmental impact index and a system stability index of each PID control; obtaining an adjusting integral gain value of the current PID control according to the change trend degree of steady-state error indexes of all PID controls before the current PID control on time sequence; and PID control is carried out on the voltage time sequence data before the current PID control based on the regulated integral gain value.
Further, in the voltage time sequence data before each PID control, according to the numerical distribution change difference condition and the data distribution association condition between every two local change segments, the system stability index of each PID control is obtained, including:
obtaining a change characteristic value of each local change section according to the distribution change degree of the numerical value in each local change section;
The voltage time sequence data in each local variation section are arranged according to a time sequence order to obtain a local sequence; taking any two different local variation sections in the voltage time sequence data before PID control each time as a time period group, and taking the voltage time sequence data with the same serial number in two local sequences in the time period group as a data comparison group for any one time period group;
obtaining an approximate index of the time period group according to the difference between the distribution conditions of the voltage time sequence data of each data comparison group in the time period group in the local change section and the difference of the change characteristic values of the local change sections of the time period group;
And taking the average value of the approximate indexes of all time period groups in the voltage time sequence data before each PID control as a system stability index of each PID control.
Further, the obtaining the variation characteristic value of each local variation section according to the distribution variation degree of the numerical value in each local variation section includes:
For any local change section, calculating the numerical value difference between every two adjacent voltage time sequence data in the local change section, and solving the average value to obtain the numerical value difference index of the local change section;
acquiring the slope of each voltage time sequence data in the local variation section; calculating the difference between every two adjacent slopes in the local variation section, and averaging to obtain a variation difference index of the local variation section;
Obtaining a change characteristic value of the local change section according to the numerical value difference index and the change difference index in the local change section; the numerical difference index and the variation difference index are positively correlated with the variation characteristic index.
Further, the expression of the approximation index is:
; in the/> Expressed as/>First/>, before secondary PID controlLocal variation segment and/>An approximation index of a time period group consisting of the partial change segments; /(I)Expressed as/>First/>, before secondary PID controlA change characteristic value of each local change segment; /(I)Expressed as/>First/>, before secondary PID controlA change characteristic value of each local change segment; /(I)Expressed as/>First/>, before secondary PID controlLocal variation segment and/>Total number of data control groups in a time period group consisting of the partial change segments; /(I)Expressed as at/>First/>, before secondary PID controlLocal variation segment and/>The/>, of a time period group consisting of partial change segmentsIn the data comparison group, correspond to the first/>The occurrence frequency of the voltage time sequence data of each local variation section in the local variation section; /(I)Expressed as at/>First/>, before secondary PID controlLocal variation segment and/>The/>, of a time period group consisting of partial change segmentsIn the data comparison group, correspond to the first/>The occurrence frequency of the voltage time sequence data of each local variation section in the local variation section; /(I)Represented as a logarithmic function with a base of a natural constant; /(I)Expressed as an absolute value extraction function; /(I)Represented as a logarithmic function with a base of a natural constant; wherein/>。
Further, the method for acquiring the environmental impact index comprises the following steps:
sequentially taking each type of environment time sequence data as reference environment data; sequentially taking voltage time sequence data before each PID control as reference voltage data;
Obtaining the environment deviation correlation degree of the reference voltage data and the reference environment according to the difference change degree between the reference environment data and the reference voltage data; calculating the pearson correlation coefficient of the reference environment data and the reference voltage data to take absolute values as the environment change correlation degree of the reference voltage data and the reference environment data;
According to the environmental deviation correlation degree and the environmental change correlation degree of the reference voltage data and the reference environmental data, acquiring environmental correlation indexes of the reference voltage data and the reference environmental data, wherein the environmental deviation correlation degree and the environmental correlation indexes are in negative correlation, and the environmental change correlation degree and the environmental correlation indexes are in positive correlation; the environment related index is a normalized value;
and calculating the average value of the relevant indexes of the reference voltage data corresponding to all types of environment time sequence data, and obtaining the environment influence index of the reference voltage data.
Further, the obtaining the correlation of the environmental deviation between the reference voltage data and the reference environment according to the degree of variation of the difference between the reference environment data and the reference voltage data includes:
Normalizing the reference environment data and the reference voltage to obtain standard environment data and standard voltage data; taking the difference between standard environment data and standard voltage data at the same position on a time sequence as difference time sequence data of reference environment data and reference voltage data;
And obtaining a differential sequence of the differential time sequence data on a time sequence, and calculating the accumulated sum of all values in the differential sequence to obtain the environment deviation correlation degree of the reference voltage data and the reference environment.
Further, the method for obtaining the steady-state error index comprises the following steps:
For any PID control, calculating the difference between the output voltage at each time sequence position after the PID control and the expected value of the preset voltage, and solving the accumulated value to obtain the expected deviation degree of the PID control;
calculating the product of the environmental impact index and the system stability index of the PID control, performing negative correlation mapping and normalization processing to obtain an error adjustment coefficient of the PID control;
And taking the product of the expected deviation degree of the PID control and the error adjustment coefficient as a steady-state error index of the PID control.
