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In this paper we propose a new distribution of Gaussian points to compute the weak form integrals of the Generalized Finite Element Method (GFEM). The relevance of this new distribution is the possibility of evaluating the integrals of oscillatory functions inside the reference triangle in an alternate way. A simple scheme of relocation of the quadrature points allows to improve the efficacy of the method. A wave propagation problem is solved with the proposed technique and its performance is compared to conventional and other existing proposals. In addition, we propose a new distribution of Gaussian points to the tetrahedral reference element

This paper presents an efficient procedure for optimizing the electric field at ground level of high surge impedance loading transmission lines. Such lines have the capacity to achieve higher power transmission rates than the conventional ones. The ellipsoidal method is applied in order to maximize the transmitted power and minimize the electric field at ground level through the variation of the conductor’s positions in the tower, given its physical and electrical constraints. By means of an efficient new approach to handle the conductors, the optimization gives birth to compact line designs combined with conventional bundles of conductors. Furthermore, it is shown that the proposed strategy can increase the transmitted power, reduce the electric field at ground level, and shorten the running time of the optimization algorithm.

The generation expansion planning (GEP) is a very relevant optimization problem in power systems research. Several factors must be considered in the management of an optimal planning that aims at expanding the generation, such as satisfying a growing demand, dealing with costs of investment, operation and maintenance, dealing with pollutant emission, among others. Considering these aspects, this problem tends to be very complex and meta-heuristics methods have been employed in order to provide satisfactory solutions. This paper aims at reviewing GEP considering five different formulations of the problem and how meta-heuristics approaches can be applied to it. For this purpose, two case studies are presented and, in order to provide a deep analysis, these methods are compared according to their performance, the solution provided by them, and also comparing results obtained among different formulations, which leads to a promising methodology and some interesting results provided.

The directional overcurrent relay coordination is often performed taking into account the scenario in which the system is expected to operate most of the time. However, such an approach frequently lead to solutions that are not robust enough to deal with perturbed conditions (e.g., topology changes and line losses), which can cause a cascade sequence of unnecessary load disconnections. A new approach to improve the robustness of directional overcurrent relay coordination is presented in this work. At first, relay settings are found to the expected conditions, using a matheuristic algorithm based on Differential Evolution, Linear Programming, and local search techniques. Afterward, the solution found by the algorithm is used as an input on a re-optimization framework, in which protection must remain coordinated and fast, but undesirable load shedding is also minimized in single line dropping scenarios (N-1 criterion). The proposed approach is applied to four test cases: a real meshed network, IEEE-30 bus, IEEE-118 bus, and IEEE-300 bus. These systems have up to 590 relays and 1,044 primary/backup relations.

A structured and robust methodology of modeling and simulation can be available through modular stochastic timed Petri nets, including experiments that allow designers to simulate the processes involved in ore production on well-founded estimates. This prerogative guides an experimental research based on real data from an Brazilian open pit mine operation. Three basic simple modules allows to achieve complex models for a real mine. The difference between simulated and measured average productivities are small when compared to an analytic model for the bottleneck and a model implemented in discrete event system language SIMAN, which also validates the simple truck dispatch rule proposed in this paper. As results of the experiment, we derived a valid simulation structure for the open-pit mining process using Petri nets. It was obtained a behavioral evaluation of the efficiency of the structure according to variations in the probability distribution function.

This chapter is devoted to optimization formulations of satellite coverage problems and their solution. They are specially useful in satellite broadcasting applications, where the information goes from one to many. Due to distance from target and power supply constraints, highly directive efficient reflector antennas are the best choice. There are many possible optimization formulations for this problem, depending fundamentally on specifications and degrees of freedom in the system. Some specific coverage problems considering broadband, radiation pattern constraints and interference are presented, where the degrees of freedom are purely geometric.

In this paper, a new methodology is applied for the optimization process of the geometry of the bundle of conductors of the overhead transmission lines. This methodology is based on the application of the Adjoint Method to calculate the sensitivity of the parameters related to the charge electric of the transmission lines. This information is used with a Gradient Method and a Gold Section algorithm for the minimize the electric field at ground level. The Surge Impedance Loading is calculated after the optimization process for check improvements in energy capacity transportation.

