Fuzzy PID (Proportion Integration Differentiation) control method and system of regional cooling system

Abstract

The invention discloses a fuzzy PID control method and system for a regional cooling system, and the method comprises the steps: collecting the real-time temperature and target room temperature of a building in the regional cooling system, and inputting a PID; fuzzifying the input quantity through a preset fuzzy rule; performing defuzzification processing by using a local weighted gravity center method to obtain a corrected PID parameter, thereby determining the adjustment amount of each sub-region; and inputting the adjustment amount into PID to realize temperature regulation and control of the regional cooling system. The advantages of fuzzy control and traditional PID control are combined, temperature data are collected in real time, PID parameters are adjusted through fuzzy rules, ambiguity resolution is conducted through a local weighting gravity center method, the cooling capacity of all sub-areas is dynamically adjusted, and therefore more accurate and stable temperature control is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of PID control, and particularly to a fuzzy PID control method and system for a district cooling system. Background Art

[0002] Due to the significant characteristics of complex processes such as complexity, nonlinearity, uncertainty, and large time delay in the flow change process during temperature regulation of the district cooling system, the classical PID control method is difficult to meet the requirements of closed-loop optimal control. As an important part of advanced process control strategies, fuzzy control has good robustness and can overcome problems caused by the time-varying nature, nonlinearity of the system itself, and external disturbances, effectively improving the ability to control the temperature during the temperature regulation process and solving problems such as excessive temperature fluctuations or too slow regulation during the temperature adjustment process.

[0003] Conventional PID controllers do not have the ability of online parameter self-tuning, making it difficult to meet the requirements of the system for PID parameter self-tuning under different temperature deviations and deviation change rates, thus affecting the control effect. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the technical problems solved by the present invention are: existing PID control methods have problems such as excessive temperature fluctuations or too slow regulation, and it is difficult to meet the requirements of the system for PID parameter self-tuning under different temperature deviations and deviation change rates.

[0006] To solve the above technical problems, the present invention provides the following technical solution: A fuzzy PID control method for a district cooling system, including:

[0007] Collect the real-time temperature of the building in the district cooling system and the target room temperature, and input them into the PID;

[0008] Fuzzify the input quantity through preset fuzzy rules;

[0009] Use the local weighted centroid method for defuzzification to obtain the corrected PID parameters, thereby determining the adjustment amount for each sub-region;

[0010] Input the adjustment amount into the PID to achieve temperature control of the district cooling system.

[0011] As a preferred solution of the fuzzy PID control method for the district cooling system of the present invention, among them: when the collected temperature data is input into the PID, calculate the difference: ΔT = T ref -T real , where ΔT is the indoor temperature T ref that the system hopes to reach and the real-time temperature T of the building in the district cooling systemreal The difference value.

[0012] As a preferred embodiment of the fuzzy PID control method for the district cooling system according to the present invention, wherein: the input quantities include the collected system temperature change amount ΔT and the temperature change rate dT / dt as input quantities;

[0013] The temperature difference change rate is:

[0014]

[0015] wherein, dT / dt is the temperature change rate; T(kΔt) represents the setting for the k-th time period with a time interval of Δt.

[0016] As a preferred embodiment of the fuzzy PID control method for the district cooling system according to the present invention, wherein: the fuzzy rules include expressing the numerical values of ΔT and dT / dt in fuzzy language according to the intervals where they are located as fuzzy subsets;

[0017] Construct a two-dimensional table, and each pair of fuzzy definitions of ΔT and dT / dt corresponds to a set of fuzzy languages of the tuning parameters; wherein, the tuning parameters K P , K I , K D represent proportional, integral, and derivative respectively;

[0018] Using the three tuning parameters K P , K I , K D of PID as the output quantity, a set of fuzzy language variables is obtained.

