ECTS - University of Pisa
Scheda programma d'esame
DIAGNOSTICS AND MONITORING OF ELECTRICAL APPARATA AND POWER SYSTEMS
MAURO TUCCI
Academic year2016/17
CourseELECTRIC ENGINEERING
Code323II
Credits6
PeriodSemester 1
LanguageItalian

ModulesAreaTypeHoursTeacher(s)
DIAGNOSTICA E MONITORAGGIO DI APPARATI E SISTEMI ELETTRICIING-IND/31LEZIONI60
MAURO TUCCI unimap
Programma non disponibile nella lingua selezionata
Learning outcomes
Knowledge
The student who successfully completes the course will demonstrate a solid knowledge of the techniques for the diagnosis and condition monitoring of equipment and electrical systems, as power transformers, rotating electrical machines, power cables etc. These include advanced knowledge of signal processing techniques, and computational intelligence methods as neural networks, fuzzy systems and optimization algorithms. The student will also be able to program algorithms related to the application of the studied methods in the Matlab environment.
Assessment criteria of knowledge
The student will be assessed on his/her demonstrated ability to discuss the main course contents using the appropriate terminology. With the oral presentation, to be made to the teacher and the other students, the student must demonstrate the ability to approach a circumscribed research problem, and organise an effective exposition of the results. The student must demonstrate the ability to put into practice and to execute, with critical awareness, the activities illustrated or carried out under the guidance of the teacher during the course.

Methods:

  • Final oral exam
Teaching methods

Delivery: face to face

Learning activities:

  • attending lectures
  • preparation of oral/written report
  • individual study
  • Practical

Attendance: Advised

Teaching methods:

  • Lectures
  • Seminar
  • Task-based learning/problem-based learning/inquiry-based learning
Syllabus
Specific topic/s: The Continuous Fourier Transform. The Discrete Fourier Transform (DFT), FFT algorithm. Stochastic processes, spectral density estimation. Identification of linear and nonlinear systems. Feed forward neural networks. Feature extraction, principal component analysis (PCA). Shewhart control chart, CUSUM chart, Hotelling control chart. Condition monitoring of power transformers: thermal models, thermoelectric analogy, winding frequency response analysis (FRA), winding deformation diagnosis. Dielectric response analysis of transformers and power cables, PDC, FDS, and DIRANA, Partial Discharge. Support Vector Machines, clustering, k-means algorithm. Fuzzy inference systems, FCM. Time-frequency signal representation: Short Time Fourier Transform, wavelet, Hilbert Huang Transform. Introduction to optimization algorithms, direct search, evolutionary algorithms. Laboratory activity: programming signal processing and computational intelligence techniques in Matlab.
Bibliography
W. H. Tang  Q. H. Wu " Condition Monitoring and Assessment of Power Transformers Using Computational Intelligence", Springer 2011. Peter Tavner, Li Ran, Jim Penman and Howard Sedding "Condition Monitoring of Rotating Electrical Machines", IET POWER AND ENERGY SERIES 56. Marco Luise, Giorgio M. Vitetta, "Teoria dei segnali", Mc graw - Hill S. Haykin, M. Moher:"Introduzione alle telecomunicazioni analogiche e digitali", Casa Editrice Ambrosiana Christopher M. Bishop "Pattern Recognition and Machine Learning (Information Science and Statistics)", Springer Simon Haykin, Neural Networks: A Comprehensive Foundation, Prentice Hall Timothy Ross, Fuzzy Logic with Engineering Applications, Wiley A.E. Eiben, J.E. Smith, Introduction to Evolutionary Computing, Springer
Work placement
Yes
Updated: 14/11/2016 17:27