Connect with us


Internet of things will empower the wind energy power plants




İnternet of Things Will Empower the Wind Energy Power Plants



Industry is uniquely positioned to tap into a potential radical change of the dominant geographical market scope of industry. Due to the cost optimization, Cyber-Physical Systems (CPS) will fundamentally change business models. A vision of tomorrow’s industry: global production methods evolve into innovative way that industry work with harmonization of future-oriented Technologies which is lead machines to interact with embedded hardware and software beyond the limits of single applications via Internet of things (IoT). For wind it provides clean, renewable energy. The core concept is simple: wind turbines spin blades to generate power. However, today’s systems are anything but simple. Modern wind turbines have blades that sweep a 120 meter circle, cost more than 1 million dollars and generate multiple megawatts of power. Each turbine may include up to 1,000 sensors and actuators – integrating strain gages, bearing monitors and power conditioning technology. The turbine can control blade speed and power generation by altering the blade pitch and power extraction. Controlling the turbine is a sophisticated job requiring many cooperating processors closing high-speed loops and implementing intelligent monitoring and optimization algorithms. But the real challenge is integrating these turbines so that they work together. A wind farm may include hundreds of turbines. They are often installed in difficult-to-access locations at sea. The farm must implement a fundamentally and truly distributed control system. Like all power systems, the goal of the farm is to match generation to load. A farm with hundreds of turbines must optimize that load by balancing the loading and generation across a wide geography. Wind, of course, is dynamic. Almost every picture of a wind farm shows a calm sea and a setting sun. But things get challenging when a storm goes through the wind farm. In a storm, the control system must decide how to take energy out of gusts to generate constant power. It must intelligently balance load across many turbines. And a critical consideration is the loading and potential damage to a half-billion-dollar installed asset. This is no environment for a slow or undependable control system. Reliability and performance are crucial.





Today’s market is characterized by demand volatility, individualized products and increasing competition due to globalization [1]. Key for companies to successfully compete in this dynamic and competitive environment is to continuously strive towards higher levels of productivity, which is particularly essential for companies producing in high-wage countries[2]. While productivity can simply be defined as the ratio between input and output, the underlying drivers behind productivity growth are manifold and include external elements, such as technology, the environment companies operate in, government regulation and competition, as well as internal elements, e.g. production processes, human capital and management [3, 4].


A new generation of computers made it possible to handle extensive calculations. This development is supported by Moore’s law which describes how to amount of operations doubles every two years and how simultaneously these operations become more affordable [5].

To get the full benefit of global production, companies must adopt an integrated perspective that extends across the value chain and covers multiple input factors from labor costs and productivity, materials, energy, and logistics through to customs, taxes, and exchange rates. Changes to product design and process technology should also be explored. As these elements can dramatically alter the economics, companies also need a new quantitative approach that does justice to the many factors involved.


1. The Coming Age of Cyber Physical Systems


The engineering research field of CPS has drawn a great deal of attention from academia, industry, and the government due to its potential benefits to society, economy, and the environment [6]. Computers were originally invented to perform compu- tation. The first computer, ENIAC, was constructed in 1946 to perform ballistic calculations [7]. In the 1990s, there began to appear much greater interest in the interaction between computational and physical systems [8]. Real-time computation researchings occured a problem that integrating the schedules due to the variable working sectors.


Around 2006, researchers, predominantly in real-time sys- tems, hybrid systems and control systems, coined the name “cyber-physical systems” to describe this increasingly impor- tant area at the interface of the cyber and the physical worlds.



