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An optimized multigrid method (NSFLEX‐MG) for the NSFLEX‐code (Navier‐Stokes solver using characteristic flux extrapolation) of MBB (Messerschmitt Bolkow Blohm GmbH) is described. The method is based on a correction scheme and implicit relaxation procedures and is applied to two‐dimensional test cases. The principal feature of the flow solver is a Godunov‐type averaging procedure based on the eigenvalues analysis of the Euler equations by means of which the inviscid fluxes are evaluated at the finite volume faces. Viscous fluxes are centrally differenced at each cell face. The performance of NSFLEX‐MG is demonstrated for a large range of Mach numbers for compressible inviscid and viscous (laminar and turbulent) flows over a RAE‐2822 airfoil and over a NACA‐0012 airfoil.

The purpose of this paper is to present an improved approach to reactive power planning in electric power systems (EPS). It is based on minimization of a transmission network's active power losses. Several operating conditions have to be fulfilled to ensure stable operation of an EPS with minimal power losses. Some new limitations such as voltage instability detection and generator capability curve limit have been added to the existing method in order to improve the reliability of reactive power planning. The proposed method was tested on a model of the Slovenian power system. The results show the achievement of significant reduction in active power losses, while maintaining adequate EPS security.

Optimal voltage profile has to be found in order to determine minimal possible active power losses of EPS. The objective function, used to find the optimal voltage profile, has integer and floating point variables and is non‐differentiable with several local minima. Additionally, to ensure secure operation of EPS, several equality and inequality boundaries and limitations have to be applied. Differential evolution (DE) was used to solve the optimization problem.

Corresponding reactive power planning can significantly reduce active power losses in EPS. However, such planning can affect the security of EPS, therefore, several additional constrains have to be considered. The presented constrains considerably improve the operational security of EPS.

DE was used to solve the minimization problem. Although this method has proven to be fast and reliable, it is theoretically possible that the obtained solution is not global minimum.

Novel approach to voltage security constrained reactive power planning with additional nonlinear constrains, such as generator capability curves and voltage instability detection.

This paper presents method to discriminate between transient voltage stability and voltage sag. The discrete wavelet transform (WT) is a powerful tool in the analysis of the transient phenomena in power systems because of its ability to extract information in both the time and frequency domain. This paper introduces a technique for accurate discrimination by combining WTs with neural networks (NNs). The WT is first applied to decompose the signals into a series of detailed wavelet components. The wavelet components are calculated and then employed to train a NN. The simulated results presented clearly show that the proposed technique can accurately discriminate between transient voltage stability and voltage sag in power system protection.

The purpose of this paper is to work on an analytical approach to test sensitivity of a maintenance‐scheduling model. Any model without sensitivity analysis is a “paper work” without advancing for wider applications. Thus, the simulation of simultaneous scheduling of maintenance and operation in a resource‐constrained environment is very important in quality problem and especially in maintenance.

The paper uses an existing model and presents a sensitivity analysis by utilising an optimal initial starting transportation tableau. This is used as input into the Gantt charting model employed in the traditional production scheduling system. The degree of responsiveness of the model parameters is tested.

The paper concludes that some of these parameters and variables are sensitive to changes in values while others are not.

The maintenance engineering community is exposed to various optimal models in the resource‐constraint‐based operational and maintenance arena. However, the models do lack the sensitivity analysis where the present authors have worked. The work seems significant since the parameters have the boundary values so the user knows where he can apply the model after considering the constraints therein.

The underlying quest for testing the sensitivity of the model parameters of a maintenance scheduling model in a multi‐variable operation and maintenance environment with resource constraints is a novel approach. An optimal solution has to be tested for robustness, considering the complexity of the variables and criteria. The objective to test the model parameters is a rather new approach in maintenance engineering discipline. The work hopefully opens a wide gate of research opportunity for members of the maintenance scheduling community.

With the continuous development of the wind power industry, wind power plant (WPP) has become the focus of resource development within the industry. Site selection, as the initial stage of WPP development, is directly related to the feasibility of construction and the future revenue of WPP. Therefore, the purpose of this paper is to study the siting of WPP and establish a framework for siting decision-making.

Firstly, a site selection evaluation index system is constructed from four aspects of economy, geography, environment and society using the literature review method and the Delphi method, and the weights of each index are comprehensively determined by combining the Decision-making Trial and Evaluation Laboratory (DEMATEL) and the entropy weight method (EW). Then, prospect theory and the multi-criteria compromise solution ranking method (VIKOR) are introduced to rank the potential options and determine the best site.

