Ventilation system distributed cooperative control method and system for optimizing incidence matrix based on impedance method

Abstract

The invention provides a ventilation system distributed cooperative control method and system for optimizing an incidence matrix based on an impedance method. According to the method, the length L and the diameter D of a target air outlet volume straight-through pipeline of each tail end node are set; the method comprises the steps that a ventilation pipeline system is equivalent to a circuit, pipeline impedance is subdivided into on-way impedance and local impedance, equivalent resistance between two air valves is calculated through the Thevenin theorem and normalized, and an incidence matrix describing the tail end relation is obtained; then, an intelligent air valve is used for collecting the actual air outlet volume qi of the tail end, the flow ratio xi is calculated, and a distributed cooperative control method is used for adjusting the angle of the air valve; and finally, the total flow ratio alpha is used for adjusting the fan voltage, so that the system achieves wind balance. According to the invention, wind balance control with low iterations can be realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent control, and particularly relates to a ventilation system distributed collaborative control method and system based on impedance method optimization of a correlation matrix. BACKGROUND

[0002] Under the background of continuous progress of technology development and continuous growth of population, the proportion of energy consumption of a heating, ventilation and air-conditioning (HVAC) system is relatively high, and how to efficiently use energy is imminent. As an important part of the HVAC system, the ventilation system provides air volume to the required space and provides high-quality air for indoor personnel to improve indoor thermal comfort. If the ventilation system fails to accurately provide air volume, it may lead to a decrease in indoor air quality, affect personnel work efficiency, and even cause health problems. Therefore, accurate and rapid air balance control of the ventilation system can not only improve indoor personnel comfort, but also improve the work efficiency of the ventilation system to achieve the purpose of energy saving.

[0003] The purpose of air balance is to accurately distribute target flow to the ventilation system pipeline, which is achieved by adjusting the source end fan voltage and the end air valve angle to avoid energy waste and comfort decline caused by unbalanced air volume distribution. As a key technology for reducing system energy consumption and improving indoor air quality, in early applications, engineers often use the proportional method for adjustment, which is essentially a trial-and-error method that requires manual repeated start-stop system adjustment. This method highly depends on personal experience and the adjustment process is tedious, resulting in a large amount of energy waste, and is no longer applicable today with complex ventilation pipelines. In order to overcome the defects of such methods, some studies have proposed offline control methods based on physical modeling or machine learning. However, such methods cannot respond to system dynamic changes in real time and are only suitable for system debugging or maintenance stages, limiting their scope of application. Today, the online control method effectively solves the problem of not being able to respond to system dynamic changes in real time, and this air balance method is being adopted. The online control method is a method of sequentially adjusting the source and end: first fix the source end fan voltage, adjust the angle of each end air valve to make the flow ratio of each end consistent, and then adjust the fan voltage through the system target flow to achieve air balance. SUMMARY

[0004] The present application is aimed at the existing three-stage DCC air balance, i.e., distributed collaborative control, which uses a correlation matrix of 01 matrix, i.e., 1 for adjacent and 0 for non-adjacent, which forcibly decouples each end node of the ventilation system, ignoring the coupling relationship between non-adjacent nodes, and has the problem of excessive iteration caused by ineffective decoupling.

[0005] To address the above problems, this invention provides a distributed collaborative control method and system for ventilation systems based on impedance method for optimizing the correlation matrix. The technical solution is as follows:

[0006] A distributed collaborative control method and system for a ventilation system based on impedance method for optimizing the correlation matrix;

[0007] The ventilation system consists of a main fan, ventilation ducts and intelligent air valves, with an intelligent air valve installed at each terminal node.

[0008] The distributed collaborative control method requires constructing an association matrix before implementing control.

[0009] The construction of the correlation matrix includes the following steps:

[0010] The ventilation system is equivalent to a corresponding equivalent circuit, where the nodes of the equivalent circuit are intelligent air valves, the current is the flow rate of the ventilation system, and the voltage is the pipeline pressure loss of the ventilation system.