Further, the method for acquiring the adjusted integral gain value comprises the following steps:
The steady-state error indexes of all PID controls before the current PID control are arranged according to the time sequence order, so as to obtain the time sequence error sequence of the current PID control; calculating the pearson correlation coefficient of the time sequence error sequence and the time sequence to obtain a trend index of the current PID control;
Calculating the difference between the trend index of the current PID control and the numerical value-1 to be used as a gain adjustment coefficient of the current PID control; taking the product of the gain adjustment coefficient of the current PID control and the integral gain value of the last PID control as the adjustment quantity of the current PID control; and taking the sum value of the integral gain value of the last PID control and the adjustment quantity of the current PID control as an adjustment integral gain value.
Further, the PID control on the voltage time sequence data before the current PID control based on the adjusted integral gain value comprises the following steps:
And inputting the regulated integral gain value, the preset proportional gain value and the preset differential gain value of the current PID control into the current PID controller, performing PID control on the voltage time sequence data before the current PID control, and outputting voltage.
The invention provides a control system based on the Internet of things, which comprises a memory and a processor, wherein the processor executes a computing program stored in the memory to realize the control method based on the Internet of things.
The invention has the following beneficial effects:
According to the invention, the characteristic of dynamic fluctuation change of voltage data is considered, so that the controller needs to adjust for a plurality of times to overcome resistance in the control process, and voltage time sequence data and environment time sequence data before PID control are obtained for operation state analysis. Firstly, analyzing the stable state of the data before each control, analyzing the change characteristics and distribution correlation between every two local change segments in the voltage time sequence data before each PID control, obtaining a system stability index, and evaluating the stable condition of the data in the system before each control according to the fluctuation similarity condition so as to analyze the error possible condition in the control process. The environmental impact index is obtained through the association of the voltage time sequence data and the environment time sequence data before each PID control, and the judgment influence caused by the environment correlation is considered in error analysis, so that the subsequent error judgment required by adjustment is more accurate. The steady-state error index of each PID control is comprehensively determined by combining the deviation of the output voltage and the expected voltage after each PID control, the system stability index and the environmental impact index, and the steady-state error elimination process of the integral gain in the PID controller has a trend of being close to expectations, so that the adjustment integral gain value of the current PID control is obtained according to the change trend of all steady-state error indexes before the current PID control, and the adjustment of the control and adjustment process is accelerated while the joint change is carried out. PID control is performed based on adjusting the integral gain value. According to the invention, through comprehensively analyzing the stability condition and the environmental influence of the voltage data in the PID control and regulation process, the current PID control parameters are adaptively regulated by combining the historical control and regulation conditions, the regulation and control efficiency is improved, and the control performance of the electric power system is more accurate and stable.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a control method based on the internet of things according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a PID controller according to an embodiment of the invention;
FIG. 3 is a schematic diagram of voltage timing data according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a voltage timing data before and after PID control according to an embodiment of the present invention;
Fig. 5 is a schematic diagram of the front-back of voltage timing data after multiple PID control according to an embodiment of the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following detailed description is given below of a control method and system based on the internet of things according to the invention, which are provided by combining the accompanying drawings and the preferred embodiment. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
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.
The invention provides a control method and a control system based on the Internet of things.
Referring to fig. 1, a flowchart of a control method based on the internet of things according to an embodiment of the present invention is shown, and the method includes the following steps:
S1: acquiring voltage time sequence data and different types of environment time sequence data in each control process in power operation; the voltage timing data is divided into two or more locally varying segments.
In the embodiment of the invention, the voltage time sequence data and the current time sequence data in the power system are monitored in real time by using the sensors, the voltage time sequence data and the current time sequence data can be used for identifying the running state and the load condition of the system, feedback information is provided for the controller, and meanwhile, various sensors including but not limited to a voltage sensor, a current sensor, a humidity sensor, a rotating speed sensor, a temperature sensor and the like are deployed in the power system, and can directly measure or sense various parameters in the power system, wherein the parameters are expressed as different types of environment time sequence data, and the acquisition frequency of the sensors is consistent for conveniently analyzing the running condition of the system data.
The data collected by the sensor is transmitted to the central control system or the cloud platform by using the internet of things technology, and the communication technology comprises Wi-Fi, bluetooth, loRa or NB-loT and the like, and the method is not limited herein. The timing data received by the control system is controlled to reach a desired threshold value, so as to ensure the stability of the system, and a common controller is a PID controller, please refer to fig. 2, which shows a schematic diagram of a PID controller according to an embodiment of the present invention, where main parameters of the PID controller include a proportional gain, an integral gain and a differential gain, and the proportional gain provides feedback control through a linear relationship between a measurement error and an output control signal, so that the influence of the system is faster and approaches to the desired value. The integral gain corrects the static error of the system by accumulating the past error, thereby eliminating the steady-state error, ensuring that the system reaches the expected value, and the differential gain inhibits the oscillation and overshoot of the system by monitoring the rate of error change, thereby improving the stability of the system. The data after proportional adjustment, integral adjustment and differential adjustment are detected in the executing mechanism, and the control result is fed back by the measuring element to carry out the adjustment control process or realize final output.
In the embodiment of the present invention, the voltage timing data is analyzed by a control and adjustment process, please refer to fig. 3, which shows a schematic diagram of the voltage timing data according to an embodiment of the present invention. The voltage time sequence data is obtained after each PID control in order to improve the efficiency of the control and regulation process, namely, the data after each proportional adjustment, integral adjustment and differential adjustment is used as the output voltage after each PID control. In order to analyze the stability of data, the voltage time sequence data before each PID control is divided into more than two local variation segments, in the embodiment of the present invention, the voltage time sequence data before each PID control is uniformly divided into a preset number of local variation segments, the preset number is greater than 1, the preset number is set to be 50, and a specific numerical value implementation person can adjust according to specific implementation conditions, which is not described herein.
