A Review of using Data Mining Techniques in Power Plants

Data mining techniques and their applications have developed rapidly during the last two decades. This paper reviews application of data mining techniques in power systems, specially in power plants, through a survey of literature between the year 2000 and 2015. Keyword indices, articles’ abstracts and conclusions were used to classify more than 86 articles about application of data mining in power plants, from many academic journals and research centers. Because this paper concerns about application of data mining in power plants; the paper started by providing a brief introduction about data mining and power systems to give the reader better vision about these two different disciplines. This paper presents a comprehensive survey of the collected articles and classifies them according to three categories: the used techniques, the problem and the application area. From this review we found that data mining techniques (classification, regression, clustering and association rules) could be used to solve many types of problems in power plants, like predicting the amount of generated power, failure prediction, failure diagnosis, failure detection and many others. Also there is no standard technique that could be used for a specific problem. Application of data mining in power plants is a rich research area and still needs more exploration

A simple fuzzy logic diagnosis system for control of internal combustion engines

Fuzzy logic (FL) systems are widely established as a technology offering an alternative system to tackle compound and ill defined problems. They can be trained from examples, are fault tolerant in the sense that they are capable to grip noisy and deficient data, are able to deal with non-linear problems, and once trained can perform prediction and generalization at high speed. in this paper a simple fuzzy logic control has been developed which is used for defining engine system faults and control and maintain them in a normal range without use any complicated mathematical equation and any fault sensor

Econometric framework for electricity infrastructure modernization in Saudi Arabia, An

2017 Summer.Includes bibliographical references.The electricity infrastructure in Saudi Arabia is facing several challenges represented by demand growth, high peak demand, high level of government subsidies, and system losses. This dissertation aims at addressing these challenges and proposing a multi-dimensional framework to modernize the electricity infrastructure in Saudi Arabia. The framework proposes four different scenarios—identified by two dimensions—for the future electric grid. The first and second dimensions are characterized by electricity market deregulation and Smart Grid technologies (SGTs) penetration, respectively. The framework analysis estimates global welfare (GW) and economic feasibility of the two dimensions. The first dimension quantifies the impact of deregulating the electricity market in Saudi Arabia. A non-linear programming (NLP) algorithm optimizes consumers surplus, producers surplus, and GW. The model indicates that deregulating the electricity market in Saudi Arabia will improve market efficiency. The second dimension proposes that allowing the penetration of SGTs in the Saudi electricity infrastructure is expected to mitigate the technical challenges faced by the grid. The dissertation examines the priorities of technologies for penetration by considering some key performance indicators (KPIs) identified by the Saudi National Transformation Program, and Saudi Vision 2030. A multi-criteria decision making (MCDM) algorithm—using the fuzzy Analytic Hierarchy Process (AHP)—evaluates the prioritization of SGTs to the Saudi grid. The algorithm demonstrates the use of triangular fuzzy numbers to model uncertainty in planning decisions. The results show that advanced metering infrastructure (AMI) technologies are the top priority for modernizing the Saudi electricity infrastructure; this is followed by advanced assets management (AAM) technologies, advanced transmission operations (ATO) technologies, and advanced distribution operations (ADO) technologies. SGTs prioritization is followed by a detailed cost benefit analysis (CBA) conducted for each technology. The framework analysis aims at computing the economic feasibility of SGTs and estimating their outcomes and impacts in monetary values. The framework maps Smart Grid assets to their functions and benefits to estimate the feasibility of each Smart Grid technology and infrastructure. Discounted cash flow (DCF) and net present value (NPV) models, benefit/cost ratio, and minimum total cost are included in the analysis. The results show that AAM technologies are the most profitable technologies of Smart Grid to the Saudi electricity infrastructure, followed by ADO technologies, ATO technologies, and AMI technologies. Considering the weights resulting from the fuzzy AHP and the economic analysis models for each infrastructure, the overall ranking places AAM technologies as the top priority of SGTs to the Saudi electricity infrastructure, followed by AMI technologies, ADO technologies, and ATO technologies. This dissertation has contributed to the existing body of knowledge in the following areas: • Proposing an econometric framework for electricity infrastructure modernization. The framework takes into account technical, economic, environmental, societal, and policy factors. • Building an NLP algorithm to optimize a counterfactual deregulation of a regulated electricity market. The algorithm comprises short run price elasticity of electricity demand (ε), level of technical efficiency improvement, and discount rate (r). • Proposing an MCDM model using AHP and fuzzy set theory to prioritize SGTs to electricity infrastructures. • Adapting a Smart Grid asset-function-benefit linkage model that maps SGTs to their respected benefits. • Conducting a detailed CBA to estimate the economic feasibility of SGTs to the Saudi electricity infrastructure, This work opens avenues for more analysis on electricity infrastructure modernization. Measuring risk impact and likelihood is one area for future research. In fact, risk assessment is an important factor in determining the economic feasibility of the modernization. Probabilistic economic analysis can be applied to assess the risk associated with the implantation of the previously mentioned dimensions. The parameters used for the economic analysis, such as economic life of a project, and the discount rate, are usually deterministic. However, a probabilistic method can be applied to capture the uncertainty of the parameters. Another area for future research is the integration of both dimensions into one model in which GW resulted from market deregulation and SGTs insertion are summed

