An automatic control method and system for an electrical system component

Abstract

This application relates to the field of automatic control technology, and particularly relates to an automatic control method and system for electrical system components. The method includes: obtaining PID control data and flow monitoring signals; determining the time delay value between the flow monitoring signals according to the correlation relationship between two flow monitoring signals; determining the signal fluctuation factor according to the amplitude distribution of the flow monitoring signals at the monitoring point in the short-time window and the amplitude distribution of the flow monitoring signals at the next monitoring point after the time delay in the short-time window, and combining the amplitude difference and phase difference of the flow monitoring signals included in all short-time windows in the frequency domain to determine the interference probability, and screening out the interference frequencies; obtaining the non-linear error probability according to the distribution of all interference frequencies; adjusting the proportional gain in the PID control data according to the non-linear error probability to complete the automatic control of the regulating electric valve. This application improves the control accuracy during the automatic control of the regulating electric valve by supplementing the non-linear error.

Description

Technical Field

[0001] The present application relates to the field of automatic control technology, and particularly relates to an automatic control method and system for electrical system components. Background Art

[0002] Regulating valves are commonly used in various equipment and subsystems in electrical systems. For example, in boilers, generators, etc. Among them, in steam turbines, valves can be used to adjust the inlet volume and pressure of steam to ensure the normal operation of the steam turbine; regulating valves can adjust the medium flow rate, pressure or flow velocity to ensure the stability of power generation equipment under different working conditions. Therefore, power station valves play an important role in ensuring the safe and stable operation of electrical systems.

[0003] In an electrical system, a PID control system is often used to achieve automatic control of a regulating valve by online monitoring the status data of the regulating valve. However, since the flow states of liquids and gases when flowing through the valve will change under different conditions, these changes will affect the sensors arranged around the regulating valve when collecting status data, such as the appearance of bubbles in the liquid, liquid diversion, etc., resulting in the status data not being able to accurately reflect the working state of the valve, causing system errors and deviations in the automatic control of the regulating valve, and even affecting the normal operation of the electrical system. Summary of the Invention

[0004] In order to solve the above technical problems, the purpose of the present application is to provide an automatic control method and system for electrical system components, and the specific technical solutions adopted are as follows:

[0005] In a first aspect, an embodiment of the present application provides an automatic control method for electrical system components, and the method includes the following steps:

[0006] Obtain the PID control data of the regulating electric valve and the flow monitoring signals at each monitoring point of the regulating electric valve;

[0007] According to the correlation between the flow monitoring signals at two monitoring points, determine the time delay value between the flow monitoring signals at the two monitoring points; according to the amplitude distribution of the flow monitoring signal at the monitoring point in the short-time window and the amplitude distribution of the flow monitoring signal at the next monitoring point after the time delay in the short-time window, determine the signal fluctuation factor of the monitoring point in each short-time window;

[0008] According to the amplitude difference, phase difference and signal fluctuation factor of the flow monitoring signals included in all short-time windows of adjacent monitoring points in the frequency domain, determine the interference probability of the flow monitoring signal at the monitoring point at each frequency; obtain the interference frequency according to the interference probability of the flow monitoring signal at the monitoring point at all frequencies;

[0009] The non - linear error probability is obtained based on the distribution of interference frequencies at all adjacent monitoring points; the proportional gain in the PID control data is adjusted according to the non - linear error probability to complete the automatic control of the regulating electric valve.

[0010] Preferably, the determination of the time - delay value between the flow monitoring signals of two monitoring points includes:

[0011] Preset an initial value of the time - delay value between the flow monitoring signals of two monitoring points and an iteration step size;

[0012] Construct a cross - correlation function using the signal values of the flow monitoring signals of two monitoring points at different sampling times and the time - delay value between the flow monitoring signals of the two monitoring points;

[0013] Calculate the function value of the cross - correlation function after each iteration during the time - delay value iteration process, and use the time - delay value corresponding to the maximum value among all the function values as the time - delay value between the flow monitoring signals of the two monitoring points.

