In recent years, computational electromagnetism has had a great development thanks to the computational systems speed increase and their cost reduction. With those improvements the mathematical algorithms are able to work properly with more practical EMC issues. The problem that arises many times is to become condent with the results, in other words, to be able to quantitatively validate the results of the numerical simulation. In this paper we present a method to evaluate the diference between results obtained by visual expert opinion and those obtained from the FSV method.Peer ReviewedPostprint (published version

Jauregui Tellería, Ricardo

Rojas, Julio

Silva Martínez, Fernando

English

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Progress In Electromagnetics Research Symposium Proceedings,Marrakesh,Morocco,Mar. 20–23, 2011 1113Study of Transient Phenomena with Feature Selective ValidationMethodR. Jauregui1 J. Rojas-Mora2, and F. Silva11Universitat Polite`cnica de Catalunya, Spain2University of Barcelona, SpainAbstract— In recent years, computational electromagnetism has had a great developmentthanks to the computational systems speed increase and their cost reduction. With those im-provements the mathematical algorithms are able to work properly with more practical EMCissues. The problem that arises many times is to become confident with the results, in otherwords, to be able to quantitatively validate the results of the numerical simulation. In this paperwe present a method to evaluate the difference between results obtained by visual expert opinionand those obtained from the FSV method.1. INTRODUCTIONIn recent years, computational electromagnetism has had a great development thanks to the com-putational systems speed increase and their cost reduction. These improvements have allowed usto analyze more complex systems and their EMC behavior.One of the most interesting signals from the viewpoint of electromagnetic compatibility is im-pulsive noise, also known as transient phenomenon. Transient interference could be described as asignal that varies between two consecutive steady states during a short period of time compared tothe time scale of interest. In other words, there must be a “momentary” change of the magnitudeseen for a very short time in the sense that this short interval of time should be much less than onecycle of the signal affected [1].The particular feature of them makes it difficult to analyze the effect of it in the different systems.For this reason, the numerical methods are a useful tool to analyze the effect of the transient signalsin other systems [2, 3]. However, this new tool requires to ensure the quality of the results, in otherwords, to make a correct validation of the results.The validation method most widely used today because of its versatility and simplicity in thefield of EMC, is the Feature Select Validation method (FSV) [4, 5]. FSV, which is now an IEEEstandard [6], has the advantage of analyzing the two major aspects that are considered in anyvalidation, the magnitude levels and the shape of the graphs.Despite all the benefits offered by the FSV in the validation processes, it has been observedin previous studies that there are some problems when trying to use this method in transientsignals [7, 8]. To overcome these drawbacks, and to make a correct validation of transient signals,an original solution was proposed [8]: to mimic what an expert does when he analyzes a transient,dividing the signal in three different regions (pre-event, event, post-event) and assigning a specificweight to each of them. But not all transients are equal, so the main problem that arises in thisnew technique is the assignment of weights and the assessment of the size of each event for differenttypes of transients.This paper presents the analysis of three types of FSV methods to analyze some of the mostrepresentative transients in the EMC area. Specifically, eight transients were evaluated using thetraditional FSV, the Weighted-FSV and the Manual-Weighted-FSV. All these methods were com-pared with expert opinion to help us make a more objective evaluation of each method.2. FSV AND TRANSIENT PHONEMENAThe FSV method is based on the decomposition of the results into two groups; the first one discussesthe difference in amplitude (Amplitude Difference Measure, ADM) and the second one the differencebetween the characteristics of the signal (Feature Difference Measure, FDM). The combination ofthese two indicators (ADM and FDM) is a measurement of the overall difference (Global DifferenceMeasure, GDM).All indicators ADM, FDM and GDM have the ability to be configured to perform a point-to-point analysis. The advantage of relying on point-to-point data is to know which areas of thedata sets have the major differences. A subscript “i” is added to consider this point-by-point1114 PIERS Proceedings, Marrakesh, MOROCCO, March 20–23, 2011feature (ADMi, FDMi and GDMi). All of the indicators are calculated accordingly to the followingequations ([6]):ADM(n) =αβ+ODM · eODM (1)where:ODM = xδ .x = (|DC1(n)|+ |DC2(n)|).