This paper deals with digital acquisition, classification and analysis of the stochastic features of random pulse signals generated by partial discharge (PD) phenomena. Focus is made on a new measuring system for the digital acquisition of PDpulse signals, which operates at a sampling rate high enough to avoid the frequency aliasing, hut that provides an amount of PD pulses which enables PD stochastic analysis. A separation and classification method, based on a fuzzy classifier, is developed for the analysis of the acquired PD-pulse shape signals. The result of the fuzzy classification is a cluster of signals homogeneous in terms of stochastic features of PD pulses. The classification efficiency is evaluated resorting to the PD-pulse height and phase distributions analysis. The instrumentation, and the associated classification methodology, are applied to measure and analyze PD data recorded for mica-insulated stator bars and coils, where typical defects, occurring during normal operations, were simulated. It is shown that the proposed procedure enables PD-source identification to solve the identification problems which arise, in particular, when different sources of PD are simultaneously active.In addition fuzzy classification provides a n efficient noise-rejection tool.

Abstract-This paper describes a modeling approach for nonlinear dynamic systems based on a modified Volterra series; by comparing the truncation error of this series with that of the classical Volterra one, we outlined that, under the assumption of short-term nonlinear memory effects, the modified series enables a single-fold nonlinear convolution integral to be adopted also in the presence of strong nonlinearities. The measurement-based identification of the first terms of the modified Volterra series is described; experimental and simulation results which confirm the theoretical considerations are also provided.

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Abstract-A new nonlinear dynamic model of large-signal amplifiers based on a Volterra-like integral series expansion is described. The new Volterra-like series is specially oriented to the modeling of nonlinear communication circuits, since it is expressed in terms of dynamic deviations of the complex modulation envelope of the input signal. The proposed model represents a generalization, to nonlinear systems with memory, of the widely-used amplitude/amplitude (AM/AM) and amplitude/phase (AM/PM) conversion characteristics, which are based on the assumption of a practically memoryless behavior. A measurement procedure for the experimental characterization of the proposed model is also outlined.

This paper deals with an application of the inverse factors method (W-matrix method) to a fast decoupled load flow procedure for steady-state contingency analysis. The W-matrix method, originally developed for the solution of sparse sets of linear equations on MIMD computers, can also be made effective for vector computers. The recurrence problem is overcome by reordering the addition operations required i n forward and backward solution. Matrix partitioning is employed to 5 d the best trade-off between the number of 5ll-ins added to the W matrix and the increased efflciency of vector operations achieved through a reduced number of partitions. The effect of different bus ordering algorithms on the partition number is also considered. Operation reordering is employed to make the bus power computation phase very fast in comparison to traditional bus-wise calculation. Tests are performed on the IEEE 118 bus system, some different configurations of the Italian EHV system and a European equivalent network with up to about 700 buses using a 4-CPU CRAY Y-MP8/4S2 and a 4-CPU Alliant PX/80 computers.

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