Abstract
Integration of solar photovoltaic (PV) generation with power distribution networks leads to many operational challenges and complexities. Unintentional islanding is one of them which is of rising concern given the steady increase in grid-connected PV power. This paper builds up on an exploratory study of unintentional islanding on a modeled radial feeder having large PV penetration. Dynamic simulations, also run in real time, resulted in exploration of unique potential causes of creation of accidental islands. The resulting voltage and current data underwent dimensionality reduction using principal component analysis (PCA) which formed the basis for the application ofQstatistic control charts for detecting the anomalous currents that could island the system. For reducing the false alarm rate of anomaly detection, Kullback-Leibler (K-L) divergence was applied on the principal component projections which concluded thatQstatistic based approach alone is not reliable for detection of the symptoms liable to cause unintentional islanding. The obtained data was labeled and aK-nearest neighbor (K-NN) binomial classifier was then trained for identification and classification of potential islanding precursors from other power system transients. The three-phase short-circuit fault case was successfully identified as statistically different from islanding symptoms.
Highlights
The distribution segment of the electricity supply network is always under stress given the variable consumption patterns in complex geographical spread
Many computational intelligence (CI) based techniques have been reported in the literature [8] but they are fundamentally based on classical techniques and seem to reinforce the reactive strategy of detecting the island formation and disconnecting the distributed generators (DGs)
This paper describes the application of anomaly detection techniques: multivariate statistical and supervised learning based for predictive detection of an unintentional islanding event
Summary
The distribution segment of the electricity supply network is always under stress given the variable consumption patterns in complex geographical spread. A segment containing certain loads even after that portion of the network, including the point of common coupling (PCC), gets disconnected from the main power system. Many computational intelligence (CI) based techniques have been reported in the literature [8] but they are fundamentally based on classical techniques and seem to reinforce the reactive strategy of detecting the island formation and disconnecting the DG This practice is not expected to remain in the future smart grid that will accommodate a large share of renewable-energy based DG power which cannot be wasted even for a few cycles.
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