WO2008069988A2

WO2008069988A2 - Method and apparatus for detecting high impedance fault

Info

Publication number: WO2008069988A2
Application number: PCT/US2007/024672
Authority: WO
Inventors: Ratan Das; Mohamed Y Haj-Maharsi; John M. Peterson
Original assignee: Abb Technology Ag
Priority date: 2006-12-01
Filing date: 2007-11-30
Publication date: 2008-06-12
Also published as: WO2008069988A3

Abstract

A method and apparatus to improve the security and dependability of high impedance fault (HIF) detection by removing prior to the detection devices one or more of a predetermined number of harmonics of the power line frequency. In one embodiment the sixth and seventh harmonics are removed to provide a first signal to one or more of the HIF detection device and from that signal the fifth harmonic is removed to provide a second signal to the other of the HIF detection devices. In another embodiment all of the above harmonics are removed from prior to the detection device(s).

Description

METHOD AND APPARATUS FOR IMPROVING THE SECURITY AND DEPENDABILITY OF DETECTING HIGH IMPEDANCE FAULT

Cross Reference To Related Application

This application claims the priority of U.S. provisional patent application Ser. No. 60/872,253 filed on December 1, 2006, entitled "Method And Apparatus For Improving The Security And Dependability Of Detecting High Impedance Fault" the contents of which are relied upon and incorporated herein by reference in their entirety, and the benefit of priority under 35 U. S. C. 119e is hereby claimed.

1. Field of the Invention

The present invention relates to an apparatus, system, and method for improving the security and dependability of High Impedance Fault (HIF) detection in electrical power systems.

2. Description of the Prior Art

High impedance faults are characterized by a high impedance at the point of fault. Accordingly, a high impedance fault typically produces a small fault current level. High impedance faults can, therefore, be generally defined as those faults that do not draw sufficient fault current to be recognized and cleared by conventional over-current devices, such as protective relays.

High impedance faults result when an energized primary conductor comes in contact with a quasi- insulating object, such as a tree, a structure or equipment, a pole cross-arm, or falls to the ground. Typically, a high impedance fault exhibits arcing and flashing at the point of contact. The significance of these hard to detect faults is that they may represent safety problems as well as a risk of arcing ignition of fires. As such, high impedance fault detection has been a major concern of protective relaying for a long time.

Protective relays are usually designed to protect equipment (line, transformer, etc.) from damage by isolating the equipment during high current conditions. High impedance faults, are typically found on distribution circuits, results in very little, if any, current. High impedance faults do not pose a threat to equipment and by their nature they can not be detected with conventional over-current devices. Nonetheless, the dangers of a downed conductor are obvious to all. Possibility of fire, property damage, and someone coming into contact with the live conductor are some of the major concerns.

Providing a comprehensive solution for high impedance faults is a difficult issue. For example, tripping the breaker following high impedance fault detection is not a clear cut choice. While the high impedance fault is a danger, tripping the feeder unnecessarily will create new problems by de-energizing homes, traffic signals, offices, etc. The effects of incorrectly determining a high impedance fault will have adverse ramifications. The utility must always have the safety of the public as a priority. However, high impedance fault detection has not been possible in the past and realistic detection algorithms are not anticipated that can detect 100% of all downed conductors, while having 100% security against misoperation. The utilities need an economic solution and a system that can reliably detect high impedance faults and are also secure in that they do not falsely detect a HIF.

In the past several techniques have been proposed in order to optimize the correct detection of high impedance faults. Although these techniques have proven to work properly to a certain extent, protection engineers still face the problem to first adequately detect the type of disturbances and then to react properly so as to prevent misoperation. Thus it is desirable to provide a solution which improves the security and dependability of High Impedance Fault (HIF) detection and which avoids as much as possible misdetections of HIF, in particular during operating conditions affected by load harmonics.

Summary of the Invention

A method for detecting a high impedance fault in an electrical power line having a predetermined power line frequency. The method: provides a high impedance fault detection means; uses the high impedance fault detection means to make a determination from a signal taken from the power line in which one or more predetermined harmonics of the power line frequency have been removed whether the high impedance fault has occurred.

A method for detecting a high impedance fault in an electrical power line having a predetermined power line frequency. The method: provides a plurality of high impedance fault detection means; provides a decision means; uses the plurality of high impedance fault detection means to make a plurality of independent determinations from a signal taken from the power line in which one or more predetermined harmonics of the power line frequency have been removed whether the high impedance fault has occurred, respectively; generates outputs representative of the independent determinations; and uses the decision means to determine whether the high impedance fault has occurred, wherein the decision means determines that the high impedance fault has occurred if any two or more of the outputs indicate that the high impedance fault has occurred.

