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Figure 1129 On-line data analysis displayed on a standard Web browser The analysis
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includes the trend of minimum, maximum, and average values of the fifth-harmonic voltage distortion along with a statistical distribution of the average values
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The process of turning raw measurement data into knowledge involves data selection and preparation, information extraction from selected data, information assimilation, and report presentation These steps (illustrated in Fig 1130) are commonly known as knowledge discovery or data mining12 The first step in the knowledge discovery is to select appropriate measurement quantities and disregard other types of measurements that do not provide relevant information In addition, during the dataselection process preliminary analyses are usually carried out to ensure the quality of the measurement For example, an expert system module is developed to retrieve a specific answer, and it requires measurements of instantaneous three-phase voltage and current waveforms to be available The data-selection task is responsible for ensuring that all required phase voltage and current waveform data are available before proceeding to the next step In some instances, it might be necessary to interpolate or extrapolate data in this step Other preliminary examinations include checking any outlier magnitudes, missing data sequences, corrupted data, etc Examination on data quality is important as the accuracy of the knowledge discovered is determined by the quality of data
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Downloaded from Digital Engineering Library @ McGraw-Hill (wwwdigitalengineeringlibrarycom) Copyright 2004 The McGraw-Hill Companies All rights reserved Any use is subject to the Terms of Use as given at the website
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Knowledge definition: The goals of the end user Answer to a specific question Extracted Information Assimilated Information
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Knowledge Interpretation and report presentation: Interpret assimilated information into knowledge Report presentation Information assimilation: Incorporate pieces of information/knowledge Resolve conflicting information
Data selection: Remove outliers Select relevant data
Data transformation: Rearrange data favorable for extraction Frequency domain, time-scale domain
Data mining: Extract features or patterns Expert systems Neural networks Machine learning Pattern recognition etc
Figure 1130 Process of turning raw data into answers or knowledge
The second step attempts to represent the data and project them onto domains in which a solution is more favorable to discover Signal-processing techniques and power system analysis are applied An example of this step is to transform data into another domain where the information might be located The Fourier transform is performed to uncover frequency information for steady-state signals, the wavelet transform is performed to find the temporal and frequency information for transient signals, and other transforms may be performed as well Now that the data are already projected onto other spaces or domains, we are ready to extract the desired information Techniques to extract the information vary from sophisticated ones, such as pattern recognition, neural networks, and machine learning, to simple ones, such as finding the maximum value in the transformed signal or counting the number of points in which the magnitude of a voltage waveform is above a predetermined threshold value One example is looking for harmonic frequencies of a distorted waveform In the second step the waveform is transformed using the Fourier transform, resulting in a frequencydomain signal A simple harmonic frequency extraction process might be accomplished by first computing the noise level in the frequencydomain signal, and subsequently setting a threshold number to severalfold that of the noise level Any magnitude higher than the threshold number may indicate the presence of harmonic frequencies
Downloaded from Digital Engineering Library @ McGraw-Hill (wwwdigitalengineeringlibrarycom) Copyright 2004 The McGraw-Hill Companies All rights reserved Any use is subject to the Terms of Use as given at the website
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The data mining step usually results in scattered pieces of information These pieces of information are assimilated to form knowledge In some instances assimilation of information is not readily possible since some pieces of information conflict with each other If the conflicting information cannot be resolved, the quality of the answer provided might have limited use The last step in the chain is interpretation of knowledge and report presentation
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