In this paper, the time domain Maxwell’s equations are solved using the discrete exterior calculus (DEC) formalism in the two-dimensional space. To truncate the computational domain, the complex frequency-shifted perfectly matched layer (CFS-PML) concept is applied to create a reflectionless artificial layer. The paper presents a new numerical procedure to easily implement the CFS-PML with curved inner boundary. In order to numerically realize the PML, in a simplicial mesh, this paper proposes to utilize the nearest neighbor algorithm to associate point sets to boundary points. The distance from points to the boundary curve defines the attenuation function inside the PML. The performance of the approach is assessed by measuring the reflection error for three numerical experiments.

Many optimization problems are complex enough that their solutions must be measured through simulation. It is also known that simulation requires a huge computational effort which impacts directly on the optimization solution. Accordingly, this paper presents a hybrid methodology faster than standard simulation tools to deal with stochastic systems subject to synchronization, delay, and decision phenomena. Such methodology aggregates Max-Plus Algebra with Markov Chain for modeling a load haulage cycle of an open-pit mine. The goal is computing the expected value for total iron production. To show that this new methodology can be applied to compute the mentioned measure, an experiment analysis was conducted to compare the results obtained. The test has shown evidence of equivalence between the results acquired by the hybrid methodology and by a standard simulation tool.

This paper presents a unit commitment algorithm that defines each unit discharge given the water head, the total plant downstream flow, the variable discharge upper limit, the unit efficiency curves, and the restricted operating zones in order to maximize power efficiency. This algorithm is part of the preprocessing phase that is intended to approximate a hydro-power production function that represents individualized unit decisions. A compact mixed-integer linear programming formulation, with fewer integer variables, based on an equivalent unit model and a piecewise linear generation function, is proposed. The unit commitment is integrated without increasing the model size and complexity due to the preprocessing phase. Moreover, the optimal aggregate decision is automatically converted to unit decisions by the proposed algorithm. The coordination with mid/long-term planning is performed by taking into account the power demand allocated to the hydro-power plants. Numerical tests on Brazilian hydro-power plants demonstrate that the proposed formulation has lower computational cost than unit individualized models considering a given accuracy level for the generation function approximation.

This paper proposes an analytic dispatch for tidal power plants that centralizes the discharge period in the tides extremes. Since the tide cycles are well predicted by a sinusoidal function, this method provides an accurate power generation as shown in the comparative tests. Furthermore, an optimal generation estimation method is derived considering that all stored water is discharged during the tides extremes, i.e., discharging at maximal water head. This method calculates a more realistic maximal power generation, which is especially simple in double-effect operation mode (power production in ebb and flood tides), since the reservoir geometry is not explicitly considered. The proposed methods are compared with a classical analytic solution and an optimal dispatch formulation.

Directional overcurrent relays are often used in the protection of electrical power transmission and distribution systems. These devices are installed along the network, and they must be coordinated to operate as fast as possible and in the adequate sequence. However, the coordination of relays is a complex problem, due to its discrete non-linear nature and its hard constraint structure. A matheuristic algorithm to provide coordination of directional overcurrent is proposed in this work. The algorithm combines a differential evolution strategy and linear programming formulations. In addition, two local search procedures are proposed to improve convergence and to handle continuous and discrete relay setups. Results for five instances show that the proposed method is able to obtain identical or better solutions than those already reported in the literature with considerably lower computational cost. Furthermore, the algorithm can directly handle robust coordination by considering possible changes in the systems conditions.

This paper introduces a cooperative parallel heuristic for the uncapacitated single allocation hub location problem. The proposed heuristic consists in combining multiple parallel ILS-RVND local search and Path-Relinking, cooperating through asynchronously exchaging solutions in a shared pool. This method as devised to tackle large scale instances up to 3038 nodes. Generally, papers in the literature solve problems up to 400 nodes. The combination of efficient methods of search allowed the proposed heuristic to explore efficiently the space of search, and outperformed four well known heuristics of the literature for the select instances.

