The performance evaluation of wind turbines operating in real-world environments typically relies... more The performance evaluation of wind turbines operating in real-world environments typically relies on analyzing the power curve, which shows the relationship between wind speed and power output. However, conventional univariate models that consider only wind speed as an input variable often fail to fully explain the observed performance of wind turbines, as power output depends on multiple variables, including working parameters and ambient conditions. To overcome this limitation, the use of multivariate power curves that consider multiple input variables needs to be explored. Therefore, this study advocates for the application of explainable artificial intelligence (XAI) methods in constructing data-driven power curve models that incorporate multiple input variables for condition monitoring purposes. The proposed workflow aims to establish a reproducible method for identifying the most appropriate input variables from a more comprehensive set than is usually considered in the litera...
Wakes between neighboring wind turbines are a significant source of energy loss in wind farm oper... more Wakes between neighboring wind turbines are a significant source of energy loss in wind farm operations. Extensive research has been conducted to analyze and understand wind turbine wakes, ranging from aerodynamic descriptions to advanced control strategies. However, there is a relatively overlooked research area focused on characterizing real-world wind farm operations under wake conditions using Supervisory Control And Data Acquisition (SCADA) parameters. This study aims to address this gap by presenting a detailed discussion based on SCADA data analysis from a real-world test case. The analysis focuses on two selected wind turbines within an onshore wind farm operating under wake conditions. Operation curves and data-driven methods are utilized to describe the turbines’ performance. Particularly, the analysis of the operation curves reveals that a wind turbine operating within a wake experiences reduced power production not only due to the velocity deficit but also due to increased turbulence intensity caused by the wake. This effect is particularly prominent during partial load operation when the rotational speed saturates. The turbulence intensity, manifested in the variability of rotational speed and blade pitch, emerges as the crucial factor determining the extent of wake-induced power loss. The findings indicate that turbulence intensity is strongly correlated with the proximity of the wind direction to the center of the wake sector. However, it is important to consider that these two factors may convey slightly different information, possibly influenced by terrain effects. Therefore, both turbulence intensity and wind direction should be taken into account to accurately describe the behavior of wind turbines operating within wakes.
2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), 2019
ABSTRACT Synchronized Wide-Area Monitoring Systems (WAMS) involve the use of power system-wide me... more ABSTRACT Synchronized Wide-Area Monitoring Systems (WAMS) involve the use of power system-wide measurements to avoid large disturbances and reduce the probability of catastrophic events. In these systems a large volume of raw data is collected by distributed sensors and sent to central servers for post processing activities. Many research works conjectured that this hierarchical monitoring paradigm could be not affordable in addressing the increasing network complexity and the massive data exchanging characterizing moderns martgrids. Unaffordable complexity, hardware redundancy, network bandwidth and data storage resources are the main barriers imposed by technology and costs. Moreover, in WAMS the global absolute time reference for sensors synchronization is typically obtained by satellite-based timing signals processing. Since these signals are extremely vulnerable to radio frequency interference(i.e.cyber-attacks), effective countermeasures aimed at increasing there silience of synchronized WAMS to external and internal interferences need to be designed. In trying and addressing these challenges,in this paper the role of decentralized consensus protocols for decentralized and synchronized wide are a smart grids monitoring is analyzed.
... comprise system state estimation, optimal power flow studies (ie, voltage control), security ... more ... comprise system state estimation, optimal power flow studies (ie, voltage control), security assessment (ie ... based on decision support systems, these historical data are extremely useful in power system analysis. ... in order to be composed with the rest of the framework, have to ...
The massive penetration of wind generators in existing electrical grids is causing several critic... more The massive penetration of wind generators in existing electrical grids is causing several critical issues, which are pushing system operators to enhance their operation functions in order to mitigate the effects produced by the intermittent and non-programmable generation profiles. In this context, the integration of wind forecasting and reliability models based on experimental data represents a strategic tool for assessing the impact of generators and grid operation state on the available power profiles. Unfortunately, field data acquired by Supervisory Control and Data Acquisition systems can be characterized by outliers and incoherent data, which need to be properly detected and filtered in order to avoid large modeling errors. To deal with this challenging issue, in this paper a novel methodology fusing Fuzzy clustering techniques, and probabilistic-based anomaly detection algorithms are proposed for wind data filtering and data-driven generator modeling
The limitation of transmission lines thermal capacity plays a crucial role in the safety and reli... more The limitation of transmission lines thermal capacity plays a crucial role in the safety and reliability of power systems. Dynamic thermal line rating approaches aim to estimate the transmission line’s temperature and assess its compliance with the limitations above. Existing physics-based standards estimate the temperature based on environment and line conditions measured by several sensors. This manuscript shows that estimation accuracy can be improved by adopting a data-driven Digital Twin approach. The proposed method exploits machine learning by learning the input–output relation between the physical sensors data and the actual conductor temperature, serving as a digital equivalent to physics-based standards. An experimental assessment on real data, comparing the proposed approach with the IEEE 738 standard, shows a reduction of 60% of the Root Mean Squared Error and a decrease in the maximum estimation error from above 10 °C to below 7 °C. These preliminary results suggest tha...