A method for detecting a high impedance fault in an electrical power line having a predetermined power line frequency. The method: provides three or more high impedance fault detection means; provides a decision means; uses the three or more high impedance fault detection means to make an independent determination whether the high impedance fault has occurred from a signal taken from the power line, each of the three or more high impedance fault detection means requiring the signal taken from the power line to have a preselected number of one or more predetermined harmonics of the power line frequency removed prior to the independent determination; generates outputs representative of the independent determinations; and uses the decision means to determine whether the high impedance fault has occurred, wherein the decision means determines that the high impedance fault has occurred if any two or more of the outputs indicate that the high impedance fault has occurred.

A system for detecting a high impedance fault in an electrical power line having a predetermined power line frequency. The system has: a high impedance fault detection means; the high impedance fault detection means for determining from a signal taken from the power line in which one or more predetermined harmonics of the power line frequency have been removed whether the high impedance fault has occurred.

A system for detecting a high impedance fault in an electrical power system having a predetermined operating frequency. The system has: an electrical power supply; one or more interconnected electrical power conductors; and a composite high impedance fault detection system connected to the one or more electrical power conductors for detecting the high impedance fault, the composite high impedance fault detection system comprising: a plurality of high impedance fault detection systems operable to respectively make a plurality of independent determinations from a signal taken from the power system in which one or more predetermined harmonics of the operating frequency have been removed whether the high impedance fault has occurred and to respectively generate outputs representative of the independent determinations; and decision means connected to the high impedance fault detection systems for determining whether the high impedance fault has occurred, the decision means determining that the high impedance fault has occurred if any two or more of the outputs indicate that the high impedance fault has occurred.

A system for detecting a high impedance fault in an electrical power line having a predetermined power line frequency. The system has: three or more high impedance fault detection means; means for deciding if a high impedance fault has occurred in the system; the three or more high impedance fault detection means for making an independent determination whether the high impedance fault has occurred from a signal taken from the power line, each of the three or more high impedance fault detection means requiring the signal taken from the power line to have a preselected number of one or more predetermined harmonics of the power line frequency removed prior to the independent determination and each of the three or more high impedance fault detection means generating outputs representative of the independent determinations; and the decision means responsive to the outputs representative of the independent determinations to determine whether the high impedance fault has occurred, wherein the decision means determines that the high impedance fault has occurred if any two or more of the outputs indicate that the high impedance fault has occurred.

Description of the Drawing

Fig. 1 shows a schematic diagram of an electrical power distribution system.

Fig. 2 shows a high impedance fault detection system.

Fig. 2a shows a system in which the fault detection system of Fig. 2 is used.

Figs 3 and 3a show arrangements for the filters of Fig. 2a.

Figs. 4, 5 and 6 show the characteristics of the notch filters for the fifth, sixth and seventh harmonics of the power line frequency.

Fig. 7 is a flowchart showing an exemplary wavelet based HIF detection application.

Fig. 8 is a flowchart showing an exemplary higher order statistics based HIF detection system. Fig. 9 is a flowchart showing neural network based HIF detection application.

Fig. 10 is a block diagram for the dynamic threshold HIF detection technique.

Figs. 11 and 12 show the harmonics in the ground current waveforms where an erroneous HIF detection occurred.

Detailed Description

Without being limited by any particular theory, applicants believe that the presence of the 5^th, 6^th, and 7^th harmonic components in ground currents at least partially contribute to erroneous HIF detection. For example, figures 11 and 12, wherein the X-axis represents the frequency in Hz. and the Y-axis is the ground current amplitude, show the recorded waveforms from two different practical examples where an erroneous HIF detection occurred. From both examples it may be observed that the recorded waveforms have a considerable amount of fifth, sixth and seventh harmonic components, with the waveform of figure 11 also showing some even harmonic components.

Figure 1 shows a schematic diagram of an electrical power distribution system having an electrical power distribution line 10 and a high impedance detection system 12. The solid vertical bars 16 in figure 1 are bus bars and represent the interconnection of multiple distribution lines. The high impedance detection system 12 preferably includes a plurality of individual high impedance fault detection systems 22, 24, 26 which are shown in figure 2. Also shown in figure 1 are the potential transformer PT and the current transformer CT which provide the typical analog inputs for a protective relay.

These individual high impedance fault detection systems have individual algorithms 18 for individually detecting high impedance faults as described in patent US 7,069,116 (""116 Patent") whose content is hereby incorporated by reference. These algorithms can use, for example, wavelets, higher order statistics, neural networks, and the like to identify the presence of high impedance fault independently of each of the other system algorithms. The individual high impedance fault detection algorithms can each have a different confidence level. A fault is identified as a high impedance fault once it is detected independently by the algorithms and processed through a decision logic. Figure 2 shows an exemplary composite high impedance fault detection system 12 including a higher order statistics based high impedance fault detection system 20 identified in figure 2 as a 2^nd order statistical system 22, a wavelet based high impedance detection system 24, and a neutral network based high impedance detection system 26. An input connection 28 labeled "Acquisition" in figure 2 and an output connection 30 labeled "Detection decision" in figure 2 are provided for communicating an electrical signal between the electrical power distribution system and the high impedance fault detection systems 22, 24, 26.

