Vertical wind profile characterization and identification of patterns based on a shape clustering algorithm

Wind power plants are becoming a generally accepted resource in the generation mix of many utilities. At the same time, the size and the power rating of individual wind turbines have increased considerably. Under these circumstances, the sector is increasingly demanding an accurate characterization of vertical wind speed profiles to estimate properly the incoming wind speed at the rotor swept area and, consequently, assess the potential for a wind power plant site. The present paper describes a shape-based clustering characterization and visualization of real vertical wind speed data. The proposed solution allows us to identify the most likely vertical wind speed patterns for a specific location based on real wind speed measurements. Moreover, this clustering approach also provides characterization and classification of such vertical wind profiles. This solution is highly suitable for a large amount of data collected by remote sensing equipment, where wind speed values at different heights within the rotor swept area are available for subsequent analysis. The methodology is based on z-normalization, shape-based distance metric solution and the Ward-hierarchical clustering method. Real vertical wind speed profile data corresponding to a Spanish wind power plant and collected by using a commercialWindcube equipment during several months are used to assess the proposed characterization and clustering process, involving more than 100000 wind speed data values. All analyses have been implemented using open-source R-software. From the results, at least four different vertical wind speed patterns are identified to characterize properly over 90% of the collected wind speed data along the day. Therefore, alternative analytical function criteria should be subsequently proposed for vertical wind speed characterization purposes.The authors are grateful for the financial support from the Spanish Ministry of the Economy and Competitiveness and the European Union —ENE2016-78214-C2-2-R—and the Spanish Education, Culture and Sport Ministry —FPU16/042

Ageneric predictive information system for resource planning and optimisation

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel UniversityThe purpose of this research work is to demonstrate the feasibility of creating a quick response decision platform for middle management in industry. It utilises the strengths of current, but more importantly creates a leap forward in the theory and practice of Supervisory and Data Acquisition (SCADA) systems and Discrete Event Simulation and Modelling (DESM). The proposed research platform uses real-time data and creates an automatic platform for real-time and predictive system analysis, giving current and ahead of time information on the performance of the system in an efficient manner. Data acquisition as the backend connection of data integration system to the shop floor faces both hardware and software challenges for coping with large scale real-time data collection. Limited scope of SCADA systems does not make them suitable candidates for this. Cost effectiveness, complexity, and efficiency-orientation of proprietary solutions leave space for more challenge. A Flexible Data Input Layer Architecture (FDILA) is proposed to address generic data integration platform so a multitude of data sources can be connected to the data processing unit. The efficiency of the proposed integration architecture lies in decentralising and distributing services between different layers. A novel Sensitivity Analysis (SA) method called EvenTracker is proposed as an effective tool to measure the importance and priority of inputs to the system. The EvenTracker method is introduced to deal with the complexity systems in real-time. The approach takes advantage of event-based definition of data involved in process flow. The underpinning logic behind EvenTracker SA method is capturing the cause-effect relationships between triggers (input variables) and events (output variables) at a specified period of time determined by an expert. The approach does not require estimating data distribution of any kind. Neither the performance model requires execution beyond the real-time. The proposed EvenTracker sensitivity analysis method has the lowest computational complexity compared with other popular sensitivity analysis methods. For proof of concept, a three tier data integration system was designed and developed by using National Instruments’ LabVIEW programming language, Rockwell Automation’s Arena simulation and modelling software, and OPC data communication software. A laboratory-based conveyor system with 29 sensors was installed to simulate a typical shop floor production line. In addition, EvenTracker SA method has been implemented on the data extracted from 28 sensors of one manufacturing line in a real factory. The experiment has resulted 14% of the input variables to be unimportant for evaluation of model outputs. The method proved a time efficiency gain of 52% on the analysis of filtered system when unimportant input variables were not sampled anymore. The EvenTracker SA method compared to Entropy-based SA technique, as the only other method that can be used for real-time purposes, is quicker, more accurate and less computationally burdensome. Additionally, theoretic estimation of computational complexity of SA methods based on both structural complexity and energy-time analysis resulted in favour of the efficiency of the proposed EvenTracker SA method. Both laboratory and factory-based experiments demonstrated flexibility and efficiency of the proposed solution.The Engineering and Physical Sciences Research Council

Microbial diversity and biogenic methane potential of a thermogenic-gas coal mine

The microbial communities and biogenic methane potential of a gas coal mine were investigated by cultivation-independent and cultivation-dependent approaches. Stable carbon isotopic analysis indicated that in situ methane in the coal mine was dominantly of a thermogenic origin. However, a high level of diversity of bacteria and methanogens that were present in the coal mine was revealed by 454 pyrosequencing, and included various fermentative bacteria in the phyla of Actinobacteria, Bacteroidetes, Firmicutes, and Proteobacteria, and acetotrophic, hydrogenotrophic, and methylotrophic methanogens. Methane was produced in enrichments of mine water samples supplemented with acetate under laboratory conditions. The microbial flora obtained from the enrichments could stimulate methane formation from coal samples. 16S rRNA gene clone library analysis indicated that the microbial community from coal cultivation samples supplemented with the enriched microbial consortium was dominated by the anaerobic fermentative Clostridiales and facultative acetoclastic Methanosarcina. This study suggests that the biogenic methane potential in the thermogenic-gas coal mine could be stimulated by the indigenous microorganisms