There are several other paths also leading to this area of in- terest. From its origins as ARPANET [9] in 1969, the Internet developed into a worldwide network connecting computers. Around 1973 was the beginning of the cellular telephony revolution. Also around 1971 was developed the ALOHA network to interconnect users across the Hawaiian islands with a mainframe computer in Oahu [10]. Its pioneering ideas, concerning how to resolve contention of the shared wireless medium, were used in Ethernet as well as packet radio networks. In 1977 DARPA tested the PRNET packet radio network [11]. In 1978, the U.S. Army deployed the SINCGARS (Single Channel Ground and Airborne Radio Sys- tem) packet radio system [12]. Subsequently in 1997, the IEEE 802.11 Wi-Fi standard was developed and proliferated across offices and homes after the introduction of IEEE 802.11b [13]. All this, including the landline telephone network, have led to a communication revolution. The goal of interconnecting computers to form a communication network has played a central role in ALOHA, the Internet and Wi-Fi. Thus we see here the convergence of communication and computation.



The appropriate framework was the frequency domain approach, developed by Nyquist [14], Bode [15], Evans [16], and others. This also led to CPSs – though based on analog computation. One can regard Ziegler-Nichols tuning rules [17], for example, as methods to adjust the overall CPS to achieve desired behavior. Already, by 1954, there was beginning to emerge the second generation of control – digital control [18]. This was spawned by the development of the digital computer. Now simple calculations on algorithms could be performed on the measured signals before closing the loops. This too required a theoretical framework, the appropriate one in this case was the state-space approach. This was developed by Bellman [19], Pontryagin [20], Kalman [21], [22], [23], [24], [25], and others under the leadership of Solomon Lefschetz at the Martin Company’s Research Institute for Advanced Study in Baltimore which was founded in 1955. This led to a very strong foundation of systems theory, with a thorough investigation of optimal control [26], stability [27], linear systems [28], nonlinear control [29], stochastic systems [30], adaptive control [31], robust control [32], infinite dimensional systems [33], decentralized control of complex systems [34], discrete event systems [35], and even attempts at integrating automata theory and control [36].


All these trends – the convergence of several disciplines, the evolution of technology in various fields, and the increasing need to build large scale systems to meet the burgeoning societal needs in an environment of resource frugality – have led to great research interest in the issues sought to be captured by the phrase of cyber-physical systems [37].


2. Future Potential of Cyber Physical Systems


Among recent technologies, cyber-physical systems (CPS) is an ever-growing terminology representing the integration of computation and physical capabilities which has vast area of application in process control, medical devices, energy control, traffic control, aviation, advanced automated systems and smart structures [38]. Businesses or rather manufacturers of varying sizes and industry segments increasingly cooperate with each other and with service providers, telecommunication suppliers, and software producers, in order to merge their competences, which are eventually needed to construct and operate cross-industry product innovation [39].


Recently, big data becomes a buzzword on everyone’s tongue. It has been in data mining since human-generated content has been a boost to the social network. It has also been called the web 2.0 era since late 2004 [40]. Lots of research organizations and companies have devoted themselves to this new research topic, and most of them focus on social or commercial mining. This includes sales prediction, user relationship mining and clustering, recommendation systems, opinion mining, etc. [41-42]. However, this research focuses on ‘human-generated or human-related data’ instead of ‘machine- generated data or industrial data’, which may include machine controllers, sensors, manufacturing systems, etc.


Cyber-physical systems contribute to finding answers to key challenges of our society and are highly relevant for numerous industries and fields of application. Cyber-physical systems provide companies with support in process optimization and therefore also in cost and time saving, and they provide help in saving energy, thus reducing CO2 emissions. For private users, the benefits of cyber-physical systems are predominantly in a higher level of comfort, for example in assistance with mobility, in networked safety, in individual medical care and for older people in the field of assisted living. In the agendaCPS study, the following four fields of application – which have particular relevance for Germany – were investigated in detailed scenarios for the period up to 2025:


•Energy – cyber-physical systems for the smart grid

•Mobility – cyber-physical systems for networked mobility

•Health – cyber-physical systems for telemedicine and remote diagnosis

•Industry – cyber-physical systems for industry and automated production. [43]


3. Cyber Physical Systems Enables Collabration Productivity via Internet of Things (IoT)


CPS expanded and related each other to operate with cooperatively and interactively. The recent developments of an Internet of Things (IOT) framework and the emergence of sensing technology have created a unified information grid that tightly connects systems and humans together, which further populates a big data environment in the industry [44]. Cyber-physical systems interconnect the physical world with the world of information technology and can be referred to as the next general purpose technology that will enable a fourth industrial revolution [45]. The role of technology is being hailed as a major force for change which has radically transformed the nature of competitive strategy in several industries. In most cases their common ground is that they are motivated by the high potential of productivity growth that bears this transformation process.