For example, the input connection 28 receives an electrical signal from a sensing device coupled to the electrical power distribution line. The sensing device can include any suitable sensing device, such as the current transformer shown in figure 1. The output of acquisition 28 is processed through data filtering means 29 which provides band limited and filtered signals to each individual high impedance fault detection systems 22, 24, 26. The filtering means 29 are preferably software filters and are implemented on the CPU board 34 illustrated in figure 2a.

As illustrated in figure 3, the filtering means 29 comprises a first pass band filter 29a, and one or more additional notch filters for different harmonic components. The band pass filter range for the sampled phase and/or ground current signal (s) is for example 297- 430 Hz for 60 Hz power systems, that is from slightly below the frequency of the fifth harmonic to slightly above the frequency of the seventh harmonic. The band pass filter range is adjusted accordingly for the 50 Hz systems. In the embodiments illustrated, the filtering means 29 preferably comprise three different notch filters, namely a sixth harmonic notch filter 29b for sixth harmonic components, a seventh harmonic notch filter 29c for seventh harmonic components, and a fifth harmonic notch filter 29d for fifth harmonic components. These filters can be used independently or as a group with all possible combinations among them.

In particular, in the embodiment illustrated in figure 3 the sampled signal is filtered in cascade first by the band pass filter 29a, then by sixth harmonic notch filter 29b and then by the seventh harmonic notch filter 29c to generate a Signal 1. The band pass filter 29a filters the sampled signal to remove all frequencies outside the filter range, the sixth harmonic filter 29b removes the sixth harmonic component from the band pass signal and the seventh harmonic filter removes the seventh harmonic component from the band pass signal without the sixth harmonic component. Therefore, Signal 1 only contains frequencies including the fifth harmonic that are within the band pass filter range without the sixth and seventh harmonic component. The fifth harmonic notch filter 29d removes the fifth harmonic component from Signal 1 to generate Signal 2.

Once filtered, the signals 1 and 2 are fed to the HIF systems 22, 24, 26 for proper processing through the algorithms 18 as will be better described hereinafter. As is described in the incorporated herein by reference ^116 Patent, the wavelet high impedance fault detection system 24 of Fig. 2 makes use of the fifth harmonic of the band limited and filtered signal from means 29 of Fig. 2 whereas the fifth harmonic is not required for the other high impedance fault detection systems 22 and 26 shown in Fig. 2. Thus Signal 1 is fed to at least the wavelet system 24.

Alternatively, as shown in figure 3a, the filters can be arranged in series one after the other with the fifth harmonic notch filter 29d which receives at its input the sampled data 28, the sixth harmonic notch filter 29b connected to the fifth harmonic notch filter 29d, and the seventh harmonic notch filter 29c connected between the sixth harmonic notch filter 29b and the band pass filter 29a. Other configurations may be adopted as well.

The characteristics of the notch filters 29b, 29c, and 29d are shown in figure 4, figure 5 and figure 6 respectively. These filters are designed to have sufficient attenuation of the related harmonic frequency for a system frequency variation up to +_ 3%.

The presence of the filter means 29, and in particular of the notch filters 29b, 29c, and 29d, allows to reduce -if not completely eliminate -possible misoperation of the HIF detection system which may be caused by the presence of large time-varying load harmonic components which are within the band pass filter range used. As illustrated in figure 2a, the data 28 are acquired, for example by means of the combination of the potential transformer PT and the current transformer CT shown in Figure 1 and are then filtered by hardware filter 29. After proper processing, the signals filtered by the filter means 29 are supplied to the high impedance systems 22, 24, 26. These systems are preferably operatively associated with and implemented on the CPU board 34. As shown in figure 2, each individual high impedance fault detection system 22, 24, 26 includes a logical output that is communicated to the composite high impedance fault detection system shown in Figure 2 as "Decision Logic" 32 which determines whether a high impedance fault has occurred. The composite high impedance fault detection system detects and identifies a fault as a high impedance fault once it determines that at least one, preferably at least two individual high impedance fault detection systems 22, 24, 26 have independently detected a high impedance fault. This composite feature provides increased security against false identification while improving the probability of detecting all high impedance faults. Each high impedance fault detection system 22, 24, 26 and its associated algorithm 18, as well as the composite algorithm are discussed in detail below.

The output connection, that is "Detection decision" 30 of Decision Logic 32 provides the logical output from each of the individual high impedance detection systems, that is, the higher order statistics based high impedance detection system 22, the wavelet based high impedance detection system 24 and the neural network based high impedance detection system 26, to the composite high impedance detection system.

