Three-stage-diagnosis-based large-scale photovoltaic array fault diagnosis and positioning method

Abstract

Disclosed in the invention is a three-stage-diagnosis-based large-scale photovoltaic array fault diagnosis and positioning method. The method is characterized in that faults of the photovoltaic array are classified into four types including an assembly short-circuit type, an assembly open-circuit type, a shadow type, and a multi-fault type under the circumstances that the photovoltaic array is in a formation mode of series connection and parallel connection. During a first-stage fault diagnosis execution process, whether a fault occurs at a branch circuit where a photovoltaic grid-connected inverter as well as the type of the fault is determined by using a power incremental ratio method; during a second-stage fault diagnosis execution process, whether faults occur at all photovoltaic assembly strings of the branch circuit of the photovoltaic grid-connected inverter is determined by using a current and voltage similarity method; and during a third-stage fault diagnosis execution process, positioning of an internal fault assembly of the fault photovoltaic assembly string is carried out by using a trained three-layer BP fault diagnosis neural network. According to the invention, on-line real-time detection of the operation state of the large-scale photovoltaic array is carried out based on the three-stage diagnoses, thereby realizing accurate and rapid fault type identification and fault positioning.

Description

technical field [0001] The invention relates to the technical field of fault diagnosis of photovoltaic arrays, and more specifically relates to a method for identifying fault types and locating faults of large photovoltaic arrays. Background technique [0002] In the prior art, the fault diagnosis for large-scale photovoltaic power plants mainly focuses on the identification of whether a fault occurs and the diagnosis of the fault type, while the fault location of the photovoltaic array is seldom studied and the positioning accuracy is low. For example in "Takashima T, Yamaguchi J, Otani K, et al. Experimental studies of failure detection methods in PV module strings [C]. Photovoltaic Energy Conversion, Conference Record of the 2006 IEEE 4th World Conference on. IEEE, 2006, 2: 2227-2230." discloses a fault location of photovoltaic module strings using time domain reflectometry TDR. This method has a certain degree of accuracy, but its diagnosis process must When multiple co...

AI Technical Summary

Problems solved by technology

However, this method needs to rearrange the original photovoltaic modules and transform the current common series-parallel connection into a cross-connection form, which is not feasible in practice

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