Abstract:
A method detects a high-impedance fault occurring in an electric distribution circuit that distributes a three-phase alternating current. The method includes the steps of applying a plurality of electrical signal analysis techniques that provide a plurality of fault detection indicators, and generating a signal that indicates a high-impedance fault depending on the outcome of the fault detection indicators. The method is characterized by determining a randomness of the residual current of the three-phase alternating current prior to determining the plurality of fault detection indicators, and generating a trigger signal depending on the randomness of the residual current. The step of determining the plurality of fault detection indicators requires that the trigger signal has been generated.

Description:
[0001]    The invention described below was developed in the context of a collaboration between Siemens AG and the faculty of Applied Sciences Bio-, Electro- and Mechanical Systems at the Free University of Brussels ULB (Université Libre de Bruxelles) under the Leadership of Professor Maun. 
         [0002]    The invention relates to methods and devices for detecting High Impedance Faults (HIF) occurring in an electric distribution circuit that distributes a three-phase alternating current. 
       BACKGROUND OF THE INVENTION 
       [0003]    The publication “Field Experience with High-Impedance Fault Detection Relays” (Alvin C. Depew, Jason M. Parsick, Robert W. Dempsey, Carl L. Benner, B. Don Russell, Mark G. Adamiak, 2006 IEEE) describes the efforts made by the Potomac Electric Power Company to reliably detect high-impedance faults. 
         [0004]    The international patent application WO 95/10815 discloses a method of detecting high-impedance faults in further detail. A plurality of electrical signal analysis techniques is applied that provide a number of fault indicators. Depending on the outcome of said fault detection indicators a signal indicating a high-impedance fault is generated or not. 
         [0005]    Under certain conditions the current of High Impedance Faults is lower than the residual current during normal operation of the network; hence overcurrent devices do not detect this fault. The difficulty of the detection depends on the configuration of the network, the worst being the multi-grounded distribution systems, which are the most common systems in America. 
         [0006]    Solidly grounded distribution systems in Europe are grounded at a single point, the substation. This practice together with the use of three-phase transformers in the MV/LV substations means that the neutral conductor under normal conditions carries barely a few amperes. In contrast, the typical configuration in America are multi-grounded systems using single-phase distribution transformers. This practice means that the current unbalance due to load switching is transferred to the primary distribution system, producing important neutral current. The stray current consequence of the multiple-grounding also contributes in the level of neutral current. 
         [0007]    The residual current in multiple-grounded systems (America), is higher than in other configurations (in Europe). The settings of the overcurrent protections are 10 or 50 times less sensitive than in protections in Europe, thus the HIF detection is more difficult, and it cannot be performed by the same detection functions (overcurrent technology). 
       OBJECTIVE OF THE PRESENT INVENTION 
       [0008]    In view of the above, an objective of the present invention is to provide a method and a device that reliably indicate a possible High Impedance Fault and avoid additional efforts in data analysing if a High Impedance Fault seems unlikely. 
       BRIEF SUMMARY OF THE INVENTION 
       [0009]    An embodiment of the invention relates to a method of detecting a high-impedance fault occurring in an electric distribution circuit that distributes a three-phase alternating current, the method comprising the steps of applying a plurality of electrical signal analysis techniques that provide a number of fault indicators, and generating a signal that indicates a high-impedance fault depending on the outcome of said fault detection indicators. The method further comprises the steps of determining the randomness of the residual current of said three-phase alternating current prior to determining said plurality of fault detection indicators, and generating a trigger signal depending on the randomness of the residual current, wherein determining said plurality of fault detection indicators requires that said trigger signal has been generated. 
         [0010]    An advantage of the present invention is that a time-consuming application of the plurality of electrical signal analysis techniques may be avoided if the occurrence of a high-impedance fault seems unlikely. To this end, the method analyzes the randomness of the residual current prior to applying the electrical signal analysis techniques and prior to determining the plurality of fault detection indicators. Depending on the randomness of the residual current, a trigger signal is generated or not. The further evaluation including the determination of said plurality of fault detection indicators may then be limited to cases when the trigger signal indicates a sufficient likelihood of the occurrence of a high-impedance fault. 
         [0011]    A further advantage of the present invention is that it addresses the drawbacks of multiple-grounded distribution networks like those presently used in America. 
         [0012]    According to a preferred embodiment, a randomness value (hereinafter also referred to as “AAD”) is calculated that describes the randomness of the residual current. Then, the trigger signal may be generated depending on the randomness value. 
         [0013]    Further, a first threshold value (hereinafter also referred to as “AAD_threshold”) may be calculated based on a given number of cycles that preceded the actual cycle wherein generating said trigger signal requires that said randomness value exceeds said first threshold value. 
         [0014]    Moreover, a second threshold value (hereinafter also referred to as “rand_AAD”) that describes the average randomness of the residual current before the actual trigger cycle (during normal operation without high-impedance fault) may be calculated, wherein generating said trigger signal requires that said randomness value exceeds said second threshold value. 
         [0015]    Preferably, generating the trigger signal requires that said randomness value exceeds said first and second threshold value. 
         [0016]    Furthermore, generating the trigger signal may also require that a reference value (hereinafter also referred to as “normal_AAD”) that indicates the average randomness of the residual current during normal conditions falls below a maximum randomness threshold value (hereinafter also referred to as “THLD nnormal     —     AAD ”) before the actual trigger cycle. 
         [0017]    In the latter case, the trigger signal is preferably generated if said randomness value exceeds said first and second threshold value and the average randomness of the residual current falls below the maximum randomness threshold value. 
         [0018]    Preferably, the method also includes the steps of evaluating the increase of each phase current of said three-phase alternating current in response to the generation of said trigger signal, and determining that no high-impedance fault occurred if all three-phases of said three-phase alternating current exhibit a similar increase of current before or after the generation of said trigger signal. In most cases, high-impedance faults are very unlikely if all three phases of the three-phase alternating current show a similar behaviour. 
         [0019]    Further, an average difference value (hereinafter also referred to as “Iextr”) may be calculated by subtracting a previous average residual current value that defines the average residual current before the generation of said trigger signal, from an actual residual current value that defines the average current after the generation of said trigger signal. 
         [0020]    The plurality of fault detection indicators is preferably determined if said trigger signal has been generated and the average difference value is between a predefined lower threshold value and a predefined upper threshold value. 
         [0021]    A counter may be incremented if said trigger signal is generated and the average difference value exceeds the predefined upper threshold value. 
         [0022]    The plurality of fault detection indicators is preferably determined if said trigger signal is generated and the counter reading equals or exceeds a predefined maximum count. 
         [0023]    An further embodiment of the invention relates to a high-impedance fault detector capable of detecting a high-impedance fault occurring in an electric distribution circuit that distributes a three-phase alternating current, the detector comprising a computer being programmed to carry out the steps of: applying a plurality of electrical signal analysis techniques that provide a number of fault indicators, and generating a signal that indicates a high-impedance fault depending on the outcome of said fault detection indicators, wherein the randomness of the residual current ( 3 I 0 ) of said three-phase alternating current is determined prior to determining said plurality of fault detection indicators, wherein a trigger signal is generated depending on the randomness of the residual current, and wherein determining said plurality of fault detection indicators requires that said trigger signal has been generated. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0024]    In order that the manner in which the above-recited and other advantages of the invention are obtained will be readily understood, a more particular description of the invention briefly described above will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings. Understanding that these drawings depict only typical embodiments of the invention and are therefore not to be considered to be limiting of its scope, the invention will be described and explained with additional specificity and detail by the use of the accompanying drawings in which 
           [0025]      FIG. 1  shows an exemplary embodiment of a high-impedance fault detector, and 
           [0026]      FIG. 2  shows a flow diagram of an exemplary embodiment of a method for detecting a high-impedance fault. 
       
