Forecasting voltage collapse when large-scale wind turbines penetrated to power systems using optimally pruned extreme learning machines (OPELM) - case study: electric power system South Sulawesi-Indonesia

• Autorzy: Chaerah Gunadin Indar , Siswanto Agus , Safrizal Safrizal , Syukriyadin Syukriyadin , Rosyadi Marwan , Muslimin Zaenab , Gassing , Rasyid Ramly

Identyfikatory

DOI

10.15199/48.2022.05.15

Warianty tytułu

PL

Prognozowanie zapadu napięcia, gdy wielkoskalowe turbiny wiatrowe przenikną do systemów energetycznych przy użyciu optymalnie przyciętych maszyn do nauki ekstremalnych (OPELM) — studium przypadku: system elektroenergetyczny South Sulawesi-Indonesia

• Języki publikacji: EN

Abstrakty

EN

The problem of voltage collapse is a major issue in the operation of the current power system, especially when the penetration of wind turbines into the system continues to increase. The intermittency of the wind turbine has an impact on the stability of the system voltage. Fast Voltage Stability Index (FVSI) is used as a parameter for the condition of the system with the phenomenon of voltage collapse. This study aims to observe and predict the value of the Line stability index using Optimally Pruned Extreme Learning Machine (OP-ELM). The test case in this study is the South Sulawesi-Indonesia Electric Power System, with a total wind turbine penetration of 142 MW. From the simulation, it can be seen that OPELM can do forecasting very well with an error rate of 0.0886%.

PL

Problem załamania napięcia jest poważnym problemem w funkcjonowaniu obecnego systemu elektroenergetycznego, zwłaszcza gdy penetracja turbin wiatrowych do systemu nadal wzrasta. Przerywalność turbiny wiatrowej ma wpływ na stabilność napięcia systemu. Wskaźnik stabilności szybkiego napięcia (FVSI) jest używany jako parametr stanu systemu ze zjawiskiem załamania napięcia. Niniejsze badanie ma na celu obserwowanie i przewidywanie wartości wskaźnika stabilności linii przy użyciu maszyny OP-ELM (ang. Optimally Pruned Extreme Learning Machine). Przykładem testowym w tym badaniu jest system elektroenergetyczny South Sulawesi-Indonesia, z całkowitą penetracją turbin wiatrowych 142 MW. Z symulacji widać, że OP-ELM może bardzo dobrze wykonywać prognozy ze wskaźnikiem błędu 0,0886%.

Słowa kluczowe

EN

voltage collapse; extreme learning machine; wind turbine; intermittency

PL

zapad napięcia; maszyna do ekstremalnego uczenia się; turbina wiatrowa

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2022
• Tom: R. 98, nr 5
• Strony: 80--84
• Opis fizyczny: Bibliogr. 15 poz., rys., tab.

Twórcy

autor

Chaerah Gunadin Indar

• Department of Electrical Engineering, Faculty of Engineering Hasanuddin University, Makassar Indonesia

• https://orcid.org/0000-0001-9516-0282

autor

Siswanto Agus

• Department of Electrical Engineering, Faculty of Engineering, 17 Agustus 1945 of University, Cirebon Indonesia

• https://orcid.org/0000-0002-5852-8161

autor

Safrizal Safrizal

• Department of Electrical Engineering Universitas Islam Nadhlatul Ulama Jepara, Jepara Indonesia

• https://orcid.org/0000-0002-8197-1313

autor

Syukriyadin Syukriyadin

• Department of Electrical Engineering Syiah Kuala University, Banda Aceh Indonesia

• https://orcid.org/0000-0002-7627-7140

autor

Rosyadi Marwan

• Department of Electrical Engineering, Faculty of Engineering Muhammadiyah University Surabaya, Indonesia

• https://orcid.org/0000-0003-2056-3292

autor

Muslimin Zaenab

• Department of Electrical Engineering, Faculty of Engineering Hasanuddin University, Makassar Indonesia

• https://orcid.org/0000-0003-4818-792X

autor

Gassing

• Department of Electrical Engineering, Faculty of Engineering Hasanuddin University, Makassar Indonesia

autor

Rasyid Ramly

• Department of Electrical Engineering, Faculty of Engineering Khairun University, Ternate Indonesia

Bibliografia

• [1]. P. Kundur, N. J. Balu, and M. G. Lauby, Power system stability and control vol. 7: McGraw-hill New York, 1994.
• [2]. Liu, Q., et al. L-Index Sensitivity Based Voltage Stability Enhancement. in 2017 IEEE 85th Vehicular Technology Conference (VTC Spring). 2017.
• [3]. Liacco, T.E.D., The Adaptive Reliability Control System. IEEE Transactions on Power Apparatus and Systems, 1967. PAS-86(5): p. 517-531.
• [4]. H. H. Goh, et al. Comparative Study of Line Voltage Stability Indices for Voltage Collapse Forecasting in Power Transmission System, International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering Vol:9, No:2, 2015
• [5]. O.P. Rahib et.al, Power System Voltage Stability Assessment through Artificial Neural Network, International Conference on Communication Technology and System Design 2011
• [6]. Gunadin I.C, et. al, The Impact of the Injection of Wind Power Plant on the Steady State Condition and the Dynamics of SULSELBAR Power System, International Journal of Electrical and Electronic Engineering & Telecommunications Vol. 8, No. 6, November 2019
• [7]. Suroso, Experience and Role Intermittent Generating Operation in the Sulawesi Electrical System, CIGRE Webinar, 26 June 2020
• [8]. I. Musirin, et. al, "Novel fast voltage stability index (Fvsi) for voltage stability analysis in power transmission system," in Research and Development, Student Conference on SCOReD 2002, pp. 265-268, 2002.
• [9]. G. K. Morison, B. Gao, and P. Kundur, "Voltage stability analysis using static and dynamic Approaches," IEEE Transactions on Power Systems, vol. 8, pp. 1159-1171, 1993.
• [10] Siswanto, A., Steady State Stability Limit Assessment when Wind Turbine Penetrated to the Systems using REI Approach. Vol. 1. 2019. 53-57.
• [11] Goh, H.H., et al. Validation of steady-state stability evaluation exerting with Dimo's approximation. in 4th IET Clean Energy and Technology Conference (CEAT 2016). 2016.
• [12] Isaac A. et. al., “Prediction of Voltage Collapse in Electrical Power System Networks using a New Voltage Stability Index.” International Journal of Applied Engineering Research ISSN 0973-4562 Volume 12, Number 2 (2017) pp. 190-199
• [13].R. Balasubramania et.al, Power System Voltage Stability Analysis Using ANN and Continuation Power Flow Methods, International Conference on Intelligent System Applications to Power Systems, 2011
• [14] Yoan Miche, et.al, " OP-ELM: Optimally Pruned Extreme Learning Machine," IEEE transactions on neural networks, Vol. 21, No. 1, January 2010
• [15] Javad Modarresi, et.al, " A comprehensive review of the voltage stability indices," Renewable and Sustainable Energy Reviews 63 (2016) 1–12

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).