Identyfikatory
DOI
Warianty tytułu
Języki publikacji
Abstrakty
Finding the most critical contingencies in a power system is a difficult task as multiple evaluations of load and generation scenarios are needed. This paper presents a mathematical formulation for selecting, ranking, and grouping the most critical N-1 net- work contingencies, based on the calculation of a Power Constraint Index (PCI) obtained from the Outage Transfer Distribution Factors (OTDF). The results show that the PCI is only affected by the impedance parameter of the transmission network, the topology, and the location of all generators. Other methods, such as the Performance Index (PI) and the Overload Index (OL) are affected by the power generation and demand variations. The proposed mathematical formulation can be useful to accelerate the calculation of other methods that evaluate contingencies in power system planning and operation. Furthermore, the fast calculation of indices makes it suitable for online evaluation and classification of multiple events considering the current topology. The results showed that the proposed algorithm easily selected and ranked the expected contingencies, with the highest values of the index corresponding to the most critical events. In the filtering process, the computational calculation time improved without losing the robustness of the results.
Czasopismo
Rocznik
Tom
Strony
247–--261
Opis fizyczny
Bibliogr. 18 poz., rys., tab.
Twórcy
autor
- Facultad de Minas, Departamento de Energía Eléctrica y Automática Universidad Nacional de Colombia, Sede Medellín Carrera 80 No. 65-223, Medellín, Colombia
autor
- Facultad de Minas, Departamento de Energía Eléctrica y Automática Universidad Nacional de Colombia, Sede Medellín Carrera 80 No. 65-223, Medellín, Colombia
Bibliografia
- [1] Morison K., Wang L., Kundur P., Power system security assessment, IEEE Power and Energy Magazine, vol. 2, no. 5, pp. 30–39 (2004), DOI: 10.1109/MPAE.2004.1338120.
- [2] Stefopoulos G.K., Yang F., Cokkinides G.J., Meliopoulos A.P., Advanced contingency selection methodology, Proc. 37th Annual North Amaerican Power Symposium, IEEE; n.d., pp. 67–73 (2005), DOI: 10.1109/NAPS.2005.1560503.
- [3] Gimenez Alvarez J.M., Mercado P.E., Online Inference of the Dynamic Security Level of Power Systems Using Fuzzy Techniques, IEEE Transactions on Power Systems, vol. 22, no. 2, pp. 717–726 (2007), DOI: 10.1109/TPWRS.2007.895161.
- [4] Chen Y., Bose A., Direct ranking for voltage contingency selection, IEEE Transactions on Power Systems, vol. 4, no. 4, pp. 1335–1344, DOI: 10.1109/59.41683.
- [5] Morison K.,Wang X., Moshref A., Edris A., Identification of voltage control areas and reactive power reserve; An advancement in on-line voltage security assessment, IEEE Power and Energy Society General Meeting – Conversion and Delivery of Electrical Energy in the 21st Century, Pittsburgh, PA, USA, pp. 1–7 (2008), DOI: 10.1109/PES.2008.4596339.
- [6] McCalley J.D., Krause B.A., Rapid transmission capacity margin determination for dynamic security assessment using artificial neural networks, Electric Power Systems Research, vol. 34, no. 1, pp. 37–45 (1995), DOI: 10.1016/0378-7796(95)00955-H.
- [7] Kezunovic M., Rikalo I., Sobajic D.J., Real-Time and off-line transmission line fault classification using neural networks, Engineering Intelligent Systems, vol. 4, no. 1, pp. 57–63 (1996).
- [8] Ghosh S., Chowdhury B.H., Design of an artificial neural network for fast line flow contingency ranking, International Journal of Electrical Power and Energy Systems, vol. 18, no. 5, pp. 271–277 (1996), DOI: 10.1016/0142-0615(94)00021-2.
- [9] Sekhar P., Mohanty S., An online power system static security assessment module using multi-layer perceptron and radial basis function network, International Journal of Electrical Power and Energy Systems, vol. 76, pp. 165–173 (2016), DOI: 10.1016/j.ijepes.2015.11.009.
- [10] Gasim Mohamed S.E., Yousif Mohamed A., Abdelrahim Y.H., Power System Contingency Analysis to detect Network Weaknesses, Zaytoonah University International Engineering Conference on Design and Innovation in Infrastructure, Amman, Jordan, pp. 1–11 (2012).
- [11] Fliscounakis S., Panciatici P., Capitanescu F., Wehenkel L., Contingency Ranking With Respect to Overloads in Very Large Power Systems Taking Into Account Uncertainty, Preventive, and Corrective Actions, IEEE Transactions on Power Systems, vol. 28, no. 4, pp. 4909–4917 (2013), DOI: 10.1109/TPWRS.2013.2251015.
- [12] Majidi-Qadikolai M., Baldick R., Integration of Contingency Analysis With Systematic Transmission Capacity Expansion Planning: ERCOT Case Study, IEEE Transactions on Power Systems, vol. 31, no. 3, pp. 2234–2245 (2016), DOI: 10.1109/TPWRS.2015.2443101.
- [13] Kumar Patra K., Contingency Analysis in Power System using Load Flow Solution, IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), Delhi, India, pp. 1–4 (2016), DOI: 10.1109/ICPEICES.2016.7853352.
- [14] Zhu J., Optimization of Power System Operation, New Jersey: John Wiley & Sons, Inc. (2009), DOI: 10.1002/9780470466971.
- [15] Eastern Interconnection Reliability Assessment Group, Study Procedure Manual 2015.
- [16] Chen Y.C., Dominguez-Garcia A.D., Sauer P.W., Generalized injection shift factors and application to estimation of power flow transients, North American Power Symposium, pp. 1–5 (2014), DOI: 10.1109/NAPS.2014.6965399.
- [17] Guo J., Fu Y., Li Z., Shahidehpour M., Direct Calculation of Line Outage Distribution Factors, IEEE Transactions on Power Systems, vol. 24, no. 3, pp. 1633–1634 (2009), DOI: 10.1109/TP-WRS.2009.2023273.
- [18] Unidad de Planeación Minero Energética – UPME., Plan de expansión de referencia generación-transmisión 2014–2028, Bogotá, Colombia (2014).
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-18cbb80d-de3f-485f-8ccb-cefc58984c5d