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Multivariate Statistics and Supervised Learning for Predictive Detection of Unintentional Islanding in Grid-Tied Solar PV Systems


Affiliations
1 Centre for Energy and Environment, Malaviya National Institute of Technology, Jawaharlal Nehru Marg, Jaipur 302017, India
2 Department of Electrical and Computer Engineering, North Dakota State University, 1340 Administration Avenue, Fargo, ND 58102, United States
 

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 of Q statistic 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 that Q statistic based approach alone is not reliable for detection of the symptoms liable to cause unintentional islanding. The obtained data was labeled and a K-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.
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  • Multivariate Statistics and Supervised Learning for Predictive Detection of Unintentional Islanding in Grid-Tied Solar PV Systems

Abstract Views: 86  |  PDF Views: 6

Authors

Shashank Vyas
Centre for Energy and Environment, Malaviya National Institute of Technology, Jawaharlal Nehru Marg, Jaipur 302017, India
Rajesh Kumar
Centre for Energy and Environment, Malaviya National Institute of Technology, Jawaharlal Nehru Marg, Jaipur 302017, India
Rajesh Kavasseri
Department of Electrical and Computer Engineering, North Dakota State University, 1340 Administration Avenue, Fargo, ND 58102, United States

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 of Q statistic 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 that Q statistic based approach alone is not reliable for detection of the symptoms liable to cause unintentional islanding. The obtained data was labeled and a K-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.