This paper presents a computational tool intended to calculate and minimize the electric and magnetic fields at ground level of High Surge Impedance Loading Transmission Lines. This type of transmission line achieves higher power transmission rates by utilizing optimized configuration for the phase conductors. This method is advantageous over other actions taken by the utility company such as increasing the maximum operation temperature of the line, increasing the size of the conductors or the utilization of multiple conductor per phase. In the present work, a Multiobjective Non Dominated Sorting Evolutionary Method is applied to find a non conventional optimized configuration for the phase conductors with reduced electric and magnetic field profiles at ground level.

This study investigates the application of short-term load forecasting (STLF), which consists of estimating a future demand within a period of time up to one week, to thermal unit commitment problems (TUCP), providing schedule for power plant operations. Both problems have fundamental importance for power system operations and good results on STLF may also influence TUCP performance. The pattern similarity approach is chosen for STLF, which allows the use of regression algorithms based on machine learning applied to time series analysis and forecasting results are used as information for generators scheduling. This study proposes a framework containing these tasks with a deep review of them and provides some statistical information regarding the performance and validation of the framework.

Este trabalho apresenta a proposta de um modelo de otimizacao, que objetiva minimizar o volume de escoria do alto-forno na Aperam. Atualmente, utiliza-se um modelo matematico para calculo do balanco de massa, que nao realiza nenhum tipo de otimizacao nas variaveis. Ainda e necessario um ajuste manual nos parâmetros de entrada do modelo pela equipe operacional, seguindo os limites das variaveis, estabelecidos a priori pela equipe do controle de processo. Atraves da identificacao das funcoes nao lineares do modelo matematico, foi aplicada uma Regressao Linear para modelagem de um problema de Programacao Linear Inteira Mista. Esse modelo e resolvido utilizando uma tecnica similar a Programacao Linear Sequencial. Os resultados apresentados demonstram a eficiencia do modelo, que nao so eliminou a necessidade de ajuste manual, mas tambem apresentou menor volume de escoria e obteve uma reducao no consumo de fundentes.

This paper presents a computational tool intended to achieves optimal configurations of conductors with capacity of transmission of energy improved. This arrangement is obtained by calculate and minimize of electric fields at ground level of High Surge Impedance Loading transmission lines. This type of transmission line has higher power transmission rates by utilizing optimized configuration for the conductors. In the present work, an enhanced Deep Cut Ellipsoidal Method is applied to find a new optimized configuration for the phase conductors. The Surge Impedance Loading of the suggested configuration is analyzed. The new proposed configuration results in reduced profiles of electric fields and higher power transmission capacity.

A possible strategy for avoiding singular material parameters in a transformation-based invisibility cloak involves an out-of-plane stretching, calculated to compensate the in-plane singular transformation. In this paper, we used numerical simulations to analyze the relation among the out-of-plane transformation, the resulting material anisotropy, and the total scattering cross width. Moreover, we show how this information can be used to optimize a cloak with homogeneous anisotropic layers.

This paper proposes a new nonlinear model for cascaded hydro-power generation and preventive maintenance scheduling. The reservoir level and the maintenance scheduling are optimized, simultaneously, to minimize the thermal generation complement and to maximize the future water value. To achieve better accuracy, the productivity was modeled considering the head effect. Since the model considers individualized plants, the water flow balance must be taken into account. By means of a transformation, this constraint can be modeled only with the reservoir volume. Also, by means of a simple nonlinear transformation, the maintenance scheduling was modeled as continuous variable, instead of the usual binary models. The maintenance start date is a continuous variable, so, it allows the maintenance to begin within the time period. These simplifications altogether define a compact continuous nonconvex problem. The proposed model was successfully solved by a Differential Evolution (DE) algorithm in Brazilian cascades.

