Integrated condition monitoring of a fleet of offshore wind turbines with focus on acceleration streaming processing

Particularly offshore there is a trend to cluster wind turbines in large wind farms, and in the near future to operate such a farm as an integrated power production plant. Predictability of individual turbine behavior across the entire fleet is key in such a strategy. Failure of turbine subcomponents should be detected well in advance to allow early planning of all necessary maintenance actions; Such that they can be performed during low wind and low electricity demand periods. In order to obtain the insights to predict component failure, it is necessary to have an integrated clean dataset spanning all turbines of the fleet for a sufficiently long period of time. This paper illustrates our big-data approach to do this. In addition, advanced failure detection algorithms are necessary to detect failures in this dataset. This paper discusses a multi-level monitoring approach that consists of a combination of machine learning and advanced physics based signal-processing techniques. The advantage of combining different data sources to detect system degradation is in the higher certainty due to multivariable criteria. In order to able to perform long-term acceleration data signal processing at high frequency a streaming processing approach is necessary. This allows the data to be analysed as the sensors generate it. This paper illustrates this streaming concept on 5kHz acceleration data. A continuous spectrogram is generated from the data-stream. Real-life offshore wind turbine data is used. Using this streaming approach for calculating bearing failure features on continuous acceleration data will support failure propagation detection

Real-time predictive maintenance for wind turbines using Big Data frameworks

This work presents the evolution of a solution for predictive maintenance to a Big Data environment. The proposed adaptation aims for predicting failures on wind turbines using a data-driven solution deployed in the cloud and which is composed by three main modules. (i) A predictive model generator which generates predictive models for each monitored wind turbine by means of Random Forest algorithm. (ii) A monitoring agent that makes predictions every 10 minutes about failures in wind turbines during the next hour. Finally, (iii) a dashboard where given predictions can be visualized. To implement the solution Apache Spark, Apache Kafka, Apache Mesos and HDFS have been used. Therefore, we have improved the previous work in terms of data process speed, scalability and automation. In addition, we have provided fault-tolerant functionality with a centralized access point from where the status of all the wind turbines of a company localized all over the world can be monitored, reducing O&M costs

Natural resources conservation management and strategies in agriculture

This paper suggests a holistic framework for assessment and improvement of management strategies for conservation of natural resources in agriculture. First, it incorporates an interdisciplinary approach (combining Economics, Organization, Law, Sociology, Ecology, Technology, Behavioral and Political Sciences) and presents a modern framework for assessing environmental management and strategies in agriculture including: specification of specific “managerial needs” and spectrum of feasible governance modes (institutional environment; private, collective, market, and public modes) of natural resources conservation at different level of decision-making (individual, farm, eco-system, local, regional, national, transnational, and global); specification of critical socio-economic, natural, technological, behavioral etc. factors of managerial choice, and feasible spectrum of (private, collective, public, international) managerial strategies; assessment of efficiency of diverse management strategies in terms of their potential to protect diverse eco-rights and investments, assure socially desirable level of environmental protection and improvement, minimize overall (implementing, third-party, transaction etc.) costs, coordinate and stimulate eco-activities, meet preferences and reconcile conflicts of individuals etc. Second, it presents evolution and assesses the efficiency of diverse management forms and strategies for conservation of natural resources in Bulgarian agriculture during post-communist transformation and EU integration (institutional, market, private, and public), and evaluates the impacts of EU CAP on environmental sustainability of farms of different juridical type, size, specialization and location. Finally, it suggests recommendations for improvement of public policies, strategies and modes of intervention, and private and collective strategies and actions for effective environmental protection

The Solway Estuary: A socio-cultural evaluation of a coastal energy landscape

No abstract available

Wind Power Forecasting Methods Based on Deep Learning: A Survey

Accurate wind power forecasting in wind farm can effectively reduce the enormous impact on grid operation safety when high permeability intermittent power supply is connected to the power grid. Aiming to provide reference strategies for relevant researchers as well as practical applications, this paper attempts to provide the literature investigation and methods analysis of deep learning, enforcement learning and transfer learning in wind speed and wind power forecasting modeling. Usually, wind speed and wind power forecasting around a wind farm requires the calculation of the next moment of the definite state, which is usually achieved based on the state of the atmosphere that encompasses nearby atmospheric pressure, temperature, roughness, and obstacles. As an effective method of high-dimensional feature extraction, deep neural network can theoretically deal with arbitrary nonlinear transformation through proper structural design, such as adding noise to outputs, evolutionary learning used to optimize hidden layer weights, optimize the objective function so as to save information that can improve the output accuracy while filter out the irrelevant or less affected information for forecasting. The establishment of high-precision wind speed and wind power forecasting models is always a challenge due to the randomness, instantaneity and seasonal characteristics

Advancing Climate Change Research and Hydrocarbon Leak Detection : by Combining Dissolved Carbon Dioxide and Methane Measurements with ADCP Data

With the emergence of largescale, comprehensive environmental monitoring projects, there is an increased need to combine state-of-the art technologies to address complicated problems such as ocean acidifi cation and hydrocarbon leak detection

Environmental management in Bulgarian agriculture

This paper presents a new framework for analysis and improvement of environmental management based on the achievements of the New Institutional and Transaction Costs Economics. Following that new framework we first, identify the major environmental problems and risks in Bulgarian agriculture. Next, we access efficiency of market, private and public modes of environmental management employed in the sector. And finally, we give prospects and major challenges of environmental management in conditions of EU Common Agricultural Policy implementation. Our analysis shows that post-communist transition of Bulgarian agriculture has changed the state of the environment and brought some new challenges such as: degradation and contamination of farmland, pollution of surface and ground waters, loss of biodiversity, significant greenhouse gas emissions etc. Badly defined and enforced environmental rights, prolonged process of privatization of agrarian resources, carrying out farming in structures not motivating in long-term investment, low appropriability of certain environmental rights and high uncertainty and assets specificity of environment related transactions, all these factors have been responsible for failure of market and private modes of environmental management. The strong needs for a public intervention have not been met by an effective government, community, international assistance etc. intervention. Consequently agrarian sustainability has been severely compromised. The assessment of likely impact of EU CAP implementation under “Bulgarian” conditions indicates that the main beneficiary of various new support measures will be the biggest operators. Income, technological and environmental discrepancy between different farms, sub-sectors and regions will be further enhanced. Our analysis has been also supported by field survey data from different type dairy farms from two major milk producing regions of the country. We have found out that a great number of farms have no sufficient capacity for adaptation to new EU requirements for the dairy sector. The bulk of milk producers expect no positive impact of CAP measures on their income, volume and technology of production, investment level, product quality, access to public programs, improvement of environmental care, improvement of animal welfare, development of infrastructure, possibilities for new income generation, and social status of farm households.environmental management; market, private and public governance; agrarian transition; CAP implementation; governing agrarian sustainability; comparative institutional analysis; transaction costs; Bulgaria

Ocean Energy in Belgium - 2019

B