The higher order statistics based high impedance detection system 22, the wavelet based high impedance detection system 24 and the neural network based high impedance detection system 26 and the decision logic 32 are stored in memory and implemented in a microprocessor which is also used for implementing non-HIF detection algorithms such as protection, other than HIF detection, and control algorithms and if desirable metering and/or monitoring algorithms. Thus in the present invention, one microprocessor is used for implementing both non-HIF detection and HIF detection algorithms.

One example of how to implement the HIF algorithms using a digital signal processor (DSP) is described in US patent application Ser. No. 11/081,042 filed on March 15, 2005 whose content is hereby incorporated by reference.

As shown in better detail in figure 2a, the filtered signals outputted by the filtering means 29 are provided to a multiplexer 23. The output of multiplexer 23 is connected by an analog to digital converter 25 to the input of a digital signal processor 27. The embodiment shown in figure 2a also includes a memory 33 and a CPU board 34 which includes a microprocessor 34a, a random access memory 34b and a read only memory 34c. As was described above in connection with figure 2, each of the individual high impedance fault detection systems 22, 24, 26 shown in that figure are implemented in microprocessor 34a. Also as was described above in connection with Figure 2, microprocessor 34a is also used for implementing non-HIF detection algorithms such as protection, other than HIF detection, and control algorithms and if desirable metering and/or monitoring algorithms. The output of CPU board 34, which is an indication that a high impedance fault or a non-high impedance fault condition was determined is connected to alarming 36.

Figure 7 is a flowchart showing an exemplary wavelet based HIF detection application. After the data is acquired in 50, it is filtered in 52, and then, as is described in detail below, it is decomposed in separate high and low pass wavelet decomposition filters in 54. The energy is then calculated in 56 and the calculated energy is compared to a threshold in 58 to determine if a HIF has occurred.

The following is an exemplary application of high impedance fault detection using a wavelet based high impedance fault detection system. The continuous wavelet transform of r(t) is

C p,s = ^∞lr(()φ(L÷-e-)dt (1)

- 00 ^ ' where, the wavelet is φ(t) , p is the position and s is the scale.

The position argument keeps track of the temporal change in current harmonics which is essential to HIF detection and the scale change keeps track of bands of frequencies of the current load. Both position and scale are continuous, therefore the above transform is not suited for computation. A discrete version of the transform is needed which is given by,

C» =Σ^) ) ⁽2) where, k, m and n are all integers. The above transform is implemented by multi resolution analysis where the signal is decomposed into a low pass and a high pass component via two separate low pass and high pass filters known as wavelet decomposition filters. After filtering, both low pass and high pass signals are down sampled by a factor of 2. The high pass signal component corresponds to the first detail look of the signal. The second detail look can be obtained by further decomposition of the current low pass signal into two new low pass and high pass components. The third, fourth, etc. detail signals can be obtained by further decomposition of subsequent low pass components.

The original signal can be reconstructed with minimal error from its low pass and high pass components in a reverse pyramidal manner. It is in these high pass components where distinct HIF features can be located and distinguished from signatures of other nonlinear loads of transient and bursty nature. The decomposition filters are associated with the type of mother wavelet used.

Most of the exemplary tests of this technique were conducted using the Daubechies-4 wavelet which is not a very smooth wavelet but requires less computation time. Use of other wavelets or other Daubechies wavelets did not show any noticeable change in performance nor in the threshold parameters used.

The exemplary HIF detection algorithm developed for the wavelet based system examines overlapping windows of the current at different scales and details via a wavelet transform. Although proper HIF detection can be accomplished using more than a single scale, experimental testing indicated that the energy component of the seventh detail signal carries the most significant HIF information that is more distinguishable from other normal arcing loads or normal nonlinear loads. The additional preprocessing needed is a FFT to render the current with all its random delay components position insensitive .

Thus, the preferred algorithm relies on evaluating the energy of the seventh detail signal of the magnitude of the FFT of a current. That energy is compared to a threshold and to the energy of the previous data segment. The combined decision results in a fault/no fault determination. This detection scheme delivers about 80% detection with about a 0.5% false alarm rate in the absence of arc welding loads. If the HIF attenuation parameters were lower limited to 0.1 (i.e. typically high impedance fault detection systems are not interested in detecting very weak currents), the detection rate increases to about 95% with about a 0.1% false alarm rate. The detection performance drops to about 65% in the presence of arc welding signals and without considering any lower limits on attenuation. The false alarm rate remains under about 1%.

