Wind power ramp event detection using a multi-parameter segmentation algorithm

Lyners, Danielle; Vermeulen, H.J.; Groch, Matthew

doi:10.1016/j.egyr.2021.08.137

Cited by 20 publications

(4 citation statements)

References 25 publications

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“…However, wind power also faces a series of technical challenges. The randomness and uncertainty of wind power can have an impact on the safe and stable operation of the power grid [2]. Wind power ramp events can be triggered when extreme weather event occurs.…”

Section: Introductionmentioning

confidence: 99%

A wind power ramp event detection method based on eigenvalue correction and trend integration

Wang,

Wu,

Gao

et al. 2024

J. Phys.: Conf. Ser.

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Wind power ramp events have the characteristic of small probability and big hazard, so it is significant to improve the recognition rate and accuracy of ramp events for the safe and stable operation of the power grid. In order to improve the efficiency of ramp event detection, a wind power ramp event detection method based on eigenvalue evaluation correction and trend integration is proposed by combining the feature information of ramp events. The original wind power data is extracted with extreme value feature points and corrected with eigenvalue evaluation to achieve the trend feature extraction effect. In order to avoid being affected by small power jitter events, a trend labeling method is used to integrate the correction sequences. The actual wind power data of a wind farm in Xinjiang is used as an example for ramp event detection. The results of the case show that compared with the original swinging door algorithm, the proposed method has both better trend extraction effect and can avoid the influence of power small jitter events, and can more accurately and more completely identify ramp events.

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Section: Introductionmentioning

confidence: 99%

A wind power ramp event detection method based on eigenvalue correction and trend integration

Wang,

Wu,

Gao

et al. 2024

J. Phys.: Conf. Ser.

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show abstract

“…This means looking at the data within a recent time range and analyzing it by mathematical techniques (autoregressive models and neural network analysis) 13–15 . The protocols can become very complex, indeed like a multiparameter segmentation algorithm to detect wind power ramps 16 . These efforts are spread in the techniques as well as in the geography; thus, incorporating a huge amount of information on a field of rapid present expansion accelerated even more by the design of new turbines.…”

Section: Introductionmentioning

confidence: 99%

“…[13][14][15] The protocols can become very complex, indeed like a multiparameter segmentation algorithm to detect wind power ramps. 16 These efforts are spread in the techniques as well as in the geography; thus, incorporating a huge amount of information on a field of rapid present expansion accelerated even more by the design of new turbines. Just to mention a couple of recent examples, we can mention a rather simple method based on wavelet transforms to detect wind ramps applied to data from Belgium and Sweden.…”

Section: Introductionmentioning

confidence: 99%

Onshore versus offshore capacity factor and reliability for wind energy production in Germany: 2010–2022

Vogel,

Saravia,

Kobe

et al. 2024

Energy Science & Engineering

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Similarities and differences between the features related to the productivity of onshore and offshore wind energy are developed with the aid of information theory techniques complemented by normal statistics. The data comes from the 13‐year period between 2010 and 2022 for the registered turbines in Germany (practically all). The information content of the generated power is dynamically measured by the mutability of the files storing the information. Monthly statistics show that in spite of the Summer months being relatively unproductive, the corresponding mutability shows the possibility of making use of short periods of intermediate productivity in the case of offshore plants. Favorable conditions for wind energy generation in Wintertime are reached for both onshore and offshore production, although the latter is favored. More homogeneity and stability in the data are still necessary to generalize algorithms, protocols, and criteria. This general study shows the success of the information theory techniques in describing wind ramps. Applications to specific zones could improve the efficiency and capacity factor of particular wind turbines depending on their exposure to wind streams or the blocking of nearby mountains and forestry. The information theory techniques presented here allow for a different and novel viewpoint to detect favorable and unfavorable wind energy periods.

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“…Cornejo-Bueno et.al predicted the occurrence of ramp events through a hybrid model that combines extreme learning machine (ELM) and SVM algorithms [19], and Y. Zhao et. al employed a statistical approach, specifically the Bayesian network, to detect ramp events [20]. Furthermore, Okada et.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Performance Evaluation Metrics for Wind Power Ramp Event Forecasting

Yu,

Hur

2023

IEEE Access

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As wind power is volatile and intermittent, an increase in the proportion of wind power generation may affect the security of power systems. Accurate wind power generation and ramp forecasting are crucial for reducing operating costs and maintaining the power balance. This study proposed an enhanced performance evaluation metric for wind power ramp event forecasting, and analyzed the forecasting results using this metric. Experimental ramp event forecasting was conducted on wind farms located in Jeju, South Korea by employing an indirect forecasting method for wind power output forecasting. Considering the evaluation of forecasting performance, the accuracy (ACC) was observed to be 0.86 for Case #1 and 0.90 for Case #2. The results highlight the advantages of the proposed metric over the widely used confusion matrix for performance evaluation, which has more detailed and visually based analytical capabilities. The simulation demonstrates that the proposed approach is poised to make a noteworthy contribution in the advancement of tool development, enabling real-time curtailment forecasting and facilitating well-informed decision-making in high wind power scenarios.INDEX TERMS Performance evaluation metric, power system security, ramp event forecasting, wind power.

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Wind power ramp event detection using a multi-parameter segmentation algorithm

Cited by 20 publications

References 25 publications

A wind power ramp event detection method based on eigenvalue correction and trend integration

A wind power ramp event detection method based on eigenvalue correction and trend integration

Onshore versus offshore capacity factor and reliability for wind energy production in Germany: 2010–2022

An Enhanced Performance Evaluation Metrics for Wind Power Ramp Event Forecasting

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