2020 IEEE International Conference on Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)
The performance evaluation of wind turbines operating in real-world environments typically relies... more The performance evaluation of wind turbines operating in real-world environments typically relies on analyzing the power curve, which shows the relationship between wind speed and power output. However, conventional univariate models that consider only wind speed as an input variable often fail to fully explain the observed performance of wind turbines, as power output depends on multiple variables, including working parameters and ambient conditions. To overcome this limitation, the use of multivariate power curves that consider multiple input variables needs to be explored. Therefore, this study advocates for the application of explainable artificial intelligence (XAI) methods in constructing data-driven power curve models that incorporate multiple input variables for condition monitoring purposes. The proposed workflow aims to establish a reproducible method for identifying the most appropriate input variables from a more comprehensive set than is usually considered in the litera...
Wakes between neighboring wind turbines are a significant source of energy loss in wind farm oper... more Wakes between neighboring wind turbines are a significant source of energy loss in wind farm operations. Extensive research has been conducted to analyze and understand wind turbine wakes, ranging from aerodynamic descriptions to advanced control strategies. However, there is a relatively overlooked research area focused on characterizing real-world wind farm operations under wake conditions using Supervisory Control And Data Acquisition (SCADA) parameters. This study aims to address this gap by presenting a detailed discussion based on SCADA data analysis from a real-world test case. The analysis focuses on two selected wind turbines within an onshore wind farm operating under wake conditions. Operation curves and data-driven methods are utilized to describe the turbines’ performance. Particularly, the analysis of the operation curves reveals that a wind turbine operating within a wake experiences reduced power production not only due to the velocity deficit but also due to increased turbulence intensity caused by the wake. This effect is particularly prominent during partial load operation when the rotational speed saturates. The turbulence intensity, manifested in the variability of rotational speed and blade pitch, emerges as the crucial factor determining the extent of wake-induced power loss. The findings indicate that turbulence intensity is strongly correlated with the proximity of the wind direction to the center of the wake sector. However, it is important to consider that these two factors may convey slightly different information, possibly influenced by terrain effects. Therefore, both turbulence intensity and wind direction should be taken into account to accurately describe the behavior of wind turbines operating within wakes.
2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), 2019
ABSTRACT Synchronized Wide-Area Monitoring Systems (WAMS) involve the use of power system-wide me... more ABSTRACT Synchronized Wide-Area Monitoring Systems (WAMS) involve the use of power system-wide measurements to avoid large disturbances and reduce the probability of catastrophic events. In these systems a large volume of raw data is collected by distributed sensors and sent to central servers for post processing activities. Many research works conjectured that this hierarchical monitoring paradigm could be not affordable in addressing the increasing network complexity and the massive data exchanging characterizing moderns martgrids. Unaffordable complexity, hardware redundancy, network bandwidth and data storage resources are the main barriers imposed by technology and costs. Moreover, in WAMS the global absolute time reference for sensors synchronization is typically obtained by satellite-based timing signals processing. Since these signals are extremely vulnerable to radio frequency interference(i.e.cyber-attacks), effective countermeasures aimed at increasing there silience of synchronized WAMS to external and internal interferences need to be designed. In trying and addressing these challenges,in this paper the role of decentralized consensus protocols for decentralized and synchronized wide are a smart grids monitoring is analyzed.
... comprise system state estimation, optimal power flow studies (ie, voltage control), security ... more ... comprise system state estimation, optimal power flow studies (ie, voltage control), security assessment (ie ... based on decision support systems, these historical data are extremely useful in power system analysis. ... in order to be composed with the rest of the framework, have to ...
The massive penetration of wind generators in existing electrical grids is causing several critic... more The massive penetration of wind generators in existing electrical grids is causing several critical issues, which are pushing system operators to enhance their operation functions in order to mitigate the effects produced by the intermittent and non-programmable generation profiles. In this context, the integration of wind forecasting and reliability models based on experimental data represents a strategic tool for assessing the impact of generators and grid operation state on the available power profiles. Unfortunately, field data acquired by Supervisory Control and Data Acquisition systems can be characterized by outliers and incoherent data, which need to be properly detected and filtered in order to avoid large modeling errors. To deal with this challenging issue, in this paper a novel methodology fusing Fuzzy clustering techniques, and probabilistic-based anomaly detection algorithms are proposed for wind data filtering and data-driven generator modeling
The limitation of transmission lines thermal capacity plays a crucial role in the safety and reli... more The limitation of transmission lines thermal capacity plays a crucial role in the safety and reliability of power systems. Dynamic thermal line rating approaches aim to estimate the transmission line’s temperature and assess its compliance with the limitations above. Existing physics-based standards estimate the temperature based on environment and line conditions measured by several sensors. This manuscript shows that estimation accuracy can be improved by adopting a data-driven Digital Twin approach. The proposed method exploits machine learning by learning the input–output relation between the physical sensors data and the actual conductor temperature, serving as a digital equivalent to physics-based standards. An experimental assessment on real data, comparing the proposed approach with the IEEE 738 standard, shows a reduction of 60% of the Root Mean Squared Error and a decrease in the maximum estimation error from above 10 °C to below 7 °C. These preliminary results suggest tha...
2020 IEEE International Conference on Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)
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