S2: in the voltage time sequence data before each PID control, according to the numerical distribution change difference condition and the data distribution association condition between every two local change segments, obtaining a system stability index of each PID control; and in the voltage time sequence data before each PID control, according to the change correlation between each type of environment time sequence data and the voltage time sequence data, obtaining the environment influence index of each control process.
The PID control system has the advantages that steady-state errors possibly existing in each PID control are analyzed through the divided local change segments, the difference between the voltage value after the control output of the controller and the expected voltage is regarded as a static error in the running process, after the control is carried out for a plurality of times, all errors are accumulated to obtain the steady-state errors in the running process, and the response of the control system is more timely and the resistance in the control process can be better overcome through adjusting the integral gain value of the controller, so that the output result reaches the expected value more quickly.
And analyzing the stable condition of the voltage time sequence data and the interference condition influenced by the environment to obtain the possible error condition of the voltage time sequence data. Referring to fig. 4, schematic diagrams of voltage timing data before and after one PID control according to an embodiment of the present invention are shown, T0 is the voltage timing data before the PID control, F is a preset voltage expected value, which is the voltage expected to be achieved during system regulation, and T1 is the output voltage after the PID control, where a higher steady-state error still exists. And firstly, analyzing the stability of the voltage time sequence data, and obtaining a system stability index of each PID control according to the numerical distribution change difference condition and the data distribution association condition between every two local change segments in the voltage time sequence data before each PID control.
Preferably, according to the distribution change degree of the numerical value in each local change section, a change characteristic value of each local change section is obtained to reflect the fluctuation condition of each local change section. The slope of each voltage time sequence data in the local change section is obtained, the difference between every two adjacent slopes in the local change section is calculated, the change difference index of the local change section is obtained by averaging, and the overall change confusion degree of the data in the local change section is reflected through the overall difference between the slopes. And obtaining a change characteristic value of the local change section according to the numerical value difference index and the change difference index in the local change section, and comprehensively reflecting the change characteristic from the numerical value change degree and the change disorder condition. The numerical difference index and the variation difference index are positively correlated with the variation characteristic index, and in the embodiment of the invention, the expression of the variation characteristic value is:
In the method, in the process of the invention, Expressed as/>First/>, before secondary PID controlChange characteristic value of each local change segment,/>Denoted as the firstFirst/>, before secondary PID controlTotal number of voltage timing data in each local variation segment,/>Expressed as/>The/>, in the local variation sectionNumerical value of the individual voltage sequence data,/>Expressed as/>The/>, in the local variation sectionNumerical value of the individual voltage sequence data,/>Expressed as/>The/>, in the local variation sectionSlope of the individual voltage timing data,/>Expressed as/>The/>, in the local variation sectionSlope of the individual voltage timing data,/>Represented as an absolute value extraction function.
Wherein,Expressed as/>The/>, in the local variation sectionTime series data of voltage and the/>Numerical difference between the voltage sequence data,/>Expressed as/>The/>, in the local variation sectionTime series data of voltage and the/>Slope difference between the voltage time series data,/>Expressed as/>Numerical difference index of each local variation segment,/>Expressed as/>The larger the numerical difference index is, the larger the variation difference index is, which indicates that the variation condition in the corresponding local variation section is more complex, the larger the variation characteristic value is, the numerical difference index and the variation difference index are both positively correlated with the variation characteristic index through the product form, in other embodiments of the invention, other basic mathematical operations can be adopted to reflect that the numerical difference index and the variation difference index are positively correlated with the variation characteristic index, such as addition, etc., without limitation.
After the analysis of each local variation section is completed, the stability of the data before each PID control can be analyzed according to the variation correlation degree among the local variation sections, and further, the voltage time sequence data in each local variation section is arranged according to the time sequence order to obtain a local sequence, namely, the sequence number of the voltage time sequence data in each local variation section is marked. And taking any two different local variation sections in the voltage time sequence data before each PID control as a time period group, and analyzing every two local variation sections of the voltage time sequence data before single PID control. And for any time period group, taking the voltage time sequence data with the same serial number in two partial sequences in the time period group as a data comparison group, and analyzing the partial change sections according to the data relative position distribution. For example, when the period groups are {2,3,6,4,5} and {7,6,4,5,6}, the data comparison group is {2,7} {3,6} {6,4} {4,5} {5,6}, and the partial variation segments are uniformly divided, that is, the number of voltage timing data between the partial variation segments is uniform, so that a uniform data comparison group can be formed.
