Sorgenia Green Srl

Milano, Italy

Sorgenia Green Srl

Milano, Italy
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Castellani F.,University of Perugia | Astolfi D.,University of Perugia | Garinei A.,Guglielmo Marconi University | Proietti S.,University of Perugia | And 3 more authors.
Energy Procedia | Year: 2015

SCADA control systems are the keystone for reliable performance optimization of wind farms. Processing into knowledge the amount of information they spread is a challenging task, involving engineering, physics, statistics and computer science skills. The present work deals with the effects on the efficiency of turbine inability of optimal aligning to the wind direction, due to meandering wind caused by wakes. The approach is tested on a judiciously chosen cluster of turbines of a wind farm sited in southern Italy. By a post-processing method based on discretization of nacelle position measurements, a set of dominant patterns of the cluster is identified. The patterns associated to best performances are individuated and it is shown that they correspond to non-trivial alignment to wind direction. © 2015 The Authors. Published by Elsevier Ltd.

Castellani F.,University of Perugia | Garinei A.,University Guglielmo Marconi | Terzi L.,Sorgenia Green Srl | Astolfi D.,University of Perugia | And 2 more authors.
Diagnostyka | Year: 2013

Monitoring wind energy production is fundamental to improve the performances of a wind farm during the operational phase. In order to perform reliable operational analysis, data mining of all available information spreading out from turbine control systems is required. In this work a SCADA (Supervisory Control And Data Acquisition) data analysis was performed on a small wind farm and new post-processing methods are proposed for condition monitoring of the aerogenerators. Indicators are defined to detect the malfunctioning of a wind turbine and to select meaningful data to investigate the causes of the anomalous behaviour of a turbine. The operating state database is used to collect information about the proper power production of a wind turbine and a number map has been codified for converting the performance analysis problem into a purely numerical one. Statistical analysis on the number map clearly helps in detecting operational anomalies, providing diagnosis for their reasons. The most operationally stressed turbines are systematically detected through the proposal of two Malfunctioning Indices. Results demonstrate that a proper selection of the SCADA data can be very useful to measure the real performances of a wind farm and thus to define optimal repair/replacement and preventive maintenance policies that play a major role in case of energy production.

Castellani F.,University of Perugia | Garinei A.,University Guglielmo Marconi | Terzi L.,Sorgenia Green Srl | Astolfi D.,University of Perugia
Wind Energy | Year: 2015

The assessment of extreme wind speeds is a crucial issue for securing structural safety of wind turbines and inquiring largest loads to which turbines must be prepared to undergo. International standards suggest applying the Gumbel method of fitting the annual maxima to their theoretic probability distribution. Yet, often, wind databases are too short to apply such methods with statistical significance, and other procedures are commonly adopted [such as peaks over threshold (POT) and independent storms], which involve dependency on arbitrary thresholds for filtering data and issues of sub-asymptocity, i.e. how well the selected dataset fits to density functions describing the distribution of peaks or extreme values. The present paper aims at contributing to such currently ongoing debate, providing a statistical analysis of the application of POT and independent storms methods on wind time series of various lengths from different geographical areas. The CERN data analysis framework ROOT has been employed for guaranteeing excellent standards of computational precision and wealth of statistical information. Analysis of uncertainties in the wind speeds estimates and tests of the goodness of fit of the datasets to the proper distributions have been carried on. An algorithm for choosing the optimum thresholds was developed, which encapsules and compromises the statistical complexity of the methods. A declustering procedure has been carried on for discriminating proper peaks in the POT method: it has been tested that such declustering provides a dramatic improvement of the statistical quality of the method. Copyright © 2014 John Wiley & Sons, Ltd.

Astolfi D.,University of Perugia | Castellani F.,University of Perugia | Terzi L.,Sorgenia Green srl
Diagnostyka | Year: 2014

Wind turbines, due to the distribution of the source, are an energy conversion system having low density on the territory, whose operational behaviour and production on the short term strongly depends on the stochastic nature of wind. They therefore need accurate assessment prior installation and careful condition monitoring in the operative phase. In the present work, smart post processing of Supervisory Control And Data Acquisition (SCADA) control system data sets is employed for fault prevention and diagnosis through the analysis of the temperatures of the machines. Automatic routines are developed for monitoring the evolution of all the temperature SCADA channels against power production. The methods are tested on an onshore wind farm sited in southern Italy, where nine turbines with 2 MW rated power are installed. The tests are performed both ex post and in real time: it is shown that in the former case, a major mechanical problem is detected, and in the latter case a significant problem to the cooling system is identified before compromising turbine functionality. © DIAGNOSTYKA 2014.

Astolfi D.,University of Perugia | Castellani F.,University of Perugia | Garinei A.,University Guglielmo Marconi | Terzi L.,Sorgenia Green srl
Applied Energy | Year: 2015

Wind turbines are an energy conversion system having a low density on the territory, and therefore needing accurate condition monitoring in the operative phase. Supervisory Control And Data Acquisition (SCADA) control systems have become ubiquitous in wind energy technology and they pose the challenge of extracting from them simple and explanatory information on goodness of operation and performance. In the present work, post processing methods are applied on the SCADA measurements of two onshore wind farms sited in southern Italy. Innovative and meaningful indicators of goodness of performance are formulated. The philosophy is a climax in the granularity of the analysis: first, Malfunctioning Indexes are proposed, which quantify goodness of merely operational behavior of the machine, irrespective of the quality of output. Subsequently the focus is shifted to the analysis of the farms in the productive phase: dependency of farm efficiency on wind direction is investigated through the polar plot, which is revisited in a novel way in order to make it consistent for onshore wind farms. Finally, the inability of the nacelle to optimally follow meandering wind due to wakes is analysed through a Stationarity Index and a Misalignment Index, which are shown to capture the relation between mechanical behavior of the turbine and degradation of the power output. © 2015 Elsevier Ltd.

Castellani F.,University of Perugia | Garinei A.,University Guglielmo Marconi | Terzi L.,Sorgenia Green Srl | Astolfi D.,University of Perugia | Gaudiosi M.,University of Perugia
IET Renewable Power Generation | Year: 2014

Usually, wind energy assessment on a new windfarm is conducted with maximum effort prior to the installation of the turbines by using both numerical and experimental investigations. Yet, often the windfarm performances during operation are not as good as expected. This issue can be investigated with a deep analysis of the operational conditions of the windfarm. The large amount of data collected by the SCADA (Supervisory Control and Data Acquisition) systems installed on the turbines can be very helpful. In the present study, the performances of a windfarm were analysed through the elaboration of the SCADA data from a windfarm in southern Italy; in this site, Sorgenia Green installed nine aerogenerators with a rated power of 2 MWeach, on a hilly area with gentle slopes. A systematic approach is proposed to isolate the downtime because of malfunctioning and a manifold investigation is applied to the operational phase: several methods (polar efficiency plot, multidimensional graphical analysis, sectorial power curve and misalignment index, respectively) are suggested and applied for unveiling the sectors where the production output is most affected by the wake interactions. Numerical windflow modelling is performed in the wind rose sectors where underproduction is highlighted by a SCADA data analysis: finally, a SCADA database is employed for testing the goodness of the simulations. © 2014 The Institution of Engineering and Technology.

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