Publications

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Understanding wind farm reliability from various data sources is highly complex because the boundary conditions for the data are often undocumented and impact the outcome of aggregation significantly. Sandia National Laboratories has been investigating the reliability of wind farms through the Continuous Reliability Enhancement Wind (CREW) project since 2007 through the use of Supervisory Control and Data Acquisition (SCADA) data from multiple wind farms in the fleet of the USA. However, data streaming from sample wind farms does not lead to better understanding as it is merely a generic status of those samples. Economic type benchmark studies are used in the industry, but these do not yield much technical understanding and give only a managerial cost perspective. Further, it is evident that there are many situations in which average benchmark data cannot be presented in a meaningful way due to discrete events, especially when the data is only based on smaller samples relative to the probability of the events and the sample size. The discrete events and insufficient descriptive tagging contribute significantly to the uncertainty of a fleet average and may even impair the way we communicate reliability. These aspects will be discussed. It is speculated that some aspects of reliability can be understood better through SCADA data-mining techniques and considering the real operating environment, as, it will be shown that there is no particular reason that two identical wind turbines in the same wind farm should have identical reliability performance. The operation and the actual environmental impact on the turbine level are major parameters in determining the remaining useful life. Methods to normalize historical data for future predictions need to be developed, both for discrete events and for general operational conditions.

As wind farms scale to include more and more turbines, questions about turbine wake interactions become increasingly important. Turbine wakes reduce wind speed and downwind turbines suffer decreased performance. The cumulative effect of the wakes throughout a wind farm will therefore decrease the performance of the entire farm. These interactions are dynamic and complicated, and it is difficult to quantify the overall effect of the wakes. This problem has attracted some attention in terms of computational modelling for siting turbines on new farms, but less attention in terms of empirical studies and performance validation of existing farms. In this report, Supervisory Control and Data Acquisition (SCADA) data from an existing wind farm is analyzed in order to explore methods for documenting wake interactions. Visualization techniques are proposed and used to analyze wakes in a 67 turbine farm. The visualizations are based on directional analysis using power measurements, and can be considered to be normalized capacity factors below rated power. Wind speed measurements are not used in the analysis except for data pre-processing. Four wake effects are observed; including wake deficit, channel speed up, and two potentially new effects, single and multiple shear point speed up. In addition, an attempt is made to quantify wake losses using the same SCADA data. Power losses for the specific wind farm investigated are relatively low, estimated to be in the range of 3-5%. Finally, a simple model based on the wind farm geometrical layout is proposed. Key parameters for the model have been estimated by comparing wake profiles at different ranges and making some ad hoc assumptions. A preliminary comparison of six selected profiles shows excellent agreement with the model. Where discrepancies are observed, reasonable explanations can be found in multi-turbine speedup effects and landscape features, which are yet to be modelled.

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Wind turbines have grown in size and capacity with today's average turbine having a power capacity of around 1.9 MW, reaching to heights of over 495 feet from ground to blade tip, and operating with speeds at the tip of the blade up to 200 knots. When these machines are installed within the line-of-sight of a radar system, they can cause significant clutter and interference, detrimentally impacting the primary surveillance radar (PSR) performance. The Massachusetts Institute of Technology's Lincoln Laboratory (MIT LL) and Sandia National Laboratories (SNL) were co-funded to conduct field tests and evaluations over two years in order to: I. Characterize the impact of wind turbines on existing Program-of-Record (POR) air surveillance radars; II. Assess near-term technologies proposed by industry that have the potential to mitigate the interference from wind turbines on radar systems; and III. Collect data and increase technical understanding of interference issues to advance development of long-term mitigation strategies. MIT LL and SNL managed the tests and evaluated resulting data from three flight campaigns to test eight mitigation technologies on terminal (short) and long-range (60 nmi and 250 nmi) radar systems. Combined across the three flight campaigns, more than 460 of hours of flight time were logged. This paper summarizes the Interagency Field Test & Evaluation (IFT&E) program and publicly- available results from the tests. It will also discuss the current wind turbine-radar interference evaluation process within the government and a proposed process to deploy mitigation technologies.

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This report investigates strategies to mitigate anticipated wind energy curtailment on Maui, with a focus on grid-level energy storage technology. The study team developed an hourly production cost model of the Maui Electric Company (MECO) system, with an expected 72 MW of wind generation and 15 MW of distributed photovoltaic (PV) generation in 2015, and used this model to investigate strategies that mitigate wind energy curtailment. It was found that storage projects can reduce both wind curtailment and the annual cost of producing power, and can do so in a cost-effective manner. Most of the savings achieved in these scenarios are not from replacing constant-cost diesel-fired generation with wind generation. Instead, the savings are achieved by the more efficient operation of the conventional units of the system. Using additional storage for spinning reserve enables the system to decrease the amount of spinning reserve provided by single-cycle units. This decreases the amount of generation from these units, which are often operated at their least efficient point (at minimum load). At the same time, the amount of spinning reserve from the efficient combined-cycle units also decreases, allowing these units to operate at higher, more efficient levels.

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