A 32 mV/69 mV input voltage booster based on a piezoelectric transformer for energy harvesting applications

This paper presents a novel method for battery-less circuit start-up from ultra-low voltage energy harvesting sources. The approach proposes for the first time the use of a Piezoelectric Transformer (PT) as the key component of a step-up oscillator. The proposed oscillator circuit is first modelled from a theoretical point of view and then validated experimentally with a commercial PT. The minimum achieved start-up voltage is about 69 mV, with no need for any external magnetic component. Hence, the presented system is compatible with the typical output voltages of thermoelectric generators (TEGs). Oscillation is achieved through a positive feedback coupling the PT with an inverter stage made up of JFETs. All the used components are in perspective compatible with microelectronic and MEMS technologies. In addition, in case the use of a ∼40 μH inductor is acceptable, the minimum start-up voltage becomes as low as about 32 mV

Design of low-voltage integrated step-up oscillators with microtransformers for energy harvesting applications

This paper describes the modeling of startup circuits in battery-less micropower energy harvesting systems and investigates the use of bond wire micromagnetics. The analysis focuses on step-up Meissner oscillators based on magnetic core transformers operating with input voltages down to ≈100 mV, e.g. from thermoelectric generators. As a key point, this paper examines the effect of core losses and leakage inductances on the startup requirements obtained with the classical Barkhausen criterion, and demonstrates the minimum transconductance for oscillations to occur. For validation purposes, a step-up oscillator IC is fabricated in a STMicroelectronics 0.32 μm technology, and connected to two bond-wire microtransformers, respectively, with a 1:38 MnZn ferrite core and with a 1:52 ferromagnetic low-temperature co-fired ceramic (LTCC) core. Coherently with the proposed model, experimental measurements show a minimum startup voltage of 228 mV for the MnZn ferrite core and of 104 mV for the LTCC core

Detection of Anomalies in Household Appliances from Disaggregated Load Consumption

The detection of anomalous power consumption in household appliances plays a key role for the optimization of grid operations and for reducing unwanted electrical absorptions in residential buildings. Smart Plugs, Smart Appliances and other appliance-level monitoring devices allow to continuously monitor the power consumption of individual appliances present in the house. This work is aimed at detecting electrical anomalies in household appliances by analyzing the disaggregated load consumption derived from appliance-level monitoring devices. For this purpose, we implemented an anomaly detection framework which monitors the hourly energy consumption of three common sources of power absorption: the baseline, the fridge and the electrical devices. Here, we focused our analysis on two kinds of anomalies: single-point deviations and anomalous trends. The analysis of single-point deviations allowed us to identify short-term power peaks due either to unexpected electrical faults or sudden variations in end-users routines. The analysis of anomalous trends revealed several cases in which the end-users gradually increased their ordinary power consumption profile towards more energy-intensive practices. In summary, the results of our work showed that the power consumption derived from appliance-level load monitoring can be used to detect several anomalous power consumption in household appliances

Non-intrusive load monitoring techniques for the disaggregation of ON/OFF appliances

Nowadays, Non-Intrusive Load Monitoring techniques are sufficiently accurate to provide valuable insights to the end-users and improve their electricity behaviours. Indeed, previous works show that commonly used appliances (fridge, dishwasher, washing machine) can be easily disaggregated thanks to their abundance of electrical features. Nevertheless, there are still many ON/OFF devices (e.g. heaters, kettles, air conditioners, hair dryers) that present very poor power signatures, preventing their disaggregation with traditional algorithms. In this work, we propose a new online clustering method exploiting both operational features (peak power, duration) and external features (time of use, day of week, weekday/weekend) in order to recognize ON/OFF devices. The proposed algorithm is intended to support an existing disaggregation algorithm that is already able to classify at least 80% of the total energy consumption of the house. Thanks to our approach, we improved the performance of our existing disaggreation algorithm from 80% to 87% of the total energy consumption in the monitored houses. In particular, we found that 85% of the clusters were identified by only using operational features, while external features allowed us to identify the remaining 15% of the clusters. The algorithm needs to collect on average less than 40 operations to find a cluster, which demonstrates its applicability in the real world

Clustering appliance operation modes with unsupervised deep learning techniques

In smart grids, consumers can be involved in demand response programs to reduce the total power consumption of their households during the peak hours of the day. Unfortunately, nowadays, utility companies are facing important challenges in the implementation of demand response programs because of their negative impact on the comfort of end-users. In this paper, we cluster the different operation modes of household appliances based on the analysis of their power signatures. For this purpose, we implement an autoencoder neural network to create a better data representation of the power signatures. Then, we cluster the different operational programs by using a K-means algorithm fitted to the new data representation. To test our methodology, we study the operation modes of some washing machines and dishwashers whose power signatures were derived from both submeters and non-intrusive load monitoring techniques. Our clustering analysis reveals the existence of multiple working programs showing well-defined features in terms of both average energy consumption and duration. Our results can then be used to improve demand response programs by reducing their impact on the comfort of end users. Furthermore, end users can rely on our framework to favor lighter operation modes and reduce their overall energy consumption

