A GA-PSO Hybrid Algorithm Based Neural Network Modeling Technique for Short-term Wind Power Forecasting

ABSTRACT

This article proposes a hybrid neural network modeling technique for forecasting of wind power generation based on an integrated algorithm combining genetic algorithm (GA) and particle swarm optimization (PSO). The share of wind energy in electric power generation keeps growing supported by favorable environmental policies aiming at achieving low-emission targets. However, due to the intermittent and uncertain nature of wind flow, integration of wind power into electric power systems brings operational challenges to address. Accurate wind power generation forecasting tools play a key role to address the challenges. A multi-layered feed-forward artificial neural network model optimized by a combination of genetic algorithm and particle swarm optimization algorithm is developed in this work for wind power generation forecasting. The proposed technique is tested based on practical information obtained from Goldwind Smart Microgrid in Beijing. The performance of the proposed method is superior to neural network models optimized using GA and PSO separately, as well as the benchmark persistence approach.

Keywords::

• Wind
• Generation Forecasting
• Genetic Algorithm
• Particle Swarm Optimization
• Neural Networks

Additional information

Notes on contributors

Yordanos Kassa Semero

ABOUT THE AUTHORS

Yordanos Kassa Semero received his B.Sc. degree in Electrical Engineering from Mekelle University, Mekelle, Ethiopia in 2008, M.Sc. degree in Electrical Power Engineering from Arba Minch University, Arba Minch, Ethiopia in 2011, and PhD degree in Electric Power System and Its Automation from North China Electric Power University, Beijing, China in 2018. He is currently with the department of Electrical and Computer Engineering of Mettu University, Mettu, Ethiopia. He was also with the Microgrid R&D Center of Goldwind Science and Technology, Beijing, China. His research interests include distributed generation, microgrid energy management systems, operation and control of microgrids. Email: [email protected]

Jianhua Zhang

Jianhua Zhang received his M.Sc. degree in electrical engineering from North China Electric Power University, Beijing, China, in 1984. He was a Visiting Scholar with the Queen's University, Belfast, U.K., from 1991 to 1992, and was a Multimedia Engineer of Electric Power Training with CORYS T.E.S.S., France, from 1997to 1998. Currently, he is a Professor and Head of the Transmission and Distribution Research Institute, North China Electric Power University, Beijing. He is also the Consultant Expert of National “973” Planning of the Ministry of Science and Technology. His research interests are in power system security assessment, operation and planning, and micro-grid. Prof. Zhang is an IET Fellow and a member of several technical committees.

Dehua Zheng

Dehua Zheng was born in Guangdong province in China, on November 26, 1955. He graduated from North China Electric Power University, and pursued further study at the University of Manitoba. His employment experience included the Manitoba Hydropower Company, University of Saskatchewan, China National Wind Power Engineering Technology Research center, and Goldwind Science and Technology. He is a Senior Member of IEEE, Registered Senior Electric Engineer of North America and IEC member. As the chief engineer of Goldwind and Etechwin, he devotes to research and development of Chinese microgrid technology.

Dan Wei

Dan Wei was born in Hubei province in China, in 1988. She graduated from Wuhan University of Technology, and pursed further study at the Shanghai Dianji University. Her employment experience includes Fiberhome Telecommunication Tech. Co., Ltd and Beijing Etechwin Electric Co., Ltd. D. Wei is an IECRE member and a micorgrid system engineer in Beijing Etechwin electric Co., Ltd. She majors in the application of hybrid energy storage in microgrids.