Mark Ahlstrom UVIG Fall Technical Workshop Portland, Oregon October 31, 2013

Wind Plant Operational Reporting Applications Lessons Learned and New Research Results

2

You miss problems unless you have very methodical ways of looking for them

Using new tools, we are able to uncover performance issues and maximize revenue

Normal Upgrades Causing Abnormal Behaviors

Historical Implied Power Curve Current Power Curve

3

Better Assessments and Better Operations

A tight relationship between preconstruction assessments, budgets, actuals, and operations is key to improvement

The linkage between these processes can be measured by variances between expected & actual

Pre-Build Assessment Budget Actuals

Feedback to enhance budget performance

Feedback to improve preconstruction assessments

4

Daily Performance Measures - Establish “should have been” production

Identify Variances to Expected -  Create reporting

metrics -  Study all the

operating data

Identify Underperformance

-  Drilldown & correct Anomaly Detection Alert & Predict

Assessing Performance

Determine Benchmarks

Predictive Analytics

Turbine Diagnostics

Operational Performance

5

Wind Plant Operations Diagnostic Tool

Important to start by exploiting all the data you already have, and understand exactly how each turbine is actually performing

Must be able to fully explore SCADA and turbine data, from annual performance to 10-minute drilldown

6

Nacelle Wind Speed &

Local Air Density Data SCADA Speed, Energy

& Status

Normal Operations?

Raw SCADA Energy Production Generation Entitlement

Turbine Specific Power Model

No No

YES

Based on this understanding, you can start pursuing the “missing megawatts” by knowing the expected generation

We correct for anemometry error and use turbine-specific implied power curves

• Ava

ilabi

lity

• Cur

tailm

ent

7

The GenE methodology was more formally developed in 2012-13 to calculate “expected generation” for given wind conditions & account for all lost energy on a “same day” basis

Generation Expected (GenE)

The GenE process enables measurement of true wind resource and accounts for all losses

Build the P50 Implied Power

Curve

Categorize +2σ & -4σ as Normal

Categorize < -4σ

as Sub-Curve

Generation Expected = (Black dots, no change) + (Blue/Green/Pink dots move to red line)

Calculate Lost MWh for all non-Normal data points

GenE is:

•  An automated measurement of expected turbine output given observed wind

•  A tool to highlight change in turbine performance

•  A standardized method to measure and categorize losses

GenE is not:

•  Equivalent to an OEM Power Curve Test

•  A detailed categorization of root cause

12-24 months of SCADA data

Anemometer

Remove Curtailments &

Availability Faults

8

By looking at every turbine every day with this level of detail, you start finding all sorts of interesting things

Examples of Under Performance

Parameter changed incorrectly

Wind Speed

Pow

er O

utpu

t

Turbine left in phasing mode after blade repair

Wind Speed

Pow

er O

utpu

t

Turbine derate accidentally left on after gearbox was change

Wind Speed

Pow

er O

utpu

t

9

The analysis can even provide upstream indication of failure

July - 2013

Gearbox Replaced

Turbine Unavailable

Turbine Under Performing

Normal Performance

Win

d Sp

eed

Pow

er

Out

put

Pow

er O

utpu

t

Wind Speed

August - 2013

Pow

er O

utpu

t

Wind Speed

September - 2013

• Po

wer

Out

put

Wind Speed

Advance Indication of Failing Components

10

Turning to wakes, better understanding of wake losses is critical for reducing pre- and post-construction variance

Key Wake Drivers •  Array design (turbine density) •  Turbine model/rotor diameter •  Atmospheric stability •  Terrain complexity •  Temperature and wind profiles •  Directional shear •  Neighbors

Terrain effects and wake recovery rate depends on vertical mixing caused by thermal instability

11

Directional Shear

No Directional Shear

Some consultants attribute losses to directional shear, but WindLogics believes that the wakes are the primary issue

Directional Shear Analysis •  Directional shear (or “veer”) is

when the wind angle changes across the rotor diameter

•  WindLogics has analyzed operating turbines to measure directional shear and its impact on production •  So far, observed directional shear

impacts are on the order of -1% or less

•  Wake issues predominate, with directional shear being a small part of the overall wake loss process

12

• Mower County Δ Wind Speed (with Neighbors) • Mower County Δ Wind Speed (Isolated)

Neighboring wind plants are a significant contributor to wake

• Colored fields give an indication of the intensity of the wake field

• Wak

e Lo

ss

• More

• Less

• Wak

e Lo

ss

• More

• Less

• Colored fields give an indication of the intensity of the wake field

Modeling and measurements show that wakes will be significantly underestimated if neighboring wind plants and “deep array”

effects are not appropriately taken into account

• L  

• H

The presence of the wind plant results in a pressure “blockage”

This “blockage” tends to further reduce the wind within a large wind plant by ~ 0.2 m/s 1

1- Based on fine-scale barometric measurements taken by Iowa State University taken at Story County,

Wake Interactions

We can measure the wake with met towers or sodars, but sampling is sparse and met towers are seldom at the optimal measurement points

14

Summary - Key Drivers of Variance

•  Wake, curtailment and availability are the top drivers –  Neighboring wind plants must be considered –  Continuous reviews and adjustments need to take place

•  General observations –  Wind speed estimates are generally very good –  Loss assumptions in going from gross to net capacity factor are

complex and can often be improved •  Goals

–  Identify and fix the correctable losses –  Improve preconstruction estimates for the fleet and for individual

wind power plants

Need to look very carefully at real data, improve operations to fix correctable losses, and improve preconstruction estimates

Discussion

Mark Ahlstrom 651-556-4262

mark@windlogics.com