Introductory Chapter: An Overview to the Analytic Principles with Business Practice in Decision Making

Decision-Making is a book based on contributions by different authors. The book synthesizes the analytic principles with business practice of decision-making. The book provides an interface between the main disciplines of engineering/technology and the organizational, administrative, and planning abilities of decision-making. It is complementary to other subdisciplines such as economics, finance, marketing, decision and risk analysis, etc

Improving the Efficiency on Decision Making Process via BDD

For a qualitatively and quantitatively analysis of a complex Decision Mak- ing (DM) process is critical to employ a correct method due to the large number of operations required. This paper presents an approach employing Binary Decision Diagram (BDD) applied to the Logical Decision Tree. LDT allows addressing a Main Problem (MP) by establishing different causes, called Basic Causes (BC) and their interrelations. The cases that have a large number of BCs generate important computational costs because it is a NP-hard type problem.. This paper presents a new approach in order to analyze big LDT. A new approach to reduce the complex- ity of the problem is hereby presented. It makes use of data derived from simpler problems that requires less computational costs for obtaining a good solution. An exact solution is not provided by this method but the approximations achieved have a low deviation from the exact

Advanced analytics for detection and diagnosis of false alarms and faults: A real case study

Onshore and offshore wind farms require a high level of advanced maintenance. Supervisory control and data acquisition (SCADA) and condition monitoring systems are now being employed, generating large amounts of data. They require robust and flexible approaches to convert dataset into useful information. This paper presents a novel approach based on the correlations of SCADA variables to detect and identify faults and false alarms in wind turbines. A correlation matrix between all the SCADA variables is used for pattern recognition. A new method based on curve fittings is employed for detecting false alarms and abnormal behaviours or faults in the components. The study is done in a real case study, validated with false alarms

Survey of maintenance management for photovoltaic power systems

The sustainability of the global energy production systems involves new renewable energies and the improvement of the existing ones. Photovoltaic industry is growing thanks to the development of new technologies that increase the performance of photovoltaic systems. These systems are commonly subject to harsh environmental conditions that decrease their energy production and efficiency. In addition, current photovoltaic technologies are more sophisticated, and the size of photovoltaics solar plants is growing. Under this framework, research on failures and degradation mechanisms, together with the improvement of maintenance management, becomes essential to increase the performance, efficiency, reliability, availability, safety, and profitability of these systems. To assess maintenance needs, this paper presents a double contribution: an exhaustive literature review and updated survey on maintenance of photovoltaic plants, and a novel analysis of the current state and a discussion of the future trends and challenges in this field. An analysis of the main faults and degradation mechanisms is done, including the causes, effects, and the main techniques to detect, prevent and mitigate them

A review of the application performances of concentrated solar power systems

The global energy production model is changing from fossil fuels to renewable and nuclear energies. Concentrated solar power is one of the growing technologies that is leading this process. This growth implies the sophistication and size of the systems and, therefore, it requires an increase in maintenance tasks to ensure reliability, availability, maintainability and safety. The aim of this paper is to describe the current context of concentrated solar power, to summarise and analyse the main degradation mechanisms and the main techniques to detect, prevent and mitigate these faults. An exhaustive literature study is presented, considering the most advanced techniques and approaches. A novel qualitative and quantitative analysis of the literature is provided. Finally, the current trends and the future challenges in this field are gathered from this study

Reliability analysis of detecting false alarms that employ neural networks: a real case study on wind turbines.

Operations and maintenance tasks are critical to the reliability of a wind turbine. The state-of-the-art demonstrates the effectiveness of reliability centred maintenance, but there are no research studies that consider false alarms to reliability of the wind turbines. This paper presents a novel approach based on artificial neural networks to reliability centred maintenance. The methodology is employed for false alarm detection and prioritization, training the artificial neural networks over the time to increase the system reliability. The approach is applied to a real dataset from a supervisory control and data acquisition system together with a vibration monitoring system of a wind turbine. The results accuracy is done by confusion matrices, studding real alarms with the estimations provided by the approach, and the results are validated with real false alarms and compared by the results given by a fuzzy logic model. The method provides accuracy results (over 90%). A novelty is to use a two real dataset from a wind turbine to create a redundant response to detect false alarms by artificial neural networks

