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Predictive maintenance is a key element for lowering Operation and Maintenance (O&M) costs of wind turbines. Predictive maintenance models are usually based on drivetrain vibration data or operational timeseries from the Supervisory Control And Data Acquisition (SCADA) system, while readily available alarms and warnings from the SCADA system are typically not utilized. In this work we present a novel Artificial Intelligence (AI) based approach for early fault detection of wind turbines using alarms and warnings from the SCADA system.

Based on data from 2083 wind turbines installed in Sweden since 1988, the annual energy production (AEP) predictions considered at the project planning phases of the wind turbines in Sweden have been compared to the wind-index-corrected production data. The production data and the predicted AEP data are taken from Vindstat, a database that collects information directly from wind turbine owners in Sweden. The mean error for all analyzed wind turbines was 11.9%,which means that, overall, the predicted AEP has been overestimated. There has been improved accuracy with time and error in prediction decreasing from 12% to 6.3% for wind turbines installed in the 2000s and 2010s, respectively. However, the overall improvement in accuracy seems to have stagnated around 2005 despite the refinement of forecasting methods and better data availability. From the results analyzed for effects of terrain, the error is smaller for wind turbines in forest areas than in open terrain, indicating that the complexity of forest terrain is not the reason behind the error. Also, there is no apparent increase of error with wind farm size, which could have been expected if the wind farm blockage effect was a primary reason for the overestimations. Comparison between significant wind turbine manufacturers Vestas and Enercon in the Swedish context, the error was more prominent for Enercon.

Based on data from 2083 wind turbines installed in Sweden since 1988, the annual energy production (AEP) predictions considered at the project planning phases of the wind turbines in Sweden have been compared to the wind-index-corrected production data. The production data and the predicted AEP data are taken from Vindstat, a database that collects information directly from wind turbine owners in Sweden. The mean error for all analyzed wind turbines was 11.9%,which means that, overall, the predicted AEP has been overestimated. There has been improved accuracy with time and error in prediction decreasing from 12% to 6.3% for wind turbines installed in the 2000s and 2010s, respectively. However, the overall improvement in accuracy seems to have stagnated around 2005 despite the refinement of forecasting methods and better data availability. From the results analyzed for effects of terrain, the error is smaller for wind turbines in forest areas than in open terrain, indicating that the complexity of forest terrain is not the reason behind the error. Also, there is no apparent increase of error with wind farm size, which could have been expected if the wind farm blockage effect was a primary reason for the overestimations. Comparison between significant wind turbine manufacturers Vestas and Enercon in the Swedish context, the error was more prominent for Enercon.

Predictive maintenance is a key element for lowering Operation and Maintenance (O&M) costs of wind turbines. Predictive maintenance models are usually based on drivetrain vibration data or operational timeseries from the Supervisory Control And Data Acquisition (SCADA) system, while readily available alarms and warnings from the SCADA system are typically not utilized. In this work we present a novel Artificial Intelligence (AI) based approach for early fault detection of wind turbines using alarms and warnings from the SCADA system.

Based on data from 2083 wind turbines installed in Sweden from 1988 onwards, the accuracy of the predictions of the annual energy production (AEP) from the project planning phases has been compared to the actual wind-index-corrected production. Both the electricity production and the predicted AEP come from Vindstat, a database that collects information directly from wind turbine owners. The mean error for all analyzed wind turbines was 13.0%, which means that, overall, the predicted AEP has been overestimated. There has been an improvement of accuracy with time with an overestimation of 8.2% for wind turbines installed in the 2010s, however, the continuous improvement seems to have stagnated around 2005 despite better data availability and continuous refinement of methods. Dividing the results by terrain, the error is larger for wind turbines in open and flat terrain than in forest areas, indicating that the reason behind the error is not the higher complexity of the forest terrain. Also, there is no apparent increase of error with wind farm size which could have been expected if wind farm blockage effect was a main reason for the overestimations. Besides inaccurate AEP predictions, a higher-than-expected performance decline due to inadequate maintenance of the wind turbines may be a reason behind the AEP overestimations. The main sources of error are insecurity regarding the source of AEP predictions and the omission of mid-life alterations of rated power. © 2021 by the authors.

