Abstract
This paper presents two-fold adaptive linear neural networks (ADALINE) to gain the current operating state of power system for a fast and accurate estimation. On the one hand, the Slave-ADALINE applies the fixed and larger step-size least mean square algorithm to accelerate the convergence speed of weights. On the other hand, the Master-ADALINE follows least mean square with a variable step-size factor to achieve the minimum of steady-state error. In this paper the IEEE-30 network of power system is used to verify the effectiveness of the proposed method, and comparisons of simulation results with Particle Swarm Optimization algorithm and single ADALINE are also provided.
Z. Wang—This work was supported by the National Natural Science Foundation of China (Grant Nos. 61473070, 61433004, 61627809), and SAPI Fundamental Research Funds (Grant No. 2013ZCX01).
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Wang, Z., Gao, H., Zhang, H. (2017). An Application of Master-Slave ADALINE for State Estimation of Power System. In: Cong, F., Leung, A., Wei, Q. (eds) Advances in Neural Networks - ISNN 2017. ISNN 2017. Lecture Notes in Computer Science(), vol 10262. Springer, Cham. https://doi.org/10.1007/978-3-319-59081-3_4
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DOI: https://doi.org/10.1007/978-3-319-59081-3_4
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