Identification of critical components of wind turbines using FTA over time

Wind energy is currently the most widely implemented renewable energy source in global scale. Complex industrial multi-MW wind turbines are continuously being installed both onshore and offshore. Projects involving utility-scale wind turbines require optimisation of reliability, availability, maintainability and safety, in order to guarantee the financial viability of large scale wind energy projects, particularly offshore, in the forthcoming years. For this reason, critical wind turbine components must be identified and monito red as cost-effectively, reliably and efficiently as possible. The condition of industrial wind turbines can be qualitatively evaluated through the Fault Tree Analysis (FTA). The quantitative analysis requires high computational cost. In this paper, the Binary Decision Diagram (BDD) method is proposed for reducing this computational cost. In order to optimise the BDD a set of ranking methods of events has been considered; Level, Top-Down-Left-Right, AND, Depth First Search and Breadth-First Search. A quantitative analysis approach in order to find a general solution of a Fault Tree (FT) is presented. An illustrative case study of a FT of a wind turbine based on different research studies has been developed. Finally, this FT has been solved dynamically through the BDD approach in order to highlight the identification of the critical components of the wind turbine under different conditions, employing the following heuristic methods: Birnbaum, Criticality, Structural and Fussell-Vesely. The results provided by this methodology allow the performance of novel maintenance planning from a quantitative point of view

A Survey of Artificial Neural Network in Wind Energy Systems

Wind energy has become one of the most important forms of renewable energy. Wind energy conversion systems are more sophisticated and new approaches are required based on advance analytics. This paper presents an exhaustive review of artificial neural networks used in wind energy systems, identifying the methods most employed for different applications and demonstrating that Artificial Neural Networks can be an alternative to conventional methods in many cases. More than 85% of the 190 references employed in this paper have been published in the last 5 years. The methods are classified and analysed into four groups according to the application: forecasting and predictions; design optimization; fault detection and diagnosis; and optimal control. A statistical analysis of the current state and future trends in this field is carried out. An analysis of each application group about the strengths and weaknesses of each ANN structure is carried out. A quantitative analysis of the main references is carried out showing new statistical results of the current state and future trends of the topic. The paper describes the main challenges and technological gaps concerning the application of ANN to wind turbines, according to the literature review. An overall table is provided to summarize the most important references according to the application groups and case studies

Optimal Dynamic Analysis of Electrical/Electronic Components in Wind Turbines

Electrical and electronic components are very important subcomponents in modern industrial wind turbines. Complex multimegawatt wind turbines are continuously being installed both onshore and offshore, continuously increasing the demand for sophisticated electronic and electrical components. In this work, most critical electrical and electronic components in industrial wind turbines have been identified and the applicability of appropriate condition monitoring processes simulated. A fault tree dynamic analysis has been carried out by binary decision diagrams to obtain the system failure probability over time and using different time increments to evaluate the system. This analysis allows critical electrical and electronic components of the converters to be identified in different conditions. The results can be used to develop a scheduled maintenance that improves the decision making and reduces the maintenance costs

A novel walking robot based system for non-destructive testing in wind turbines

The constant increasing of renewable energy demand is leading wind turbines to become very complex and sophisticated devices. These technological developments imply new methods and tools to ensure the reliability of the systems. For this purpose, non-destructive testing techniques are widely employed in the field of wind turbine maintenance. This work presents the implementation of a walking robot-based system that allows non-destructive testing to be carried out in difficult access areas of wind turbines. The paper is divided as: a brief literature overview is done to identify those limitations of current procedures that could be overcome by using the proposed tool; a detailed explanation of the novel system is given, where the different components and features of the robot are described; several applications of the proposed systems are also shown. These applications can be classified regarding to the type of sensor and the area to inspect: Acoustic emission, visual inspection, guided wave testing, noise analysis or thermographic inspections are some of the non-destructive testing techniques that can be aided by this tool. Moreover, external and internal surfaces of blades, tower, nacelle and other difficult access areas can be reached by the robot. Finally, some advantages of this system are enhanced with respect to the conventional methodologies. The usefulness of the proposed system is demonstrated in terms of safety and efficiency with respect to other procedures