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT 
       [0027]    The preferred embodiment of the present invention will be best understood by reference to the drawings, wherein identical or comparable parts are designated by the same reference signs throughout. 
         [0028]    It will be readily understood that the present invention, as generally described and illustrated in the figures herein, could vary in a wide range. Thus, the following more detailed description of the exemplary embodiments of the present invention, as represented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of presently preferred embodiments of the invention. 
         [0029]      FIG. 1  shows an embodiment of a high-impedance fault detector  10 . The detector  10  comprises a computer  20  having a microprocessor unit  30  and a memory  40 . The memory  40  stores a computer program CP that may be carried out by the microprocessor unit  30  in order to detect a high-impedance fault occurring in an electric distribution circuit. 
         [0030]    The detector  10  analyzes the residual current  3 I 0  and the 3-phase currents I 1 , I 2 , I 3  of a three-phase alternating current and generates a signal ST indicating whether a high-impedance fault is likely (“HIF”), possible (“Possible HIF”) or unlikely (“No HIF”). 
         [0031]    An exemplary embodiment of an algorithm that may be applied by the detector  10  of  FIG. 1  is depicted in further detail in  FIG. 2 . The algorithm uses the three phase currents I 1 -I 3  and outputs the label of “HIF”, “No HIF”, or “Possible HIF”. 
         [0032]    If a high-impedance fault appears, an increase of randomness is expected, thus the algorithm monitors the randomness (see step  100  in  FIG. 2 ) and triggers when there is an important increase (see step  110  in  FIG. 2 ). To this end, the current of the high-impedance fault is superposed to the residual current of the pre-fault situation, thus the algorithm removes the current before the trigger from the current after the trigger so the extracted current is the current of the event that produced the trigger (possibly a HIF, see step  110  in  FIG. 2 ). The extracted current is analyzed and classified as “HIF” or as “Other event”. Apart from this process there are other criteria that complement the algorithm. The final decision is made using information accumulated during a pre-defined period of time Δt decision. A complete description of the algorithm is presented hereinafter in further detail. 
         [0033]    The inputs to the algorithm are the 3-phase currents I 1 -I 3  and, if available, the sensitive measure of the residual current  3 I 0 . If the residual current  3 I 0  is not directly available it is calculated by the sum of the 3-phase currents I 1 -I 3 . 
         [0034]    A randomness value AAD is computed for the residual current  3 I 0 , as well as a first threshold value AAD_threshold and a second threshold value rand_AAD. The second threshold value rand_AAD is calculated based on a reference value normal_AAD that defines the average randomness of the residual current  3 I 0  during normal operation (see step  100  in  FIG. 2 ). These magnitudes measure, respectively, the instantaneous randomness of the residual current  3 I 0 , the randomness of the residual current  3 I 0  under normal conditions of the network, the level above which the residual current  3 I 0  is considered random and the level above which the algorithm triggers. The definitions and expressions of each magnitude are shown in the following Table 1 which includes definitions and expressions of the magnitudes used in the description of the algorithm: 
         [0000]    
       