[0011] Calculate the friction impedance and local impedance based on the target flow rate at the end, and then convert the ventilation system into an equivalent circuit.

[0012] The equivalent resistance between each intelligent air valve is solved using Thevenin's theorem.

[0013] Based on the obtained equivalent resistance, construct the first correlation matrix;

[0014] The first correlation matrix is ​​normalized to obtain the second correlation matrix;

[0015] The voltage of the main fan and the angle of each intelligent air valve are adjusted based on the second correlation matrix.

[0016] As one possible implementation method:

[0017] Set the target flow rate at each end point, based on the friction factor λ, fluid density ρ, and straight pipe length L. m Target air volume Q in straight-through duct m and the diameter D of the straight pipe m Calculate the friction impedance Z m Based on the local resistance coefficient ξ, fluid density ρ, and target air volume Q in the pipeline. j and pipe diameter D j Calculate the local impedance Z f .

[0018] Based on the calculated friction impedance and local impedance, the equivalent circuit is obtained, and the equivalent resistance is calculated using Thevenin's theorem.

[0019] As one possible implementation method:

[0020] The construction of the correlation matrix based on the equivalent resistance includes the following steps:

[0021] The impedance and the local impedance are equivalent and simplified, so as to obtain an equivalent circuit describing the connection relationship between the end nodes; based on the equivalent circuit, the equivalent resistance between the end nodes is solved respectively; when the equivalent resistance between the end nodes is solved, the remaining end nodes are short-circuited; and finally a first correlation matrix based on the equivalent resistance is obtained.

[0022] As an implementable manner:

[0023] The step of normalizing the first correlation matrix based on the equivalent resistance is:

[0024] The reference correlation matrix is constructed based on the experimental test, the maximum value and the minimum value of the reference correlation matrix are extracted; the first correlation matrix based on the equivalent resistance is normalized based on the maximum value and the minimum value, to obtain a second correlation matrix; the method of normalization is that the minimum value of the first correlation matrix based on the equivalent resistance is mapped to the maximum value of the second correlation matrix, the maximum value of the first correlation matrix based on the equivalent resistance is mapped to the minimum value of the second correlation matrix, and other parameters in the first correlation matrix based on the equivalent resistance are normalized in proportion.

[0025] As an implementable manner:

[0026] The reference correlation matrix constructed based on the experimental test includes the following steps:

[0027] The fan voltage is set as a first reference voltage, the angle of all end intelligent air valves is set as a first reference angle, the current flow of each end is collected after the fan is stable, then the angle of the end intelligent air valve 1 is set as a second reference angle, the flow of each end at this time is collected after the reference time, and the ratio of the flow difference value of the end intelligent air valves 2-5 to the flow difference value of the end intelligent air valve 1 is calculated as the first row weight coefficient of the reference correlation matrix;

[0028] According to the above method, the angles of the air valves 2-5 are sequentially changed to the second reference angle, and then the ratio of the flow difference value of the remaining air valves to the current air valve is calculated as the weight coefficient of the next several rows of the reference correlation matrix.

[0029] As an implementable manner:

[0030] The range of the first reference voltage is 2-7V;

[0031] The first reference angle is less than the second reference angle.

[0032] As an implementable manner:

[0033] The first reference voltage is 4V;

[0034] The first reference angle is 20°;

[0035] The second reference angle is 70°;

[0036] The reference time is 30s.

[0037] As an implementable manner:

[0038] The reference correlation matrix constructed based on the experimental test is:

[0039]

[0040] The maximum value is 0.45 and the minimum value is 0.03.

[0041] As an implementable manner:

[0042] The balance control method of distributed collaborative control includes three control stages.

