Method and system for detecting running state of automatic control loop

Abstract

The invention discloses an operation state detection method and system for an automatic control loop, and relates to the technical field of loop state detection, and the method comprises the steps: collecting and storing unit operation data; preprocessing the unit operation data; based on the performance evaluation indexes, constructing an automatic control system performance evaluation index system; constructing an automatic control loop operation state detection algorithm based on the automatic control system performance evaluation index system; and judging the operation state of the control loop through the automatic control loop operation state detection algorithm. The invention also discloses a method.

Description

Technical Field

[0001] This invention relates to the field of power grid technology, and in particular to a method and system for detecting the operating status of an automatic control loop. Background Technology

[0002] Large thermal power generating units commonly suffer from poor control quality due to factors such as aging of local equipment, actuator failure, and unreasonable controller parameters. Furthermore, the lack of a control loop performance evaluation mechanism makes it difficult to understand the system's status from routine operating data, resulting in low unit operating efficiency and impacting economic benefits. Therefore, developing a software system for real-time monitoring of the control loop's operating status and evaluation of its control performance is of paramount importance.

[0003] Most thermal automatic control loops are characterized by multivariable coupling and severe nonlinearity. The key to their performance evaluation lies in how to select effective evaluation indicators and construct reasonable evaluation methods. Therefore, a data-driven approach is needed, and by integrating various performance evaluation indicators (deterministic indicators, stochastic indicators, and suggestive indicators), a set of evaluation methods for thermal automatic control loops with good versatility and strong adaptability is proposed. Summary of the Invention

[0004] In view of the problems existing in the existing methods for detecting the operating status of automatic control loops, this invention is proposed.

[0005] Therefore, the problem to be solved by this invention is how to adopt a data-driven approach and propose a set of universal and adaptable methods for detecting the operating status of automatic control loops by integrating multiple performance evaluation indicators (deterministic indicators, stochastic indicators, and advisory indicators).

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, embodiments of the present invention provide a method for detecting the operating status of an automatic control loop, comprising: collecting and storing unit operating data; preprocessing the unit operating data; constructing an automatic control system performance evaluation index system based on performance evaluation indicators; constructing an automatic control loop operating status detection algorithm based on the automatic control system performance evaluation index system; and determining the operating status of the control loop through the automatic control loop operating status detection algorithm.

[0007] As a preferred embodiment of the automatic control loop operation status detection method of the present invention, the unit operation data is collected from the SIS database according to a certain time period and stored in the real-time database for application in the automatic control system performance evaluation index system.

[0008] In a preferred embodiment of the method for detecting the operating status of the automatic control loop described in this invention, the performance evaluation indicators include deterministic evaluation indicators, statistical evaluation indicators, and advisory evaluation indicators; the deterministic evaluation indicators include overshoot, decay rate, settling time, and rise time; when both the step process start flag and the validity flag are valid, historical data of process variables and setpoints are stored; when both the step process end flag and the validity flag are valid, the overshoot of the step process is calculated. σ Attenuation rate η, settling time t s and rising time t r ; wherein, the overshoot σ It is the maximum peak value of the system's step response. With steady-state value The difference between the steady-state values ​​and the steady-state values The percentage of the ratio; the adjustment time t s The rise time refers to the time required for the system's step response curve to enter the specified allowable error band and no longer exceed it; t r It refers to the time it takes for the system's step response curve to reach its first steady-state value from time zero; it is usually defined as the time required for the response curve to rise from 10% of the steady-state value to 90% of the steady-state value.

[0009] As a preferred embodiment of the automatic control loop operation status detection method of the present invention, the statistical evaluation index includes a minimum variance index and a Kendall coefficient index; the calculation of the minimum variance index includes the following steps: determining whether the Enable flag is valid; if invalid, the time series counter is cleared and returned; if valid, the PV time series is collected and stored; determining whether the time series counter has reached a given length; if not, the time series counter is incremented by 1 and returned; if reached, the least squares method is used to establish an AR (Advanced Variable Arithmetic) with a time delay of k. n The AR model was modeled, and the order of the AR model was determined using the AIC criterion; the minimum variance estimate was calculated. variance of actual output Then calculate performance indicators. η And provide the evaluation results.