However, the producing industry itself is responsible to initiate measures to profit from the social and technological change [46]. In order to do so, this paper proposes to create necessary preconditions in the production system. The required preconditions in a pro- duction system can be classified on two levels: The first level is the allocation to the cyber or the physical world and the second is the distinction between hard or soft component.


On the production side, the two major mechanisms are in- tegration and self-optimisation. Integration means a revolu- tionary short value chain. Through an improved information basis and decision-making ability more functions can be inte- grated and combined in one process step or person [47]. More- over collaboration enables more employees to work together in order to address challenges in different areas [48]. Self- optimisation means that one can improve beyond the theoreti- cal boundaries and therefore become better as expected. Cy- bernetic effects and structural changes of the system at the right time can change the parameters and framework condi- tions to constantly improve the production system [49].


Complete self-optimising production systems are theoreti- cally already possible [50]. Until now only their implementation fails as the enablers from section 2 are not yet established. Once selfoptimising production systems are working accurately they reduce the workload and efficiently work at the optimal operating point. With their high flexibility and reactivity they can adapt to sudden impacts or changes in the production process. Already existing self-learning machines can only reach the theoretically expected maximum. The advantage of self-optimising systems of the future is their aim for an even higher goal. In fact they are supposed to be con- structed in such a way that they surpass the previously expected efficiency. In order to enable such an efficient system, it is necessary to consider cybernetic effects. That means, the structural change of the system as a result of considering different boundary conditions enables new opening possibilities. This implies to approach sudden changes from a different perspective. They can be used to improve the system by ad- justing its structures and rules. An assembly line with a natively planned output of 20.000 units and an improved output of 25.000 units after one year using the same resources can serve as a theoretical example.


4. Opportunities For Cyber-Physical Systems


A grid with deep renewables, as represented by the dynamics in the scaled supply blend above, presents a family of CPS challenges and opportunities that go far beyond those in the grid in operation today. At core, the challenges arise from the shift from primarily modulated, dispatchable supply to primarily uncontrolled, non-dispatchable supply, and the resultant opportunities from the increased intelligence and communication needed to allow the energy network to function as a system. Here we explore some of the dominant CPS thrusts relative to the temporal dynamics of our year in a grid with deep renewables: the coordinated management of the entire portfolio, the potential to modulate (or dispatch) demand, the utilization of storage resources, and grid-driven demand reduction [51]. Ultimately, all of these aspects need to come together in a manner that addresses the additional level of fidelity associated with transmission constraints, plant dynamics, demand adjustment mechanisms, and markets.




Cyber-physical systems (CPS) will transform how humans interact with and control the physical world. Correct, affordable and flexible deployment of CPS can only be made possible by fundamental advances in science, engineering and education. CPS technologies must be scalable across time and space, and must deal with multiple time-scales, uncertainty, privacy concerns and security issues. A new CPS science will define new mathematical foundations with formalisms to specify, analyze, verify and validate systems that monitor and control physical objects and entities. Cyber-physical sensing systems and green communications put a new emphasis on the task of energy management for wireless communications and favor the use of energyharvesting technologies.


Without any questions, CPPS can be considered as an important step in the development of manufacturing systems. Whether this step would be regarded as the fourth industrial revolution will be decided by the coming generations, but certainly, this will happen with no zero probability.




I would like to thank you my lecturer Asst.Prof.Dr. Melih S. Çeliktaş for his generous advice, inspiring guidance and encouragement throughout my research for his work.




