Fuzzy control method for dual-system cabinet air conditioner

Abstract

The application discloses a fuzzy control method of a double-system cabinet air conditioner. Firstly, the double-system cabinet air conditioner is in a compressor operation mode, and then temperature is set and input variables are obtained. Next, the input variables and a first compressor frequency increment are fuzzified to obtain fuzzy variables, and a fuzzy rule base is established. Then, the input variables are discriminated according to the fuzzy rule base, and the first compressor frequency increment is output. Then, the value of the input variables is input into a module controller and adjustment parameters are output. Then, adaptive adjustment of a proportional factor is completed. Next, the adaptive adjustment of the proportional factor is based on the fuzzy control. The double-system air conditioner is controlled by the fuzzy control, and the online adaptive adjustment is also realized. The method has the functions of quick response and reduction of oscillation of a refrigeration system, the energy efficiency ratio of the double-system air conditioner is improved, the decoupling control is relatively well completed, and the anti-interference performance is relatively strong.

Description

Technical Field

[0001] This invention relates to the field of dual-system cabinet air conditioning control technology, specifically to a fuzzy control method for dual-system cabinet air conditioning. Background Technology

[0002] In recent years, the PUE (Power Usage Efficiency) requirements for data centers have been continuously increasing, leading to a growing demand for energy-efficient rack air conditioners, a major energy consumer in data centers. Single-system air conditioners often struggle to improve their energy efficiency ratio, resulting in the development of dual-system rack air conditioners. Dual-system rack air conditioners combine gravity heat pipe cooling with compressor cooling. When the ambient temperature is low, they enter energy-saving mode, utilizing only gravity heat pipes for cooling without the compressor. Conversely, when the ambient temperature does not support the required cooling capacity, the compressor cooling mode is activated.

[0003] Currently, due to the time-delay, time-varying, and nonlinear nature of air conditioning systems, and the complex internal coupling, precise model control and function control are difficult to achieve. Conventional fuzzy control strategies suffer from rising characteristic curves and less than ideal parameters such as adjustment time. Therefore, it is necessary to design a fuzzy control method for dual-system cabinet air conditioners. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and to better and more effectively solve the problems that current air conditioning systems are often time-delayed, time-varying, and nonlinear, and the internal coupling of the system is also relatively complex, making precise model control and function control difficult. Conventional fuzzy control strategies have problems such as the rising characteristics of the characteristic curve and the fact that parameters such as adjustment time are not ideal. This invention provides a fuzzy control method for dual-system cabinet air conditioners, which has the functions of fast response and reducing refrigeration system oscillation, improving the energy efficiency ratio of dual-system air conditioners, and can also achieve relatively good decoupling control with strong anti-interference ability.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] A fuzzy control method for a dual-system cabinet air conditioner includes the following steps:

[0007] Step (A): Set the dual-system cabinet air conditioner to compressor operation mode, set the temperature, and obtain the input variables;

[0008] Step (B) involves fuzzifying the input variables and the frequency increment of the first compressor to obtain fuzzy values, and then establishing a fuzzy rule base.

[0009] Step (C) involves judging the input variables based on the fuzzy rule base and outputting the first compressor frequency increment.

[0010] Step (D) inputs the value of the input variable to the module controller and outputs the adjustment parameter to complete the adaptive adjustment of the scaling factor;

[0011] Step (E) involves adjusting the frequency increment of the first compressor based on the adaptive adjustment of the proportional factor, obtaining the frequency increment of the second compressor, and then using the frequency increment of the second compressor to complete the fuzzy control of the dual-system cabinet air conditioner.

[0012] Preferably, in step (A), the dual-system cabinet air conditioner is set to compressor operation mode, the temperature is set, and input variables are obtained. The input variables include temperature difference E and temperature change rate Ec. Temperature difference E is the difference between the set temperature and the output temperature, and temperature change rate Ec is the trend of temperature difference E and is derived from the derivative of E. The upper and lower limits of temperature difference E and temperature change rate Ec are limited. If it is greater than the upper limit, the value is the maximum value Xe. If it is less than the lower limit, the value is the minimum value -Xe.