U.S. Latino parents are often characterized by educators as uninvolved in school and the formal education of their children because they do not value education. While research indicates otherwise, stereotypes still exist among many educators that one reason Latinos do poorly in school is because they do not care. Masked behind stereotypes is the significant variation in levels of parent involvement in education among U.S. Latinos. The literature does not address why parents with similar socioeconomic, ethnic, and cultural backgrounds respond to schools in such distinct ways. This ethnographic case study addresses variation in the involvement of Mexican immigrant mothers by focusing on how some use their cultured capacities to construct strategies of action in the formal education of their children in the United States. The implications for policy and practice include a greater understanding of why some Mexican immigrant parents routinely participate in the school affairs of their children while others with similar characteristics and backgrounds do not; and how institutions, like schools, may have the means to impact the strategies of action chosen by parents through the use of widely disseminated semiotic codes. This study also finds that culture's role in the participation of Mexican immigrant parents could not fully be accounted for in Epstein's (1995, 2001) Six Types of Involvement for Parents. Socializing was identified as a seventh type of involvement that may actually serve as a catalyst for involvement in the other six types and a means for strengthening home-school connections. In addition, a significant finding not seen elsewhere in the literature was an overwhelming amount of evidence that the majority of Mexican immigrant mothers in this study believed that schools were better in Mexico than the U.S.

U.S. Latino parents are often characterized by educators as uninvolved in school and the formal education of their children because they do not value education. While research indicates otherwise, stereotypes still exist among many educators that one reason Latinos do poorly in school is because they do not care. Masked behind stereotypes is the significant variation in levels of parent involvement in education among U.S. Latinos. The literature does not address why parents with similar socioeconomic, ethnic, and cultural backgrounds respond to schools in such distinct ways. This ethnographic case study addresses variation in the involvement of Mexican immigrant mothers by focusing on how some use their cultured capacities to construct strategies of action in the formal education of their children in the United States. The implications for policy and practice include a greater understanding of why some Mexican immigrant parents routinely participate in the school affairs of their children while others with similar characteristics and backgrounds do not; and how institutions, like schools, may have the means to impact the strategies of action chosen by parents through the use of widely disseminated semiotic codes. This study also finds that culture's role in the participation of Mexican immigrant parents could not fully be accounted for in Epstein's (1995, 2001) Six Types of Involvement for Parents. Socializing was identified as a seventh type of involvement that may actually serve as a catalyst for involvement in the other six types and a means for strengthening home-school connections. In addition, a significant finding not seen elsewhere in the literature was an overwhelming amount of evidence that the majority of Mexican immigrant mothers in this study believed that schools were better in Mexico than the U.S.

Thirty soil samples (12 residential gardens and 18 allotments) were collected from the Cherwell District of north Oxfordshire in south-central England. The underlying parent geology of the area is dominated by Jurassic ironstone. The samples were analysed for their total contents of As, Cr, and Ni by X-ray fluorescence spectroscopy and for the bioaccessible fractions of these elements using a physiologically based extraction test. Four soils (two residential soils and two allotment soils) were chosen for further determination of their element solid phase distribution. The study showed that whilst total concentrations of As, Cr, and Ni are elevated due to the soil parent material, the bioaccessibility test showed that only a small proportion of the total concentration is available for absorption into the human body (<15%). The sequential extraction test showed that the nonmobile forms of the elements are strongly sorbed on to iron oxides. Parent material geology has a significant effect on the total element concentrations and the bioaccessibility of potentially harmful element (PHE). Land use does not show such a large effect but the allotment bioaccessibility data show a bigger spread and possibly higher values for As and Cr which may be due to agronomic (cultivation) practices such as addition of fertilisers and organic matter.

Thirty soil samples (12 residential gardens and 18 allotments) were collected from the Cherwell District of north Oxfordshire in south-central England. The underlying parent geology of the area is dominated by Jurassic ironstone. The samples were analysed for their total contents of As, Cr, and Ni by X-ray fluorescence spectroscopy and for the bioaccessible fractions of these elements using a physiologically based extraction test. Four soils (two residential soils and two allotment soils) were chosen for further determination of their element solid phase distribution. The study showed that whilst total concentrations of As, Cr, and Ni are elevated due to the soil parent material, the bioaccessibility test showed that only a small proportion of the total concentration is available for absorption into the human body (<15%). The sequential extraction test showed that the nonmobile forms of the elements are strongly sorbed on to iron oxides. Parent material geology has a significant effect on the total element concentrations and the bioaccessibility of potentially harmful element (PHE). Land use does not show such a large effect but the allotment bioaccessibility data show a bigger spread and possibly higher values for As and Cr which may be due to agronomic (cultivation) practices such as addition of fertilisers and organic matter.