[0043] The first stage includes the following steps:

[0044] Based on the second correlation matrix, the actual flow of each end node is collected by the intelligent air valve, and the flow ratio of each end node is calculated;

[0045] Based on the actual flow of the end node and the set flow ratio x i and the average flow ratio of the system The relative standard deviation value is calculated;

[0046] When the relative standard deviation is greater than or equal to the first threshold value, based on the proportional coefficient k i , the second correlation matrix, the current air valve angle q i (k) and the flow ratio between air valves, the air valve angle change value θ i (k+1) is obtained, and the iteration adjustment of the angle of each end air valve is carried out until the relative standard deviation value is less than the first threshold value, and the first stage is completed.

[0047] As an implementable manner, the second stage includes the following steps:

[0048] The intelligent air valve with the smallest air valve angle when the first stage is completed is taken as the key air valve, and the angle of the key air valve is denoted as θ c .

[0049] Based on the iteration adjustment of the flow ratio of each end, until the standard deviation is less than the second threshold value and θ c =0°, wherein the second threshold value is less than the first threshold value, and θ c is the air valve angle of the key air valve.

[0050] The standard deviation is obtained by the ratio of the actual flow of the end node to the set flow x i and the system average flow ratio ;

[0051] Based on the proportional coefficient k i , the second correlation matrix, the current angle of the air valve θ i (k), the flow ratio between the air valves and the penalty term coefficient βθ c Obtain the angle change value of all air valves.

[0052] As an implementable manner, the third stage includes the following steps:

[0053] Based on the actual flow of each end node, the total actual flow of the ventilation system is obtained, and compared with all target total flows, the total flow ratio of the ventilation system is calculated;

[0054] When the total air volume error is greater than the balance state threshold, iterative adjustment is performed based on the total air volume ratio until the total air volume error is less than the balance state threshold, and the fan voltage is adjusted based on the corresponding total air volume ratio at each iteration step;

[0055] As an implementable manner:

[0056] The fan voltage is adjusted according to the following formula:

[0057]

[0058] Wherein:

[0059] V is the adjusted fan voltage;

[0060] V0 is the current fan voltage;

[0061] q toal is the sum of the actual flow;

[0062] is the sum of the target flow;

[0063] α is the total flow ratio consensus value.

[0064] As an implementable manner:

[0065] The value range of the first threshold is 0.08-1, the value range of the second threshold is 0.02-0.08, and the value range of the balance state threshold is 5%-10%.

[0066] As an implementable manner:

[0067] The value of the first threshold is 0.08;

[0068] The second threshold value is 0.02

[0069] The balanced state threshold value is 5%.

[0070] The system provided by the application has the beneficial effects that:

[0071] Compared with the problem of iteration redundancy and large final flow error caused by the distributed collaborative control using the 01 matrix, the wind balance control method provided by the application can accurately explain the complex coupling relationship of each end node by introducing key factors such as pipe length and diameter to decouple the fluid dynamics of each end node, expanding the weight coefficient from the traditional 01 value to a continuous value in the interval [0, 1], making the air volume of each end air outlet quickly converge to the target air volume, compressing the iteration number to less than 95% of the 01 matrix method, and keeping the relative error of the flow within 5%. BRIEF DESCRIPTION OF DRAWINGS

[0072] In order to more clearly illustrate the technical solutions in the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only make some embodiments of the application, and for those skilled in the art, other drawings can be obtained without creative labor based on these drawings.

[0073] Figure 1 It is a flow chart of the ventilation system control method of the application.

[0074] Figure 2 It is a communication topology diagram of the ventilation system.

[0075] Figure 3 It is an equivalent circuit diagram after the ventilation system is analogized to a circuit.

[0076] Figure 4 It is a correlation matrix diagram under the target flow of experimental verification.

[0077] Figure 5 It is a curve diagram of the flow ratio and RSD of each end.

[0078] Figure 6 It is a curve diagram of the angle change of each end air valve.

[0079] Figure 7 It is a curve diagram of the voltage and total air volume error of the fan. DETAILED DESCRIPTION

[0080] In order to make the technical problems, technical solutions and beneficial effects of the application more clear and obvious, the following will further describe the application in combination with the drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the application, and are not used to limit the application.