[0019] As a preferred embodiment of the fuzzy PID control method for the district cooling system according to the present invention, wherein: the local weighted centroid method includes having a fuzzy set A = {(W1, μ1, ΔT1), (W2, μ2, ΔT2), …, (W n , μ n , ΔT n )};

[0020] The local weighted centroid method adjusts the contributions of each part according to the membership functions of different regions;

[0021]

[0022] wherein, μ i is the membership value of the temperature deviation in the i-th region; w i is the local weight, representing the contribution weight of the temperature deviation in the i-th region in that region, which is determined according to the system state or region division; C is the defuzzified control value; ΔT iThe temperature deviation of the i-th region in the fuzzy set is represented, and n represents the number of regions.

[0023] As a preferred embodiment of the fuzzy PID control method for the district cooling system described in the present invention, wherein: the membership degree includes being calculated using a triangular membership function after the fuzzy subset;

[0024] The local weight includes being determined according to a preset criterion.

[0025] As a preferred embodiment of the fuzzy PID control method for the district cooling system described in the present invention, wherein: each sub-region adjustment amount includes, after obtaining the corresponding ΔK P 、ΔK I 、ΔK D , making corresponding adjustments to the original parameters:

[0026] K P (t) = K P (t - 1) + αΔK P

[0027] K I (t) = K I (t - 1) + βΔK I

[0028] K D (t) = K D (t - 1) + γΔK D

[0029] Wherein, ΔK P 、ΔK I 、ΔK D are respectively the adjustment amounts of the corresponding proportional coefficient, integral coefficient, and differential coefficient calculated by the fuzzy PID controller; α, β, γ are respectively the set correction coefficients corresponding to ΔK P 、ΔK I 、ΔK D , specifically set according to the operating characteristics of different regional cooling systems, with a setting range of 0 - 1; K P (t - 1) is the proportional coefficient before correction; K I (t - 1) is the integral coefficient before correction; K D (t - 1) is the differential coefficient before correction; K P (t) is the proportional coefficient after correction; K I (t) is the integral coefficient after correction; K D (t) is the differential coefficient after correction.

[0030] A fuzzy PID control system for a district cooling system adopting the method described in the present invention, wherein:

[0031] The acquisition unit collects the real-time temperature of the building in the district cooling system and the target room temperature, and inputs them into the PID;

[0032] The processing unit fuzzifies the input quantity according to the preset fuzzy rules;

[0033] The calculation unit uses the local weighted centroid method for defuzzification processing to obtain the corrected PID parameters, thereby determining the adjustment amount of each sub-region;

[0034] The regulation unit inputs the adjustment amount into the PID to achieve temperature regulation of the district cooling system.

[0035] A computer device includes: a memory and a processor; the memory stores a computer program, wherein: when the processor executes the computer program, the steps of the method described in any one of the present inventions are implemented.

[0036] A computer-readable storage medium stores a computer program thereon, wherein: when the computer program is executed by a processor, the steps of the method described in any one of the present inventions are implemented.

[0037] The beneficial effects of the present invention: The fuzzy PID control method of the district cooling system provided by the present invention combines the advantages of fuzzy control and traditional PID control. By collecting temperature data in real time, using fuzzy rules to adjust the PID parameters, and then defuzzifying through the local weighted centroid method, the cooling capacity of each sub-region is dynamically adjusted, thereby achieving more accurate and stable temperature control. Description of the Drawings

[0038] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0039] Figure 1 It is the structural diagram of the fuzzy PID control system of the district cooling system for the fuzzy PID control method of the district cooling system provided by the first embodiment of the present invention;

[0040] Figure 2 It is the effect comparison diagram for the fuzzy PID control method of the district cooling system provided by the second embodiment of the present invention;

[0041] Figure 3 It is the error comparison diagram for the fuzzy PID control method of the district cooling system provided by the second embodiment of the present invention;

[0042] Figure 4Graphical representation of membership degree calculation for the fuzzy PID control method of the district cooling system provided in the third embodiment of the present invention. Detailed implementation manners

[0043] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following provides a detailed description of the specific implementation manners of the present invention in conjunction with the accompanying drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0044] Embodiment 1, referring to Figure 1 , which is an embodiment of the present invention, providing a fuzzy PID control method for a district cooling system, including:

[0045] S1: Collect the real-time temperature and target room temperature of the buildings in the district cooling system and input them into the PID.

