Analizadores de red de bajo coste

El uso masivo de dispositivos electrónicos, tanto en entornos domésticos como industriales, tiene un impacto directo e inmediato en la creciente y compleja red de distribución eléctrica a la que se conectan. De aquí la necesidad de analizar la calidad de la señal eléctrica y su energía asociada en la propia red e instalaciones afectadas. Por otra parte, la evolución exponencial de microcontroladores y micro PC´S y su aplicación al procesado de señales, convierte a estos dispositivos en candidatos excepcionales para cubrir la mencionada necesidad del análisis de la calidad eléctrica. Esta es justamente la propuesta que se hace en este trabajo. La detección de las perturbaciones eléctricas de mayor incidencia en la calidad de la señal de red se puede realizar de diferentes formas. En este caso, se propone el uso de una potente herramienta matemática como es la Transformada Wavelet (TW), con una contrastada aplicabilidad en este campo. Su traducción a nivel de programación mediante un complejo algoritmo es implementada en dispositivos de bajo coste, particularmente en Arduino y Raspberry Pi. A partir de este algoritmo es posible la detección, análisis y clasificación de distintas perturbaciones eléctricas de forma más intuitiva. Se ha diseñado un sistema capaz de adquirir y analizar la señal de la tensión eléctrica y monitorizar dichos resultados, demostrando la aptitud de estos sistemas de bajo coste para dicho análisis.The massive use of electronic devices, both in domestic and industrial environments, has a direct and immediate impact on the electrical network in which they are connected. This fact generates the need for a power quality analysis in the electrical distribution network and affected installations. On the other hand, the exponential evolution of microcontrollers and micro PC'S and their application to signal processing, makes these devices exceptional candidates to cover the aforementioned power quality analysis. This is precisely the proposal made in this work. The detection of electrical disturbances with greater incidence in power quality can be made in different ways. In this case, the use of a powerful mathematical tool such as the Wavelet Transform (WT), with a proven applicability in this field, is proposed. Its translation at the programming level through a complex algorithm is implemented in low cost devices, particularly in Arduino and Raspberry Pi. From this algorithm it is possible to detect, analyze and classify different electrical disturbances in a more intuitive way. A system capable of acquiring and analyzing the voltage signal, as well as monitoring the results, has been designed, showing the capacity of these low cost devices for such analysis.Plan Propio de la Universidad de Sevilla Proyecto: 2017/0000096

Load Disaggregation Using One-Directional Convolutional Stacked Long Short-Term Memory Recurrent Neural Network

Reliable information about the active loads in the energy system allows for effective and optimized energy management. An important aspect of intelligent energy monitoring system is load disaggregation. The proliferation of direct current (dc) loads has spurred the increasing research interest in extra low voltage (ELV) dc grids. Artificial intelligence, such as deep learning algorithms of stacked recurrent neural network (RNN), improved results on a variety of regression and classification tasks. This paper proposes a 1-D convolutional stacked long short-term memory RNN technique for the bottom-up approach in load disaggregation using single sensor multiple loads ELV dc picogrids. This eliminates the requirement for communication and intelligence on every load in the grid. The proposed technique was applied on two different dc picogrids to test the algorithm's robustness. The proposed technique produced excellent result of over 98% accuracy for smart loads and over 99% accuracy for dumb loads in ELV dc picogrid

IoT Load Classification and Anomaly Warning in ELV DC Pico-grids using Hierarchical Extended k-Nearest Neighbors

The remote monitoring of electrical systems has progressed beyond the need of knowing how much energy is consumed. As the maintenance procedure has evolved from reactive to preventive to predictive, there is a growing demand to know what appliances reside in the circuit (classification) and a need to know whether any appliance requires attention and maintenance (anomaly warning). Targeting at the increasing penetration of dc appliances and equipment in households and offices, the described low-cost solution consists of multiple distributed slave meters with a single master computer for extra low voltage dc pico-grids. The slave meter acquires the current and voltage waveform from the cable of interest, conditions the data and extracts four features per window block that are sent remotely to the master computer. The proposed solution uses a hierarchical extended k-nearest neighbors (HE-kNN) technique that exploits the use of distance in kNN algorithm and considers a window block instead of individual data point for classification and anomaly warning to trigger the attention of the user. This solution can be used as an ad hoc standalone investigation of suspicious circuit or further expanded to several circuits in a building or vicinity to monitor the network. The solution can also be implemented as part of an Internet of Things application. This paper presents the successful implementation of HE-kNN technique in three different circuits: lightings, air-conditioning and multiple load dc pico-grids with accuracy of over 93%. Its performance is superior over other anomaly warning techniques with the same set of data

Computational intelligence in extra low voltage direct currrent pico-grids

Ph. D. ThesisThe modern power system has gone through a lot of changes over the past few years. It is no longer about providing one-way power from sources to various loads. Power monitoring and management have become an increasingly essential task with the growing trend to provide users more information about the status of the loads within their energy consumption so that they can make an informed decision to reduce usage and cost or request desired maintenance. Computational intelligence has been successfully implemented in the electrical power systems to aid the user, but these research studies about this are generally conducted on the conventional alternative current (AC) macro-grids. Until now, little work has been done on direct current (DC) and the focus on smaller DC grids has been even less. In recent years, the evolution of electrical power system has seen the proliferation of direct current (DC) appliances and equipment such as buildings, households and office loads. This number keeps increasing with the advancement in technology and consumer lifestyles changes. Given that DC power supplies are getting more popular in the form of photovoltaic panels and batteries, it is possible for Extra Low Voltage (ELV) DC households or office pico-grids to come into use soon. This research recognises and addresses this research gap in the monitoring and managing of the DC picogrids. It recommends and applies the bottom-up monitoring and management approach in smaller scale grids and in larger scale grids. It innovatively categorises the loads in the grids into dumb loads that do not have intelligence and communication features and smart loads that have these features. While targeting at these ELV DC pico-grids, this research presents solutions that provide users useful information on load classification, load disaggregation, anomaly warning and early fault detection. It provides local and remote sensing with the alternative use of hardware to lessen the computational burden from the main computer. The inclusion of remote monitoring has opened a window of opportunities for Internet of Things (IoT) implementation. These solutions involve the blending of computational intelligence techniques with enhanced algorithms, such as K-Means algorithm, k-Nearest Neighbours (kNN) classification, Naïve Bayes Classification (NBC) Theorem, Statistical Process Control (SPC) and Long Short-Term Memory Recurrent Neural Network (LSTM RNN). As demonstrated in this research, these solutions produce high accuracy results in load classification and early anomaly detection in both AC and DC pico-grids. In addition to the load side, this research features a short-term PV energy forecasting technique that is easily comprehensible to users. This research contributes to the implementation of the Smart Grid with possible IoT features in DC pico-grids