Energy and Power Engineering, 2013, 5, 962-969
doi:10.4236/epe.2013.54B185 Published Online July 2013 (http://www.scirp.org/journal/epe)
Distributed Adaptive Learning Framework for Wide
Area Monitoring of Power Systems Integrated with
Distributed Generations
Kang Li1,2, Yuanjun Guo1, David Laverty1, Haibo He3, Minrui Fei2,4
1School of Ele ctronics, E lectrical Engineering and Computer Science, Queens University Belfast, Belfast, UK
2UK-China Science Bridge Joint Laboratory on Energy and Automation, Shanghai University,
China and Queens University Belfast, UK
3Department of Electrical, Computer, and Biomedical Engineering University of Rhode Island, USA
4School of Mechatronics and Automation Shanghai Uni-versity, China
Received April, 2013
ABSTRACT
This paper presents a preliminary study of developing a novel distributed adaptive real-time learning framework for
wide area monitoring of power systems integrated with distributed generations using synchrophasor technology. The
framework comprises distributed agents (synchrophasors) for autonomous local condition monitoring and fault detec-
tion, and a central unit for generating global view for situation awareness and decision making. Key technologies that
can be integrated into this hierarchical distributed learning scheme are discussed to enable real-time information extrac-
tion and knowledge discovery for decision making, without explicitly accumulating and storing all raw data by the cen-
tral unit. Based on this, the configuration of a wide area monitoring system of power systems using synchrophasor
technology, and the fun ctionalities for locally installed open-phasor-measurement-units (OpenPMUs) an d a central unit
are presented. Initial results on anti-islanding protection using the proposed approach are given to illustrate the effec-
tiveness.
Keywords: Smart Grid; Anti-islanding Protection; Distributed Agents; Incremental Learning; Monitoring and Control
1. Introduction
We first consider a generic scenario where a distributed
adaptive real-time learning system is often placed within
a data intensive environment and actively interacts with
it [1-3]. The application environment may be structured
such as monitoring and control of large-scale power sys-
tems or unstructured where the objects to be monitored
and operated are dynamic and uncertain, such as moni-
toring and contingency control of large scale renewable
generations [4]. Figure 1 illustrates such a generic sce-
nario for smart grid monitoring, operation and control.
For such applications, a large amount of spatial-tem-
poral and often heterogeneous data is acquired and proc-
essed through a distributed network of static and mobile
data acquisition nod es/agents. A learning system is put in
place to adaptively learn and accumulate meaningful
information and knowledge to generate a global model
for situational awareness and to make decisions to be
implemented by a distributed network of actuators. Each
node in the distributed sensor and actuator networks
possesses some computation power to either pre-process
local and temporal data or to locally implement the deci-
sions from the core or central unit through task decom-
position and coordination among the nodes. In addition
to communication with the core, the nodes may commu-
nicate with each other or a node may communicate with
other nod es of close proxi mity.
Among various issues to be addressed for such com-
plex systems, we mainly consider the data fusion to sup-
port overall system operation and control. The core aim
is to build a dynamic, global, and abstract model, based
on which consequences are inferred and decisions are
made. However, the major challenge is th at data obtained
Figure 1. A generic scenario in smart grid applications.

K. LI ET AL. 963
from such systems are often of high dimension, imbal-
anced, multi-modal, multi timescale, spatial-temporal,
heterogonous, unsynchronized and uncertain nature,
coupled with large interdependencies and interactions
between human and machine systems. It presents a major
challenge to effectively and efficiently collect, transmit,
store, monitor, process, integrate and analyze these data
for information integration and accumulation, extraction,
knowledge discovery, situation awareness, and decision
making in operation and control of the whole systems. In
particular, we consider the following challenges [3,5-7]:
1) Dimension of time and multi-modality. In a com-
plex system, time has a multidimensional characteristic
as different types of events involve the assembly of many
finer events with each having different time scale. Fur-
ther, the measurement for even a single event may in-
volve multiple modes using different sensors.
