Intelligent system applications in power engineering: evolutionary programming and neural networks
Cutting-edge research indicates that evolutionary programming is set to emerge as the dominant optimisation technique in the fast-changing power industry. Combining theory and practice, Intelligent System Applications in Power Engineering capitalises on the potential of neural networks and evolutionary computation to resolve real-world power engineering problems such as load forecasting, power system operation and planning optimisation. Unlike existing optimisation methods, these novel computational intelligence techniques provide power utilities with innovative solutions for improved performance. Features include:
* Introduction to evolutionary programming and neural networks serving as a foundation for later discussion of the benefits of hybrid systems
* Practical application of evolutionary programming to reactive power planning and dispatch for speedy, cost-effective increases in transmission capacity plus generator parameter estimation
* Examination of economic dispatch, power flow control in FACTS and co-generation scheduling and fault diagnosis for HVDC systems and transformers
* Consideration of power frequency and harmonic evaluation to maximise supply quality
* Employment of distance protection, faulty section estimation and calculation of fault clearing time for transient stability assessment
Graduate students in electric power engineering will value Lai s broad coverage of the applications of evolutionary programming and neural networks in the field. This unique reference will be a boon to engineers, computer application specialists, consultants and utility managers wishing to understand the benefits intelligent systems can bring to the power industry.
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HYBRID EVOLUTIONARY ALGORITHMS AND ARTIFICIAL NEURAL
AN EVOLUTIONARY PROGRAMMING APPROACH TO REACTIVE
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activation function analysis approach architecture artificial neural networks backpropagation BFGS BFGS method Boundary buses chapter chromosomes co-generation system codes control variables convergence cost crossover developed diagram distance relay domain object dynamic EANN electricity EP-ANN equation error estimation evolution evolutionary algorithms Evolutionary Computation evolutionary programming evolutionary programming EP fault diagnosis fitness value frequency genetic algorithms harmonic HVDC IEEE Transactions implementation individual initial initialise input learning load forecasting maximum minimise mutation neurons non-linear object model object-oriented objective function obtained operation optimal output parameters performance phase population Power Delivery power flow power losses power system reactive power reactive power source represented selection shown in Figure simulation solution solve Table tap-setting techniques testing thermal thyristor tion training algorithms training patterns Transactions on Power transformer transformer fault transient stability transmission lines unsupervised learning vector weights