This paper proposes a revaluation of the Brazilian wind energy policy framework and the energy auction requirements. The proposed model deals with the four major issues associated with the wind policy framework that are: long-term wind speed sampling, wind speed forecasting reliability, energy commercialization, and the wind farm profitability. Brazilian wind policy, cross-checked against other countries policies, showed to be too restrictive and outdated. This paper proposes its renewal, through the adoption of international standards by Brazilian policy-makers, reducing the wind time sampling necessary to implement wind farms. To support such a policy change, a new wind forecasting method is designed. The method is based on fuzzy time series shaped with a statistical significance approach. It can be used to forecast wind behavior, by drawing the most-likely wind energy generation intervals given a confidence degree. The proposed method is useful to evaluate a wind farm profitability and design the biding strategy in auctions.

This work presents an analysis of the QuasiNewton methods applied to the parameter estimation of a two-layer soil model. The main goal is to represent apparent resistivity measurements due to Frank Wenner's method with an equivalent model of the soil for use in grounding simulations. The DFP and BFGS methods are compared with the Huang and Biggs variations taking into account the convergence, number of iterations and evaluations of the objective function for different measurements of soil resistivity. The simulated resistivity cases are obtained from the analytical formulation of the problem and, in a specific case, considers the presence of random noise in order to be statistically representative. The results are consistent. It is shown that the Huang variation with true golden ration tends to present better solutions in the absence of random noise. Furthermore, the Biggs variation, also with true golden ratio, is the best choice when the random noise is considered.

O planejamento de expansao da transmissao e um problema da area de sistemas eletricos de potencia bastante estudado que possui muitas abordagens. Neste trabalho, a abordagem multiobjetivo e estudada considerando perdas de rede e, para resolver o problema, tres metodos meta heuristicos sao empregados em dois estudos de casos recorrentes na literatura e tres cenarios de planejamento sao propostos, sendo um destes cenarios considerando incertezas de demanda. Os resultados obtidos sao promissores e apontam especificidades de cada metodo nos casos estudados.

This paper presents a prediction algorithm for features detection in Ground Penetrating Radar (GPR) based surveys. Based on signal processing and soft-computing techniques, the coupled use of principal-component analysis and neural networks enable a definition of an efficient method for analyzing GPR electromagnetic data. To guarantee a low error rate, a study of the algorithm main numerical parameters was performed by means of electromagnetic synthetic-data models. Results for detecting features of geological layers demonstrate not only the method predictions accuracy but also the simple interpretation of its output through scenarios reconstructed images.

This paper proposes using the Parallel Layer Perceptron (PLP) network, instead of the Single Layer Feedforward neural network (SLFN) in the Extreme Learning Machine (ELM) framework. Differently from the SLFNs which consider cascade layers, the PLP is designed to accomplish also parallel layers, being the SLFN its particular case. This paper explores a particular PLP configuration which considers a nonlinear layer in parallel with a linear layer. For n inputs and m nonlinear neurons, it provides ( n + 1 ) m linear parameters, while the SLFN would have only m linear parameters (one for each hidden neuron). Since the ELM is based on adjusting only the linear parameters using the least squares estimate (LSE), the PLP network provides more freedom for the proper adjustment. Results from 12 regression and 6 classification problems are presented considering the training and test errors, the linear vector norm and the system condition number. They point out that the PLP-ELM framework is more efficient than the SLFN-ELM approach.

In this paper, we solve the $ \mathcal {E}$ and $ \mathcal {B}$ mixed finite element time domain formulation using the alternating-direction implicit (ADI) scheme in time. To save computational cost, the algorithm is improved using a recursive sparsification procedure that approximates the inverse matrix needed to calculate the electric field. Although the method is unconditionally stable, we show that the numerical performance of the algorithm is strongly related to the time step. Numerical results demonstrated that for the same accuracy our approach is much faster than the ADI with Cholesky factorization.

This paper presents a computational tool intended to calculate and minimize the electric fields at ground level of high surge impedance loading transmission lines (TLs). This type of TL achieves higher power transmission rates utilizing optimized configuration for the phase conductors. This method is advantageous over other actions taken by the utility company, such as increasing the maximum operation temperature of the line, increasing the size of the conductors, or the utilization of multiple conductors per phase. In this paper, an enhanced deep-cut ellipsoidal method is applied to find a new optimized configuration for the phase conductors. The new proposed configuration results in reduced profiles of electric fields. Two different TLs are analyzed and optimized.