2) Discreteness. Complex systems often exhibit dis-
crete behavior. Also, microscopic granularity will help
with the emergence of macroscopic objects in both space
and time dimensions. It is therefore important to embed
such a feature in the system model.
3) Connections and dynamic networks. The emergence
of overall behavior of a complex system depends on the
dynamic and often nonlinear interaction among all its
parts and elements. There is a need for a network theory
and framework to reflect these connections over various
time and space horizons.
4) Too many or too few data. Most methods for situa-
tion awareness are data-driven approaches. However, on
one hand, due to the many variables and interactions, it is
difficult to obtain sufficient data to bu ild accurate model.
For example, suppose a system has a moderate of 10 di-
mensions with each dimension falling into an interval of
[0,1]. By first uniformly dividing each interval into ten
subintervals, the whole domain is equally divided into
1020 small cubes. Assume that the function value at a
fixed point in each cube is to be estimated and ten meas-
urements in each cube are adequate to get reliable esti-
mates. Then the total number of measurements on de-
mand is up to 1021, which is impossible to obtain in prac-
tice. This is the well-known curse of dimensionality issu e.
This situation is even worse for fault diagnosis where
faulty situations are often rare, while large portion of
data are normal data. On the other hand, many real-world
applications involve millions or billions of data records,
for example, in power system monitoring, the sampling
rate of portable devices for detecting the pulse discharge
in electricity facilities is often at 20MHz, which means
even in one second, tens of millions of records are logged
and need to be processed, and many off-the-shelf tech-
niques for data-driven modeling become computationally
impractical.
To deal with these challenges, this paper proposes to
use an incremental learning framework originally pro-
posed in [1] to facilitate real-time learning and knowl-
edge acquisition. In particular, th e details to implement a
wide area power monitoring system using synchrophasor
technology will be discussed, and initial results will be
presented.
2. The Incremental Learning Framework
For real-time learning and knowledge acquisition for
situation awareness in large-scale applications, He et al
[1-3] proposed an incremental learning scheme, as illus-
trated in Figure 2. The proposed framework has three
layers of organization and three directions of data flow.
In the following, details will be discussed on how to im-
plement such a framework.
Figure 2. Incremental learning framework [1-3].

K. LI ET AL.
964
Layer 1: Global situation generation using streams of
processed data transmitted from the distributed sensing
network with locally featured information.
This layer integrates all data streams from the distrib-
uted sensing network and generates a global view of the
situation using data fusion techniques, such as grid based
models, distributed probability network, and Bayesian
filtering technologies, etc. to deal with the integration of
spatial-temporal multi-modal d ata. It should b e noted that
the streams of data transmitted from different sensors or
local agents are often processed data by local sensor nodes
which often have some limited computation capacity.
To avoid heavy transmission traffic (often through
wireless and wired networks) and also to deal with data
imbalance, the data transmitted to the second layer are
often down sampled with larger time scale or are only
small segment of data of interest, while local computing
facilities (e.g. embedded systems) perform real-time ini-
tial data processing using e.g. filters or de-noising tech-
niques, and data analysis using e.g. fast classification and
fault diagnosis techniques to extract useful data segments
or features. This helps to reduce the amount of data
transmitted to the upper layer and thus enables more ef-
fective and efficient data fusion. Further, in this layer,
particular attentions should also be paid to the consis-
tence and completeness of the model to represent the
global situation.
Layer 2: Knowledge extraction and representation
based on the accumulation of previous experience.
This layer extracts patterns of attributes from the global
view generated from layer 1 and builds a global knowl-
edge base from the previous experience. The knowledge
representation in this layer depends on the specific ap-
plication, and they can be in the form of distribution
functions, fuzzy rules, statistic or deterministic global
models of the occurrence of certain situations or faults,
or other forms that can effectively represent high- level of
knowledge accumulated from previous experience.
