Minimum‐features‐based ANN‐PSO approach for islanding detection in distribution system

Raza, Safdar; Mokhlis, Hazlie; Arof, Hamzah; Naidu, Kanendra; Laghari, J. A.; Khairuddin, Anis Salwa Mohd

doi:10.1049/iet-rpg.2016.0080

Cited by 35 publications

(30 citation statements)

References 27 publications

(32 reference statements)

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“…In this concept, the cells are introduced as neurons, which requires an existing or pre-defined set of inputs. Each of these inputs is trained and weighted inside the neurons to be matched with the desired target [35]. Fig 2 shows a typical diagram of ANN architecture which consists of three layers known as the input layer, hidden layer and output layer.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Optimized artificial neural network to improve the accuracy of estimated fault impedances and distances for underground distribution system

et al. 2020

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This paper proposes an approach to accurately estimate the impedance value of a high impedance fault (HIF) and the distance from its fault location for a distribution system. Based on the three-phase voltage and current waveforms which are monitored through a single measurement in the network, several features are extracted using discrete wavelet transform (DWT). The extracted features are then fed into the optimized artificial neural network (ANN) to estimate the HIF impedance and its distance. The particle swarm optimization (PSO) technique is employed to optimize the parameters of the ANN to enhance the performance of fault impedance and distance estimations. Based on the simulation results, the proposed method records encouraging results compared to other methods of similar complexity for both HIF impedance values and estimated distances.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Optimized artificial neural network to improve the accuracy of estimated fault impedances and distances for underground distribution system

et al. 2020

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“…Recently, mathematical tools have been applied as the strong classifiers to categorise islanding and normal operational modes [15][16][17][18][19][20]. For instance, a decision tree-based intelligent relay has been recommended by Cui et al [15].…”

Section: Introductionmentioning

confidence: 99%

New voltage feedback‐based islanding detection method for grid‐connected photovoltaic systems of microgrid with zero non‐detection zone

Bakhshi‐Jafarabadi

Sadeh

2020

IET Renewable Power Generation

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This study proposes a fast and precise voltage feedback-based islanding detection method (IDM) for grid-connected photovoltaic systems (GCPVSs) based microgrid. In this algorithm, a disturbance containing the absolute deviation of the output voltage is injected into the inverter's d-axis reference current which tunes the active power output. In islanded mode, the applied disturbance reduces the active power output and consequently point of common coupling voltage beyond the standard setting while its effect at the presence of the grid is negligible. The assessment of the proposed IDM has been conducted in the MATLAB/Simulink platform under extensive scenarios defined by IEEE 1547-2008 and UL 1741 standards for a case study system with two large-scale GCPVSs. The provided outputs remark accurate islanding classification in all cases within 810 ms, much lower than the maximum permissible time postulated in islanding standards. This time is short enough to restore GCPVS for autonomous operation of microgrid as well. The comparative analysis of the proposed strategy with a few existing IDMs confirmed its overall superiorities in the terms of non-detection zone, detection time, being applicable into the microgrid, simple threshold determination, and straightforward and cost-effective implementation.

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“…Particle swarm optimisation (PSO) is used as an optimisation tool for neural network parameters to obtain minimum non‐detection zone (NDZ) as investigated in [8]. Evolutionary programming and PSO are both employed in [9] to maximise the learning rate, and to minimise the number of neurons in the hidden layers so as to give better accuracy. Features obtained from phase space method are fed to extreme learning machine (ERM) to detect islanding as explained in [10].…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network and phasor data‐based islanding detection in smart grid

Kumar

Bhowmik

2018

IET Generation, Transmission & Distribution

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Minimum‐features‐based ANN‐PSO approach for islanding detection in distribution system

Cited by 35 publications

References 27 publications

Optimized artificial neural network to improve the accuracy of estimated fault impedances and distances for underground distribution system

Optimized artificial neural network to improve the accuracy of estimated fault impedances and distances for underground distribution system

New voltage feedback‐based islanding detection method for grid‐connected photovoltaic systems of microgrid with zero non‐detection zone

Artificial neural network and phasor data‐based islanding detection in smart grid

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