[0014] Preferably, the determination of the signal fluctuation factor of a monitoring point in each short - time window includes:

[0015] Obtain a plurality of short - time windows of the flow monitoring signal;

[0016] Take the similarity measurement result between the amplitude distributions of the flow monitoring signals of each monitoring point and the next adjacent monitoring point in the short - time windows of the same order as the signal fluctuation factor of each monitoring point in the short - time windows of the same order.

[0017] Preferably, the method for obtaining the short - time window is: perform short - time Fourier transform on each flow monitoring signal respectively to obtain a plurality of short - time windows of each flow monitoring signal.

[0018] Preferably, the determination of the interference probability of the flow monitoring signal of a monitoring point at each frequency includes:

[0019] For any two adjacent monitoring points, use the window obtained by adding the time - delay value to each short - time window of the flow monitoring signal of the previous monitoring point among the two monitoring points as the time - delay window of each short - time window;

[0020] Count all the overlapping sampling times in all the short - time windows of the flow monitoring signal of the latter monitoring point among the two monitoring points with each time - delay window, and use the window formed by combining all the short - time windows where the overlapping sampling times are located as the combined window of each short - time window of the flow monitoring signal of the previous monitoring point;

[0021] Based on the amplitude and phase at the same frequency in each short - time window of the flow monitoring signal of the previous monitoring point and the combined window, determine the fluctuation situation recovery coefficient of each frequency in each short - time window;

[0022] The ratio of the signal fluctuation factor of each short-time window of the flow monitoring signal at the previous monitoring point to the fluctuation condition recovery coefficient is denoted as the parameter ratio, and the mean value of the accumulated results of the difference between the constant parameter and the normalized result of the parameter ratio over all short-time windows is used as the interference probability for each frequency within each short-time window.

[0023] Preferably, the determining of the fluctuation condition recovery coefficient for each frequency within each short-time window includes:

[0024] Taking the ratio of the combined window of each short-time window and the amplitudes at the same frequency within each short-time window as the amplitude change amount;

[0025] Taking the ratio of the combined window of each short-time window and the phases at the same frequency within each short-time window as the phase change amount;

[0026] The fluctuation condition recovery coefficient for each frequency within each short-time window of the flow monitoring signal at the previous monitoring point consists of two parts: the amplitude change amount and the phase change amount; wherein, the fluctuation condition recovery coefficient is positively correlated with the amplitude change amount and the phase change amount respectively.

[0027] Preferably, the obtaining of the interference frequency based on the interference probabilities of the flow monitoring signals at all frequencies of the monitoring point specifically includes:

[0028] Calculating the interference probability for each frequency within all short-time windows of the flow monitoring signal of each monitoring point respectively, and taking the frequency with the interference probability greater than the preset threshold as the interference frequency.

[0029] Preferably, the obtaining of the non-linear error probability based on the distribution of the interference frequencies at all adjacent monitoring points specifically includes:

[0030] Obtaining all the interference frequencies of the flow monitoring signal of each monitoring point respectively; obtaining the intersection of all the interference frequencies of the flow monitoring signals of all monitoring points, and taking the interference frequencies in the intersection as the characteristic frequencies;

[0031] Taking the mean value of the distribution variances of the signal amplitudes of the flow monitoring signals of each monitoring point and the next monitoring point of each monitoring point at all characteristic frequencies as the non-linear error generated by the interference of the flow monitoring signal of each monitoring point;

[0032] Normalizing all the non-linear errors based on the non-linear errors generated by the interference of the flow monitoring signals of all monitoring points, and taking the normalized result of each non-linear error as a non-linear error probability.

[0033] Preferably, adjusting the proportional gain in the PID control data according to the non - linear error probability to complete the automatic control of the regulating electric valve includes:

[0034] Taking the product of the sum of the non - linear error probability of each monitoring point and the constant parameter and the proportional gain of the PID controller at the historical adjustment moment as the proportional gain of each monitoring point at the adjustment moment;

[0035] Taking the mean value of the proportional gains of all monitoring points at the adjustment moment as the actual proportional gain of the PID controller at the adjustment moment, generating a control signal using the PID controller based on the actual proportional gain, and completing the automatic control of the opening of the regulating electric valve using the control signal.

[0036] In a second aspect, the embodiments of the present application also provide an automatic control system for an electrical system component, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of the automatic control method for an electrical system component described in any one of the above.

