非侵入式電器識(shí)別算法的研究
發(fā)布時(shí)間：2018-07-25 19:40
【摘要】：對(duì)于電力系統(tǒng)的智能化發(fā)展,負(fù)荷監(jiān)測(cè)具有非常重要的意義。傳統(tǒng)的負(fù)荷監(jiān)測(cè)方法一般是在每個(gè)負(fù)荷配電輸出端,安裝傳感器等監(jiān)測(cè)設(shè)備,這種侵入式的負(fù)荷監(jiān)測(cè)方法在安裝和維護(hù)方面需要大量的時(shí)間和金錢,且硬件維護(hù)成本較高。因此,研究人員提出非侵入式負(fù)荷監(jiān)測(cè)(NILM)方式,只需要在電力入口處安裝監(jiān)測(cè)設(shè)備,通過監(jiān)測(cè)人口處的電壓、電流等信號(hào)就可以分解得到系統(tǒng)內(nèi)單個(gè)負(fù)荷類別和運(yùn)行情況。對(duì)于能源提供者來說,NILM有助于電力提供方了解用戶的負(fù)荷構(gòu)成,用電習(xí)慣和能源使用情況,加強(qiáng)負(fù)荷用電的監(jiān)測(cè)和管理,合理安排負(fù)荷的使用時(shí)間,調(diào)節(jié)峰谷差、降低輸電損耗等；單從技術(shù)本身考慮,有助于改善電力負(fù)荷的預(yù)測(cè)精度,為負(fù)荷監(jiān)測(cè)的仿真分析、系統(tǒng)規(guī)劃提供更準(zhǔn)確的數(shù)據(jù)；對(duì)于電力用戶來說,通過NILM可以對(duì)負(fù)荷能耗數(shù)據(jù)進(jìn)行有效的分析,減少不必要的能源消耗,達(dá)到節(jié)能降耗的目的。家用電器用電情況在線監(jiān)測(cè)是在智能電表中加入非侵入式家用電器用電監(jiān)測(cè)模塊,為滿足在線用電管理提供有效且全面的數(shù)據(jù)支持。本文從三個(gè)方面進(jìn)行非侵入式負(fù)荷識(shí)別的簡(jiǎn)單研究,首先根據(jù)空調(diào)負(fù)荷在夏季是家用負(fù)荷用電的主要耗能元件,基于k-means算法的改進(jìn)應(yīng)用于空調(diào)負(fù)荷的分解,使用邊緣檢測(cè)和k-means聚類方法將數(shù)據(jù)進(jìn)行分類,利用數(shù)據(jù)確定空調(diào)行為的關(guān)鍵參數(shù),這個(gè)參數(shù)用于確認(rèn)空調(diào)的啟停事件；其次提取負(fù)荷電流參數(shù),選用電流最大值、平均值和均方差作為負(fù)荷識(shí)別特征參數(shù),進(jìn)行簡(jiǎn)單的識(shí)別。負(fù)載啟動(dòng)瞬態(tài)電流波形可以被獲取到,激勵(lì)瞬態(tài)特性的幾個(gè)數(shù)值提取自獲取到的與三個(gè)特性參數(shù)相關(guān)的瞬態(tài)電流波形,提取到瞬態(tài)特性參數(shù),將其進(jìn)行訓(xùn)練完善,標(biāo)識(shí)為負(fù)荷識(shí)別特征參數(shù),進(jìn)而進(jìn)行仿真驗(yàn)證識(shí)別效果；最后,根據(jù)提取到的電流、電壓波形,計(jì)算負(fù)荷的多特征參數(shù),加權(quán)賦值法來完成負(fù)荷類型的匹配,選擇用電負(fù)荷仿真,將實(shí)驗(yàn)數(shù)據(jù)代入識(shí)別算法,驗(yàn)證算法的準(zhǔn)確性與可應(yīng)用性。具體工作如下：(1)首先檢測(cè)到負(fù)荷的啟停,根據(jù)電流波形的差分,獲得投切負(fù)荷的波形圖,之后對(duì)每個(gè)電流周期強(qiáng)度進(jìn)行差分運(yùn)算,得到總的瞬態(tài)時(shí)間,進(jìn)而提取到該時(shí)間段內(nèi)負(fù)荷電流的最大值、平均值和均方差作為負(fù)荷識(shí)別的參數(shù)設(shè)定。提取多負(fù)荷的這三個(gè)瞬態(tài)識(shí)別參數(shù),進(jìn)而可仿真驗(yàn)證算法準(zhǔn)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(shù)。以16種家用電器作為參照設(shè)備進(jìn)行實(shí)驗(yàn),采樣穩(wěn)態(tài)運(yùn)行的電壓、電流波形數(shù)據(jù),計(jì)算其多特征參數(shù),建立特征參數(shù)模型庫作為電器類型辨識(shí)數(shù)據(jù)庫。(3)提出家用電器類型辨識(shí)算法。選取參照電器以外的某種電器進(jìn)行仿真識(shí)別,將電壓電流波形數(shù)據(jù)帶入辨識(shí)過程進(jìn)行計(jì)算分析,結(jié)果證明該辨識(shí)算法的正確性。選取兩個(gè)家用電器做混合類型識(shí)別實(shí)驗(yàn),利用上述方式進(jìn)行分析。結(jié)果證明提出的辨識(shí)算法可以成功辨識(shí)多個(gè)設(shè)備同時(shí)在線運(yùn)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(xué)位授予單位】：中國(guó)海洋大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TM715
本文編號(hào)：2144869
[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(xué)位授予單位】：中國(guó)海洋大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TM715
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，本文編號(hào)：2144869
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