Optimisation of Emergency Power Restoration in Distribution Networks with Distributed Generation Integration
DOI:
https://doi.org/10.13052/dgaej2156-3306.4141Keywords:
Distributed generation, stochastic power flow, semi-invariants, feeder clustering, residual current, faulty user localizationAbstract
The integration of distributed generation (DG) such as photovoltaic and wind power systems into distribution networks significantly alters power flow patterns and operational characteristics, introducing stochasticity and uncertainty into voltage and loss behavior. Higher-order semi-invariants (cumulants) yield greater accuracy and computational efficiency than traditional Monte Carlo simulations; therefore, they can be employed as the preferred and more reliable method of performing effective stochastic power flow analysis of very complex power systems. Meanwhile, wiring errors at the user side, such as neutral-to-earth misconnection, compromise residual current device (RCD) protection and elevate electric shock risks. Feeder clustering via the CFSFDP algorithm is integrated with this stochastic loss modeling framework to identify user groups and isolate faulty users, providing a cohesive methodological approach for practical distribution network analysis. An analysis performed on wiring error hazard mechanisms and the associated ground scheme’s performance against RCDs; examining the stochastic power flow and line loss distribution models by utilizing higher-order cumulative distributions for evaluating the voltage and loss statistical distributions. Cumulative distribution methodologies have been shown to have an order of magnitude less compute time than Monte Carlo simulation and equivalent performance accuracy. The user grouping algorithm developed in this study (CFSFDP) does not require the specification of input cluster(s) to create a grouping. Fault localization is accomplished through an adaptive lasso-type model. The method developed for validating the algorithms used in this study on the IEEE 34 Bus network have shown to be very accurate and efficient.
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