According to the difference between the distribution conditions of the voltage time sequence data of each data comparison group in the time period group in the local change section and the difference of the change characteristic values among the local change sections of the time period group, obtaining an approximate index of the time period group, wherein the approximate index reflects the similarity stability degree among the local change sections, and the approximate index has the following expression:
In the method, in the process of the invention, Expressed as/>First/>, before secondary PID controlLocal variation segment and/>An approximation index of a time period group consisting of the partial change segments; /(I)Expressed as/>First/>, before secondary PID controlA change characteristic value of each local change segment; /(I)Expressed as/>First/>, before secondary PID controlA change characteristic value of each local change segment; /(I)Expressed as/>First/>, before secondary PID controlLocal variation segment and/>Total number of data control groups in a time period group consisting of the partial change segments; /(I)Expressed as at/>First/>, before secondary PID controlLocal variation segment and/>The/>, of a time period group consisting of partial change segmentsIn the data comparison group, correspond to the first/>The occurrence frequency of the voltage time sequence data of each local variation section in the local variation section; /(I)Expressed as at/>First/>, before secondary PID controlLocal variation segment and/>The/>, of a time period group consisting of partial change segmentsIn the data comparison group, correspond to the first/>The occurrence frequency of the voltage time sequence data of each local variation section in the local variation section; /(I)Represented as a logarithmic function with a base of a natural constant; /(I)Expressed as an absolute value extraction function; /(I)Represented as a logarithmic function with a base of a natural constant; wherein/>。
Wherein,Reflect the/>Local variation segment and/>The difference condition of the local variation sections on the variation characteristic value indicates that the two local variation sections have obvious difference on fluctuation variation when the variation characteristic value is large, the approximation degree is low, and the stability reflected from the local variation sections is poor. /(I)Expressed as the relative magnitude between the distribution probabilities, i.e. the occurrence frequencies, reflecting the/>The likelihood of simultaneous occurrence of data in the individual data control groups also reflects the/>The data in the data comparison group are similar in information quantity, stability is analyzed from the relevance of the local change sections on the numerical value distribution, and when the value is smaller, the information quantity provided by the two local change sections on the whole numerical value relevance distribution is closer, so that the stability of the system is better. Thus byNegative correlation and normalization are carried out in the form of negative index, when/>And/>The smaller the variation characteristic difference between the two local variation sections is, the closer the information quantity distribution is, the higher the stability of the data is represented, and the larger the approximate index is.
In other embodiments of the present invention, the association of data distribution may be reflected by using a form of joint entropy, for any period group, the joint entropy of each data comparison group in the period group is calculated, and the accumulated value of the joint entropy of all the data comparison groups is used as a distribution association index of the period group, to reflect the distribution association degree between local segments, where the smaller the distribution association index, the higher the approximation degree on the numerical distribution association is illustrated. And taking the difference between the ratio of the change characteristic values of the two local change sections of the time period group and the numerical value 1 as a change related index of the time period group to reflect the similarity degree of the change characteristic values, and when the change related index is smaller, indicating that the similarity degree on the numerical value change characteristic is high. And obtaining the approximate index of the time period group according to the distribution association index and the change association index of the time period group, wherein the distribution association index and the change association index are in negative correlation with the approximate index.
And finally, integrating integral approximate index analysis in single PID control, taking the average value of the approximate indexes of all time period groups in the voltage time sequence data before each PID control as the system stability index of each PID control, and comprehensively reflecting the stability of the data of each PID control.
And secondly, adjusting from external environment factors, wherein in the operation process of the power equipment, data change possibly caused by environmental influence exists, the change is not an error generated in the operation process and cannot be used as a reference for error analysis, so that the analysis of the environmental influence is necessary, and in the voltage time sequence data before each PID control, the environmental influence index of each control process is obtained according to the change correlation between each type of environmental time sequence data and the voltage time sequence data.
Preferably, each type of environmental time series data is sequentially used as reference environmental data, the voltage time series data before each PID control is sequentially used as reference voltage data, and the voltage time series data of the single PID control and each type of environmental time series data are analyzed one by one. According to the difference change degree between the reference environment data and the reference voltage data, the environment deviation correlation degree between the reference voltage data and the reference environment is obtained, and the change deviation degree between the reference environment data and the reference environment is reflected. It should be noted that the standardization is a technical means well known to those skilled in the art, and is not described herein. And taking the difference between the standard environment data and the standard voltage data at the same position on the time sequence as difference time sequence data of the reference environment data and the reference voltage data, wherein the standardized difference reflects the distribution difference condition between time sequence values. And obtaining a differential sequence of the differential time sequence data on a time sequence, calculating the accumulation sum of all values in the differential sequence, obtaining the environment deviation correlation degree of the reference voltage data and the reference environment, reflecting the change of the distribution difference through the accumulation sum of the values in the differential sequence, and indicating that the reference voltage data and the reference environment data have a correlated change relation on the time sequence when the environment deviation correlation degree is smaller.
And calculating the pearson correlation coefficient of the reference environment data and the reference voltage data to obtain absolute values, wherein the absolute values are used as the environment change correlation degree of the reference voltage data and the reference environment data, and the associated change influence of the reference environment data and the reference voltage data is reflected through the pearson correlation coefficient. It should be noted that, the pearson correlation coefficient and the differential sequence are all technical means well known to those skilled in the art, and are not described herein.
