Optimal Dynamic Reactive Power Reserve for Wind Farms Addressing Short-Term Voltage Issues Caused by Wind Turbines Tripping
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In regional power grids with high wind power penetration, wind turbine tripping poses great challenges to short-term voltage stability. Dynamic reactive power (VAR) compensation (DVC) plays an important role in securing wind farm operation. To address short-term voltage stability issues, voltage disturbance index (DI) and voltage supporting index (SI) are defined to evaluate the degree of voltage fluctuation and voltage supporting ability of a bus, respectively. Then corresponding vector-type features, called disturbance vector (DV) and supporting vector (SV) are proposed based on the defined indexes. The Kendall rank correlation coefficient is adopted to evaluate the matching degree of DV and SV, so as to determine the influenced area of each wind farm. Candidate locations for DVC are determined sequentially. By comprehensively considering the probability of combined disturbance in each wind farm, a site selection method is proposed and then genetic algorithm is applied to optimize the DVC capacity considering short-term voltage security. The proposed method is applied on a modified NE 39-bus system and a real power grid. Comparison with the engineering practice-based method validates its effectiveness.Keywords:
Tripping
The wind power generation capacity exceeds 40GW in the world. There are about 800 turbines of 730MW in Japan. Growing market requires newer and larger wind turbines. So, rated output of wind turbine has increased double every four years since last 10 years. It's a very hard target for turbine manufacturers. Mitsubishi Heavy Industries, Ltd. is the only one manufacturer of MW class large wind turbines in Japan. We have produced first 2MW wind turbine in Japan at Okinawa in march 2003. The technical problems about large wind turbines and tactics to solve them are described. First tactics is how to extend the relative strength of turbine equipmemts. Second tactics is how to reduce the load acting to turbines. Wind turbines at Miyako island have been severely damaged by typhoon No.14 in 2003. It revealed that wind turbines are not so strong enough when yaw control has been lost by power failure. The new technology to survive power failure, named "SmatYaw" is explained.
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Wind‐turbine operations are associated with bat mortality worldwide; minimizing these fatalities is critically important to both bat conservation and public acceptance of wind‐energy development. We tested the effectiveness of raising wind‐turbine cut‐in speed – defined as the lowest wind speed at which turbines generate power to the utility system, thereby reducing turbine operation during periods of low wind speeds – to decrease bat mortality at the Casselman Wind Project in Somerset County, Pennsylvania, over a 2‐year period. Observed bat mortality at fully operational turbines was, on average, 5.4 and 3.6 times greater than mortality associated with curtailed (ie non‐operating) turbines in 2008 and 2009, respectively. Relatively small changes to wind‐turbine operation resulted in nightly reductions in bat mortality, ranging from 44% to 93%, with marginal annual power loss (≤ 1% of total annual output). Our findings suggest that increasing turbine cut‐in speeds at wind facilities in areas of conservation concern during times when active bats may be at particular risk from turbines could mitigate this detrimental aspect of wind‐energy generation.
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Condition monitoring of wind turbines is gaining importance as turbines become larger and move to more inaccessible locations, such as offshore. Condition monitoring based on methods conventionally used in the power generation industry have been demonstrated to work successfully on large wind turbines when attention is paid to data collection. In view of the large number of wind turbines deployed this paper proposes a methodology for wind turbine condition monitoring that compares conventional condition monitoring signals with operational signals, such as load or energy, which could be applied automatically. A multi-parameter approach, based on comparison of independent signals, should increase confidence in fault signal interpretation and alarms generated, potentially reducing the risk of false alarms.
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Modern utility-scale wind farms consist of a large number of wind turbines. In order to improve the power generation efficiency of wind turbines, accurate quantification of power generation levels of multi-turbines is critical, in both wind farm design and operational controls. One challenging issue is that the power output levels of multiple wind turbines are different, due to complex interactions between turbines, known as wake effects. In general, upstream turbines in a wind farm absorb kinetic energy from wind. Therefore, downstream turbines tend to produce less power than upstream turbines. Moreover, depending on weather conditions, the power deficits of downstream turbines exhibit heterogeneous patterns. This study proposes a new statistical approach to characterize heterogeneous wake effects. The proposed approach decomposes the power outputs into the average pattern commonly exhibited by all turbines and the turbine-to-turbine variability caused by multi-turbine interactions. To capture the wake effects, turbine-specific regression parameters are modeled using a Gaussian Markov random field. A case study using actual wind farm data demonstrates the proposed approach's superior performance.
