Edge Processing and Event Detection using Phasor Data. Raymond de Callafon and Sai Akhil Reddy

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1 Edge Processing and Event Detection using Phasor Data Raymond de Callafon and Sai Akhil Reddy University of California, San Diego & OSIsoft JSIS Meeting, April 26-28, Salt Lake City

University of California, San Diego & OSIsoft JSIS

2 Motivation Smart PMU: local signal processing and detect/store events centrally How to implement local signal processing? Can local processing used to detect individual events? Can event detection be distributed on each PMU? May 30 data: data points (30Hz sampling noon-9pm) 2

Can local processing used to detect individual events?

3 Approach Infrastructure based on real-time local processing of PMU data: Decoding of C data messages of PMUs Filtering of phasor data to obtain Filtered Rate of Change signal Formulate event detection based on FRoC signal Store finite batch size of PMU data in event only PMUs (micropmu) Decoding and filtering implemented in Python on Raspberry PI with wired and wireless TPC/IP 3

Formulate event detection based on FRoC signal Store finite batch size of PMU data in event only

4 Power Standards Lab (PSL) micro-pmu Three phase 512 samples/cycle Produces 3 phase voltage and current phasors & 60Hz Data streamed in real-time to Raspberry PI model B+ using IEEE C standard over the Ethernet using TCP port 4713 Python socket programming to read data from the TCP port 4713 in a predefined frame-size Decoding C data End Result: real-time streaming phasor data on our Raspberry PI 4

118 standard over the Ethernet using TCP port 4713 Python socket programming to read data from the TCP

5 Real-time streaming phasor data of interest are voltage V(k) and angle 60Hz Data is processed via parallel real-time filters V d k = b 0 V k + b 1 V k b n V k n a 1 V d k 1 a n V d k n α d k = c 0 α k + c 1 α k c n α k n d 1 α d k 1 d n α d k n Implemented in NumPy (for scientific computing in Python) The real-time signal V d (k) and α d (k) can be interpreted as V d (k) = Filtered Rate of Change (FROC) of voltage phasor amplitude α d (k) = FROC of voltage phasor angle/frequency Coefficients b i, a i, c i and d i via LS optimization for min. variance Event detection at k = l when Event Detection V d k > γ V OR α d k > γα for k [l, l + 1,, N] End Result: real-time phasor data event detection on our Raspberry PI 5

be interpreted as V d (k) = Filtered Rate of Change (FROC) of voltage phasor amplitude α d (k) = FROC of voltage phasor angle/frequency Coefficients b i, a i, c i and d i via LS

6 Filter Coefficients + Bounds Computation of filter: Select small part of data Model noise as output noise Add fixed noise filter (low pass) Compute filter via LS minimization Define a Filtered Rate of Change (FRoC) signal f t for detection via differentiation (high pass) filter H Compute 3σ level of filtered signal End Result: Go/Ho Ho/Go _ 1/Go Go/Ho 6 Filter coefficients b i, a i, c i and d i 3σ levels γ V and γ α f(t) H ε(t, θ) G(θ)

signal f t for detection via differentiation (high pass) filter H Compute 3σ level of filtered signal End

7 Filtering of Phasor Data Select small part of data Phasor data (amplitude and phase angle) used for the identification of the real-time filter 7

8 Filtering of Phasor Data Define FRoC signal + error bounds NOTE: FROC signal is defined for both amplitude and phase angle of phasor! 8

9 Local/edge Event Detection 9 Automatically: Detect events (via threshold on Filtered Rate of Change signal) Store event data Notification/ Note: Not every disturbances is an event! Hard to see, but clear with edge processing

event data Notification/email Note: Not every

10 Local/edge Event Detection 10 Automatically: Detect event. (via threshold on Filtered Rate of Change signal) Store event data Notification/ Note: Not every disturbances is an event! Hard to see, but clear with edge processing

11 Local/Edge processing of PMU data Intermediate C decoding on local processor (in this case a Raspberry PI) Implement real-time filtering on local processor Create FRoC signals on phasor (V and α) for event detection Automatically detect events via adaptive thresholding Coordinate central data management in case of events Summary Excellent results from lab experiments Only store data in case of event (large data compression) 11

processor Create FRoC signals on phasor (V and α) for event detection Automatically detect events via

12 For more info/pubs see also 12 Callafon - SyGMA Lab, JSIS Meeting, April 2016

Phasor Data for Event Detection, Feedback Control and On-line Generator Modelling in the SyGMA lab

Phasor Data for Event Detection, Feedback Control and On-line Generator Modelling in the SyGMA lab R.A. de Callafon 1, C.H Wells 2, Sai Akhil Reddy 1 callafon@ucsd.edu Joint work with H. Ghoudjehbaklou,