DATA VALIDATION - Wind Resource Assessment: A Practical Guide to Developing a Wind Project - page 117

10 7

Speed
Speed standard deviation
Temperature

9

6

8

5

7

4

6

5

3

4

2

3

1

2

Suspected icing event

0

1

0 − 1

Time (10-min observations)

Figure 9-1. A plot of 10-min average wind speed, standard deviation of speed, and temperature
showing a suspected icing event. Time series plots such as these can be a valuable tool for
verification of suspected bad data. Source : AWS Truepower.

the review phase, whereas good records usually receive no further scrutiny. Another
reason is that failing to reject even a small number of bad values can significantly
bias a wind resource analysis, whereas excluding a moderate amount of good data
rarely has such an impact. However, care must be taken in designing the automated
screening so as not to overwhelm the review phase with an excessive number of false
positives. Finding the right balance takes trial and error.

9.2.1 Validation Routines
Validation routines are designed to screen each measured parameter and flag suspect
values for review. They can be grouped into two main categories: general system
checks and measured parameter checks.

General System Checks. Two simple tests described below assess the com-
pleteness of the collected data:

Data Records. The number of data fields must equal the expected number of mea-
sured parameters for each record.
Time Sequence. The time and date stamp of each data record are examined to see
if there are any missing or out-of-sequence data.