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The aim of the current paper is to present an approach to a wind turbine selection based on an annual wind measurements. The proposed approach led to a choice of an optimal device for the given wind conditions. The research was conducted for two potential wind farm locations, situated on the north of Poland. The wind measurements pointed out a suitability of the considered localizations for a wind farm development. Six types of wind turbines were investigated in each localization. The power of the wind turbines were in the range of 2.0 to 2.5 MW and with a medium size of the rotor being in the range of 82 to 100 m. The purpose of the research was to indicate a wind turbine with the lowest sensitivity to the variation of wind speed and simultaneously being most effective energetically. The Weibull density distribution was used in the analyses for three values of a shape coefficients k. The energy efficiency of the considered turbines were also assessed. In terms of the hourly distribution of the particular wind speeds, the most effective wind turbines were those with a nominal power of 2 MW, whereas the least effective were those with the nominal power of 2.3–2.5 MW. The novelty of the proposed approach is to analyze the productivity for many types of wind turbines in order to select the one which is the most effective energy producer. The analyses conducted in the paper allowed to indicate a wind turbine which generates the highest amount of energy independently on the wind speed variation.
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This paper examines the effect of different wind turbine classes on the electricity production of wind farms in two areas of Cyprus Island, which present low and medium wind potentials: Xylofagou and Limassol. Wind turbine classes determine the suitability of installing a wind turbine in a particulate site. Wind turbine data from five different manufacturers have been used. For each manufacturer, two wind turbines with identical rated power (in the range of 1.5 MW–3 MW) and different wind turbine classes (IEC II and IEC III) are compared. The results show the superiority of wind turbines that are designed for lower wind speeds (IEC III class) in both locations, in terms of energy production. This improvement is higher for the location with the lower wind potential and starts from 7%, while it can reach more than 50%.
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With the rise of new energy power generation technology, the installed capacity of wind turbines continues to increase. At the same time, the potential faults of wind turbines have also increased with the increase of wind turbines. Therefore, early prediction of potential faults of wind turbines and ensuring the safe and stable operation of wind turbines is of great significance for improving power generation efficiency and reducing maintenance costs. In order to realize the fault early warning of the main bearing of the wind turbine, an early warning method of the main bearing of the wind turbine based on Stacked Auto encoder (SAE) is proposed.
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This paper focus is on the small wind turbines resource potential estimation. Assessment is done for seven selected small wind turbines and one measured set of wind speed data with the micropower optimization modeling tool HOMER. Goal was to investigate how estimated energy production and economical parameters are sensitive to the selection of small wind turbine. Selected turbines have similar rated power, but different blades diameter and aerodynamic characteristics. Energy production was quantified for one year with hourly resolution. Results from all different wind turbines were compared on the power production base, and on the economical base. Two sensitivity cases related to the wind speed and installation lifetime were also simulated. Results are showing significant importance of the small wind turbine selection for the both total energy production and economical feasibility. This makes small wind turbine characteristics such as reliability and power curves testing very important.
Small wind turbine
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Power production of a wind turbine system is strongly dependent on both the wind regime at the site and the operating parameters of the wind turbine (design parameters). In order to evaluate the performance of a wind turbine in a given location, an accurate estimation of the turbine's Capacity Factor (CF) is required. This parameter shows the degree of match between the characteristics of the turbine and the wind patterns on the site. In this paper, four widely used empirical models are presented and compared using the method of bins, which is based on the manufacturer-provided power curves. The generic models considered in this paper are Linear Model (LM), Quadratic Model (QM), Cubic Model (CM), and General Model (GM). The validity of these models was investigated using a case study of four locations across Morocco which are namely: Tetouan, Essaouira, Taza and Ouarzazate. Four small scale wind turbines presenting different ranges of characteristic speeds and rated powers (10 kW, 20 kW, and 50 kW) were used to conduct the comparative study. From the obtained results, the recommended models are the Quadratic and the Cubic models. These two models present a good description of the turbines' power curves.
Empirical modelling
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