CN115729132A - Intelligent control method for single-system air conditioner - Google Patents
Intelligent control method for single-system air conditioner Download PDFInfo
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- CN115729132A CN115729132A CN202211390789.7A CN202211390789A CN115729132A CN 115729132 A CN115729132 A CN 115729132A CN 202211390789 A CN202211390789 A CN 202211390789A CN 115729132 A CN115729132 A CN 115729132A
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Abstract
The invention discloses an intelligent control method of a single-system air conditioner, which comprises the steps of firstly, setting the single-system air conditioner in a compressor running mode, setting temperature and obtaining an input variable, then fuzzifying the input variable and a first compressor frequency increment to obtain a fuzzy quantity, establishing a fuzzy rule base, judging the input variable according to the fuzzy rule base, outputting the first compressor frequency increment, then inputting the value of the input variable to a module controller and outputting an adjusting parameter, then completing the self-adaptive adjustment of a proportional factor, and then performing the self-adaptive adjustment based on the proportional factor; according to the method, the single-system air conditioner is controlled in a fuzzy control-based mode, and online self-adaptive adjustment can be realized, so that the method has the functions of quickly responding and reducing the oscillation of a refrigerating system, the energy efficiency ratio of the single-system air conditioner is improved, decoupling control can be relatively well completed, and the anti-interference performance is relatively high.
Description
Technical Field
The invention relates to the technical field of single-system air conditioner control, in particular to an intelligent control method of a single-system air conditioner.
Background
China is a large energy-consuming country, the current energy cannot meet the requirements of the social development of China, and under the environment, china advocates energy conservation and emission reduction vigorously. At present, various single-system air conditioners are arranged in most buildings in China, a large number of single-system refrigeration air conditioners exist in industrial production and application, and a large amount of energy is consumed in the operation of a heating ventilation refrigeration system every year, so that the optimization and control of the air conditioning refrigeration system are necessary, and the air conditioning refrigeration system is developed to low energy consumption.
At present, because an air conditioning system is time-lag, time-varying and nonlinear, and the coupling inside the system is complex, the modes of accurate model control, function control and the like are difficult, the rising characteristic of a characteristic curve exists in a conventional fuzzy control strategy, and parameters such as adjusting time are not particularly ideal; therefore, it is necessary to design an intelligent control method of a single system air conditioner.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides an intelligent control method of a single-system air conditioner, which has the functions of quickly responding and reducing the oscillation of a refrigerating system, improves the energy efficiency ratio of the single-system air conditioner, can relatively well finish decoupling control and has strong anti-interference performance, in order to better and effectively solve the problems that the conventional fuzzy control strategy has rising characteristics of a characteristic curve and parameters such as adjusting time are not particularly ideal because the air conditioning system is usually time-delayed, time-varying and nonlinear and the coupling in the system is also complex.
In order to achieve the purpose, the invention adopts the technical scheme that:
an intelligent control method of a single-system air conditioner comprises the following steps,
step (A), setting the temperature of a single-system air conditioner and obtaining an input variable;
step (B), fuzzifying the input variable and the first compressor frequency increment to obtain a fuzzy quantity, and then establishing a fuzzy rule base;
step (C), judging the input variable according to the fuzzy rule base, and outputting the frequency increment of the first compressor;
step (D), inputting the value of the input variable into the module controller, and outputting an adjusting parameter to finish the self-adaptive adjustment of the proportional factor;
and (E) correcting the frequency increment of the first compressor and obtaining the frequency increment of the second compressor based on the self-adaptive adjustment of the scale factor, and then completing the fuzzy control of the single-system air conditioner by utilizing the frequency increment of the second compressor.
Preferably, in the step (a), the temperature of the single-system air conditioner is set and an input variable is obtained, wherein the input variable comprises a temperature difference E and a temperature change rate Ec, the temperature difference E is a difference value between the set temperature and the output temperature, the temperature change rate Ec is a change trend of the temperature difference E and is obtained by differentiation of E, upper and lower limits of the temperature difference E and the temperature change rate Ec both have limits, if the upper limit is larger than the upper limit, the value is a maximum value Xe, and if the lower limit is smaller than the lower limit, the value is a minimum value-Xe.
