TY - JOUR
T1 - Genetic algorithm-assisted design of adaptive predictive filters for 50/60 Hz power systems instrumentation
AU - Ovaska, Seppo J.
AU - Bose, Tamal
AU - Vainio, Olli
N1 - Funding Information:
Manuscript received January 19, 2004; revised January 21, 2005. This work was supported by the Academy of Finland under Grant 80100. S. J. Ovaska is with the Institute of Intelligent Power Electronics, Department of Electrical and Communications Engineering, Helsinki University of Technology, FIN-02150 Espoo, Finland (e-mail: [email protected]). T. Bose is with the Center for High-Speed Information Processing, Department of Electrical and Computer Engineering, Utah State University, Logan, UT 84322, USA (e-mail: [email protected]). O. Vainio is with the Department of Information Technology, Tampere University of Technology, FIN-33101 Tampere, Finland (e-mail: [email protected]). Digital Object Identifier 10.1109/TIM.2005.853230
PY - 2005/10
Y1 - 2005/10
N2 - We introduce a genetic algorithm-based method for structural optimization of multiplicative general parameter (MGP) finite impulse response (FIR) filters. These computationally efficient reduced-rank adaptive filters are robust, suitable for predictive configurations, and they have numerous applications in 50/60 Hz power systems instrumentation. The design process of such filters has three independent stages: Lagrange multipliers-based optimization of the sinusoid-predictive basis filter, genetic algorithm-based search of optimal FIR tap cross-connections and, finally, the online MGP-adaptation phase guided by variations in signal statistics. Thus, our multistage design procedure is a complementary fusion of hard computing (HC) and soft computing (SC) methodologies. Such advantageous fusion (or symbiosis) thinking is emerging among researchers and practicing engineers, and it can potentially lead to competitive combinations of individual HC and SC methods.
AB - We introduce a genetic algorithm-based method for structural optimization of multiplicative general parameter (MGP) finite impulse response (FIR) filters. These computationally efficient reduced-rank adaptive filters are robust, suitable for predictive configurations, and they have numerous applications in 50/60 Hz power systems instrumentation. The design process of such filters has three independent stages: Lagrange multipliers-based optimization of the sinusoid-predictive basis filter, genetic algorithm-based search of optimal FIR tap cross-connections and, finally, the online MGP-adaptation phase guided by variations in signal statistics. Thus, our multistage design procedure is a complementary fusion of hard computing (HC) and soft computing (SC) methodologies. Such advantageous fusion (or symbiosis) thinking is emerging among researchers and practicing engineers, and it can potentially lead to competitive combinations of individual HC and SC methods.
KW - Adaptive filtering
KW - Control instrumentation
KW - Electric power systems
KW - Genetic algorithms
KW - Predictive filtering
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U2 - 10.1109/TIM.2005.853230
DO - 10.1109/TIM.2005.853230
M3 - Article
AN - SCOPUS:27644451329
SN - 0018-9456
VL - 54
SP - 2041
EP - 2048
JO - IEEE Transactions on Instrumentation and Measurement
JF - IEEE Transactions on Instrumentation and Measurement
IS - 5
ER -