Abstract:
An arc fault detector which includes a filter which receives an input signal and filters the input signal, an amplifier which amplifies a signal output from the filter, an analog-to-digital converter disposed to receive the amplified signal from the amplifier and convert the amplified signal into a digital signal, and a processing unit responsive to computer executable instructions when executed thereon that receives samples of data associated with the digital signal and performs an arc detection algorithm on the data using fuzzy logic.

Description:
BACKGROUND OF THE INVENTION 
       [0001]    This invention relates to arc fault detection in electric power systems. More particular, this invention relates to arc fault detection using fuzzy logic. 
         [0002]    Arcing is a luminous discharge of electricity across an insulating medium. There are two types of arcing faults in AC electrical systems, which include parallel arcing faults or series arcing faults. Series arcing faults may exist when arcing occurs across a break in the line or neutral conductors or at a loose terminal in a branch circuit of a distribution network. The conductors are carrying current to a load derived from the line voltage. Since the current through the series arcing fault is limited by the impedance of the load itself and the fault is in series with the load, the fault is known as a series arcing fault. Parallel arcing faults occur when there is an unintentional conducting path between two conductors of opposite polarity, such as between a line conductor and ground. Parallel arcing faults generally involve relatively high currents, and are limited primarily by the available fault current of the circuit. 
         [0003]    Many conventional arc fault detection methods analyze waveforms or captured data based on probability, frequency content, or amplitude. These methods can be very complex and processor intensive to determine whether arcing is present. Therefore, it is desired to provide an arc fault detection method utilizing fuzzy logic to more efficiently and detect arc faults. 
       BRIEF DESCRIPTION OF THE INVENTION 
       [0004]    An exemplary embodiment of the present invention provides an arc fault detector which includes a filter which receives an input signal and filters the input signal, an amplifier which amplifies a signal output from the filter, an analog-to-digital converter disposed to receive the amplified signal from the amplifier and convert the amplified signal into a digital signal, and a processing unit responsive to computer executable instructions when executed thereon that receives samples of data associated with the digital signal and performs an arc detection algorithm on the data using fuzzy logic. 
         [0005]    Another exemplary embodiment of the present invention provides an arc fault detection method. The method includes capturing samples of electronic data representative of measured current on a conductor of an electrical circuit, and performing an arc fault detection algorithm on the captured data using fuzzy logic to detect and protect against arc faults. 
         [0006]    Additional features and advantages are realized through the techniques of exemplary embodiments of the invention. Other embodiments and aspects of the invention are described in detail herein and are considered a part of the claimed invention. For a better understanding of the invention with advantages and features thereof, refer to the description and to the drawings. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0007]      FIG. 1  is a front plan view of a circuit breaker that can be implemented within embodiments of the present invention. 
           [0008]      FIG. 2  is a top perspective view of the circuit breaker of  FIG. 1  with an arcing fault module that can be implemented within embodiments of the present invention. 
           [0009]      FIG. 3  is a circuit schematic diagram an arc fault detector of the arc fault module of  FIG. 2  that can be implemented within embodiments of the present invention. 
           [0010]      FIG. 4  is a graph of  15 A resistive arcing that can be implemented within embodiments of the present invention. 
           [0011]      FIG. 5  is a flowchart illustrating an arcing fault detection method performed by the arc fault detector of  FIG. 3  that can be implemented within embodiments of the present invention. 
           [0012]      FIG. 6  is a graph illustrating an operation of capturing  15 A arcing data of the method shown in  FIG. 5  that can be implemented within embodiments of the present invention. 
           [0013]      FIG. 7  is a graph illustrating an operation of obtaining an absolute value of the captured  15 A arcing data of  FIG. 6  that can be implemented within embodiments of the present invention. 
           [0014]      FIG. 8  is a graph illustrating an operation of determining a bit level waveform of the  15 A arcing data shown in  FIG. 7  that can be implemented within embodiments of the present invention. 
