symptom project - royal institute of · pdf filee.g.: model in modelica validated against a...

SYMPTOM Project SYnchrophasor-based Model calibration for Power systems and conTrol OptiMization

Maxime Baudette, L. Vanfretti

May 2015

Why “Model Validation”?

The quality of the models used by off-line and on-line tools will affect the result of any analysis for planning and operation •  Good model: approximates the

simulated response as “close” to the “measured response” as possible

Validating models helps in having a model with better accuracy: •  increasing the capability of reproducing

actual power system behavior 2 3 4 5 6 7 8 9

-2

-1.5

-1

-0.5

0

0.5

1

Δ P

(pu)

Time (sec)

Measured ResponseModel Response

WECC Break-up 1996

BAD Model for Dynamic Security Assessment!!!

15-05-08 SYMPTOM PROJECT PRESENTATION 2

Near RT Model Calibration App

Real-Time Measurements Pre-processing Mode Estimation

CIM Harmonized

Model

Initialization

Mode Estimation

Linear Analysis Tools

Pre-processing Simulation

Assessment

Performance Indicator

Criteria for parameter selection

Parameter Selection

Parameter Variation

Non-linear analysis

Linear Analysis

Critical Modes Optimization of

Controller Settings and

Control Actions

Performance indicator not satisfied

Real-time alarms from mode estimation

PMU / WAMS

SCADA / EMS

Critical Modes


Near RT Model Calibration

Source of information •  PMU measurements (live or archived) •  Harmonized model from SCADA

snapshot and dynamic components definitions

Calibration based on small signal response Calibrated models: •  Validated representation of the power

system at any instant for further use

2

1

min max

min

s.t.

(Parameters ranges)

N

i ii

f

P P P

λ λ=

= −

≤ ≤

∑)


Near RT Control Redesign/Retuning

Control optimization: •  Model reduction for control redesign •  Tuning/ optimization of control schemes toward small

signal stability •  Control always optimized for the current operating

conditions

à Better overview of the actual health conditions


Main Project Work

•  Reworked Mode Estimation app to support real-time PMU measurements with SDK: •  Magnitudes, angles, frequencies, angle differences can

be used with the method •  Integration in WAMS Display with GUI elements

•  Modelica / PacDyn comparison for linear analysis •  Test for Statnett SDK Licensing


Collaboration STRONg2rid: •  Assisting Almas with supervision of MSc

Student Gudrun iTesla: •  RaPId Toolbox:

•  Upcomming publication on RaPId Toolbox

•  CIM2ModelicaFactory:

•  Participation in specifications development

•  Collaboration for interfacing the Simulation Engine

STINT •  Visit at UFRJ, begin of work on

Modelica/PacDyn comparison


Mode Estimation Application

Implemented with S3DK to use RT- PMU data Implementation of two algorithms to increase precision •  Ambient data (general case)

[Stochastic State Space Modeling or ARMA Modelling] •  Ring-down data (post fault case)

[ERA]

PMU Data Preprocessing Mode Estimation


(PMU/WAMS)

Ambient data

Ring-down data


Mode Estimation Application

Switch logic between the two algorithms: •  Detection of the presence of Ring-down

data •  Detection of large discontinuities in the

measurements

Oscillation detector from previous work appeared well suited for the detection •  Detecting oscillatory ”activity” within a

predefined frequency range [0.1 – 2 Hz]

Measurements

Ambiant Data Algo.

Ringdown Data Algo.

Merging

Faults ? NO YES

Combined Estimation


Mode Estimation Application Interface

The fault detection works well after calibration (Test with Nordic32) Both method show good results so far •  ERA method gives more accurate estimates

-> Higher confidence! •  How to “merge the results” and derive general confidence ?

-> Graphically for now Ongoing Work: •  Test on synthetic data •  Test with linearized Nordic 32 with synthetized data using knowledge

from dominant paths to carefully select measurement points •  Test on PMU data (RT simulation and ambient from STRONg2rid

PMUs and/or Statnett PMUs). To be done.


