The BLADEcontrol system for wind turbine blades was developed by Weidmüller and has been successfully used in more than 3,000 sets of various types of wind turbines.
With the continuous development of wind power planning and technology, the trend of large-scale wind turbines is becoming more and more obvious, and the size of wind turbine blades is also rising. The increase in blade length not only increases the efficiency of wind energy capture, but also increases the probability of blade cracking and damage. Generally, the primary causes of blade cracking include poor process control during production, incomplete resin curing in the local area of the blade root, reduced strength and stiffness, wind speed overrun, wind turbine stall, electrical faults and lightning strikes. With the increase of the operating life, the current monitoring of the condition of the wind turbine blades is insufficient, resulting in the operation and maintenance level of the blades far behind, making the failure of the blades impossible to guess, and unable to play its normal efficiency in production, resulting in the loss of power generation and safety.
Whether from the perspective of safety or economic benefits, the study of how to monitor the operation of blades is of great significance.
The position of the BLADEcontrol channel in the production system architecture is shown in the figure below, covering the Level0, Level1, and Level2 levels.
The BLADEcontrol channel can complete the functions of oscillation sensing, condition evaluation, fault diagnosis, situation trend guessing, and protection management of wind turbine blades.
Architecture
The architecture of the BLADEcontrol channel is shown in the figure. Among them, the hardware mainly includes high-precision acceleration sensors, data collection and measurement units, wireless access points in the cabin, and data evaluation servers. The software mainly includes a situation monitoring and health management system and an alarm system for abnormal blade conditions.
function
The blade condition monitoring system collects the oscillation signals of the blade oscillation and waving direction through high-precision acceleration sensors, and then determines the natural frequency model of the blade through artificial intelligence-specific algorithms and data models. After the blade oscillation model is established, the on-line condition monitoring system of the blade collects, calculates and analyzes in real time, and gives positive warning or alarm signals about the abnormal situation generated by the blade.
Through the blade condition monitoring system, the external damage, lightning damage, internal structure damage, dynamic imbalance and other problems of the blade itself can be found in time, and the icing situation of the blade in winter can be accurately detected, so as to achieve accurate start and stop, increase production benefits, and reduce safety hazards.
characteristic
Outstanding features of the BLADEcontrol system are as follows:
Detection of abnormal conditions: According to the data accumulation of the daily operation of each equipment, the abnormal conditions of the wind turbine blades can be detected at the first time.
Location of abnormal situations: After the abnormal situation is discovered, it can quickly locate the fault point and the cause of the problem, reduce the problem investigation time, and reduce the scale of the investigation.
Guessable protection: Faults can be guessed in advance, so unplanned downtime can be prevented, greatly improving the availability of the wind turbine equipment.
Guessable quality control: Continuously monitor the quality of wind turbine blades and their quality deviation trends, prevent a large number of product testing operations and reduce the waste of raw materials, and facilitate guessing the inventory of blades.
Power Analysis: Continuously monitor the power generation of the wind turbine, give optimization suggestions, and reduce costs by reducing load fluctuations and preventing load imbalances, together with predictable protection, which can greatly improve the life expectancy of the wind turbine and blades, thereby further reducing the LCOE.
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