Figure 8 is a flowchart showing an exemplary higher order statistics based HIF detection system. The data is acquired in 50, it is filtered in 52. The data acquisition and filtering in this application are both the same as the data acquisition and filtering described for the wavelet based HIF detection system of figure 7 and thus have in figure 8 the same reference numerals as is used in figure 7 for those functions. The energy is then calculated in 60 and the calculated energy is compared to a threshold in 62 to determine if a HIF has occurred.

An exemplary detection system and algorithm based on examining the higher order statistical features of normal currents has been developed and tested, as discussed below. Higher order spectra, namely the bispectrum and trispectrum are traditionally recognized as important feature extraction mechanisms that are associated with the third and fourth order cumulants of random signals. The bispectrum and the trispectrum are by definition the two dimensional and three dimensional Fourier transform of the third and fourth order cumulants defined as,

C 2 (m, n) = E{r (t) r (t + m) r (t + n) } (3) C 3 (m, n, k) = E{r (t) r (t + m) r (t + n) r (t + k)}(4) where E stands for the expected value. The exemplary algorithm implemented in this study is due in part to Tugnait (see, J. Tugnait, "Detection of Random Signals by Integrated Polyspectral Analysis", IEEE Transactions on Signal processing, Vol. 44, No. 8, pp. 2102-2108, August 1996) and utilizes the integrated polyspectra of single-phase current loads. This reference in incorporated herein by reference in its entirety.

The detector is developed such that a detection decision is made either using second order statistics at a preliminary stage or using third and fourth order statistics at an additional stage. The basic concept is as follows: what is the achievable detection decision assuming accessibility to second, third, and fourth order statistics for a given set of data and a fixed false alarm rate. First, it is determined whether a fault exists using only second order statistics. If the detection cannot be made, an alternative test based on third and fourth order cumulants is triggered. Both tests combined are designed such that the probability of false alarm is fixed and predetermined by the system operator. Clearly, this detector uses additional information beyond energy signatures.

Preferably, this detector relies on all current spectra including the in-between harmonics as generated by the pre-processing filter described earlier. The HIF detector is itemized as follows:

I. Declare a fault, if s_ecj>T_a

Where, the signature s_ec/ denotes the second order statistics of the data r(t) and T_Us is the threshold. s_ecj is defined as,

*ed= Λ- Σ r²(<), (5) σ t, / = 1 where, is the variance of r(t) given Hypothesis H(O) which is a no fault situation. The threshold r_α? is chosen such that,

where, χ^ (θ) denotes the non-centered chi-squared distribution of N degrees of freedom.

The parameters a_s and α_/, are set by the designer such that, a_s +(\-a_s)a_h =α (7)

where, a is the predetermined probability of false alarm. II. If a detection cannot be made with the previous test, then the following step is used. Declare a fault, if,

H > Ta₁₁

where, the signature s^ denotes the third order statistics of the data r(t) and T_Oh is the threshold. The threshold T_Oh is chosen such that,

where, Z%L(Q) denotes the non-centered chi-squared distribution of N degrees of freedom.

The parameters a_s and a_h are set by the designer such that,

a_s +(\-a_s)a_h =a (9) where, a is the predetermined probability of false alarm. The data of length N is divided into L segments each of length N _B . The third order statistics s^ is a scalar and defined as, ** = ²∑ ^yf MDh m o Y¹Yh K ) , do ⁾

The vector V_f1 is defined as ,

y_h K )A^R^_{c2r r} M}^_C2rr {m)}R^_Cirr {rn)}^_{C3r r} {_m)f ( 11 )

The transpose symbol used is T and ω_m = JΞL with

N _B m = 1,2, , L .

Thus, ω_m represents all the spectral components of the recorded current. The real and imaginary components are denoted by Re and Im respectively. The inverted matrix D_f1nQ used in the example above is defined as a diagonal matrix with elements representing the integrated polyspectra of the no fault signal .

D_{h m} K^ (ω_m )}] ( 12 )

The integrated bispectral and trispectral components are defined as,

where, C_\r = FFT{c_/r } and the cumulants are defined as, c_\r=r{t), c_2r=r²{t) and _C3r=r³(f)-^∑r²(t) (14)

Finally, R(ω_fc)is the Fourier Transform r(t).

Figure 9 is a flowchart showing neural network based

HIF detection application. The data is acquired in 50, it is filtered in 52. The data acquisition and filtering in this application are both the same as the data acquisition and filtering described for the wavelet based

HIF detection system of figure 7 and thus have in figure 9 the same reference numerals as is used in figure 7 for those functions. The samples are transformed in 64 using a fast Fourier transform (FFT) which is used only in the second neural network embodiment described below, and then mapped into the HIF plane in 66 using the neural network algorithm and compared to a threshold in 68 to determine if a HIF has occurred.

