Black-Box modeling of the Radio Frequency Emission of an Air-conditioner Indoor Unit

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Black-Box modeling of the Radio Frequency Emission of an Air-conditioner Indoor Unit
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  Australian Journal of Basic and Applied Sciences , 8(1) January 2014, Pages: 189-196 AENSI Journals Australian Journal of Basic and Applied Sciences  Journal home page: www.ajbasweb.com Corresponding Author:  Ammar Ahmed Alkahtani, Center of Signal Processing and Control System (SPaCS), College of Engineering, Universiti Tenaga Nasional (UNITEN), Jalan IKRAM-UNITEN, 43000 Kajang, Selangor, Malaysia Malaysia. Phone:+60389212020; Ext. 6263.   E-mail:   ammar@uniten.edu.my  or  dr.eng.alkahtani@gmail.com   Black-Box modeling of the Radio Frequency Emission of an Air-conditioner Indoor Unit   Ammar Ahmed Alkahtani, Farah Hani Nordin, Z.A.M Sharrif Center of Signal Processing and Control System (SPaCS), College of Engineering, Universiti Tenaga Nasional,Putrajaya Campus, Jalan  IKRAM-UNITEN, 43000 Kajang, Selangor, Malaysia.   ARTICLE INFO ABSTRACT  Article history: Received 20 November 2013 Received in revised form 18 January 2014 Accepted 29 January 2014 Available online 25 February 2014  Keywords: RF emission source modeling, Emission measurement, ARX, Order selection, EMC. Background:  Radio Frequency (RF) emission of electrical equipment has become a major concern with regard to electromagnetic compatibility (EMC) and its relevant international standards. To confirm the compliance with regulations, RF emission around the electrical equipment should be investigated. This can be achieved through descriptive models of the RF emission sources to allow further studies on the characteristics of their emissions. However, reliable models can only be obtained through a systematic data collection and modeling process. Objective:  To present a measurement methodology and an RF emission source model for an indoor air-conditioner unit in the time domain. Results:  The methodology starts by taking the measurement of the RF emission from the air-conditioner using an active loop antenna. An Auto-Regressive model with eXogenous inputs (ARX) is then developed using the measured data to represent the source of the RF emission. In addition, an effective  process is proposed to improve the order selection of the model. The selected model is tested using two approaches namely; mean square error and correlation test. The test results indicate that the model is accurate with a fit percentage of 91.31% and a mean square error of 0.009. On the other hand, the auto-correlation and cross-correlation analysis show that the residuals of the model are uncorrelated which confirms the efficiency of this model. Conclusion:  This paper has presented the identification  process of the RF emission source for an indoor air-conditioner unit. The proposed model has been validated using MSE and correlation tests. Furthermore, a systematic  process to select the best model order has been introduced. This process can be used to select the optimum order for the model and help to avoid the complexity than can be resulted from a higher model order. As a future work, it is aimed to improve the  proposed model for generating different types of fault data. © 2014 AENSI Publisher All rights reserved . To Cite This Article: Ammar Ahmed Alkahtani, Farah Hani Nordin, Z.A.M Sharrif., Black-Box modeling of the Radio Frequency Emission of an Air-conditioner Indoor Unit.  Aust. J. Basic & Appl. Sci.,  8(1): 189-196, 2014   INTRODUCTION Radio Frequency (RF) emission of electrical equipment has become an important topic that attracted researchers of both academia and industry in the last few decades. RF emission in this context is referred to as the unintentional release of electric and magnetic fields or both around electrical equipment and machines while operating. This emission has become very critical, especially with the increased use of electrical equipment and machines. As a result, parallel concerns with regard to the safety and exposure limits are developed for electromagnetic compatibility (EMC) and its related standard bodies (Johansson et al ., 2012; Hernando et al ., 2008; Friedrich and Leone, 2010; de Adana et al ., 2007). The RF emission not only presents disturbances to sensitive devices operating nearby, it may, however, cause health problems to people if exposed to it constantly for a long time (Elmas, 2013; Söker et al ., 2011; Sun et al ., 2011). To determine whether an emission source has exceeded the exposure limits, a radiation test is required to verify that the emitted fields of this source do not exceed the limits prescribed by the international standards. A common example of such test is the use of a tuned receiver operating in the same range of the test  parameters as explained  by Malarić et al . (Malarić, 2009). However, to accurately describe the RF emission around the equipment under test (EUT) as the emission source, it is essential to develop a mathematical model that represents the behavior of this source when it is in steady state operation. To this end, a measurement of the RF emission is required to obtain a good database that will optimize the modeling process. Measurements of the RF emission can be performed in both time and frequency domains. A basic measurement system includes an antenna and a display device such as a spectrum analyzer or an oscilloscope  190 Alkahtani et al  , 2014   Australian Journal of Basic