[0013] Preferably, in step (B), the input variable and the frequency increment of the first compressor are fuzzified to obtain fuzzy values, and then a fuzzy rule base is established. The specific steps are as follows:

[0014] Step (B1) involves fuzzifying the input variables and the compressor operating frequency increment to obtain fuzzy quantities. The specific steps are as follows:

[0015] Step (B11) fuzzifies the input variables and obtains fuzzy values. The linguistic variables of temperature difference E and temperature change rate Ec are set as NB negative large, NM negative medium, NS negative small, ZO zero, PS positive small, PM positive medium and PB positive large. The universes of discourse of temperature difference E and temperature change rate Ec are -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5 and 6, and their membership functions are selected as Gaussian functions.

[0016] Step (B12) fuzzify the frequency increment of the first compressor and obtain the fuzzy quantity. The linguistic variables of the frequency increment ΔU of the first compressor are set as NL negative giant, NB negative large, NM negative medium, NS negative small, ZO zero, PS positive small, PM positive medium, PB positive large and PL positive giant. The universe of discourse of the frequency increment ΔU of the first compressor is set to -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8 and 9. The membership function is selected as the Gaussian function.

[0017] Step (B2) establishes a fuzzy rule base, which summarizes control methods based on experimental experience and judgment for specific controlled objects or processes into control rules with conditions if and results then.

[0018] Preferably, step (C) involves judging the input variables based on the fuzzy rule base and outputting the first compressor frequency increment. The specific steps are as follows:

[0019] Step (C1), identifying and outputting fuzziness, involves a two-dimensional fuzzy controller with two input quantities E and Ec and one output quantity ΔU. The specific control rules are shown in formula (1).

[0020] 1.if E is A1 and Ec is B1, then△U is C1

[0021] 2.if E is A2 and Ec is B2, then△U is C2 (1)

[0022] n. if E is A n and Ec is B n , then△U is C n

[0023] Among them, A1, A2 and A n B1, B2 and B n It is a fuzzy subset of the input, while C1, C2, and C n It is a fuzzy subset of the output, let E = e0 and E c =ec0, according to the membership function The membership function formula is μ(x)=exp[-(xc)]. 2 / σ 2 The result of the synthetic reasoning can be obtained, as shown in formula (2).

[0024]

[0025] Step (C2) involves defuzzification. The result of fuzzy discrimination is a fuzzy quantity, which cannot directly control the controlled object. Therefore, it is necessary to defuzzify and convert the fuzzy quantity into a precise quantity, as shown in formula (3).

[0026]

[0027] Preferably, in step (D), the values ​​of the input variables are input to the module controller, and the adjustment parameters are output to complete the adaptive adjustment of the scaling factor. The linguistic variables of the adjustment parameter P are set as NB negative large, NM negative medium, NS negative small, ZO zero, PS positive small, PM positive medium, and PB positive large. The universe of discourse of the adjustment parameter P is set to -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, and 6. The output adjustment parameter P must be combined with the scaling factor Ku, as shown in formulas (4) and (5).

[0028] Q = a|P| + b (4)

[0029] Ku 调整 =Ku+Q (5)

[0030] Where a is the weight value and b is the correction value.

[0031] Preferably, in step (E), based on the adaptive adjustment of the proportional factor, the frequency increment of the first compressor is corrected and the frequency increment of the second compressor is obtained. Then, the frequency increment of the second compressor is used to complete the fuzzy control of the dual-system cabinet air conditioner. In this case, after receiving the frequency increment signal of the second compressor, the inverter will modify its output power supply frequency, thereby completing the frequency control of the inverter compressor.

[0032] The beneficial effects of this invention are as follows: The fuzzy control method for a dual-system cabinet air conditioner first places the dual-system cabinet air conditioner in compressor operation mode, then sets the temperature and obtains the input variables. Next, the input variables and the first compressor frequency increment are fuzzified to obtain fuzzy quantities. A fuzzy rule base is then established. Subsequently, the input variables are judged according to the fuzzy rule base, and the first compressor frequency increment is output. The value of the input variables is then input to the module controller, and adjustment parameters are output. Adaptive adjustment of the proportional factor is then completed. Following this, based on the adaptive adjustment of the proportional factor, the first compressor frequency increment is corrected to obtain the second compressor frequency increment. The second compressor frequency increment is then used to complete the fuzzy control of the dual-system cabinet air conditioner. This method, using fuzzy control to control the dual-system air conditioner, also allows for online adaptive adjustment, effectively achieving the functions of rapid response and reduced refrigeration system oscillation, improving the energy efficiency ratio of the dual-system air conditioner, and achieving relatively good decoupling control with strong anti-interference capabilities. Attached Figure Description

