Algo APM Description
The Core high-level Functionality available in AlgoAPM© is enlisted below:
1. Continuous energy reconciliation.
The AlgoAPM© performs energy reconciliation based on millions of calculations every day. It calculates, given the wind, what energy the wind farm is expected to produce? What energy the farm has actually produced? And what reasons/losses can reconcile the difference between the two?
2. Continuous Measurement of Performance
deterioration due to dust accumulation/contamination on the turbine blades. Unfortunately, in the Wind corridors of Pakistan, the average rainfall is <180 mm per annum. Not enough to regularly wash the turbine blades. Evidence gathered over the last 5 years proves that the dust accumulation/contamination of turbine blades, without sufficiently regular rainfall, is the single largest source of production losses; right after grid curtailments. The contamination causes more production losses than all other causes combined together. The AlgoAPM© uses at least three separate and proprietary statistical methods, using rain/dew data, spot power, and wind data, to estimate losses due to dust accumulation/contamination on the blades and updates the state of the blades weekly.
3. Continuous Measurement of Actual power curve performance against Turbine OEMs declared power curve.
Algorithm Consulting, prior installation of the AlgoAPM©, measures and compares the met mast wind data with turbine mounted anemometers. The algorithm develops its own Nacelle Transfer functions and uses them to calculate the true wind speed recorded on the anemometers. This fixes any errors due to the turbine mounted anemometers. The power curve performance can then be measured more precisely. Every week, the APM takes an overview of the power curve performances throughout the
preceding week and updates the overall performance.
1. Continuous energy reconciliation.
The AlgoAPM© performs energy reconciliation based on millions of calculations every day. It calculates, given the wind, what energy the wind farm is expected to produce? What energy the farm has actually produced? And what reasons/losses can reconcile the difference between the two?
2. Continuous Measurement of Performance
deterioration due to dust accumulation/contamination on the turbine blades. Unfortunately, in the Wind corridors of Pakistan, the average rainfall is <180 mm per annum. Not enough to regularly wash the turbine blades. Evidence gathered over the last 5 years proves that the dust accumulation/contamination of turbine blades, without sufficiently regular rainfall, is the single largest source of production losses; right after grid curtailments. The contamination causes more production losses than all other causes combined together. The AlgoAPM© uses at least three separate and proprietary statistical methods, using rain/dew data, spot power, and wind data, to estimate losses due to dust accumulation/contamination on the blades and updates the state of the blades weekly.
3. Continuous Measurement of Actual power curve performance against Turbine OEMs declared power curve.
Algorithm Consulting, prior installation of the AlgoAPM©, measures and compares the met mast wind data with turbine mounted anemometers. The algorithm develops its own Nacelle Transfer functions and uses them to calculate the true wind speed recorded on the anemometers. This fixes any errors due to the turbine mounted anemometers. The power curve performance can then be measured more precisely. Every week, the APM takes an overview of the power curve performances throughout the
preceding week and updates the overall performance.
Open up