China is used as a case study, and the robustness and reliability of the methodology are demonstrated through sensitivity analysis, comparative analysis and ablation experiment analysis. This paper aims to provide a useful reference for WPP siting research.

In this paper, DEMATEL and EW are used to determine the weights of indicators, which overcome the disadvantage of single assignment. Prospect theory and VIKOR are combined to construct a decision model, which also considers the attitude of the decision-maker and the compromise solution of the decision result. For the first time, this framework is applied to WPP siting research.

Generation scheduling (GS) is the most prominent and hard-hitting problem in the electrical power industry especially in an integrated power system. Countless techniques have been used so far to solve this GS problem for proper functioning of the units in the power system to dispatch the load economically to consumers at once. Therefore, this work aims to study for the best possible function of integrated power plants to obtain the most favourable solution to the GS problem.

An appropriate method works in a proper way and assures to give the best solution to the GS problem. The finest function of incorporated power plants should be mathematically devised as a problem and via that the aim of the GS problem to minimize the total fuel cost subject to different constraints will be achieved. In this research work, the latest meta-heuristic and swarm intelligence-based technique called grey wolf optimization (GWO) technique is used as an optimization tool that will work along with the formulated problem for correct scheduling of generating units and thus achieve the objective function.

The recommended GWO technique provides the best feasible solution which is optimal in its performance for different test cases in the GS problem of integrated power plant. It is further found that the obtained solutions using GWO method are better than the former reports of other traditional methods in terms of solution excellence. The GWO method is found to be unique in its performance and having superior computational efficiency.

Decision making is significant for effective operation of integrated power plants in an electrical power system. The recommended tactic implements a modern meta-heuristic procedure that is applied to diverse test systems. The method that is proposed is efficient in providing the best solutions of solving GS problems. The suggested method surpasses the early techniques by offering the most excellent feasible solutions. Thus, it is obvious that the proposed method may be the appropriate substitute to attain the optimal operation of GS problem.

Renewable energy sources are discontinuous and infrequent in nature, and it is tough to predict them in general. Further, integrating renewable energy source-based plants with the conventional plant is extremely difficult to operate and maintain. Operation of integrated power system is full of challenges and complications. To handle those complications and challenges, the GWO algorithm is suggested for solving the GS problem and thus obtain the optimal solution in integrated power systems by considering the reserve requirement, load balance, equality and inequality constraints.

The proposed system should be further tested on diverse test systems to evaluate its performance in solving a GS problem and the results should be compared. Computation results reveal that the proposed GWO method is efficient in attaining best solution in GS problem. Further, its performance is effectively established by comparing the result obtained by GWO with other traditional methods.

The purpose of this paper is to examine the feasibility of an ideal power network that combines many different renewable energy technologies such as wind power, concentrated solar power (CSP) and hydroelectric power. This paper emphasizes in finding the benefits arising from hydrothermal coordination compared to the non-regulated integration of the hydroelectric units, as well as the benefits from the integration of wind power and CSP.

Artificial Neural Networks were used to estimate wind power output. As for the CSP system, a three-tier architecture which includes a solar field, a transmission-storage system and a production unit was used. Each one of those separate sections is analyzed and the process is modeled. As for the hydroelectric plant, the knowledge of the water’s flow rated has helped estimating the power output, taking into account the technical restrictions and losses during transmission. Also, the economic dispatch problem was solved by using artificial intelligence methods.

Hydrothermal coordination leads to greater thermal participation reduction and cost reduction than a non-regulated integration of the hydrothermal unit. The latter is independent from the degree of integration of the other renewable sources (wind power, CSP).

Hydrothermal coordination in a power system which includes thermal units and CSP for cost and emissions reduction.

There has been extensive research on techniques to assign mechanics to machines in order to maximize their availability in corrective maintenance systems. However, the focus has been on achieving high machine availability, while disregarding the utilization of mechanics. The maintenance brigade system (MBS), a team‐based mechanic‐assignment technique based on the relay‐type, self‐balancing bucket brigade system created by Bartholdi and Eisenstein, was thus developed and tested. The MBS was compared to a traditional assignment technique via machine availability and mechanic utilization, each at different levels according to the industrial partner’s opinion of high and low, with medium levels taken to be the status quo. Each system was modeled and, after a validation and verification process, simulated under the same number of machines and mechanics. Results and implications are discussed