本文選題：通信基站 + 相變儲能技術　； 參考：《湖南大學》2015年碩士論文

【摘要】：隨著電子行業(yè)以及通信技術的快速發(fā)展,通信基站的數(shù)量與日劇增。通信基站需要全天候不間斷通信,導致其能耗增長迅速,在能耗構成中,空調(diào)能耗占基站總能耗的43%,因此,如何降低基站內(nèi)空調(diào)能耗成為專家學者的研究熱點。目前,眾多專家學者從室外自然冷源利用角度對基站空調(diào)節(jié)能進行研究,但由于室外環(huán)境氣溫周期性變化,使得室外自然能源在利用時出現(xiàn)供需不平衡的現(xiàn)象。相變儲能技術通過充分利用相變材料在相變過程時儲存或釋放能量的特性來解決自然冷源利用中存在時空不匹配問題,實現(xiàn)基站能耗降低,同時可以解決基站出現(xiàn)如斷電原因而不能使用空調(diào)等應急問題。本文通過現(xiàn)有基站節(jié)能技術的發(fā)展情況,結合相變儲能技術開發(fā)出相變儲能空調(diào)機組,機組包括新風工況、新風儲能工況以及放能工況。在焓差臺實驗室對機組進行儲能量、放能量以及能效比實驗,結果顯示:機組5小時內(nèi)儲存26005k J冷量,平均能效比EER為11.87;釋放過程中,機組3.75釋放18370k J的冷量,平均能效比EER為9.64。由于相變空調(diào)機組性能實驗所耗費的時間多以及代價高,本文采用BP神經(jīng)網(wǎng)絡預測模型對機組進行性能預測,減小相變儲能空調(diào)機組的開發(fā)及改進成本,同時為機組的智能儲放能研究作前期準備。將預測結果與實驗結果對比發(fā)現(xiàn)所提出的預測模型能很好地對機組進行預測,幾個主要性能指標的誤差在接受范圍內(nèi)不超過2%。因相變儲能空調(diào)機組處于開發(fā)階段,考慮到實際應用的控制問題,本文結合控制節(jié)能思路采用模糊PID控制器,通過MATLAB/Simulink程序?qū)C組在基站中的應用進行控制仿真研究,與常規(guī)PID控制器比較,結果顯示:模糊PID控制效果明顯優(yōu)于PID控制,其調(diào)節(jié)時間更快,超調(diào)量更小,魯棒性更強。
[Abstract]:With the rapid development of electronic industry and communication technology, the number and daily increase of communication base stations. The communication base station needs all-weather continuous communication, which leads to the rapid growth of its energy consumption. In the energy consumption composition, the energy consumption of air conditioning accounts for 43 percent of the total energy consumption of the base station. Therefore, how to reduce the energy consumption of air conditioning in the base station has become a hot research topic for experts and scholars. At present, many experts and scholars study the energy saving of base station air conditioning from the angle of outdoor natural cold source utilization. However, due to the periodic change of outdoor ambient temperature, there is imbalance between supply and demand when outdoor natural energy is used. Phase change energy storage technology solves the problem of space-time mismatch in the use of natural cold source by making full use of the characteristics of energy storage or release of phase change materials during the phase change process, thus reducing the energy consumption of base station. At the same time, it can solve the emergency problems of base station such as power failure and air conditioning. Based on the development of energy saving technology of base station and combined with phase change energy storage technology, a phase change energy storage air conditioning unit is developed in this paper. The unit includes fresh air condition, fresh air energy storage condition and release energy condition. The energy storage, release energy and energy efficiency ratio of the unit are tested in the enthalpy difference laboratory. The results show that the unit stores 26005kJ cooling capacity within 5 hours, the average EER is 11.87, and during the release process, the unit 3.75 releases 18370kJ, The average energy efficiency ratio (EER) is 9.64. Because the performance experiment of phase change air-conditioning unit is time-consuming and costly, the BP neural network prediction model is used to predict the performance of the unit to reduce the cost of development and improvement of phase change energy storage air-conditioning unit. At the same time, preparation for the intelligent energy storage and discharge research of the unit is made. By comparing the prediction results with the experimental results, it is found that the proposed prediction model can predict the units well, and the errors of several main performance indexes are not more than 2% within the accepted range. Because the phase change energy storage air conditioning unit is in the development stage, considering the actual application control problem, this paper combines the control energy saving idea to adopt the fuzzy pid controller, carries on the control simulation research to the unit in the base station through MATLAB / Simulink program. Compared with the conventional pid controller, the results show that the fuzzy pid control effect is better than pid control, the adjustment time is faster, the overshoot is smaller, and the robustness is stronger.
【學位授予單位】：湖南大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TU831

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本文編號：2037814