[0081] This invention provides a distributed control method for ventilation systems based on impedance method to optimize the correlation matrix, for use in the air balance control of ventilation systems.

[0082] The ventilation system consists of a main fan, ventilation ducts, and multiple intelligent air valves. The input voltage of the main fan can control the total air volume of the system. The intelligent air valves are installed at each end of the ventilation ducts, and the actual air volume at each end is adjusted by controlling the angle of the air valves.

[0083] Communication topology diagram of the ventilation system, such as Figure 2 As shown, the system has one main fan and five terminal units, each of which is equipped with an intelligent air valve. The intelligent air valves can communicate with each other.

[0084] Overall process reference for wind balance control method Figure 1 Based on the equivalent circuit of the ventilation system, an correlation matrix is ​​created using the impedance method, and then the three-stage distributed collaborative control is implemented.

[0085] S100: Creating an correlation matrix describing the terminal connection relationships based on the impedance method:

[0086] The airflow rate at each terminal is set, and the airflow rate of the ventilation system is analogous to the current in the corresponding circuit; the pressure loss of the ventilation system's duct network is analogous to the voltage in the corresponding circuit, and the pressure loss of the duct network includes friction loss and local resistance loss; the intelligent air valves of the ventilation duct are used as nodes of the corresponding equivalent circuit, and the friction impedance and local impedance of the ventilation duct are calculated. The equivalent resistance between each terminal is solved using Thevenin's theorem, and a first correlation matrix composed of equivalent resistances is obtained. Then, the first correlation matrix is ​​normalized to obtain a second correlation matrix, and distributed collaborative control will be carried out based on the second correlation matrix.

[0087] The specific execution steps are as follows.

[0088] S110: Preparation phase;

[0089] In this embodiment, the target air volume of each terminal node, the initial angle of the air valve, the initial voltage of the fan, the pipe length, the pipe diameter, the air density, the friction coefficient, and the local resistance coefficient are set.

[0090] Start the ventilation system and wait for the system to stabilize. In this embodiment, the terminal flow rate is considered to have reached a stable state 60 seconds after the fan starts, and the system is determined to be stable.

[0091] S120: Calculate the correlation matrix A;

[0092] S121: Calculate the friction impedance and local impedance of the ventilation duct;

[0093] The friction impedance and local impedance are obtained based on fluid density, friction coefficient, local resistance coefficient, target flow rate of the pipeline, and pipeline length and diameter.

[0094] The calculation formula is as follows:

[0095]

[0096] Among them, Z m The friction impedance of the pipe; ΔP m λ is the friction loss; ρ is the friction coefficient; Q is the air density; m The target air volume for the straight-through duct m; L m D is the length of the straight pipe m; m Z is the diameter of the straight pipe m; j Let ΔP be the local impedance of pipe j; j Let ξ be the local resistance loss of pipe j; ξ be the local resistance coefficient. j D represents the target airflow of pipe j; j Let j be the diameter of pipe j;

[0097] S122: Calculate the equivalent resistance using Thevenin's theorem and establish the correlation matrix A;

[0098] In this embodiment, the friction impedance and local impedance obtained above are simplified to equivalent circuits to obtain the equivalent circuit describing the connection relationship between end nodes, that is, the equivalent circuit describing the connection relationship between smart air valves.

[0099] Based on the equivalent circuit, the equivalent resistance between the end nodes is calculated respectively.

[0100] In this embodiment, when solving for the equivalent resistance between each pair of end nodes, the remaining end nodes are short-circuited, and finally an correlation matrix composed of the equivalent resistance between each pair of nodes is obtained, thus obtaining the first correlation matrix.

[0101] The first correlation matrix is ​​normalized to obtain the second correlation matrix, which is the aforementioned correlation matrix A.