           [0015]      FIG. 9  is a graph illustrating operation of obtaining a rolling average of the data shown in  FIG. 8  that can be implemented within embodiments of the present invention. 
           [0016]      FIG. 10  is a graph illustrating a spread length membership function that can be implemented within embodiments of the present invention. 
           [0017]      FIG. 11  is a graph illustrating an amount of dead time membership function that can be implemented within embodiments of the present invention. 
           [0018]      FIG. 12  is a graph illustrating an arcing membership function that can be implemented within embodiments of the present invention. 
           [0019]      FIG. 13  is a graph illustrating an example a fuzzification operation of the spread length membership function shown in  FIG. 10  that can be implemented within embodiments of the present invention. 
           [0020]      FIG. 14  is a graph illustrating an example of a fuzzification operation of the amount of dead time membership function shown in  FIG. 11  that can be implemented within embodiments of the present invention. 
           [0021]      FIG. 15  is a graph illustrating the determination of an arcing membership degree using fuzzy logic that can be implemented within embodiments of the present invention. 
           [0022]      FIG. 16  is a graph illustrating an example of a defuzzification operation that can be implemented within embodiments of the present invention. 
           [0023]      FIG. 17  is a graph illustrating an arcing determination that can be implemented within embodiments of the present invention. 
       
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
       [0024]    Turning now to the drawings in greater detail, it will be seen that in  FIG. 1 , there is a circuit breaker  10  having an operating handle  9  and formed in a case  11 . Current from a line of a circuit transfers from a load terminal lug  12 , to a trip unit  14 , to fixed and movable contacts  21  and  22  and then to a line terminal stab  13 . The trip unit  14  includes a magnet  15  and an armature  16  that are operable when short circuit and short time fault conditions occur within a protected circuit. Long time overcurrent protection may be provided via a bimetal  17  of the circuit breaker  10 . In addition, an operating mechanism  18  interacts with the armature  16  to hold the contacts  21  and  22  from being separated by the bias provided by an operating spring  19 . 
         [0025]      FIG. 2  illustrates an arcing fault module  20  which may be combined with the circuit breaker  10  shown in  FIG. 1 . In  FIG. 2 , the arcing fault module  20  is formed of a case  23  and includes an air core sensing transformer  25 . The current on the load terminal lug  12  is applied to the wire  8  which passes through an aperture  26  in the air core sensing transformer  25 . A printed circuit board (PCB)  24  including an arc fault detection circuit (as depicted in  FIG. 3 ) is provided. A trip solenoid  27  projects a trip armature  28  into contact with the operating mechanism  18 . The arcing fault module  20  may be combined with the circuit breaker  10  via rivets  31  inserted into apertures  29 . The circuit breaker  10  is connected between a line conductor  56  and a neutral conductor  58  (as depicted in  FIG. 3 , for example) of a protected circuit (not shown). Alternatively, according to another exemplary embodiment, the circuit breaker  10  and the arc fault module  20  maybe in integrally formed as an arc fault circuit interrupter (AFCI). 
