Abstract
This paper proposes a new forecasting-aided state estimation (FASE) method for distribution systems that mitigates issues with uncertain distributed generation (DG) and lost measurements. We utilize an Improved Particle Swarm Optimization (IPSO)-optimized Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for examination of historical DG output data. Based on identified DG output modes, it facilitates precise state prediction and data reconstruction,. The proposed method employs a Bidirectional Gated Recurrent Unit (BiGRU) neural network for state prediction and a particle filter (PF)
for final state filtering. The method verification is provided through Python simulations of the Distribution Transformer Unit (DTU)7k distribution network system, demonstrating improved accuracy and robustness against sudden load change and bad data in measurements .
for final state filtering. The method verification is provided through Python simulations of the Distribution Transformer Unit (DTU)7k distribution network system, demonstrating improved accuracy and robustness against sudden load change and bad data in measurements .
Original language | English |
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Article number | 109797 |
Pages (from-to) | 1-23 |
Number of pages | 23 |
Journal | International Journal of Electrical Power & Energy Systems |
Volume | 157 |
DOIs | |
Publication status | Published - 2024 |
Keywords
- Density-based spatial clustering of applications with noise, forecasting-aided state estimation, improved particle swarm optimization, machine learning, neural network, state estimation