         
               
               
               
             
           
               
                   
               
               
                 Acronym 
                 Meaning 
                 Description/Equation 
               
               
                   
               
             
             
               
                 3I0 
                 Residual Current 
                 The sum of the 3phase current 
               
               
                 AAD 
                 Accumulated 
                 AAD measure the randomness 
               
               
                   
                 Absolute 
                 of the signal by quantifying 
               
               
                   
                 Differences cycle 
                 the changes in the amplitude 
               
               
                   
                 per cycle 
                 and the content of 
               
               
                   
                   
                 non-harmonic components 
               
               
                   
               
               
                   
                   
                 
                   
                     
                       
                         AAD 
                         = 
                         
                           
                             ∑ 
                             
                               k 
                               = 
                               i 
                             
                             Nacc 
                           
                            
                           
                              
                             
                               
                                 3 
                                  
                                 I 
                                  
                                 
                                     
                                 
                                  
                                 
                                   0 
                                   k 
                                 
                               
                               - 
                               
                                 3 
                                  
                                 I 
                                  
                                 
                                     
                                 
                                  
                                 
                                   0 
                                   
                                     k 
                                     + 
                                     spc 
                                   
                                 
                               
                             
                              
                           
                         
                       
                     
                   
                 
               
               
                   
               
               
                 Nacc 
                 Number of samples 
                 Nacc determine the number 
               
               
                   
                 accumulated 
                 of differences cycle per 
               
               
                   
                   
                 cycle that are accumulated 
               
               
                   
                   
                 for calculating the AAD. 
               
               
                 spc 
                 Samples per cycle 
                   
               
               
                 normal_AAD 
                 Value of AAD when 
                 It represents the randomness 
               
               
                   
                 the network 
                 of the residual current  
               
               
                   
                 works under normal 
                 3I0 during normal operation 
               
               
                   
                 conditions 
                 of the network. It is calculated 
               
               
                   
                   
                 as the average of AAD during  
               
               
                   
                   
                 20 cycles during which the 
               
               
                   
                   
                 algorithm does not trigger. 
               
