A. Testa

Bio: A. Testa is an academic researcher. The author has contributed to research in topics: Second-generation wavelet transform & Harmonic analysis. The author has an hindex of 3, co-authored 4 publications receiving 340 citations.

A measurement method based on the wavelet transform for power quality analysis

Abstract: The paper presents a measurement method for power quality analysis in electrical power systems. The method is the evolution of an iterative procedure already set up by the authors and allows the most relevant disturbances in electrical power systems to be detected, localized and estimated automatically. The detection of the disturbance and its duration are attained by a proper application, on the sampled signal, of the continuous wavelet transform (CWT). Disturbance amplitude is estimated by decomposing, in an optimized way, the signal in frequency subbands by means of the discrete time wavelet transform (DTWT). The proposed method is characterized by high rejection to noise, introduced by both measurement chain and system under test, and it is designed for an agile disturbance classification. Moreover, it is also conceived for future implementation both in a real-time measurement equipment and in an off-line analysis tool. In the paper firstly the theoretical background is reported and briefly discussed. Then, the proposed method is described in detail. Finally, some case-studies are examined in order to highlight the performance of the method.

A multiple deep attenuation frequency window for harmonic analysis in power systems

Abstract: A novel window is presented and applied in electrical power system harmonic analysis. The goal of increasing the resolvability of low magnitude nonharmonic tones close in frequency to higher magnitude harmonics and the detectability of very low magnitude high frequency harmonics is pursued. The proposed window is derived from the Tseng window; its spectrum can be modeled in the synthesis stage and it is characterized by a narrow width main lobe and by sidelobes which are very low in correspondence to some specified frequencies. Numerical experiments demonstrate the performances and the usefulness of the new window in resolving periodic distorted waveforms in power systems. >

Detection and Classification of Power Quality Disturbances Using S-Transform and Probabilistic Neural Network

Abstract: This paper presents an S-Transform based probabilistic neural network (PNN) classifier for recognition of power quality (PQ) disturbances. The proposed method requires less number of features as compared to wavelet based approach for the identification of PQ events. The features extracted through the S-Transform are trained by a PNN for automatic classification of the PQ events. Since the proposed methodology can reduce the features of the disturbance signal to a great extent without losing its original property, less memory space and learning PNN time are required for classification. Eleven types of disturbances are considered for the classification problem. The simulation results reveal that the combination of S-Transform and PNN can effectively detect and classify different PQ events. The classification performance of PNN is compared with a feedforward multilayer (FFML) neural network (NN) and learning vector quantization (LVQ) NN. It is found that the classification performance of PNN is better than both FFML and LVQ.

Wavelet-based neural network for power disturbance recognition and classification

Abstract: In this paper, a prototype wavelet-based neural-network classifier for recognizing power-quality disturbances is implemented and tested under various transient events. The discrete wavelet transform (DWT) technique is integrated with the probabilistic neural-network (PNN) model to construct the classifier. First, the multiresolution-analysis technique of DWT and the Parseval's theorem are employed to extract the energy distribution features of the distorted signal at different resolution levels. Then, the PNN classifies these extracted features to identify the disturbance type according to the transient duration and the energy features. Since the proposed methodology can reduce a great quantity of the distorted signal features without losing its original property, less memory space and computing time are required. Various transient events tested, such as momentary interruption, capacitor switching, voltage sag/swell, harmonic distortion, and flicker show that the classifier can detect and classify different power disturbance types efficiently.

Time-frequency and time-scale domain analysis of voltage disturbances

Abstract: This paper discusses the analysis of voltage disturbance recordings in the time-frequency domain and in the time-scale domain. The discrete short-time Fourier transform (STFT) is used for the time-frequency domain; dyadic and binary-tree wavelet filters for the time-scale domain. The theory is explained with special emphases on the analysis of voltage disturbance data. Dyadic wavelet filters are not suitable for the harmonic analysis of disturbance data. Filter center frequencies and bandwidths are inflexible, and the results do not give easy insight in the time behavior of the harmonics. On the other hand, band-pass filter outputs from discrete STFT are well associated with harmonics and are thus more useful for power system analysis. With a properly chosen window size, the discrete STFT is also able to detect and analyze transients in a voltage disturbance. Overall, the STFT is more suitable than wavelet filters for the analysis of power system voltage disturbance data.

A critical review of detection and classification of power quality events

Abstract: Requirement of green supply with higher quality has been consumers’ demand around the globe The electrical power system is expected to deliver undistorted sinusoidal rated voltage and current continuously at rated frequency to the consumers This paper presents a comprehensive review of signal processing and intelligent techniques for automatic classification of the power quality (PQ) events and an effect of noise on detection and classification of disturbances It is intended to provide a wide spectrum on the status of detection and classification of PQ disturbances as well as an effect of noise on detection and classification of PQ events to the researchers, designers and engineers working on power quality More than 150 research publications on detection and classification techniques of PQ disturbances have been critically examined, classified and listed for quick reference

A measurement method based on the wavelet transform for power quality analysis

Abstract: The paper presents a measurement method for power quality analysis in electrical power systems. The method is the evolution of an iterative procedure already set up by the authors and allows the most relevant disturbances in electrical power systems to be detected, localized and estimated automatically. The detection of the disturbance and its duration are attained by a proper application, on the sampled signal, of the continuous wavelet transform (CWT). Disturbance amplitude is estimated by decomposing, in an optimized way, the signal in frequency subbands by means of the discrete time wavelet transform (DTWT). The proposed method is characterized by high rejection to noise, introduced by both measurement chain and system under test, and it is designed for an agile disturbance classification. Moreover, it is also conceived for future implementation both in a real-time measurement equipment and in an off-line analysis tool. In the paper firstly the theoretical background is reported and briefly discussed. Then, the proposed method is described in detail. Finally, some case-studies are examined in order to highlight the performance of the method.