Layer 3: Multiple hypotheses development by effec-
tive weight adjustments in the incremental learning life.
This layered learning architecture enables consideration
of all previous domain data and accumulates knowledge
from layer 2 to any future time instance without explicit
access to pr e v iously o bserved raw data.
In the following, details will be presented on how to
implement the scheme in building a wide-area monitor-
ing system for smart grid integrated with distributed ge-
nerators, often from renewables, using synchrophasor
technologies.
3. Automatic Anti-islanding Detection Using
Synchrophasors
To address the twin challenges of tackling climate change
and maintaining energy security, many countries are
committed to decarbonize their energy systems. As a
result, the renewable energy solution becomes an in-
creasingly attractive and important topic, and it contrib-
utes to the sustainable development of the society. The
development of hybrid power systems incorporating re-
newable sources represents a big step towards distributed
generation (DG) and smart grid [8]. Distributed genera-
tion concerns the generation and interconnection of elec-
tricity from many small-scale and geographically distrib-
uted energy sources to the main power utility. The en-
ergy sources include renewables such as wind, tidal and
wave, solar PV, and bio-fuels, etc, but also traditional
ones like diesel generators. Today, some networks fre-
quently operate with greater than 10% of their power
supplied by distributed generation, and Ireland has at
times operated with over 40 % of generation supplied by
wind energy [9]. High penetration of distributed genera-
tions has placed considerable impact on the power sys-
tem planning, scheduling, operation, control, protection,
and maintenance. Of particular interest here is the
islanding situation where a distributed generator is sup-
plying power to a location without power from the utility
being present.
Islanding can occur intentionally, bu t also unintention-
ally, for the latter case, this is often caused by an unex-
pected interruption to the utility supply in a region where
an embedded generator is operating in parallel to the u til-
ity grid. Islanding may pose a risk of damaging utility
plant and customer connected equipment, and it also
presents a danger to utility personnel working to restore
the utility supply and to the public as the utility is no
longer in control of power quality and earth ing. For these
reasons, islanded operation while connected to th e utility
network is generally forbidden. Methods of preventing
islanding are known as anti-islanding detection, and it
has become the most challenging and important aspect in
designing the electrical power system with cogenera-
tions.
An important technique in anti-islanding detection is
to reliably detect the power islanding condition, and the
two most common methods are Rate-of-Change -of-
Frequency (ROCOF) and Vector Shift which rely on a
power imbalance to detect islanding [8,10,11]. These
techniques can fail when the power imbalance may not
be large enough to activate the protection to trip the in-
ter-tie break to disconn ect the power island from the util-
ity, or an event may occur across the grid which is how-
ever misjudged by ROCOF or Vector Shift relay as an
islanding event, leading to the nuisance tripping which
can further cause cascade tripping, causing significant
economic and social loss. Therefore, to improve the reli-
able islanding condition detection has become an impor-
tant research topic.

K. LI ET AL. 965
3.1. Introduction to Automatic Anti-islanding
Detection Using Synchrophasors
Wide-area Synchrophasor technology provides the anti-
islanding detector at distributed generators with real-time
information, delivered using secure Internet Protocol
technologies, so that nuisance trip s may be avoided, sen-
sitivity is improved and cascade tripping can be pre-
vented [10-1 2].
In general, the pure sinusoidal waveform is commonly
represented as a phasor


/2 j
ri m
YYY Ae

 .
According to IEEE Standard C37.118.1-2011, this basic
concept is adapted as the representation of AC power
system sinusoidal signals. In wide-area Synchrophasor
technology, PMU phasor measurements are required to
be synchronized to UTC time with sufficient accuracy.
Laverty et al introduced synchrophasor for anti-island-
ing detection [11], and a prototype of the detector has
been tested. In the method, the thresholds the detector
should operate were dependent on analysis of the net-
work, and a system of phase monitoring stations has been
installed to acquire this data.