[0102] As one possible implementation method:

[0103] First, a reference correlation matrix is ​​obtained based on experimental testing. The maximum and minimum values ​​of the reference correlation matrix are then extracted. Based on the maximum and minimum values, the first correlation matrix is ​​normalized to obtain the final correlation matrix. The normalization method is as follows: the minimum value of the first correlation matrix is ​​mapped to the maximum value of the reference correlation matrix, and the maximum value of the first correlation matrix is ​​mapped to the minimum value of the reference correlation matrix. Other parameters in the first correlation matrix are normalized proportionally to obtain the corresponding second correlation matrix.

[0104] The specific implementation steps are as follows:

[0105] Set the fan voltage as the first reference voltage, with a range of 2 to 7V. In this example, the first reference voltage is set to 4V. Set the angles of all terminal smart valves as the first reference angle, which is set to 20° in this example. After the fan stabilizes, collect the current flow rate of each terminal. Then, set the angle of terminal smart valve 1 as the second reference angle, which is set to 70° in this example. After the reference time has elapsed, collect the flow rate of each terminal. Calculate the ratio of the flow rate difference between terminal smart valves 2 to 5 to the flow rate difference of terminal smart valve 1, and use it as the weight coefficient of the first row of the new correlation matrix.

[0106] Following the above method, change the angles of air valves 2 to 5 to the second reference angle in sequence. Then calculate the ratio of the flow difference between the remaining air valves and the current air valve, and use it as the weight coefficient for the next few rows of the new correlation matrix.

[0107] A reference correlation matrix is ​​constructed based on experimental tests, and the maximum and minimum values ​​of the reference correlation matrix are extracted. The minimum value of the first correlation matrix constructed based on equivalent resistance is mapped to the maximum value of the reference correlation matrix, and the maximum value of the first correlation matrix constructed based on equivalent resistance is mapped to the minimum value of the reference correlation matrix. Other parameters in the first correlation matrix constructed based on equivalent resistance are normalized according to the proportion to obtain the second correlation matrix finally applied to the distributed cooperative control method.

[0108] In the embodiments of the present invention, a fully connected topology is adopted between the valve nodes, and the reference correlation matrix obtained based on the experimental testing method is as follows:

[0109]

[0110] The maximum value is 0.45 and the minimum value is 0.03, which are used to normalize the equivalent resistance.

[0111] S200: The first stage of distributed collaborative control, selecting key air valves:

[0112] In the first stage, it is determined whether the system RSD is less than a first threshold. The first threshold ranges from 0.08 to 1. In this example, the first threshold is set to 0.08. Based on the basic consensus algorithm, each terminal smart damper is regarded as a node in the system, and all dampers are synchronously adjusted. The specific implementation steps are as follows.

[0113] S210: Using the second correlation matrix, the actual outlet flow rate q is collected through the intelligent dampers of each end node. i Calculate the ratio x of the actual flow rate to the set flow rate at each end node. i The system average flow ratio is calculated based on the flow ratios of all end nodes. And the relative standard deviation RSD, where RSD is the consistency index of each air valve, i∈(1,n), n is the number of air outlets, and i represents the i-th air outlet;

[0114]

[0115] Where, q i This represents the actual airflow of the i-th damper; Let x be the target airflow of the i-th damper. i Let be the flow ratio corresponding to the i-th damper. This indicates the average flow rate ratio.

[0116] S220: Determine whether RSD is less than the first threshold. If it is not less than the first threshold, adjust the angle of the terminal intelligent air valve in a unified and coordinated manner according to the second correlation matrix and the flow ratio of each terminal, so that the flow ratio of each terminal gradually converges until RSD is less than the first threshold.