and Applied Sciences, 8(1) January 2014, Pages: 189-196  based on the measurement system used. In the time domain measurements (Wefky et al ., 2012), an antenna is used for capturing the RF emission and is connected to an oscilloscope for displaying the signal and transferring it to a personal computer (PC) for offline processing. Typically, the use of anti-aliasing filter and Preamplifier may be introduced because the recorded signal is relatively small. On the other hand, frequency domain is widely used to measure the RF emission as can be seen in (Zhenfei et al ., 2010) and (Alkahtani et al ., 2012). The antenna in this type of measurements is connected to a spectrum analyzer to visualize the captured RF emission from the EUT in the form of electric field strength. In this paper, all measurements are performed in the time domain using an active-loop antenna which is capable of capturing the low RF emission within the range of 1KHz up to 30MHz. This range is proved to accommodate the RF emission of the EUT in this piece of work (Mariscotti, 2007; de Adana et al ., 2007; Alkahtani et al ., 2012). Modeling the RF emission can aid the understanding of electromagnetic phenomenon and help to simplify its complexity which is difficult to handle by experimental methods. It is crucial to test how a specific piece of equipment performs in the presence of different emission sources and whether it has negative effects on other systems operating nearby. The test result can be used as an indicator about the operating status of these equipment as well as their health condition (Shihab and Wong, 2000). By modeling the RF emission, it is  possible for manufacturers and designers of sensitive systems to conceptualize the RF emission and perform necessary tests for compliance prior to any design stage. To date, a number of models have been proposed to estimate the RF emission from various electrical equipment and machines. These models are constructed based on experimental methods, mathematical approaches or simulation techniques. An example of such models is a model proposed by Wefky et al . (Wefky et al ., 2012) for estimating the radiated emissions from electrical drives. In this model, the measured radiated emissions are studied with regard to the input voltage. It is concluded that there is a relation between the variation of input voltage and the radiated emissions level of the electrical drive. However, this paper did not  provide enough information regarding the radiated emissions since the authors’ focus is more on the relationship  between the radiated emissions and the input voltage. Such missing information are those related to the exact  bandwidth of the received signals as well as the frequency range of the antenna used. Another model for predicting the emissions radiated by induction-machine motor drive is introduced by Della Torre and Morando (Della Torre and Morando, 2009). This model is developed based on computer simulation and can be used to verify other proposed models of the radiated emissions for the considered induction machines. However, this model needs to be tested on real measured data to prove its efficiency in reproducing the emitted fields of the induction machine. Antonini et al . (Antonini et al ., 1999) proposed a numerical model of electromagnetic Interferences from industrial power drive systems (PDS). This model gives a good understanding of PDS radiated emissions. However, there is more interest in building source models from real measured data which has motivated us to build a source model from real measured RF emission signals. This paper aims at introducing a time-domain measurement and a black-box model of the RF emission source when operating in steady state. An effective process for improving the order selection of the model is introduced. The model is then validated and its accuracy is tested using the mean squared error (MSE) and correlation tests. MATERIALS AND METHODS This section presents the description of the RF emission measurement system and methodology. The EUT in this measurement is dealt with as a black-box radiation source and all measurements are performed while it is in steady state operation. The measurements are carried out in the Digital Signal Processing Laboratory, College of Engineering, Universiti Tenaga Nasional (UNITEN), Malaysia. The laboratory is equipped with various electrical equipment and devices, and hence, a measurement of the background noise is carried out to ensure that no other RF emission signals are captured else than those of the EUT. Figure 1 shows the RF emission measurement setup. The EUT used in this measurement is an indoor air-conditioner ceiling unit. An active loop antenna with a range of 1 KHz to 30 MHz is used for capturing the RF emission from the EUT. In addition, a digital oscilloscope is used for displaying the captured signals. The oscilloscope has a bandwidth of 70 MHz and a sampling rate of 2 Gsa/s (i.e. Giga sample per second). It also has a built-in USB interface that eliminates the need to use External General-Purpose Interface Bus (GPIB) which is widely used for transferring data to the PC. The PC used for analyzing the recorded signals has 4 GB of RAM and 2 GHz processor. The measurement procedure for recording the RF emission signals is summarized  by the following steps: First, the antenna is placed 1 meter away from the EUT. Second, the EUT is switched on and the RF emission is recorded for 3 minutes during the steady state operation. Finally, recorded signals are stored and