Identifying multiple interacting bad data in power system state estimation

This paper presents an intelligent search strategy for the conforming bad data errors identification in the generalized power system state estimation, by using the tabu search meta heuristic. The main objective is to detect critical errors involving both analog and topology errors. These errors are represented by conforming errors, whose nature affects measurements that actually do not present bad data and also the conventional bad data identification strategies based on the normalized residual methods. ©2005 IEEE.

Transmission network expansion planning in full open market considering security constraints

This paper presents a mathematical model and a methodology to solve the transmission network expansion planning problem with security constraints in full competitive market, assuming that all generation programming plans present in the system operation are known. The methodology let us find an optimal transmission network expansion plan that allows the power system to operate adequately in each one of the generation programming plans specified in the full competitive market case, including a single contingency situation with generation rescheduling using the security (n-1) criterion. In this context, the centralized expansion planning with security constraints and the expansion planning in full competitive market are subsets of the proposal presented in this paper. The model provides a solution using a genetic algorithm designed to efficiently solve the reliable expansion planning in full competitive market. The results obtained for several known systems from the literature show the excellent performance of the proposed methodology.

Allocation of the cost of transmission losses in a multimarket framework

The paper addresses the issue of apportioning of the cost of transmission losses to generators and demands in a multimarket framework. Line flows are unbundled using equivalent bilateral exchanges on a DC-network model and allocated to generators and demands. Losses are then calculated based on unbundled flows and straightforwardly apportioned to generators and demands. The proposed technique is particularly useful in a multimarket framework, where all markets have a common grid operator with complete knowledge of all network data, as is the case of the Brazilian electric-energy system. The methodology proposed is illustrated using the IEEE Reliability Test System and compared numerically with an alternative technique. Appropriate conclusions are drawn. © The Institution of Engineering and Technology 2006.

Chronological Monte Carlo-based assessment of distribution system reliability

Regulatory authorities in many countries, in order to maintain an acceptable balance between appropriate customer service qualities and costs, are introducing a performance-based regulation. These regulations impose penalties, and in some cases rewards, which introduce a component of financial risk to an electric power utility due to the uncertainty associated with preserving a specific level of system reliability. In Brazil, for instance, one of the reliability indices receiving special attention by the utilities is the Maximum Continuous Interruption Duration per customer (MCID). This paper describes a chronological Monte Carlo simulation approach to evaluate probability distributions of reliability indices, including the MCID, and the corresponding penalties. In order to get the desired efficiency, modern computational techniques are used for modeling (UML -Unified Modeling Language) as well as for programming (Object- Oriented Programming). Case studies on a simple distribution network and on real Brazilian distribution systems are presented and discussed. © Copyright KTH 2006.

Neural network approach for surface roughness prediction in surface grinding

Several systems are currently tested in order to obtain a feasible and safe method for automation and control of grinding process. This work aims to predict the surface roughness of the parts of SAE 1020 steel ground in a surface grinding machine. Acoustic emission and electrical power signals were acquired by a commercial data acquisition system. The former from a fixed sensor placed near the workpiece and the latter from the electric induction motor that drives the grinding wheel. Both signals were digitally processed through known statistics, which with the depth of cut composed three data sets implemented to the artificial neural networks. The neural network through its mathematical logical system interpreted the signals and successful predicted the workpiece roughness. The results from the neural networks were compared to the roughness values taken from the worpieces, showing high efficiency and applicability on monitoring and controlling the grinding process. Also, a comparison among the three data sets was carried out.

A neural approach to evaluate the effect of lightning in power transformers

This paper proposes the application of computational intelligence techniques to assist complex problems concerning lightning in transformers. In order to estimate the currents related to lightning in a transformer, a neural tool is presented. ATP has generated the training vectors. The input variables used in Artificial Neural Networks (ANN) were the wave front time, the wave tail time, the voltage variation rate and the output variable is the maximum current in the secondary of the transformer. These parameters can define the behavior and severity of lightning. Based on these concepts and from the results obtained, it can be verified that the overvoltages at the secondary of transformer are also affected by the discharge waveform in a similar way to the primary side. By using the tool developed, the high voltage process in the distribution transformers can be mapped and estimated with more precision aiding the transformer project process, minimizing empirics and evaluation errors, and contributing to minimize the failure rate of transformers. © 2009 The Berkeley Electronic Press. All rights reserved.