         [0026]      FIG. 3  illustrates a circuit diagram of an arc fault detector that can be implemented within embodiments of the present invention. The arc fault detector  100  detects an arc fault condition. The arc fault detector  100  includes a high pass filter  42 , for example, an amplifier  44 , a DC offset  46 , an analog-to-digital (A/D) converter  48  and a processing unit  50 . As shown in  FIG. 3 , current on a line passes through the bimetal  17  to get to a load  40 . The arc fault detector  100  measures the current via the bimetal  17 . However, the present invention is not limited hereto and the current may be measured via current transformers (CT) or a Hall effect sensor, for example. The bimetal  17  then provides a signal representative of an input signal to the high pass filter  42  to remove  60  Hz line frequency from the measured current. According to an exemplary embodiment, by removing the 60 Hz line frequency, arcing may be accurately determined. The output of the high pass filter  42  is sent to the amplifier  44  which amplifies the signal, and sends the amplified signal to the DC offset  46 , for example. According to one embodiment, the DC offset  46  shifts the signal into a range to be converted by the A/D converter  48 . That is, the signal from the bimetal  17  may be positive or negative, therefore, the DC offset  46  shifts the signal to positive to be in a range to be converted by the A/D converter  48 . The A/D converter  48  is disposed to receive the amplified signal from the amplifier and convert the amplified signal into a corresponding digital signal for input to the processing unit (e.g. a microprocessor)  50  to be processed. According to an exemplary embodiment, the block of samples can be captured on a per cycle basis or at various intervals of a cycle. The microprocessor  50  obtains the block of samples from the A/D converter  48 , reads the samples of data and performs an arc detection algorithm using fuzzy logic which is described below in connection with  FIGS. 5 through 17 , to detect and protect against arc faults. As used herein, the term “fuzzy logic” is intended to mean a set of logical rules established by observation of empirical data that when exercised provides an indication of the presence or the absence of an arc fault condition. The microprocessor  50  repeatedly captures samples of data and performs the arc fault detection algorithm using fuzzy logic on the captured data over a specified period of time. When a predetermined number of arcs are detected by the microprocessor  50  in the specified time period, the microprocessor  50  generates a trip signal to turn on a switching device  52  such as a silicon-controlled rectifier (SCR). The switching device  52  then energizes a trip solenoid  54  to trip the circuit breaker  10 .  FIG. 3  illustrates one exemplary embodiment of the arc fault detector  100 . Alternatively, the high pass filter  42  may be located in the microprocessor  50 . 
         [0027]    According to an exemplary embodiment, the arc fault detector  100  may be used for both series and parallel arc detection. 
         [0028]    When reading the samples, the microprocessor  50  determines a spread (e.g., duration of an arcing signal) and an amount of dead time (e.g., time between arc ignitions) of the data per cycle or per specified intervals of a cycle, for example, and detects arcing using fuzzy logic based on the determined spread and the amount of dead time.  FIG. 4  is a graph of  15 A resistive arcing that can be implemented within embodiments of the present invention. As shown in  FIG. 4 , the graph illustrates a shape of a waveform of the current measures at the bimetal  17 , a spread of arcing and an amount of dead time (i.e., time between arc ignitions) per one cycle, for a sample which is utilized to determine arcing and non-arcing loads. In this waveform, the spread is the length of time a burst of arcing is present and the amount of dead time is the number of times no burst of arcing occurs. 
         [0029]    According to an exemplary embodiment, as mentioned above, the microprocessor  50  performs an arc fault detection algorithm using fuzzy logic to determine whether arcs are present.  FIG. 5  is a flow chart illustrating an arc fault detection method employing the arc fault detection algorithm according to an exemplary embodiment of the present invention, and  FIGS. 6-9  and  13 - 17  illustrate the various operations performed in the arc fault detection method of  FIG. 5  that can be implemented within embodiments of the present invention. As shown in  FIG. 5 , at operation  200 , samples of data are captured at the microprocessor  50  (as depicted in  FIG. 6 ) for one cycle, for example, which are representative of the measured current on a line of the circuit which is filtered through the high pass filter, converted by the A/D converter and sent to the microprocessor  50  for processing. As mentioned above, the samples may be captured on a per cycle basis or at various intervals of a cycle. From operation  200 , the process moves to operation  205  where an absolute value of the captured data is obtained (as depicted in  FIG. 7 ). 
         [0030]    From operation  205 , the process moves to operation  210  where the absolute value of the captured data is converted into a bit level (i.e., 0 or 1) based on a predetermined threshold (as depicted in  FIG. 8 ). According to an exemplary embodiment, the predetermined threshold is an amplitude of approximately 5, for example. However, the present invention is not limited hereto, and may vary accordingly. As shown in  FIG. 8 , if an amplitude of the captured data is over the predetermined threshold, it is converted a bit level of 1 and if the amplitude of the captured data is below the predetermined threshold, it is converted to a bit level of 0. 