[0010] As a preferred embodiment of the automatic control loop operation status detection method of the present invention, the calculation of the Kendall coefficient index includes the following steps: determining whether the control loop operation status abnormality flag, i.e., the Enable flag, is valid; if invalid, the control loop operation status is abnormal, and the process returns; if valid, the control loop operation status is normal, determining whether the system is in automatic state and whether the steady-state flag is valid; if invalid, the time series counter is cleared, and the process returns; if valid, the data is collected and stored. ER and PV The time series function checks if a time series counter has reached a given length. If not, it increments the counter and returns; otherwise, it returns. PV Calculation and ER sequential Kendall Correlation coefficient COEFF ;according to Kendall Correlation coefficient COEFF Give evaluation results FEN .

[0011] As a preferred embodiment of the operating status detection method for the automatic control loop described in this invention, the suggested evaluation indicators include the Hurst exponent, the relative damping exponent, and the impulse response algorithm; wherein, when the enable flag is active, the Hurst exponent is a stored process variable. PV Historical data is used to construct a time series. The Hurst exponent of the time series is calculated to determine the state of the control system and provide recommendations. The specific formula is as follows: ; in, L Indicates the window length; α The slope of the straight line is represented by the Hurst exponent; if α =0.5 indicates that the control system has good performance. K p , T i The parameters are reasonable; if 0 ≤ α A value less than 0.5 indicates that the control system is over-adjusted and needs to be reduced appropriately. K p , T i If 0.5≤ α A value less than 1 indicates that the control system is under-adjusted and needs to be appropriately increased. K p , T i ;if α> 1 indicates that the control system is under-adjusted and requires a slight increase. K p , T i .

[0012] The relative damping index is calculated based on the damping coefficient of the second-order model to determine the control performance index. The impulse response algorithm includes the following steps: determining whether the Enable flag, which indicates an abnormal control loop state, is valid; if invalid, the control loop state is abnormal, and the algorithm returns; if valid, the control loop state is normal, determining whether the system is in automatic mode and whether the steady-state flag is valid; if invalid, the time series counter is cleared, and the algorithm returns; if valid, the PV time series is collected and stored, and the time series counter is checked to see if it has reached the given length; if not, the time series counter is incremented by 1, and the algorithm returns; if it has reached the given length, the PV value is zero-mean processed; the least squares method is used to establish the AR(n) model, and the AIC criterion is used to determine the order of the AR model; the AR model residuals are calculated, and the ARMA (ARMA) model is established. p , q The applicability of the ARMA model is tested using the autocorrelation coefficient and cross-correlation coefficient. The GREEN function sequence of the ARMA model is calculated to obtain its minimum value MING, the location of the minimum value LOCMING, and the number of oscillations OSC, and the evaluation results are given.

[0013] As a preferred embodiment of the automatic control loop operation status detection method of the present invention, the automatic control loop operation status detection algorithm includes the following steps: determining whether the system is in automatic mode; if it is in manual mode, no determination is made; if it is in automatic mode, determining whether the lengths of the setpoint SP sequence and the controlled variable PV sequence have reached the set sequence length based on the sequence length counter; if the lengths of the setpoint SP sequence and the controlled variable PV sequence have reached the set sequence length, calculating the average value of the setpoint SP sequence and the controlled variable PV sequence collected within that length time. , The time counter is incremented by 1 if the absolute value of the difference is greater than the allowable deviation; otherwise, the time counter is cleared. If the product of the time counter value and the sampling time is greater than the given time threshold, it is determined that the long-term deviation between the set value SP and the controlled variable PV is large, and the control loop status abnormality flag is set to OFF; otherwise, the control loop status abnormality flag is set to ON.

[0014] Secondly, embodiments of the present invention provide an automatic control loop operation status detection system, comprising: a data acquisition module for acquiring unit operation data from the SIS database at a certain time period and storing it in a real-time database; a data preprocessing module for preprocessing the acquired unit operation data to prepare for subsequent analysis; a performance evaluation index system construction module for constructing an automatic control system performance evaluation index system based on performance evaluation indicators; an operation status detection algorithm module for constructing a detection algorithm for judging the operation status of the control loop based on the performance evaluation index system; and a status judgment and output module for judging the operation status of the control loop using the operation status detection algorithm and outputting the evaluation results.

[0015] Thirdly, embodiments of the present invention provide a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement any step of the above-described automatic control loop operation status detection method.

[0016] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the above-described method for detecting the operating status of an automatic control loop.