The following is an exemplary application of high impedance fault detection using a neural network based high impedance fault detection system. Artificial Neural Networks (ANNs) have been successfully used in many applications to solve complex classification problems due to their ability to create non-linear decision boundaries. The most common and flexible neural network is the multi-layer perceptron (MLP) which is constructed from a series of neurons.

One embodiment of a neural network design used the spectrum of the 3-cycle window of data. The magnitude of the FFT of the 1000 samples was truncated at the 13th harmonic. This resulted in a reduction to only 40 input nodes for the neural network. This network had fewer weights and biases and could be trained almost an order of magnitude faster. The best results occurred when 30 nodes were used in the hidden layer. The network was trained with 600 cases and had a sum-squared error of 11.8 (8 missed detections and 4 false alarms). Generalization testing on 3600 new inputs resulted in about an 86.06% detection rate with about a 17.06% false alarm rate. The increased performance of this network over the previous network is likely due to the invariance of the frequency spectrum to phase shifts. These performance figures are once again based upon using about 0.5 as the output threshold for indicating a detected HIF. An attempt was made to reduce the false alarm rate by increasing the output threshold to about 0.75. This resulted in about a 83.7% detection rate with about a 14.8% false alarm rate. Increasing the threshold to about 0.95 resulted in about a 77.7% detection rate and about a 11.8% false alarm rate.

Another exemplary network architecture was a combination of the two previous networks operating in parallel. If the output of both networks was greater than 0.5, then a positive HIF decision was indicated. A decision that no HIF was present was made if the output of both networks was less than 0.5. For the cases in which the two neural networks disagreed as to the presence of a HIF current, the output of the two networks was summed and a variable threshold was used to make the decision. A threshold of 1.0 corresponded to making the final decision based upon which network was more confident in its own decision.

For example, if the output of network 1 was 0.9867 and the output of network 2 was 0.0175, then the sum would be less than 1.0 and a no HIF decision would be made because the output of network 2 is closer to the ideal value of 0 than the output of network 1 is to the ideal value of 1.

On the other hand, if a more conservative approach were desired in which one chose to reduce the false alarm rate, a larger threshold approaching 1.5 could be selected. In essence, a larger threshold gives more weight to the network that indicates a no HIF situation.

The network using the spectrum (FFT) of the monitored current would appear to be more capable of detecting HIF than the network using the actual current samples. Using the sampled current network in tandem with the spectrum based network can reduce the false alarm rates, however, it doesn't appear to increase the detection rate significantly. The lack of synchronizing the current's zero-crossing during training and generalization may prohibit this neural network from detecting some of the patterns or features attributed to HIFs, such as asymmetry of half cycles and variations from cycle to cycle.

Referring once again to Figure 2 there is shown an exemplary composite HIF detection system 12 that includes all of the three different techniques described above.

As can be seen from the test results of the three different HIF techniques 22, 24, 26 described above, none of them can detect all HIF faults while assuring no false alarms. The present invention evaluates the presence of HIF fault with all the above techniques and uses a multi- resolution framework having a decision logic 32 to detect the presence of high impedance fault. A fault is identified as a high impedance fault once it is independently detected by any two of a plurality of individual high impedance fault detection systems.

An exemplary decision logic is described below:

if (Technique 1 = true) ; and if ( (Technique 2 = true) OR (Technique 3 = true) ) ,

then HIF = TRUE end; else, if (Technique 1 = false) ; and if ( (Technique 2 = true) AND (Technique 3 = true) ) then HIF = TRUE end end. where, Technique 1 is the logical output (true or false) from the wavelet based algorithm; Technique 2 is the logical output from the algorithm based on higher order statistics; and Technique 3 is the logical output from the ANN based technique. For the above example, the logical output of any individual technique is true if that technique detects an HIF, otherwise it is false.

