Analysis and assessment software for multi-user collaborative cognitive radio networks

Computer simulations are without a doubt a useful methodology that allows to explore research queries and develop prototypes at lower costs and timeframes than those required in hardware processes. The simulation tools used in cognitive radio networks (CRN) are undergoing an active process. Currently, there is no stable simulator that enables to characterize every element of the cognitive cycle and the available tools are a framework for discrete-event software. This work presents the spectral mobility simulator in CRN called “App MultiColl-DCRN”, developed with MATLAB’s app designer. In contrast with other frameworks, the simulator uses real spectral occupancy data and simultaneously analyzes features regarding spectral mobility, decision-making, multi-user access, collaborative scenarios and decentralized architectures. Performance metrics include bandwidth, throughput level, number of failed handoffs, number of total handoffs, number of handoffs with interference, number of anticipated handoffs and number of perfect handoffs. The assessment of the simulator involves three scenarios: the first and second scenarios present a collaborative structure using the multi-criteria optimization and compromise solution (VIKOR) decision-making model and the naïve Bayes prediction technique respectively. The third scenario presents a multi-user structure and uses simple additive weighting (SAW) as a decision-making technique. The present development represents a contribution in the cognitive radio network field since there is currently no software with the same features

Spectral opportunity selection based on the hybrid algorithm AHP-ELECTRE

Due to an ever-growing demand for spectrum and the fast-paced developmentof wireless applications, technologies such as cognitive radio enablethe efficient use of the spectrum. The objective of the present article is todesign an algorithm capable of choosing the best channel for data transmission.It uses quantitative methods that can modify behavior by changing qualityparameters in the channel. To achieve this task, a hybrid decision-makingalgorithm is designed that combinesanalytical hierarchy process(AHP)algorithms and adjusts the weights of each channel parameter, using a prioritytable. TheElimination Et Choix Tranduisant La Realité(ELECTRE)algorithm processes the information from each channel through a weightmatrix and then delivers the most favorable result for the transmitted data. Theresults reveal that the hybrid AHP-ELECTRE algorithm has a suitableperformance, which improves the throughput rate by 14% compared to similaralternatives

Failed handoffs in collaborative Wi-Fi networks

Cognitive radio networks enable a more efficient use of the radioelectric spectrum through dynamic access. Decentralized cognitive radio networks have gained popularity due to their advantages over centralized networks. The purpose of this article is to propose the collaboration between secondary users for cognitive Wi-Fi networks, in the form of two multi-criteria decision-making algorithms known as TOPSIS and VIKOR and assess their performance in terms of the number of failed handoffs. The comparative analysis is established under four different scenarios, according to the service class and the traffic level, within the Wi-Fi frequency band. The results show the performance evaluation obtained through simulations and experimental measurements, where the VIKOR algorithm has a better performance in terms of failed handoffs under different scenarios and collaboration levels

Herramienta de simulación para el análisis de flujo óptimo clásico utilizando multiplicadores de Lagrange

El análisis del flujo óptimo es un problema complejo y desafiante por sus características no lineales. La inclusión de restricciones de potencia y los modelos de las líneas de transmisión hacen complejo determinar el respectivo despacho. Los multiplicadores de Lagrange son un método de optimización clásico que permite solucionar problemas de despacho económico de múltiples variables sujetas con diversas restricciones. Este articulo presenta el desarrollo de una herramienta de simulación denominada SOPF (Software Optimal Power Flow), desarrollada en Guide-Matlab y que permite analizar el problema de flujo óptimo clásico de un sistema de potencia con pérdidas y con restricciones de potencia activa, el simulador desarrollado es un herramienta académica de apoyo para los estudiantes, profesores y personas interesadas en la aplicación de algoritmos de optimización para la operación económica de sistemas eléctricos de potencia. Como métricas, el simulador determina el despacho de la potencia activa de cada generador, los costos de generación de la potencia despachada, el aporte de cada máquina, los costos incrementales y las pérdidas de acuerdo al balance de potencia. Finalmente, los resultados se presentan a través de dos casos de estudio: flujo óptimo clásico con pérdidas y sin restricciones de potencia activa y flujo óptimo clásico con pérdidas y con restricciones de potencia activa. Para ambos casos, se obtienen errores inferiores al 1 %.The optimal flow analysis is a complex and challenging problem because of its non-linear characteristics. It is difficult to determine the respective flow of active power due to the inclusion of power restrictions and models of the transmission lines. Lagrange multipliers are a classical optimization method that allows solving the economic flow of multiple variables subject to various limits. This article presents a simulation tool called SOPF (Software Optimal Power Flow) developed in Guide-Matlab. This tool analyzes the classical optimal flow problem of a power system with leaks and energetic power limitations. This simulator is an academic support tool for students, professors, and people interested in applying optimization algorithms for economic electrical power systems. The software not only determines the flow of the power of each generator, the costs of the generated flow power, the contribution of each machine, the incremental costs, and the leaks according to the power balance. Finally, the results are presented through two case studies: classic optimal flow with losses and without active power restrictions and classical optimal flow with leaks and brisk power restrictions. For both cases, errors of less than 1 % are obtained.Universidad Tecnológica de Bolíva

