Power converters systems aimed at renewable energy applications have become a common option for sustainable electricity and distributed generation, since their performance has improved, and prices have steadily been reduced in the last years. However, there are still several drawbacks that hinder their widespread installation, such as the simultaneous minimization of cost and volume, efficiency maximization, size reduction, etc. Quite often, accomplishing these goals requires dealing with complicated optimization problems, which are difficult to solve by classical methods. Metaheuristic techniques provide a viable alternative to solve complex intricate optimization problems, such as those encountered in the development of power electronics converters. This paper presents a comprehensive coverage of metaheuristic methodologies applied in the area of power converters. The review includes a classification of the methodologies and main objective functions in each paper surveyed. An aim for this paper is to highlight the importance of the optimization tools, and the many benefits they provide to tackle the challenges encountered in the design, operation, and control of power converters.

Metaheuristic Optimization Methods Applied to Power Converters: A Review

In electrolyzers, Faraday’s efficiency is a relevant parameter to assess the amount of hydrogen generated according to the input energy and energy efficiency. Faraday’s efficiency expresses the faradaic losses due to the gas crossover current. The thickness of the membrane and operating conditions (i.e., temperature, gas pressure) may affect the Faraday’s efficiency. The developed models in the literature are mainly focused on alkaline electrolyzers and based on the current and temperature change. However, the modeling of the effect of gas pressure on Faraday’s efficiency remains a major concern. In proton exchange membrane (PEM) electrolyzers, the thickness of the used membranes is very thin, enabling decreasing ohmic losses and the membrane to operate at high pressure because of its high mechanical resistance. Nowadays, high-pressure hydrogen production is mandatory to make its storage easier and to avoid the use of an external compressor. However, when increasing the hydrogen pressure, the hydrogen crossover currents rise, particularly at low current densities. Therefore, faradaic losses due to the hydrogen crossover increase. In this article, experiments are performed on a commercial PEM electrolyzer to investigate Faraday’s efficiency based on the current and hydrogen pressure change. The obtained results have allowed modeling the effects of Faraday’s efficiency by a simple empirical model valid for the studied PEM electrolyzer stack. The comparison between the experiments and the model shows very good accuracy in replicating Faraday’s efficiency.

https://www.mdpi.com/1996-1073/13/18/4792/pdf

Faraday’s Efficiency Modeling of a Proton Exchange Membrane Electrolyzer Based on Experimental Data

This investigation applies fuzzy-set qualitative comparative analysis (fsQCA) and an artificial neural networks method (ANN) with the aim of addressing the determinants of votes regarding managerial proposals presented in corporate meetings. The data refer to companies in the United States banking industry and they cover the period from 2003 to 2013. The results show that the variables that contribute to explain the voting support have changed over time. Thus, during the 2003–2006 sub-period the number of funds voting appears as the most clearly outstanding variable. On the contrary, in the 2007–2009 sub-period there is a heterogeneous set of explanatory features that includes the total volume of assets, the leverage ratio and the return on assets ratio, among others, as the most remarkable factors. Finally, in the 2010–2013 sub-period, there are no specific features or combinations that contribute to voting support, indicating that the explanatory factors are yet to be consolidated after the financial downturn.

Selecting explanatory factors of voting decisions by means of fsQCA and ANN

The step response for hydraulic automatic gauge control (HAGC) determines the steel rolling speed and the steel sheet thickness in the process of rolling production. In this paper, the step response test process of HAGC was analysed, and a test approach was proposed for it. Based on that, the transfer function model of the step response test was established and simulated by using Matlab. In order to reduce the settling time and the overshoot, an adaptive proportional-integral-derivative (APID) link was presented in order to compensate for the input signal by using back propagation neural networks (BPNN). The experimental results show that the improved step response test model reaches the process requirements of HAGC, eliminates the jitter of the HAGC system at the start-up phase, and has better stability as well as faster response for steel sheet rolling.