In order to reduce to ensure a good HIF detection performance by making the threshold calculation in each of the algorithms independent of the load a dynamic energy threshold calculation can be used according to the solution described in US patent 7,085,659 serial code 10/966,432 filed on October 15, 2004 whose content is hereby incorporated by reference. As shown for example in figure 10, an input signal comprising of phase (load) currents and/or neutral (residual) current, is input to the HIF detection algorithm 18 for processing. The HIF detection algorithm 18 may be one of the three algorithms previously described. The output of the HIF algorithm 18 is the energy of the input signal. This input signal energy is then multiplied by a factor, called Threshold Margin 14, that can be set to anywhere from about 110% to about 300% depending on the security of detection required and the result of that multiplication, known as Threshold Energy, is stored into a First-In First-Out (FIFO) buffer and control logic 13. Trials with captured field data indicate that there may be an unacceptable number of false detections when using a Threshold Margin lower than 125%. In general, the user of this technique would increase the Threshold Margin if the protected line has normally large and quickly varying frequency components of interest and the user wanted to reduce the probability of false detection. The FIFO buffer 13 has N elements and each element is updated every T seconds. The total delay from the input to the output of the buffer 13 is T*N seconds. The values used for T and N in one embodiment of the present invention are T = 10 seconds and N = 8 for a total delay through buffer 16 of 80 seconds or one (1) minute and 20 seconds. The updating period, T, is in that one embodiment selected as 10 seconds because it is the shortest time that produced acceptable detections given the sampling rate of 32 samples per cycle (about 2 kHz) in that embodiment. The value of 8 for N in that one embodiment is chosen to give the desired separation in number of minutes, one (1) in that embodiment, of lead-time between the present calculated energy and the Threshold Energy value: N = (number of minutes * 6) + 2 [where six (6) is the number of 10 second intervals in one (1) minute] . This value for N provides a clear distinction between pre-fault and fault values. The number of minutes or unit of time should be the maximum amount of time that it is expected to detect the fault. After that time expires, the fault energy begins to appear in the Threshold Energy which then makes detection less and less likely. The number of minutes or unit of time should be short enough that the HIF algorithm 18 can track normal changes in the load. Any element of the FIFO buffer 13 can be used as the threshold energy and is compared at 15 to the present energy signal. In one embodiment the three oldest values of the FIFO buffer 13, that is the three oldest values of the Threshold Energy, are used in a filter (not shown) to produce the one threshold value. There is a tradeoff between keeping enough elder values to provide sufficient time for detection versus keeping even more elder values and not using them which wastes memory. The filter provides for a smoother transition of the threshold outputs and because the data is updated so slowly (once every 10 seconds) , any type of low-pass filter should be adequate to perform that function. When the input signal energy has a value greater than the Threshold Energy, an HIF detection signal is generated and that signal can be used to raise an HIF detection flag by any means, not shown but well known to those of ordinary skill in the art. It should be appreciated that while the embodiment described above uses the three oldest values of Threshold Energy stored in buffer 13 as the input to the filter to produce the one threshold value used for comparison that any or all of the values in the buffer 13 can be used for that purpose. In that one embodiment it was decided to use a filter that was easy to implement and that filter happens to use only the three oldest values.

During a reset of the algorithm, as would occur during initialization, all elements in the FIFO buffer 13 are assigned a reset value. The reset value is a relatively large value that prevents the comparator 15 from being activated and thus prevents a false detection while the system adapts to the input signal it is monitoring. Since the largest Threshold Margin is 300% or three times the typical load value a suitable reset value might be 10 times the typical load value that is obtained from the field data. During normal operation, a HIF detection signal is generated when the computed input signal energy is larger than the Threshold Energy. This detection signal causes all elements of the FIFO buffer 13 to be set to the present output Threshold Energy threshold value. This provides a type of seal-in for the detection since an algorithm that has picked up, that is detected a HIF, will not drop out because the next Threshold Energy in the FIFO buffer 13 is larger. This action also clears the threshold pipeline of any values that may have been influenced by the fault before the Threshold Energy was exceeded.

It is to be understood that the description of the preferred embodiment (s) is (are) intended to be only illustrative, rather than exhaustive, of the present invention. Those of ordinary skill will be able to make certain additions, deletions, and/or modifications to the embodiment (s) of the disclosed subject matter without departing from the spirit of the invention or its scope, as defined by the appended claims.

Claims

What is claimed is:

1. A method for detecting a high impedance fault in an electrical power line having a predetermined power line frequency comprising: providing a high impedance fault detection means; using said high impedance fault detection means to make a determination from a signal taken from said power line in which one or more predetermined harmonics of said power line frequency have been removed whether said high impedance fault has occurred.

2. The method of claim 1 further comprising removing said one or more predetermined harmonics from said signal taken from said power line.

3. The method of claim 1 wherein said one or more removed predetermined harmonics of said power line frequency is the fifth harmonic of said power line frequency.

4. The method of claim 1 wherein said one or more removed predetermined harmonics of said power line frequency is other than the fifth harmonic of said power line frequency.

5. The method of claim 3 wherein said one or more removed predetermined harmonics of said power line frequency is also the sixth and seventh harmonics of said power line frequency.

6. The method of claim 1 wherein some of said one or more predetermined harmonics of said power line frequency have been removed from said signal taken from said power line.

7. The method of claim 1 wherein all of said one or more predetermined harmonics of said power line frequency have been removed from said signal taken from said power line.

8. A method for detecting a high impedance fault in an electrical power line having a predetermined power line frequency comprising: providing a plurality of high impedance fault detection means; providing a decision means; using said plurality of high impedance fault detection means to make a plurality of independent determinations from a signal taken from said power line in which one or more predetermined harmonics of said power line frequency have been removed whether said high impedance fault has occurred, respectively; generating outputs representative of said independent determinations; and using said decision means to determine whether said high impedance fault has occurred, wherein said decision means determines that said high impedance fault has occurred if any two or more of said outputs indicate that said high impedance fault has occurred.