[1] Schuh, G.; Lenders, M.; Nussbaum, C.; Kupke, D. (2009) Design for Changeability. In ElMaraghy, H. A. (Ed.): Changeable and Reconfigurable Manufacturing Systems. London: Springer, pp. 251-266.

[2] Wagels, C.; Schmitt, R. (2012) Benchmarking of Methods and Instruments for Self-Optimization in Future Production Systems. In 45th CIRP Conference on Manufacturing Systems 2012, pp. 161–166.

[3] Syverson, C. (2011) What Determines Productivity? In Journal of Economic Literature 49 (2), pp. 326-365.

[4] Bartelsman, E. J; Doms, M. (2000) Understanding Productivity: Lessons from Longitudinal Microdata. In Journal of Economic Literature 38 (3), pp. 569-594.

[5] Moore G (1965) Cramming More Components onto Integrated Circuits. Electronics 38(8):114–117.

[6] Proceedings of the IEEE. [Online]. Available: publications standards/publications/proceedings/index.html

[7] J. P. Eckert and J. Mauchly. (1946) Outline of plans for development of electronic computers. [Online]. Avail- able: 102660910-05-01-acc.pdf

[8] T. A. Henzinger and S. Sastry, Eds., Proceedings of the First International Workshop on Hybrid Systems: Computation and Control. Springer, 1998.

[9] Kagermann, H.; Wahlster, W.; Helbig J. (2013) Recommendations for implementing the strategic initiative Industrie 4.0. Acatech. pp. 13-78.

[10] Schuh, G.; Potente, T.; Varandani, R.; Hausberg, C.; Fränken, B. (2014) Collaboration Moves Productivity To The Next Level. To be published in 47th CIRP Conference on Manufacturing Systems 2014.

[11] Wagels, C.; Schmitt, R. (2012) Benchmarking of Methods and Instru- ments for Self-Optimization in Future Production Systems. In 45th CIRP Conference on Manufacturing Systems 2012, pp. 161–166.

[12] Lu, S. C-Y.; ElMaraghy, W.; Schuh, G.; Wilhelm, R. (2007) A Scien- tific Foundation of Collaborative Enginereering. In CIRP Annals – Man- ufacturing Technology 56 (2), pp. 605–634.

[13] Schuh, G.; Potente, T.; Fuchs, S.; Thomas, C.; Schmitz, S.; Hausberg, C.; Hauptvogel, A.; Brambring, F. (2013) Self-Optimising Decision- Making in Production Control. In Robust Manufacturing Control. Ber- lin: Springer, pp. 443-454.

[14] Graham, P. (2005). Web 2.0. Consultado (21/12/2008) en: http://www. nosolousabilidad. com/articulos/Web20. htm.

[15] McAfee, A., & Brynjolfsson, E. (2012). Big data: the management revolution. Harvard business review, 90(10), 60-68.

[16] Provost, F., & Fawcett, T. (2013). Data Science and its Relationship to Big Data and Data-Driven Decision Making. Big Data, 1(1), 51-59.

[17] A history of the ARPANET: The first decade. [Online]. Available: html&identifier=ADA115440 [18] N. Abramson, “The ALOHA system: another alternative for computer communications,” in Proceedings of the November 17-19, 1970, joint computer conference, 1970, pp. 281–285. [19] J. Jubin and J. Tornow, “The DARPA packet radio network protocols,” Proceedings of the IEEE, vol. 75, no. 1, pp. 21–32, 1987.

[20] Air Land Sea Application Center, “Talk II – SINCGARS,” Army, Marine Corps, Navy, Combat Air Forces, Tech. Rep., 1996.

[21] B. Crow, I. Widjaja, L. Kim, and P. Sakai, “IEEE 802.11 Wireless Local Area Networks,” IEEE Communications Magazine, vol. 35, no. 9, pp. 116–126, 1997.