To achieve this, a reference signal is acquired from a
dependable utility site, for example a transmission sub-
station, a power station or a major node on the network
that would be considered to have a utility connection at
all times excluding extremely unusual circumstances.
This reference signal is acquired using Phasor Measure-
ment Unit (PMU) technology, meaning that the signal
will contain at minimum the voltage amplitude, fre-
quency and phase angle at a specific and accurate time.
The standard for representing Synchrophasors is IEEE
C37.118 [11,12]. The reference signal is transmitted to
local distributed generators around the power system, by
means of Internet Protocol (IP) telecommunications. This
may be via the Internet (public network) or via a secure
utility network. Internet Protocol is the preferred com-
munications protocol due to its flexibility and ease of
reconfiguration which become important should the gen-
erator be required to use a different reference site if the
first becomes unavailable due to a fault or maintenance.
The telecoms can be secured using IPSec tunneling,
which is an integral part of the IPv6 standard.
At the generator site, the reference signal is compared
to the Synchrophasor acquired at the generator terminals.
The time signatures of the Synchrophasors are aligned so
that phasors taken at the same instant are compared. The
phase difference between the generator and the reference
site are compared against known typical operating condi-
tions. When the phase between the generator and the
reference site becomes abnormally large, the generator is
considered to be islanded and will be disconnected from
the utility.
In principle, if at the moment islanding occurs the
power imbalance between the generator and the islanded
load is low than certain threshold, the generator may
continue operating at the same frequency as when grid
connected. However, natural variations in the grid fre-
quency will be enough to lead to a phase difference be-
tween the generator and the utility an d allow islanding to
be detected.
If the power imbalance is large, the frequency of the
generator will be different to that of the utility and is-
landing will be detected either by the frequency discrep-
ancy, or by the rapid swing of the generators phase angle.
Therefore, synchrophasor anti-islanding detector can
operate in two modes. Firstly, using the phase difference,
the detector will eventually detect that islanding has oc-
curred. After approximately 2 seconds, the generator is
out-of-sync with the utility supply and continuing to drift.
Depending on how wide the threshold for phase differ-
ence is set, the time delay may prove excessiv e. Alterna-
tively, the detector can use the rate of change of phase, or
the slip frequency of the generator. This is the rate at
which the generators phase slips against the utility with
respect to time.
3.2. Real-time Anti-islanding Detection using
Synchrophasors
While wide-area system monitoring using Synchrophasor
offers improved opportunities for automatic detection of
islanding conditions, the challenges include 1) a large
amount of data are collected from each node and major-
ity are normal data. Continuously transmitting large
amount of data from a number of local sensing nodes to
the core (host server or central co ntrol unit) will not help
with real-time situation awareness and power system
operation and control. 2) Current anti-islanding detection
technologies such as Rate-of-Change-of-Frequency (RO-
COF), Vector Shift, as well as phase and frequency
analysis using multiple reference signals all need careful
network and operation condition an alysis to set appropri-
ate thresholds. The difficulty is that the operation condi-
tions vary with time, detection based on current single
window of power system conditions may fail either to
detect the islanding condition or to cause nuisance trip-
ping. Therefore, the aforementioned incremental learning
framework provides a great potential in improving the
near real-time monitoring and detection performance.
To implement the proposed framework on wide-area
monitoring system using Synchrophasor technologies,
the distributed PMUs and the central unit are designed
with different functionalities.
1) PMUs at distributed sites
PMUs used in this monitoring system are based on the
OpenPMUs developed at Queen’s University Belfast
[11]. The OpenPMU can sample 6 channels (3 voltages,
3 currents) at 6.4 kHz, which gives 128 samples per cy-
cle at 50 Hz or 60 Hz frequency. The ADC starts acquisi-