δ = 1N∑Ni=1(|DC1(i)|+ |DC2(n)|).DC-data is the “very low” component of the original data.Lo-data is the low-frequency component of the original data.Hi-data is the hi-frequency component of the original data.FDM(n) = 2(|FDM1 + FDM2 + FDM3|) (2)whereFDM1(n) =(|Lo′1(n)| − |Lo′2(n)|2N∑Ni=1 |(|Lo′1(i)|+ |Lo′2(n)||.FDM2(n) =(|Hi′1(n)| − |Hi′2(n)|6N∑Ni=1 |(|Hi′1(i)|+ |Hi′2(n)||.FDM3(n) =(|Hi′′1(n)| −Hi′′2(n)|7.2N∑Ni=1 |(|Hi′′1(i)|+ |Hi′′2(i)|)|.GDMi(n) =√ADM(n2) + FDM(n)2 (3)Another way to qualitatively analyse the FSV indicators is represented by a probability densityfunction. This indicator is useful for a rapid and comprehensive analysis of the results. Histogramsare sorted according to the quality of the results in excellent, very good, good, fair, poor and verypoor.This efficient and rapid method of analysis is ideal for most signals, but as mentioned above,some studies showed that the use of FSV for transient analysis is inadequate. For this reason, avariation of the method was created, Weighted-FSV [8]. This method divides the signal in threedifferent sectors or regions and assigns a fixed weight to each of them. The first region (pre-event)is defined from t = 0 to the time the signal amplitude reached 5% of peak value. The pre-eventrepresents 5% of the total weight on the transient analysis.The second region (the event) is defined as the transition itself, ranging from the end of thepre-event up to a point that contains 65% of the signal energy (the largest region is chosen whenthe two sets of data match each other). Since this region is the most important of the entiredisturbance, it is assigned a weight of 70% of the total.Finally, the third region, the post-event, is defined from the end of the event to the end of thesignal. Usually, this is the region of longer duration, but it is not the most important, for this reasona weight of 25% is assigned. However, despite this method has proven to be a good alternative fortransient analysis, it has not been tested against a wide variety of them.3. ANALYSIS OF THE TRANSIENTSIn this section we have analyzed eight typical EMC transients (Table 1). Figure 1 shows two shortand very similar transients. In Figure 2, the transients analyzed have a longer settlement time anda larger ripple in each area of the event. The transients in Figures 3, 4 and 5 are more complex,with settlement times longer and with more ripples throughout the signal. Figure 6 shows twotransients (Modulated Gaussian pulse) with equal amplitude but different modulation frequencies.Figure 7 shows two transient phenomena without pre-event and lengthy settlement times. In thelast figure (Figure 8), shows two completely different transient signals.Progress In Electromagnetics Research Symposium Proceedings,Marrakesh,Morocco,Mar. 20–23, 2011 1115The transients were carefully chosen and have a unique feature that will help us better under-stand the interpretation of the FSV, the “Weight-FSV” (W-FSV) and the “Dynamic-Region-FSV”(D-FSV). In the D-FSV method the weights are assigned in each zone manually, depending on thelength and breadth that has the transient in that area in particular.Finally, the results are compared with those obtained from a survey based on Likert scalequestions [9], carried out with a sample of 18 experts in the area, who evaluated the transientbased on previous studies [5]. To ease the comparison between the different methods and expertopinion, the mean square difference (see equation 4) is calculated for each indicator and each expertD-FSV method.Diff(GDmexpert, GDMiFSV ) =√(∑ni=1(GDMexpert(i) −GDMiFSV (i))2n)(4)where: “i” is an element in the set of subjective values of the GDM indicator (from excellent tovery poor).4. DISCUSSIONTable 1 shows, as expected, that the worst results compared with the expert opinion are found inthe FSV method. In the specific case of the transient signals, the FSV method has two problems.The first one is found in the ODM indicator (see Equation (1)), as it is severely affected by theoffset levels. The reason can be found in the smaller offset in the signals when the value of “δ”decreases and the ODM grows. This error directly affects to the ADM indicator and, through it, tothe GDM indicator. This is an important drawback for transients because most of them have offsetTable 1: Different type of transients analyzed.Figure 1: Weights used in D-FSV: pre=2%;event=95%; Post=3%.Figure 2: Weights used in D-FSV: pre=5%;event=70%; Post=25%.Figure 3: Weights used in D-FSV: pre=5%;event=85%; Post=10%.Figure 4: Weights used in D-FSV: pre=5%;event=80%; Post=15%.1116 PIERS Proceedings, Marrakesh, MOROCCO, March 20–23, 2011Figure 5: Weights used in D-FSV: pre=5%;event=70%; Post=25%.Figure 6: Weights used in D-FSV: pre=5%;event=85%; Post=10%.Figure 7: Weights used in D-FSV: pre=0%;event=80%; Post=20%.Figure 8: Weights used in D-FSV: pre=5%;event=80%; Post=15%.levels close to zero. The second problem with FSV is, as stated above, that it gives equal weightto the three regions of the