[0017] The beneficial effects of this invention are significant. Firstly, the system, through its data acquisition and preprocessing modules, can efficiently process large amounts of operational data, ensuring data accuracy and reliability. Secondly, by constructing a performance evaluation index system and operational status detection algorithm, the system can comprehensively evaluate the performance of the control loop and promptly identify potential problems and anomalies. This not only improves the stability and reliability of the control system but also helps prevent faults and reduce maintenance costs. Furthermore, the application of the status judgment and output modules makes the evaluation results intuitive and easy to understand, facilitating quick judgment and adjustments by operators. In summary, through the coordinated work of these modules, this invention greatly improves the monitoring and evaluation efficiency of automatic control loops, contributing to enhanced overall system performance and safety. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein: Figure 1 This is an overall flowchart of the method for detecting the operating status of an automatic control loop.

[0019] Figure 2 The step response curve of a process control system for detecting the operating status of an automatic control loop. Detailed Implementation

[0020] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0021] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0022] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0023] This invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of this invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not adhering to the usual scale. Furthermore, the schematic diagrams are merely examples and should not be construed as limiting the scope of protection of this invention. In actual fabrication, the three-dimensional spatial dimensions of length, width, and depth should be included.

[0024] Furthermore, in the description of this invention, it should be noted that the terms "upper," "lower," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used solely for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. In addition, the terms "first," "second," or "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0025] Unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" in this invention should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; similarly, they can refer to mechanical connections, electrical connections, or direct connections, or indirect connections through an intermediate medium, or internal connections between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0026] Example 1 Reference Figure 1 and Figure 2 This is the first embodiment of the present invention, which provides a method for detecting the operating status of an automatic control loop. Based on the structure of the automatic control loop, the thermal automatic control system of Unit 1 of Fujian Huadian Kemen Power Generation Co., Ltd. is divided into: a single-loop feedback control loop, a single-loop feedback feedforward control loop, a cascade control loop, a cascade feedforward control loop, and other control loops. For all control loops, an automatic control loop operating status detection algorithm is designed to determine whether the actuator is functioning normally and whether the deviation between the setpoint and the feedback value is consistently large. For different types of automatic control loops, a corresponding performance evaluation index system is proposed, and a calculation method for the performance index is designed. The main work content is as follows: Automatic control loops include single-loop feedback control loops, single-loop feedback feedforward control loops, cascade control loops, cascade feedforward control loops, and other control loops.

[0027] The single-loop feedback control loop identifies the main characteristic parameters of the object, such as gain coefficient, inertia time constant, and pure delay time. It calculates the deterministic indices (attenuation rate, overshoot, rise time, steady-state time), statistical indices (variance of control loop output), and suggested indices (Hurst exponent, relative damping exponent, impulse response exponent) of the control loop online. The results are compared and analyzed with the standards in the operating guidelines for power plant thermal control systems, providing corresponding evaluation results and adjustment suggestions.

[0028] The single-loop feedback feedforward control loop identifies the main characteristic parameters of the object; calculates the deterministic indices (attenuation rate, overshoot, rise time, steady-state time), statistical indices (control loop output variance), and advisory indices (Hurst exponent, relative damping exponent, impulse response exponent) of the control loop online; calculates the performance index (Kendall coefficient) and evaluation score of the feedforward controller, and provides a comprehensive evaluation result and adjustment suggestions for the control loop.

[0029] The cascade control loop identifies the main characteristic parameters of the primary and secondary objects; it calculates the deterministic indices (attenuation rate, overshoot, rise time, steady-state time), statistical indices (output variance of the primary loop), and advisory indices (Hurst exponent, relative damping exponent, impulse response exponent) of the primary loop online; it calculates the performance indices (output variance of the secondary loop) and evaluation scores of the secondary loop, and provides a comprehensive evaluation result and adjustment suggestions for the control loop.

[0030] The cascade feedforward control loop identifies the main characteristic parameters of the primary and secondary objects; it calculates the deterministic indices (attenuation rate, overshoot, rise time, steady-state time), statistical indices (output variance of the primary loop), and advisory indices (Hurst exponent, relative damping exponent, impulse response exponent) of the primary loop online; it calculates the performance indices (output variance of the secondary loop) and evaluation scores of the feedforward controller; it calculates the performance indices (Kendall coefficient) and evaluation scores of the feedforward controller; and it provides a comprehensive evaluation result and adjustment suggestions for the control loop.

[0031] A method for detecting the operating status of an automatic control loop includes the following steps: S1: Collect and store unit operation data.

[0032] Specifically, unit operating data is collected from the SIS database at certain time intervals and stored in a real-time database for use in the performance evaluation index system of the automatic control system. Based on the plant-level SIS system database, the collection and storage of unit operating data are carried out to complete the online performance evaluation of each thermal automatic control system of the unit. The evaluation results are then presented in the form of graphs, tables, etc., to relevant personnel in thermal engineering and management at Fujian Huadian Kemen Power Generation Co., Ltd. through a client interface, providing technical support for improving the maintenance level and optimizing the quality of the unit's automatic control system.

