【摘要】：變流量空調(diào)系統(tǒng)是中央空調(diào)系統(tǒng)的主角,而冷凍水系統(tǒng)又是空調(diào)系統(tǒng)的主要組成部分,也是影響整個(gè)系統(tǒng)能耗的重要因素。隨著空調(diào)系統(tǒng)的發(fā)展和復(fù)雜程度的日益提高,對(duì)其控制環(huán)節(jié)的要求也越來(lái)越高。對(duì)于變流量空調(diào)系統(tǒng),采用較多的是壓差控制包括定壓差控制和變壓差控制,因?yàn)閷?shí)際使用中負(fù)荷是不斷變化的,系統(tǒng)的工作狀態(tài)也在不斷變化,固定的壓差設(shè)定值往往使系統(tǒng)的工作狀態(tài)具有較大起伏,不利于穩(wěn)定控制和提高設(shè)備使用壽命,也造成很多不必要的能耗。而變壓差控制可以隨負(fù)荷變化的情況合理的調(diào)整壓差設(shè)定值,從而提高了系統(tǒng)的適應(yīng)能力,逐漸成為近些年研究的熱點(diǎn)。本文以變流量空調(diào)冷凍水系統(tǒng)為控制對(duì)象,介紹了最不利熱力環(huán)路的概念,并在此概念的基礎(chǔ)上引入變壓差控制策略,詳細(xì)說(shuō)明了采用變壓差控制的優(yōu)勢(shì)和如何實(shí)現(xiàn)變壓差控制,介紹了變壓差設(shè)定值的線性調(diào)整算法。在控制器方面采用的是基于魚(yú)群算法的PID神經(jīng)網(wǎng)絡(luò)控制器,PID控制簡(jiǎn)單可靠,但是不適用于非線性系統(tǒng),在復(fù)雜系統(tǒng)和時(shí)變系統(tǒng)中的應(yīng)用效果也不盡如人意,抗干擾和自適應(yīng)能力較弱；神經(jīng)網(wǎng)絡(luò)具有很強(qiáng)的非線性能力和容錯(cuò)能力,適應(yīng)能力較強(qiáng),但是常規(guī)的神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu)復(fù)雜,計(jì)算量大。空調(diào)系統(tǒng)是一個(gè)復(fù)雜的時(shí)變的非線性系統(tǒng),將PID與神經(jīng)網(wǎng)絡(luò)優(yōu)勢(shì)互補(bǔ)形成的PID神經(jīng)網(wǎng)絡(luò)則正好解決空調(diào)系統(tǒng)的控制問(wèn)題。在此基礎(chǔ)上,再采用具有強(qiáng)大尋優(yōu)能力的人工魚(yú)群算法對(duì)PID神經(jīng)網(wǎng)絡(luò)進(jìn)行訓(xùn)練,對(duì)其進(jìn)一步優(yōu)化。將優(yōu)化好的PID神經(jīng)網(wǎng)絡(luò)應(yīng)用到壓差控制回路中,可以使末端壓差快速高效地穩(wěn)定在設(shè)定值。另一方面,變壓差設(shè)定的線性調(diào)整算法在負(fù)荷大幅度變化時(shí)無(wú)法很好地跟蹤負(fù)荷的變化,壓差設(shè)定值的調(diào)整速度不夠,精確度也有待加強(qiáng)。為此,再次應(yīng)用魚(yú)群算法對(duì)線性調(diào)整算法進(jìn)行優(yōu)化,根據(jù)負(fù)荷變化調(diào)整算法的參數(shù),增強(qiáng)算法調(diào)整壓差設(shè)定值的能力。最后,建立相關(guān)設(shè)備的模型,利用MATLAB仿真,對(duì)比定壓差控制與變壓差控制效果,驗(yàn)證魚(yú)群算法在優(yōu)化PID神經(jīng)網(wǎng)絡(luò)和優(yōu)化變壓差設(shè)定值線性調(diào)整算法方面的效果。仿真結(jié)果表明,魚(yú)群算法的優(yōu)化效果明顯。
[Abstract]:Variable flow air conditioning system is the main role of the central air conditioning system, and the chilled water system is the main component of the air conditioning system, and it is also an important factor affecting the energy consumption of the whole system. With the development of air conditioning system and the increasing complexity, the requirements of its control links are becoming higher and higher. For the variable flow air conditioning system, the pressure difference control includes constant pressure difference control and variable pressure difference control, because the load is constantly changing in actual use, and the working state of the system is also changing. The fixed pressure-difference value often makes the working state of the system fluctuate greatly, which is not conducive to stable control and increase the service life of the equipment, and also results in a lot of unnecessary energy consumption. The variable pressure difference control can adjust the pressure-difference setting value reasonably with the change of load, thus improving the adaptability of the system, and gradually becoming the hot spot of research in recent years. In this paper, the concept of the most unfavorable thermal loop is introduced, and the variable pressure difference control strategy is introduced on the basis of the variable flow air conditioning chilled water system as the control object. The advantages of variable pressure difference control and how to realize variable pressure difference control are described in detail. The linear adjustment algorithm of variable pressure difference setting value is introduced. In the controller, the PID neural network controller based on fish swarm algorithm is simple and reliable, but it is not suitable for nonlinear system, and the application effect in complex system and time-varying system is not satisfactory. The ability of anti-interference and self-adaptation is weak, and the neural network has strong nonlinear and fault-tolerant ability, but the conventional neural network structure is complex and the computation is large. Air conditioning system is a complex and time-varying nonlinear system. PID neural network, which combines PID and neural network, can solve the control problem of air conditioning system. On this basis, the artificial fish swarm algorithm with strong optimization ability is used to train the PID neural network and optimize it further. By applying the optimized PID neural network to the differential pressure control loop, the end pressure difference can be quickly and efficiently stabilized at the set value. On the other hand, the linear adjustment algorithm of variable pressure difference setting can not track the change of load well when the load changes greatly, the adjustment speed of the pressure difference setting value is not enough, and the accuracy needs to be strengthened. Therefore, the linear adjustment algorithm is optimized by using the fish swarm algorithm again, and the ability of adjusting the pressure-difference setting value is enhanced according to the load change adjustment algorithm parameters. Finally, the model of related equipment is established, and the effects of constant pressure difference control and variable pressure difference control are compared by using MATLAB simulation. The effect of fish swarm algorithm in optimizing PID neural network and optimizing linear adjustment algorithm of variable pressure difference setting value is verified. The simulation results show that the optimization effect of fish swarm algorithm is obvious.
【學(xué)位授予單位】：沈陽(yáng)建筑大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TB657.2;TP18

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