transient. This produces a grossly overestimating in the pre-event andthe post event, skewing the final assessment away from the expert opinion.The previous problems related with the FSV method were corrected with the W-FSV method.The ODM indicator deviations are solved by applying a large enough offset level to both signals toensure an objective interpretation. On the other hand, the division of the transitional phenomenoninto regions makes the results closer to the expert opinion in most of the cases.However, this method still has some drawbacks. The main problem in W-FSV is that the weightremains fixed in all the proposed regions, regardless of the type of transient. Some good examplesof this problem could be seen in the previous figures, where the importance of each event variesdepending on the extent and duration of the event. An additional problem with this techniqueis that the possibility that the pre-event could be zero (Figure 7) or that the post-event couldbe extremely long (Figure 1) are not taken into account, making the results drift away from theassessments made by experts.Finally, the results obtained by the method D-FSV presented in this paper, show an improvementover the W-FSV. The main reason for this improvement is that the weight assigned to each regionof the event is selected manually in terms of magnitude and duration (Figures 1, 4, 6 and 7).An important feature of this method is the procedure used to select the regions in the transientevent. It was noted that if the power method (70% of total energy) is used to divide the event andpost-event regions in the transient with very long or very short time of settlements (Figures 1, 4and 6), some errors still appears in the interpretation of the transient. This division creates veryshort event and very long post-event regions in the transient, having a direct effect on the resultsof FSV. To solve this problem we use an approach based in the classical control theory [10] todivide the transient in the regions of interest. The theory defines the post-event region when thesignal level decreases to 5% of the maximum. Conducting a study with the experts, the best valueProgress In Electromagnetics Research Symposium Proceedings,Marrakesh,Morocco,Mar. 20–23, 2011 1117obtained for most of the transients was when the signal reached 10% of maximum.5. CONCLUSIONSThis paper has addressed some problems related to validation of transient signals using differentvariants of the FSV method. The principal conclusion of this work is that, to use the FSV asa validation method for transient signals, it is not only necessary to divide each region of thetransient carefully, but that a specific weighting for each region must also be carried out. Theresults obtained in this test show that it is necessary to assign weights to each region dependingon both, its duration and amplitude.Our results suggest that it is necessary to continue investigating ways of improving the analysisof transients in time domain through the FSV.REFERENCES1. Jauregui, R., “Electromagnetics analysis in time domain of complex structure using FDTD,”Thesis, 2009.2. Riu, P. J., R. Jauregui, S. Verdaguer, R. Santos, and F. Silva, “Transient radiated fields in14/42V powernet systems FDTD assessment,” EMC Europe, Barcelona, Spain, 2006.3. Portugues, I. E., P. J. Moore, I. A. Glover, and R. J. Watson, “A portable wideband impul-sive noise location system,” IEEE Transactions on Instrumentation and Measurement, IEEEInstrumentation and Measurement Society, Vol. 57, 2059–2066, 2008.4. Duffy, A. P., A. J. M. Martin, A. Orlandi, G. Antonini, T. M. Benson, and M. S. Woolfson,“Feature selective validation (FSV) for validation of computational electromagnetics (CEM).Part I: The FSV method,” IEEE Transactions on Electromagnetic Compatibility, Vol. 48, 449–459, 2006.5. Orlandi, A., A. P. Duffy, B. Archambeault, G. Antonini, D. E. Coleby, and S. Connor, “Fea-ture selective validation (FSV) for validation of computational electromagnetics (CEM). PartII: Assessment of FSV performance,” IEEE Transactions on Electromagnetic Compatibility,Vol. 48, 460–467, 2006.6. “IEEE 1597.1 Standard for validation of computational electromagnetics computer modelingand simulations,” The IEEE Electromagnetic Compatibility Society, P.1 W.G., Jun. 2008.7. Jauregui, R., P. J. Riu, and F. Silva, “Transient FDTD simulation validation,” IEEE Interna-tional Symposium on Electromagnetic Compatibility, 257–262, 2010.8. Jauregui, R., F. Silva, A. Orlandi, H. Sasse, and A. Duffy, “Factors influencing the successfulvalidation of transient phenomenon modelling,” Asia-Pacific Electromagnetic CompatibilitySymposium and Technical Exhibition, 2–5, Beijing, 2010.9. Likert, R., “A technique for the measurement of attitudes,” s.n., 1932.10. Ogata, K., Modern Control Engineering, 2001.

Study of transient phenomena with feature selective validation method

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Study of transient phenomena with feature selective validation method

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