[0033] S2: Preprocess the unit's operating data.

[0034] S3: Based on performance evaluation indicators, construct a performance evaluation index system for automatic control systems.

[0035] Specifically, performance evaluation indicators include deterministic evaluation indicators, statistical evaluation indicators, and advisory evaluation indicators.

[0036] like Figure 2 As shown, deterministic evaluation metrics, also known as time-domain metrics, mainly include overshoot, decay rate, settling time, and rise time. When both the start and end flags of the step process are valid, historical data of the process variables and setpoints are stored; when both the end and end flags of the step process are valid, the four time-domain metrics of the step process are calculated: overshoot... σ Attenuation rate η, settling time ts and rising time t r .

[0037] Specifically, overshoot σ It is the maximum peak value of the system's step response. With steady-state value The difference between the steady-state values ​​and the steady-state values The percentage of the ratio is calculated using the following formula: ; The formula for calculating the attenuation rate η is as follows: ; Specifically, among them, y 1. y 3 See Figure 2 .

[0038] Adjusting time t s This refers to the system's step response curve entering the specified allowable error band range. And the time required to no longer exceed the error band is calculated using the following formula: ; Ascent Time t r It refers to the time it takes for the system's step response curve to reach its first steady-state value from time zero; it is usually defined as the time required for the response curve to rise from 10% of the steady-state value to 90% of the steady-state value.

[0039] Furthermore, statistical evaluation indicators include the minimum variance indicator and the Kendall coefficient indicator.

[0040] The calculation of the minimum variance index includes the following steps: Check if the Enable flag is valid. If invalid, clear the time series counter and return.

[0041] If valid, collect and store the PV time series, determine if the time series counter has reached the given length, and if not, increment the time series counter by 1 and return.

[0042] If this is achieved, the least squares method is used to establish an AR(k) with a time delay of k. n The AR model is modeled and its order is determined using the AIC criterion. Specifically, the AR( n )Model parameters : N : Output data length n The order of the AR model is given by the following formula: ; ; ; ; ; ; AIC Criteria: ; AIC takes the order of the minimum value as the order of the AR model.

[0043] Calculate the minimum variance estimate variance of actual output Then calculate performance indicators. η The evaluation result, Evaluate, is given, and the specific formula is as follows: Minimum variance estimate for: ; The actual output variance for: ; Then performance indicators for: .

[0044] Furthermore, the Kendall coefficient index includes the following: (1) Calculation PV and ER The number of consistent element pairs C and the number of inconsistent element pairs D in a time series: when and At that time, if and or and When this happens, the two elements are considered to be the same, and C = C + 1.

[0045] like and or and When this happens, the two elements are considered inconsistent, and D = D + 1.

[0046] in, , N for ER and PV The length of the sequence.

[0047] (2) Calculation PV and ERThe number of elements in each subset of the sequence that consists of identical elements YPAIR and EPAIR : First PV All numbers in the sequence and PV (1) Compare and obtain the results with PV (1) The number of elements with the same value is recorded as follows: YPAIR (1), then reset the numbers at the corresponding positions of these identical elements to 0. Then select PV The next non-zero number in the sequence is then compared, and the number of elements obtained is recorded as follows. YPAIR (2) and set the values ​​at the corresponding positions of these elements to 0, and so on, until... PV The sequence ends when all zeros are present.

[0048] EPAIR Obtained using the same method described above.

[0049] (3) Calculation kendall Correlation coefficient COFEE : ; in, ; ; ; u and v They represent YPAIR and EPAIR The length of the sequence.

[0050] In summary, by calculating the interference signal of the feedforward channel and the system output signal... Kendall The correlation coefficient is used to determine the feedforward compensation situation. The main steps include: (1) First, determine whether the control loop working state abnormal flag Enable flag is valid. If it is invalid, the control loop working state is abnormal and return.

[0051] (2) If valid, the control loop is in normal working state. Determine whether the system is in automatic state and whether the steady state flag is valid. If invalid, clear the time series counter and return.

[0052] (3) If valid, collect and store. ER and PV The time series function checks if the time series counter has reached the given length. If not, it increments the time series counter by 1 and returns.

[0053] (4) If achieved, PV Calculation and ER sequential Kendall Correlation coefficientCOEFF .

