1. QUANTITATIVE PERFORMANCE
EVALUATION OF A WIND
TURBINE GENERATOR
CLUSTER USING STATISTICAL
TECHNIQUES
S. V. Joshi1*; S. Sainis1; M. D’Souza2
1 School
of Mechanical and Building Sciences, VIT
University, Vellore, India
2 Suzlon Energy Limited, One Earth, Pune, India
* Tel: 0763-956-7408, E-mail: saumitra.vjoshi@gmail.com

2. INTRODUCTION
•
Power generation from wind has emerged as a major sector in the energy
market.
•
Besides technology development, wind power companies focus equally on
development of a strong customer base.
•
The performance of a Wind Turbine Generator (WTG) is directly linked with
monetary profit or loss to the customer.
•
The important factors that play a vital role in the performance of a wind site
are:
• Wind at site
• Machine availability (MA): This is the factor Wind Energy companies
have a control over
• Grid availability (GA)

3. INTRODUCTION
•
Estimation of power generation losses is crucial for evaluating the
performance of a WTG Cluster.
•
A suitable technique that can effectively calculate the total power generation
losses due to lack of MA and GA is imperative to the company.
•
•
Power generated from the WTG cluster was predicted using WAsP.
Wind Atlas, Analysis and Application Program (WAsP) is a powerful PC
program that can predict power generation from Wind Turbines and Wind
Farms. The predictions are based on wind data measured at meteorological
stations in the same region. The program includes a complex terrain flow
model, a roughness change model, a model for sheltering obstacles, and a
wake flow model.

4. RESEARCH PROBLEM
Where is
the
shortfall?
How
much is
the
shortfall?
How do
we
quantify
it?
Fig. 2 WAsP-predicted and actual
generation at centum MA and GA
• Problem statement: Identify reasons for shortfall in actual and predicted
power generation, and quantify these losses using actual data
• Approach: Define different techniques of calculating losses caused due to
lack of MA and GA in order to determine the best suited method based on
accuracy of results as well as practical utility

5. SITE DESCRIPTION
•
For our research, the WTG cluster
scrutinized has 10 WTGs, located in
Kuchchh region in India and is
maintained by Suzlon Energy
Limited, India.
•
Relatively flat terrain (5 meter rise
for every kilometer distance)
•
Proximity of mast to area of study
•
Generator Model: S82 (Rated
Capacity: 1.5 MW)
Fig. 1 Location of cluster WTGs
and Mast. X and Y scale is in
UTM

6. DATA COLLECTION AND VALIDATION
•
•
•
Data obtained from Suzlon Energy Group’s Supervisory Control and Data
Acquisition (SCADA) Monitoring Centre.
Missing data was completed using two approaches:
• Measure-Correlate-Predict (MCP) Method: for data points with lack of
MA + GA
• Cluster Nacelle Velocity Average (CNVA) Method: for lack of MA only
Validation:
Fig. 3 Scatter plots of Wind
Vane 1(65m Height) reading vs.
Wind Vane 2(50m Height)
reading
Fig. 4 Scatter Plot of (V65/V50)
vs. Wind Direction
Fig. 5 Wind speed at 65m
(Blue) and at 50m(Red) vs.
Time

7. PROPOSED STATISTICAL METHODS
•
The statistical correlation techniques used in this study for prediction is based on a
simple approach of determining the weightage of points (xi, yi) on the argand plane
to find the best fitting polynomial through the set.
•
The basic steps followed in this technique are as follows:
• Identification of an independent variable x. In our case, we have used wind speed
which was obtained from completed data sets obtained by MCP and CNVA methods
as described in the previous section
• Selection of a suitable dependent variable y such that y = f(x) + c
• Obtaining the spatial distribution of y with x to carry out a weight analysis on the
argand plane
• Expressing y as a polynomial function of x.
• Calculation of yj at conditionally selected points xj

8. PROPOSED STATISTICAL METHODS
Identification
of x
Determination
of missing x
Calculation
Curtailment
Loss
MCP Method
Generation
Loss (100
MA, GA)
Velocity
Turbine
Cluster
Average
Generation
Loss (100 GA)
Fig. 6 Schematic of procedure

9. CURTAILMENT LOSSES
•
Data analysis of three quarantined WTG for pitch angle variations with
wind speed show that curtailment exists for T002 only
Fig. 7 Scatter plot of pitch angles
versus wind speed for T002.
Notice the anomalous trend at
higher speeds.
Fig. 8 Actual Power Generation
versus wind speed for T002
Fig. 9 Actual Power Generation
for T002 versus Wind Speed
showing fitted polynomial after
filtering the curtailment points

10. CURTAILMENT LOSSES
Statistically correlated, ninth-degree, centered-and-scaled data polynomial is
calculated as shown below that relates actual T002 power generation to wind
speed
Using above equation, curtailment losses are evaluated as follows:

11. LACK OF MACHINE AND GRID AVAILABILITY
•
Main reason for shortfall: Lack of Machine Availability in high wind season
•
Numbering in figure is based on fiscal months (1represents April, and so on)
Fig. 10 Lack of MA for T002, T003 and T005 is in high wind season

12. CALCULATION OF MA + GA LOSSES
I.
Extraction Factor Method (EFM)
Determination of a relation between the ratio () of maximum theoretical
extractable power to actual power generated and MCP wind speed.
Fig. 11 Plot of  versus
Wind Speed for T002

13. OTHER METHODS
•
Power generation loss due to lack of MA+GA
II. Monthly Correlation Method (MCM)
- Correlation of actual monthly power generation to MCP wind speed
•
Power generation loss due to lack of MA
I. Turbine Specific Power Curve Method (TSPCM)
- Correlation of actual power generation to CNVA wind speed
II. Rated Power Curve Method (RPCM)
- Correlation of rated power to CNVA wind speed

14. RESULTS
Lack of MA + GA
Lack of MA

15. CONCLUSIONS
• TSPCM gives best and most consistent results
• TSPCM manages to bring down the error in calculation of actual gene
ration
losses well below 10%.
• Calculates generation losses due to lack of MA, which is of more utility
to
WTG companies.
• EFM calculates results very well for a temporally distributed lack of M
A
• MCM is well suited for a temporally concentrated lack of MA
• RPCM provides a good way to compare two machines’ performance –
not
suggested for quantification

16. SCOPE OF THIS WORK
• Refinement of the TSPA technique by incorporation of more complex
concepts of statistics to make calculations more accurate.
• Making all methods computationally less tedious by determining a
way of automation of polynomial feeding into prediction codes.