Preferably, step (B) is to fuzzify the input variable and the first compressor frequency increment to obtain a fuzzy amount, and then establish a fuzzy rule base, and the specific steps are as follows,
step (B1), fuzzifying the input variable and the compressor running frequency increment to obtain a fuzzy quantity, and the concrete steps are as follows,
step (B11), fuzzifying input variables and obtaining fuzzy quantities, wherein linguistic variables of the temperature difference E and the temperature change rate Ec are set to be NB negative large, NM negative medium, NS negative small, ZO zero, PS positive small, PM positive medium and PB positive large, discourse domains of the temperature difference E and the temperature change rate Ec are-6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5 and 6, and membership functions of the temperature difference E and the temperature change rate Ec select Gaussian functions;
step (B12), fuzzifying the first compressor frequency increment and obtaining a fuzzy quantity, wherein linguistic variables of the first compressor frequency increment delta U are set as NL negative large, NB negative large, NM negative middle, NS negative small, ZO zero, PS positive small, PM middle, PB positive large and PL positive large, the domain of the first compressor frequency increment delta U is set as-9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8 and 9, and the membership function of the first compressor frequency increment delta U is a Gaussian function;
and (B2) establishing a fuzzy rule base, which is a control rule integrating the condition if and the result then according to the experimental experience and judgment on the control method of the specific controlled object or process.
Preferably, the step (C) of outputting the first compressor frequency increment by discriminating the input variable according to the fuzzy rule base comprises the following specific steps,
step (C1), judging and outputting the fuzzy, namely, a two-dimensional fuzzy controller of two input quantities E and Ec and an output quantity delta U is used, the specific control rule is shown as formula (1),
wherein A is 1 、A 2 And A n ，B 1 、B 2 And B n Is a fuzzy subset of the input, and C 1 、C 2 And C n Is a fuzzy subset of the output, let E = E 0 And E c ＝ec 0 According to membership functions
And membership function formula mu (x) = exp [ - (x-c) 2 /σ 2 ]The resultant reasoning result can be obtained, as shown in equation (2),
step (C2), resolving the blur, wherein the result of the blur discrimination is a blur amount, and the controlled object cannot be directly controlled, and at this time, resolving the blur is needed and the blur amount is converted into an accurate amount, as shown in formula (3),
preferably, step (D) of inputting the value of the input variable to the module controller and outputting the tuning parameter P, wherein the linguistic variables of the tuning parameter P are set to NB negative large, NM negative medium, NS negative small, ZO zero, PS positive small, PM positive medium and PB positive large, the domains of the tuning parameter P are set to-6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5 and 6, and the outputted tuning parameter P is combined with the scaling factor Ku, as shown in formula (4) and formula (5),
Q＝a│P│+b (4)
Ku adjustment of ＝Ku+Q (5)
Wherein, a is a weight value, and b is a correction value.
Preferably, in the step (E), the frequency increment of the first compressor is corrected and the frequency increment of the second compressor is obtained based on the adaptive adjustment of the scale factor, and then the fuzzy control of the single-system air conditioner is completed by using the frequency increment of the second compressor, wherein the frequency converter modifies the output power supply frequency of the second compressor after receiving the frequency increment signal of the second compressor, thereby completing the frequency control of the inverter compressor.
The beneficial effects of the invention are: the invention relates to an intelligent control method of a single-system air conditioner, which comprises the steps of firstly enabling the single-system air conditioner to be in a compressor running mode, setting temperature and obtaining an input variable, then fuzzifying the input variable and a first compressor frequency increment to obtain a fuzzy quantity, then establishing a fuzzy rule base, then judging the input variable according to the fuzzy rule base, then outputting a first compressor frequency increment, then inputting the value of the input variable to a module controller and outputting an adjusting parameter, then completing self-adaptive adjustment of a proportional factor, next completing self-adaptive adjustment based on a proportional factor, correcting the first compressor frequency increment and obtaining a second compressor frequency increment, and then completing the fuzzy control of the single-system air conditioner by utilizing the second compressor frequency increment.
Drawings
FIG. 1 is an overall flowchart of the control method of the present invention;
FIG. 2 is a program flow diagram of the fuzzy algorithm of the present invention;
FIG. 3 is a schematic diagram of the fuzzy rule base established by the present invention;
FIG. 4 is a schematic diagram of an adjustment parameter adjustment rule base according to the present invention.