[0117] Then proceed to step S300;

[0118] If it is less than, then proceed directly to step S300;

[0119] Methods for gradually converging the flow ratios at each terminal until the RSD is less than the first threshold include:

[0120] S221: Treat the ventilation duct system as a multi-node communication system, and the intelligent damper as a node in the system. Adjust the angle of the intelligent damper according to the following control protocol:

[0121]

[0122] Where k is the sampling time; θ i (k) represents the angle of the intelligent damper i at sampling time k; θ i (k+1) represents the angle of the intelligent damper i at sampling time k+1; q i (k) represents the actual airflow of intelligent air valve i at sampling time k; q j(k) represents the actual air output of the intelligent air valve j at sampling time k; The target airflow of intelligent air valve i at sampling time k; τ represents the target airflow of the intelligent air valve j at sampling time k; s k is the discrete time step; i J is the proportional coefficient for voltage regulation; ii The elements of the Jacobian matrix J map flow rate changes to valve angle changes; a ij The communication relationship between end node i and end node j is represented by the final correlation matrix.

[0123] S300: The second phase of distributed collaborative control, specifying key dampers and introducing penalty terms:

[0124] In the second stage, the smart damper with the smallest damper angle at the end of the first stage is selected as the critical damper, and the angle of the critical damper is denoted as θ. c The damper angle refers to the angle of the intelligent damper disc relative to its fully closed position (0° represents fully open, 90° represents fully closed). Based on the first stage, a time-varying weighted penalty term is introduced to further adjust all dampers. The specific implementation steps are as follows:

[0125] S310: Determine whether RSD is less than the second threshold and θ c Equal to 0°, if not less than and θ c The value is not equal to 0. A time-varying weighted penalty term βθ is introduced into the valve angle control protocol. c Continue to coordinate and adjust the angle of the intelligent air valve until RSD is less than the second threshold and θ c It equals 0°.

[0126] Then proceed to step S400;

[0127] If it is less than, then proceed directly to step S400;

[0128] In this embodiment, the value range of the second threshold is 0.02 to 0.08, for example, it can be set to 0.02.

[0129] Adjust the flow ratios at each terminal gradually until they converge until RSD is less than the second threshold and θ c Methods to equal 0° include:

[0130] S311: Based on a one-stage method, the angle of the intelligent damper is adjusted according to the following control protocol:

[0131]

[0132] β=β0(1+e -λk (10)

[0133] Where k is the sampling time; θ i (k) represents the angle of the intelligent damper i at sampling time k; θ i (k+1) represents the angle of the intelligent damper i at sampling time k+1; q i (k) represents the actual airflow of intelligent air valve i at sampling time k; q j (k) represents the actual air output of the intelligent air valve j at sampling time k; The target airflow of intelligent air valve i at sampling time k; τ represents the target airflow of the intelligent damper j at sampling time k; β is the penalty term coefficient, β0 is the initial penalty weight, and its value is negative; s k is the discrete time step; i J is the proportional coefficient for voltage regulation; ii The elements of the Jacobian matrix J map flow rate changes to valve angle changes; a ij This represents the communication relationship between intelligent air valve i and intelligent air valve j, that is, the value in the second correlation matrix indicating the relationship between end node i and end node j.

[0134] S400: The third stage of distributed collaborative control, regulating the fan voltage:

[0135] In the third stage, when the standard deviation is less than 0.02, it is determined whether the total flow error ε is less than 5%. The specific execution steps are as follows:

[0136] S410: Collects the airflow at the outlet of each terminal node through intelligent air valves and sums them to obtain the total actual flow rate q. total The total target flow is obtained by summing the target flows at each endpoint. Calculate the total air volume error ε, where ε is the standard for measuring whether each terminal has achieved air balance;

[0137]

[0138] Where, q total This represents the total actual flow rate. The total target flow;

[0139] S420: Determine if ε is less than the equilibrium threshold. The equilibrium threshold ranges from 5% to 10%, and in this implementation example, the equilibrium threshold is set to 5%. Calculate the total flow ratio consensus value α, and adjust the fan voltage according to the following formula:

[0140]

[0141] Where V is the adjusted fan voltage; V0 is the current fan voltage; q total This represents the total actual flow rate. The target total traffic; α is the consensus value of the total traffic ratio.

[0142] In embodiments of the present invention, it is considered that when RSD is less than the second threshold, ε is less than the equilibrium state threshold, and θ c At 0°, the system achieves wind balance, and all terminals have reached the target flow rate.

