Computational Intelligence in Power Engineering by M. A. Abido (auth.), Bijaya Ketan Panigrahi, Ajith Abraham,

By M. A. Abido (auth.), Bijaya Ketan Panigrahi, Ajith Abraham, Swagatam Das (eds.)

Computational Intelligence (CI) is likely one of the most vital robust instruments for learn within the assorted fields of engineering sciences starting from conventional fields of civil, mechanical engineering to enormous sections of electric, electronics and machine engineering and particularly the organic and pharmaceutical sciences. the prevailing box has its starting place within the functioning of the human mind in processing info, spotting development, studying from observations and experiments, storing and retrieving info from reminiscence, and so on. particularly, the ability being at the verge of epoch altering because of deregulation, the ability engineers require Computational intelligence instruments for correct making plans, operation and keep an eye on of the facility procedure. many of the CI instruments are certainly formulated as a few kind of optimization or selection making difficulties. those CI ideas give you the strength utilities with leading edge options for effective research, optimum operation and keep an eye on and clever choice making. This edited quantity bargains with diverse CI ideas for fixing genuine international energy difficulties. The technical contents may be tremendous useful for the researchers in addition to the training engineers within the energy industry.

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2 explains the details for implementing AIS-ACO algorithm for solving the reconfiguration problem. 36 A. Ahuja, S. Das, and A. 2 Implementation Details of the Algorithm A solution to the multi-objective distribution system reconfiguration problem is referred to as an antibody. We do not use any population for antigens. The equivalence of antibody-antigen affinity in this case is the concept of Pareto dominance among solutions as explained in section 2. This antibody-antigen affinity provides information about the solution quality.

These observations motivated the use of the concept of Pareto Optimality for multi-objective framework for distribution network reconfiguration, in which, instead of just one optimal solution, the set of all optimal solutions is preserved. Section 2 presents the formal definition of Pareto Optimality and explains the multi-objective framework used. Further it presents the mathematical formulation for multi-objective distribution system reconfiguration problem. Section 3 describes the theoretical basis of Artificial Immune System and Ant Colony Optimization and presents the AIS-ACO hybrid algorithm.

These cells have a molecule attached to their surface called antibody (Ab). Each B cell secretes a single type of antibody, which is relatively specific for an antigen. The primary purpose of an antibody is to recognize and bind to an antigen for marking it for elimination by other cells of the immune system. Those B cells, whose antibodies best recognize and bind the antigen, are cloned into a number of copies. This step is called proliferation or cloning. Some of these clones finally maturate into terminal (non-dividing) antibody secreting cells called plasma cells.

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