Abstract
We propose a suite of intelligent tools based on the integration of methods of agent modeling and machine learning for the improvement of protection systems and emergency automatics. We propose an online approach to the assessment and management of dynamic security of electric power systems (EPS) with the use of a streaming modification of the random forest algorithm. The suite allows to recognize dangerous modes of complex closed-loop EPS, preventing the risk of emergencies on early stages. We show results of experimental tests on IEEE test systems.
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Voropai, N.I., Tomin, N.V., Kurbatskii, V.G., et al., Kompleks intellektual’nykh sredstv dlya predotvrashcheniya krupnykh avarii v elektroenergeticheskikh sistemakh (A Suite of Intelligent Means of Prevention Large-Scale Failures in Electric Power Systems), Novosibirsk: Nauka, 2016.
Voropai, N.I. and Saratova, N.E., Analyzing the Statistics of RZA Failures Based on Microprocessors from the Point of View of Accounting for them in Modeling Cascade Failures, Probl. Energetiki, 2008, no. 11/12 (1), pp. 66–71.
Voropai, N.I., Snizhenie riskov kaskadnykh avarii v elektroenergeticheskikh sistemakh (Reducing the Risks of Cascade Failures in Electric Power Systems), Novosibirsk: Sib. Otd. Ross. Akad. Nauk, 2011.
IEEE PES CAMS Task Force on Understanding, Prediction, Mitigation and Restoration of Cascading Failures, “Initial Review of Methods for Cascading Failure Analysis in Electric Power Transmission Systems,” Proc. IEEE PES General Meeting, Pittsburgh, July, 2008.
Negnevitsky, M., An Expert System Application for Clearing Overloads, Int. J. Power Energy Syst., 1995, vol. 15, no. 1, pp. 9–13.
Barkans, E. and Zhalostiba, D., Zashchita ot razvalov i samovosstanovlenie energosistem (Protection against Critical Failures and Self-Restoration of Power Systems), Cheboksary: RITs “SRZAU,” 2014.
Kessel, P. and Glavitsch, H., Estimating the Voltage Stability of a Power System, IEEE Trans. Power Delivery, 1986, vol. 1, no. 3, pp. 346–354.
Voitov, O.N., Voropai, N.I., Gamm, A.Z., et al., Analiz neodnorodnostei elektroenergeticheskikh sistem (Analysis of Nonuniformities in Electric Power Systems), Novosibirsk: Nauka, 1999.
Goh, H., Comparative Study of Different Kalman Filter Implementations in Power System Stability, Am. J. Appl. Sci., 2014, vol. 11, no. 8, pp. 1379–1390.
Karbalaei, F., Soleymani, H., and Afsharnia, S., A Comparison of Voltage Collapse Proximity Indicators, IPEC, 2010 Conf. Proc., 2010, pp. 429–432.
Sayed Shah, D.M., Voltage Stability in Electric Power System: A Practical Introduction, Berlin: Logos Verlag, 2015.
Kurbatsky, V.G., Sidorov, D.N., Spiryaev, V.A., and Tomin, N.V., The Hybrid Model Based on Hilbert-Huang Transform and Neural Networks for Forecasting of Short-Term Operation Conditions of Power Systems, Proc. IEEE PES Trondheim PowerTech, Trondheim, 2011, pp. 1–7.
Zhukov, A., Tomin, N., Sidorov, D., Panasetsky, D., and Spirayev, V., A Hybrid Artificial Neural Network for Voltage Security Evaluation in a Power System, Proc. 2015 Int. Youth Con. Energy (IYCE), Pisa, 2015, pp. 1–8.
Kurbatsky, V., Tomin, N., Sidorov, D., and Spiryaev, V., Application of Two Stages Adaptive Neural Network Approach for Short-Term Forecast of Electric Power Systems, Proc. 10 Int. Conf. Environ. Electr. Engineer., Rome, 2011, pp. 1–4.
