Product Description:
Molas CL tower clearance lidar is a type of lidar system that
continuously monitors the distance between blade tips in real-time.
By alerting the main controller when the blade clearance approaches
the critical limit, immediate protective actions such as
deceleration or retraction can be implemented. The integration of
tower clearance lidar technology onto current units can effectively
prevent tower collisions, mitigate power constraints in risky
units, and enhance power output.
Implementing tower clearance lidar on upcoming units has the
potential to reduce blade expenses and alleviate structural
pressure on the units' design. By leveraging this advanced
technology, future units can optimize their performance and lower
maintenance costs.
Features:
- Product Name: Wind Measurement Lidar
- Beam 1: 0°
- Repeat Frequency: 20KHz per channel
- Laser Safety Level: Class 1M
- Range of Working Temperature: -40℃ to +60℃
- Beam 3: 4.09±0.2°
Technical Parameters:
| Ambient Light Resistance | 100Klux |
| Enclosure Rating | IP65 |
| Measurement Accuracy | ±0.2m |
| Repeated Measurement Accuracy | ±0.2m |
| Distance Resolution | ≤0.1m |
| Laser Safety Level | Class 1M |
| Beam 3 | 4.09±0.2° |
| Range of Working Temperature | -40℃~+60℃ |
| Ranging Method | ToF |
| Working Acceleration Range | -0.5g～0.5g |
Applications:
Single point precise feedback
Single-point precise feedback refers to receiving accurate and
specific feedback at a particular point in time or during a
specific event. This type of feedback provides detailed information
and helps individuals or systems to make improvements or
corrections based on the received data.
Threshold detection
Threshold detection involves setting a specific level or limit
within a system or process to trigger a response when that
threshold is reached or exceeded. This mechanism is commonly used
in various fields to monitor and control different parameters,
ensuring that certain conditions are met or avoided.
Trend detection
Trend detection is the identification of patterns or trends in data
over time. By analyzing historical information, trends can be
observed, allowing for predictions or adjustments to be made based
on the direction in which the data is heading. This analysis is
crucial in decision-making and planning for future actions.