9. The method of claim 8 wherein each of said plurality of high impedance fault detection means make said independent determination from a signal taken from said power line in which all of said one or more predetermined harmonics of said power line frequency have been removed.

10. The method of claim 8 wherein each of said plurality of high impedance fault detection means make said independent determination from a signal taken from said power line in which a preselected number of said one or more predetermined harmonics of said power line frequency have been removed.

11. The method of claim 10 wherein said preselected number is different for each of said plurality of high impedance fault detection means.

12. A method for detecting a high impedance fault in an electrical power line having a predetermined power line frequency comprising: providing three or more high impedance fault detection means; providing a decision means; using said three or more high impedance fault detection means to make an independent determination whether said high impedance fault has occurred from a signal taken from said power line, each of said three or more high impedance fault detection means requiring said signal taken from said power line to have a preselected number of one or more predetermined harmonics of said power line frequency removed prior to said independent determination; generating outputs representative of said independent determinations; and using said decision means to determine whether said high impedance fault has occurred, wherein said decision means determines that said high impedance fault has occurred if any two or more of said outputs indicate that said high impedance fault has occurred.

13. The method of claim 12 wherein said preselected number of said one or more predetermined harmonics removed is identical for less than all of said three or more high impedance fault detection means.

14. The method of claim 13 wherein said removed one or more predetermined harmonics is identical for said less than all of said three or more high impedance fault detection means.

15. A system for detecting a high impedance fault in an electrical power line having a predetermined power line frequency comprising: a high impedance fault detection means; said high impedance fault detection means for determining from a signal taken from said power line in which one or more predetermined harmonics of said power line frequency have been removed whether said high impedance fault has occurred.

16. The system of claim 15 further comprising a filter for removing said one or more predetermined harmonics from said signal taken from said power line.

17. The system of claim 15 wherein said one or more removed predetermined harmonics of said power line frequency is the fifth harmonic of said power line frequency.

18. The system of claim 15 wherein said one or more removed predetermined harmonics of said power line frequency is other than the fifth harmonic of said power line frequency.

19. The system of claim 17 wherein said one or more removed predetermined harmonics of said power line frequency is also the sixth and seventh harmonics of said power line frequency.

20. The system of claim 15 wherein some of said one or more predetermined harmonics of said power line frequency have been removed from said signal taken from said power line.

21. The system of claim 15 wherein all of said one or more predetermined harmonics of said power line frequency have been removed from said signal taken from said power line.

22. A system for detecting a high impedance fault in an electrical power system having a predetermined operating frequency, comprising: an electrical power supply; one or more interconnected electrical power conductors; and a composite high impedance fault detection system connected to said one or more electrical power conductors for detecting said high impedance fault, said composite high impedance fault detection system comprising: a plurality of high impedance fault detection systems operable to respectively make a plurality of independent determinations from a signal taken from said power system in which one or more predetermined harmonics of said operating frequency have been removed whether said high impedance fault has occurred and to respectively generate outputs representative of said independent determinations; and decision means connected to said high impedance fault detection systems for determining whether said high impedance fault has occurred, said decision means determining that said high impedance fault has occurred if any two or more of said outputs indicate that said high impedance fault has occurred.

23. The system of claim 22 wherein each of said plurality of high impedance fault detection means make said independent determination from a signal taken from said system in which all of said one or more predetermined harmonics of said operating frequency have been removed.

24. The system of claim 22 wherein each of said plurality of high impedance fault detection means make said independent determination from a signal taken from said system in which a preselected number of said one or more predetermined harmonics of said operating frequency have been removed.

25. The system of claim 24 wherein said preselected number is different for each of said plurality of high impedance fault detection means.

26. A system for detecting a high impedance fault in an electrical power line having a predetermined power line frequency comprising: three or more high impedance fault detection means; means for deciding if a high impedance fault has occurred in said system; said three or more high impedance fault detection means for making an independent determination whether said high impedance fault has occurred from a signal taken from said power line, each of said three or more high impedance fault detection means requiring said signal taken from said power line to have a preselected number of one or more predetermined harmonics of said power line frequency removed prior to said independent determination and each of said three or more high impedance fault detection means generating outputs representative of said independent determinations; and said decision means responsive to said outputs representative of said independent determinations to determine whether said high impedance fault has occurred, wherein said decision means determines that said high impedance fault has occurred if any two or more of said outputs indicate that said high impedance fault has occurred.

27. The system of claim 26 wherein said preselected number of said one or more predetermined harmonics removed is identical for less than all of said three or more high impedance fault detection means.

28. The system of claim 27 wherein said removed one or more predetermined harmonics is identical for said less than all of said three or more high impedance fault detection means.