Abstract
Power ramp estimation has wide ranging implications for wind power plants and power systems which will be the focus of this paper. Power ramps are large swings in power generation within a short time window. This is an important problem in the power system that needs to maintain the load and generation at balance at all times. Any unbalance in the power system leads to price volatility, grid security issues that can create power stability problems that leads to financial losses. In addition, power ramps decrease the lifetime of turbine and increase the operation and maintenance expenses. In this study, power ramps are detected by data mining and optimization. For detection and prediction of power ramps, data mining K means clustering approach and optimisation scoring function approach are implemented [1]. Finally association rules of data mining algorithm is employed to analyze temporal ramp occurrences between wind turbines for both clustering and optimization approaches. Each turbine impact on the other turbines are analyzed as different transactions at each time step. Operational rules based on these transactions are discovered by an Apriori association rule algorithm for operation room decision making. Discovery of association rules from an Apriori algorithm will serve the power system operator for decision making.
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References
Sevlian, R., Rajagopal, R.: Detection and statistics of wind power ramps. IEEE Trans. Power Syst. 28(4), 3610–3620 (2013)
Ouyang, X.Z.T., Qin, L.: A survey of wind power ramp forecasting. Energy Power Eng. 5(4B), 368–372 (2013)
Sevlian, R., Rajagopal, R.: Wind power ramps: detection and statistics. In: Power and Energy Society General Meeting, pp. 1–8. IEEE, July 2012
Kamath, C.: Associating weather conditions with ramp events in wind power generation. In: Power Systems Conference and Exposition (PSCE), IEEE/PES, vol. 2011, pp. 1–8. IEEE (2011)
Uzunoglu, B., Bayazit, D.: A generic resampling particle filter joint parameter estimation for electricity prices with jump diffusion. In: 10th International Conference on the European Energy Market (EEM), pp. 1–7. IEEE (2013)
Ülker, M.A.: Balancing of wind power: optimization of power systems which include wind power systems (2011)
Aflori, C., Craus, M.: Grid implementation of the Apriori algorithm. Adv. Eng. Softw. 38(5), 295–300 (2007)
Guo, Z., Chi, D., Wu, J., Zhang, W.: A new wind speed forecasting strategy based on the chaotic time series modelling technique and the Apriori algorithm. Energy Convers. Manag. 84, 140–151 (2014)
Tong, C., Guo, P.: Data mining with improved Apriori algorithm on wind generator alarm data. In: Control and Decision Conference (CCDC),: 25th Chinese, vol. 2013, pp. 1936–1941. IEEE (2013)
Kusiak, A., Verma, A.: Prediction of status patterns of wind turbines: a data-mining approach. J. Sol. Energy Eng. 133(1), 011008 (2011)
Yıldırım, N., Uzunoglu, B.: Association rules for clustering algorithms for data mining of temporal power ramp balance. In: Cyberworlds Visby. IEEE (2015)
Uzunoglu, B., Albayrak, A.: Data mining of wind data generated by CFD solutions. In; CFD and Optimization. ECCOMAS Antalya TURKEY (2011)
Yıldırım, N., Uzunoglu, B.: Spatial clustering for temporal power ramp balance and wind power estimation. In: Greentech. IEEE (2015)
Kusiak, A., Zheng, H.: Data mining for prediction of wind farm power ramp rates. In: IEEE International Conference on Sustainable Energy Technologies, ICSET 2008, vol. 2008, pp. 1099–1103. IEEE(2008)
Pena, J.M., Lozano, J.A., Larranaga, P.: An empirical comparison of four inilitiazion methods for the k means algorithm. Pattern Recogn. Lett. 20(10), 1027–1040 (1999)
Gan, G., Ma, C., Wu, J.: Data Clustering,: Theory, Algorithms, and Applications, vol. 20. Siam, Philadelphia (2007)
Charrad, M., Ghazzali, N., Boiteau, V., Niknafs, A.: Nbclust: An R package for determining the relevant number of clusters in a data set. J. Stat. Softw. 61(6), 1–36 (2014)
Sevlian, R.: Wind ramp detection (2012). http://web.stanford.edu/rsevlian/WindRampDetect.html
Tan, P.-N., Kumar, V.: Chapter 6. association analysis: basic concepts and algorithms. In: Introduction to Data Mining, Addison-Wesley (2005). ISBN 321321367
Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. In: ACM SIGMOD Record, vol. 22, no. 2, pp. 207–216. ACM (1993)
Acknowledgements
We would like to acknowledge the financial support given by Vindforsk and Swedish Energy Agency grant “Bayesian methods for preventive maintenance”. The authors would like to acknowledge the financial support given by Computational Renewables LLC for the duration of this study. The second author, Bahri Uzunoğlu, would like to acknowledge visiting scientist exchange granted at Florida State University, Department of Mathematics with Prof. Yousuff Hussaini in the context of this research.
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Yıldırım, N., Uzunoğlu, B. (2016). Data Mining via Association Rules for Power Ramps Detected by Clustering or Optimization. In: Gavrilova, M., Tan, C., Sourin, A. (eds) Transactions on Computational Science XXVIII. Lecture Notes in Computer Science(), vol 9590. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-53090-0_9
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