Sample results from statistical analysis Synthetic Model

15-05-08 11 SYMPTOM PROJECT PRESENTATION

Am

bien

t

ER

A

Sample results from statistical analysis Synthetic Data from Linearized Nordic32


Am

bien

t

ER

A

0.47 0.48 0.49 0.5 0.51 0.52 0.53 0.54 0.55 0.56

0

10

20

30

40

50

60

70

80

90

100

AMB mode1 frequency after zero removal

Prob

abilit

y De

nsity

Probability Density Function

empiricaltlocationscaleloglogisticlogisticlognormal

0 0.02 0.04 0.06 0.08 0.1 0.12

10

20

30

40

50

60

AMB mode1 damping after zero removal

Prob

abilit

y De

nsity


empiricaltlocationscalelogisticnormalgeneralized extreme value

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.90

2

4

6

8

10

12

ERA mode1 frequency after zero removal

Prob

abilit

y De

nsity


empiricaltlocationscalelogisticloglogisticnormal

-0.05 0 0.05 0.1 0.15 0.20

5

10

15

20

25

ERA mode1 damping after zero removal

Prob

abilit

y De

nsity


empiricalgeneralized extreme valuegeneralized paretotlocationscalelogistic

Modelica / PacDyn

Comparison of Modelica for linear analysis to the PacDyn reference software: •  SMIB •  2 areas system •  AKD (SS properties?) •  Nordic44 If good results, Modelica will be used for linear analysis


Modelica / PacDyn SMIB system


•  Preliminary comparisons show large difference between the Modelica and PacDyn results.

à  Modification of the initialization of the Modelica models (the linearization is performed at initialization, from the powerflow data.)

•  New results more satisfactory for the critical mode, but the Modelica models seem to still require additional checking.

# f (Hz) ζ (%) f (Hz) ζ (%)

1 0.8494 -5.2 0.8498 -7.34 2 1.2147 78.03 1.9385 79.28

# f (Hz) ζ (%) f (Hz) ζ (%)

1 0.8494 -5.2 0.8498 -7.34 2 1.2147 78.03 1.9385 79.28

No PSS PSS

Development Status


Real-Time Measurements Pre-processing Mode

Estimation

CIM Harmonized

Model

Initialization

Mode Estimation

Linear Analysis Tools

Pre-processing Simulation

Assessment

Performance Indicator

Criteria for parameter selection

Parameter Selection

Parameter Variation

Non-linear analysis

Linear Analysis

Critical Modes Optimization of Controller Settings and

Control Actions

Performance indicator not satisfied


PMU / WAMS

SCADA / EMS

Critical Modes

Orange: Developed, not completely tested yet Red: Not yet developed, work for 2015

Future Work

•  Wrapping of the script for model building and model simulation to interface with LabVIEW when necessary

•  Develop the interface for linear analysis with Modelica

•  Develop and implement the performance indicator


THANK YOU


Future Development

•  Develop user interface of the mode meter module to fit requirements

•  Implementation of data export in HDF5 for collaboration within iTesla

•  Test the tools at Statnett control center, Alta


iTesla: Power System Library


•  The FP7 iTESLA project develops a high level library for modeling power grid components

•  Generators, •  Governors, •  Excitation Systems •  Stabilizers •  Branches, •  Loads, •  Buses, •  Events •  Connectors •  Non Electrical Components

iTesla / Mango: Power System Library

E.g.: Model in modelica validated against a PSSE model


iTesla: Model Execution Engine

Open-source software for cyber-physical system simulation Plug-in different compilers and solvers: Use of Python Scripts Simulation of .fmu using JModelica.

•  JModelica contains Python scripts for compile models into .fmu and simulate .fmu

Simulation of .mo with OpenModelica / Dymola •  OpenModelica comes with JAVA libs (.jar files) leading calls to OMC (Open

Modelica Compiler) •  Dymola script needs run-time license for executing simulations


Pro

perti

es

Res

ults

HDF5

JM OMC

PYTHON

JAVA

Dymola

iTesla: Model Execution Engine


symptom project - royal institute of · pdf filee.g.: model in modelica validated against a...

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