Anomaly detection of consumption in Hotel Units: A case study comparing isolation forest and variational autoencoder algorithms

Buildings are responsible for a high percentage of global energy consumption, and thus, the improvement of their efficiency can positively impact not only the costs to the companies they house, but also at a global level. One way to reduce that impact is to constantly monitor the consumption levels of these buildings and to quickly act when unjustified levels are detected. Currently, a variety of sensor networks can be deployed to constantly monitor many variables associated with these buildings, including distinct types of meters, air temperature, solar radiation, etc. However, as consumption is highly dependent on occupancy and environmental variables, the identification of anomalous consumption levels is a challenging task. This study focuses on the implementation of an intelligent system, capable of performing the early detection of anomalous sequences of values in consumption time series applied to distinct hotel unit meters. The development of the system was performed in several steps, which resulted in the implementation of several modules. An initial (i) Exploratory Data Analysis (EDA) phase was made to analyze the data, including the consumption datasets of electricity, water, and gas, obtained over several years. The results of the EDA were used to implement a (ii) data correction module, capable of dealing with the transmission losses and erroneous values identified during the EDA’s phase. Then, a (iii) comparative study was performed between a machine learning (ML) algorithm and a deep learning (DL) one, respectively, the isolation forest (IF) and a variational autoencoder (VAE). The study was made, taking into consideration a (iv) proposed performance metric for anomaly detection algorithms in unsupervised time series, also considering computational requirements and adaptability to different types of data. (v) The results show that the IF algorithm is a better solution for the presented problem, since it is easily adaptable to different sources of data, to different combinations of features, and has lower computational complexity. This allows its deployment without major computational requirements, high knowledge, and data history, whilst also being less prone to problems with missing data. As a global outcome, an architecture of a platform is proposed that encompasses the mentioned modules. The platform represents a running system, performing continuous detection and quickly alerting hotel managers about possible anomalous consumption levels, allowing them to take more timely measures to investigate and solve the associated causes.info:eu-repo/semantics/publishedVersio

Advanced Battery Technologies: New Applications and Management Systems

In recent years, lithium-ion batteries (LIBs) have been increasingly contributing to the development of novel engineering systems with energy storage requirements. LIBs are playing an essential role in our society, as they are being used in a wide variety of applications, ranging from consumer electronics, electric mobility, renewable energy storage, biomedical applications, or aerospace systems. Despite the remarkable achievements and applicability of LIBs, there are several features within this technology that require further research and improvements. In this book, a collection of 10 original research papers addresses some of those key features, including: battery testing methodologies, state of charge and state of health monitoring, and system-level power electronics applications. One key aspect to emphasize when it comes to this book is the multidisciplinary nature of the selected papers. The presented research was developed at university departments, institutes and organizations of different disciplines, including Electrical Engineering, Control Engineering, Computer Science or Material Science, to name a few examples. The overall result is a book that represents a coherent collection of multidisciplinary works within the prominent field of LIBs

Prognostics and Health Monitoring for ECU Based on Piezoresistive Sensor Measurements

This dissertation presents a new approach to prognostics and health monitoring for automotive applications using a piezoresistive silicon stress sensor. The stress sensor is a component with promising performance for monitoring the condition of an electronic system, as it is able to measure stress values that can be directly related to the damage sustained by the system. The primary challenge in this study is to apply a stress sensor to system-level monitoring. To achieve this goal, this study firstly evaluates the uncertainties of measurement conducted with the sensor, and then the study develops a reliable solution for gathering data with a large number of sensors. After overcoming these preliminary challenges, the study forms a framework for monitoring an electronic system with a piezoresistive stress sensor. Following this, an approach to prognostics and health monitoring involving this sensor is established. Specifically, the study chooses to use a fusion approach, which includes both model-based and data-driven approaches to prognostics; such an approach minimizes the drawbacks of using these methods separately. As the first step, the physics of failure model for the investigated product is established. The process of physics of failure model development is supported by a detailed numerical analysis of the investigated product under both active and passive thermal loading. Accurate FEM modeling provides valuable insight into the product behavior and enables quantitative evaluation of loads acting in the considered design elements. Then, a real-time monitoring of the investigated product under given loading conditions is realized to enable the system to estimate the remaining useful life based on the existing model. However, the load in the design element may abruptly change when delamination occurs. A developed data-driven approach focuses on delamination detection based on a monitoring signal. The data driven methodology utilizes statistical pattern recognition methods in order to ensure damage detection in an automatic and reliable manner. Finally, a way to combine the developed physics-of-failure and data-driven approaches is proposed, thus creating fusion approach to prognostics and health monitoring based on piezoresistive stress sensor measurements

Thermal characterisation and reliability analysis of power electronic devices in wind and solar energy systems

Power electronic converters (PECs) are used for conditioning the flow of energy between renewable energy applications and grid or stand-alone connected loads. Insulated gate bipolar transistors (IGBTs) are critical components used as switching devices in PECs. IGBTs are multi-layered devices made of different coefficient of thermal expansion (CTE) based materials. In wind and solar energy applications, IGBT’s reliability is highly influenced by the operating conditions such as variable wind speed and solar irradiance. Power losses occur in switching transient of high current/voltage which causes temperature fluctuations among the layers of the IGBTs. This is the main stress mechanism which accelerates deterioration and eventual failures among IGBT layers due to the dissimilar CTEs. Therefore, proper thermal monitoring is essential for accurate estimation of PECs reliability and end lifetime. Several thermal models have been proposed in literature, which are not capable of representing accurate temperature profiles among multichip IGBTs. These models are mostly derived from offline modelling approaches which cannot take operating conditions and control mechanisms of PECs into account and unable to represent actual heat path among each chip. This research offers an accurate and powerful electro thermal and reliability monitoring tool for such devices. Three-dimensional finite element (FE) IGBT models are implemented using COMSOL, by considering complex heat interactions among each layer. Based on the obtained thermal characteristics, electro thermal and thermo mechanical models were developed in SIMULINK to determine the thermal behaviour of each layer and provide total lifetime consumption analysis. The developed models were verified by real-time (RT) experiments using dSPACE environment. New materials, such as silicon carbide (SiC) devices, were found to exhibit approximately 20°C less thermal profile compared to conventional silicon IGBTs. For PECs used within wind energy systems, PECs driving algorithms were derived within the proposed models and by adjusting switching frequency PECs cycling temperatures were reduced by 12°C which led to a significant reduction in thermal stress; approximately 27 MPa. Total life consumption for the proposed method was calculated as 3.26x10-5 which is approximately 1x10-5 less compared to the other both methods. Effects of maximum power tracking algorithms, used in photovoltaic solar systems, on thermal stress were also explored. The converter’s thermal cycling was found approximately 3 °C higher with the IC algorithm. The steady state temperature was 52.7°C for the IC while it was 42.6 °C for P&O. In conclusion, IC algorithm offers more accurate tracking accuracy; however, this is on the expense of harsher thermal stress which has led to approximately 1.4 times of life consumption compared to P&O under specific operating conditions