DOWNLOAD MATLAB CODE FOR CALCULATION OF SAIFI, SAIDI, CAIDI, ASAI, ASUI AND EENS-GENETIC ALGORITHM
DESIGN DETAILS
In a distribution network varying load demand makes the operation and control more complex mostly in the high load density zones. The power loss of a distribution network will not be minimum with varying load demand for a fixed configuration of the network. Hence, timely reconfiguration of the network is required. Network reconfiguration is the process of varying the topological structure of feeders by altering the open/closed status of sectionalising and tie switches. In general, reconfiguration of the network is preferred to relive the overloaded feeders and to minimize real power loss and to improve the reliability in the distribution system. In this design, feeder reconfiguration is performed to improve reliability, minimize voltage deviation, and power loss. Genetic Algorithm optimization handles the reconfiguration. The algorithm has been tested using Matlab on two standard bus system i.e., IEEE 33 and 69 by using Backward-Forward Sweep load flow analysis to verify the performance.
RELIABILITY INDICES (Reference paper 2&3)
1. System Average Interruption Frequency Index (SAIFI)
2. System Average Interruption Frequency Index (SAIDI)
3. Customer Average Interruption Duration Index (CAIDI)
4. Average service availability index (ASAI)
5. Average Service Unavailability Index (ASUI)
6. Expected energy not supplied (EENS)
DOWNLOAD THE MATLAB CODE FROM HE BELOW URL,
https://drive.google.com/file/d/13GWc...
REFERENCES
Reference Paper-1: Reconfiguration and optimal capacitor placement for losses reduction
Author’s Name: Diana P. Montoya and Juan M. Ramirez
Source: IEEE
Year: 2012
Reference Paper-2: DG Placement Using Loss Sensitivity Factor Method for Loss Reduction and Reliability Improvement in Distribution System
Author’s Name: G.Sasi Kumar, Dr.S.Sarat Kumar, Dr.S.V.Jayaram Kumar
Source: IJET
Year: 2018
Reference Paper-3: Distribution network reconfiguration for power quality and reliability
improvement using Genetic Algorithms
Author’s Name: Nikhil Gupta, Anil Swarnkar, and K.R. Niazi
Source: Elseivier
Year: 2013
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