Finally, according to the environment deviation correlation degree and the environment change correlation degree of the reference voltage data and the reference environment data, environment correlation indexes of the reference voltage data and the reference environment data are obtained, and the influence degree of the environment on the voltage is reflected. The environment deviation correlation degree and the environment correlation index are in negative correlation, the environment change correlation degree and the environment correlation index are in positive correlation, and the environment correlation index is a normalized value; in the embodiment of the invention, the expression of the environment-related index is:
In the method, in the process of the invention, Expressed as/>Voltage sequence data and the/>, before secondary PID controlEnvironmental correlation between types of environmental time series data,/>Expressed as/>Voltage sequence data and the/>, before secondary PID controlPearson correlation coefficient between types of environmental time series data,/>Expressed as/>Total number of voltage timing data before secondary PID control,/>Expressed as/>Voltage sequence data and the/>, before secondary PID controlType of environmental time series data/>Time series data of difference value,/>Expressed as/>Voltage sequence data and the/>, before secondary PID controlType of environmental time series data/>Time series data of difference value,/>Expressed as an absolute value extraction function,/>Expressed as a preset adjustment coefficient, is set to 0.001 in the embodiment of the present invention, in order to prevent the case where the denominator is zero to make the formula meaningless. /(I)It should be noted that, normalization is a technical means well known to those skilled in the art, and the normalization function may be selected by linear normalization or standard normalization, and the specific normalization method is not limited herein.
Wherein,Expressed as/>Voltage sequence data and the/>, before secondary PID controlEnvironmental change correlation between types of environmental time series data,/>Expressed as a single value in a differential sequence,/>Expressed as/>Voltage sequence data and the/>, before secondary PID controlThe environmental deviation correlation degree between the types of environmental time sequence data indicates that the environmental time sequence data and the voltage time sequence data have a more obvious correlation change relation when the environmental deviation correlation degree is smaller and the environmental change correlation degree is larger, and the environmental correlation index is larger.
And finally, obtaining an influence condition by combining comprehensive correlation conditions of the reference voltage data and all types of environment time sequence data, calculating an average value of correlation indexes of the reference voltage data corresponding to all types of environment time sequence data, and obtaining an environment influence index of the reference voltage data.
So far, the system stability index and the environment influence index of each PID control are obtained from the voltage time sequence data of each PID control and the related condition with the environment.
S3: obtaining a steady-state error index of each PID control according to the deviation degree of the output voltage after each PID control and a preset voltage expected value, and an environmental impact index and a system stability index of each PID control; obtaining an adjusting integral gain value of the current PID control according to the change trend degree of steady-state error indexes of all PID controls before the current PID control on time sequence; and PID control is carried out on the voltage time sequence data before the current PID control based on the regulated integral gain value.
The steady-state error in the overshoot can be further calculated, the steady-state error is used as the resistance to be eliminated in the control process, the system can reach the expected value faster and keep the stability of the system, and the steady-state error generated in the operation of the system is analyzed by combining the influence of the data stability of each control and the error judgment caused by the environment. Namely, the steady-state error index of each PID control is obtained according to the deviation degree of the output voltage after each PID control and the expected value of the preset voltage, the environmental impact index and the system stability index of each PID control.
Preferably, for any one PID control, the difference between the output voltage at each time sequence position after the PID control and the expected value of the preset voltage is calculated, and the accumulated value is calculated to obtain the expected deviation degree of the PID control, wherein the expected deviation degree reflects the difference between the voltage output by the control process and the expected value, and the smaller the difference is, the closer the running state is to the expected state. And calculating the product of the environmental impact index and the system stability index of the PID control, and carrying out negative correlation mapping and normalization processing to obtain an error adjustment coefficient of the PID control, wherein the smaller the environmental impact index is, the smaller the degree of influence of the environmental factors on the voltage time sequence data is, the more likely the error is a steady-state error to be eliminated, and when the smaller the system stability index is, the higher the instability of the voltage time sequence data per se is, the more severe the fluctuation is, and the higher the possibility of the error as the steady-state error is. The preset voltage expected value is set by the practitioner according to the specific implementation, and is not limited in detail herein.
Therefore, the product of the expected deviation degree of the PID control and the error adjustment coefficient is used as a steady-state error index of the PID control, and a more accurate steady-state error is obtained through the adjustment coefficient, so that the subsequent adjustment of the elimination degree is facilitated. In the embodiment of the invention, the expression of the steady state error index is:
In the method, in the process of the invention, Expressed as/>Steady state error index of secondary PID control,/>Expressed as/>Desired degree of deviation of secondary PID control,/>Expressed as/>Environmental impact index of secondary PID control,/>Expressed as/>The system stability index of the secondary PID control,Represented as a normalized processing function.
Wherein,Expressed as/>The greater the error adjustment coefficient of the secondary PID control, the greater the likelihood of a stable error at that time, and the greater the resulting steady state error indicator.
The steady-state error index of the multi-time PID control can be further combined to adjust the integral gain value, so that the elimination probability of the steady-state error is improved. And obtaining the adjustment integral gain value of the current PID control according to the change trend degree of steady-state error indexes of all PID controls before the current PID control in time sequence.
Preferably, steady-state error indexes of all PID controls before the current PID control are arranged according to a time sequence to obtain a time sequence error sequence of the current PID control, and the pearson correlation coefficient of the time sequence error sequence and the time sequence is calculated to obtain a trend index of the current PID control; when the trend index is negative and smaller, the steady-state error index is reduced, the efficiency of the regulation process is higher, and the required adjustment degree of the gain value is lower.