Detailed Description
The invention will be further described with reference to the accompanying drawings.
As shown in fig. 1, the intelligent control method of a single system air conditioner of the present invention includes the following steps,
and (A) setting the temperature of the single-system air conditioner and obtaining input variables, wherein the input variables comprise a temperature difference E and a temperature change rate Ec, the temperature difference E is a difference value between the set temperature and the output temperature, the temperature change rate Ec is a change trend of the temperature difference E and is obtained by differentiation of E, the upper limit and the lower limit of the temperature difference E and the temperature change rate Ec are both limited, if the temperature difference E and the temperature change rate Ec are larger than the upper limit, the value is the maximum value Xe, and if the temperature change rate Ec is smaller than the lower limit, the value is the minimum value-Xe.
As shown in fig. 2, step (B), fuzzifying the input variable and the first compressor frequency increment to obtain a fuzzy amount, and then establishing a fuzzy rule base, which comprises the following specific steps,
step (B1), fuzzifying the input variable and the compressor running frequency increment to obtain a fuzzy quantity, and the concrete steps are as follows,
step (B11), fuzzifying input variables and obtaining fuzzy quantities, wherein linguistic variables of the temperature difference E and the temperature change rate Ec are set to be NB negative large, NM negative medium, NS negative small, ZO zero, PS positive small, PM positive medium and PB positive large, discourse domains of the temperature difference E and the temperature change rate Ec are-6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5 and 6, and membership functions of the temperature difference E and the temperature change rate Ec select Gaussian functions;
the membership function has a sharp shape, the resolution is higher, the control sensitivity is higher, the stability is weaker, the oscillation is easily caused, the shape is gentle, the control characteristic is gentle, and the stability is strong;
step (B12), fuzzifying the first compressor frequency increment and obtaining a fuzzy quantity, wherein linguistic variables of the first compressor frequency increment delta U are set as NL negative huge, NB negative large, NM negative middle, NS negative small, ZO zero, PS positive small, PM positive middle, PB positive large and PL positive huge, the discourse domain of the first compressor frequency increment delta U is set as-9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8 and 9, and the membership function of the first compressor frequency increment delta U is a Gaussian function;
the fuzzification of the frequency increment delta U of the first compressor is used for more accurately adjusting the temperature;
as shown in fig. 3, in step (B2), a fuzzy rule base is established, which is a control rule that combines the condition if and the result then with the control method for a specific controlled object or process by means of experimental experience and judgment.
Step (C), the input variable is distinguished according to the fuzzy rule base, and the frequency increment of the first compressor is output, the specific steps are as follows,
step (C1), judging and outputting the fuzzy, namely, a two-dimensional fuzzy controller of two input quantities E and Ec and an output quantity delta U is used, the specific control rule is shown as formula (1),
wherein, A 1 、A 2 And A n ，B 1 、B 2 And B n Is a fuzzy subset of the input, and C 1 、C 2 And C n Is a fuzzy subset of the output, let E = E 0 And E c ＝ec 0 According to membership functions
And membership function formula mu (x) = exp [ - (x-c) 2 /σ 2 ]The obtained synthetic reasoning resultAs shown in the formula (2),
step (C2), resolving the ambiguity, wherein the result of the ambiguity discrimination is an ambiguity quantity, and the controlled object cannot be directly controlled, and at the moment, the ambiguity resolution is needed and the ambiguity quantity is converted into an accurate quantity, as shown in formula (3),
as shown in fig. 4, step (D) of inputting the values of the input variables to the module controller and outputting the tuning parameters, completing the adaptive adjustment of the scaling factor, wherein the linguistic variables of the tuning parameter P are set to NB negative large, NM negative medium, NS negative small, ZO zero, PS positive small, PM positive medium and PB positive large, the domains of the tuning parameter P are set to-6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5 and 6, and the outputted tuning parameter P is to be combined with the scaling factor Ku as shown in formula (4) and formula (5),
Q＝a│P│+b (4)
Ku adjustment of ＝Ku+Q (5)
The method comprises the following steps of firstly, calculating a scale factor Ku, calculating a temperature difference E and a temperature change rate Ec of the air conditioner, wherein a is a weight value, b is a correction value, the influence of the scale factor Ku on the dynamic and static characteristics of fuzzy control is large, different Ku values are needed to optimize a characteristic curve when the temperature difference E and the temperature change rate Ec are different, then, a small Ku is needed when the temperature difference is small and the change rate is small to a stable stage, and a large Ku is needed to enable the temperature difference to be rapidly reduced when the temperature difference is large and the change rate is large, so that the adjusting time is shortened, the energy efficiency of the air conditioner is further improved, and a large weight needs to be given to an adjusting value P which generates change to generate a large Ku adjusting quantity.
