Скачать или смотреть SEQUENTIAL AND NON-SEQUENTIAL APPROACH-OPTIMAL DG PLACEMENT-GRADIENT PARTICLE SWARM OPTIMIZATION

DESIGN DETAILS
The configuration of a distribution network can be changed by opening/closing the tie switches of the network. The advantages of this technique are reduction in real power loss and enhanced system reliability as well as power quality. The radiality of the network is to be maintained while performing this technique to comply with the protective relaying requirements. This Matlab design is based on reconfiguration of the distribution network in the presence of distributed generations (DGs) by considering two bus types i.e., P bus and PQV bus (remotely voltage-controlled bus). The ‘P’ bus is represented by active power specification only whereas the PQV bus is one whose voltage is remotely controlled by the P bus. By selecting the P bus for controlling the voltage magnitude of remotely located PQV bus. A sensitivity analysis approach is used for selecting the buses for the placement of DGs operating based on power factor. The placement of DGs is done in two ways i.e., non-sequential placement and sequential placement of DG in a distribution network. Gradient particle swarm optimization is used for the optimization of DGs followed by network reconfiguration.

The main contributions of this design are:
•Incorporation of P and PQV buses into the distribution network and selecting P bus to control the voltage magnitude of a remotely located PQV bus.
•Reconfiguration of the distribution network for power loss minimization with and without considering DGs operating in the presence of P and PQV buses.
•Non-sequential and sequential approach of DG placement on distribution networks with DG sizes optimized using GPSO.

The design tested using Matlab 2022b version for IEEE 33 bus and 69 bus distribution networks.

REFERENCES
Reference Paper-1: Reconfiguration of distribution networks with optimal placement of distributed generations in the presence of remote voltage-controlled bus.
Author’s Name: Sangeeta Das, Debapriya Das and, Amit Patra
Source: Elsevier
Year: 2017

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