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: [email protected]
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)
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.
RESEARCH PROBLEM
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
Where is the
shortfall?
How much is
the shortfall?
How do we
quantify it?
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
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
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
PROPOSED STATISTICAL METHODS
Calculation
Determination of
missing x
Identification of x
Velocity
MCP Method
Curtailment Loss
Generation Loss (100 MA, GA)
Turbine Cluster Average
Generation Loss (100
GA)
Fig. 6 Schematic of procedure
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
CURTAILMENT LOSSESStatistically 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:
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
CALCULATION OF MA + GA LOSSESI. 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
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
RESULTS
Initial comparison
Coarse Extrapolation
PFM
MCM
TSPCM
RPCM
Lack of MA + GA Lack of MA
CONCLUSIONS• TSPCM gives best and most consistent results
• TSPCM manages to bring down the error in calculation of ac-tual generation 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 MA
• MCM is well suited for a temporally concentrated lack of MA
• RPCM provides a good way to compare two machines’ per-formance – not suggested for quantification
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.
QUESTIONS?