[0054] (5) According to Kendall Correlation coefficient COEFF Give evaluation results FEN .

[0055] Furthermore, suggested evaluation metrics include the Hurst exponent, relative damping exponent, and impulse response algorithm.

[0056] Among them, the Hurst exponent, when the enable flag is valid, is a stored procedure variable. PV Historical data is used to construct a time series, and the Hurst exponent of the time series is calculated to determine the state of the control system and provide recommendations.

[0057] (1) For those containing N Time series of data Y Calculate its average value ; ; (2) Calculation Y cumulative deviation sequence ; (3) For the cumulative deviation series Based on window length L Divide it into W Non-overlapping intervals of equal length, ( W = N / L (take integers); for each interval, use the least squares method to evaluate its constituent elements. L Perform a first-order linear fit on each data point: set up L The data points are as follows: Then we have: ; In the j The fitting results over the intervals are ; Calculate the first j The sum of the mean squared errors of each interval after filtering out the trend: ; Calculate the DFA wave function F ( L ): ; (4) Select different window lengths L Multiple sets can be obtained ( L , F ( L)),but F ( L )and L Satisfies the power law relationship: ; The algorithm's time series length N =450, based on window length L The selection range is ,Pick L ={ A total of 10 groups were obtained. L , F ( L )).

[0058] (5) In double logarithmic coordinates (ln( L ), ln( F ( L In the process, the least squares method is used to fit the data points, resulting in... ; The slope of the straight section α This refers to the Hurst exponent.

[0059] if α =0.5 indicates that the control system has good performance. K p , T i The parameters are reasonable; if 0 ≤ α A value less than 0.5 indicates that the control system is over-adjusted and needs to be reduced appropriately. K p , T i If 0.5≤ α A value less than 1 indicates that the control system is under-adjusted and needs to be appropriately increased. K p , T i ;if α> 1 indicates that the control system is under-adjusted and requires a slight increase. K p , T i .

[0060] The relative damping index is used as a benchmark for calculating control performance indicators based on the damping coefficient of the second-order model. The specific process is as follows: (1) Find the autocorrelation sequence of the PV time series. R : in, YK This is a PV time series; N The length of the PV time series; , (2) Least squares fitting: ; in, n This refers to the model order, which is 2 in this case. u=1; (3) Based on parameters Find the pulse curve: ; (4) Calculate the damping coefficient SACT : pass R 1 = abs( G (i)- G (1) This form can transform the autocorrelation sequence into a step response form, and then the damping coefficient can be calculated using the following formula. SACT : ; ; (5) Calculate the relative damping exponent ( RDI ): ; in, SAGG The damping of the system under controlled oscillation conditions; SSLUG This represents the damping of the system under sluggish control conditions. When... SACT = SSLUG hour, RDI =0. When RDI When >0, control is good; when RDI When -1 < -1, control is sluggish; when -1 < RDI When <0, control the oscillation.

[0061] In summary, the algorithm calculates the control performance index based on the damping coefficient of the second-order model. The main steps include: (1) First, determine whether the Enable flag of the control loop working state is valid. If it is invalid, the control loop working state is abnormal and return.

[0062] (2) If valid, the control loop is in normal working state. Determine whether the system is in automatic state and whether the steady state flag is valid. If invalid, clear the time series counter and return.

[0063] (3) If valid, collect and store the PV time series, determine whether the time series counter has reached the given length, if not, increment the time series counter by 1 and return.

[0064] (4) If the value is reached, calculate the autocorrelation coefficient R of the PV value.

[0065] (5) If R (1) < 0.5, return; otherwise, fit the autocorrelation curve using the least squares method and calculate the damping coefficient of the second-order model. SACT .

[0066] (6) Calculate the relative damping exponent RDI And give the evaluation result FEN.

[0067] Furthermore, the impulse response algorithm uses the impulse response as the evaluation benchmark to calculate control performance indicators. The specific process is as follows: (1) Zero-mean processing of PV time series: ; ; in, N The length of the PV time series; Y The sequence is a PV time series; y The sequence is the processed time series.

[0068] (2) Find AR( n )Model parameters : First, construct matrices X and Y: ; ; Then calculate the parameter Φ: in, N The length of the time series; n The AR model order is K; K = Tau / DT is the system delay time; Tau is the delay time. ; ; (3) Find the residual sequence of the AR model. : (4) Determine the order of the AR model using the AIC criterion: AIC Guidelines: ; in, n Take 10 to 40 and iterate from (1) sequentially. The order of the AR model is taken as . AIC The smallest value n The value is recorded, and the residual sequence at this moment is saved. .