Calculating the difference between the trend index of the current PID control and the numerical value-1 to be used as a gain adjustment coefficient of the current PID control; taking the product of the gain adjustment coefficient of the current PID control and the integral gain value of the last PID control as the adjustment quantity of the current PID control; and taking the sum value of the integral gain value of the last PID control and the adjustment quantity of the current PID control as an adjustment integral gain value. In the embodiment of the invention, the expression for adjusting the integral gain value is:
In the method, in the process of the invention, Expressed as/>Adjusting integral gain value of secondary PID control,/>Expressed as/>Adjusting integral gain value of secondary PID control,/>Expressed as/>Trend index of secondary PID control.
Wherein,Expressed as/>Gain adjustment coefficient of secondary PID control,/>Expressed as/>The adjusting quantity of the secondary PID control is that the closer the pearson correlation coefficient is to-1, the closer the correlation of the two groups of data is to negative correlation, namely the steady-state error index is changed in a descending trend along with the time sequence, so that when the trend index is closer to-1, the more excellent the surface area division gain value is, the more adjustment is not needed, and when the change is larger than the difference of-1, the worse the elimination process of the steady-state error is, and the integral gain value is needed to be improved to improve the elimination efficiency.
When the PID control is not the last PID control, that is, the PID control is the first PID control, the preset integral gain value is used as the integral gain value of the last PID control of the first PID control. Referring to fig. 5, a schematic diagram of the voltage sequence data after multiple PID control according to an embodiment of the present invention is shown, where T0 is the voltage sequence data before PID control, F is a preset voltage expected value, and T1 is the output voltage after PID control, and the steady state error is eliminated more.
Finally, PID control is performed on the voltage time sequence data before the current PID control based on the regulated integral gain value, and in one embodiment of the invention, the regulated integral gain value, the preset proportional gain value and the preset differential gain value of the current PID control are input into the current PID controller, PID control is performed on the voltage time sequence data before the current PID control, and voltage is output.
In the embodiment of the present invention, the proportional gain value and the differential gain value of the PID controller are a preset proportional gain value and a preset differential gain value, which can be obtained by using the Ziegler-Nichols rule, which is a method for determining parameters of the PID controller based on steady state characteristics and corresponding characteristics of the system, and it should be noted that the Ziegler-Nichols rule is a technical means well known to those skilled in the art, and will not be described herein. In other embodiments of the present invention, other PID data tuning methods, such as a trial-and-error method, a critical oscillation method, a reference model method, a genetic algorithm, an optimization algorithm, etc., may be used to obtain the initial scaling factor, the initial integral factor, and the initial differential factor, which are not limited herein.
In other embodiments of the present invention, the preset proportional gain value may also be obtained by performing experimental adjustment on the PID controller, selecting one hundred pieces of experimental data, respectively recording the response speed of each piece of experimental data output in the control system, and taking the proportional gain value when the response speed of the system changes least as the preset proportional gain value, which is not limited herein.
It should be noted that, in order to facilitate the operation, all index data involved in the operation in the embodiment of the present invention is subjected to data preprocessing, so as to cancel the dimension effect. The specific means for removing the dimension influence is a technical means well known to those skilled in the art, and is not limited herein.
In summary, the invention considers the characteristic of dynamic fluctuation change of voltage data to enable the controller to need to adjust for a plurality of times to overcome the resistance in the control process, and acquire the voltage time sequence data and the environment time sequence data before each PID control to analyze the running state. Firstly, analyzing the stable state of the data before each control, analyzing the change characteristics and distribution correlation between every two local change segments in the voltage time sequence data before each PID control, obtaining a system stability index, and evaluating the stable condition of the data in the system before each control according to the fluctuation similarity condition so as to analyze the error possible condition in the control process. The environmental impact index is obtained through the association of the voltage time sequence data and the environment time sequence data before each PID control, and the judgment influence caused by the environment correlation is considered in error analysis, so that the subsequent error judgment required by adjustment is more accurate. The steady-state error index of each PID control is comprehensively determined by combining the deviation of the output voltage and the expected voltage after each PID control, the system stability index and the environmental impact index, and the steady-state error elimination process of the integral gain in the PID controller has a trend of being close to expectations, so that the adjustment integral gain value of the current PID control is obtained according to the change trend of all steady-state error indexes before the current PID control, and the adjustment of the control and adjustment process is accelerated while the joint change is carried out. PID control is performed based on adjusting the integral gain value. According to the invention, through comprehensively analyzing the stability condition and the environmental influence of the voltage data in the PID control and regulation process, the current PID control parameters are adaptively regulated by combining the historical control and regulation conditions, the regulation and control efficiency is improved, and the control performance of the electric power system is more accurate and stable.
The invention provides a control system based on the Internet of things, which comprises a memory and a processor, wherein the processor executes a computing program stored in the memory to realize the control method based on the Internet of things.
It should be noted that: the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
Claims (10)
1. The control method based on the Internet of things is characterized by comprising the following steps:
Acquiring voltage time sequence data before each PID control, different types of environment time sequence data and output voltage after each PID control in power operation; dividing voltage time sequence data before PID control into more than two local variation sections;
In the voltage time sequence data before each PID control, according to the numerical distribution change difference condition and the data distribution association condition between every two local change segments, obtaining a system stability index of each PID control; in the voltage time sequence data before each PID control, according to the change relativity between each type of environment time sequence data and the voltage time sequence data, obtaining the environment influence index of each control process;
Obtaining a steady-state error index of each PID control according to the deviation degree of the output voltage after each PID control and a preset voltage expected value, and an environmental impact index and a system stability index of each PID control; obtaining an adjusting integral gain value of the current PID control according to the change trend degree of steady-state error indexes of all PID controls before the current PID control on time sequence; and PID control is carried out on the voltage time sequence data before the current PID control based on the regulated integral gain value.
