Compensation Admittance Load Flow: A Computational Tool for the Sustainability of the Electrical Grid

Compensation Admittance Load Flow (CALF) is a power flow analysis method that was developed to enhance the sustainability of the power grid. This method has been widely used in power system planning and operation, as it provides an accurate representation of the power system and its behavior under different operating conditions. By providing a more accurate representation of the power system, it can help identify potential problems and improve the overall performance of the grid. This paper proposes a new approach to the load flow (LF) problem by introducing a linear and iterative method of solving LF equations. The aim is to obtain fast results for calculating nodal voltages while maintaining high accuracy. The proposed CALF method is fast and accurate and is suitable for the iterative calculations required by large energy utilities to solve the problem of quantifying the maximum grid acceptance capacity of new energy from renewable sources and new loads, known as hosting capacity (HC) and load capacity (LC), respectively. Speed and accuracy are achieved through a properly designed linearization of the optimization problem, which introduces the concept of compensation admittance at the node. The proposed method was validated by comparing the results obtained with those coming from state-of-the-art methods

Accurate Design of Array Coils for Transcranial Magnetic Stimulation by means of Continuous FSO

The closed forms of the Continuous Flock-of-Starling Optimization (CFSO) are applied to the optimization of the array coils usually used for TranscranialMagnetic Stimulation. The CFSO is the continuous equivalent model of the FSO algorithm, and it is expressed in terms of a state space representation. The trajectories of the CFSO particles, which explore the space solutions of the optimization problem, are obtained by solving a differential equations system within suitable Time-Windows (TWs). Thanks to the representation in terms of differential equations, it is possible to drive the trajectory by passing from convergence to divergence or vice-versa, and then from exploration to exploitation. Moreover, it is possible to refine the solution by reducing the amplitude of the TWs, during the optimization procedure, enhancing the performance of numerical FSO algorithm. The use of closed forms makes the CFSO easy to be implemented and accurate in quality of solution. Validation results are presented and the performances of different optimal array coils configurations have been compared

Modern Techniques for the Optimal Power Flow Problem: State of the Art

Due to its significance in the operation of power systems, the optimal power flow (OPF) problem has attracted increasing interest with the introduction of smart grids. Optimal power flow developed as a crucial instrument for resource planning effectiveness as well as for enhancing the performance of electrical power networks. Transmission line losses, total generation costs, FACTS (flexible alternating current transmission system) costs, voltage deviations, total power transfer capability, voltage stability, emission of generation units, system security, etc., are just a few examples of objective functions related to the electric power system that can be optimized. Due to the nonlinear nature of optimal power flow problems, the classical approaches may become locked in local optimums, hence, metaheuristic optimization techniques are frequently used to solve these issues. The most recent optimization strategies used to solve optimal power flow problems are discussed in this paper as the state of the art (according to the authors, the most pertinent studies). The presented optimization techniques are grouped according to their sources of inspiration, including human-inspired algorithms (harmony search, teaching learning-based optimization, tabu search, etc.), evolutionary-inspired algorithms (differential evolution, genetic algorithms, etc.), and physics-inspired methods (particle swarm optimization, cuckoo search algorithm, firefly algorithm, ant colony optimization algorithm, etc.)

Fast and simple numerical computation of Maximum Power Point in PV systems

The increasing implementation of Photovoltaics sys-tem for energy production in small scale equipment makes feasible the possibility of develop smart module. One of the aspect really of interest is the accurate and fast computation of maximum power point from available data. Several mathematical approaches have been proposed in the last years and, in this works, a very simple robust and fast methodology is presented, based on the one-diode model. Few algebraic manipulations of one-diode equation allow to define a computational scheme which can be implemented in any kind of computing system (from workstation to small microcontroller). The proposed algorihm was test against data available in literature and implemented also in a low cost microcontroller, with computation time in this configuration of the order of few ms

Magnetic Hysteresis Simulation by Using a Deep Neural Network for Non-sinusoidal Excitations

Here we present an effective and performing hysteresis model, based on a deep neural network, with the ability to reproduce the evolution of the magnetization processes under arbitrary excitation waveforms. The proposed model consists of an autonomous multilayer feed-forward neural network, with input neurons reserved for the past values of both input (H) and output (M), aimed at reproducing the memorization mechanism typical of hysteretic systems. The training set was suitably prepared starting from a set of simulations, carried out using the Preisach hysteresis model. The optimized training procedure, based on multi-stage control of the model performance, will be extensively discussed. The comparative analysis between the neural network-based model, implemented at a low level of abstraction, and the Preisach model covers further hysteresis processes, different from those involved in the training, will be also presented

Automatic Aircraft Target Recognition by ISAR Image Processing based on Neural Classifier

"This work proposes a new automatic target classifier, . based on a combined neural networks’ system, by ISAR image . processing. The novelty introduced in our work is twofold. We . first present a novel automatic classification procedure, and then . we discuss an improved multimedia processing of ISAR images . for automatic object detection. The classifier, composed by a . combination of 20 feed-forward artificial neural networks, is used . to recognize aircraft targets extracted from ISAR images. A . multimedia processing by two recently introduced image . processing techniques is exploited to improve the shape and . features extraction process. Performance analysis is carried out . in comparison with conventional multimedia techniques and . standard detectors. Numerical results obtained from wide . simulation trials evidence the efficiency of the proposed method . for the application to automatic aircraft target recognition.

Neural Fem approach for the analysis of hysteretic materials in unbounded domain

"Purpose – This paper aims the application of a novel synergy between a neural network (NN) and the finite element method (FEM) in the solution of electromagnetic problem involving hysteretic material in unbounded domain.. . Design\/methodology\/approach – The hysteretic nature of the material is taken into account by an original NN able to perform the modelling of any kind of quasi-static loop (saturated and non-saturated, symmetric or asymmetric). An appositely developed iterative FEM procedure is presented for the solution of this kind of problems in unbounded domains.. . Findings – By starting from a small set of measured loops, the NN manages the values of the magnetic field, H, and the flux density, B, as inputs while the differential permeability is the output. In particular, the proposed NN is capable to perform the modelling of saturated and non-saturated, symmetric or asymmetric hysteresis loops.. . Practical implications – The development of an efficient method for the solution of a complicated electromagnetic problem in unbounded domain by using an iterative approach and NNs, which can be implemented also in existing FEM code.. . Originality\/value – The paper shows that the combination of FEM, iterative procedure and NNs allows us to produce effective solutions of electromagnetic problems in unbounded domains involving also nonlinear hysteretic magnetic materials with an acceptable computational cost.