[0143] This method is a distributed collaborative control approach for ventilation systems based on an optimized correlation matrix. By introducing the concept of equivalent resistance from circuit theory, it fully considers the coupling relationship between the terminals, achieving precise control of the angle of intelligent air valves and realizing air balance more quickly. The proposed impedance method changes the weight coefficients of the correlation matrix from a single 0 or 1 value to a continuous value in the [0,1] interval. This avoids the problems of redundant iterations and large final flow error caused by the correlation matrix of traditional distributed collaborative control failing to better represent the relationship between the terminals.

[0144] Experimental verification:

[0145] The target flow rates of the five terminal nodes of the ventilation system in this experiment decreased progressively, each with a flow rate of 80 m³ / h. 3 / h, 100m 3 / h, 120m 3 / h, 140m 3 / h and 160m 3 / h, thus calculating the second correlation matrix under this working condition, such as Figure 4 As shown; Figure 5 This is a graph showing the variation curves of the flow ratio and RSD at each terminal under this operating condition, based on... Figure 5 The curve shown indicates that after 16 iterations, the flow ratios at each end of the system tend to be consistent, and the RSD is less than the second threshold, thus entering the third stage. Figure 6 The graph shows the angle variation of each terminal damper, based on... Figure 6 The graph shown shows that valve T3, which was identified as a critical valve in the first stage, was fully open in the end. Figure 7 The graph shows the error variation of the fan voltage and total air volume, based on... Figure 7 The graph shows that changes in voltage cause changes in the total air volume error. After 7 iterations, the total air volume error is less than the equilibrium threshold, which proves that air balance has been achieved at this point.

[0146] At the beginning of the experiment, the target flow rate at the end of the system was first set. Using the corresponding second correlation matrix, the initial angle of the T1 to T5 dampers was set to 45°, and the initial voltage of the fan was set to 4V. After the system started, it entered the first stage. The damper angles were adjusted according to formula (6), such as... Figure 5 and Figure 6As shown, after two iterations, the RSD is less than the first threshold; the system enters the second stage, at which point the angle of damper T3 is the smallest, and this damper is selected as the key damper, with the angle denoted as θ. c Further adjustments to the air valve are made according to formulas (9) and (10), such as... Figure 5 and Figure 6 As shown, after 16 iterations, RSD is less than the second threshold, and the critical damper angle is 0° at this time; entering the third stage, the system adjusts the fan voltage through formulas (14) and (15), such as Figure 7 As shown, after 7 iterations, the total flow rate is less than 5% of the consensus value, indicating that wind balance has been achieved and the system is out of control.

[0147] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A distributed collaborative control method and system for a ventilation system based on impedance method for optimizing the correlation matrix: The ventilation system consists of a main fan, ventilation ducts, and several intelligent air valves located at the end nodes, with each end node corresponding to one of the intelligent air valves. Its features are, The ventilation system is equivalent to a circuit. The equivalent resistance between the two smart air valves is calculated using Thevenin's theorem and normalized to obtain the corresponding target correlation matrix. Distributed collaborative control is then performed based on the target correlation matrix. The construction of the target correlation matrix includes the following steps: Calculate the friction impedance and local impedance based on the target flow rate at the end, and then convert the ventilation system into an equivalent circuit to obtain the corresponding equivalent circuit. The nodes of the equivalent circuit are intelligent air valves, the current is the flow rate of the ventilation system, and the voltage is the pipeline pressure loss of the ventilation system. The equivalent resistance between each intelligent air valve is solved using Thevenin's theorem. Based on the obtained equivalent resistance, construct the first correlation matrix; The first correlation matrix is ​​normalized to obtain the second correlation matrix, and the second correlation matrix is ​​used as the target correlation matrix.

