Summary In the last 3 years, the fractional conformable derivative and its properties have been introduced. Unlike other definitions, this new fractional derivative is based on the basic limit definition of the derivative and satisfies the same formulas of derivation, such as product and quotient of 2 functions and the chain rule. Using this new derivative, we obtain a new class of linear ordinary differential equations with noninteger power variable coefficients for the Resistance Capacitance (RC), Inductance Capacitance (LC), and Resistance, Inductance Capacitance (RLC) electric circuits. The numerical solutions are solved through the Matlab software. Solutions depend on time and on the fractional order parameter 0 < γ ≤ 1. The computing using this new derivative is much easier than using other definitions of fractional derivative. It has been shown that in the particular case γ = 1, these solutions become the ordinary ones. Also, a comparison has been made with the Caputo fractional derivative for the case of the RC circuit with constant source.

The objective of this study is to present a voltage compensator based on a matrix converter without energy storage, which can cope with common power quality problems presented in power distribution systems. The proposed scheme acquires from the grid the necessary energy during the disturbance, which eliminates the drawbacks imposed by the use of a dc-link and the need of energy storage components. A matrix converter study under unbalanced input voltage conditions is exposed, as well as a detailed explanation of the proposed modified direct space vector modulation (MDSVM). It has been verified that even when the supply voltage exhibits unbalanced conditions and harmonic distortion, the control strategy does not exhibit difficulties to synthesise the compensation voltages, provided the restrictions imposed by the formulation are fulfilled. Numerical simulations and experimental results from a laboratory scale prototype are presented to validate the performance of the compensator.

Summary In this paper, we study a supercapacitor model represented by an equivalent RC circuit considering five different types of derivatives: Caputo, Caputo‐Fabrizio, and Atangana‐Baleanu fractional derivatives and the conformable and integer‐order derivatives. A set of experimental data from six commercial supercapacitors are used to estimate the parameter values for each derivative model by applying interior point optimization. The results show that the most accurate approach is achieved with the conformable derivative followed by the Caputo fractional derivative.

This paper presents the development of a heuristic-based algorithm for a Home Electric Energy Management System (HEEMS). The novelty of the proposal resides in the fact that solutions of the Pareto front, minimizing both the energy consumption and cost, are obtained by a Genetic Algorithm (GA) considering the renewable energy availability as well as the user activity level (AL) inside the house. The extensive solutions search characteristic of the GAs is seized to avoid the calculation of the full set of Pareto front solutions, i.e., from a reduced set of non-dominated solutions in the Pareto sense, an optimal solution with the best fitness is obtained, reducing considerably the computational time. The HEEMS considers models of the air conditioner, clothes dryer, dishwasher, electric stove, pool pump, and washing machine. Models of the wind turbine and solar PV modules are also included. The wind turbine model is written in terms of the generated active power exclusively dependent on the incoming wind profiles. The solar PV modules model accounts for environmental factors such as ambient temperature changes and irradiance profiles. The effect of the energy storage unit on the energy consumption and costs is evaluated adapting a model of the device considering its charge and discharge ramp rates. The proposed algorithm is implemented in the Matlab® platform and its validation is performed by comparing its results to those obtained by a freeware tool developed for the energy management of smart residential loads. Also, the evaluation of the performance of the proposed HEEMS is carried out by comparing its results to those obtained when the multi-objective optimization problem is solved considering weights assigned to each objective function. Results showed that considerable savings are obtained at reduced computational times. Furthermore, with the calculation of only one solution, the end-user interaction is reduced making the HEEMS even more manageable than previously proposed approaches.

No abstract

SUMMARYInduction motors are key elements of every industrial process. A faulty motor produces interruptions on production lines, with consequences in cost, product quality, and safety. The relevance in induction motor monitoring is the ability to detect faults in incipient state. Many proposed methods consider direct connection of motors to the power supply; however, the common practice in industry is to connect them through variable speed drives (VSD), which introduce harmonics into the current supply signal that make the fault identification extremely difficult. This work proposes a statistical analysis through mean, variance, and information entropy computation, combined with sensorless rotating speed estimation for classifying different faults in induction motors using an artificial neural network. The proposed methodology examines the voltage and current signals provided by an industrial VSD that ensures a high certainty on identifying the treated faults at different rotational speed. A field programmable gate array-based implementation is developed to offer an online, system-on-chip solution for real-time condition monitoring.

No abstract

In this paper, an efficient implementation of the four-step current commutation technique for controlling bidirectional power switches in a Matrix Converter (MC) is proposed. This strategy is based on the enhanced pulse width modulation peripheral included in the C 2000 Delfino 32-bit microcontroller of Texas Instruments. By tuning the algorithmic parameters contained in this module, the four-step commutation process is carried out on the Microcontroller Unit (MCU) without overloading the full complex processor and avoiding the use of additional special hardware such as Field-Programmable Gate Arrays (FPGA) or Complex Programmable Logic Devices (CPLD) when controlling the MC. The algorithm is implemented on the TMS320F28379D MCU and operationally validated on an MC prototype, where the functionality of the proposal is demonstrated.