               
                 rand_AAD 
                 Minimum AAD  
                 Value of AAD above which 
               
               
                   
                 indicating  
                 the signal is considered 
               
               
                   
                 randomness 
                 random. It depends on  
               
               
                   
                   
                 normal_AAD: 
               
               
                   
                   
                 if normal_AAD &lt;= 
               
               
                   
                   
                 C 1  * spc * Nacc, rand_AAD = 
               
               
                   
                   
                 C 2  * spc * Nacc 
               
               
                   
                   
                 if normal_AAD&gt; 
               
               
                   
                   
                 C 1  * spc * Nacc, rand_AAD = 
               
               
                   
                   
                 normal_AAD * 2.5 C 1  = 1,  
               
               
                   
                   
                 25E-4; C 2  = 3, 125E-4 
               
               
                 THLD  normal AAD   
                 Threshold that  
                 THLD normal_AAD indicates 
               
               
                   
                 indicates the 
                 the maximum level of  
               
               
                   
                 superior limit for 
                 normal_AAD that allows  
               
               
                   
                 normal_AAD 
                 the algorithm to work  
               
               
                   
                   
                 correctly. Above this value, 
               
               
                   
                   
                 the randomness of the  
               
               
                   
                   
                 residual current 3I0 under 
               
               
                   
                   
                 normal conditions is too 
               
               
                   
                   
                 random and the algorithm 
               
               
                   
                   
                 cannot work. 
               
               
                   
                   
                 THLD normal AAD  = C 3 ; C 3  =  
               
               
                   
                   
                 1E-3 * spc * Nacc 
               
               
                 AADmean 
                 mean value of AAD 
                 AADmean is calculated each 
               
               
                   
                 during 5 cycles 
                 5 cycles as the average 
               
               
                   
                   
                 value during this period 
               
               
                 AAD_Threshold 
                 Threshold for AAD 
                 It is a dynamic threshold, 
               
               
                   
                 that determines 
                 updated each 5 cycles, 
               
               
                   
                 when the algorithm 
                 which value is calculated 
               
               
                   
                 triggers 
                 based on the avarage 
               
               
                   
                   
                 value of AAD during the 
               
               
                   
                   
                 previous 5 cycles 
               
               
                   
                   
                 Threshold j  = C 4  * AADmean j−1 ; 
               
               
                   
                   
                 C 4  = 1, 4 
               
               
                 ΔI 3_phases 
                 Extracted current 
                 There are 3 magnitudes 
               
               
                   
                 in each of the 3 
                 measuring the change of 
               
               
                   
                 phases 
                 the phase currents after 
               
               
                   
                   
                 each trigger. There are 3 
               
               
                   
                   
                 15-cycle currents obtained 
               
               
                   
                   
                 by deducting the average 
               
               
                   
                   
                 current before the trigger 
               
               
                   
                   
                 from the current after the 
               
               
                   
                   
                 trigger. 
               
               
                 Iextr 
                 Extracted current 
                 Magnitude measuring the 
               
               
                   
                 in the residual 
                 change of the residual 
               
               
                   
                 current 3I0 
                 current 3I0 after each 
               
               
                   
                   
                 trigger. It represent the 
               
               
                   
                   
                 current of the new event 
               
               
                   
                   
                 that causes the trigger 
               
               
                   
                   
                 (possibly a HIF). It is a 
               
               
                   
                   
                 15-cycle current obtained 
               
               
                   
                   
                 by deducting the average 
               
               
                   
                   
                 current before the trigger 
               
               
                   
                   
                 from 15-cycles of current 
               
               
                   
                   
                 after the trigger. 
               
               
                 THLD  Sup _Iextr 
                 Superior threshold 
                 Amplitude above which  
               
               
                   
                 for Iextr 
                 the Iextr is considered too 
               
               
                   
                   
                 high for being the corre- 
               
               
                   
                   
                 spondent to HIF current. 
               
               
                   
                   
                 THLD  Sup Iextr  ~150A 
               
               
                 THLD  Inf _Iextr 
                 Inferior threshold 
                 Amplitude below which  
               
               
                   
                 for Iextr 
                 the Iextr is mostly noise,  
               
               
                   
                   
                 so it will not be analysed. 
               
               
                   
                   
                 THLD  Inf Iextr  ~1A, 2A 
               
               
                 Δt  decision   
                 Period of time for 
                 Time during which the  
               
               
                   
                 taking the decision 
                 information of the number  
               
               
                   
                   
                 of triggers and the output of  
               
               
                   
                   
                 the classifier is accumulated 
               
               
                   
                   
                 so the decision can be done. 
               
