Abnormal Detection in Photovoltaic Array Based on Artificial Neural Network

Sholikhah, Evi Nafiatus; Habibi, Muhammad Nizar; Windarko, Novie Ayub; Anggriawan, Dimas Okky

doi:10.1109/eeccis49483.2020.9263457

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…To reduce the effect of partial shading is to install a bypass diode on each PV module. As a result of the installation of this bypass diode, the PV array characteristics have several power peaks, namely global maximum power point (GMPP) and local maximum power point (LMPP) [2]- [4].…”

Section: Introductionmentioning

confidence: 99%

Tunicate swarm algorithm based maximum power point tracking for photovoltaic system under non-uniform irradiation

Sholikhah

Windarko²,

Sumantri³

2022

IJECE

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<span>A new maximum power point tracking (MPPT) technique based on the bio-inspired metaheuristic algorithm for photovoltaic system (PV system) is proposed, namely tunicate swarm algorithm-based MPPT (TSA-MPPT). The proposed algorithm is implemented on the PV system with five PV modules arranged in series and integrated with DC-DC buck converter. Then, the PV system is tested in a simulation using PowerSim (PSIM) software. TSA-MPPT is tested under varying irradiation conditions both uniform irradiation and non-uniform irradiation. Furthermore, to evaluate the performance, TSA-MPPT is compared with perturb & observe-based MPPT (P&O-MPPT) and particle swarm optimization-based MPPT (PSO-MPPT). The TSA-MPPT has an accuracy of 99% and has a reasonably practical capability compared to the MPPT technique, which already existed before.</span>

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Section: Introductionmentioning

confidence: 99%

Tunicate swarm algorithm based maximum power point tracking for photovoltaic system under non-uniform irradiation

Sholikhah

Windarko²,

Sumantri³

2022

IJECE

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“…Figures 17,18,19,20, 21, 22 and 23 respectively, show how the neural network identifies free fault condition and the type of fault along its location in partial shading, short circuit and open circuit, using MATLAB/Simulink. In the MATLAB/Simulink, input display from top to bottom are the parameters of the voltage string 1 (V1), current string 1 (I1), voltage string 2 (V2) and current string 2 (I2).…”

mentioning

confidence: 99%

Identify and Locating the Faults in the Photovoltaic Array Using Neural Network

Pratama

Suharyanto²,

Arif³

2021

JNTE

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In making the PV array system work optimally without a hitch, it is important to recognize and know where the fault occurs. The current and voltage represent the conditions of a PV array, so that, in this paper, the proposed method is based on the current and voltage values for each string, four identified conditions, namely free fault conditions, partial shading, short circuit and open circuit. Neural network is used as a tool for predicting the type and location of faults, fault samples are obtained from simulations through PSIM and the learning process is carried out through MATLAB/Simulink, the algorithms used in the learning process are also compared to see which are the best. As a result, neural network was able to identify the type and location of faults on the PV array. This proves that the condition of a PV array can be explained through its voltage and current values.

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Abnormal Detection in Photovoltaic Array Based on Artificial Neural Network

Cited by 2 publications

References 12 publications

Tunicate swarm algorithm based maximum power point tracking for photovoltaic system under non-uniform irradiation

Tunicate swarm algorithm based maximum power point tracking for photovoltaic system under non-uniform irradiation

Identify and Locating the Faults in the Photovoltaic Array Using Neural Network

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