[22] H. Nyquist, “Regeneration theory,” Bell System Technical Journal, vol. 11, pp. 126–147, 1932.

[23] H. W. Bode, Network analysis and feedback amplifier design, ser. Bell Telephone Laboratories series. D. Van Nostrand, 1952.

[24] W. R. Evans, “Control system synthesis by root locus method,” Transactions of the American Institute of Electrical Engineers, vol. 69, no. 1, pp. 66–69, 1950.

[25] J. Ziegler and N. B. Nichols, “Optimum settings for automatic con- trollers,” Transactions of the ASME, vol. 64, pp. 759–768, 1942.

[26] S. Bennett, “Control and the digital computer: The early years,” in Proceedings of the International Federation of Automatic Control, 2002.

[27] R. Bellman, Dynamic Programming. Princeton University Press, 1957.

[28] L. S. Pontryagin, V. G. Boltyanskii, R. V. Gamkrelidze, and E. F. Mishechenko, The Mathematical Theory of Optimal Processes. Wiley, 1963.

[29] R. E. Kalman, “Design of a self-optimizing control system,” Transac- tions of the ASME, vol. 80, pp. 468–478, 1958.

[30] ——, “On the general theory of control systems,” in Proceedings of the 1st Internatinal Conference on Automatic Control, 1960, pp. 481–492.

[31] R. E. Kalman and R. S. Bucy, “New results in linear filtering and prediction theory,” Transactions of the ASME, vol. 83, pp. 95–108, 1961.

[32] R. E. Kalman, “Canonical structure of linear dynamical systems,” Proceedings of the National Academy of Sciences of the United States of America, vol. 48, no. 4, pp. 596–600, 1962.

[33] ——, “Mathematical description of linear dynamical systems,” Journal of the Society for Industrial and Applied Mathematics Series, vol. 1, no. 2, pp. 152–192, 1963.

[34] J. A. E. Bryson and Y.-C. Ho, Applied Optimal Control: Optimization, Estimation and Control. Taylor & Francis, 1975.

[35] A. N. Michel, L. Hou, and D. Liu, Stability of Dynamical Systems: Continuous, Discontinuous, and Discrete Systems. Birkha¨user Boston, 2007.

[36] S. P. Bhattacharyya, A. Datta, and L. H. Keel, Linear Control Theory: Structure, Robustness, and Optimization. CRC Press, 2009.

[37] H. K. Khalil, Nonlinear Systems, 3rd ed. Prentice Hall, 2001.

[38] P. R. Kumar and P. Varaiya, Stochastic Systems: Estimation, Identifi- cation and Adaptive Control. Prentice Hall, 1986.

[39] K. J. A¸ stro¨m and B. Wittenmark, Adaptive Control, 2nd ed. Prentice Hall, 1994.

[40] G. E. Dullerud and F. Paganini, A Course in Robust Control Theory: A Convex Approach. Springer, 2010.

[41] H. O. Fattorini, Infinite Dimensional Linear Control Systems: The Time Optimal and Norm Optimal Problems. North Holland, 2005.

[42] D. D. Sˇ iljak, Decentralized control of complex systems. Academic Press, 1991.

[43] Proceedings of the IEEE. [Online]. Available: publications standards/publications/proceedings/index.html

[44] acatech POSITION PAPER December 2011, Cyber-Physical Systems Driving force for innovation in mobility, health, energy and production

[45] Lee J, Lapira E, Bagheri B, Kao H (2013) Recent advances and trends in predictive manufacturing systems in big data environment. Manuf Lett 1(1):38–41

[46] Johansson JE, Krishnamurthy C, Schlissberg HE. Solving the solutions problem. McKinsey Quarterly, 3, 2003, p. 116-125

[47] Lee J., Kao H., Yang S., (2014), Service innovation and smart analytics for Industry 4.0 and big data environment, Procedia CIRP 16 (2014), 3 – 8

[48] Kagermann, H.; Wahlster, W.; Helbig J. (2013) Recommendations for implementing the strategic initiative Iindustrie 4.0. Acatech. pp. 13-78.