[0069] (5) Find ARMA( p,q Model parameters: ; make N 2=1, p =2* N 2, q =2* N 2-1, First construct matrices X6, Y1: ; Then calculate the parameters. β : in, N The length of the time series; n For AR model order; p , q For the order of the ARMA model; parameter express ; ; (6) Find the residual sequence of the ARMA model e : (7) Test the applicability of the model: ; e autocorrelation coefficient sequence r : e and y Cross-correlation sequence v : The model is applicable when both the r and v sequences gradually approach 0; otherwise... N 2=N 2+1, p =2* N 2, q =2* N 2-1, Continue iterative calculation from (5) until the condition is met.

[0070] (8) Find the Green's function for the ARMA model: ; In summary, the algorithm completes the calculation of control performance index based on impulse response. The main steps include: (1) First, determine whether the control loop working state abnormal flag Enable flag is valid. If it is invalid, the control loop working state is abnormal and return.

[0071] (2) If valid, the control loop is in normal working state. Determine whether the system is in automatic state and whether the steady state flag is valid. If invalid, clear the time series counter and return.

[0072] (3) If valid, collect and store the PV time series, determine whether the time series counter has reached the given length, if not, increment the time series counter by 1 and return.

[0073] (4) If the target is reached, the PV value is treated as zero mean.

[0074] (5) Establish AR( using the least squares method) n The AR model was modeled and its order was determined using the AIC criterion.

[0075] (6) Calculate the AR model residuals and establish ARMA( p,q The applicability of the ARMA model was tested using the autocorrelation coefficient and cross-correlation coefficient.

[0076] (7) Calculate the GREEN function sequence of the ARMA model, obtain its minimum value MING, the position of the minimum value LOCMING and the number of oscillations OSC, and give the evaluation result FEN.

[0077] S4: Based on the performance evaluation index system of automatic control systems, construct an algorithm for detecting the operating status of automatic control loops.

[0078] Specifically, the automatic control loop operation status detection algorithm refers to the setpoint and controlled variable deviation detection algorithm. The function of this algorithm is to detect whether the setpoint SP and the controlled variable PV have a large long-term deviation, in order to determine whether the control loop status is abnormal.

[0079] (1) At time t For those containing N Time series of data Calculate its average value : ; ; (2) If ,but ,otherwise .

[0080] (3) If ,but: ; otherwise .

[0081] In summary, the automatic control loop operation status detection algorithm includes the following steps: (1) Determine whether the system is in automatic state; if it is in manual state, no determination is made. (2) If it is in automatic mode, the sequence length counter determines whether the lengths of the set value SP sequence and the controlled value PV sequence have reached the set sequence length.

[0082] (3) If the lengths of the setpoint SP sequence and the controlled variable PV sequence reach the set sequence length, then calculate the average value of the setpoint SP sequence and the controlled variable PV sequence collected within that time period. , The time counter is incremented by 1 if the absolute value of the difference is greater than the allowable deviation, and otherwise the time counter is reset to zero.

[0083] (4) If the product of the time counter value and the sampling time is greater than the given time threshold, it is determined that the long-term deviation between the set value SP and the controlled variable PV is large, and the control loop status abnormality flag is set to OFF; otherwise, the control loop status abnormality flag is set to ON.

[0084] S5: The operating status of the control loop is determined through an automatic control loop operation status detection algorithm.

[0085] In summary, the automatic control loop operation status detection system of the present invention has significant beneficial effects. First, through its data acquisition and preprocessing modules, the system can efficiently process large amounts of operational data, ensuring data accuracy and reliability. Second, by constructing a performance evaluation index system and an operation status detection algorithm, the system can comprehensively evaluate the performance of the control loop and promptly detect potential problems and anomalies. This not only improves the stability and reliability of the control system but also helps prevent faults and reduce maintenance costs. Furthermore, the application of the status judgment and output modules makes the evaluation results intuitive and easy to understand, facilitating quick judgment and adjustments by operators. Overall, through the coordinated work of these modules, the present invention greatly improves the monitoring and evaluation efficiency of automatic control loops, contributing to enhanced overall system performance and safety.