And (E) correcting the frequency increment of the first compressor and obtaining the frequency increment of the second compressor based on the self-adaptive adjustment of the scale factor, and then completing the fuzzy control of the single-system air conditioner by utilizing the frequency increment of the second compressor, wherein the frequency converter modifies the output power supply frequency of the second compressor after receiving the frequency increment signal of the second compressor, thereby completing the frequency control of the variable frequency compressor.
Under fuzzy control, after the input quantity of the fuzzy control system is fuzzified and the output quantity is subjected to inverse fuzzy, when the input temperature difference is extremely small, the fuzzy control system has the possibility of considering that the fuzzy control system does not have the temperature difference, if the fuzzy control system has a high-precision control requirement, the deviation processing is carried out after the set temperature, if E is larger than zero, the positive deviation is carried out, so that the actual set temperature of the system is slightly larger than the input set temperature, and if E is smaller than zero, the negative deviation is carried out, so that the actual set temperature of the system is slightly smaller than the set temperature.
In order to better illustrate the use effect of the present invention, a specific embodiment of the present invention is described below;
when the single system air conditioner is in the operation mode, the compressor performs refrigeration supplement at the moment, the direct output quantity is the frequency increment of the compressor so as to control the temperature of the air conditioner, and because the refrigeration oscillation of the single system air conditioner is large and the stable speed is slow, an online self-adjusting Ku mode is adopted, the mode is that when the temperature difference curve deviates from the set value greatly, a large Ku is used for realizing the curve to be quickly close to the set value, when the temperature difference curve approaches the set value, the Ku is gradually reduced so that the oscillation is small and the set value is smoothly reached, so that the refrigeration is quickly responded and the oscillation is small, and because the complete percent precision to the set value is difficult to achieve in the system control, the deviation can be set in the system according to the requirement, and the input display set value is reasonably deviated and then used as the set value of the control system.
In summary, the intelligent control method for the single-system air conditioner provided by the invention controls the single-system air conditioner in a fuzzy control-based manner, and can also perform online adaptive adjustment, so that the method has the functions of quickly responding and reducing the oscillation of a refrigeration system, the energy efficiency ratio of the single-system air conditioner is improved, the decoupling control can be relatively well completed, and the anti-interference performance is relatively strong.
The foregoing shows and describes the general principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (6)
1. An intelligent control method of a single-system air conditioner is characterized in that: comprises the following steps of (a) carrying out,
step (A), setting the temperature of a single-system air conditioner and obtaining an input variable;
step (B), fuzzifying the input variable and the first compressor frequency increment to obtain a fuzzy quantity, and then establishing a fuzzy rule base;
step (C), judging the input variable according to the fuzzy rule base, and outputting the frequency increment of the first compressor;
step (D), inputting the value of the input variable into the module controller, and outputting an adjusting parameter to finish the self-adaptive adjustment of the proportional factor;
and (E) correcting the frequency increment of the first compressor and obtaining the frequency increment of the second compressor based on the self-adaptive adjustment of the scale factor, and then completing the fuzzy control of the single-system air conditioner by utilizing the frequency increment of the second compressor.
2. The intelligent control method of the single-system air conditioner according to claim 1, wherein: and (A) setting the temperature of the single-system air conditioner and obtaining an input variable, wherein the input variable comprises a temperature difference E and a temperature change rate Ec, the temperature difference E is a difference value between the set temperature and the output temperature, the temperature change rate Ec is a change trend of the temperature difference E and is obtained by differentiation of the temperature difference E, the upper limit and the lower limit of the temperature difference E and the temperature change rate Ec are both limited, if the temperature difference E is larger than the upper limit, the value is the maximum value Xe, and if the temperature difference E is smaller than the lower limit, the value is the minimum value-Xe.
