Development of a wavelet-based algorithm to detect and determine ...

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7.3. MATLAB IMPLEMENTATION 61 7.3.2 Summary Figure 7.7: Structure chart of the program module which identifies the disturbance It is possible to add new pattern for other disturbances. The calculation is the same as with already integrated disturbances. It is not necessary to find new criteria to detect these new disturbances, as it is necessary if the identifying is estimated with changing of the RMS values (for example for a short circuit the criterias are: decreasing voltage and increasing current, but this can be a load switching also, thus additional criterias are need for this identifying). The characteristic pattern described are calculated for a voltage frequency of 60Hz at an unknown sampling rate. So these pattern have to be adopted for 50Hz and a known sampling frequncy. Through simulations with Matlab it is known that for getting the same pattern as described, a sampling rate of 8333,33Hz is necessary. Because this is not possible, the new pattern have to be calculated. To calculate them, it is necessary to have very clear disturbance recordings. Of course this is possible with a simulator (power network model or computer based simulator). It is very interesting to do this simulation with a sampling rate of 12,8kHz, because the Simeas R disturbance recorder can be used to get the disturbance recordings or 6,4kHz because the disturbance recording can be downsampled from the recordings from the Simeas R. The pattern for several equal disturbances are not the same exactly but in a small range. So several disturbances for each fault have to be calculated to get the range of the pattern. In the time for this diploma thesis, it was not possible to make these simulations. It is obvious to use this small peaks, which are described above for localizing the dis-
7.3. MATLAB IMPLEMENTATION 62 turbance to detect also involving faults. So they can be separated and each one can be calculated for its own. The result is a higher accuracy in the identification of the disturbances. Also possible is the adaptation of the Matlab programs for a very fast digital signal processor. So the disturbances can be detected very fast. And it can be integrated in a digital measuring or recording device. It is only necessary to store the pattern for each disturbance. Each pattern has ten values only, so their is a very small need of memory.
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Development of a wavelet-based algo
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Eidesstattliche Versicherung Ich ve
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2.5.2 Software modules . . . . . .
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Chapter 1 Introduction Electrical p
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Chapter 2 Power Quality Recorder Si
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2.2. FUNCTIONS 5 2.2.2 Power data r
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2.3. INTERFACES 7 The binary inputs
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2.4. OPERATION MODES 9 cessed. Figu
Page 18 and 19: 2.5. SOFTWARE (OSCOP P) 11 2.5.2 So
Page 20 and 21: 2.5. SOFTWARE (OSCOP P) 13 Figure 2
Page 22 and 23: 3.1. SIMEAS R CONFIGURATION 15 •
Page 24 and 25: 3.2. LABORATORY USE 17 Then the SIM
Page 26 and 27: 4.2. SOURCES OF TRANSIENT DATA 19 c
Page 28 and 29: 4.3. FILES FOR DATA EXCHANGE 21 4.3
Page 30 and 31: 4.3. FILES FOR DATA EXCHANGE 23 p=
Page 32 and 33: 4.3. FILES FOR DATA EXCHANGE 25 ft
Page 34 and 35: 4.4. DIFFERENCES BETWEEN THE 1991
Page 36 and 37: Figure 5.1: Structure chart of the
Page 38 and 39: 5.1. DATA STRUCTURE OF DATA STORAGE
Page 40 and 41: Chapter 6 Wavelets 6.1 Introduction
Page 42 and 43: 6.2. BASIC IDEAS OF THE WAVELET TRA
Page 60 and 61: Chapter 7 Algorithm to determine ce
Page 62 and 63: 7.2. ANALYZATION OF THE DISTURBANCE
Page 70 and 71: Chapter 8 Conclusion and Look-Out T
Page 72 and 73: LIST OF FIGURES 65 6.8 Scale level
Page 74 and 75: Bibliography [1] Ronald W. Schafer
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