Abstract
Mathematically, an optimal power flow (OPF) is in general a non-linear, non-convex and large-scale problem with both continuous and discrete control variables. This paper approaches the OPF problem using a modified Artificial Immune System (AIS). The AIS optimization methodology uses, among others, two major immunological principles: hypermutation, which is responsible for local search, and receptor edition to explore different areas in the solution space. The proposed method enhances the original AIS by combining it with a gradient vector. This concept is used to provide valuable information during the hypermutation process, decreasing the number of generations and clones, and, consequently, speeding up the convergence process while reducing the computational time. Two applications illustrate the performance of the proposed method.
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de Mello Honório, L., da Silva, A.M.L., Barbosa, D.A. (2007). A Gradient-Based Artificial Immune System Applied to Optimal Power Flow Problems. In: de Castro, L.N., Von Zuben, F.J., Knidel, H. (eds) Artificial Immune Systems. ICARIS 2007. Lecture Notes in Computer Science, vol 4628. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73922-7_1
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DOI: https://doi.org/10.1007/978-3-540-73922-7_1
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