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Showing 2 results for Photovoltaic Array
Maximum Power Point Tracking Using Sliding Mode Control for Photovoltaic Array
J. Ghazanfari, M. Maghfoori Farsangi,
Volume 9, Issue 3 (9-2013)
Abstract
In this paper, a robust Maximum Power Point Tracking (MPPT) for PV array has been proposed using sliding mode control by defining a new formulation for sliding surface which is based on increment conductance (INC) method. The stability and robustness of the proposed controller are investigated to load variations and environment changes. Three different types of DC-DC converter are used in Maximum Power Point (MPP) system and the results obtained are given. The simulation results confirm the effectiveness of the proposed method in the presence of load variations and environment changes for different types of DC-DC converter topologies.
Analysis of Fault Detection and Classification in Photovoltaic Arrays Using Neural Network-Based Methods
Arizadayana Zahalan, Samila Mat Zali, Ernie Che Mid, Noor Fazliana Fadzail,
Volume 21, Issue 2 (6-2025)
Abstract
Photovoltaic (PV) systems are vital in the global renewable energy landscape because of their capability to harness solar energy efficiently. Ensuring the continuous and efficient operation of PV systems is crucial in maximizing their energy contribution. However, these systems' reliability and safety remain critical because they are prone to various faults, mainly when operating in harsh environmental conditions. This study addresses these issues by exploring fault detection and classification in PV arrays using neural network (NN) -based techniques. A PV array model, consisting of 3x6 PV modules, was simulated using MATLAB Simulink to replicate real-world conditions and analyse various fault scenarios. An open circuit, a short circuit, and a degrading fault are the three types of faults considered in this study. The NN was trained on a dataset generated from the MATLAB Simulink model, encompassing normal operating and fault conditions. This training enables the network to learn the distinctive patterns associated with each fault type, enhancing its detection accuracy and classification capabilities. Simulation results demonstrate that the NN-based approach effectively identifies and classifies the three types of faults.
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