Method for online calculating number of wind field fault shutdown more than 24 hours based on defect list

Abstract

The invention discloses a method for online calculating the number of wind field fault shutdown more than 24 hours based on defect lists. The method comprises the following steps: 1) acquiring defect lists which are in an unclosed state on the current day of a statistical day T0 and have defect list creation time earlier than that on the current day of the T0 from a wind field production management system to form a set A0; 2) rejecting defect lists of which the states are temporary storage, canceling and discarding states from the set A0 to form a set A; 3) collecting defect lists which are closed on the same day of the T0 and have the defect list creation time of tcreate from a wind field production management system, a set B is formed, wherein the tcreate is 24 hours earlier than the closing time; 4) combining the set A and the set B to form a defect single set C0; 5) rejecting defect sheets for reducing output and eliminating defects from the set C0 to form a set C; 6) querying in the defect set C according to the fan ID, if the same fan has a plurality of defect sheets, only retaining one defect sheet, and finally obtaining a defect set M; and 7) enabling the number n of the defects in the set M to be the number of the wind fields which are shut down for more than 24 hours in the statistical day T0. The invention provides a scientific and rapid index acquisition method for a production management department.

Description

technical field [0001] The invention belongs to the technical field of wind power operation and maintenance, and in particular relates to a single-online defect-based method for calculating the number of wind farm failure shutdowns exceeding 24 hours. Background technique [0002] The number of wind farms that have been down for more than 24 hours due to failure refers to the number of wind turbines that have been shut down due to failure before the statistical day and lasted for more than 24 hours until 24:00 on the statistical day. It is an important indicator for the management department to supervise and evaluate the long-term shutdown of wind turbine failures in wind farms, and is an important content of long-stop wind turbine management. The current method is for wind farm operators to manually judge by consulting the historical data of wind farm fan operation and maintenance. However, due to the large number of wind turbines, the large time span, and the heavy workloa...

AI Technical Summary

Problems solved by technology

It is an important indicator for the management department to supervise and evaluate the long-term shutdown of wind turbine failures in wind farms, and is an important content of long-stop wind turbine management. The current method is for wind farm operators to manually judge by consulting the historical data of wind farm fan operation and maintenance. However, due to the large number of wind turbines, the large time span, and the heavy workload of analysis, it is difficult for the staff to make accurate judgments, and the entire process cannot be effectively supervised and controlled. As a result, the management department cannot grasp the real fault and long-term shutdown information of the wind farm, which affects production decisions.

In addition, this indicator cannot be calculated based on the real-time data of the fan. The fan fault status from the fan PLC only represents the status during the period when the fan reports a fault and then shuts down. , offline and other states, it is impossible to find a judgment logic that can effectively identify the start and recovery time of the fault shutdown, which affects the application of this method

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