[0086] Example 2 Building upon the first embodiment, this embodiment further provides an automatic control loop operation status detection system, comprising: a data acquisition module for collecting unit operation data from the SIS database at certain time periods and storing it in a real-time database; a data preprocessing module for preprocessing the collected unit operation data to prepare for subsequent analysis; a performance evaluation index system construction module for constructing an automatic control system performance evaluation index system based on performance evaluation indicators; an operation status detection algorithm module for constructing a detection algorithm for judging the operation status of the control loop based on the performance evaluation index system; and a status judgment and output module for judging the operation status of the control loop using the operation status detection algorithm and outputting the evaluation results.

[0087] This embodiment also provides a computer device applicable to the method for detecting the operating status of an automatic control loop, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the method for detecting the operating status of an automatic control loop as proposed in the above embodiment.

[0088] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0089] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the method for detecting the operating status of an automatic control loop as proposed in the above embodiments.

[0090] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0091] Example 3 This is the third embodiment of the present invention. Based on the first two embodiments, this embodiment provides a method for detecting the operating status of an automatic control loop. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0092] The real-time database serves as a bridge between the power plant's SIS database and the performance evaluation support platform, playing a crucial role in storing raw data and reading / writing evaluation results. Therefore, establishing a robust storage system is essential for smooth communication and convenient data retrieval. In this project, a total of 120 tables were established. The "quanchang" table contains evaluation information for the entire power plant's control loop, such as the number of control loops rated as excellent, good, average, or poor, and monthly automatic start-up rates. The remaining 119 tables each correspond to a control loop: TB and DB series represent boiler-side loops, BL series represent turbine-side loops, and ZL series represent other side loops. Each table primarily contains calculated values ​​for various indicators, as well as statistical quantities such as average, maximum, and minimum values. Specific loop classification information is shown in Table 1. Table 1. Circuit Classification Information Table

[0093] According to the automatic control loop operation status detection method, the following steps are performed: S1: Collect and store unit operation data.

[0094] S2: Preprocess the unit's operating data.

[0095] S3: Based on performance evaluation indicators, construct a performance evaluation index system for automatic control systems.

[0096] S4: Based on the performance evaluation index system of the automatic control system, construct an algorithm for detecting the operating status of the automatic control loop.

[0097] S5: The operating status of the control loop is determined by the automatic control loop operation status detection algorithm.

[0098] The results are shown in Table 2: Table 2 Comparison with Existing Technologies

[0099] Therefore, the method of this invention improves detection accuracy through enhanced data analysis techniques. More efficient algorithms enable faster data processing. The method optimizes the system structure, enhancing stability. Improved user interface and interaction design increase user-friendliness. By reducing system complexity, this method reduces maintenance costs.

[0100] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method of detecting an operating state of an automatic control loop, characterized by: Comprising, collecting and storing unit operation data; preprocessing the unit operation data; constructing an automatic control system performance evaluation index system based on performance evaluation indexes; constructing an automatic control loop operation state detection algorithm based on the automatic control system performance evaluation index system; judging the control loop operation state through the automatic control loop operation state detection algorithm.

2. The method of claim 1, wherein: The unit operation data is collected from the SIS database according to a certain time period and stored in the real-time database for application of the automatic control system performance evaluation index system.

3. The method of claim 2, wherein: the automatic control loop is a proportional-integral-derivative (PID) control loop. The performance evaluation indexes include deterministic evaluation indexes, statistical evaluation indexes and suggested evaluation indexes; The deterministic evaluation indexes include overshoot, decay rate, regulation time and rise time; When both the step process start flag and the valid flag are valid, store the history data of the process variable and the set value; when both the step process end flag and the valid flag are valid, calculate the overshoot of the step process The statistical evaluation indexes include minimum variance indexes and Kendall coefficient indexes; , the damping rate η, the regulation time t s and the rise time t r ; wherein the overshoot The calculation of the minimum variance indexes includes the following steps: is the percentage of the difference between the maximum peak value of the step response of the system and the steady state value to the ratio of the difference to the steady state value ; the adjustment time t s the time required for the system step response curve to enter the prescribed allowable error band and no longer to exceed the error band; the rise time t r refers to the time for the system step response curve to reach the steady state value for the first time; usually defined as the time required for the response curve to rise from 10% of the steady state value to 90% of the steady state value.

4. The method of claim 3, wherein: determining whether the Enable flag is valid, if not, the time series counter is cleared and returned; if valid, collecting and storing the PV time series, determining whether the time series counter reaches the given length, if not, the time series counter is incremented by 1 and returned; The calculation of the Kendall coefficient indexes includes the following steps: determining whether the control loop working state abnormal flag, i.e. the Enable flag, is valid, if not, the control loop working state is abnormal and returned; If this is achieved, the least squares method is used to establish an AR(k) with a time delay of k. n The AR model was modeled and its order was determined using the AIC criterion. Computing minimum variance estimates With actual output variance , and computing performance indicators if valid, the control loop working state is normal, determining whether the system is in automatic state and the steady state flag bit is valid, if not, the time series counter is cleared and returned; And giving evaluation results.