         [0031]    From operation  210 , the process moves to operation  215  where a rolling average of the captured data is performed to smooth out the bit level (as depicted in  FIG. 9 ). As shown in  FIG. 9 , the rolling average is performed to smooth out the data. 
         [0032]    From operation  215 , the process moves to  220  where a spread of the data is calculated. According to an exemplary embodiment, the spread is calculated by determining the average amount of time the data has a bit level of 1. That is, the spread is calculated by adding the number of ones in each block and dividing by the total number of blocks of ones to obtain the average which equals the spread of the data. From operation  220 , the process moves to operation  225  where an amount of dead time is then calculated. According to an exemplary embodiment, the amount of dead time is calculated by counting the number of blocks of zeros there are between the blocks of ones. Alternatively, other methods may be used to calculate the spread, for example, a maximum, minimum or standard variation method may be used. 
         [0033]    From operation  225 , the process moves to operation  230  where membership degrees for the spread and the amount of dead time are calculated based on predetermined membership functions stored in the microprocessor  50 . The predetermined membership functions will now be described with reference to  FIGS. 10 through 12 . The predetermined membership functions map continuous variables to a truth value in the 0 to 1 range. In fuzzy logic, a membership function represents the degree of truth as an extension of valuation. According to an exemplary embodiment, these mappings are produced based on statistical data. The spread and the amount of dead time are input variables and the arcing is an output variable.  FIG. 10  illustrates a graph of a spread length (i.e., samples) membership function where the spread is linked to fuzzy logic variables such as low, medium or high.  FIG. 11  illustrates a graph of an amount of dead time membership function where the amount of dead time is linked to fuzzy logic variables such as small, medium or large.  FIG. 12  illustrates a graph of an arcing membership function where the arcing level is linked to fuzzy logic variables such as low, medium and high. According to an exemplary embodiment, each fuzzy logic variable has a corresponding membership function mapped to the input variables, (i.e., the spread and the amount of dead time), and the output variable (i.e., arcing). The fuzzy logic variable is not limited to any particular shape and may vary accordingly.  FIGS. 13 and 14  will now be described with respect to operation  230  shown in  FIG. 5 . In  FIGS. 13 and 14 , a fuzzification operation is performed to map the calculated spread and the amount of dead time to membership functions, respectively. As shown in  FIG. 13 , if a spread length of 65 is calculated at operation  220 , a low membership degree of approximately 0.5 is determined at operation  230 . As shown in  FIG. 14 , if an amount of dead time of 14 is calculated at operation  225 , then a large membership degree of approximately 0.8 is determined at operation  230 . 
         [0034]    From operation  230 , the process moves to operation  235  where a set of fuzzy logic rules are used to determine an arcing membership degree and an arcing level (as depicted in  FIG. 15 , for example). A set of fuzzy logic rules is a set whose elements have degrees of membership. Fuzzy logic rules are if/then statements and typically include the following operators: OR, AND, and NOT where OR=MAX(A,B); AND=MIN(A,B); and NOT=1−A. According to an exemplary embodiment of the present invention, the following set of fuzzy logic rules is used to determine an arcing membership degree: 1) if the spread is high then arcing is low; 2) if the spread is low and the amount of dead time is large then arcing is high; 3) if the amount of dead time is low then arcing is low; and 4) if the spread is medium and amount of dead time is medium then arcing is medium. According to an exemplary embodiment, each fuzzy logic rule is executed in order. The present invention is not limited to any particular set of fuzzy logic rules, and may vary as necessary. 
