Massimo Lazzaroni

A calibration procedure for a digital instrument for electric power quality measurement

The measurement of power quality is becoming an impelling need in a deregulated electricity market where the sources producing distortion and disturbances are steadily increasing in number and power. The instruments for power quality measurement are based on complex digital processing of the input signals, whose waveforms are highly variable. The calibration of this kind of instrument is therefore an open topic, especially when the uncertainty propagation through the DSP algorithm is concerned. This paper proposes an innovative procedure that, starring from the determination of the probability density function of the uncertainty contribution of every device from the signal input stage to the A/D conversion stage, estimates the final measurement standard uncertainty and the probability density function of the measurement results. The result of the experimental work done on a prototype of power quality measurement instrument is reported.

A linearisation method for commercial Hall-Effect current transducers

The need for high-current transducers featuring wide-band and high-insulation levels is becoming a more and more impelling need in the electric power network, as the current distortion increases due to the proliferation of nonlinear, time-variant loads. The closed-loop Hall-effect current transducers show a good compromise between cost and metrological performance. On the other hand, they may introduce an unacceptable nonlinearity error when the dynamic range of the input current is a reduced portion of the dynamic range of the transducer and may show a large drift of their gain with temperature. The paper proposes a simple method for reduction of the nonlinearity error when the input signal is an unbiased sinewave and the automatic calibration of the gain. Experimental results are also reported, showing the method effectiveness.

Quality assessment of biometric systems: a comprehensive perspective based on accuracy and performance measurement

Despite the efforts of the international biometric community, the measurement of the accuracy of a biometric system is far from being completely investigated and, eventually, standardized. This paper presents a critical analysis of the accuracy and performance measurement of a biometric system. Current approaches to the problem and procedural error have been described and criticized. Finally, a methodology for the measurement of the accuracy of biometric systems with nonsymmetric matching function will be proposed and discussed.

Accuracy and performance of biometric systems

Although efforts of the entire international biometric community, the measurement of accuracy of a biometric system is far to be completely investigated and, eventually, standardized. The paper presents a critical analysis of the measurement of an accuracy and performance of a biometric system. Current approaches to the problem and procedural error have been described and criticized. Finally, a methodology for the measurement of the accuracy of biometric system with not-symmetric matching function is proposed and discussed.

FMECA technique on photovoltaic module

The solar photovoltaic industry has seen rapid expansion in the past decade with an ever-increasing share of the electricity-generating capacity for the world. For the emerging photovoltaic (PV) industry the assessment of the quality and reliability of its products is becoming more and more important. To this aim and to ensure an optimal design and material choice, a failure modes, effects and criticality analysis methodology (FMECA) to classify the occurrence, the severity and the impact of all possible failure mechanisms on the PV system has been introduced. This helps to eliminate or reduce the impact of potential failure modes before the completion of the design and before failures occur in the field. By means of this analysis it can be noticed that a crucial aspect in PV systems is the cleaning status of the panel surface. In this paper this problem has been analyzed by means of an experimental activity. Finally a method for the assessment of the PV panel condition has been proposed.

A neuro-fuzzy approach for the detection of partial discharge

Dielectric surfaces exposed to partial discharges (PD) undergo aging, which is reflected by changes in the discharge pulse form. An approach is described in which fuzzy logic and neural networks are used in conjunction with the wavelet transform to identify the parameters in the PD pulse form for the purpose of classifying the aging phenomena due to partial discharge degradation.

Diagnostic of a Faulty Induction Motor Drive via Wavelet Decomposition

An approach to the analysis of ac side current is presented for the purpose of identification of faults in the stator phase resistance of an ac induction motor drive. The method relies on the correlation between wavelet decomposition coefficients of the current in healthy and faulty conditions. The findings highlight that the fault causes waveform variations that are localized at specific decomposition levels. The presented approach may open the way to efficient training for fault recognition systems.

A genetic algorithm for fault identification in electrical drives: a comparison with neuro-fuzzy computation

Industrial applications require suitable monitoring systems able to identify any decrement in the production efficiency involving economical losses. The information coming from a general purpose monitoring system can be usefully exploited to implement a sensorless instrument monitoring an AC motor drive and a diagnostic tool providing useful risk coefficients. The method is based on a complex digital processing of the line signals acquired by means of a virtual instrument. In this paper a genetic algorithm, implemented in a Mathcad environment, performs the evaluation of the risk indexes from the processed line signals. The combination of genetic algorithms and neural network is also investigated as a promising possibility for the development of a reliable diagnostic tool. The risk coefficients derived from this approach are evaluated, discussed and compared to other indexes - in particular fuzzy indexes - introduced by the authors in previous papers.

A neuro-fuzzy application for AC motor drives monitoring system

Nowadays industrial applications require suitable monitoring systems able to identify any decrement in the efficiency involving economical losses. This paper shows that the information coming from a general purpose monitoring system can be usefully exploited to realize a sensorless instrument able to monitor an ac motor drive and diagnostic tools providing useful risk coefficients. The method is based on a complex digital processing of the line signals acquired by means of a Virtual Instrument. The employed wavelet algorithms have been implemented in a Matlab environment and risk coefficients are elaborated by means of suitable neuro fuzzy algorithms.

Design and implementation of a on-board device for photovoltaic panels monitoring

Nowadays the world energy infrastructure is subjected to a progressive transformation. In fact it is already possible to notice the growing number of distributed small generation units, based on different technologies, directly connected to the power grid. At present the energy that can be produced by the sun and wind seem to be the most promising renewable energy sources. The use of the solar energy requires to optimize the efficiency of the converting chain and to maintain the performances of the system. These requirements highlight the need to equip photovoltaic (PV) plants with efficient condition monitoring tools. In this paper a hardware solution for monitoring system that allows implementing algorithm devoted to check the dependability of the PV panels and to improve the overall system efficiency is presented.