[0033] Figure 1 This is an overall flowchart of the control method of the present invention;

[0034] Figure 2 This is a flowchart of the fuzzy algorithm of the present invention;

[0035] Figure 3 This is a schematic diagram of the fuzzy rule base established by the present invention;

[0036] Figure 4 This is a schematic diagram of the adjustment parameter regulation rule library of the present invention. Detailed Implementation

[0037] The present invention will now be further described with reference to the accompanying drawings.

[0038] like Figure 1 As shown, a fuzzy control method for a dual-system cabinet air conditioner according to the present invention includes the following steps:

[0039] Step (A): Set the dual-system cabinet air conditioner to compressor operation mode, set the temperature and obtain input variables, including temperature difference E and temperature change rate Ec. Temperature difference E is the difference between the set temperature and the output temperature, while temperature change rate Ec is the trend of temperature difference E and is derived from the derivative of E. The upper and lower limits of temperature difference E and temperature change rate Ec are limited. If it is greater than the upper limit, the value is the maximum value Xe; if it is less than the lower limit, the value is the minimum value -Xe.

[0040] like Figure 2 As shown, step (B) involves fuzzifying the input variables and the frequency increment of the first compressor to obtain fuzzy values, and then establishing a fuzzy rule base. The specific steps are as follows:

[0041] Step (B1) involves fuzzifying the input variables and the compressor operating frequency increment to obtain fuzzy quantities. The specific steps are as follows:

[0042] Step (B11) fuzzifies the input variables and obtains fuzzy values. The linguistic variables of temperature difference E and temperature change rate Ec are set as NB negative large, NM negative medium, NS negative small, ZO zero, PS positive small, PM positive medium and PB positive large. The universes of discourse of temperature difference E and temperature change rate Ec are -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5 and 6, and their membership functions are selected as Gaussian functions.

[0043] Among them, the more pointed the membership function is, the higher its resolution and the higher its control sensitivity, but the weaker its stability and the more prone it is to oscillation. On the other hand, the more gently shaped its control characteristics are more gentle and the stronger its stability.

[0044] Step (B12) fuzzify the frequency increment of the first compressor and obtain the fuzzy quantity. The linguistic variables of the frequency increment ΔU of the first compressor are set as NL negative giant, NB negative large, NM negative medium, NS negative small, ZO zero, PS positive small, PM positive medium, PB positive large and PL positive giant. The universe of discourse of the frequency increment ΔU of the first compressor is set to -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8 and 9. The membership function is selected as the Gaussian function.

[0045] The fuzzification of the frequency increment ΔU of the first compressor is for the purpose of more accurate temperature adjustment.

[0046] like Figure 3 As shown, step (B2) establishes a fuzzy rule base, which summarizes the control methods for specific controlled objects or processes based on experimental experience and judgment into control rules of condition if and result then.

[0047] Step (C) involves judging the input variables based on the fuzzy rule base and outputting the first compressor frequency increment. The specific steps are as follows:

[0048] Step (C1), identifying and outputting fuzziness, involves a two-dimensional fuzzy controller with two input quantities E and Ec and one output quantity ΔU. The specific control rules are shown in formula (1).

[0049] 1.if E is A1 and Ec is B1, then△U is C1

[0050] 2.if E is A2 and Ec is B2, then△U is C2 (1)

[0051] n. if E is A n and Ec is B n , then△U is C n

[0052] Among them, A1, A2 and A n B1, B2 and B n It is a fuzzy subset of the input, while C1, C2, and C n It is a fuzzy subset of the output, let E = e0 and E c =ec0, according to the membership function The membership function formula is μ(x)=exp[-(xc)]. 2 / σ 2 The result of the synthetic reasoning can be obtained, as shown in formula (2).

[0053]

[0054] Step (C2) involves defuzzification. The result of fuzzy discrimination is a fuzzy quantity, which cannot directly control the controlled object. Therefore, it is necessary to defuzzify and convert the fuzzy quantity into a precise quantity, as shown in formula (3).