         [0035]    As shown in  FIG. 15 , for example, when inputting the calculated spread and the calculated amount of dead time to each of the fuzzy logic rules mentioned above, the results are as follows: 1) If the spread (65) is high (0) then arcing is low (0); 2) If the spread (65) is low (0.5) and the amount of dead time (14) is large (0.8) then arcing is high (0.5); 3) If the amount of dead time (14) is low (0) then arcing is low (0); and 4) if the spread (65) is medium (0) and the amount of dead time (14) is medium then arcing is high (0). Therefore, in this example, as shown in  FIG. 15 , based on a calculated spread of 65 and a calculated amount of dead time of 14, the arcing membership degree is 0.5 and the arcing level is high at approximately 0.13, for example. 
         [0036]    From operation  235  in  FIG. 15 , the process moves to operation  240  where a shape of an arcing membership function is generated on the determined arcing membership degree using the calculated arcing membership degree (as depicted in  FIG. 16 ). As shown in  FIG. 16 , a defuzzification process which produces a quantifiable result in fuzzy logic is performed where the shape of the arcing membership function is mapped by the arcing level and the membership degree determined in operation  235 . Therefore, as shown in  FIG. 16 , the arcing level of 0.13 and the membership degree of 0.5 form a centroid at the high fuzzy logic variable. The present invention is not limited to the arcing level and the membership degree forming a particular shape and may vary, as necessary. That is, the shape of the arcing membership function is dependent upon the determined arcing membership degree. From operation  240 , the process moves to operation  245  where a center of mass (denoted with a dot) of the shape on the arcing membership function is calculated. As shown in  FIG. 16 , the center of mass is approximated by finding Xcm and Ycm for a rectangle where Xcm equals b/2=0.1, b is the base of the centroid and Ycm equals h/2=0.25, h is the height of the centroid, and solving for r which equals the square root of (Xcm 2 +Y cm 2 ) which in this case equals 0.269. Then, from operation  245 , the process moves to operation  250  where the calculated center of mass is compared to specified thresholds to determine whether arcing is present (as depicted in  FIG. 17 ). In the current exemplary embodiment, the specified thresholds are as follows: when the calculated center of mass is greater than approximately 0.12, the microprocessor  50  determines that arcing is present, when the calculated center of mass is between approximately 0.06 and 0.12, the microprocessor  50  determines that arcing may be present, and when the calculated center of mass is less than 0.06, the microprocessor  50  determines that arcing is not present. Therefore, since the calculated center of mass is equal to 0.269 in this example, it is determined that arcing is present. The present invention is not limited to any particular specified thresholds. Therefore, the specified thresholds may vary, as necessary. According to an exemplary embodiment, the specified thresholds are based on statistical information corresponding to arcing and non-arcing events. 
         [0037]    According to an exemplary embodiment, the microprocessor  50  repeatedly captures samples of data and performs the arc fault detection algorithm using fuzzy logic on the capture data over a specified period of time. Therefore, when a predetermined number of arcs occur in the specified period of time, the microprocessor  50  generates a trip signal to the switching device  52  to energize the trip solenoid  54  to trip the circuit breaker  10 . For example, if it is determined that arcing has occurred in 3 out of 4 cycles analyzed, then the microprocessor  50  generates the trip signal to the switching device  52 . According exemplary embodiment, the specified period of time maybe varied, as necessary. 
         [0038]    Since the arc fault detector  100  identifies arcs by calculating a spread and an amount of dead time and using fuzzy logic to determine arcing, the present invention provides the advantage of accurately distinguishing between arcing and non-arcing events. 
         [0039]    While the invention has been described with reference to exemplary embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents maybe substituted for elements thereof without departing from the scope of the invention. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the invention without departing from the essential scope thereof. Therefore, it is intended that the invention not be limited to the particular embodiment disclosed as the best mode contemplated for carrying out this invention, but that the invention will include all embodiments falling within the scope of the appended claims. Moreover, the use of the terms first, second, etc. do not denote any order or importance, but rather the terms first, second, etc. are used to distinguish one element from another.