By W.H. Tang
In fresh years, fast adjustments and enhancements were witnessed within the box of transformer tracking and overview, in particular with the advances in computational intelligence ideas. Condition tracking and overview of energy Transformers utilizing Computational Intelligence applies a wide variety of computational intelligence suggestions to house useful transformer operation difficulties. The methods brought are awarded in a concise and flowing demeanour, tackling advanced transformer modelling difficulties and uncertainties taking place in transformer fault analysis. Condition tracking and overview of strength Transformers utilizing Computational Intelligence covers either the basic theories and the main up to date study during this speedily altering box. Many examples were integrated that use real-world measurements and lifelike working situations of strength transformers to completely illustrate using computational intelligence ideas for quite a few transformer modelling and fault analysis difficulties. Condition tracking and review of strength Transformers utilizing Computational Intelligence is an invaluable publication for pro engineers and postgraduate scholars. It additionally presents an organization beginning for complicated undergraduate scholars in strength engineering.
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Extra resources for Condition Monitoring and Assessment of Power Transformers Using Computational Intelligence
IEEE Trans Power Deliv 13(4):1224-1229 20. Lin CE, Ling JM, Huang CL (1993) An expert system for transformer fault diagnosis and maintenance using dissolved gas analysis. IEEE Trans Power Deliv 8(1):231–238 21. Islam SM, Wu T, Ledwich G (2000) A novel fuzzy logic approach to transformer fault diagnosis. IEEE Trans Dielectr Electr Insul 7(2):177–186 22. Dick EP, Erven CC (1978) Transformer diagnostic testing by frequency response analysis. IEEE Trans Power Apparatus Syst, PAS-97 (6):2144–2150 References 13 23.
The hill-climbing and branch-bound algorithms. 3. Random search algorithms have achieved increasing popularity, as researchers have recognised the shortcomings of calculus-based and enumerative schemes. A random search is the simplest stochastic search strategy, as it simply evaluates a given number of randomly selected solutions. A random walk is very similar amongst each other, except that the next solution evaluated is selected randomly using the last evaluated solution as a starting point. 2 Overview of Evolutionary Computation Evolutionary computation techniques or evolutionary algorithms (EAs) work on a population of potential solutions in a search space.
Pi ð2:8Þ Pi ¼ Xi : f ðXi Þ\Pi Pg 2 fP0 ; P1 ; . ; Pm gjf ðPg Þ ¼ minðf ðP0 Þ; f ðP1 Þ; . 4 Particle Swarm Optimisation 31 where f is the objective function, m is the number of particles, r1 and r2 are the elements from two uniform random sequence on the interval ½0; 1 : r1 $ Uð0; 1Þ; r2 $ Uð0; 1Þ and x an inertia weight which is typically chosen in the range of [0,1]. A larger inertia weight facilitates the global exploration and a smaller inertia weight tends to facilitate the local exploration to fine-tune the current search area .