[0046] Collect the temperature change amount ΔT and the temperature change rate dT / dt of the system; calculate the temperature difference ΔT = T ref -T real by comparing the measured value with the reference given value, and the temperature difference change rate is Take both ΔT and dT / dt as input quantities, and define the fuzzy subsets of ΔT and dT / dt as {Negative Big [NB], Negative Medium [NM], Negative Small [NS], Zero [Z0], Positive Small [PS], Positive Medium [PM], Positive Big [PB]}, and the corresponding basic domains of ΔT and dT / dt are {-3, -2, -1, 0, 1, 2, 3}.

[0047] S2: Fuzzify the input quantities through preset fuzzy rules.

[0048] Take the three tuning parameters K P , K I , K D of the PID as output quantities, define its fuzzy language variable set as {Z0, S, M, B}, the elements in the subset represent {Zero, Small, Medium, Big} respectively, and define the basic domains of K P , K I as {0, 2}, and the basic domain of K D as {0, 1}.

[0049] Take the three tuning parameters K P , K I , K D of the PID as output quantities, define its fuzzy language variable set as {Z0, S, M, B}, the elements in the subset represent {Zero, Small, Medium, Big} respectively, and define the basic domains of K P, K I The basic domain of is {0, 2}, and K D The basic domain of is {0, 1}.

[0050] It should be noted that the fuzzy PID controller consists of two parts: a conventional PID control part and fuzzy inference.

[0051] The fuzzy inference part is essentially a fuzzy controller. The input variables are the temperature deviation ΔT and the rate of change of temperature deviation dT / dt, and the output is the PID parameter K P , K I , K D .

[0052] Considering the characteristics of the parameters K P , K I , K D and their relationships, the following fuzzy control rule tables are established, as shown in Tables 1, 2, and 3:[[]]END

[0053] Table 1

[0054]

[0055] Table 2

[0056]

[0057] Table 3

[0058]

[0059] S3: Use the local weighted centroid method for defuzzification to obtain the corrected PID parameters, thereby determining the adjustment amounts for each sub-region.

[0060] Using the local weighted centroid method for defuzzification, it calculates the weighted centroid by assigning different weights to subsets of the fuzzy set. This method is applicable to the situation of dealing with different fuzzy sets in different regions or different working states of the regional refrigeration system, and has strong versatility and scalability.

[0061] Suppose there is a fuzzy set A = {(W1, μ1, ΔT1), (W2, μ2, ΔT2), …, (W n , μ n , ΔT n )};

[0062] The local weighted centroid method adjusts the contributions of each part according to the membership functions of different regions;

[0063]

[0064] Among them, μ i ​is the membership value of the temperature deviation of the i-th region; w i is the local weight, representing the contribution weight of the temperature deviation of the i-th region in that region, determined according to the system state or regional division; C is the defuzzified control value; ΔT i represents the temperature deviation of the i-th region in the fuzzy set, and n represents the number of regions.

[0065] The local weight w i is usually determined according to preset criteria (such as error range, dynamic changes of the system, distribution of input values, etc.). The local weight w i is dynamically changing, and it changes according to the membership function of each element of the fuzzy set or different regions of the current control environment. The defuzzification process not only considers the membership value μ i of the fuzzy set, but also weights the influence of different regions or parts, and can better handle the contribution of different regions to the performance of the control system.

[0066] To achieve a balance among the coverage range of the universe of discourse, sensitivity, and convenience of adjusting membership parameters, after determining the above fuzzy subsets, the triangular membership function is used to calculate the membership degree.

