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Finally, testing results were used to ascertain the feasibility of the PV power generation fault diagnosis meter discussed in this paper.
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This scheme handles decreased power generation faults in a wind farm caused by turbine blade erosion and debris buildup on the blades over time.
Then, this paper establishes a photovoltaic power generation system fault diagnosis method based on extension theory.
This matter-element model and the PV power generation system fault diagnosis program are implemented using a PIC microcontroller.
The PV power generation system fault diagnosis based on extension theory discussed previously requires only small amounts of data in order to construct matter-element models for normal operations and different fault situations.
Equal power-rating inverters operating with different power set-points in either an islanded or grid-connected mode may lead to inter-unit circulating power caused by a large mismatch between power generation and demand during network contingencies (faults on the heavy load side or unintentional islanding).
This diagnosis method does not require learning procedures to be used in fault diagnosis of PV power generation systems.
Therefore, a number of facts need to be considered while configuring a large MTDC network protection such as infrequency of DC fault events, inconsistency of power generation from wind farms and investment in protection cost.
If timely diagnostics and maintenance actions are not undertaken, then these faults can cause instabilities, inefficient power generation, and other losses.
Nowadays, it is a common practice in power generation utilities to monitor the generation units using digital fault recorders (DFRs).
The main purpose of this paper is to develop a fault diagnostic meter for photovoltaic (PV) power generation systems.
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