Power systems outage possibilities analysis by Paraconsistent Logic

Abstract

ϑ Abstract--Para-consistent Logic is a non-classic logic whose foundations allow for the contradictions treatment without invalidating the conclusions. Several researches have been demonstrating that through algorithms named PANs -Paraconsistent Analysis Nodes is possible to establish equations determined through signals representing restrictions, risks and configurations of electric power systems networks. In this paper we presented a model with this logic where each electric power grid component is modeled to create a Net of Analysis capable to offer a risk degree for analysis and diagnosis. In that way the model offers a diagnosis and the optimal restorative strategy proposition to the electrical power after an outage. The Paraconsistent logic type used for the models is the Annotated Paraconsistent Logic (APL). The APL utilization brings certain advantages in comparison with the classic logic because allow to manipulate contradictory signals, and like this presenting a faster and reliable action to decision in some situations where the information can be vague, ambiguous or inconsistent. This method is being validated through off-line tests applied at the Eletropaulo-Eletricidade de São Paulo, subtransmission power system. Eletropaulo is an electric power distribution utility of São Paulo state, in Brazil.

Survey on knowledge based methods to assist fault restoration in power distribution networks

“…Moreover, fuzzy and paraconsistent logic, Petri nets and multi-agent systems have been used to support these techniques. Fuzzy logic has been used to express the approximate expression of the heuristics rules or goals while paraconsistent logic allows to handle contradictions with a new logic [15], [16], [17]. Petri nets help represent the problem [7].…”

Treatment of Uncertainties with Algorithms of the Paraconsistent Annotated Logic

“…For this, the signals which will represent the evidence in relation to the proposition in analyses must be normalized and all the processing will be done in real closed interval between 0 and 1 [9]. This process for modelling the evidence degrees with linear variation can be made in its simpler form with the algorithm that will be described in the next section [13][14][15].…”

“…The real applications of PANnet in Analysis Systems of Artificial Intelligence are described in [13][14][15][16] and an example of this application is shown to follow: Now we show an example of application of the paraconsistent analyses to evaluate risks of overloads in an electric circuit [14,15].…”

“…From measured values; Temperature T, Electric Tension V and Electric Current A are obtained the normalized values in the form of Evidence Degrees according to the linear variation in specified universe discourse. After the Paraconsistent Analysis between Temperature, Tension Electric and Current Electric the Resulting Evidence Degree (μ ER ) considered as the Overload Risk Degree of the measured point.This method of treatment of Uncertainties is being used in Electric Power System of the AES-Eletropaulo Company and EDP Bandeirantes in Brazil[13][14][15].…”

Artificial Intelligence and Machine Learning in Energy Conversion and Management

“…Integrated Renewable Energy System Management (#2). This cluster combines the publications related to the management of integrated renewable energy systems [58], power generation using the organic Rankine cycle [59] and power systems outage outages [60], biomass energy conversion [61,62], and hybrid systems [63] using knowledge-based design tools, mixed-integer linear programming, genetic algorithm, decision support systems, and ANNs. These topics were abundant in the late 1990s and 2005-2015.…”