[0067] An example of defuzzification calculation using the specific local weighted centroid method:

[0068] Suppose the regional refrigeration system has 5 regions, each with a different size, so the heat capacity is different, and the heat dissipation capacity is also different, so different weights are assigned.

[0069] When calculating ΔK D , when the data is as follows:

[0070] <![CDATA[Temperature difference ΔT i > <![CDATA[Membership degree μ i > <![CDATA[Local weight w i > -3 0.1 0.5 -1 0.4 0.7 0 1.0 1.0 1 0.5 1.2 3 0.2 0.8

[0071] The temperature difference ΔT is each element (-3, -1, 0, 1, 3) in the fuzzy set. μ i is the membership value of this element, representing the membership degree of this temperature difference in the corresponding temperature difference interval. w i is the weight in the local weighted centroid method

[0072] Step 1: Calculate the weighted centroid:

[0073] According to the formula of the local weighted centroid method:

[0074]

[0075] Substitute specific values for calculation:

[0076] Calculate the numerator:

[0077] Molecular = (0.5·0.1·(-3))+(0.7·0.4·(-1))+(1.0·.0·)+(1.2·.5·)+(0.8·.2·)

[0078] = (-0.15)+(-0.28)+0+0.6+0.48

[0079] = 0.65

[0080] Calculate the denominator:

[0081] Denominator = (0.5·0.1)+(0.7·0.4)+(1.0·1.0)+(1.2·0.5)+(0.8·0.2)

[0082] = 0.05+0.28+1.0+0.6+0.16

[0083] = 2.09

[0084] Step 2: Calculate the weighted centroid value

[0085]

[0086] Result:

[0087] The ΔK calculated by the local weighted centroid method D is 0.311.

[0088] Similarly, ΔK P , ΔK I are also solved using the same method (the values are the same), but due to different coefficients, namely α, β, γ, the final corrected coefficients will be different.

[0089] After obtaining the corresponding ΔK P , ΔK I , ΔK D , make the corresponding adjustments to the original parameters. The formula is as follows:

[0090] K P (t) = K P (t - 1)+αΔK P

[0091] K I (t) = K I (t - 1)+βΔK I

[0092] K D (t) = K D (t - 1)+γΔK D

[0093] Among them, ΔK P , ΔKI , ΔK D are the adjustment amounts of the corresponding proportional coefficient, integral coefficient, and differential coefficient calculated by the fuzzy PID controller, respectively. α, β, and γ are the set correction coefficients corresponding to ΔK P , ΔK I , ΔK D , and are specifically set according to the operating characteristics of the refrigeration system in different regions, with a setting range of 0 - 1.

[0094] K P (t - 1) is the proportional coefficient before correction, K I (t - 1) is the integral coefficient before correction, K D (t - 1) is the differential coefficient before correction, K P (t) is the proportional coefficient after correction, K I (t) is the integral coefficient after correction, K D (t) is the differential coefficient after correction.

[0095] S4: Input the adjustment amount into the PID to achieve temperature control of the district cooling system.

[0096] Determine the adjustment amount of each sub - region according to the corrected PID parameters:

[0097]

[0098] Among them, ΔP i is the magnitude of the refrigeration power that needs to be adjusted in the i - th region, ΔK P , ΔK I , ΔK D are the proportional adjustment coefficient, integral adjustment coefficient, respectively, and ΔT i is the temperature difference between the indoor temperature and the target temperature in the i - th region.

[0099] Finally, adjust through the magnitude of the refrigeration power in different regions to finally achieve stable and rapid adjustment of the overall temperature of the district cooling system.

[0100] It should be noted that the control function in the PID control module is the traditional PID control function, that is:

[0101]

[0102] Fuzzy inference transmits the inference result to the PID controller to update the parameters K P , K I , K D in the PID controller in real - time. G(s) is the transfer function of the district cooling system.

[0103] When the district cooling system is operating, it is necessary to consider the influence of external heat sources such as equipment heat generation, personnel activities, lighting, and ambient temperature. Therefore, a working model of the district cooling system combined with heat sources is established. The transfer function of the system not only considers the relationship between the cooling power input and temperature but also takes into account the influence of external heat sources.

