Open AccessDC‐side fault detection for photovoltaic energy conversion system using fractional‐order dynamic‐error‐based fuzzy Petri net integrated with intelligent meters
Author(s) -
Chen JianLiung,
Kuo ChaoLin,
Chen ShiJaw,
Kao ChihCheng,
Zhan TungSheng,
Lin ChiaHung,
Chen YingShin
Publication year - 2016
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
eISSN - 1752-1424
pISSN - 1752-1416
DOI - 10.1049/iet-rpg.2015.0517
Subject(s) - photovoltaic system , maximum power point tracking , fault (geology) , control theory (sociology) , voltage , engineering , computer science , electrical engineering , inverter , control (management) , artificial intelligence , seismology , geology
Fault occurrence or voltage disturbance, such as mismatch operations or electrical faults caused by structural changes in photovoltaic (PV) panels, local/remote faults, or heavy load operation, can disturb a PV energy conversion system (PVECS) on both the DC and AC sides. On the AC side, any serious disturbance can be isolated using power fuses, overcurrent protection and ground‐fault protection devices. Therefore, the authors propose the use of fractional‐order dynamic‐error‐based fuzzy Petri net (FPN) to detect disturbance events in a microdistribution system. PV energy conversion depends on solar radiation and temperature, and a maximum power point tracking control is used to maintain stable output power and voltage to microdistribution loads. When the desired maximum power is estimated, a bisection approach algorithm is used to regulate the output voltage of the PVECS by adjusting the duty ratios of a buck–boost converter. The maximum power drops, which are compared with meter‐reading power from intelligent meters, are used to detect faults on the DC side. Then, fractional‐order dynamic errors between the desired and estimated powers and a FPN are employed to detect faults. For a small‐scale PVECS, computer simulations are conducted to show the effectiveness of the proposed model.