Avaliação das principais t́cnicas para obtenção de MPPT de paińis fotovoltaicos

Electrical energy from photovoltaic panels (PV) has became an increasing viable alternative because of the great concern for environmental preservation and the possibility of the reduction of the conventional fuels, and this natural energy source is free, abundant and clean. In addition, Brazil is a privileged country because of the high levels of irradiation throughout its territory all over the year. Thus the exploitation of the energy from PV is one of the best alternatives to overcome the supply electrical energy issues. However, nowadays the energy conversion efficiency is low and the initial costs are high for these energy systems. Therefore, in order to increase the efficiency of these systems the extraction of the maximum power point (MPP) from PV is extremely necessary, and it is done using the maximum power point tracking (MPPT) techniques. The MPP of the PV varies non linearly with the environmental conditions and several MPPT techniques are available in literature, and this paper presents a careful comparison among the most usual techniques, doing meaningful comparisons with respect to the amount of energy extracted, PV voltage ripple, dynamic response and use of sensors, considering that the models are implemented via MatLab/Simulink®. © 2010 IEEE.

Multi-area decentralized optimal VAr planning using the Dantzig-Wolfe decomposition principle

This paper presents a new methodology for solving the optimal VAr planning problem in multi-area electric power systems, using the Dantzig-Wolfe decomposition. The original multi-area problem is decomposed into subproblems (one for each area) and a master problem (coordinator). The solution of the VAr planning problem in each area is based on the application of successive linear programming, and the coordination scheme is based on the reactive power marginal costs in the border bus. The aim of the model is to provide coordinated mechanisms to carry out the VAr planning studies maximizing autonomy and confidentiality for each area, assuring global economy to the whole system. Using the mathematical model and computational implementation of the proposed methodology, numerical results are presented for two interconnected systems, each of them composed of three equal subsystems formed by IEEE30 and IEEE118 test systems. © 2011 IEEE.

Parameterization technique for the continuation power flow using the trivial and tangent predictor

This paper presents efficient geometric parameterization techniques using the tangent and the trivial predictors for the continuation power flow, developed from observation of the trajectories of the load flow solution. The parameterization technique eliminates the Jacobian matrix singularity of load flow, and therefore all the consequent problems of ill-conditioning, by the addition of the line equations which pass through the points in the plane determined by the variables loading factor and the real power generated by the slack bus, two parameters with clear physical meaning. This paper also provides an automatic step size control around the maximum loading point. Thus, the resulting method enables not only the calculation of the maximum loading point, but also the complete tracing of P-V curves of electric power systems. The technique combines robustness with ease of understanding. The results to the IEEE 300-bus system and of large real systems show the effectiveness of the proposed method. © 2012 IEEE.

Artificial neural network model of discharge lamps in the power quality context

This paper presents a methodology for modeling high intensity discharge lamps based on artificial neural networks. The methodology provides a model which is able to represent the device operating in the frequency of distribution systems, facing events related to power quality. With the aid of a data acquisition system to monitor the laboratory experiment, and using $$\text{ MATLAB }^{\textregistered }$$ software, data was obtained for the training of two neural networks. These neural networks, working together, were able to represent with high fidelity the behavior of a discharge lamp. The excellent performance obtained by these models allowed the simulation of a group of lamps in a distribution system with shorter simulation time when compared to mathematical models. This fact justified the application of this family of loads in electric power systems. The representation of the device facing power quality disturbances also proved to be a useful tool for more complex studies in distribution systems. © 2013 Brazilian Society for Automatics - SBA.

Uma nova metodologia de projeto e controle para o inversor Boost (CSI) monofásico, para o aproveitamento de fontes alternativas e renováveis de energia elétrica

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Inversor Buck-Boost integrado para aplicações com micro-geradores eólicos

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Metodologias de medição de energia elétrica reativa (varh) e erros de medição em equipamentos eletrônicos de (varh)

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Comparação entre modelos do dispositivo FACTS STATCOM para o estudo da estabilidade a pequenas perturbações

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Decomposição de Dantzig-Wolfe aplicada ao problema de planejamento de reativos em sistemas de potência multi-áreas

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)