transferred to a PC.  191 Alkahtani et al  , 2014   Australian Journal of Basic and Applied Sciences, 8(1) January 2014, Pages: 189-196 Fig. 1: Radio Frequency Emission Measurement Setup Figure 2 shows a sample of the recorded RF emission from the EUT. The recorded signal consists of the RF emission as well as the background noise captured by the antenna. However, the background noise is neglected since measurements are performed with all equipment and devices in the lab switched off. Fig. 2: Radio Frequency Emission of a Normal Operating Air-conditioner RESULTS AND DISCUSSION This section will introduce the basic steps of RF emission source modeling including the model structure and order selection. The proposed model and its validation will be discussed in details. For simplicity, a number of notations have been used for the mathematical model parameters and are presented in Table 1. Table 1:  Notations  Notation Definition u ( t  ) Input signal  y ( t  ) Output signal  A (  z ) Polynomial of the model transfer function (denominator)  B (  z ) Polynomial of the model transfer function (numerator) α  Maximum targeted fit value of the model  Max(n)  Maximum number of free coefficients  fit   Resulted fit of the model n  Number of free coefficients ( i.e. n = n a + n b + n k  ) n a  Order of  A (  z ) and also represents the no. of poles n b  Order of  B (  z ) and also represents the no. of zeros n k  H(z) Input delay Model transfer function  192 Alkahtani et al  , 2014   Australian Journal of Basic and Applied Sciences, 8(1) January 2014, Pages: 189-196 e(t)  Model noise  A.    Model Structure: Model structure selection is an important step in the modeling process. Selecting a suitable model structure depends mainly on the type of the measured data and the preferences of the intended system to be modeled. In most cases, the structure is chosen based on testing different selection methods such as ARX, State Space, and Transfer Function models. In this paper, the model structure being used is the ARX which is a polynomial model that implements the use of least squares for estimating the model’s parameters. ARX model is chosen for the following reasons (Ljung, 1999; Mahmoud, 2011): i) It is simple to obtain. (ii) It has less complexity. (iii) ARX models are results of solving linear regression equations and analytic forms. (iv) ARX models always have unique solutions. ARX model is constructed based on a discrete time transfer function as expressed in Eq. (1):  A (  z )  y ( t  ) =  B (  z ) u ( t −   n k  ) + e ( t  )  , (1) where u ( t  ) and  y ( t  ) are the pair of input-output signals of system identification, while  A (  z ) and  B (  z ) are  polynomials with degrees of n a (Number of poles) and n b (Number of zeros). The symbol, n k represents the input delay of the transfer function. The last term of the model general form is e ( t  ) which referred to as the noise of the model.  A (  z ) and  B (  z ) are expressed in Eq. (2) and Eq. (3), respectively,  A (  z ) = 1 + 11 −  z a  + 22 −  z a  + .....  aa nn  z a  −   (2)  B (  z ) = 1 + 11 −  z b  + 22 −  z b + ..... 1 +− bb nn  z b   (3)   where a n and b n are the coefficients of both polynomials and are referred to as the number of poles and zeros, respectively. To create the model of the RF emission source, one needs to identify a pair of input-output signals of the system. In this paper, u ( t  ) represents the input signal to the EUT which is a sine wave signal of 240V amplitude and a 50 Hz frequency. On the other hand, the output signal (i.e.  y ( t  )) represents the measured RF emission from the respective EUT.  B.   Order Selection:   Selecting the right order of model is an important and a difficult step in modeling. The choice of a model order is also influenced by the amount of delay. Thus, estimating this delay is a key step in choosing the optimum order. In this paper, a process is proposed to ease the model order selection process as shown in Figure 3. The order selection process started by estimating the input delay of the system which is found to be zero ( k  n = 0) . Increasing the order may give a good data fit, however this choice will increase the complexity of the model. To avoid such complexity, the maximum number of free coefficients, max(n), of the model is set to 20. On the other hand, a maximum targeted fit value of the model ( α ) is set to 100%. The process starts by selecting an initial value of the free coefficients ( n = 1). While the number of free coefficients n is less than max(n) , the resulted fit is compared to α . If the resulted fit is less than α , the number of free coefficients is increased ( n = n + 1) to select a higher free coefficients number. If the maximum number of free coefficients is reached, n = max(n) ,while the targeted fit has not been achieved, α is decreased by 1% and the process is repeated until the  best value of the fit is obtained. C.   The Proposed Model: Figure 4 shows the pair of input-output signals used for modeling the RF emission source. The model output is shown in Figure 5. The measured RF emission of the EUT is plotted against the RF emission simulated  by ARX model. The figure indicates that model output accurately matches the trend of the measured data although not perfectly.  193 Alkahtani et al  , 2014   Australian Journal of Basic and Applied Sciences, 8(1) January 2014, Pages: 189-196 Fig. 3: Model   Order Selection Process. Fig. 4: Input-output Pair of the Modeled RF Emission Source
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