[0037] The present application has at least the following beneficial effects:

[0038] By analyzing the non - linear error problem caused by phenomena such as turbulence and bubbles in the flow monitoring signal during the water supply process in the pipeline connected to the regulating electric valve in the process of using boiler heating to drive steam turbine power generation, this application calculates the time - delay value of the flow monitoring signal by analyzing the flow monitoring signals of adjacent monitoring points. Secondly, by analyzing the synchronous fluctuation of the flow monitoring signals of adjacent monitoring points during fluid flow, the signal fluctuation factor is obtained, which represents the possibility of the flow monitoring signal fluctuating due to the change in fluid velocity at the monitoring point. Further, based on the processing of the flow monitoring signal using the short - time Fourier transform, the frequency - domain change of the flow monitoring signal is analyzed. According to the time - delay value, the time - delay window and the combined window of each short - time window are obtained, and the amplitude difference and phase difference of the flow monitoring signals of adjacent monitoring points in the frequency domain are analyzed. Combining the signal fluctuation factor, the interference probability of each frequency is obtained, and then all possible interfering frequencies are screened out to obtain the non - linear error probability. Then, according to the occurrence expectation of the non - linear error, the PID gain component is adjusted, so that the PID controller can effectively compensate for the non - linear error that may be caused by bubbles at the regulating electric valve, and improve the control accuracy of the regulating electric valve. Description of the Drawings

[0039] To more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0040] Figure 1 It is a flowchart of the steps of an automatic control method for an electrical system component provided by an embodiment of the present application. Specific embodiments

[0041] The following will clearly and completely describe the technical solutions in the embodiments of the present application in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. Without conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0042] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present application belongs.

[0043] The following specifically describes the specific solutions of an automatic control method and system for an electrical system component provided by the present application in conjunction with the accompanying drawings.

[0044] Please refer to Figure 1 , which shows a flowchart of the steps of an automatic control method for an electrical system component provided by an embodiment of the present application. The method includes the following steps:

[0045] S1. Obtain the PID control data of the regulating electric valve and the flow monitoring signals at each monitoring point of the regulating electric valve.

[0046] When the regulating electric valve acts in the electrical system, it can be used for different media. For example, controlling the opening of the electric valve to regulate the water supply of the boiler, or regulating the steam flow to achieve the control of rotor rotation for power generation, etc. In an embodiment of the present application, when the boiler burns to heat the feed water, the subsequent automatic control process of the regulating electric valve is described with the application scenario of controlling the water supply by adjusting the opening of the regulating electric valve. Sensors are arranged around the regulating electric valve to obtain monitoring signals, and the arrangement positions include but are not limited to the pipeline connecting the electric valve and the electric valve itself. The purpose is to obtain monitoring signals at different monitoring points, and the specific number of monitoring points is set by the implementer according to the actual scenario, and the present application does not make any restrictions.

[0047] Specifically, in an embodiment of the present application, an electromagnetic flowmeter is used to obtain the flow monitoring signals of the regulating electric valve at M monitoring points with the same sampling frequency. In this embodiment, M is set to 5 and the sampling frequency is set to 500 HZ. Secondly, for each collected flow monitoring signal, a data filtering algorithm is used, including but not limited to mean filtering, Gaussian filtering, and median filtering. Data filtering is a well-known technique in the field of data processing, and the specific process will not be elaborated here. Preferably, in an embodiment of the present application, mean filtering is used to process the flow monitoring signals at each monitoring point respectively.

[0048] Further, from the log data of the electrical system, the control parameters when the regulating electric valve performs automatic control at each historical regulation moment in the historical time period are obtained. The control parameters include the preset component gains of the PID control module in the PID controller, including the proportional gain , integral gain , and derivative gain .

[0049] S2. According to the correlation relationship between the flow monitoring signals at two monitoring points, determine the time delay value between the flow monitoring signals at the two monitoring points; according to the amplitude distribution of the flow monitoring signal at the monitoring point in the short-time window and the amplitude distribution of the flow monitoring signal at the next monitoring point after the time delay in the short-time window, determine the signal fluctuation factor of the monitoring point in each short-time window.