[0104] Define the transfer function G(s) model of the district cooling system as follows:

[0105]

[0106] The meanings of each parameter and variable:

[0107] 1. G(s): The transfer function of the controlled object.

[0108] This is the transfer function of the district cooling system, representing the relationship between the cooling power input and the temperature output, and reflecting the inertia and response characteristics of the system.

[0109] 2. K: The gain of the system, representing the proportional relationship between the input and the output. For the cooling system, K represents the temperature change caused by a unit change in cooling power. When K = 0.5, it means that a 1W change in cooling power will cause a 0.5°C change in temperature.

[0110] 3. τ: The time constant of the system, describing the response speed of the system. A smaller τ value indicates a faster system response, while a larger τ value indicates a slower system response. The time constant of the system varies according to the working characteristics of different district cooling systems and mainly depends on variables such as the thermal inertia of the equipment, the heat capacity of the pipeline, the area of the room, and the temperature conduction coefficient.

[0111] 4. H(s): The transfer function of the heat source.

[0112] The heat source H(s) describes the influence of the external heat source on the system temperature. The external heat source is generally the heat generated by electrical equipment (such as lighting equipment, computers, air conditioners, etc.); the heat generated by personnel activities; the influence of ambient temperature changes on the system (such as changes in the outside air temperature).

[0113] Generally, the influence of the heat source on the temperature gradually increases over time. Therefore, the transfer function of the heat source is selected as a low-pass filter, showing a lag characteristic. This part of the model is represented in the following form:

[0114]

[0115] 5. K H : The gain of the heat source, representing the influence of a unit change in the heat source on the temperature change. When K H = 0.2, it means that each unit of heat source (such as every 1W increase in the heat source) will cause a 0.2°C change in temperature.

[0116] 6. τ H : The time constant of the heat source, representing the response delay of the heat source to the change in the system temperature. A smaller τ H indicates that the heat source has a more rapid impact on the temperature, and a larger τ H indicates that the response of the heat source is more sluggish.

[0117] 7. β: The feedback coefficient of the heat source influence

[0118] β is a feedback coefficient with a value range between 0 and 1, which describes how the system feedbacks and adjusts the influence of the external heat source. It reflects the degree of influence of the heat source part on the dynamic response of the system. The larger β is, the more significant the dynamic influence of the change in the external heat source on the system is. When β = 0.5, this means that when the heat source changes, the response of the system in the frequency domain will have a larger feedback effect, resulting in a larger amplitude of temperature change or a faster reaction.

[0119] On the other hand, this embodiment also provides a fuzzy PID control system for the district cooling system, which includes:

[0120] A collection unit that collects the real-time temperature of the building in the district cooling system and the target room temperature, and inputs them into the PID.

[0121] The processing unit fuzzifies the input quantity through preset fuzzy rules.

[0122] A calculation unit that uses the local weighted centroid method for defuzzification processing to obtain the corrected PID parameters, thereby determining the adjustment amounts for each sub-region.

[0123] A regulation unit that inputs the adjustment amount into the PID to achieve the temperature regulation of the district cooling system.

[0124] If the above functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. And the aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0125] The logic and / or steps represented in the flowchart or otherwise described herein can, for example, be considered as a definite sequence list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch instructions from the instruction execution system, apparatus, or device and execute the instructions), or used in conjunction with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

[0126] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection portion with one or more wirings (electronic device), a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or other suitable processing as necessary, and then stored in a computer memory.

[0127] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one or a combination of the following techniques well-known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), and the like.

[0128] Example 2, referring to Figure 2 、 Figure 3 , which is an embodiment of the present invention, provides a fuzzy PID control method for a district cooling system. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0129] The proposed algorithm is implemented by MATLAB R2024a. All experiments were conducted on a Windows 11 platform using an Intel Core i9-14900HX CPU and an NVIDIA GeForce RTX 4060 GPU (32GB RAM).

