High-performance predictor for critical unstable generators based on scalable parallelized neural networks

Journal of Modern Power Systems and Clean Energy - Tập 4 - Trang 414-426 - 2016
Youbo LIU1, Yang LIU1, Junyong LIU1, Maozhen LI2, Zhibo MA3, Gareth TAYLOR2
1School of Electrical Engineering and Information, Sichuan University, Chengdu, China
2Electronic and Computer Engineering, Brunel University, London, UK
3Senior Power System Engineer, National Grid, Wokingham, UK

Tóm tắt

A high-performance predictor for critical unstable generators (CUGs) of power systems is presented in this paper. The predictor is driven by the MapReduce based parallelized neural networks. Specifically, a group of back propagation neural networks (BPNNs), fed by massive response trajectories data, are efficiently organized and concurrently trained in Hadoop to identify dynamic behavior of individual generator. Rather than simply classifying global stability of power systems, the presented approach is able to distinguish unstable generators accurately with a few cycles of synchronized trajectories after fault clearing, enabling more in-depth emergency awareness based on wide-area implementation. In addition, the technique is of rich scalability due to Hadoop framework, which can be deployed in the control centers as a high-performance computing infrastructure for real-time instability alert. Numerical examples are studied using NPCC 48-machines test system and a realistic power system of China.

Tài liệu tham khảo

Baldick R, Chowdhury B, Dobson I et al (2009) Vulnerability assessment for cascading failures in electric power systems. In: Proceedings of the 2009 power systems conference and exposition (PSCE’09), Seattle, WA, 15–18 Mar 2009, 9 pp Vaiman M, Bell K, Chen Y et al (2012) Risk assessment of cascading outages: methodologies and challenges. IEEE Trans Power Syst 27(2):631–641 Miao L, Fang JK, Wen JY et al (2013) Transient stability risk assessment of power systems incorporating wind farms. J Mod Power Syst Clean Energy 1(2):134–141. doi:10.1007/s40565-013-0022-2 De La Ree J, Centeno V, Thorp JS et al (2010) Synchronized phasor measurement applications in power systems. IEEE Trans Smart Grid 1(1):20–27 Makram EB, Vutsinas MC, Girgis AA et al (2012) Contingency analysis using synchrophasor measurements. Electr Power Syst Res 88(1):64–68 Alvarez JMG, Mercado PE (2007) Online inference of the dynamic security level of power systems using fuzzy techniques. IEEE Trans Power Syst 22(2):717–726 Chow JH, Chakrabortty A, Arcak M et al (2007) Synchronized phasor data based energy function analysis of dominant power transfer paths in large power systems. IEEE Trans Power Syst 22(2):727–734 Cepeda JC, Rueda JL, Colomé DG et al (2014) Real-time transient stability assessment based on centre-of-inertia estimation from phasor measurement unit records. IET Gener Transm Distrib 8(8):1363–1376 Liu JH, Chu CC (2014) Wide-area measurement-based voltage stability indicators by modified coupled single-port models. IEEE Trans Power Syst 29(2):756–764 Makarov YV, Du PW, Lu S et al (2012) PMU-based wide-area security assessment: Concept, method, and implementation. IEEE Trans Smart Grid 3(3):1325–1332 Gomez FR, Rajapakse AD, Annakkage UD et al (2011) Support vector machine-based algorithm for post-fault transient stability status prediction using synchronized measurements. IEEE Trans Power Syst 26(3):1474–1483 Kamwa I, Samantaray SR, Joos G (2010) Development of rule-based classifiers for rapid stability assessment of wide-area post-disturbance records. In: Proceedings of the 2010 IEEE PES general meeting, Minneapolis, MN, 25–29 Jul 2010, 1 pp Xu Y, Dong ZY, Zhao JH et al (2012) A reliable intelligent system for real-time dynamic security assessment of power systems. IEEE Trans Power Syst 27(3):1253–1263 Guo TY, Milanović JV (2014) Probabilistic framework for assessing the accuracy of data mining tool for online prediction of transient stability. IEEE Trans Power Syst 29(1):377–385 Hashiesh F, Mostafa HE, Khatib AR et al (2012) An intelligent wide area synchrophasor based system for predicting and mitigating transient instabilities. IEEE Trans Smart Grid 3(2):645–652 Al-Masri AN, Ab Kadir MZA, Hizam H et al (2013) A novel implementation for generator rotor angle stability prediction using an adaptive artificial neural network application for dynamic security assessment. IEEE Trans Power Syst 28(3):2516–2525 Cui MJ, Ke DP, Gan D et al (2015) Statistical scenarios forecasting method for wind power ramp events using modified neural networks. J Mod Power Syst Clean Energy 3(3):371–380. doi:10.1007/s40565-015-0138-7 Gu R, Shen FR, Huang YH (2013) A parallel computing platform for training large scale neural networks. In: Proceedings of the 2013 IEEE international conference on big data, Silicon Valley, CA, 6–9 Oct 2013, pp 376–384 Rizwan M, Jamil M, Kothari DP (2012) Generalized neural network approach for global solar energy estimation in India. IEEE Trans Sustain Energy 3(3):576–584 Yuan JW, Yu SC (2014) Privacy preserving back-propagation neural network learning made practical with cloud computing. IEEE Trans Parallel Distrib Syst 25(1):212–221 Ikram AA, Ibrahim S, Sardaraz M et al (2013) Neural network based cloud computing platform for bioinformatics. In: Proceedings of the 2013 IEEE Long Island conference on systems applications and technology (LISAT’13), Farmingdale, NY, 3 May 2013, 6 pp Long LN, Gupta A (2008) Scalable massively parallel artificial neural networks. J Aerosp Comput Inf Commun 5(1):3–15 Alham NK (2011) Parallelizing support vector machines for scalable image annotation. PhD Thesis. Brunel University, London Kundur P (2012) Power system stability and control, 3rd edn. McGraw-Hill, New York Rajapakse AD, Gomez F, Nanayakkara K et al (2010) Rotor angle instability prediction using post-disturbance voltage trajectories. IEEE Trans Power Syst 25(2):947–956 Hagan MH, Demuth HB, Beale MH (1996) Neural network design. PWS Publishing Company, Boston Dean J, Ghemawat S (2008) MapReduce: simplified data processing on large clusters. Commun ACM 51(1):107–113 He BS, Fang WB, Govindaraju NK et al (2008) Mars: a MapReduce framework on graphics processors. In: Proceedings of the 17th international conference on parallel architectures and compilation techniques (PACT’08), Toronto, 25–29 Oct 2008, pp 260–269 Taura K, Kaneda K, Endo T et al (2003) Phoenix: a parallel programming model for accommodating dynamically joining/leaving resources. ACM SIGPLAN Notices 38(10):216–229 Apache Hadoop. http://hadoop.apache.org/. Accessed 07 June 2016 Liu YB, Liu Y, Liu JY et al (2014) A cloud computing framework for cascading failure simulation and analysis of large-scale transmission systems. In: Proceedings of the 2014 international conference on power system technology (POWERCON’14), Chengdu, 20–22 Oct 2014, pp 287–293