[0050] The larger the flow velocity and the pipe diameter of the water supply in the pipe connecting the regulating electric valve, the more likely it is to generate bubbles and a more complex flow state; too fast flow velocity is likely to cause local turbulence and oscillation at the regulating electric valve, thereby promoting the generation of bubbles, changing the flow characteristics, and making the flow measurement result more complex; this is manifested when the electromagnetic flowmeter collects the flow monitoring signal as: the trend of the flow signal shows small non-linear fluctuations.

[0051] Specifically, when collecting flow monitoring signals at different monitoring points, the local turbulence and oscillation caused by too fast flow velocity will cause the flow monitoring signals at some collection moments to have different time delays at different monitoring points; in order to obtain the time delay between the flow monitoring signals at adjacent positions during the fluid flow process, according to the correlation relationship between the flow monitoring signals at two monitoring points, obtain the time delay value between the two positions.

[0052] Specifically, first calculate the cross-correlation function between the flow monitoring signals at two monitoring points ; where N represents the number of sampling moments of the flow monitoring signal, represents the value of the flow monitoring signal at the first monitoring point at the t-th moment, represents the value of the flow monitoring signal at the second monitoring point at the -th moment, is the time delay value between two monitoring points.

[0053] It should be noted that when iterating, the step size is set to , the initial value , and the iteration process is T, , 2T…, The maximum iteration value of is set to 1 s, and the value of T is taken as the empirical value 0.1 s. Calculate the function values of the relevant functions after each iteration in the iteration process in turn, and use the

[0054] when the function value of the cross-correlation function F is the largest as the time delay value between two monitoring points.

[0054] The maximum cross-correlation function represents the maximum correlation between the previous flow monitoring signal and the latter flow monitoring signal after a time delay of , which conforms to the flow characteristics of the fluid.

[0055] Furthermore, the bubbles are formed by the air mixed in during the water supply process. When there are bubbles, the internal resistance of the fluid is large, and the fluid speed is slower than that of the fluid without bubbles. However, the bubbles will rise and aggregate in the fluid of the pipeline. Therefore, the bubbles in the water flow should gradually decrease with the flow, and the fluctuations of the signal trend term caused by the bubbles will gradually disappear; and after the bubbles decrease, the flow rate of the fluid will tend to be normal and stable.

[0056] Specifically, in order to analyze the flow monitoring signals, first perform short-time Fourier transform on each flow monitoring signal respectively. Set the short-time window length to L, and the value range of L is from 0.1 s to 1 s. In this embodiment, the value of L is set to the empirical value 0.1 s to obtain several short-time windows of each flow monitoring signal. Among them, the short-time Fourier transform is a well-known technology in the field of data processing, and the specific process will not be elaborated here.

[0057] Furthermore, if the water flow rate is normal when water is supplied in the pipeline connected to the regulating electric valve, the amplitude distribution of the flow monitoring signal at the first monitoring point in the short-time window should be relatively close to the amplitude distribution of the flow monitoring signal at the second monitoring point after a time delay in the short-time window.

[0058] Therefore, for any short-time window in a sequence, taking the p-th short-time window as an example, the similarity measurement result between the amplitude distribution of the flow monitoring signal at the first monitoring point within the p-th short-time window and the amplitude distribution of the flow monitoring signal at the second monitoring point within the p-th short-time window is used as the signal fluctuation factor of the flow monitoring signal at the first monitoring point within the p-th short-time window. The signal fluctuation factor reflects whether the flow monitoring signal at the first monitoring point within the short-time window of the same sequence has the characteristic of stable delay towards the flow monitoring signal at the second monitoring point. The larger the signal fluctuation factor, the smoother the water supply from the first monitoring point to the second monitoring point.

[0059] It should be noted that the similarity measurement is used to measure the similarity between the amplitude distributions within two short-time windows. Therefore, under the premise of achieving the purpose of similarity measurement, the methods of similarity measurement include but are not limited to value variance, Pearson correlation coefficient, and cosine similarity. This application does not impose special restrictions on the specific method of similarity measurement. As an example, in this embodiment, the Pearson correlation coefficient between the amplitude distributions within two short-time windows is calculated as the similarity measurement result.