[0130] The accuracy of the present invention is higher than that of traditional methods, and the error is lower than that of traditional methods. The experimental results prove that compared with traditional PID control, the fuzzy PID control method proposed in this patent better realizes the control of the temperature of the district cooling system and reduces the temperature error during actual regulation.

[0131] Example 3, referring to Figure 4 , which is an embodiment of the present invention, provides a fuzzy PID control method for a district cooling system, including the calculation strategies of membership degrees and weights.

[0132] Suppose there are 3 zones in the district cooling system. Each zone has a different size, so the heat capacity is different, and the heat dissipation capacity is also different. Therefore, different weights are assigned.

[0133] Example: Weight distribution of different zones in a district cooling system

[0134] Suppose a district cooling system includes the following three zones:

[0135] Small meeting room (area 30㎡, low heat capacity, no external windows, poor heat dissipation capacity).

[0136] Medium-sized office (area 100㎡, medium heat capacity, partial ventilation).

[0137] Large exhibition hall (area 500㎡, high heat capacity, open space, strong heat dissipation capacity).

[0138] Step 1: Analyze the characteristics of the zone.

[0139] Heat capacity: Related to the volume of the zone and building materials, it can be simplified as area × heat capacity coefficient (assuming the heat capacity coefficient is 1.0).

[0140] Small meeting room: 30×1.0 = 30.

[0141] Medium-sized office: 100×1.0 = 100.

[0142] Large exhibition hall: 500×1.0 = 500.

[0143] Heat dissipation capacity: Related to ventilation conditions and heat insulation performance, it is defined as the heat dissipation coefficient (0 - 1, the larger the value, the faster the heat dissipation).

[0144] Small meeting room: 0.3 (enclosed space, poor heat dissipation).

[0145] Medium-sized office: 0.6 (partial ventilation).

[0146] Large exhibition hall: 0.9 (open, strong heat dissipation).

[0147] Step 2: Calculate the weight.

[0148] The weight needs to comprehensively consider the heat capacity and heat dissipation ability, and the formula is:

[0149] w i = α · heat capacity + β · heat dissipation coefficient

[0150] Among them, α and β are adjustment factors (for example, α = 0.7, β = 0.3, emphasizing the influence of heat capacity).

[0151] Small meeting room: w1 = 0.7 × 30 + 0.3 × 0.3 = 21 + 0.09 = 21.09

[0152] Medium-sized office: w2 = 0.7 × 100 + 0.3 × 0.6 = 70 + 0.18 = 70.18

[0153] Large exhibition hall: w3 = 0.7 × 500 + 0.3 × 0.9 = 350 + 0.27 = 350.27

[0154] Normalized weight (total weight = 21.09 + 70.18 + 350.27 = 441.54):

[0155] Small meeting room: w1' = 21.09 / 441.54 ≈ 0.048

[0156] Medium-sized office: w'2 = 70.18 / 441.54 ≈ 0.159

[0157] Large exhibition hall: w'3 = 350.27 / 441.54 ≈ 0.793

[0158] Finally, the regional characteristics (heat capacity, heat dissipation ability) are quantified and dynamic weights are assigned.

[0159] 3. Design of triangular membership function parameters

[0160] (1) Domain division and vertex assignment

[0161]

[0162]

[0163] 4. Example of membership value calculation, graphical representation as Figure 4 :

[0164] According to the table of "universe of discourse division and vertex allocation", determine the interval into which the input variable ΔT (represented by x) falls. Obtain the fuzzy sets that fall into the interval. Let the left endpoint be a, the vertex be b, and the right endpoint be c, and calculate the membership degrees:

[0165]

[0166] Scenario 1: The input value e = -2.5 (after normalization).

[0167] Determine the fuzzy sets of membership: e = -2.5 falls in the overlapping region of NM (a = -5, b = -3.33, c = -1.67) and NS (a = -3.33, b = -1.67, c = 0).

