JOURNAL ARTICLE

Optimal Distributed Generation placement in distribution network

Abstract

This paper presents a method for optimal Distributed Generation placement with goal of reducing active power system losses and voltage level regulation. Active power losses in radial distribution network are determined using an Artificial Neural Network (ANN) by simultaneous formulation for the determination process based on voltage level control and injected power. Adequate installed power of distributed generation and the appropriate terminal for distributed generation utilization are selected by means of a genetic algorithm (GA), performed in a distinct manner that fits the type of decision-making assignment. The training data for ANN is obtained by means of load flow simulation performed in DIgSILENT PowerFactory software on a part of the Croatian distribution network. The active power losses and voltage conditions are simulated for various operation scenarios in which the back propagation ANN model has been tested to predict the power losses and voltage levels for each system terminal, and GA is used to determine the optimal terminal for distributed generation placement.

Keywords:
Distributed generation Genetic algorithm Voltage Terminal (telecommunication) Artificial neural network Computer science AC power Power (physics) Electric power system Control theory (sociology) Software Electricity generation Process (computing) Control engineering Engineering Electrical engineering Control (management) Artificial intelligence Computer network Machine learning

Metrics

9
Cited By
1.11
FWCI (Field Weighted Citation Impact)
17
Refs
0.83
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Optimal Power Flow Distribution
Physical Sciences →  Engineering →  Electrical and Electronic Engineering
Microgrid Control and Optimization
Physical Sciences →  Engineering →  Control and Systems Engineering
Power Quality and Harmonics
Physical Sciences →  Engineering →  Electrical and Electronic Engineering
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