[0060] S3. According to the amplitude difference, phase difference, and signal fluctuation factor of the flow monitoring signals included in all short-time windows of adjacent monitoring points in the frequency domain, determine the interference probability of the flow monitoring signals at each frequency of the monitoring point; obtain the interference frequency based on the interference probabilities of the flow monitoring signals at all frequencies of the monitoring point.

[0061] Specifically, when the water flow in the water supply process passes through two sensors successively, the signal components of most frequencies should have the same phase, while the signal amplitudes of a small number of frequencies will increase and the phases will decrease. The signal frequencies with this characteristic may be the frequency components corresponding to the bubbles in the fluid.

[0062] Therefore, in order to analyze the amplitude change of the frequency signal and extract the bubble frequency, this application first obtains the amplitude spectrum and phase spectrum of the flow monitoring signal at the first monitoring point included in each short-time window; the amplitude spectrum represents the instantaneous flow rate of signals at different frequencies, and the phase represents the time offset of signals at different frequencies.

[0063] Specifically, taking the first monitoring point as an example, according to the correlation relationship of all amplitude spectra and phase spectra in each short-time window between the time-delayed flow monitoring signal at the second monitoring point and the flow monitoring signal at the first monitoring point, the frequency components corresponding to the signal fluctuations caused by the influence are extracted. First, for each monitoring point except the last one, obtain the window obtained by adding the time delay value to each short-time window in the flow monitoring signal of the monitoring point, which is denoted as the time-delay window of each short-time window. The window composed of all short-time windows that overlap the time-delay window of the flow monitoring signal of the first monitoring point in the flow monitoring signal of the second monitoring point is denoted as the combined window.

[0064] Specifically, taking the first monitoring point and the second monitoring point as examples, calculate each moment in each short-time window of the flow monitoring signal of the first monitoring point at the corresponding sampling moment in the flow monitoring signal of the second monitoring point, that is, the v-th sampling moment corresponds to the -th sampling moment; all sampling moments in the first short-time window a1 of the flow monitoring signal of the first monitoring point, after adding the time delay value the determined time window is the time-delay window ab1 of a1, and ab1 involves two short-time windows b1 and b2 in the flow monitoring signal of the second monitoring point. The combination of the two short-time windows is called the combined window of a1.

[0065] Furthermore, align the two amplitude spectra and two phase spectra within the short-time window and its combined window according to the same frequency; if a certain frequency within the short-time window has no value at the same frequency position in the amplitude spectrum and phase spectrum within the combined window, it is replaced by the constant 1. For each monitoring point except the last one, according to the differences in the amplitude spectra and phase spectra of the flow monitoring signals included in each short-time window of the monitoring point and its combined window in the flow monitoring signal of the next monitoring point, obtain the fluctuation situation recovery coefficient of the flow monitoring signals included in each short-time window of the monitoring point at each frequency, which is used to reflect the situation where the data fluctuations that occur in the flow monitoring signal at the previous monitoring point during the water supply process return to normal in the flow monitoring signal at the next monitoring point. The calculation formula for the fluctuation situation recovery coefficient of the flow monitoring signal of the first monitoring point at the i-th frequency within the p-th short-time window is:

[0066]

[0067] where represents the fluctuation situation recovery coefficient of the flow monitoring signal of the first monitoring point at the i-th frequency within the p-th short-time window;

[0068] 、 represent the amplitude and phase of the \(i\)-th frequency in the amplitude spectrum and phase spectrum of the flow monitoring signal at the first monitoring point within the \(p\)-th short-time window, respectively, and represent the amplitude and phase of the \(i\)-th frequency in the amplitude spectrum and phase spectrum of the flow monitoring signal at the \(p\)-th short-time window within the combined window of the second monitoring point, respectively.

[0069] When and are both larger, it represents that the \(i\)-th frequency has the characteristics of amplitude increase and phase decrease between the flow monitoring signal of the previous monitoring point and the flow monitoring signal of the next monitoring point; when there are bubbles when the fluid passes through the previous monitoring point, the fluid containing bubbles has greater resistance and slower flow velocity than normal fluid, and there is a delay at the previous monitoring point; while when the fluid flows from the previous monitoring point to the next monitoring point, the bubbles float up and gradually decrease, the flow velocity returns to normal, and the delay disappears.

