9781032864655 - Fault Diagnosis for Electric Power Systems and Electric Vehicles von Rigatos, G.; Abbaszadeh, M.; Hamida, M.A.; Siano, P. (4 Ergebnisse)

Zustand: New.

Paperback. Zustand: Brand New. 238 pages. 6.14x0.57x9.21 inches. In Stock.

Zustand: New. Dr. Gerasimos Rigatos obtained his diploma (1995) and his Ph.D. (2000) both from the Department of Electrical and Computer Engineering, of the National Technical University of Athens (NTUA), Greece. In 2001 he was a post-doctoral researcher at IRI.

Taschenbuch. Zustand: Neu. Neuware - The present monograph offers a detailed and in-depth analysis of the topic of fault diagnosis for electric power systems and electric vehicles. It explores both model-based and model-free techniques for fault diagnosis and provides a solution for the problem of control of the marine-turbine and synchronous-generator unit and Fault diagnosis of the marine turbine and synchronous-generator unit. Additionally, it introduces innovative approaches for diagnosing faults in electricity microgrids and gas processing units.The new fault detection and isolation methods with statistical procedures for defining fault thresholds enable early fault diagnosis and reveal incipient changes in the parameters of the monitored systems.Key Features: - Analyzes model-based fault detection and isolation methods. Known models about the dynamics of the monitored system are used by nonlinear state observers and Kalman Filters, which emulate the system's fault-free condition - Analyzes model-free fault detection and isolation methods. Raw data are processed by neural networks and nonlinear regressors to generate models that emulate the fault-free condition of the monitored system - Utilizes statistical tests based on residual processing, which compare outputs from the monitored system to those of a fault-free model, providing objective and highly reliable criteria for identifying failures - Enables early fault diagnosis through new detection and isolation methods that use statistical procedures for defining fault thresholds, effectively revealing incipient changes in the parameters of monitored systems.