Conference of the Industrial Electronics Society 

About: Conference of the Industrial Electronics Society is an academic conference. The conference publishes majorly in the area(s): Control theory & Inverter. Over the lifetime, 19458 publications have been published by the conference receiving 175101 citations.

Hierarchical control of droop-controlled DC and AC microgrids — a general approach towards standardization

Abstract: DC and AC Microgrids are key elements to integrate renewable and distributed energy resources as well as distributed energy storage systems. In the last years, efforts toward the standardization of these Microgrids have been made. In this sense, this paper present the hierarchical control derived from ISA-95 and electrical dispatching standards to endow smartness and flexibility to microgrids. The hierarchical control proposed consist of three levels: i) the primary control is based on the droop method, including an output impedance virtual loop; ii) the secondary control allows restoring the deviations produced by the primary control; and iii) the tertiary control manage the power flow between the microgrid and the external electrical distribution system. Results from a hierarchical-controlled microgrid are provided to show the feasibility of the proposed approach.

A Comparative Study of Wireless Protocols: Bluetooth, UWB, ZigBee, and Wi-Fi

Abstract: Bluetooth (over IEEE 802.15.1), ultra-wideband (UWB, over IEEE 802.15.3), ZigBee (over IEEE 802.15.4), and Wi-Fi (over IEEE 802.11) are four protocol standards for short- range wireless communications with low power consumption. From an application point of view, bluetooth is intended for a cordless mouse, keyboard, and hands-free headset, UWB is oriented to high-bandwidth multimedia links, ZigBee is designed for reliable wirelessly networked monitoring and control networks, while Wi-Fi is directed at computer-to-computer connections as an extension or substitution of cabled networks. In this paper, we provide a study of these popular wireless communication standards, evaluating their main features and behaviors in terms of various metrics, including the transmission time, data coding efficiency, complexity, and power consumption. It is believed that the comparison presented in this paper would benefit application engineers in selecting an appropriate protocol.

A review of induction motors signature analysis as a medium for faults detection

Abstract: This paper is intended as a tutorial overview of induction motors signature analysis as a medium for fault detection. The purpose is to introduce in a concise manner the fundamental theory, main results, and practical applications of motor signature analysis for the detection and the localization of abnormal electrical and mechanical conditions that indicate, or may lead to, a failure of induction motors. The paper is focused on the so-called motor current signature analysis (MCSA) which utilizes the results of spectral analysis of the stator current. The paper is purposefully written without "state of the art" terminology for the benefit of practicing engineers in facilities today who may not be familiar with signal processing.

Advanced Grid Synchronization System for Power Converters under Unbalanced and Distorted Operating Conditions

Abstract: This paper proposes a new technique for grid synchronization under unbalanced and distorted conditions, ie, the dual second order generalised integrator - frequency-locked loop (DSOGI-FLL) This grid synchronization system results from the application of the instantaneous symmetrical components method on the stationary and orthogonal alphabeta reference frame The second order generalized integrator concept (SOGI) is exploited to generate in-quadrature signals used on the alphabeta reference frame The frequency-adaptive characteristic is achieved by a simple control loop, without using either phase-angles or trigonometric functions In this paper, the development of the DSOGI-FLL is plainly exposed and hypothesis and conclusions are verified by simulation and experimental results

Building energy load forecasting using Deep Neural Networks

Abstract: Ensuring sustainability demands more efficient energy management with minimized energy wastage. Therefore, the power grid of the future should provide an unprecedented level of flexibility in energy management. To that end, intelligent decision making requires accurate predictions of future energy demand/load, both at aggregate and individual site level. Thus, energy load forecasting have received increased attention in the recent past. However, it has proven to be a difficult problem. This paper presents a novel energy load forecasting methodology based on Deep Neural Networks, specifically, Long Short Term Memory (LSTM) algorithms. The presented work investigates two LSTM based architectures: 1) standard LSTM and 2) LSTM-based Sequence to Sequence (S2S) architecture. Both methods were implemented on a benchmark data set of electricity consumption data from one residential customer. Both architectures were trained and tested on one hour and one-minute time-step resolution datasets. Experimental results showed that the standard LSTM failed at one-minute resolution data while performing well in one-hour resolution data. It was shown that S2S architecture performed well on both datasets. Further, it was shown that the presented methods produced comparable results with the other deep learning methods for energy forecasting in literature.