[0070] Secondly, according to the fluctuation recovery coefficient and signal fluctuation factor of each frequency of the flow monitoring signal at the monitoring point in all short-time windows, the interference probability of the flow monitoring signal at the monitoring point at each frequency is obtained, which is used to reflect the influence probability of the flow monitoring signal collected at each monitoring point during the water supply process due to phenomena such as turbulence and bubbles caused by velocity changes. The calculation formula for the interference probability of the flow monitoring signal at the first monitoring point at the \(i\)-th frequency is:

[0071]

[0072] where represents the interference probability of the flow monitoring signal at the first monitoring point at the \(i\)-th frequency, \(n\) is the number of short-time windows corresponding to the flow monitoring signal at the first monitoring point, is the signal fluctuation factor of the flow monitoring signal at the first monitoring point in the \(p\)-th short-time window, represents the fluctuation recovery coefficient of the flow monitoring signal at the first monitoring point at the \(i\)-th frequency in the \(p\)-th short-time window.

[0073] It should be noted that the more severe the signal fluctuation of the flow monitoring signal at the first monitoring point in the \(p\)-th short-time window, and the greater the possibility that the signal fluctuation of the flow monitoring signal at the second monitoring point returns to normal at the \(i\)-th frequency in the combined window corresponding to the \(p\)-th short-time window, the smaller the value of the parameter ratio , indicating that the probability of the component of the flow monitoring signal at the first monitoring point being interfered at the \(i\)-th frequency is higher.

[0074] Further, calculate the interference probability of the flow monitoring signal at the first monitoring point at each frequency, and compare it with a preset threshold. In this embodiment, the preset threshold is set to 0.6. The frequencies with interference probability greater than the threshold are used as the interference frequencies where the flow monitoring signal is affected by phenomena such as turbulence and bubbles caused by the change in flow velocity near the connecting pipe of the regulating electric valve during the water supply process, resulting in data fluctuations.

[0075] S4. Obtain the non - linear error probability based on the distribution of the interference frequencies at all adjacent monitoring points; adjust the proportional gain in the PID control data according to the non - linear error probability to complete the automatic control of the regulating electric valve.

[0076] The non - linear error indicates that during the water supply process, when water flows in the connecting pipe before reaching the regulating electric valve, it may be affected by phenomena such as turbulence and bubbles, resulting in error fluctuations in the fluid flow velocity. This non - linear error may cause the sensor at the regulating electric valve to obtain incorrect flow measurement results, and further lead to valve control deviation. Among the three gain components of the PID controller, the proportional gain compensates for the real - time error. Therefore, in order to eliminate the deviation in this application, the non - linear error is calculated based on the bubble interference frequency, and the proportional gain among the three gain components of the PID controller is regulated.

[0077] Specifically, first, according to the above process, obtain all the interference frequencies of the flow monitoring signals at each monitoring point respectively; secondly, obtain the intersection of all the interference frequencies of the flow monitoring signals at all monitoring points, and use the interference frequencies in the intersection as the characteristic frequencies.

[0078] Further, calculate the distribution variances of the signal amplitudes of the flow monitoring signals at the first monitoring point and the second monitoring point at all the characteristic frequencies respectively, and use the mean of the two distribution variances as the non - linear error generated by the interference of the flow monitoring signal at the first monitoring point during the water supply process.

[0079] When automatically controlling the regulating electric valve, the change in flow velocity during the water supply process is real - time. Therefore, it is necessary to supplement based on the non - linear error and the proportional gain of the PID controller to eliminate the influence brought by the error.

[0080] Specifically, based on the non - linear errors generated by the interference of the flow monitoring signals at all monitoring points, normalize the non - linear errors generated by the interference of the flow monitoring signals at each monitoring point, use the normalized result of each non - linear error as a non - linear error probability, and determine the proportional gain at the adjustment moment in combination with the proportional gain. The specific calculation formula for the proportional gain at the adjustment moment at the first monitoring point is:

[0081]

[0082] In the formula, is the proportional gain of the first monitoring point at the adjustment moment, is the proportional gain of the PID controller controlling the regulating electric valve at the historical moment, is the non - linear error probability of the first monitoring point.

