Stability Performance Analysis for Variable-Speed Variable-Pitch WECS Based on Dynamic Feedforward Neural Network Control

• Autorzy: Li T. , Ji Z.

Warianty tytułu

PL

Analiza stabilności system konwersji energi wiatru o różnej prędkości z wykorzystaniem sterowania bazującego na sieci neuronowej

• Języki publikacji: EN

Abstrakty

EN

Wind energy conversion system (WECS) is a complex nonlinear system, when the wind speed is above the rated value. For a smooth integration of wind generators into the utility grids, two subsystems are built for the WESC based on two-time-scale. NNPID compensator is designed to compensate slow dynamics blade pitch angle, in order to reduce fluctuations of the power output. Compensator for the slow dynamics blade pitch angle is designed based on dynamic feedforward neural network (DFNN), its approximation capabilities are verified by the SCADA (supervisory control and data acquisition) wind farm data collected. Control performances of the DFNN with different structure are compared and analysed, results show that the method can effectively reduce the interference caused by disturbed parameters of the WECS. Safety of the system is improved, and a better idea is provided for application of the DFNN in wind power systems field.

PL

System konwersji energii wiatrowej jest szczególnie złożony gdy prędkość wiatru przekracza założone wartości. Zaproponowano dynamiczny układ sterowania z siecią neuronową DFNN. Osiągnięto lepsze bezpieczeństwo pracy systemu i zmniejszenie zakłóceń.

Słowa kluczowe

EN

wind energy conversion system; sensitivity analysis; dynamic feedforward neural network; power output

PL

konwersja energii wiatru; sieci neuronowe

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2012
• Tom: R. 88, nr 7b
• Strony: 243--247
• Opis fizyczny: Bibliogr. 8 poz., schem., tab., wykr.

Twórcy

autor

Li T.

• Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi,214122, China

autor

Ji Z.

• Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi,214122, China

Bibliografia

• [1] Kittipong Methaprayoon, Chitra Ying vivatana pong, Wei-Jen Lee, James R. Liao, et al. An Integration of ANN Wind Power Estimation Into Unit Commitment Considering the Forecasting Uncertainty[J].IEEE Transactions on Industray Applications, 43 (2007), No.6:1441-1448.
• [2] Ahmet Serdar Yilmaz, Zafer OZer. Pitch Angle Control in Wind Turbines above the Rated Wind Speed by Multi-Layer Perceptron and Radial Basis Function Neural Networks[J]. Expert Systems with Applications, 36 (2009) ,No.6: 9767-9775.
• [3] Luis F.C, Alberto. Uniform Approach For Stablility Analysis of Fast Subsystem of Two–Time–Scale Nonlinear Systems [J]. International Journal of Bifurcation and Chaos, 17(2006) No.(11): 4195-4203.
• [4] Irmela Zentner,Stefano Tarantola,E.de Rocquigny.Sensitivity Analysis for Reliable Design Verification of Nuclear Turbosets[J].Reliability Engineering & System Safety, 96 (2011) No.3:391-397.
• [5] Andrea Saltelli,Marco Ratto,Stefano Tarantola,et al. Sensitivity Analysis Practices: Strategies for Model-Based Inference[J].Reliability Engineering and System Safety,91 (2006), No.(11-12):1109-1125.
• [6] Andrew Kusiak,Zijun Zhang,Mingyang Li.Optimization of Wind Turbine Performance With Data-Driven Models[J].IEEE Transactions on Sustainable Energy,1 (2010), No. 2: 66-76.
• [7] Pirabakaran,V.M.. Becerra.PID Autotuning Using Neural Networks and Model Reference Adaptive Control[C]. Proceeding of 15th the International Federation of Automatic Control, Spain :Barcelona,15(2002):.
• [8] Munteanu I., Barcu A. I., Cutululis N. A.,et al. Optimal control of Wind Energy Systems[M].London:Springer-Verlag, (2008).