[0055]

[0056] like Figure 4 As shown, in step (D), the values ​​of the input variables are input to the module controller, and the adjustment parameters are output to complete the adaptive adjustment of the scaling factor. The linguistic variables of the adjustment parameter P are set as NB negative large, NM negative medium, NS negative small, ZO zero, PS positive small, PM positive medium, and PB positive large. The universe of discourse of the adjustment parameter P is set to -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, and 6. The output adjustment parameter P must be combined with the scaling factor Ku, as shown in formulas (4) and (5).

[0057] Q = a|P| + b (4)

[0058] Ku 调整 =Ku+Q (5)

[0059] Where a is the weight value and b is the correction value, the proportional factor Ku has a significant impact on the dynamic and static characteristics of fuzzy control. Different Ku values ​​are needed to optimize the characteristic curve when the temperature difference E and the rate of temperature change Ec are different. Then, when the temperature difference and the rate of change are small and reach the stable stage, a smaller Ku is needed, while when the temperature difference and the rate of change are large, a larger Ku is needed to make the temperature difference decrease rapidly, thereby reducing the adjustment time and further improving the air conditioning energy efficiency. Therefore, a larger weight needs to be given to the adjustment value P that produces the change to generate a larger Ku adjustment amount.

[0060] Step (E) involves adjusting the frequency increment of the first compressor based on the adaptive adjustment of the proportional factor, obtaining the frequency increment of the second compressor, and then using the frequency increment of the second compressor to complete the fuzzy control of the dual-system cabinet air conditioner. The inverter will modify its output power supply frequency after receiving the frequency increment signal of the second compressor, thereby completing the frequency control of the inverter compressor.

[0061] In fuzzy control, the input quantity is fuzzified and the output quantity is inversely fuzzified. When the temperature difference of the input is extremely small, it may be considered that there is no temperature difference. If there is a requirement for high-precision control, an offset processing will be performed after the temperature is set. If E is greater than zero, a positive offset is performed, so that the actual set temperature of the system is slightly greater than the input set temperature. If E is less than zero, a negative offset is performed, so that the actual set temperature of the system is slightly less than the set temperature.

[0062] To better illustrate the effects of the present invention, a specific embodiment of the present invention is described below;

[0063] The method of the present invention can still be used for the energy-saving mode of dual-system air conditioners. It is only necessary to convert the frequency increment of the second compressor after fuzzy control processing into the control quantity of the fan. Therefore, the fuzzy controller can be shared in both modes. Only one control center with two paths is needed. Then, it is determined which mode is currently in use, and the program runs the path in the current mode.

[0064] When the dual-system cabinet air conditioner is in energy-saving mode, the ambient temperature is low and the air conditioner compressor does not run. It can meet the cooling requirements by relying on the gravity heat pipe and the fan. The gravity heat pipe uses the ambient temperature for cooling, and its cooling capacity is related to the operating power of the fan. Therefore, the output can be converted into the control quantity of the fan power according to this control, thereby controlling the air conditioner temperature.

[0065] When the dual-system cabinet air conditioner is in hybrid mode, the ambient temperature is not low enough and the cooling capacity of the gravity heat pipe is insufficient. The compressor starts to supplement the cooling. In the initial transition phase, the inverter compressor runs at the lowest frequency. When there is excess cooling capacity, the cooling capacity and power consumption are reduced by controlling and adjusting the fan. As the cooling demand increases or the ambient temperature rises further, the cooling capacity of the heat pipe decreases. After adjusting the fan to the maximum power, the cooling capacity is increased by controlling the inverter compressor.

[0066] When the dual-system cabinet air conditioner is in compressor mode, the compressor provides supplemental cooling, and the direct output is the compressor frequency increment to control the air conditioner temperature. Since the cooling oscillation of the dual-system air conditioner is relatively large and the stabilization speed is relatively slow, an online self-adjusting Ku method is adopted. This method uses a larger Ku when the temperature difference curve deviates significantly from the set value to make the curve quickly approach the set value. When it approaches the set value, the Ku is gradually reduced to reduce the oscillation and reach the set value more smoothly. This makes the cooling response fast and the oscillation small. Since it is generally difficult to achieve 100% accuracy to the set value in system control, a set offset can be built into the system as needed. The input display set value is reasonably offset and used as the set value of the control system.