[0083] Since multiple sensors are arranged around the regulating electric valve, in order to avoid the influence of the sensor accuracy on the monitoring results of a single monitoring point, the mean value of the proportional gains of all monitoring points at the same adjustment moment is calculated as the actual proportional gain of the PID controller at the adjustment moment. Among them, the adjustment time can be based on a preset fixed time interval at the last sampling moment, and the time interval can be 5 minutes, 10 minutes, etc., which is set by the implementer according to the specific situation. For example, adjusting the proportional gain of the PID - controlled regulating electric valve every 10 minutes during the water supply process can enable the PID controller to effectively compensate for the non - linear error when automatically controlling the regulating electric valve according to the flow monitoring signal.

[0084] Specifically, taking the integral gain , the differential gain , and the actual proportional gain at the adjustment moment as the inputs of the PID controller, the PID outputs a control signal, which is received by the positioner and converted into the specific opening position of the valve. For example, if the control signal requires the valve to open 50%, then the positioner will ensure that the opening of the valve is exactly 50%. Receive the position signal of the actuator valve feedback by the actuator valve position sensor, convert this signal into a digital signal through an A / D converter and calibrate it; according to the calibrated actuator valve position signal, decode the commutation control logic of the corresponding brushless DC motor winding. Subsequently, according to the calibrated actuator valve position signal and the target opening signal provided by the actuator valve control system, generate a pulse - width modulation (PWM) sequence. According to this PWM sequence and the winding commutation control logic, control the winding current of the brushless DC motor, and adjust the opening of the actuator valve through the brushless DC motor and the gear system.

[0085] Based on the same inventive concept as the above - mentioned method, the embodiment of the present application also provides an automatic control system for an electrical system component, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps in the above - mentioned automatic control method for an electrical system component are implemented.

[0086] Through the above description of the embodiments in conjunction with the accompanying drawings, those skilled in the art can understand that for the convenience and brevity of description, only the above division of each functional module is used as an example. In actual applications, the above functions can be allocated to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above.

[0087] The above content is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should be covered by the protection scope of the present application.

Claims

1. An automatic control method for an electrical system component, characterized in that, The method includes the following steps: Obtain the PID control data of the regulating electric valve and the flow monitoring signals at each monitoring point of the regulating electric valve; Determine the time delay value between the flow monitoring signals at two monitoring points according to the correlation relationship between the flow monitoring signals at the two monitoring points; determine the signal fluctuation factor of each monitoring point in each short-time window according to the amplitude distribution of the flow monitoring signal of the monitoring point in the short-time window and the amplitude distribution of the flow monitoring signal of the next monitoring point after the time delay in the short-time window; Determine the interference probability of the flow monitoring signal of each monitoring point at each frequency according to the amplitude difference, phase difference and signal fluctuation factor of the flow monitoring signals included in all short-time windows of adjacent monitoring points in the frequency domain; obtain the interference frequency according to the interference probability of the flow monitoring signal of the monitoring point at all frequencies; Obtain the non-linear error probability according to the distribution of the interference frequencies at all adjacent monitoring points; adjust the proportional gain in the PID control data according to the non-linear error probability to complete the automatic control of the regulating electric valve; The determination of the interference probability of the flow monitoring signal of each monitoring point at each frequency includes: For any two adjacent monitoring points, use the window obtained by adding the time delay value to each short-time window of the flow monitoring signal of the previous monitoring point among the two monitoring points as the time delay window of each short-time window; Count all the overlapping sampling moments within all the short-time windows of the flow monitoring signal of the latter monitoring point among the two monitoring points and the time delay window of each short-time window, and use the window formed by combining all the short-time windows where the overlapping sampling moments are located as the combined window of each short-time window of the flow monitoring signal of the previous monitoring point; Based on the amplitude and phase at the same frequency of each short-time window of the flow monitoring signal of the previous monitoring point and the combined window, determine the fluctuation condition recovery coefficient of each frequency within each short-time window; Record the ratio of the signal fluctuation factor of each short-time window of the flow monitoring signal of the previous monitoring point to the fluctuation condition recovery coefficient as the parameter ratio, and use the mean value of the accumulated results of the difference between the constant parameter and the normalized result of the parameter ratio over all short-time windows as the interference probability of each frequency within each short-time window.