2. The control method based on the internet of things according to claim 1, wherein the obtaining the system stability index of each PID control according to the numerical distribution variation difference condition and the data distribution association condition between every two local variation segments in the voltage time sequence data before each PID control comprises:
obtaining a change characteristic value of each local change section according to the distribution change degree of the numerical value in each local change section;
The voltage time sequence data in each local variation section are arranged according to a time sequence order to obtain a local sequence; taking any two different local variation sections in the voltage time sequence data before PID control each time as a time period group, and taking the voltage time sequence data with the same serial number in two local sequences in the time period group as a data comparison group for any one time period group;
obtaining an approximate index of the time period group according to the difference between the distribution conditions of the voltage time sequence data of each data comparison group in the time period group in the local change section and the difference of the change characteristic values of the local change sections of the time period group;
And taking the average value of the approximate indexes of all time period groups in the voltage time sequence data before each PID control as a system stability index of each PID control.
3. The control method based on the internet of things according to claim 2, wherein the obtaining the variation characteristic value of each local variation segment according to the distribution variation degree of the numerical value in each local variation segment includes:
For any local change section, calculating the numerical value difference between every two adjacent voltage time sequence data in the local change section, and solving the average value to obtain the numerical value difference index of the local change section;
acquiring the slope of each voltage time sequence data in the local variation section; calculating the difference between every two adjacent slopes in the local variation section, and averaging to obtain a variation difference index of the local variation section;
Obtaining a change characteristic value of the local change section according to the numerical value difference index and the change difference index in the local change section; the numerical difference index and the variation difference index are positively correlated with the variation characteristic index.
4. The control method based on the internet of things according to claim 2, wherein the expression of the approximate index is:
; in the/> Expressed as/>First/>, before secondary PID controlLocal variation segment and/>An approximation index of a time period group consisting of the partial change segments; /(I)Expressed as/>First/>, before secondary PID controlA change characteristic value of each local change segment; /(I)Expressed as/>First/>, before secondary PID controlA change characteristic value of each local change segment; /(I)Expressed as/>First/>, before secondary PID controlLocal variation segment and/>Total number of data control groups in a time period group consisting of the partial change segments; /(I)Expressed as at/>First/>, before secondary PID controlLocal variation segment and/>The/>, of a time period group consisting of partial change segmentsIn the data comparison group, correspond to the first/>The occurrence frequency of the voltage time sequence data of each local variation section in the local variation section; /(I)Expressed as at/>First/>, before secondary PID controlLocal variation segment and/>The/>, of a time period group consisting of partial change segmentsIn the data comparison group, correspond to the first/>The occurrence frequency of the voltage time sequence data of each local variation section in the local variation section; /(I)Represented as a logarithmic function with a base of a natural constant; /(I)Expressed as an absolute value extraction function; /(I)Represented as a logarithmic function with a base of a natural constant; wherein/>。
5. The control method based on the internet of things according to claim 1, wherein the method for acquiring the environmental impact index comprises:
sequentially taking each type of environment time sequence data as reference environment data; sequentially taking voltage time sequence data before each PID control as reference voltage data;
Obtaining the environment deviation correlation degree of the reference voltage data and the reference environment according to the difference change degree between the reference environment data and the reference voltage data; calculating the pearson correlation coefficient of the reference environment data and the reference voltage data to take absolute values as the environment change correlation degree of the reference voltage data and the reference environment data;
According to the environmental deviation correlation degree and the environmental change correlation degree of the reference voltage data and the reference environmental data, acquiring environmental correlation indexes of the reference voltage data and the reference environmental data, wherein the environmental deviation correlation degree and the environmental correlation indexes are in negative correlation, and the environmental change correlation degree and the environmental correlation indexes are in positive correlation; the environment related index is a normalized value;
and calculating the average value of the relevant indexes of the reference voltage data corresponding to all types of environment time sequence data, and obtaining the environment influence index of the reference voltage data.
6. The control method based on the internet of things according to claim 5, wherein the obtaining the degree of correlation of the environmental deviation between the reference voltage data and the reference environment according to the degree of variation of the difference between the reference environment data and the reference voltage data comprises:
Normalizing the reference environment data and the reference voltage to obtain standard environment data and standard voltage data; taking the difference between standard environment data and standard voltage data at the same position on a time sequence as difference time sequence data of reference environment data and reference voltage data;
And obtaining a differential sequence of the differential time sequence data on a time sequence, and calculating the accumulated sum of all values in the differential sequence to obtain the environment deviation correlation degree of the reference voltage data and the reference environment.
7. The control method based on the internet of things according to claim 1, wherein the method for obtaining the steady state error index comprises:
For any PID control, calculating the difference between the output voltage at each time sequence position after the PID control and the expected value of the preset voltage, and solving the accumulated value to obtain the expected deviation degree of the PID control;
calculating the product of the environmental impact index and the system stability index of the PID control, performing negative correlation mapping and normalization processing to obtain an error adjustment coefficient of the PID control;
And taking the product of the expected deviation degree of the PID control and the error adjustment coefficient as a steady-state error index of the PID control.
