IEEE Power & Energy Magazine - September/October 2015 - 67

14

400

12

Ringdown Data

Ambient Data

300

Damping Ratio (%)

Line Power (MW)

16
500

200
100

Mode 1 (0.48 Hz ~ 0.55 Hz)
Mode 2 (0.71 Hz ~ 0.72 Hz)
Mode 3 (0.36 Hz ~ 0.43 Hz)

10
8
6
4
2

0

0
0

2

4

6

8

10

0

1

2

3

4

Time (min)
(a)

5

6

7

8

9

10 11

Time (min)
(b)

figure 7. Identifying an LFO event: (a) power oscillation on 21 April 2008 in the CSG and (b) identification results obtained using the ARMA method.

Given that disturbances in real power systems are relatively
rare, the identification of oscillation based on ambient data is practical for early warning and decision making. Several improved
methods based on the auto-regressive moving average (ARMA)
model and recursive algorithms have been studied and implemented by Tsinghua University and Beijing Sifang Automation
Company as a key function in the CSG's data-mining system.
We use an LFO event that occurred on 21 April 2008 as
an example. The ambient and ringdown signals are shown
in Figure 7(a). Figure 7(b) demonstrates the identification
results of three interarea oscillation modes using the ARMA
method. The results can be validated by Prony calculations
based on the ringdown data. The software interface is shown
in Figure 8. The oscillation frequencies and damping ratios
of the different oscillation modes are estimated for an hour.
This system helps in realizing constant monitoring of small
signal stability in the CSG.

Frequency (Hz)
YN-GD Mode

Identifying Power Grid Disturbances
When a disturbance occurs in power systems, the control
center operators receive numerous warnings and information
about the sequence of events. However, this information usually only contains breaker trips and protection relay actions.
Operators cannot directly determine the kind of fault that actually led these disturbances to occur. The causes can generally
be identified by analyzing fault recording wave files after the
fault events. However, this analysis is time consuming. Based
on the real-time dynamic data of PMUs, online identification
methods for different kinds of faults or disturbances have been
designed and implemented. The types of faults or disturbances
identified include short circuit faults, generator breaker trips,
phase-shift failures by dc converters, and islanding.
In some cases, the message-relaying action cannot be transmitted to the dispatching center because of issues such as communication traffic under system faults. However, the dynamic

Damping Ratio (%)
YN-GZ Mode

Hainan Mode

0.58 Hz
8.40%

YN-GD Mode

0.40 Hz
16.40 %
YN-GZ Mode
0.76 Hz
0.33%

Damping Ratio (%)
Hainan Mode

figure 8. Real-time LFO monitoring software interface of the CSG data-mining system. YN: Yunnan; GD: Guangdong;
GZ: Guangzhou.
september/october 2015

ieee power & energy magazine

67



Table of Contents for the Digital Edition of IEEE Power & Energy Magazine - September/October 2015

IEEE Power & Energy Magazine - September/October 2015 - Cover1
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IEEE Power & Energy Magazine - September/October 2015 - Cover3
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