               
                   
               
             
          
         
       
     
         [0035]    The main condition for the good performance of the algorithm is that the residual current  3 I 0  during normal operation of the network is regular or not random, so that normal_AAD is low. Therefore, the value of normal_AAD has to be checked. If it is lower than a maximum randomness threshold value THLD normal_AAD then the residual current  3 I 0  is considered regular enough and the algorithm for triggering runs. Otherwise, the algorithm breaks, indicating that the load of the network is too random. 
         [0036]    The value of normal_AAD is updated several times per day in order to be adapted to the changes in the network. So the algorithm will be aware of the moments when the conditions of the network are so bad that high-impedance fault detection cannot be done. 
         [0037]    The algorithm is designed to trigger when there is a change in the residual current  3 I 0  linked to an increase of randomness. High-impedance faults cause changes in the residual current  3 I 0  and increase the randomness of the current, but also inrush currents or load switching activities do. The algorithm has to trigger in any of those cases, and later it will distinguish between high-impedance faults and other events. 
         [0038]    There are two requirements for triggering: that the instantaneous value of AAD is higher than the threshold AAD_threshold and that the value of the instantaneous AAD is high enough so it indicates randomness. The AAD_threshold adapts its value each 5 cycles of current. If the instantaneous value of AAD passes this threshold, it means that the random of the residual current  3 I 0  at that moment has notably increased, because a change in the residual current  3 I 0  has occurred. On the other hand, the instantaneous value of AAD has to be representative, has to be higher than a minimum level of AAD that reveals randomness. This minimum level is rand_AAD, which is updated depending on the value of normal_AAD (further explanation in Table 1). 
         [0039]    When a trigger is produced the algorithm extracts the component of the current related to the change that made the algorithm trigger (see step  120  in  FIG. 2 ). This current component, Iextr, (hereinafter also referred to as average difference value Iextr) is analyzed in order to decide if it is related to a high-impedance fault or to another event. The algorithm also considers some other cases: the triggers related to 3-phase events, the very low amplitude extracted currents, and the too high amplitude extracted current. 
         [0040]    If the trigger is due to a 3-phase event (see step  130  in  FIG. 2 ), the event is not a high-impedance fault because high-impedance faults are single-phase faults. Therefore, after each trigger, the algorithm obtains the extracted currents in each of the 3 phases (ΔI in 3 phases). They are calculated by subtracting the phase current before the trigger from the phase current after the trigger. AI in 3 phases represents the 3-phase current of the event that causes the trigger. If the event is a single-phase-event, the extracted currents in two phases have to be negligible, and the extracted current in one phase has to be similar to the extracted current of the residual current  3 I 0 , Iextr. On the contrary, if the extracted currents in the 3-phases have similar amplitudes, the event is a 3-phase event, so it is not a high-impedance fault and the algorithm breaks and outputs the label “No HIF”. 
         [0041]    If the average difference value Iextr is higher than THLD sup_Iextr the output is “No HIF”. By definition, the amplitude of high-impedance faults is low, e.g. between 1 A and 70 A-100 A. In practice it needs to be considered that high-impedance fault detection is complementary to overcurrent protection, thus the maximum amplitude considered by high-impedance fault detection is the setting of the overcurrent protection. THLD sup_Iextr is given by the limit of the overcurrent protection of each network, and we estimate this value between 100 A and 200 A. 
         [0042]    If the amplitude of Iextr is lower than THLD inf_Iextr, the algorithm memorizes the trigger by increasing a counter by “1” (see step  140  in  FIG. 2 ), but the algorithm does not compute the classification of Iextr. Due to the inaccuracy of the current measurement and of the extraction method there is noise in Iextr. If the amplitude of Iextr is not much higher than the amplitude of the estimated noise, Iextr is considered too noisy to be analyzed. However, the fact that the algorithm triggered is taken into account is meaningful. In case the event analyzed is a high-impedance fault the algorithm will trigger several consecutive times during a long period, which can be several seconds or even days. Consequently, the information of the numbers of triggers during a period of time of decision is an input for deciding if the event is high-impedance fault or is not. 
         [0043]    If the amplitude of Iextr is between THLD inf_Iextr and THLD sup_Iextr the algorithm memorizes the trigger by increasing the counter by “1”, and Iextr is classified as high-impedance fault or as “Other event” (see step  150  in  FIG. 1 ). 
         [0044]    In case the event is a high-impedance fault the extracted current Iextr is the current of the fault, so it would have the typical characteristics of high-impedance faults (main harmonic the 3rd harmonic, phase of the 3rd harmonic constant around 180°, effect of the arc at the current zero-crossing . . . ). Therefore, a given list of indicators (for instance  14  indicators as listed in the following Table 2) that reveal the typical characteristics of high-impedance faults may be calculated from the Iextr. Using this input, the classifier offers the output “HIF” or “Other event”. The output of the classifier is accumulated during the period of time Δt decision, and is used for taking the final decision. The following Table 2 lists indicators and their characteristics in an exemplary fashion: 
         [0000]    
       
