Detection and clustering of an Neutral Section faults using machine learning techniques for SMART railways

Abstract: Fault detection and diagnosis plays an important role particularly in railways were abnormal events are detected and a detailed root causes analysis is performed to prevent similar occurrence. The current method used to detect immediate and long-term faults is through foot inspections and inspection trolleys fitted with cameras proving to be inefficient and time consuming when analyzing the data. This paper examines the smart fault detection system on the overhead wires by applying machine learning techniques for accurate assessment of the neutral section before and after failure thereby grouping the events into fault bins. Modern computational intelligence has enabled the fault diagnostic and fault detection to be accurate from the data generated from the sensors. The interaction between the pantograph and contact wire will be monitored using accelerometers and non-contact infrared thermometer sensors were should there be a deviation from the normal signal spectrum it will be detected. The measured data from onsite will be conveyed to ThingSpeak for cloud computation thereby providing notifications in real-time which allows the end user to visualize, analyze and act on data online. A prototype has been built and tested which shows that the system works reasonably with data collected from sensors

Proactive Monitoring, Anomaly Detection, and Forecasting of Solar Photovoltaic Systems Using Artificial Neural Networks

The world of energy sustainability landscape is witnessing high proliferation of smartgrids and microgrids, it has become significant to use intelligent tools to design, operate and maintain such crucial systems in our lives. Solar energy is an intermittent source and purely Photovoltaic (PV) based, or PV and storage based smartgrids require characterization and modelling of PV resources for an effective planning and effective operations. This dissertation familiarizes briefly the existing tools for design, monitoring, forecasting and operation of a solar system in smart electric grids infrastructure and proposes a unique application-based infrastructure to monitor, operate, forecast and troubleshoot a working PV of a smartgrid. A resilient smartgrid communication is proposed which enables monitoring and control of different elements in any PV system. This communication architecture is used to facilitate a feedback-oriented monitoring of different elements in a microgrid ecosystem and investigated thoroughly. This integrated architecture which is a combination of sensors, network elements, database and computation elements is designed specifically for solar photovoltaic (PV) powered grids on modular basis. Apart from this, the network resilience and redundancy for smooth and loss less communication is another characteristic factor in this research work. Subsequently, a deep neural network algorithm is developed to diagnose the underperformance in the generation of a PV system connected to a smartgrid. As PV generation is predominantly dependent on climatic parameters, it is necessary to have a mechanism for understanding and diagnosing performance of the system at any given instance. To address this challenge, this deep neural network architecture is presented for instantaneous performance diagnosis. The proposed architecture enabled modeling and diagnose of soiling and partial shade conditions prevalent with an accuracy of 90+%. Features of monitoring and regulating the generation and demand side of the grid were integrated through network along with feedback-based measures for effective performance in the PV system of a smartgrid or microgrid using the same network. The novelty in this work lies in real-time calculation of ideal performance and comparison for diagnosing critical performance issues of solar power generation like soiling and partial shading. Furthermore, long-short term memory (LSTM), which is a recurrent neural network model, is created for forecasting the PV solar resources, in which can assist in quantifying PV generation in various time intervals (hourly, daily, weekly). PV based smartgrids often experience expensive or inaccurate resources planning due to the lack of accurate forecasting tools where the projected methodology would eliminate such losses. This research work in its whole provides a different proposition of vertical integration which can transform into a new concept called Internet of Microgrid (IoMG). Planning, monitoring and operation form the core of smartgrids administration and if intelligent tools intertwined with network are being used as integral part in each of these aspects, then it forms a holistic view of smartgrids

THERMAL ENERGY HARVESTING IN WIRELESS SENSOR NODES USED FOR CONDITION MONITORING

Presently, wireless sensor notes (WSN) are widely investigated and used in condition monitoring on industrial process monitoring and control, based on their inherent advantages of lower maintenance cost, easy installation and the ability to be installed in places not reached easily. However, current WSN based monitoring system still need dedicated power line or regular charging / replacing the batteries, which not only makes it difficult to deploy it in the fields but also degrades the operational reliability. This PhD research focuses on an investigation into energy harvesting approaches for powering WSN so as to develop a cost-effective, easy installation and reliable wireless measurement system for monitoring mission critical machinery such as multistage gearbox. Among various emerging energy harvest approaches such as vibrations, inductions, solar panels, thermal energy harvesting is deemed in this thesis to be the most promising one as almost all machines have frictional losses which manifest in terms of temperature changes and more convenient for integration as the heat sources can be close to wireless nodes. In the meantime, temperature based monitoring is adopted as its changes can be more sensitive to early health conditions of a machine when its tribological behaviour is starting to be degraded. Moreover, it has much less data output and more suitable for WSN application compared the mainstream vibration based monitoring techniques. Based on these two fundamental hypothesis, the research has been carried out according to two main milestones: the development of a thermoelectric harvesting (TEH) module and the evaluation of temperature based monitoring performances based on an industrial gearbox system. The first one involves the designing, fabricating and optimising the thermal EH module along with a WSN based temperature node and the second investigates the analysis methods to detect the temperature changes due to various faults associated with tribological mechanisms in the gearbox. In completing the first milestone, it has successfully developed a TEH module using cost-effective thermoelectric generator (TEG) devices and temperature gradient enhancement modules (heat sinks). Especially, the parameters such as their sizes and integration boundary conditions have been configured optimally by a proposed procedure based on the fine element (FE) analysis and the heat generation characteristics of machines to be monitored. The developed TEG analytic models and, FE models along with simulation study show that three different specifications of heat sinks with a Peltier TEG module are able to produce power that are consistently about 85% of the experimental values from offline tests, showing the good accuracy in predicting power output based on different applications and thus the reliability of the models proposed. And further investigation shows that a Peltier TEG module based that the thermal energy harvesting system produces is nearly 10 mW electricity from the monitored gearbox. This power is demonstrated sufficient to drive the WSN temperature node fabricated with low power consumption BLE microcontroller CC2650 sensor tags for monitoring continuously the temperature changes of the gearbox. Moreover, it has developed model based monitoring using multiple temperature measurements. The monitoring system allows two common faults oil shortages and mechanical misalignment to be detected and diagnosed, which demonstrates the specified performance of the self-power wireless temperature system for the purpose of condition monitoring

Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems

Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions

Survey on Security Issues and Protective Measures in Different Layers of Internet of Things (IoT)

In general perspective, Internet of things is defined as a network of physical objects by connecting” things to things” through the sensors, actuators and processors, to communicate and exchange data and information among each other along with other related devices and systems spread over different locations, without human-to-human or human-to-computer interactions. This survey summarises all the security threats along with privacy issues that may be confronted by the end users in Internet of Things (IoT). The majority of survey is to gather information about the current security requirements for IoT, the further scope and the challenges in IoT and the measures to prevent attacks upon the IoT systems

IoT Applications Computing

The evolution of emerging and innovative technologies based on Industry 4.0 concepts are transforming society and industry into a fully digitized and networked globe. Sensing, communications, and computing embedded with ambient intelligence are at the heart of the Internet of Things (IoT), the Industrial Internet of Things (IIoT), and Industry 4.0 technologies with expanding applications in manufacturing, transportation, health, building automation, agriculture, and the environment. It is expected that the emerging technology clusters of ambient intelligence computing will not only transform modern industry but also advance societal health and wellness, as well as and make the environment more sustainable. This book uses an interdisciplinary approach to explain the complex issue of scientific and technological innovations largely based on intelligent computing