2. The automatic control method for an electrical system component according to claim 1, wherein The determination of the time delay value between the flow monitoring signals of two monitoring points includes: Preset the initial value of the time delay value between the flow monitoring signals of two monitoring points and the iteration step size; Construct a cross-correlation function by using the signal values of the flow monitoring signals of two monitoring points at different sampling moments and the time delay value between the flow monitoring signals of the two monitoring points; Calculate the function value of the cross-correlation function after each iteration during the time delay value iteration process, and use the time delay value corresponding to the maximum value among all the function values as the time delay value between the flow monitoring signals of the two monitoring points.

3. An automatic control method for an electrical system component according to claim 1, characterized in that, The determination of the signal fluctuation factor of each monitoring point in each short-time window includes: Obtain several short-time windows of the flow monitoring signal; Use the similarity measurement result between the amplitude distributions of the flow monitoring signals of each monitoring point and the next adjacent monitoring point of each monitoring point within the short-time windows in the same order as the signal fluctuation factor of each monitoring point within the short-time windows in the same order.

4. An automatic control method for an electrical system component as claimed in claim 3, characterized in that, The method for obtaining the short-time window is as follows: perform short-time Fourier transform on each traffic monitoring signal respectively to obtain a number of short-time windows of each traffic monitoring signal.

5. An automatic control method for an electrical system component as described in claim 1, characterized in that, The determination of the fluctuation condition recovery coefficient of each frequency within each short-time window includes: Taking the ratio of the combined window of each short-time window and the amplitudes at the same frequency within each short-time window as the amplitude change amount; Taking the ratio of the combined window of each short-time window and the phases at the same frequency within each short-time window as the phase change amount; The fluctuation condition recovery coefficient of each frequency within each short-time window of the traffic monitoring signal at the previous monitoring point consists of two parts: the amplitude change amount and the phase change amount; among them, the fluctuation condition recovery coefficient is positively correlated with the amplitude change amount and the phase change amount respectively.

6. The automatic control method for an electrical system component according to claim 1, characterized in that, The obtaining of the interference frequency according to the interference probability of the traffic monitoring signal at the monitoring point at all frequencies specifically includes: Calculating the interference probability of each frequency within all short-time windows of the traffic monitoring signal of each monitoring point respectively, and taking the frequency with the interference probability greater than the preset threshold as the interference frequency.

7. An automatic control method for an electrical system component as claimed in claim 1, characterized in that, The obtaining of the non-linear error probability according to the distribution of the interference frequencies at all adjacent monitoring points specifically includes: Obtaining all the interference frequencies of the traffic monitoring signal of each monitoring point respectively; obtaining the intersection of all the interference frequencies of the traffic monitoring signals of all monitoring points, and taking the interference frequencies in the intersection as the characteristic frequencies; Taking the mean value of the distribution variances of the signal amplitudes of the traffic monitoring signals of each monitoring point and the traffic monitoring signal of the next monitoring point of each monitoring point at all characteristic frequencies as the non-linear error generated by the interference of the traffic monitoring signal of each monitoring point; Performing normalization processing on all the non-linear errors based on the non-linear errors generated by the interference of the traffic monitoring signals of all monitoring points, and taking the normalization processing result of each non-linear error as a non-linear error probability.

8. An automatic control method for an electrical system component as claimed in claim 1, wherein, The adjustment of the proportional gain in the PID control data according to the non-linear error probability to complete the automatic control of the regulating electric valve includes: Taking the product of the sum value of the non-linear error probability of each monitoring point and the constant parameter and the proportional gain of the PID controller at the historical adjustment moment as the proportional gain of each monitoring point at the adjustment moment; Taking the mean value of the proportional gains of all monitoring points at the adjustment moment as the actual proportional gain of the PID controller at the adjustment moment, and generating a control signal by using the PID controller based on the actual proportional gain, and completing the automatic control of the opening of the regulating electric valve by using the control signal.

9. An automatic control system for an electrical system component, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the automatic control method for an electrical system component as described in any one of claims 1-8.

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