Modelo de decisión para el diseño conceptual de un sistema de suministro sostenible de energía para la Sede Leticia de la Universidad Nacional de Colombia

ilustraciones, gráficas, tablasThe decentralized model of energy generation has emerged as a solution to provide electricity to isolated areas, ensuring energy security and increasing coverage. This model frequently leads to a dependency on a unique energy source; thus, it is necessary to change the paradigm of energy generation by adding other more sustainable sources. Unfortunately, there is not a well-defined route to establish which energy sources should be linked and in what way, making this restructuring a very complex problem involving a decision-making process. Generally, decisions are made only considering the economic or technical dimensions, ignoring the other dimensions such as environmental, social, and political, which could provide a more contextualized perspective. The aim of this study is to develop and test a methodology to find an optimal arrangement of energy sources in a decentralized electricity production model considering all sustainability dimensions. A methodology as the proposed in this work can support the stakeholders during the planning stages of energy supply systems. The methodology was applied to a specific case in Colombia, the campus Amazonia of the Universidad Nacional de Colombia, located in Leticia, a municipality where on-site generators are employed due to the difficulty of access. As a result, the proposed methodology generated nine different scenarios of energy arrangements according to an evaluation of energy sources using a sustainability approach that considered context aspects along with a carefully selected set of indicators and stakeholders' preferences.El modelo descentralizado de generación de energía surgió como una solución para el suministro de energía en áreas aisladas, asegurando la seguridad energética e incrementando la cobertura. No obstante, este modelo frecuentemente conlleva a una dependencia a una única fuente de energía, por lo que es necesario cambiar el paradigma de la generación de energía añadiendo otras fuentes más sostenibles. Desafortunadamente, no existe una ruta definida para establecer cuales fuentes de energía deben ser agregadas y de qué manera, convirtiendo esta reestructuración en un problema muy complejo que involucra la toma de decisiones. Generalmente, estas decisiones se toman considerando aspectos económicos o técnicos, dejando de lado otras dimensiones como la ambiental, social y política, que podrían proporcionar una perspectiva más contextualizada. El objetivo de este estudio es desarrollar y probar una metodología que permita encontrar un arreglo óptimo de fuentes de energía en un modelo de producción de electricidad descentralizado teniendo en cuenta todas las dimensiones de la sostenibilidad. La metodología propuesta en este trabajo puede ayudar a los principales involucrados durante las fases de planeación de sistemas de suministro de energía. Esta metodología fue aplicada a un caso específico en Colombia, la sede Amazonas de la Universidad Nacional de Colombia, ubicada en Leticia, un municipio donde generadores in situ son empleados debido al difícil acceso. Como resultado, la metodología propuesta generó nueve escenarios diferentes de arreglos energéticos de acuerdo a una evaluación de fuentes de energía en un enfoque de sostenibilidad considerando aspectos de contexto junto a una selección cuidadosa de indicadores y las preferencias de las partes interesadas. (Texto tomado de la fuente).Incluye anexosMaestríaBiorefinerías y biorefinació

Eco-friendly Naturalistic Vehicular Sensing and Driving Behaviour Profiling

PhD ThesisInternet of Things (IoT) technologies are spurring of serious games that support training directly in the field. This PhD implements field user performance evaluators usable in reality-enhanced serious games (RESGs) for promoting fuel-efficient driving. This work proposes two modules – that have been implemented by processing information related to fuel-efficient driving – to be employed as real-time virtual sensors in RESGS. The first module estimates and assesses instantly fuel consumption, where I compared the performance of three configured machine learning algorithms, support vector regression, random forest and artificial neural networks. The experiments show that the algorithms have similar performance and random forest slightly outperforms the others. The second module provides instant recommendations using fuzzy logic when inefficient driving patterns are detected. For the game design, I resorted to the on-board diagnostics II standard interface to diagnostic circulating information on vehicular buses for a wide diffusion of a game, avoiding sticking to manufacturer proprietary solutions. The approach has been implemented and tested with data from the enviroCar server site. The data is not calibrated for a specific car model and is recorded in different driving environments, which made the work challenging and robust for real-world conditions. The proposed approach to virtual sensor design is general and thus applicable to various application domains other than fuel-efficient driving. An important word of caution concerns users’ privacy, as the modules rely on sensitive data, and provide information that by no means should be misused

