robust data filtering in wind power systems by: andrés llombart-estopiñán circe foundation –...

Robust data filtering in wind power systems

By: Andrés Llombart-EstopiñánCIRCE Foundation – Zaragoza University

Index

ObjectiveIntroduction: the need of filteringThe LMS fitting techniqueThe LMedS methodologyExperimental resultsConclusions

Index

ObjectiveIntroduction: the need of filteringThe LMS fitting techniqueThe LMedS methodologyExperimental resultsConclusions

Objective

To assess the performance of the Least Median of Squares method when it is used to filter wind power data

Index

Objective

Introduction: the need of filteringThe LMS fitting techniqueThe LMedS methodologyExperimental resultsConclusions

Introduction

Why it is needed?OperationMaintenanceProduction Control

Characterization of the P – v curves

High quality P – v data

Introduction

Circumstances that affect the data qualitySensor accuracyEMI Information processing errorsStorage faultsFaults in the communication systemsAlarms in the wind turbineetc

Introduction

An example of P – v data

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Introduction

P – v data after considering the SCADA alarms

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Index

ObjectiveIntroduction: the need of filtering

The LMS fitting techniqueThe LMedS methodologyExperimental resultsConclusions

The LMS fitting technique

Gets the curve that minimizes the Mean Square Error

All measurements can be interpreted with the same model

Very sensitive to outliersBreakdown of 0% of spurious data

The LMS fitting technique

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LMS line

Index

ObjectiveIntroduction: the need of filteringThe LMS fitting technique

The LMedS methodologyExperimental resultsConclusions

The LMedS fitting technique

It is based in the existence of redundancyLMedS method uses the Median whereas

the LMS method uses the meanUnfortunately the LMedS method don’t

have analytical solution

The LMS fitting technique

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LMS line

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The LMedS fitting technique

ExampleFitting with a polynomial with 4 coefficientsn measurements

m possible solutions, where

!4!4

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The LMedS fitting technique

Steps to get the fitting:

1. Calculate the m subsets of the minimum number of measurements required to fit your curve

2. For each subset S, we compute a power curve in closed form PS

3. For each solution PS, the median MS of the squares of the residue with respect to all the measurements is computed

4. We store the solution PS which gives the least median MS

The LMedS fitting technique

Rejection of wrong data:Estimate de standard deviation

Probability of accepting a measure being good: 99 %

Threshold = 2.57

SMn 45148.1ˆ

Index

ObjectiveIntroduction: the need of filteringThe LMS fitting techniqueThe LMedS methodology

Experimental resultsConclusions

Experimental results

Methodology

A year of historical data 5 different tests

Alarm Records (AR) AR + classical

statistic method AR + robust statistic Classical statistic Robust statistic

Experimental results

Rough data Considered Alarms

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Experimental results

AR + Class. Stat AR + Robust Stat.

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Experimental results

Classic Stat. Robust Stat.

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Index

ObjectiveIntroduction: the need of filteringThe LMS fitting techniqueThe LMedS methodologyExperimental results

Conclusions

Conclusions

A robust filtering method has been proposed It has been proved successfullyThe method have shown a good

robustnessSome research is needed

Considering the wind direction

Robust data filtering in wind power systems

Thanks for your attention

The LMedS fitting technique

Example: fitting a polynomial of 4 coefficients for a 3 months period of data, that implies ~ 12.750 data

The computational cost is huge

151!4!4

!E

n

nm

The LMedS fitting technique

Solution: selecting randomly subsets Compromise:

Minimizing the number of subsetsWarranting a reasonable probability of not

failingSo, the first method step is substituted

by a Monte Carlo technique to randomly select k subsets of 4 elements

The LMedS fitting technique

How many subsets?A selection of k subsets is good if at least in

one subset all the measurements are goodPns is the probability that a measurement is

not spuriousPm is the probability of not reaching a good

solution

41log

log

ns

m

P

Pk

The LMedS fitting technique

In our example considering:Pns = 75 %Pm = 0,001

191log

log4

ns

m

P

Pk