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Structural health monitoring and ice detection for connected wind turbines with Wendelin

A complete use case of Nexedi technologies (Wendelin, NEO) for the implementation of a Big Data infrastructure in the field of Wind Energy.
  • Last Update:2018-11-11
  • Version:003
  • Language:en

Structural health monitoring and ice detection for connected wind turbines with Wendelin

Wölfel Logo
Nexedi Logo

Dr.-Ing. Steffen Pankoke

Pankoke (at) woelfel (dot) de

Dr. Klaus Wölfel

klaus (at) nexedi (dot) com
 

IDD.Blade

 

Structural health monitoring and ice detection for connected wind turbines with Wendelin

Wölfel Logo
Nexedi Logo

Dr.-Ing. Steffen Pankoke

Pankoke (at) woelfel (dot) de

Dr. Klaus Wölfel

klaus (at) nexedi (dot) com
 

IT Problems

  • Reliable data collection
  • Scalable storage
  • Parallel processing
  • Out-of-core processing
  • Predictive algorithms
 

Introducing Wendelin

Wendelin Logo
 

Reliable data collection: Fluentd

Fluentd Illustration
 

Scalable storage: NEO

NEO Illustration
 

Parallel Processing: ERP5

# Initialize data
data_size = 1e6
server_count = 1000
chunk_size = data_size / server_count
data = zbigarray(data_size)

# Process data in parallel on each server (Map Reduce, Batch, etc)
for server in server_count:
  data.activate().process(server*chunk_size, chunk_size)
 

Out-of-core processing: wendelin.core

# Numpy
np.ndarray(shape=(2,2), dtype="float")

# Out-of-core data
ZBigArray(shape=(1e18,2), dtype="float")

# Full out-of-core
ZBigArray(shape=(1e9,2e9), dtype="float")
 

Predictive algorithms: scikit-learn

scikit-learn Illustration
 

User Interface

Mic Wind Map Screenshot
 

Data Visualisation

Mic Wind Graph
 

Thank You

Image Nexedi Office
  • Nexedi GmbH
  • Agnes-Pockels-Bogen 1
  • 80992 München
  • Germany