K. LI ET AL.
966
tion on the rising edge of the external hardware sampling
trigger whose signal is a 50 Hz / 60 Hz square wave
which by means of a GPS receiver oscillates in phase
with Coordinated Universal Time (UTC). The GPS re-
ceiver used is a Garmin GPS-18x which outputs a TTL
level one-pulse-per-second (1PPS) signal, a 1 Hz square
wave disciplined and in phase with the transition of the
UTC second. Firmware on a PIC microcontroller oper-
ated as a fuzzy logic phase-locked-loop (PLL) which
multiplies the 1PPS b y the Synchrophasor repor ting rates
recommended in IEEE C37.118.1. The recommended
reporting ra tes are 10/25 /50 repor ts per second in a 50 H z
system, and 10/12/20/30/60 rep orts per second in a 60 Hz
system. The PIC microcontroller also provides a time
transfer mechanism to the Labview environment so that
Synchrophasors can be time coded with UTC derived
time.
At present, synchrophasors in OpenPMU are com-
puted using standard Windows PC from the analog
waveform data within the Labview environment. The
spectrum of nominally 4-cycles at line frequency is
found using FFT. The three d ominan t frequen cy bands of
the spectrum are used in an iterative curve fitting algo-
rithm. The frequency, phase and amplitude of the meas-
ured waveform are determined from the parameters of
the synthesized waveform generated during curve fitting.
Then, estimated Synchrophasor parameters are exported
from Labview in CSV format, which contains the time
code, amplitude, frequency and phase angle in ASCII
strings representing decimal numbers. The CSV string is
transmitted by Labview by means of UDP/IP (User Da-
tagram Protocol over Internet Protocol) to a destination
IP address and port number.
To support the incremental learning based on the
afore-mentioned method, additional functionalities are
needed to integrate into the existing OpenPMU: a) The
PMUs as local agents should be able to communicate
with other PMUs, such as for reference signals from a
utility dependent site of close proximity; b) Additional
pre-processing step is added, including filtering and out-
liers removal. c) Fast anomalies detection using ROCOF,
Vector Shift, linear PCA, and dynamic PCA, etc. Once
anomalies are detected, the corresponding data segment
will be transmitted from the PMU to the core (central
unit). If no abnormal situation is detected, then the data
transmitted to the core will be further down-sampled
significantly to reduce the overall data tran smission load.
2) Central unit. At present, the central unit (server) at
Queen’s University is only used for data storage for
post-event analysis. To implement the proposed incre-
mental learning scheme, it is used as the core of the mon-
itoring system. Basically, the central unit in the system
collects all data transmitted from the PMUs, and builds
an overall system view using distributed temporal-spatial
model. It also uses advanced fault detection methods to
monitor the whole system operation, such as PCA tech-
niques (linear, dynamic, kernel based, nonlinear), model
based, and support vector machine (SVM), etc. In this
paper, we use linear PCA to detect islanding situations.
Further, various islanding modes and conditions are re-
corded, accumulated, and used to build a fuzzy system
for fast inference. Thus, knowledge and experience are
accumulated, and the incremental learning framework is
implemented.
Given the above design guidelines, a schematic of the
wide-area system monitoring network using Synchro-
phasor is shown in Figure 3. The original network of
PMUs has already been deployed across the Great Brit-
ain and Ireland electrical networks. This includes four
PMUs in southern England, one in Manchester, one in
Tealing, five on the Orkn ey islands, and two in Shetland.
Additional PMUs operate on the Irish network providing
similar data.
Data is transmitted to a central unit (server) in the host
organization QUB in an ASCII plain text format and
stored and for further processing and analysis, such as for
post analysis of events, determination of suitable anti-
islanding relay event thresholds and online simulation of
anti-islanding relays.
3.3. Preliminary Results
Three PMUs (PMU-1, PMU-4, PMU-5) in the network
captured one data set containing a frequency dip caused
by a 1 GW loss in bulk generation as well as an islanding
event occurred in response to the frequency dip. In the
following, we show how the proposed framework is im-
plemented.
Figures 4, 5 and 6 show that three PMUs (PMU-1,
PMU-4, PMU-5) at three different sites simultaneously
Figure 3. Synchrophasor Anti -Islanding detection scheme.

