Artificial Intelligence Based Distribution System Management and Control

The electrical transmission and distribution systems are working on their own independence in operation. The operation of these systems can be modified by manual switching process. The switching process takes place only there is a need for transmission line alteration and transmission line fault attendance period. The manual switching operation during fault occurrence period consumes lot of time for the trained person to reach the place and it may leads to severe damages to the transmission system, also it’s a threat to human safety. In order to avoid such drawbacks circuit breakers and automatic trippers were installed to the transmission lines and distribution systems. The circuit breakers and trippers are able to switch off the system only after the fault observation in the transmission line system. The proposed artificial intelligence based management and control system consists of several sensor elements and wireless IoT transmission to predict and avoid the fault occurrence by monitoring the physical and atmosphere condition of the transmission and distribution line. The control structure fitted with the transmission line monitors the environment and line fault condition and the IoT transmission unit gives a possible communication from the remote monitoring system to the transmission line system for switching operations.

[1]  Rui Liang,et al.  Fault location of transmission lines connecting with short branches based on polarity and arrival time of asynchronously recorded traveling waves , 2019, Electric Power Systems Research.

[2]  Yang Mi,et al.  A New Single Ended Fault Location Method for Transmission Line Based on Positive Sequence Superimposed Network During Auto-Reclosing , 2019, IEEE Transactions on Power Delivery.

[3]  R Vinothkanna,et al.  A SECURE STEGANOGRAPHY CREATION ALGORITHM FOR MULTIPLE FILE FORMATS , 2019, Journal of Innovative Image Processing.

[4]  Chinmoy Kumar Panigrahi,et al.  A method for fault section identification in High voltage direct current transmission lines using one End measurements , 2019, Electric Power Systems Research.

[5]  Haoxiang Wang,et al.  An intelligent hybrid model for power flow optimization in the cloud-IOT electrical distribution network , 2017, Cluster Computing.

[6]  Ali Khaleghi,et al.  Single-Phase Fault Location in Four-Circuit Transmission Lines Based on Wavelet Analysis Using ANFIS , 2019 .

[7]  Zaibin Jiao,et al.  A Novel Fault-Location Method for HVDC Transmission Lines , 2010, IEEE Transactions on Power Delivery.

[8]  Mugunthan S. R Dr.  SECURITY AND PRIVACY PRESERVING OF SENSOR DATA LOCALIZATION BASED ON INTERNET OF THINGS , 2019 .

[9]  Yuping Li,et al.  A Novel Fault Location Method for HVDC Transmission Lines , 2019, 2019 IEEE PES GTD Grand International Conference and Exposition Asia (GTD Asia).

[10]  S. Smys,et al.  A Multihoming ACO-MDV Routing for Maximum Power Efficiency in an IoT Environment , 2019, Wirel. Pers. Commun..

[11]  Smys S Dr,et al.  INTERNET OF THINGS AND BIG DATA ANALYTICS FOR HEALTH CARE WITH CLOUD COMPUTING , 2019, Journal of Information Technology and Digital World.

[12]  Zaibin Jiao,et al.  Fault Location Technology for Power System Based on Information About the Power Internet of Things , 2020, IEEE Transactions on Industrial Informatics.

[13]  Yuhong Wang,et al.  Transmission Line Fault Identification Based on BP Neural Network , 2019, 2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia).

[14]  Wang Haoxiang.  TRUST MANAGEMENT OF COMMUNICATION ARCHITECTURES OF INTERNET OF THINGS , 2019 .

[15]  Ahmed Abu-Siada,et al.  A new on-line technique to identify fault location within long transmission lines , 2019, Engineering Failure Analysis.

[16]  D Sivaganesan,et al.  DESIGN AND DEVELOPMENT AI-ENABLED EDGE COMPUTING FOR INTELLIGENT-IOT APPLICATIONS , 2019, Journal of Trends in Computer Science and Smart Technology.