Fault prediction method for photovoltaic inverter

Abstract

The invention discloses a fault prediction method for a photovoltaic inverter. The fault prediction method comprises the following steps that historical monitoring signals of a photovoltaic inverter cluster of the same photovoltaic power station serve as an original feature library; a main feature matrix of the photovoltaic inverter cluster at each sampling time is extracted from the original feature library through a sparse self-encoding algorithm; a cluster center photovoltaic inverter at each sampling time is searched based on a fast clustering algorithm; a cumulative eccentric distance matrix of the photovoltaic inverter cluster is calculated; and the cumulative eccentric distance matrix is subjected to normalization processing, an early warning threshold value is set, and finally, prediction of the fault of the photovoltaic inverter is achieved. According to the fault prediction method, prediction of the fault of the photovoltaic inverter is achieved, on-line operation can be conducted, calculation is convenient, special requirement limitation is avoided, the fault prediction method is suitable for photovoltaic inverter clusters with different scales, portability is good, overhaul personnel can establish a reasonable and effective maintenance plan advantageously, and safe and stable operation of a microgrid is ensured.

Description

technical field [0001] The invention relates to a fault prediction method for a photovoltaic inverter, which belongs to the technical field of micro-grids. Background technique [0002] As a sustainable, renewable and clean energy generation method, solar photovoltaic power generation has become an important part of the world's energy demand and supply. Photovoltaic inverter is a key component of photovoltaic power generation system, and its health status directly affects the safety and stability of the entire photovoltaic power generation system. With the continuous increase of the capacity of the photovoltaic power generation system, the microgrid also puts forward higher requirements for the health status assessment technology of the photovoltaic inverter. Therefore, real-time monitoring of the operating status of photovoltaic inverters and timely and accurate prediction of photovoltaic inverter failures are conducive to establishing reasonable and effective maintenance ...

AI Technical Summary

Problems solved by technology

[0003] At present, the maintenance of photovoltaic inverters usually adopts after-the-fact maintenance, and it is difficult for maintenance personnel to grasp the health status of photovoltaic inverters in real time

Fault prediction technology can help maintenance personnel to predict possible faults of photovoltaic inverters in advance. However, most of the existing fault prediction methods rely on the operation data of the entire life cycle of the equipment, and the established fault prediction model is only applicable to a single device. The portability of the model is poor, and there is still a lack of an effective and scalable photovoltaic inverter fault prediction method

Images