Chang, L.-Y.; Pai, N.-S.; Chou, M.-H.; Chen, J.-L.; Kuo, C.-L.; Lin, C.-H. Multiple Fault Location in a Photovoltaic Array Using Bidirectional Hetero-Associative Memory Network in Micro-Distribution Systems. Crystals2018, 8, 327.
Chang, L.-Y.; Pai, N.-S.; Chou, M.-H.; Chen, J.-L.; Kuo, C.-L.; Lin, C.-H. Multiple Fault Location in a Photovoltaic Array Using Bidirectional Hetero-Associative Memory Network in Micro-Distribution Systems. Crystals 2018, 8, 327.
Chang, L.-Y.; Pai, N.-S.; Chou, M.-H.; Chen, J.-L.; Kuo, C.-L.; Lin, C.-H. Multiple Fault Location in a Photovoltaic Array Using Bidirectional Hetero-Associative Memory Network in Micro-Distribution Systems. Crystals2018, 8, 327.
Chang, L.-Y.; Pai, N.-S.; Chou, M.-H.; Chen, J.-L.; Kuo, C.-L.; Lin, C.-H. Multiple Fault Location in a Photovoltaic Array Using Bidirectional Hetero-Associative Memory Network in Micro-Distribution Systems. Crystals 2018, 8, 327.
Abstract
In manual maintenance inspections of large-scaled photovoltaic (PV) or rooftop PV systems, several days are required to survey the entire PV field. To improve reliability and shorten the amount of time involved, this study proposes an electrical examination based method for locating multiple faults in the PV array. The maximum power point tracking (MPPT) algorithm is used to estimate the maximum power of each PV panel; this is then compared with metering the output power of PV array. Power degradation indexes are parameterized to quantify the degradation between maximum power and metered output power. Bidirectional hetero-associative memory (BHAM) networks are then used to locate multiple faults within the entire PV field. For a rooftop PV system with two strings, as seen in Figure 1, experimental results demonstrate that the proposed model has computational efficiency for real-time applications and that its algorithm is easily implemented in a mobile intelligent vehicle.
Keywords
Rooftop Photovoltaic (PV) System, Maximum Power Point Tracking (MPPT), Power Degradation Index, Bidirectional Hetero-Associative Memory Network (BHAM).
Subject
Engineering, Energy and Fuel Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