[0168] Calculate the membership degrees:

[0169] Membership degree of NM:

[0170] Membership degree of NS:

[0171] The membership degrees of other fuzzy sets are 0.

[0172] Result: μ = {NB:0, NM:0.5, NS:0.5, ZO:0, PS:0, PM:0, PB:0}

[0173] Scenario 2: The input value e = +1.0 (after normalization).

[0174] Determine the fuzzy sets of membership: e = +1.0 falls in the overlapping region of ZO (a = -1.67, b = 0, c = +1.67) and PS (a = 0, b = +1.67, c = +3.33).

[0175] Calculate the membership degrees:

[0176] Membership degree of ZO:

[0177] Membership degree of PS:

[0178] The membership degrees of other fuzzy sets are 0.

[0179] Result: μ = {NB:0, NM:0, NS:0, ZO:0.4, PS:0.6, PM:0, PB:0}

[0180] 1. Calculation of the membership degree of the input value x = -3:

[0181] Fuzzy sets of membership: NM (Negative Medium), NS (Negative Small)

[0182] Calculation formula (triangle function):

[0183]

[0184] Result: μ = {NB:0, NM:0.8, NS:0.2, ZO:0, PS:0, PM:0, PB:0}

[0185] 2. Calculation of membership degree when the input value x = -1:

[0186] Fuzzy sets to which it belongs: NS (Negative Small), ZO (Zero)

[0187] Calculation formula:

[0188]

[0189] Result: μ = {NB:0, NM:0, NS:0.6, ZO:0.4, PS:0, PM:0, PB:0}

[0190] 3. Calculation of membership degree when the input value x = 0:

[0191] Fuzzy set to which it belongs: ZO (Zero).

[0192] Calculation formula: μ ZO (0) = 1 (the input value is at the vertex of ZO).

[0193] Other fuzzy sets:

[0194] The right endpoint of NS is 0, but the membership degree is 0 when x = 0 (beyond the range of NS).

[0195] The left endpoint of PS is 0, but the membership degree is 0 when x = 0 (beyond the range of PS).

[0196] Result: μ = {NB:0, NM:0, NS:0, ZO:1, PS:0, PM:0, PB:0}

[0197] When calculating ΔK D when the data is as follows:

[0198] Region <![CDATA[Temperature difference ΔT i > <![CDATA[Membership degree μ i > <![CDATA[Local weight w i > Region 1 -3 NM = 0.8, NS = 0.2 0.5 Region 2 -1 NS = 0.6, ZO = 0.4 0.2 Region 3 0 ZO = 1 0.3

[0199] The temperature difference ΔT is each element in the fuzzy set (-3, -1, 0, 1, 3). μ i is the membership degree value of this element, indicating the membership degree of this temperature difference in the corresponding temperature difference interval. w i is the weight in the local weighted centroid method

[0200] Step 1: Calculate the weighted centroid:

[0201] According to the formula of the local weighted centroid method:

[0202]

[0203] Substitute specific values for calculation:

[0204] Due to normalization, it is 1.

[0205] For ΔK D , its value on NM is 0.8 * 0.5 * (-3) = -1.2.

[0206] Its value on NS is 0.2 * 0.5 * (-3) + 0.6 * 0.2 * (-1) = -0.42.

[0207] Its value on ZO is 0.4 * 0.2 * (-1) + 0 * 1 * 0.3 = -0.8.

[0208] Then the sum is -1.2 - 0.42 - 0.8 = -2.42.

[0209] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. Fuzzy PID control method for district cooling system, characterized in that, Including: Collect the real-time temperature and target room temperature of the buildings in the district cooling system and input them into the PID; Fuzzify the input quantity according to the preset fuzzy rules; Use the local weighted centroid method for defuzzification to obtain the corrected PID parameters, thereby determining the adjustment amount for each sub-region; Input the adjustment amount into the PID to achieve temperature control of the district cooling system.