8. The control method based on the internet of things according to claim 1, wherein the obtaining method for adjusting the integral gain value comprises:
The steady-state error indexes of all PID controls before the current PID control are arranged according to the time sequence order, so as to obtain the time sequence error sequence of the current PID control; calculating the pearson correlation coefficient of the time sequence error sequence and the time sequence to obtain a trend index of the current PID control;
Calculating the difference between the trend index of the current PID control and the numerical value-1 to be used as a gain adjustment coefficient of the current PID control; taking the product of the gain adjustment coefficient of the current PID control and the integral gain value of the last PID control as the adjustment quantity of the current PID control; and taking the sum value of the integral gain value of the last PID control and the adjustment quantity of the current PID control as an adjustment integral gain value.
9. The control method based on the internet of things according to claim 1, wherein the PID control of the voltage timing data before the current PID control based on the adjustment of the integral gain value comprises:
And inputting the regulated integral gain value, the preset proportional gain value and the preset differential gain value of the current PID control into the current PID controller, performing PID control on the voltage time sequence data before the current PID control, and outputting voltage.
10. A control system based on the internet of things, comprising a memory and a processor, wherein the processor executes a computing program stored in the memory to implement the control method based on the internet of things according to any one of claims 1-9.
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Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH0573104A (en) * | 1991-09-11 | 1993-03-26 | Hitachi Ltd | Method for automatically tuning pid parameter |
| US5714831A (en) * | 1995-11-13 | 1998-02-03 | Wisconsin Alumni Research Foundation | Method and apparatus for improved control of piezoelectric positioners |
| US6081751A (en) * | 1997-12-19 | 2000-06-27 | National Instruments Corporation | System and method for closed loop autotuning of PID controllers |
| US20030014165A1 (en) * | 1999-12-21 | 2003-01-16 | Baker Brian C | Spatial avoidance method and apparatus |
| US20030153990A1 (en) * | 2002-01-22 | 2003-08-14 | Igor Boiko | Method and apparatus for tuning a PID controller |
| CN107688291A (en) * | 2017-08-23 | 2018-02-13 | 苏州大学 | The feedforward of piezoelectric ceramics platform and closed loop composite control method, system based on single neuron |
| CN109557811A (en) * | 2019-01-22 | 2019-04-02 | 绍兴文理学院 | Using the pid parameter setting method of intelligence cosine similarity amount in monodrome and genetic algorithm |
| CN113625762A (en) * | 2021-08-30 | 2021-11-09 | 吉林大学 | Unmanned aerial vehicle obstacle avoidance method and system, and unmanned aerial vehicle cluster obstacle avoidance method and system |
| CN117349778A (en) * | 2023-12-04 | 2024-01-05 | 湖南蓝绿光电科技有限公司 | Online real-time monitoring system of consumer based on thing networking |
| CN117856455A (en) * | 2024-03-07 | 2024-04-09 | 太原理工大学 | Intelligent regulation and control method for power equipment based on fuzzy control |
| CN117891161A (en) * | 2024-03-14 | 2024-04-16 | 漳浦玮柏自行车有限公司 | Bicycle bottom bracket bearing forging process adjusting method based on self-adaptive control algorithm |
-
2024
- 2024-05-14 CN CN202410592422.6A patent/CN118170004B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH0573104A (en) * | 1991-09-11 | 1993-03-26 | Hitachi Ltd | Method for automatically tuning pid parameter |
| US5714831A (en) * | 1995-11-13 | 1998-02-03 | Wisconsin Alumni Research Foundation | Method and apparatus for improved control of piezoelectric positioners |
| US6081751A (en) * | 1997-12-19 | 2000-06-27 | National Instruments Corporation | System and method for closed loop autotuning of PID controllers |
| US20030014165A1 (en) * | 1999-12-21 | 2003-01-16 | Baker Brian C | Spatial avoidance method and apparatus |
| US20030153990A1 (en) * | 2002-01-22 | 2003-08-14 | Igor Boiko | Method and apparatus for tuning a PID controller |
| CN107688291A (en) * | 2017-08-23 | 2018-02-13 | 苏州大学 | The feedforward of piezoelectric ceramics platform and closed loop composite control method, system based on single neuron |
| CN109557811A (en) * | 2019-01-22 | 2019-04-02 | 绍兴文理学院 | Using the pid parameter setting method of intelligence cosine similarity amount in monodrome and genetic algorithm |
| CN113625762A (en) * | 2021-08-30 | 2021-11-09 | 吉林大学 | Unmanned aerial vehicle obstacle avoidance method and system, and unmanned aerial vehicle cluster obstacle avoidance method and system |
| CN117349778A (en) * | 2023-12-04 | 2024-01-05 | 湖南蓝绿光电科技有限公司 | Online real-time monitoring system of consumer based on thing networking |
| CN117856455A (en) * | 2024-03-07 | 2024-04-09 | 太原理工大学 | Intelligent regulation and control method for power equipment based on fuzzy control |
| CN117891161A (en) * | 2024-03-14 | 2024-04-16 | 漳浦玮柏自行车有限公司 | Bicycle bottom bracket bearing forging process adjusting method based on self-adaptive control algorithm |
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