         
               
               
               
             
           
               
                   
                   
               
               
                   
                 Indicators 
                 Characteristics 
               
               
                   
                   
               
             
             
               
                   
                 rms_value 
                 Typical amplitude and limit 
               
               
                   
                   
                 of algorithm 
               
               
                   
                 AADaverage 
                 Randomness 
               
               
                   
                 Fast change 
               
               
                   
                 Slow change 
               
               
                   
                 Main Harmonic 
                 3 rd  harmonic as main harmonic 
               
               
                   
                 Mean Amplitude of 3 rd  H 
                 component 
               
               
                   
                 Correlation 3 rd  H/fund 
                 Amplitude of 3 rd  harmonic is 
               
               
                   
                   
                 not proportional to fundamental 
               
               
                   
                   
                 amplitude 
               
               
                   
                 Mean Phase 3 rd  H 
                 Constant phase of the third 
               
               
                   
                 Standard deviation 
                 harmonic, close to 180° 
               
               
                   
                 of Phase 3 rd  H 
               
               
                   
                 Ratio 0 Cross 
                 Effect of the arc at the 
               
               
                   
                   
                 current-zero-crossing 
               
               
                   
                 Min ADPN 
                 Asymmetry between the negative 
               
               
                   
                 Mean ADPN 
                 and positive part of the 
               
               
                   
                 Ratio Alternance 
                 cycle 
               
               
                   
                 sign 
               
               
                   
                 Ratio H3 
                 Asymmetry at the current- 
               
               
                   
                   
                 zero-crossing 
               
               
                   
                   
               
             
          
         
       
     
         [0045]    It is evident that more or less indicators or other types of indicators than those listed in Table 2 may be used. The list in Table 2 represents a preferred embodiment, only. 
         [0046]    The decision logic (see step  160  in  FIG. 2 ) indicates the final decision (“HIF”, “No HIF” or “Possible HIF”) based on the information of the numbers of triggers (see Table 2) and the output of the classifier during Δt decision (see steps  140  and  160  in  FIG. 2 ). The output will be “HIF” if there were several triggers and a determined number of them were classified as high-impedance faults. The output will be “No HIF” if there were not enough triggers or if the number of them classified as “HIF” was lower than the minimum number needed for being suspicious of a high-impedance fault. And the output will be “Possible HIF” if there were several triggers and most of them were related to an Iextr lower than THLD inf_Iextr, or if the number of triggers classified as high-impedance fault was higher than the minimum number needed for being suspicious of a high-impedance fault but lower than the number that determines it as a high-impedance fault. 
         [0047]    The extraction of the “suspicious event” as detailed above represents an important advantage compared to existing methods. By removing the component of the residual current that is due to the background load, the current of the event is obtained that has just appeared. So even if the current of the event is very low, it is extracted and analysed looking for characteristics of high-impedance faults. 
         [0048]    The classification may be developed using data-mining techniques, and it can be improved as the database of residual currents in case of a high-impedance fault and residual currents in case of other suspicious events is extended. The classifier may be a one-class classifier using a Support Vector Machine. A Support Vector Machine may be trained and tested using a database of previous high-impedance faults and other events. Adding and removing data from the original database may be carried out to improve the classifier. An automatic system design for this function may be used. Some parameters such as normal_AAD and rand_AAD are specific for each network and each moment, so the method may adapt to the customer. 
         [0049]    The design of the algorithm allows the possibility of future improvements that will be possible after testing the high-impedance fault detection method and increasing the training database. These improvements are related to the definition of THLDnormal_AAD, to the extraction algorithm and to the data-mining technique. 
         [0050]    Instead of defining THLDnormal_AAD as a constant (C3=1E-3*spc*Nacc) it could depend on the amplitude of Iextr. Concerning the extraction method, the calculation of Iextr can be improved if the two currents that are subtracted (current before the trigger and after the trigger) are synchronized considering the possible error in frequency. 
         [0051]    Related to the data-mining technique, the algorithm may use a one-class support vector machine with negative examples, but with a complete database it can be considered a two-class classification, such as random forest, decision rules . . . , etc.