Observations from study of pre-islanding behaviour in a solar PV system connected to a distribution network

IET Renewable Power Generation

2018

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Accidental disconnection of a live feeder section is a major concern accompanying large distributed solar photovoltaic (PV) generation integration. High localised penetration can alter power flows leading to anomalous occurrences. Load-inverter power balance during grid-side disturbances, for different load models, may cause unique situations that can trigger the interconnection point protective devices. One such phenomenon, identified as a potential cause of unintentional islanding on radial feeder models (a modified IEEE feeder in simulation and verified on a laboratory-hardware network), has been used in this work. A pre-emptive detection strategy has been implemented to identify such islanding initiators among other power system transients. Computational geometry concepts have been utilised to create an optimisation-derived feature extraction methodology for effective training of a classifier module realised in a Raspberry Pi microcomputer. This module predicts the class labels of test data points transmitted from simulations carried on a personal computer for the feeder model. The proposed pre-emptive islanding detection strategy can trigger an appropriate change in a PV inverter's operating mode before a feeder protective device is tripped by such island initiating anomalies. The online classification accuracy and speed indicate a possible integration of the proposed methodology and strategy with the inverter's control circuitry.

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Section: Explored Islanding Initiating Phenomenonmentioning

confidence: 99%

Section: Explored Islanding Initiating Phenomenonmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

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Computational geometry‐based methodology for identification of potential islanding initiators in high solar PV penetration distribution feeders

IET Renewable Power Generation

2018

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“…The PV inverter operates on unity power factor and thus the loads on lateral 671-675 had to be scaled according to the fixed PV penetration and feeder capacitor bank size for attaining -balance required for the exploratory islanding study described in the next section. The details of the system component modeling are given in our earlier work [18] from which the same model is taken here. This section only produces the results that describe the system model functioning and verification.…”

Section: Power System Modelmentioning

confidence: 99%

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

Applied Computational Intelligence and Soft Computing

Kavasseri

2016

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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.

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Data analytics and computational methods for anti-islanding of renewable energy based Distributed Generators in power grids

Renewable and Sustainable Energy Reviews

Kavasseri

2017