Applications of Artificial Intelligence Techniques in Engineering SIGMA 2018, Volume 1 /

The book is a collection of high-quality, peer-reviewed innovative research papers from the International Conference on Signals, Machines and Automation (SIGMA 2018) held at Netaji Subhas Institute of Technology (NSIT), Delhi, India. The conference offered researchers from academic and industry the...

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Λεπτομέρειες βιβλιογραφικής εγγραφής
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Άλλοι συγγραφείς: Malik, Hasmat (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Srivastava, Smriti (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Sood, Yog Raj (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Ahmad, Aamir (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Singapore : Springer Singapore : Imprint: Springer, 2019.
Έκδοση:1st ed. 2019.
Σειρά:Advances in Intelligent Systems and Computing, 698
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
Πίνακας περιεχομένων:
  • Chapter 1: Annual Energy Savings with Multiple DG and D-STATCOM Allocation using PSO in DNO Operated Distribution Network
  • Chapter 2: Optimized 2DOF PID for AGC of Multi Area Power System using Dragon-Fly
  • Chapter 3:Wide Area Monitoring System using Integer Linear Programming
  • Chapter 4: Fault Classification and Faulty Phase Selection Using Symmetrical Components of Reactive Power for EHV Transmission Line
  • Chapter 5: Optimal Bidding Strategy in Deregulated Power Market Using Krill Herd Algorithm
  • Chapter 6: A Novel Intelligent Bifurcation Classification Model Based on Artificial Neural Network (ANN)
  • Chapter 7: Teaching Learning Based Optimization for Frequency Regulation in Two Area Thermal-Solar Hybrid Power System
  • Chapter 8: An Approach to Minimize the Transmission Loss and Improves the Voltage Profile of Load Bus Using Interline Power Flow Controller (IPFC)
  • Chapter 9: A Novel Intelligent Transmission Line Fault Diagnosis Model Based on EEMD And Multiclass PSVM.