K. LI ET AL. 967
Figure 4. Frequenc y dip captured by PMU-1.
Figure 5. Frequenc y dip captured by PMU-4.
Figure 6. Frequenc y dip captured by PMU-5.
captured abnormal dip in frequency during a day. Fig-
ures 7, 8 and 9 show the ROCOF results, where the fre-
quency dip lasted for 5 seconds. The detection of fre-
quency dips using ROCOF in local PMUs can be fast
Figure 7. ROCOF results from PMU-1.
Figure 8. ROCOF results from PMU-4.
Figure 9. ROCOF results from PMU-5.
enough. In our lab simulatio n, it only u sed aroun d 2.3 ms
to process 5 samples, with a Dell PC with Intel(R)
Core(TM) i5-2400 CPU @ 3.10 GHz, RAM 4.00 GB and

K. LI ET AL.
968
with a 32-bit Operating system.
Once the PMUs detect the significant change of fre-
quency, the relevant data segments are then transmitted
to the central unit from these PMUs, and PCA [13][14] is
used by the central unit to perform statistic analysis on
the islanding situation. Figure 10 shows the detection
results when PCA is used in the central unit, where par-
ticular high values indicating possible islanding event
occurred.
To further distinguish the potential islanding site, the
PCA based fault reconstruction method [14] is performed
in the central unit, and Figure 11 shows the fault recon-
struction results. While these lines in Figure 11 are
straight but the two sections of fluctuations on each of
them represent the response behavior after the frequency
dip occurs. The green line of PMU-4 is different from
PMU-1 and PMU-5 and with a relative high magnitude.
Figure 10. PCA analysis results.
Figure 11. Fault reconstruction using P CA model.
Figure 12. Normal operation conditions.
The frequency plots also show that PMU-4 was unsyn-
chronized with the entire grid after the two big frequency
changes, and PMU-4 signal kept generating power on its
own frequency for a while before resynchronization.
From previously accumulated experience, this case indi-
cates the islanding occurs on the site where PMU-4 is
installed. In terms of the processing time, the same PC
only used 73.5 ms to detect the islanding situation. Fi-
nally, Figure 12 shows the PCA analysis of the central
unit under normal operation conditions, which illustrates
that the system can reduce false alarms if no fault event
occurred.
In summary, this preliminary study has shown that the
proposed wide-area power system monitoring scheme
using synchrophasor technology can offer great potential
in fast capturing of islanding events while reducing the
number of false alarms and minimizing the amount of
data transmissions from distributed synchrophasors to the
central processing unit.
4. Conclusions
This paper has discussed the application and extension of
a recently proposed novel distributed adaptive real-time
learning framework for supporting wide area monitoring
of distributed power generation for anti-islanding protec-
tion. The framework comprises distributed agents (Open-
PMUs) for autonomous local operation, and incremental
learning is used to generate global dynamic view. Initial
results show that the proposed approach can correctly
identify islanding events, with the benefit of significantly
reduced amount of data transmitted through the network,
making use of the available though limited computing
capabilities in local agents. This has enabled the central
unit to focus on generating a global view for situation
awareness and a high-level knowledge database can be
established for real-time system operation and control. It

K. LI ET AL.
969
should be noted that this paper only presents an early
stage of the development of wide-area power monitoring
system using the proposed distributed incremental learn-
ing scheme, and future work will involve the develop-
ment of a comprehensive suite of technologies for each
PMU and for the central unit for different monitoring
scenarios, and assessment of the long-term benefit of
such scheme in real-time power system monitoring and
control.
5. Acknowledgements
This work was partially supported by Engineering and
Physical Sciences Research Council (EPSRC) under
grants EP/F021070/1, the Key Project of Science and
Technology Commission of Shanghai Municipality under
Grant No.11ZR1413100, National Natural Science
Foundation of China under Grants 61273040 and
Shanghai Rising-Star Program (12QA1401100).
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