The main roles of the biologic immune system are to recognize and eliminate foreign antigens (e.g., bacteria, virus, etc), and to act as a defensive barrier. From an information-processing perspective, the immune system is a highly parallel and distributed intelligent system that has learning, recognition, memory, and associative retrieval capabilities. In recent years, the artificial immune system (AIS), inspired by biologic immune system, has sprung up since it provides a powerful capability of information process and a novel paradigm of solving problem.The basic principles of the biologic immune system, the Clone Selection Theory, the Theory of Idiotype Immune Network, the researches and typical applications of AIS are introduced in this dissertation. A novel artificial immune algorithm and its applications in electric power systems are studied in the following fields:(1) The causes of the premature convergence of the genetic algorithm (GA) are analyzed. Inspired by the immune principles, a new artificial immune algorithm (AIA or IA) is presented and its global convergence is proved theoretically. Several De Jong's benchmark functions are used to test the convergent performances of the IA, in which the parameters are float-coded and a novel mutation operator is designed. The simulation results demonstrate that the proposed algorithm has faster convergent speed and higher convergent accuracy.(2) The IA is used to reconfigure electric power distribution networks for loss minimization. A strategy is adopted to shorten the encoding length of the antibodies. When the population is initialized, vaccinations are applied to modify some genes of the individuals, which can increase the percentage of feasible solutions comparing to random initialization. A heuristic rule is used to correct infeasible solutions to feasible ones by opening up the loops and connecting the islands. Furthermore, double hypermutation and immune recruitment operators are used to maintain the diversity of the population, which can prevent the algorithm from prematurity. The results of 69-bus system reconfiguration prove the proposed algorithm has a higher computational efficiency.(3) A new immune chaotic algorithm (ICA) is proposed for constrained optimization problems (COPs). The algorithm mixes the merits of both IA and chaotic optimization (CO) method. The former has a powerful capability of global exploration, and the latter is fit for local exploitation. A group of feasible solutions is set for ICA firstly. During the course of optimization, the memory cells, namely the approximate solutions, can be obtained by IA with clonal selection, clonal proliferation, hypermutation and censoring steps, and then the accurate optimal solutions can be reached by using censoring and CO, which locally searches the neighborhoods of the approximate solutions according to the rules of chaotic motion. Thereinto, the censoring process consists of handling constrains. It censors the newcomers, and only the ones that are feasible are reserved. Using ICA tests several classical COPs, and the results excel those reported, which proves that ICA for COPs is effective. ICA is modified slightly and used to solve the economic dispatch (ED) problems, and satisfactory results are obtained.(4) The general frameworks of immune algorithm and swarm algorithms are discussed, and their similarities are pointed out. A novel immune chaotic particle optimization (ICPSO) algorithm is put forward, in which the memory cells, immune recruitment and chaotic optimization operators are integrated with the particle swarm optimization (PSO) algorithm. ICPSO is applied to solve the ED problems, and the results show that it has better convergent performances than the basic PSO. |