R.A. de Callafon
1, C.H Wells
2,Sai Akhil Reddy
1Joint work with H. Ghoudjehbaklou, T. Rahman, S. Sankaran, at Diego Gas and Electric (SDG&E)
1SyGMA Lab, University of California, San Diego (UCSD) 2OSIsoft
JSIS Talk, April 26-28, 2006
Phasor Data for Event Detection, Feedback Control
and On-line Generator Modelling in the SyGMA lab
Official Opening
The new SyGMA lab at SDSC, UCSD opened on
March 17, 2016
Mission Statement
The SyGMA lab: key player in the emerging technology on electric grid instrumentation.
Development of new data processing, modeling
and model validation tools
Advanced grid monitoring and automatic control
of electric networks
Real-time Event Detection
Real-time event detection algorithm is currently being
developed to run on Linux using Python on Raspberry PI.
Automatically detect events in real-time.
Send only the data with events to the data server, which can
Real-time data Live on SyGMA website
Funded by CEC
Application to Anza Borrego microgrid
Tom Bialek, Neal Bartek SDG&E, Main PI Objective Control P/Q at PCC via P/Q of distributed smart inverters (while maintaining constrains)
Feedback Control with Phasor Data
PCC
control
See: Borrego Spring Microgrid Demonstration Project http://www.energy.ca.gov/2014publications/CEC-500-2014-067/CEC-500-2014-067.pdf
Feedback Control with Phasor Data
Detailed simulations with Simulink/SimPower Systems
Simulations reveal:
Typical oscillations Coupling between
P/Q at PCC that is
both static and dynamic
Feedback Control with Phasor Data
Phasor based Feedback Control
Feedback control algorithm uses phasor feedback (V,I, + angles) Feedback algorithm takes into account:
Grid dynamics (oscillation response)
Communication delays (PMU data + actuation)
Non-linear dynamic coupling (trig. between phasors and P/Q) Disturbance rejection + tracking (to follow P/Q references)
Detailed simulations with Simulink/SimPower Systems
Simulations reveal:
Damping of oscillations
Reduced coupling between
P/Q at PCC that is
both static and dynamic
Tracking and disturbance
rejection of P/Q at PCC
Generator Model Validation
Disturbance (change in f or P/Q) generated by “grid”
Measurements of f, V/I and P/Q at high/low side
In addition to
Koserev/Yang
approach:
Rotor “phasor” angle 𝜃
and rotor frequency 𝜔
Field 𝑉𝑓 𝐼𝑓
𝑇
𝜔
𝑉
𝐼
𝑉
𝐼
low side high side
𝑉
𝑓𝐼
𝑓Overview of data
Instrumentation for rotor angle measurements
Rotor phasor angle via zero-crossing detection Rotor frequency via timing
Typical Rotor Angle Data
𝜃(𝑘) 𝜃𝑢 𝑘 = 𝑢𝑢(𝜃 𝑘 ) 𝜔 𝑘 = 𝜃𝑢 𝑘 − 𝜃𝑢(𝑘 − 1) 0.03333Note: 𝜃 𝑘 constant if rotor frequency = 60Hz (not absolute rotation)
Comparison of rotor angle and frequency
𝑓(𝑘) = 60+ 𝜃𝑢 𝑘 − 𝜃𝑢(𝑘 − 1)
0.03333 ∙ 2 ∙ 180
Final Transient Data: Field V,I and P,Q
15
Generator Models
Callafon - SyGMA Lab, JSIS Meeting, April 2016
More advanced models
(simplified CIGRE or GT1) Still “simplified” model
Ham et al. “Development and Experience in Digital Turbine Control” IEEE Trans. on Energy Conversion, (1988)
Features:
Logic for feedback (P/PI/PID) 2nd order model for
gas turbine dynamics
Possibility to model power
output as function of heat/speed
Similar to GGVO1
CIGRE Technical Brochure 238, Modeling of Gas Turbines and Steam Turbines in Combined-Cycle Power Plants (2003)
Results of “fitting” measured rotor frequency
Due to simple dynamics between
POI PMU frequency and rotor
frequency and excellent fit
is obtained
Results
Results of “fitting” Ifield and Vfield
Dynamic effects are captured reasonably well
Results
Results of “fitting” positive sequence real P and reactive Q
Dynamic effects are captured, but model needs more features
Results
Wrap Up
Additional rotor angle/angular speed
𝜔
allows
characterization of PMU/transformer dynamics
Additional rotor angle, filed current and field voltage
can be exploited
distinguish generator dynamics from
PSS dynamics
𝑇
𝜔
𝑉
𝐼
𝑉
𝐼
low side high side
𝑉
𝑓𝐼
𝑓