Surya Santoso

Bio: Surya Santoso is an academic researcher from University of Texas at Austin. The author has contributed to research in topics: Electric power system & Wind power. The author has an hindex of 32, co-authored 263 publications receiving 6271 citations. Previous affiliations of Surya Santoso include Eindhoven University of Technology & McGraw Hill Financial.

Power quality assessment via wavelet transform analysis

Abstract: In this paper we present a new approach to detect, localize, and investigate the feasibility of classifying various types of power quality disturbances. The approach is based on wavelet transform analysis, particularly the dyadic-orthonormal wavelet transform. The key idea underlying the approach is to decompose a given disturbance signal into other signals which represent a smoothed version and a detailed version of the original signal. The decomposition is performed using multiresolution signal decomposition techniques. We demonstrate and test our proposed technique to detect and localize disturbances with actual power line disturbances. In order to enhance the detection outcomes, we utilize the squared wavelet transform coefficients of the analyzed power line signal. Based on the results of the detection and localization, we carry out an initial investigation of the ability to uniquely characterize various types of power quality disturbances. This investigation is based on characterizing the uniqueness of the squared wavelet transform coefficients for each power quality disturbance.

Characterization of distribution power quality events with Fourier and wavelet transforms

Abstract: It is the objective of this paper to present unique features that characterize power quality events and methodologies to extract them from recorded voltage and/or current waveforms using Fourier and wavelet transforms. Examples of unique features include peak amplitudes, RMS, frequency, and statistics of wavelet transform coefficients. These features are derived from well documented theories, power engineers' heuristics gained through long years of experience, and power quality data collected in recent years. Converter operation, transformer energization, and capacitor energization (which includes normal, back-to-back, and re-strike on opening energization), representing three common power quality events at the distribution level, are presented. These examples provide the basis for further characterization of other power quality events.

Power quality disturbance waveform recognition using wavelet-based neural classifier. I. Theoretical foundation

Abstract: Existing techniques for recognizing and identifying power quality disturbance waveforms are primarily based on visual inspection of the waveform. It is the purpose of this paper to bring to bear advances, especially in wavelet transforms, artificial neural networks, and the mathematical theory of evidence, to the problem of automatic power quality disturbance waveform recognition. Unlike past attempts to automatically identify disturbance waveforms where the identification is performed in the time domain using an individual artificial neural network, the proposed recognition scheme is carried out in the wavelet domain using a set of multiple neural networks. The outcomes of the networks are then integrated using decision making schemes such as a simple voting scheme or the Dempster-Shafer theory of evidence. With such a configuration, the classifier is capable of providing a degree of belief for the identified disturbance waveform.

Power quality disturbance data compression using wavelet transform methods

Abstract: In this paper, the authors present a wavelet compression technique for power quality disturbance data. The compression technique is performed through signal decomposition, thresholding of wavelet transform coefficients and signal reconstruction. Threshold values are determined by weighting the absolute maximum value at each scale. Wavelet transform coefficients whose values are below the threshold are discarded, while those that are above the threshold are kept along with their temporal locations. The authors show the efficacy of the technique by compressing actual disturbance data. The file size of the compressed data is only one-sixth to one-third that of the original data. Therefore, the cost related to storing and transmitting the data is significantly reduced.

Understanding Power System Hannonics

Abstract: Harmonics – Past to Present Power systems are designed to operate at frequencies of 50 or 60Hz. However, certain types of loads produce currents and voltages with frequencies that are integer multiples of the 50 or 60 Hz fundamental frequency. These higher frequencies are a form of electrical pollution known as power system harmonics. Power system harmonics are not a new phenomenon. Concern over harmonic distortion has ebbed and flowed during the history of electric power systems. Steinmetz published a book in 1916 that devoted considerable attention to the study of harmonics in three-phase power systems. His main concern was third harmonic currents caused by saturated iron in transformers and machines, and he was the first to propose delta connections for blocking third harmonic currents. Later, with the advent of rural electrification and telephone service, power and telephone circuits were often placed on common rights-of-way. Harmonic currents produced by transformer magnetizing currents caused inductive interference with open-wire telephone systems. The interference was so severe at times that voice communication was impossible. This problem was studied and alleviated by filtering and by placing design limits on transformer magnetizing currents. Today, the most common sources of harmonics are power electronic loads such as adjustable-speed drives (ASDs) and switch-mode power supplies. These loads use diodes, silicon-controlled rectifiers (SCRs), power transistors, and other electronic switches to chop waveforms to control power or to convert 50/60Hz AC to DC. In the case of ASDs, the DC is then converted to variable-frequency AC to control motor speed. Example uses of ASDs include chillers and pumps. Due to tremendous advantages in efficiency and controllability, power electronic loads are proliferating and can be found at all power levels – from low voltage appliances to high voltage converters. Hence, power systems harmonics are once again an important problem.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

A review on the forecasting of wind speed and generated power

Abstract: In the world, wind power is rapidly becoming a generation technology of significance. Unpredictability and variability of wind power generation is one of the fundamental difficulties faced by power system operators. Good forecasting tools are urgent needed under the relevant issues associated with the integration of wind energy into the power system. This paper gives a bibliographical survey on the general background of research and developments in the fields of wind speed and wind power forecasting. Based on the assessment of wind power forecasting models, further direction for additional research and application is proposed.

Power quality assessment via wavelet transform analysis

Abstract: In this paper we present a new approach to detect, localize, and investigate the feasibility of classifying various types of power quality disturbances. The approach is based on wavelet transform analysis, particularly the dyadic-orthonormal wavelet transform. The key idea underlying the approach is to decompose a given disturbance signal into other signals which represent a smoothed version and a detailed version of the original signal. The decomposition is performed using multiresolution signal decomposition techniques. We demonstrate and test our proposed technique to detect and localize disturbances with actual power line disturbances. In order to enhance the detection outcomes, we utilize the squared wavelet transform coefficients of the analyzed power line signal. Based on the results of the detection and localization, we carry out an initial investigation of the ability to uniquely characterize various types of power quality disturbances. This investigation is based on characterizing the uniqueness of the squared wavelet transform coefficients for each power quality disturbance.