Contemporary Affirmation of Machine Learning Models for Sensor Validation and Recommendations for Future research Directions

Wireless Sensor Networks (WSNs) are important and needed systems for the future as the notion "Internet of Things" has emerged lately. They're used for observation, tracking, or controlling of several uses in sector, health care, home, and military. Yet, the quality of info collected by sensor nodes is changed by anomalies that happen because of various grounds, including node failures, reading errors, unusual events, and malicious assaults. Thus, fault detection is a necessary procedure before it's used in making selections to make sure the quality of sensor information. A multitude of methods can be called multiple-changeable systems/agents. For example methods such as for example creating heating system, ventilation and air conditioner(HVAC) methods are changeable methods / agents . Multiple-changeable methods /agents such as for instance these commonly don't meet performance expectations imagined at design time. Such failings can be a result of a number of factors, for example difficulties due to improper installment, substandard maintenance, or products failure. These issues, or "faults," can comprise mechanical disappointments, management difficulties, design mistakes, and improper operator treatment

Studies on SI engine simulation and air/fuel ratio control systems design

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.More stringent Euro 6 and LEV III emission standards will immediately begin execution on 2014 and 2015 respectively. Accurate air/fuel ratio control can effectively reduce vehicle emission. The simulation of engine dynamic system is a very powerful method for developing and analysing engine and engine controller. Currently, most engine air/fuel ratio control used look-up table combined with proportional and integral (PI) control and this is not robust to system uncertainty and time varying effects. This thesis first develops a simulation package for a port injection spark-ignition engine and this package include engine dynamics, vehicle dynamics as well as driving cycle selection module. The simulations results are very close to the data obtained from laboratory experiments. New controllers have been proposed to control air/fuel ratio in spark ignition engines to maximize the fuel economy while minimizing exhaust emissions. The PID control and fuzzy control methods have been combined into a fuzzy PID control and the effectiveness of this new controller has been demonstrated by simulation tests. A new neural network based predictive control is then designed for further performance improvements. It is based on the combination of inverse control and predictive control methods. The network is trained offline in which the control output is modified to compensate control errors. The simulation evaluations have shown that the new neural controller can greatly improve control air/fuel ratio performance. The test also revealed that the improved AFR control performance can effectively restrict engine harmful emissions into atmosphere, these reduce emissions are important to satisfy more stringent emission standards

Towards 2050 net zero carbon infrastructure:a critical review of key decarbonization challenges in the domestic heating sector in the UK

One of the most challenging sectors to meet “Net Zero emissions” target by 2050 in the UK is the domestic heating sector. This paper provides a comprehensive literature review of the main challenges of heating systems transition to low carbon technologies in which three distinct categories of challenges are discussed. The first challenge is of decarbonizing heat at the supply side, considering specifically the difficulties in integrating hydrogen as a low-carbon heating substitute to the dominant natural gas. The next challenge is of decarbonizing heat at the demand side, and research into the difficulties of retrofitting the existing UK housing stock, of digitalizing heating energy systems, as well as ensuring both retrofits and digitalization do not disproportionately affect vulnerable groups in society. The need for demonstrating innovative solutions to these challenges leads to the final focus, which is the challenge of modeling and demonstrating future energy systems heating scenarios. This work concludes with recommendations for the energy research community and policy makers to tackle urgent challenges facing the decarbonization of the UK heating sector.</p

Modeling and Simulation in Engineering

The Special Issue Modeling and Simulation in Engineering, belonging to the section Engineering Mathematics of the Journal Mathematics, publishes original research papers dealing with advanced simulation and modeling techniques. The present book, “Modeling and Simulation in Engineering I, 2022”, contains 14 papers accepted after peer review by recognized specialists in the field. The papers address different topics occurring in engineering, such as ferrofluid transport in magnetic fields, non-fractal signal analysis, fractional derivatives, applications of swarm algorithms and evolutionary algorithms (genetic algorithms), inverse methods for inverse problems, numerical analysis of heat and mass transfer, numerical solutions for fractional differential equations, Kriging modelling, theory of the modelling methodology, and artificial neural networks for fault diagnosis in electric circuits. It is hoped that the papers selected for this issue will attract a significant audience in the scientific community and will further stimulate research involving modelling and simulation in mathematical physics and in engineering