[49] Gielen D, Taylor P. Indicators for industrial energy efficiency in India. Energy 2009;34:962–9.

[50] Zhang, Lichen, Jifeng He, and Wensheng Yu., 2012 “Challenges and Solutions of Cyber-Physical Systems.”, SoftTech 2012, Online

[51] Givehchi O., Jasperneite J. Industrial automation services as part of the Cloud: First experiences. Proceedings of the Jahreskolloquium Kommunikation in der Automation – KommA, Magdeburg, November 13-14, 2013, Magdeburg, Germany: 10.

[52] Monostori L., Csáji, BCs. Stochastic dynamic production control by neurodynamic programming. CIRP Annals – Manufacturing Technology 2006; 55/1: 473–478.


IEA and OPEC debated the current situation in global oil markets



Leaders expressed deep concerns about the grave global health crisis caused by the coronavirus (COVID-19) and its related impacts on the stability of economies and markets, notably in developing countries.

The Executive Director of the International Energy Agency (IEA), Dr Fatih Birol, and the Secretary General of the Organization of the Petroleum Exporting Countries (OPEC), Mohammad Sanusi Barkindo, spoke by phone to review the current situation in global oil markets.

The two leaders expressed deep concerns about the coronavirus (COVID-19), which is already a grave and unprecedented global health crisis with potentially far-reaching economic and social consequences. Dr Birol and SG Barkindo assessed the impact of the virus and the recent broad-based financial and oil market volatility on the global economy. In particular, they discussed the inherent risks of the fast-evolving dynamics, including the most recent developments in global oil markets. They agreed that these create material impacts, particularly for citizens of developing countries including those that rely heavily on income from oil and gas production for essential services and that are especially vulnerable to market volatility.

Dr Birol and SG Barkindo reviewed the impact on vulnerable developing countries and noted that if current market conditions continue, their income from oil and gas will fall by 50% to 85% in 2020, reaching the lowest levels in more than two decades, according to recent IEA analysis. This is likely to have major social and economic consequences, notably for public sector spending in vital areas such as healthcare and education.

They both underscored the importance of market stability, as the impacts of extreme volatility are felt by producers, particularly in terms of much needed income, and by both producers and consumers, who are affected by an unstable and unpredictable market.

SG Barkindo and Dr Birol emphasised the importance of finding ways to minimise the impact of the current situation on vulnerable developing countries. They agreed to remain in close contact on the matter and continue their regular consultations on oil market developments.

Continue Reading


Early to assess impact of COVID-19 on European wind energy



The European wind industry is the global leader in the wind turbine market, realising projects in more than 80 countries world-wide. As such, our companies rely on both European and global supply chains for raw materials and components. The COVID-19 virus is impeding international trade, creating delays and uncertainties for different industrial sectors. As the number of infections rises, the European wind industry is likely to be impacted.

First analysis suggests that COVID-19 will have moderate effects on international supply chains for wind energy. With the outbreak of COVID-19 still at a relatively early stage in Europe and other countries, it is too soon to judge its impact on production and revenues in the sector. However, the first logistical delays in the supply chain can be observed already.

“A knock-on effect of a slowdown in China’s manufacturing output is already visible in other countries. The wind industry is, of course, not the only industry feeling the pinch from quarantines, travel restrictions and closed factories. Vehicle and vessel manufacturers, solar-PV panel and battery producers are being similarly affected. We will need to take a strategic approach to ensure that disruption is minimised”, comments WindEurope CEO Giles Dickson with regards to the EU Commission’s Industrial Strategy presented earlier this week.

“With COVID-19 we are likely to see delays in the development of new wind farm projects which could cause developers to miss the deployment deadlines in countries’ auction systems and face financial penalties. Governments should be flexible on how they apply their rules. And if ongoing auctions are undersubscribed because developers can’t bid in time, governments should award what they can and auction the non-awarded volumes at a later stage”, says Dickson.

