•The methodology for compression of electrical power signals from waveform records uses GA and ANN.•The genetic algorithm is used to select and preserve the points that better characterize the ...waveform contours.•The ANN is used in the compression of the points not selected by GA and as well as on the signal reconstruction process.•The proposed methodology preserves a percentage of the original signal samples.
This paper proposes a methodology for compression of electrical power signals from waveform records in electric systems, using genetic algorithm (GA) and artificial neural network (ANN). The genetic algorithm is used to select and preserve the points that better characterize the waveform contours; and the artificial neural network is used in the compression of other points as well as on the signal reconstruction process. Thus, the data resulting from the proposed methodology are formed by a part of the original signal and by a compressed complementary part in the form of synaptic weights. The proposed methodology selects and preserves a percentage of the original signal samples, which are aspects not explored in the literature. The method was tested using field data obtained from an oscillographic recorder installed in a 230kV electrical power system. The results presented compression rates ranging from 8.59:1.00 to 24.16:1.00 for preservation rates ranging from 2.5% to 10%, respectively.
This paper proposes a new approach to fault diagnosis in electrical power systems, which presents an aspect little explored in the literature that is the protective device failure detection together ...with the fault section estimation, since the majority of the methodologies so far proposed to fault diagnosis are limited to the fault section estimation alone. The proposed methodology makes use of operation states of protective devices as well as information related to the protection philosophy. Initially, these data undergo a preprocessing step to convert the format of 0 and 1 to percentage values. The conversion to percentage values allows the use of artificial neural networks, whose numbers of inputs do not depend on the number of alarms of the protection philosophy, or the type of bus arrangement or the number of circuit breakers. This allows the same set of neural networks to be trained and applied in different power systems with different protection schemes and bus arrangements. The proposed system has five neural networks, each containing few neurons and requiring 30 μs to perform fault diagnosis. The proposed system was trained considering the IEEE 57-bus system, containing different selective protection schemes, and subsequently tested in the IEEE 14-bus, 30-bus, and 118-bus systems, and Eletronorte 230-kV real power system.
A novel method is used for projecting and implementing a current control loop as a means for regulating the power flow originated of a photovoltaic generator, based on three-phase nonautonomous ...static converter dc-ac functioning as an inverter. The maximum efficiency is the main issue presented in this work. A strategy for tracking the maximum power point (MMPT) for several solar insolation levels has been used in this paper. The control loop circuit, a three-phase nonautonomous static converter dc-ac, operating as inverter, is experimentally mounted to verify the proposed control strategy performance. Since the characteristic volt-ampere of a photovoltaic array depends on solar cell temperature, on sunshine incident, and on load, it is very difficult to achieve an optimum matching at all insolation levels and at all temperature variations. In this work the temperature effect will be neglected, since in our region (north of Brazil), the temperature remains approximately constant, around 30 degrees Celsius. In this regard, the temperature variations will be neglected.
