A robust distribution network state estimation method based on enhanced clustering Algorithm: Accounting for multiple DG output modes and data loss

• Published in: International journal of electrical power & energy systems Vol. 157; p. 109797
• Main Authors: Yu, Yue, Jin, Zhaoyang, Ćetenović, Dragan, Ding, Lei, Levi, Victor, Terzija, Vladimir
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.06.2024
• Subjects: Density-based spatial clustering of applications with noise; Forecasting-aided state estimation; Improved particle swarm optimization; Machine learning; Neural network; State estimation; Improved particle swarm optimization; Forecasting-aided state estimation; Neural network; State estimation; Machine learning; Density-based spatial clustering of applications with noise
• ISSN: 0142-0615; 1879-3517
• DOI: 10.1016/j.ijepes.2024.109797

•Distinct DG Output Modes Validation: The simulation validates distinct DG output modes, impacting prediction.•Mode-Based State Estimation: The paper proposes an innovative mode-based state estimation using IPSO-DBSCAN clustering, overcoming k-means limitations.•Enhanced Data Recovery using DBSCAN: Enhanced data recovery employs DBSCAN with improved secondary clustering, guided by similarity and homogeneity, outperforming traditional methods.•BiGRU-PF State Estimation: BiGRU-PF enhances state estimation accuracy and robustness in distribution networks by detecting DG output modes, addressing k-means limitations, and improving data recovery. 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.