2. The distributed collaborative control method and system for a ventilation system based on impedance method for optimizing the correlation matrix, as described in claim 1, is characterized in that: Set the target flow rate for each terminal; Based on the friction coefficient λ, fluid density ρ, and straight pipe length L m Target air volume Q in straight-through duct m and the diameter D of the straight pipe m Calculate the friction impedance Z m ; Based on the local resistance coefficient ξ, fluid density ρ, and target air volume Q in the pipeline j and pipe diameter D j Calculate the local impedance Z f ; The friction impedance and local impedance are simplified to equivalent values ​​to obtain the equivalent circuit describing the connection relationship between the end nodes. Based on the equivalent circuit, the equivalent resistance between the end nodes is solved respectively, and the first correlation matrix is ​​constructed based on the equivalent resistance. When solving the equivalent resistance between any two end nodes, the remaining end nodes are short-circuited.

3. The distributed collaborative control method and system for a ventilation system based on impedance method for optimizing the correlation matrix according to claim 1, characterized in that: Based on the reference correlation matrix constructed from experimental tests, the maximum and minimum values ​​of the reference correlation matrix are extracted; The first correlation matrix constructed based on the equivalent resistance is normalized based on the maximum and minimum values ​​to obtain the second correlation matrix. The normalization method is to map the minimum value of the first correlation matrix constructed based on the equivalent resistance to the maximum value of the second correlation matrix, map the maximum value of the first correlation matrix constructed based on the equivalent resistance to the minimum value of the second correlation matrix, and normalize other parameters in the correlation matrix constructed based on the equivalent resistance according to the proportion.

4. A distributed collaborative control method and system for a ventilation system based on impedance method for optimizing the correlation matrix, as described in any one of claims 1 to 3, characterized in that: The distributed collaborative control method comprises three stages: the first stage, the second stage, and the third stage. The first stage includes the following steps Based on the second correlation matrix, the actual flow rate of each end node is collected through intelligent air valves, and the flow rate ratio of each end node is calculated. Based on the ratio of actual flow to set flow at the end node x i Ratio of system average flow Calculate the relative standard deviation; When the relative standard deviation is greater than or equal to the first threshold, based on the scaling factor k i Second correlation matrix, current valve angle q i (k) and the flow ratio between the dampers are used to obtain the damper angle change value θ. i (k+1) Iteratively adjust the angle of each terminal air valve until the relative standard deviation value is less than the first threshold, and complete the first stage.

5. The distributed collaborative control method and system for a ventilation system based on impedance method for optimizing the correlation matrix according to claim 4, characterized in that, The second phase includes the following steps: The smart valve with the smallest valve angle upon completion of the first stage is designated as the critical valve, and its angle is denoted as θ. c ; The flow rate is iteratively adjusted based on the flow rate ratio at each terminal until the standard deviation is less than the second threshold and θ c = 0°, where the second threshold is less than the first threshold, θ c The valve angle for the critical air valve; The standard deviation is calculated as x, which is the ratio of the actual flow rate at the end node to the set flow rate. i Ratio of system average flow Calculated; Based on the proportionality coefficient k i Second correlation matrix, current damper angle θ i (k), inter-valve flow ratio and penalty term coefficient βθ c Obtain the angle change values ​​of all air valves.

6. The distributed collaborative control method and system for a ventilation system based on impedance method for optimizing the correlation matrix according to claim 4, characterized in that, The third stage includes the following steps: Based on the actual flow rate of each end node, the total actual flow rate of the ventilation system is obtained, and compared with the total target flow rate, the total flow rate ratio of the ventilation system is calculated. When the total air volume error is greater than the preset balance state threshold, iterative adjustment is performed based on the total air volume ratio α until the total air volume error is less than the balance state threshold. In each iteration step, the fan voltage is adjusted based on the corresponding total air volume ratio.

7. The distributed collaborative control method and system for a ventilation system based on impedance method for optimizing the correlation matrix according to claim 6, characterized in that: The equilibrium threshold ranges from 5% to 10%.

8. A distributed collaborative control system and system for a ventilation system based on impedance method for optimizing the correlation matrix, characterized in that, Used to perform the method described in any one of claims 1 to 7.

Images