A Contribution to the study of the distribution of <i>Medicago-Sinorhizobium</i> symbiosis in Sardinia (Italy)

The paper summarizes the results of a Medicago-Sinorhizobium germplasm survey and collection carried out in the island of Sardinia (Italy) in 1998/99 and subsequent laboratory isolation of microbial strains, soil sample analyses and determination of Medicago species. According to a stratified sampling methodology, the major ecological characteristics of island's habitats were taken into account, collecting and surveying mostly in natural or semi-natural habitats (no roadside sites nor cultivated fields were sampled). Forty-six sites, widely distributed in semi-natural representative areas of Sardinia, ranging from sea level to above 1000 m asl, were sampled and 24 were surveyed to gather additional data on species distribution. Root nodules were collected from 15 species (13 annuals) out of the total 21 Medicago species recorded in Sardinia. Isolation of root nodule bacteria accessions and identification of 29 strains were achieved. A total number of 17 species were surveyed and mapped. The present study gives a first contribution to the knowledge of the present distribution of the species of the genus Medicago in Sardinia with special concern to the species recorded or sampled during the survey and highlights the presence of Medicago biodiversity hot spots

Ion Implanted Phosphorous for 4H-SiC VDMOSFETs Source Regions: Effect of the Post Implantation Annealing Time

Van der Pauw devices have been fabricated by double ion implantation processes, namely P+ and Al+ co-implantation. Similarly to the source area in a SiC VD-MOSFET, a 5 × 1018 cm-3 P plateau is formed on the top of a buried 3 × 1018 cm-3 Al distribution for electrical isolation from the n- epilayer. The post implantation annealing temperature was 1600 °C. Annealing times equal to 30 min and 300 min have been compared. The increase of the annealing time produces both an increase of electron density as well as electron mobility. For comparison a HPSI 4H-SiC wafer, 1×1020 cm-3 P+ ion implanted and 1700 °C annealed for 30 min was also characterized.ISSN:0255-5476ISSN:1662-975

UNA NUOVA STAZIONE A VULCANO PER IL MONITORAGGIO DEL FLUSSO DI CALORE DAL SUOLO

Una stazione per il monitoraggio delle variazioni del flusso di calore dal suolo è stata installata all’isola di Vulcano sul cono attivo di La Fossa, all’esterno del campo fumarolico di alta temperatura. La stazione misura i valori di temperatura del suolo a sei differenti profondità, lungo uno stesso profilo verticale. Le misure consentono di ottenere il gradiente di temperatura (°C/m) nel suolo ed il coefficiente di correlazione lineare (R2) delle temperature registrate lungo il profilo. Tali parametri (R2 e °C/m) consentono di determinare il flusso di calore nei periodi in cui la componente conduttiva è la principale forma di trasporto del calore. La stazione è stata progettata ed assemblata nel laboratorio di elettronica della sezione di Palermo dell’INGV e utilizza un programma di acquisizione ed un sistema di trasmissione interamente progettati e sviluppati dallo stesso personale. Dopo un primo periodo di osservazione dei dati e di verifiche tecniche la stazione, denominata Bordosud, è entrata nel sistema di monitoraggio geochimico dell’attività di Vulcano gestito dalle Sezione di Palermo dell’INGV. L’energia termica rilasciata da un sistema vulcanico è un parametro di primaria importanza per la sorveglianza dell’attività vulcanica. Durante i periodi intereruttivi, il calore rilasciato attraverso la circolazione dei fluidi idrotermali e l’energia termica associata all’emissione di vapore attraverso i campi fumarolici costituiscono una buona parte dell’energia totale rilasciata dal vulcano

A novel infrastructure for the continuous monitoring of soil CO2 emissions: a case study at the alto Tiberina near fault observatory in Italy

Static and dynamic stress, along with earthquakes, can trigger the emission and migration of crustal fluids, as frequently observed on the surface and within the upper crust of tectonically active areas such as the northern Apennines of Italy. To investigate the origin of these fluids and their interconnection with the seismogenic process, we complemented The Alto Tiberina Near Fault Observatory (TABOO-NFO), a multidisciplinary monitoring infrastructure composed of a dense array of seismic, geodetic, strain, and radon sensors, with a proper geochemical network grounded on four soil CO2 flux monitoring stations and weather sensors, placed near the main vents of the superficial manifestations. The TABOO-NFO is a state-of-the-art monitoring infrastructure, which allows for studying various geophysical parameters connected to the deformation processes active along a crustal fault system dominated by the Alto Tiberina fault (ATF), which is a 60 km long normal fault dipping at a low angle (&lt;15°–20°). The region is favourable for conducting geochemical studies, as it is characterised by the presence of over-pressurised fluids trapped at certain depths and superficial manifestations associated with the emission of large quantities of fluids. After describing the theoretical framework and the technological aspects based on which we developed the geochemical monitoring network, we described the data recorded in the first months. Over the studied period, the results showed that soil CO2 flux was primarily influenced by environmental parameters, and that the selected sites received a regular supply of deep-origin CO2