Continue Reading


Leosphere launches Windcube: Power to customers with data and analytics



Windcube Insights is the first out-of-the-box software solution to leverage lidar and turbine data for cost-efficient power performance testing

Leosphere, a Vaisala company that specializes in developing, manufacturing, and servicing turnkey wind lidar (light detection and ranging) instruments for wind energy, aviation, meteorology, and air quality, today announced the launch of Windcube Insights at Wind Operations Europe 2020 in Munich. Windcube Insights is a proprietary data analytics software designed specifically for the Windcube Nacelle (previously called Wind Iris) nacelle-mounted lidar that simplifies the wind turbine power performance testing process.

“This new tool empowers operators with International Electrotechnical Commission (IEC)-compliant data to verify that turbines are performing as promised so they can maximize the energy output of their wind farm,” said Alexandre Sauvage, CEO of Leosphere. “The easy-to-use software allows operators to perform power performance testing and suggest operational optimization — quickly, accurately and efficiently.”

Windcube Insights enables true and fully transparent data analysis and reporting for Windcube Nacelle customers — all within a web-based user interface. The software is the first in the industry to enable the upload of both Windcube Nacelle lidar and supervisory control and data acquisition (SCADA) turbine performance data with a simplified data synchronization process.

The method of operation:

  • A variety of standardized lidar and turbine data filters are available and fully configurable by the user, simplifying preparation of the data sets.
  • The software leverages those data sets to calculate and display the power curve, and the complete set of IEC requirements can be applied with embedded guidelines that reference the proper IEC standard sections, making the service fully transparent and understandable.
  • The production data, along with standardized uncertainties, are calculated and can be exported in the form of a traditional report table.
Wind Turbine Power Curve Determination Value

The handling of IEC standard requirements for issuing a power performance test (PPT) is complex, requires deep expertise, and represents a potential source of error. However, such testing is necessary for regulatory compliance, warranty verification and turbine performance verification during both the development and operations phases. Determining the power curve of a wind turbine in accordance with recognized standards is valuable because the power curve is one of the most important characteristics of the economic value of a wind project.

The Windcube Nacelle lidar measures the wind conditions at hub height ahead of the turbine, enabling operators and wind turbine original equipment manufacturers (OEMs) to efficiently and accurately assess performance and optimize design and production efficiency. When fully integrated within the wind turbine, Windcube Nacelle enables load reduction, design costs reduction, and continuous production gains.

Seeing the global energy demand accelerate at its fastest pace in more than a decade, wind energy and other clean energies are increasingly becoming further engrained into the world’s equation for energy demand. Since 2010, the size of the global wind power market has increased by 35%, and the global market is expected to approach USD 125 billion by 2030.

“Without Windcube Insights, lidar users would have to build their own software programs to analyze the data being collected by the nacelle lidar and the wind turbine,” said Sauvage. “We’ve simplified the process of applying filters, calculating the uncertainties described in recognized IEC standards and displaying data, ultimately creating a simple way to support the utilization of nacelle-mounted lidar following IEC standards and industry best practices. Windcube Insights is just another example of Leosphere’s commitment to innovation and to making our customers’ lives better by introducing tools to help them maximize efficiency and productivity.”

Vaisala, a global leader in weather, environmental, and industrial measurements, acquired Leosphere in 2018. For more information on how Leosphere provides the most comprehensive lidar-based wind measurement systems to markets around the globe, please visit us on March 5-6 in Munich at Wind Operations Europe 2020 (booth #12).

Continue Reading


Copyright © 2011-2018 Moneta Tanıtım Organizasyon Reklamcılık Yayıncılık Tic. Ltd. Şti. - Canan Business Küçükbakkalköy Mah. Kocasinan Cad. Selvili Sokak No:4 Kat:12 Daire:78 Ataşehir İstanbul - T:0850 885 05 01 -