5. The method of claim 4, wherein: the automatic control loop is a proportional-integral-derivative (PID) control loop. The suggested evaluation indexes include Hurst index, relative damping index and pulse response algorithm. The relative damping index is based on the damping coefficient of the second-order model for control performance index calculation; The pulse response algorithm includes the following steps: determining whether the control loop working state abnormal flag Enable flag is valid, if not, the control loop working state is abnormal and returned; if valid, the control loop working state is normal, determining whether the system is in automatic state and the steady state flag bit is valid, if not, the time series counter is cleared and returned; If valid, collect and store if valid, collecting and storing the PV time series, determining whether the time series counter reaches the given length, if not, the time series counter is incremented by 1 and returned; if valid, the PV value is processed to zero mean; and establishing an AR(n) model using the least square method and using AIC criterion to determine the order of the AR model; time series, determine if time series counter reaches given length, if not, time series counter +1, return; if reached, calculating the GREEN function sequence of the ARMA model, obtaining the minimum value MING, the position of the minimum value LOCMING and the oscillation number OSC, and giving the evaluation result. calculations and The automatic control loop operation state detection algorithm includes the following steps: sequences of determining whether the system is in automatic state, if in manual state, no judgment is made; correlation coefficients if in automatic state, determining whether the length of the set value SP sequence and the length of the regulated variable PV sequence reach the set sequence length according to the sequence length counter; ; According to ​ correlation coefficient ​ give the evaluation result ​ .

6. The method of claim 5, wherein: ​ Wherein, the Hurst index when the enable flag is valid state, storage process variable ​ History data, constitute time series, calculate the Hurst index of time series, judge the state of control system and give suggestions, the specific formula is as follows: ； wherein, L represents the window length; α represents the slope of the straight line part, i.e. the Hurst index; if α = 0.5, it indicates that the control system performance is good, K p , T i the parameters are reasonable; if 0 α < 0.5, it indicates that the control system is in over-tuning, and the K p , T i tuning needs to be appropriately increased; α < 1, it indicates that the control system is in under-tuning, and the K p , T i tuning needs to be appropriately increased; α> 1, it indicates that the control system is in under-tuning, and the K p , T i tuning needs to be slightly increased; ​ ​ ​ ​ Calculate the AR model residual, establish ARMA( p , q ) model, use autocorrelation coefficient and cross-correlation coefficient to test the applicability of ARMA model; ​ 7. The method of claim 6, wherein: the automatic control loop is a proportional-integral-derivative (PID) control loop. ​ ​ ​ If the length of the set value SP sequence and the length of the regulated quantity PV sequence reaches the set sequence length, the average value of the set value SP sequence and the regulated quantity PV sequence collected in the length of time is calculated , , and the difference is calculated; if the absolute value of the difference is greater than the allowed deviation, the time counter is incremented by 1; otherwise, the time counter is cleared. If the product of the value of the time counter and the sampling time is greater than a given time threshold, it is determined that the set value SP and the controlled variable PV are long-term deviated, and the control loop state abnormality flag is set OFF; otherwise, the control loop state abnormality flag is set ON.

8. A system for detecting an operating state of an automatic control loop, based on the method for detecting an operating state of an automatic control loop according to any one of claims 1 to 7, characterized in that: The method comprises the following steps: A data acquisition module is configured to acquire unit operation data from an SIS database according to a certain time period and store the unit operation data in a real-time database. A data preprocessing module is configured to preprocess the acquired unit operation data to prepare for subsequent analysis. A performance evaluation index system construction module is configured to construct a performance evaluation index system of the automatic control system based on performance evaluation indexes. An operation state detection algorithm module is configured to construct a detection algorithm for judging the operation state of the control loop based on the performance evaluation index system. A state judgment and output module is configured to judge the operation state of the control loop by using the operation state detection algorithm and output an evaluation result. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is characterized in that: The processor executes the computer program to implement the steps of the operation state detection method of the automatic control loop according to any one of claims 1-7.

10. A computer readable storage medium having stored thereon a computer program, characterized in that: The computer program is executed by the processor to implement the steps of the operation state detection method of the automatic control loop according to any one of claims 1-7.

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