2. The fuzzy PID control method for the district cooling system according to claim 1, characterized in that: When the collected temperature data is input into the PID, the difference is calculated: ΔT = T ref - T real , where ΔT is the indoor temperature T ref that the system hopes to reach and the real-time temperature T real of the building in the district cooling system.

3. The fuzzy PID control method for the district cooling system according to claim 2, characterized in that: The input quantity includes the temperature change amount ΔT and the temperature change rate dT / dt of the collection system as the input quantity; The temperature difference change rate is: where dT / dt is the temperature change rate; T(kΔt) represents the setting for the k-th time period with a time interval of Δt.

4. The fuzzy PID control method for the district cooling system as claimed in claim 3, characterized in that: The fuzzy rules include expressing the numerical values of ΔT and dT / dt in fuzzy language according to the intervals where they are located as fuzzy subsets; Construct a two-dimensional table, where each pair of fuzzy definitions of ΔT and dT / dt corresponds to a set of fuzzy languages of tuning parameters; among them, the tuning parameters K P 、K I 、K D represent proportional, integral and derivative respectively; Taking the three tuning parameters K P , K I , K D of the PID as the output quantity, a set of fuzzy language variables is obtained.

5. The fuzzy PID control method for the district cooling system according to claim 4, characterized in that: The local weighted centroid method includes setting up a fuzzy set A = {(W1, μ1, ΔT1), (W2, μ2, ΔT2), …, (W n , μ n , ΔT n )}; The local weighted centroid method adjusts the contributions of each part according to the membership functions of different regions; Among them, μ i is the membership value of the temperature deviation in the i-th region; w i is the local weight, indicating the contribution weight of the temperature deviation in the i-th region in this region, which is determined according to the system state or region division; C is the control value after defuzzification; ΔT i represents the temperature deviation in the i-th region in the fuzzy set, and n represents the number of regions.

6. The fuzzy PID control method for the district cooling system according to claim 5, characterized in that: The membership degree includes being calculated using a triangular membership function after the fuzzy subset; The local weight includes being determined according to the preset criteria.

7. The fuzzy PID control method for the district cooling system according to claim 6, characterized in that: The adjustment amounts of the respective sub-regions include that after obtaining the corresponding ΔK P , ΔK I , ΔK D , the original parameters are adjusted accordingly: K P K(t) = P K(t - 1)+αΔK P K I K(t) = I K(t - 1)+βΔK I K D K(t) = D K(t - 1)+γΔK D Among them, ΔK P , ΔK I , ΔK D are the adjustment amounts of the corresponding proportional coefficient, integral coefficient, and differential coefficient calculated by the fuzzy PID controller respectively; α, β, and γ are the set correction coefficients corresponding to ΔK P , ΔK I , ΔK D , which are specifically set according to the operating characteristics of the refrigeration system in different regions, and the setting range is 0 - 1; K P (t - 1) is the proportional coefficient before correction; K I (t - 1) is the integral coefficient before correction; K D (t - 1) is the differential coefficient before correction; K P (t) is the proportional coefficient after correction; K I (t) is the integral coefficient after correction; K D (t) is the differential coefficient after correction.

8. A fuzzy PID control system for a district cooling system adopting the method according to any one of claims 1-7, characterized in that: A collection unit that collects the real-time temperature and target room temperature of the buildings in the district cooling system and inputs them into the PID; A processing unit fuzzifies the input quantity according to the preset fuzzy rules; A calculation unit uses the local weighted centroid method for defuzzification to obtain the corrected PID parameters, thereby determining the adjustment amount for each sub-region; A regulation unit inputs the adjustment amount into the PID to achieve temperature control of the district cooling system.

9. A computer device, comprising: A memory and a processor; the memory stores a computer program, characterized in that: when the processor executes the computer program, the steps of the method according to any one of claims 1-7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, the steps of the method according to any one of claims 1-7 are implemented.

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