Analog controllers using digital stochastic logic

Stochastic logic is based on digital processing of a random pulse stream, where the information is codified as the probability of a high level in a finite sequence. The probability of the pulse stream codifies a continuous time variable. Subsequently, this pulse stream can be digitally processed to perform analog operations. In this paper we propose a stochastic approach to the digital implementation of complex controllers. This is approach allows for the realization of the controllers, and A/D and D/A converters within a digital programmable device, leading to simple circuit implementations. A practical realization of a classical PID and nonlinear dissipative controllers for the series switching power resonant converter is presented

Deep Learning Implementation of Model Predictive Control for Multi-Output Resonant Converters

Flexible-surface induction cooktops rely on multi-coil structures which are powered by means of advanced resonant power converters that achieve high versatility while maintaining high efficiency and power density. The study of multi-output converters has led to cost-effective and reliable implementations even if they present complex control challenges to provide high performance. For this scenario, model predictive control arises as a modern control technique that is capable of handling multivariable problems while dealing with nonlinearities and constraints. However, these controllers are based on the computationally-demanding solution of an optimization problem, which is a challenge for high-frequency real-time implementations. In this context, deep learning presents a potent solution to approximate the optimal control policy while achieving a time-efficient evaluation, which permits an online implementation. This paper proposes and evaluates a multi-output-resonant-inverter model predictive controller and its implementation on an embedded system by means of a deep neural network. The proposal is experimentally validated by a resonant converter applied to domestic induction heating featuring a two-coil 3.6 kW architecture controlled by means of a FPGA. Autho

A plug-and-play ripple mitigation approach for DC-links in hybrid systems

© 2016 IEEE.In this paper, a plug-and-play ripple mitigation technique is proposed. It requires only the sensing of the DC-link voltage and can operate fully independently to remove the low-frequency voltage ripple. The proposed technique is nonintrusive to the existing hardware and enables hot-swap operation without disrupting the normal functionality of the existing power system. It is user-friendly, modular and suitable for plug-and-play operation. The experimental results demonstrate the effectiveness of the ripple-mitigation capability of the proposed device. The DC-link voltage ripple in a 110 W miniature hybrid system comprising an AC/DC converter and two resistive loads is shown to be significantly reduced from 61 V to only 3.3 V. Moreover, it is shown that with the proposed device, the system reliability has been improved by alleviating the components' thermal stresses

Demand and Storage Management in a Prosumer Nanogrid Based on Energy Forecasting

Energy efficiency and consumers' role in the energy system are among the strategic research topics in power systems these days. Smart grids (SG) and, specifically, microgrids, are key tools for these purposes. This paper presents a three-stage strategy for energy management in a prosumer nanogrid. Firstly, energy monitoring is performed and time-space compression is applied as a tool for forecasting energy resources and power quality (PQ) indices; secondly, demand is managed, taking advantage of smart appliances (SA) to reduce the electricity bill; finally, energy storage systems (ESS) are also managed to better match the forecasted generation of each prosumer. Results show how these strategies can be coordinated to contribute to energy management in the prosumer nanogrid. A simulation test is included, which proves how effectively the prosumers' power converters track the power setpoints obtained from the proposed strategy.Spanish Agencia Estatal de Investigacion ; Fondo Europeo de Desarrollo Regional

Neural Networks for Modeling and Control of Particle Accelerators

We describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.Comment: 21 p

A Neural Controller for quasi-resonant converters

A neural controller implementing an energy feedback control law is proposed to improve the stability and dynamic characteristics of the series resonant converter (SRC). The cnert)' feedback control is introduced and analysed in discrete time domain. A novel formulation of the control law is suggested. The adaptive control law is learnt by an analog neural network (ANN). An easy imple· mentation of this controller is proposed and applied to a SRC circuit. Simulation results show a good improvement in the SRC response and confirm the validity of the controller

Survey on Photo-Voltaic Powered Interleaved Converter System

Renewable energy is the best solution to meet the growing demand for energy in the country. The solar energy is considered as the most promising energy by the researchers due to its abundant availability, eco-friendly nature, long lasting nature, wide range of application and above all it is a maintenance free system. The energy absorbed by the earth can satisfy 15000 times of today’s total energy demand and its hundred times more than that our conventional energy like coal and other fossil fuels. Though, there are overwhelming advantages in solar energy, It has few drawbacks as well such as its low conversion ratio, inconsistent supply of energy due to variation in the sun light, less efficiency due to ripples in the converter, time dependent and, above all, high capitation cost. These aforementioned flaws have been addressed by the researchers in order to extract maximum energy and attain hundred percentage benefits of this heavenly resource. So, this chapter presents a comprehensive investigation based on photo voltaic (PV) system requirements with the following constraints such as system efficiency, system gain, dynamic response, switching losses are investigated. The overview exhibits and identifies the requirements of a best PV power generation system