Price elasticity of electricity demand in the mining sector: South Africa

This study estimates the price and income elasticity coefficients of electricity demand in the mining sector of South Africa for the period ranging from April 2006 to March 2019. A time varying parameter (TVP) model with the Kalman filter is applied to monitor the evolution of the elasticity estimates. The TVP model can provide a robust estimation of elasticities and can detect any outliers and structural breaks. The results indicate that income and price elasticity coefficients of electricity demand are lower than unit. The income elasticity of demand has a positive sign and it is statistically significant. This indicates that mining production – used as a proxy for mining income – is a significant determinant of electricity consumption in the mining sector. In its final state income elasticity is estimated at 0.15 per cent. On the contrary, price does not play a significant role in explaining electricity demand. In fact, the price elasticity coefficient was found to be positive which is contrary to normal economic convention. This lack of response is attributed mainly to the mining sector’s inability to respond, rather than an unwillingness to do so. A fixed coefficient model in a form of Ordinary Least Squares (OLS) is used as a benchmark model to estimate average price and income elasticity coefficients for the period. The results of the OLS regression model confirm that price does not play a significant role in explaining electricity consumption in the mining sector. An average price elasticity coefficient of -0.007 has been estimated. Income elasticity was estimated at 0.11 for the period under review. The CUSUM of squares test indicate that parameters of the model are unstable. The Chow test confirms 2009 as a breakpoint in the data series. This means that elasticity coefficients of electricity demand in the mining sector are time variant. Thus the OLS results cannot be relied upon for inference purposes. The Kalman filter results are superior. This study cautions policy makers not to interpret the seeming lack of response to price changes as an indication that further prices increases could be implemented without hampering electricity consumption in the sector. Furthermore, it recommends that the electricity pricing policy should take into account both the negative impacts of rapid price increases and the need to invest in long-term electricity infrastructure in order to improve the security of energy supply. A long term electricity price path should be introduced in order to provide certainty and predictability in the price trajectory. This would allow all sectors of the economy sufficient time and space to make investment and operational decisions that would have the least adverse effects on economic growth and job creation.EconomicsM. Com. (Economics

Distributed energy resources and the application of AI, IoT, and blockchain in smart grids

Smart grid (SG), an evolving concept in the modern power infrastructure, enables the two-way flow of electricity and data between the peers within the electricity system networks (ESN) and its clusters. The self-healing capabilities of SG allow the peers to become active partakers in ESN. In general, the SG is intended to replace the fossil fuel-rich conventional grid with the distributed energy resources (DER) and pools numerous existing and emerging know-hows like information and digital communications technologies together to manage countless operations. With this, the SG will able to “detect, react, and pro-act” to changes in usage and address multiple issues, thereby ensuring timely grid operations. However, the “detect, react, and pro-act” features in DER-based SG can only be accomplished at the fullest level with the use of technologies like Artificial Intelligence (AI), the Internet of Things (IoT), and the Blockchain (BC). The techniques associated with AI include fuzzy logic, knowledge-based systems, and neural networks. They have brought advances in controlling DER-based SG. The IoT and BC have also enabled various services like data sensing, data storage, secured, transparent, and traceable digital transactions among ESN peers and its clusters. These promising technologies have gone through fast technological evolution in the past decade, and their applications have increased rapidly in ESN. Hence, this study discusses the SG and applications of AI, IoT, and BC. First, a comprehensive survey of the DER, power electronics components and their control, electric vehicles (EVs) as load components, and communication and cybersecurity issues are carried out. Second, the role played by AI-based analytics, IoT components along with energy internet architecture, and the BC assistance in improving SG services are thoroughly discussed. This study revealed that AI, IoT, and BC provide automated services to peers by monitoring real-time information about the ESN, thereby enhancing reliability, availability, resilience, stability, security, and sustainability

Artificial Intelligence in Process Engineering

In recent years, the field of Artificial Intelligence (AI) is experiencing a boom, caused by recent breakthroughs in computing power, AI techniques, and software architectures. Among the many fields being impacted by this paradigm shift, process engineering has experienced the benefits caused by AI. However, the published methods and applications in process engineering are diverse, and there is still much unexploited potential. Herein, the goal of providing a systematic overview of the current state of AI and its applications in process engineering is discussed. Current applications are described and classified according to a broader systematic. Current techniques, types of AI as well as pre- and postprocessing will be examined similarly and assigned to the previously discussed applications. Given the importance of mechanistic models in process engineering as opposed to the pure black box nature of most of AI, reverse engineering strategies as well as hybrid modeling will be highlighted. Furthermore, a holistic strategy will be formulated for the application of the current state of AI in process engineering

Medical Informatics and Data Analysis

During recent years, the use of advanced data analysis methods has increased in clinical and epidemiological research. This book emphasizes the practical aspects of new data analysis methods, and provides insight into new challenges in biostatistics, epidemiology, health sciences, dentistry, and clinical medicine. This book provides a readable text, giving advice on the reporting of new data analytical methods and data presentation. The book consists of 13 articles. Each article is self-contained and may be read independently according to the needs of the reader. The book is essential reading for postgraduate students as well as researchers from medicine and other sciences where statistical data analysis plays a central role