Reducing the scattering and enhancing the bandwidth (BW) are two desirable aspects in cloak applications. In this paper, we propose their optimization using a simplified objective function, which avoids a costly detailed frequency sweep to get the BW at each iteration. The multi-objective characteristic of our optimization permits us to obtain a set of possible solutions (the Pareto-Front), which represents different compromises between the two objectives. Two examples of dielectric cloaks are presented and discussed.

This paper analyzes the mathematical properties related to the hydro-power generation optimization problem. From the problem’s physical nature, it can be concluded that the hydro-power generation is a function of the plant productivity and the water discharge. Since the plant productivity must be an increasing function of the net height, it was shown, under mild conditions, that the resulting generation is a strongly increasing function. Given that, it was established some mathematical properties that guarantee a unique maximum inside the feasible set and, therefore, global optimality. Under this analysis it is possible to derive optimization problems with optimality guarantees. Finally, a problem concerning the minimization of the deficit between supply and demand during a time window for a single power plant is explored. A numerical example based on a real case is given. Other existing formulations could also take advantage of our main results to confirm global optimality or to prove multimodality. These results are also useful to define the best optimization strategies for a given optimization problem.

This paper presents a modified direct approach for the forward/backward sweep power flow method. Taking advantage of the special topological characteristics of the radial network, an algorithm with linear storage complexity is defined. These features are summarized in the incidence matrix, which becomes a lower triangular matrix after the vertex ordering. This new formulation allows to solve linear systems of equations instead of explicitly inverting matrices during the iterative process, leading to a lower computational burden. Therefore, the proposed method is time and memory-efficient. The results show that the proposed method improves the storage and time complexity without any loss of accuracy, making it a robust and efficient method.

This paper proposes the application of advanced fuzzy time series in order to provide short-term load forecasting, which consists of predicting future demands from up to a week. An accurate forecast can influence significantly the availability and reliability of electrical systems. In this work, we tested different fuzzy time series algorithms in order to provide hourly, daily and weekly forecast of the demand in the Polish Electric System. The presented methods have achieved some interesting results to the problem.

Lightning arresters are equipments with a fundamental role in the protection of surges in substations. Thermographic images have been used in large scale to prevent faults in these devices. This paper presents a new framework aimed to nondestructive inspection of surges and medium voltage equipments. It applies a fuzzy clustering algorithm to automatic identify fault patterns in thermographs of medium voltage equipments, minimizing the needs of qualified manpower. The technic can also be used in thermography drones in a way to acquire the images remotely and automatic processes the fault triggering the staff only when a fault is detected. Experimental results prove the effectiveness of the method identifying hot spots in the devices.

The coordination of electrical power system protection relays is a hard problem to solve, due to its discrete/nonlinear nature and its complex constraint structure. A multiobjective algorithm to coordinate directional overcurrent relays is proposed in this work. Two objective functions are considered: minimization of the sum of relay operation times, and; maximization of the minimum coordination time interval between relay pairs (primary and backup). The second objective is novel, and we prove to be very useful to improve the coordination robustness. The search for efficient solutions is performed by a matheuristic algorithm, which combines Non-dominated Sorting Genetic Algorithm II, Differential Evolution and Mathematical Programming formulations. Results for four case studies from the literature suggest that the proposed approach is able to generate solutions that are equivalent, or even better, than the ones obtained with mono-objective approaches. In addition, the proposed algorithm can find some solutions that are considerably more robust taking into account variations in system conditions.

Design of Hub-and-spoke networks is an extension of classical facility location problem and it is very important due to its applications in cargo, passenger and telecommunication systems. The problem consists in determining the number and location of the hubs, besides define the allocation of non-hub nodes to the installed hubs, aiming to minimize the total costs. This problem is known to be NP-hard and it has been tackled by heuristic based approaches. In this paper it is proposed an efficient multi-start heuristic composed by a simple construction phase, a perturbation mechanism and an adaptive local search. Computational experiments using standard benchmark problems shows that the proposed approach is competitive when compared with the best heuristics in the literature.