On the Matricial Formulation of Iterative Sweep Power Flow for Radial and Meshed Distribution Networks with Guarantee of Convergence

This paper presents a general formulation of the classical iterative-sweep power flow, which is widely known as the backward–forward method. This formulation is performed by a branch-to-node incidence matrix with the main advantage that this approach can be used with radial and meshed configurations. The convergence test is performed using the Banach fixed-point theorem while considering the dominant diagonal structure of the demand-to-demand admittance matrix. A numerical example is presented in tutorial form using the MATLAB interface, which aids beginners in understanding the basic concepts of power-flow programming in distribution system analysis. Two classical test feeders comprising 33 and 69 nodes are used to validate the proposed formulation in comparison with conventional methods such as the Gauss–Seidel and Newton–Raphson power-flow formulations

Black hole optimizer for the optimal power injection in distribution networks using DG

The optimal sizing of Distributed Generators (DG) in electric power distribution networks is carried out through a metaheuristic optimization strategy. To size DG it is proposed an optimal power flow model is formulated by considering that the location of these sources has been previously defined by the distribution company. The solution of the optimal power flow is reached with the Black Hole Optimizer (BHO). A methodology is used master-slave optimization methodology, where the BHO (i.e., master stage) defines the sizes of the DG and the slave stage evaluates the objective function with a load flow algorithm, this work using the triangular-based power flow method. Numerical results in the 33-node and the 69-node test system demonstrates the effectiveness and robustness of the proposed approach when compared with literature results. © 2021 Institute of Physics Publishing. All rights reserved

Optimal economic-environmental dispatch in MT-HVDC systems via sine-cosine algorithm

This paper addresses the problem of optimal economic-environmental dispatch in Multi-Terminal High-Voltage Direct Current (MT-HVDC) networks using the Sine-Cosine Algorithm (SCA). This optimization methodology allows working with nonlinear non-convex large-scale optimization problems via sequential programming. The SCA works with an initial population and rules of advance based on the best current solution and sine and cosine functions that define the direction of the next solution. Three variants of the SCA are evaluated in a standard six-node MT-HVDC system considering a linear combination of the objective functions (i.e., greenhouse emissions and energy production costs). The main advantage of the proposed evolutionary approach lies in its pure algorithmic structure. Thus, it can be easily adapted to any continuous optimization problem. All numerical calculations are performed using MATLAB software. © 202

Electric and magnetic field calculation software in transmission lines

There is an interest in the biological effects of exposure to low-frequency electromagnetic fields issued by transmission lines on animals and humans. The fields generated by the lines are relevant for the design and operation of power systems. The study of the electric and magnetic fields in the transmission networks implemented commercial simulators bases on the finite element method. These commercial simulators are characterized by accuracy and high hardware and software requirements. This work presents CEM-LT, a tool that accurately precisely the electric and magnetic field in the transmission lines, with simple and intuitive handling and low processing times, making it ideal for being implemented together with optimization methods. The electric and magnetic field in the servant area for two case studies is analyzed to evaluate the accuracy and processing times. The level of accuracy is characterized by comparing the results with COMSOL obtaining errors of less than 2.4%. The case study with the highest computational requirement achieved a processing time of 3,027 seconds

Optimized Two-Level Control of Islanded Microgrids to Reduce Fluctuations

The main problem in the operation of micro-grids is controlling the voltage and frequency. The inertia of the whole grid is low, so the operation of the system is interrupted by sudden changes in load or incidence in the absence of a proper control system. In order to solve this issue, various control structures have been proposed. In this paper, an optimal distributed control strategy for coordinating multiple distributed generation instances is presented in an islanded microgrid. A secondary frequency control method is implemented in order to eliminate voltage deviation and reduce the small signal error. In this layer, an optimized PID controller is used. PID controller optimization is carried out via the Honey Badger Algorithm, and results are obtained using the MATLAB software. According to the results, inadequate adjustment of a secondary loop leads to poor and unacceptable outcomes, and the necessary power quality is not achieved. However, by using the proposed method, a proper performance of the microgrid in the face of disturbances is achieved

Using an intelligent method for microgrid generation and operation planning while considering load uncertainty

The integration of distributed generation (DG), energy storage systems (ESS), and controllable loads near the place of consumption has led to the creation of microgrids. However, the uncertain nature of renewable energy sources (wind and photovoltaic), market prices, and loads have caused issues with guaranteeing power quality and balancing generation and consumption. To solve these issues, microgrids should be managed with an energy management system (EMS), which facilitates the minimization of operating (performance) costs, the emission of pollutants, and peak loads while meeting technical constraints. To this effect, this research attempts to adjust parameters by defining indicators related to the best possible conditions of the microgrid. Generation planning, the storage of generated power, and exchange with the main grid are carried out by defining a dual-purpose objective function, which includes reducing the operating cost of power generation, as well as the pollution caused by it in the microgrid, by means of the SALP optimization algorithm. Moreover, in order to make the process more realistic and practical for microgrid planning, some parameters are considered as indefinite values, as they do not have exact values in their natural state. The results show the effect of using the introduced intelligent optimization method on reducing the objective function value (cost and pollution)