[0067] In summary, the fuzzy control method for a dual-system cabinet air conditioner of the present invention uses fuzzy control to control the dual-system air conditioner and can also adaptively adjust online. This method effectively achieves the functions of fast response and reduced refrigeration system oscillation, improves the energy efficiency ratio of the dual-system air conditioner, can also achieve relatively good decoupling control, and has strong anti-interference ability.

[0068] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A fuzzy control method for a dual-system cabinet air conditioner, characterized in that: Includes the following steps, Step (A): Set the dual-system cabinet air conditioner to compressor operation mode, set the temperature and obtain input variables, including temperature difference E and temperature change rate Ec. Temperature difference E is the difference between the set temperature and the output temperature, while temperature change rate Ec is the trend of temperature difference E and is derived from the derivative of E. The upper and lower limits of temperature difference E and temperature change rate Ec are limited. If it is greater than the upper limit, the value is the maximum value Xe; if it is less than the lower limit, the value is the minimum value -Xe. Step (B) involves fuzzifying the input variables and the frequency increment of the first compressor to obtain fuzzy values, and then establishing a fuzzy rule base. The specific steps are as follows: Step (B1) involves fuzzifying the input variables and the compressor operating frequency increment to obtain fuzzy quantities. The specific steps are as follows: Step (B11) fuzzify the input variables and obtain fuzzy values. The linguistic variables of temperature difference E and temperature change rate Ec are set as NB negative large, NM negative medium, NS negative small, ZO zero, PS positive small, PM positive medium and PB positive large. The universes of discourse of temperature difference E and temperature change rate Ec are -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5 and 6, and their membership functions are selected as Gaussian functions. Step (B12): The frequency increment of the first compressor is fuzzified and the fuzzy quantity is obtained. The linguistic variables of the frequency increment ΔU of the first compressor are set as NL negative giant, NB negative large, NM negative medium, NS negative small, ZO zero, PS positive small, PM positive medium, PB positive large and PL positive giant. The universe of discourse of the frequency increment ΔU of the first compressor is set as -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8 and 9. The membership function is selected as Gaussian function. Step (B2) establishes a fuzzy rule base, which summarizes control methods based on experimental experience and judgment for specific controlled objects or processes into control rules of condition if and result then. Step (C) involves judging the input variables based on the fuzzy rule base and outputting the first compressor frequency increment. The specific steps are as follows: Step (C1), identifying and outputting fuzziness, involves a two-dimensional fuzzy controller with two input quantities E and Ec and one output quantity ΔU. The specific control rules are shown in formula (1). 1.if E is A1 and Ec is B1, then △U is C1 2.if E is A2 and Ec is B2, then △U is C2 (1) n.if E is A n and Ec is B n ,then △U is C n Among them, A1, A2 and A n B1, B2 and B n It is a fuzzy subset of the input, while C1, C2, and C n It is a fuzzy subset of the output, let E = e0 and E c =ec0, according to the membership function , (i=1,2,...,n) and membership function formula The result of the synthetic reasoning can be obtained, as shown in formula (2). （2）； Step (C2) involves defuzzification. The result of fuzzy discrimination is a fuzzy quantity, which cannot directly control the controlled object. Therefore, it is necessary to defuzzify and convert the fuzzy quantity into a precise quantity, as shown in formula (3). （3）； Step (D) inputs the values ​​of the input variables to the module controller and outputs the adjustment parameters to complete the adaptive adjustment of the scaling factor. The linguistic variables of the adjustment parameter P are set as NB negative large, NM negative medium, NS negative small, ZO zero, PS positive small, PM positive medium, and PB positive large. The universe of discourse of the adjustment parameter P is set to -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, and 6. The output adjustment parameter P must be combined with the scaling factor Ku, as shown in formulas (4) and (5). Q = a|P| + b (4) You 调整 =Ku+Q(5); Where a is the weight value and b is the correction value; Step (E): Based on the adaptive adjustment of the proportional factor, the frequency increment of the first compressor is corrected and the frequency increment of the second compressor is obtained. Then, the frequency increment of the second compressor is used to complete the fuzzy control of the dual-system cabinet air conditioner. After receiving the frequency increment signal of the second compressor, the inverter will modify its output power supply frequency, thereby completing the frequency control of the inverter compressor.

Images