Manov, N.S., Khokhlov, M.V., Chukreev, Yu.Ya., et al., Metody i modeli issledovaniya nadezhnosti elektroenergeticheskikh sistem (Methods and Models for Reliability Studies of Electric Power Systems), Syktyvkar: Komi Nauchn. Tsentr, Ural. Otd. Ross. Akad. Nauk, 2010.
Kurbatskii, V.G., Sidorov, D.N., Spiryaev, V.A., and Tomin, N.V., On the Neural Network Approach for Forecasting of Nonstationary Teme Series on the Basis of the Hilbert–Huang Transform, Autom. Remote Control, 2011, vol. 72, no. 7, pp. 1405–1414.
Kalyani, S. and Shanti Swarup, K., Design of Pattern Recognition System for Static Security Assessment and Classification, Patt. Anal. Appl., 2012, vol. 15, pp. 299–311.
Jothinathan, K. and Ganapathy, S., Transient Security Assessment in Power Systems Using Deep Neural Network, Int. J. Appl. Engin. Res., 2012, vol. 10, no. 15, pp. 787–790.
Diao, R., Sun, K., Vittal, V., et al., Decision Tree-Based Online Voltage Security Assessment Using PMU Measurements, IEEE Trans. Power Syst., 2009, vol. 24, no. 2, pp. 832–839.
Arkhipov, I.L., Ivanov, A.M., Kholkin, D.V., et al., A Multiagent Control System for Voltage and Reactive Power, Proc. 22nd Conf. “Relay Protection and Automation of Power Systems,” Moscow, 2014, pp. 243–252.
Belkacemi, R., Babalola, S., and Zarrabian, A., Experimental Implementation of Multi-Agent System Algorithm to Prevent Cascading Failure after N -1 -1 Contingency in Smart Grid Systems, IEEE Power & Energy Society General Meeting, Denver, 2015, pp. 1–5.
Panasetskii, D.A., Improving the Structure and Algorithms for Failure Protection Control of an Electric Power Station to Prevent Voltage Avalanche and Cascade Outing of Lines, Cand. Sci. Dissertation, Irkutsk: ISEM SB RAS, 2015.
Negenborn, R.R., De Schutter, B., and Hellendoorn, J., Multi-agent Model Predictive Control for Transportation Networks: Serial Versus Parallel Schemes, Eng. Appl. Artific. Intelligence, 2008, vol. 21, pp. 353–366.
Zhukov, A.V. and Sidorov, D.N., A Modification of the Random Forest Algorithm for Classification of Nonstationary Streaming Data, Vest. YuUrGU, Mat. Modelir. Programmir., 2016, vol. 9, no. 4, pp. 86–95.
Zhukov, A.V., Sidorov, D.N., and Foley, A.M., Random Forest Based Approach for Concept Drift Handling, Commun. Comput. Inform. Sci., 2017, vol. 661, pp. 69–77.
Voropai, N.I., Negnevitskii, M., Tomin, N.V., et al., An Intelligent System for Preventing Large Failures in Power Systems, Elektrichestvo, 2014, no. 8, pp. 1–7.
Geurts, P., Ernst, D., and Wehenkel, L., Extremely Randomized Trees, Machine Learning, 2006, vol. 63, no. 1, pp. 3–42.
Scornet, E., Random Forests and Kernel Methods, IEEE Trans. Inform. Theory, 2016, vol. 62, no. 3, pp. 1485–1500.
Kundur, P., Power System Stability and Control, New York: McGraw Hill, 1994.
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Original Russian Text © N.I. Voropai, N.V. Tomin, D.N. Sidorov, V.G. Kurbatsky, D.A. Panasetsky, A.V. Zhukov, D.N. Efimov, A.B. Osak, 2018, published in Avtomatika i Telemekhanika, 2018, No. 10, pp. 6–25.
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Voropai, N.I., Tomin, N.V., Sidorov, D.N. et al. A Suite of Intelligent Tools for Early Detection and Prevention of Blackouts in Power Interconnections. Autom Remote Control 79, 1741–1755 (2018). https://doi.org/10.1134/S0005117918100016
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DOI: https://doi.org/10.1134/S0005117918100016