3. The intelligent control method of the single-system air conditioner according to claim 2, wherein: step (B), fuzzifying the input variable and the first compressor frequency increment to obtain a fuzzy quantity, and establishing a fuzzy rule base, which comprises the following specific steps,
step (B1), fuzzifying the input variable and the compressor running frequency increment to obtain a fuzzy quantity, and the concrete steps are as follows,
step (B11), fuzzifying input variables and obtaining fuzzy quantities, wherein linguistic variables of the temperature difference E and the temperature change rate Ec are set to be NB negative large, NM negative medium, NS negative small, ZO zero, PS positive small, PM positive medium and PB positive large, discourse domains of the temperature difference E and the temperature change rate Ec are-6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5 and 6, and membership functions of the temperature difference E and the temperature change rate Ec select Gaussian functions;
step (B12), fuzzifying the first compressor frequency increment and obtaining a fuzzy quantity, wherein linguistic variables of the first compressor frequency increment delta U are set as NL negative large, NB negative large, NM negative middle, NS negative small, ZO zero, PS positive small, PM middle, PB positive large and PL positive large, the domain of the first compressor frequency increment delta U is set as-9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8 and 9, and the membership function of the first compressor frequency increment delta U is a Gaussian function;
and (B2) establishing a fuzzy rule base, which is a control rule integrating the condition if and the result then according to the experimental experience and judgment on the control method of the specific controlled object or process.
4. The intelligent control method of the single-system air conditioner according to claim 3, wherein: step (C), the input variable is judged according to the fuzzy rule base, and the frequency increment of the first compressor is output, the specific steps are as follows,
step (C1), judging and outputting the fuzzy, namely, a two-dimensional fuzzy controller of two input quantities E and Ec and an output quantity delta U is used, the specific control rule is shown as a formula (1),
wherein A is 1 、A 2 And A n ，B 1 、B 2 And B n Is a fuzzy subset of the input, and C 1 、C 2 And C n Is a fuzzy subset of the output, let E = E 0 And E c ＝ec 0 According to membership functions
And membership function formula mu (x) = exp [ - (x-c) 2 /σ 2 ]The resultant of the synthetic reasoning can be obtained, as shown in equation (2),
step (C2), resolving the ambiguity, wherein the result of the ambiguity discrimination is an ambiguity quantity, and the controlled object cannot be directly controlled, and at the moment, the ambiguity resolution is needed and the ambiguity quantity is converted into an accurate quantity, as shown in formula (3),
5. the intelligent control method of the single-system air conditioner according to claim 4, wherein: and (D) inputting the values of the input variables into the module controller, outputting an adjusting parameter and finishing the adaptive adjustment of the scaling factor, wherein the linguistic variables of the adjusting parameter P are set to be NB negative large, NM negative middle, NS negative small, ZO zero, PS positive small, PM positive middle and PB positive large, the domain of the adjusting parameter P is set to be-6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5 and 6, and the output adjusting parameter P is combined with the scaling factor Ku as shown in formula (4) and formula (5),
Q＝a│P│+b (4)
Ku adjustment of ＝Ku+Q (5)
Wherein, a is a weight value, and b is a correction value.
6. The intelligent control method of the single-system air conditioner according to claim 5, wherein: and (E) correcting the frequency increment of the first compressor and obtaining the frequency increment of the second compressor based on the self-adaptive adjustment of the scale factor, and then completing the fuzzy control of the single-system air conditioner by utilizing the frequency increment of the second compressor, wherein the frequency converter modifies the output power supply frequency of the second compressor after receiving the frequency increment signal of the second compressor, thereby completing the frequency control of the variable frequency compressor.
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| CN118584793A (en) * | 2024-08-05 | 2024-09-03 | 森特士兴环保科技有限公司 | Intelligent frequency modulation method of soil vapor extraction vacuum pump based on fuzzy control |
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| CN118584793A (en) * | 2024-08-05 | 2024-09-03 | 森特士兴环保科技有限公司 | Intelligent frequency modulation method of soil vapor extraction vacuum pump based on fuzzy control |
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