The shift towards qualify performance and accounting for results has dramatically changed the way Government executes public policy objectives. The advent of the Government Performance and Results Act (GPRA) places the responsibility for gathering this information upon each Federal activity subject to its provisions. The Army SBIR program must now find a way to qualify its performance and determine what results are derived from a program that expends in excess of $100,000,000 annually on research. This thesis analyses Army Small Business Innovation Research (SBIR) commercialization rates against a National Science Foundation study of DoD Fast Track and DoD Control Group awards. It provides an objective measure of program results that program officials can use to submit their annual GPRA performance reports. The thesis studied 37 SBIR phase II firms and established a performance baseline. The thesis concludes that Army SBIR awards are outperforming DoD Fast Track and DoD Control Groups in the critical area of average commercial sales per award. It recommends a reduced focus on outside investment and a survey strategy that uses small sample sizes to qualify program performance. It concludes with a proposed survey instrument that Army SBIR managers can use to capture future program outcomes.

There is a growing interest in robust and nonparametric methods with engineering applications, due to the nature of the data. Here, we study two power systems engineering applications that employ or recommend robust and nonparametric methods; topology error identification and voltage calibration. Topology errors are a well-known, well-documented problem for utility companies. A topology error occurs when a line's status in a power network, whether active or deactive, is misclassified. This will lead to an incorrect Jacobian matrix used to estimate the unknown parameters of a network in a nonlinear regression model. We propose a solution using nonlinear regression techniques to identify the correct status of every line in the network by deriving a statistical model of the power flows and injections while employing Kirchhoff's Current Law. Simulation results on the IEEE-118 bus system showed that the methodology was able to detect where topology errors occurred as well as identify gross measurement errors. The Friedman Two-Way Analysis of Variance by Ranks test is advocated to calibrate voltage measurements at a bus in a power network. However, it was found that the Friedman test was only slightly more robust or resistant in the presence of discordant measurements than the classical F-test. The resistance of a statistical test is defined as the fraction of bad data necessary to switch a statistical conclusion. We mathematically derive the maximum resistance to rejection and to acceptance of the Friedman test, as well as the Brown-Mood test, and show that the Brown-Mood test has a higher maximum resistance to rejection and to acceptance than the Friedman test. In addition, we simulate the expected resistance to rejection and to acceptance of both tests and show that on average the Brown-Mood test is slightly more robust to rejection while on average the Friedman test is more robust to acceptance.

There is a growing interest in robust and nonparametric methods with engineering applications, due to the nature of the data. Here, we study two power systems engineering applications that employ or recommend robust and nonparametric methods; topology error identification and voltage calibration. Topology errors are a well-known, well-documented problem for utility companies. A topology error occurs when a line's status in a power network, whether active or deactive, is misclassified. This will lead to an incorrect Jacobian matrix used to estimate the unknown parameters of a network in a nonlinear regression model. We propose a solution using nonlinear regression techniques to identify the correct status of every line in the network by deriving a statistical model of the power flows and injections while employing Kirchhoff's Current Law. Simulation results on the IEEE-118 bus system showed that the methodology was able to detect where topology errors occurred as well as identify gross measurement errors. The Friedman Two-Way Analysis of Variance by Ranks test is advocated to calibrate voltage measurements at a bus in a power network. However, it was found that the Friedman test was only slightly more robust or resistant in the presence of discordant measurements than the classical F-test. The resistance of a statistical test is defined as the fraction of bad data necessary to switch a statistical conclusion. We mathematically derive the maximum resistance to rejection and to acceptance of the Friedman test, as well as the Brown-Mood test, and show that the Brown-Mood test has a higher maximum resistance to rejection and to acceptance than the Friedman test. In addition, we simulate the expected resistance to rejection and to acceptance of both tests and show that on average the Brown-Mood test is slightly more robust to rejection while on average the Friedman test is more robust to acceptance.

The shift towards qualify performance and accounting for results has dramatically changed the way Government executes public policy objectives. The advent of the Government Performance and Results Act (GPRA) places the responsibility for gathering this information upon each Federal activity subject to its provisions. The Army SBIR program must now find a way to qualify its performance and determine what results are derived from a program that expends in excess of $100,000,000 annually on research. This thesis analyses Army Small Business Innovation Research (SBIR) commercialization rates against a National Science Foundation study of DoD Fast Track and DoD Control Group awards. It provides an objective measure of program results that program officials can use to submit their annual GPRA performance reports. The thesis studied 37 SBIR phase II firms and established a performance baseline. The thesis concludes that Army SBIR awards are outperforming DoD Fast Track and DoD Control Groups in the critical area of average commercial sales per award. It recommends a reduced focus on outside investment and a survey strategy that uses small sample sizes to qualify program performance. It concludes with a proposed survey instrument that Army SBIR managers can use to capture future program outcomes., http://archive.org/details/armysmallbusines1094510965