【摘要】：對于電力系統(tǒng)的智能化發(fā)展,負荷監(jiān)測具有非常重要的意義。傳統(tǒng)的負荷監(jiān)測方法一般是在每個負荷配電輸出端,安裝傳感器等監(jiān)測設備,這種侵入式的負荷監(jiān)測方法在安裝和維護方面需要大量的時間和金錢,且硬件維護成本較高。因此,研究人員提出非侵入式負荷監(jiān)測(NILM)方式,只需要在電力入口處安裝監(jiān)測設備,通過監(jiān)測人口處的電壓、電流等信號就可以分解得到系統(tǒng)內單個負荷類別和運行情況。對于能源提供者來說,NILM有助于電力提供方了解用戶的負荷構成,用電習慣和能源使用情況,加強負荷用電的監(jiān)測和管理,合理安排負荷的使用時間,調節(jié)峰谷差、降低輸電損耗等；單從技術本身考慮,有助于改善電力負荷的預測精度,為負荷監(jiān)測的仿真分析、系統(tǒng)規(guī)劃提供更準確的數據；對于電力用戶來說,通過NILM可以對負荷能耗數據進行有效的分析,減少不必要的能源消耗,達到節(jié)能降耗的目的。家用電器用電情況在線監(jiān)測是在智能電表中加入非侵入式家用電器用電監(jiān)測模塊,為滿足在線用電管理提供有效且全面的數據支持。本文從三個方面進行非侵入式負荷識別的簡單研究,首先根據空調負荷在夏季是家用負荷用電的主要耗能元件,基于k-means算法的改進應用于空調負荷的分解,使用邊緣檢測和k-means聚類方法將數據進行分類,利用數據確定空調行為的關鍵參數,這個參數用于確認空調的啟停事件；其次提取負荷電流參數,選用電流最大值、平均值和均方差作為負荷識別特征參數,進行簡單的識別。負載啟動瞬態(tài)電流波形可以被獲取到,激勵瞬態(tài)特性的幾個數值提取自獲取到的與三個特性參數相關的瞬態(tài)電流波形,提取到瞬態(tài)特性參數,將其進行訓練完善,標識為負荷識別特征參數,進而進行仿真驗證識別效果；最后,根據提取到的電流、電壓波形,計算負荷的多特征參數,加權賦值法來完成負荷類型的匹配,選擇用電負荷仿真,將實驗數據代入識別算法,驗證算法的準確性與可應用性。具體工作如下：(1)首先檢測到負荷的啟停,根據電流波形的差分,獲得投切負荷的波形圖,之后對每個電流周期強度進行差分運算,得到總的瞬態(tài)時間,進而提取到該時間段內負荷電流的最大值、平均值和均方差作為負荷識別的參數設定。提取多負荷的這三個瞬態(tài)識別參數,進而可仿真驗證算法準確性。(2)研究家用電器的穩(wěn)態(tài)和暫態(tài)特征,提取家用電器的多特征參數。以16種家用電器作為參照設備進行實驗,采樣穩(wěn)態(tài)運行的電壓、電流波形數據,計算其多特征參數,建立特征參數模型庫作為電器類型辨識數據庫。(3)提出家用電器類型辨識算法。選取參照電器以外的某種電器進行仿真識別,將電壓電流波形數據帶入辨識過程進行計算分析,結果證明該辨識算法的正確性。選取兩個家用電器做混合類型識別實驗,利用上述方式進行分析。結果證明提出的辨識算法可以成功辨識多個設備同時在線運行情況。
[Abstract]:The load monitoring is very important for the intelligent development of the power system. The traditional load monitoring method is usually in the output end of each load, the installation of sensors and other monitoring equipment. This intrusion detection method needs a lot of time and money in installation and maintenance, and the cost of hardware maintenance is high. In this case, the researchers propose a non intrusive load monitoring (NILM) method, which only needs to install monitoring equipment at the power entrance. By monitoring the voltage of the population, the current and other signals can decompose the single load category and operation in the system. For the energy provider, NILM helps the power provider to understand the user's load composition. With the use of electricity and energy, the monitoring and management of load power is strengthened, the use time of load is reasonably arranged, peak and valley difference is adjusted, transmission loss is reduced, and the prediction accuracy of power load is improved by the single technology itself, and more accurate data for the simulation analysis of load monitoring, and more accurate data for the system planning; For the household, the data of the load energy consumption can be analyzed effectively by NILM to reduce the unnecessary energy consumption and achieve the purpose of saving energy and reducing consumption. The on-line monitoring of household electrical appliances is to add non intrusive household electrical monitoring module to the intelligent meter to provide effective and comprehensive data support for line management. In this paper, a simple study of non intrusive load identification is carried out from three aspects. First, according to the air conditioning load in summer is the main energy consumption component of the household load, based on the improvement of the k-means algorithm, the air conditioning load is decomposed. The data are classified by the edge detection and K-means clustering method, and the air conditioning behavior is determined by the data. The key parameter, this parameter is used to confirm the start and stop event of the air conditioning. Secondly, the load current parameters are extracted, the maximum current value, the mean value and the mean square deviation are selected as the characteristic parameters of the load identification. The load starting transient current waveform can be obtained, and several values of the transient characteristics are extracted from the acquisition to three. The transient characteristic parameters related to the characteristic parameters are extracted and the transient characteristic parameters are extracted. They are trained and improved. The characteristic parameters of the load identification are identified, and then the simulation verification and recognition results are carried out. Finally, according to the extracted current, voltage waveform, the multiple characteristic parameters of the load, the weighted assignment method is used to match the load type, and the selection of the load type is completed. Using the electrical load simulation, the experimental data are replaced by the recognition algorithm to verify the accuracy and applicability of the algorithm. The specific work is as follows: (1) first, the load starts and stops are detected, and the waveform diagram of the load is obtained according to the difference of the current waveform, then the difference calculation is carried out for each current cycle strength, and the total transient time is obtained, then the extraction is obtained. The maximum value, average value and mean square of load current in this period are set as parameters of load identification. The three transient identification parameters of multi load are extracted, and then the accuracy of the algorithm can be verified. (2) study the steady and transient characteristics of household electrical appliances and extract the multiple characteristic parameters of household appliances. 16 kinds of household appliances are used as reference equipment. In the experiment, the voltage and current waveform data are sampled in the steady state, and the multiple characteristic parameters are calculated. The characteristic parameter model library is set up as the identification database of the electrical type. (3) the identification algorithm of the household electrical type is proposed. The results prove the correctness of the algorithm. Two household appliances are selected to do the hybrid type identification experiment, and the above method is used to analyze. The results show that the proposed identification algorithm can identify the on-line operation of multiple devices at the same time.
【學位授予單位】：中國海洋大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TM715

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本文編號：2144869