/pdf/modelling-and-analysis-of-step-response-test-for-hydraulic-3j4rwk7p1i.pdf

Modelling and Analysis of Step Response Test for Hydraulic Automatic Gauge Control

Cascaded H-bridge Asymmetrical Seven-level Inverter Using THIPWM for High Power Induction Motor

A neural implementation of a harmonic elimination strategy for the control a uniform step asymmetrical 11-level inverter is proposed and described in this paper. A Multi-Layer Perceptrons (MLP) neural network is used to approximate the mapping between the modulation rate and the required switching angles. After learning, the neural network generates the appropriate switching angles for the inverter. This leads to a low-computational-cost neural controller which is therefore well suited for real-time applications. This neural approach is compared to the well-known sinusoidal Pulse-Width Modulation (PWM). Simulation results demonstrate the technical advantages of the neural implementation of the harmonic elimination strategy over the conventional method for the control of an uniform step asymmetrical 11-level inverter. The approach is used to supply an asynchronous machine and results show that the neural method ensures a highest quality torque by efficiently canceling the harmonics generated by the inverter.

Harmonic Elimination Control of an Inverter Based on an Artificial Neural Network Strategy

Harmonic Elimination Strategy (HES) has been a widely researched alternative to traditional PWM techniques. This paper presents the harmonic elimination strategy of a Uniform Step Asymmetrical Multilevel Inverter (USAMI) using Particle Swarm Optimization (PSO) which eliminates specified higher order harmonics while maintaining the required fundamental voltage. This method can be applied to USAMI with any number of levels. As an example, in this paper a seven-level USAMI is considered and the optimum switching angles are calculated to eliminate the fifth and seventh harmonics. The HES-PSO approach is compared to the well-known Sinusoidal Pulse-Width Modulation (SPWM) strategy. Simulation results demonstrate the better performances and technical advantages of the HES-PSO controller in feeding an asynchronous machine. Indeed, the harmonic distortions are efficiently cancelled providing thus an optimized control signal for the asynchronous machine. Moreover, the technique presented here substantially reduces the torque undulations. Streszczenie. W artykule zaprezentowano strategie eliminacji harmonicznych HES w przeksztaltniku wielopoziomowym asymetrycznym USAMI. Wykorzystano metode algorytmow rojowych i porownano te metode z klasyczną metoda PWM. (Sterowanie wielopoziomowym symetrycznym przeksztaltnikiem USAMI przy wykorzystaniu algorytmow rojowych)

https://automatika.korema.hr/index.php/automatika/article/view/117/313

Control of a Uniform Step Asymmetrical Multilevel Inverter using Particle Swarm Optimization

In this paper, a neural approach is applied to determine the switching angles for a uniform step asymmetrical multilevel inverter by eliminating specified higher-order harmonics while maintaining the required fundamental voltage. A Multi-Layer Perceptron (MLP) neural network is used to approximate the function between the modulation rate and the required switching angles. After learning, the artificial neural network is able to determine the appropriate switching angles for the inverter. This leads to a low-computational-cost neural controller which is therefore well suited for real-time applications. This technique can be applied to multilevel inverters with any number of levels. As an example, a nine-level inverter and an eleven-level inverter are considered and the optimum switching angles are calculated on-line. The neural approach is compared to the well-known sinusoidal Pulse-Width Modulation (PWM) strategy. Simulation results demonstrate the better performances and technical advantages of the neural controller in feeding an asynchronous machine. Indeed, the harmonic distortions are efficiently cancelled providing thus an optimized control signal for the asynchronous machine. Moreover, the technique presented here substantially reduces the torque undulations.

An Artificial Neural Network Approach for Solving the Harmonic Distortions Elimination in Multilevel Converters

Commande des onduleurs multiniveaux asymétriques pour l'entraînement des machines électriques

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Papers

Harmonic Elimination Control of an Inverter Based on an Artificial Neural Network Strategy

Control of a Uniform Step Asymmetrical Multilevel Inverter using Particle Swarm Optimization

An Artificial Neural Network Approach for Solving the Harmonic Distortions Elimination in Multilevel Converters

An Artificial Neural Network Approach for Solving the Harmonic Distortions Elimination in Multilevel Converters

Commande des onduleurs multiniveaux asymétriques pour l'entraînement des machines électriques