Contingency analysis

1. XXX-X-XXXX-XXXX-X/XX/$XX.00 ©20XX IEEE
On Artificial Intelligence Approaches for
Contingency Analysis in Power System Security
Assessment
1, 2
Lili Wu, Jinfeng Gao
1
Department of Electrical Engineering
Zhengzhou University
Zhengzhou, China
wuli1988me@gmail.com
G. Kumar Venayagamoorthy
2
Real-Time Power and Intell. Systems Lab.
Clemson, USA
School of Engineering
University of Kwazulu-Natal
Durban 4041, South Africa
gkumar@ieee.org
Ronald G. Harley
School of Electrical and Computer
Engineering
Georgia Institute of Technology
Atlanta, USA
Deceased October 30, 2017
Abstract— This paper presents a review of artificial-
intelligence methodologies for contingency analysis for power
system security. In the planning process, offline artificial
intelligence methods can assist the operator with anticipatory
information useful to preserve the steady state security of the
system. In online operation stages, artificial intelligence
techniques have been proposed to speed up and improve the
accuracy of voltage-stability analysis and for contingency
selection, without requiring a traditional mathematical model of
the power grid. Furthermore, when the power grid is stressed,
artificial intelligence approaches to dynamic contingency analysis
are capable of empowering the operator to control the power
grid in a more reliable and rapid fashion.
Keywords—artificial intelligence, contingency analysis, online,
power system security, voltage stability analysis
I. INTRODUCTION
Artificial intelligence (AI) adopts nature-inspired
methodologies and algorithms for solving challenging
problems involving systems that behave intelligently. The
methodology has been adopted with enthusiasm on a number
of technical fields, including intelligent control, robotics,
speech and text recognition, genetic analysis, etc. [1] [2]. The
application of AI techniques in the security analysis of power
systems has experienced increasing momentum over the recent
decades because they have proven to be able to overcome
significant drawbacks of more traditional methods [3]. Given
the extensive level of power grid interconnections and the
penetration of intermittent energy resources, many of the
traditional static and deterministic power system problems
become dynamic and complex. Moreover, the transformation
of the electric power industry from a traditional power system
to a smart grid makes the power system become more
controllable and intelligent. Artificial intelligence techniques
are promising tools because they can provide effective
solutions to complex problems where more traditional
mathematical methods are ineffective or intractable.AI
technologies organized into four categories, namely, Artificial
Neural Network (ANN), Fuzzy Logic, Swarm Intelligence, and
Support Vector Machine (SVM).
Various applications of artificial intelligence techniques in
contingency analysis research reveal that this approach is
highly promising as tools for carrying out effective security
analysis of power systems. More significantly, some of the
inherent characteristics of AI, such as automated learning,
pattern recognition, fast mapping, and optimization
capabilities, make it suitable for online applications. This paper
reviews the state-of-the-art on the application of AI
methodologies for the analysis of power system security under
contingencies. The primary motivation for this study is that AI
is rapidly rising in relevance in this domain.
This paper is organized as follows. Section II introduces
AI-based approaches in contingency planning stage. Section III
focuses on reactive power and voltage stability assessment for
power systems. Section IV provides examples of AI
applications for contingency selection. Section V considers
different aspects of online power system security. Finally,
Section VI presents conclusions and suggests potentially
promising future directions.
II. AI IN THE CONTIGENCY PLANNNING STAGE
Contingency analysis is widely adopted over the entire life
circle of a power grid, to maintain safety operation of the
power system not only in the planning process but also in the
operation stage. In the design of an electrical network,
contingency analysis is an integral part of power system
planning. During the operation of a power system, voltage-
stability security, contingency selection, and online
contingency analysis are common concerns.
In the system planning stage, contingencies are normally
taken into consideration for the purpose of guaranteeing power
system reliability and security. Embedded generations (EGs)
deployment in a distribution network considering contingency
analysis is studied in [4]. Genetic algorithms (GA) are used to
determine the optimal location and size of EGs in a power
system. The resulting optimal installation of EGs is intended to
achieve good performance both before and after contingencies.
Contingency planning considering the system
reconfiguration is another topic of concern in power system
modeling. Mendes et al. [5] use Genetic Algorithms to design
the switching strategy for the electrical network
This work is supported by China Scholarship Council (No.2015070400
63) and National Natural Science Foundation of China (No. 51507155)
978-1-5386-7703-2/18/$31.00 ©2018 IEEE

2. reconfiguration under an N-1 contingency. The results
demonstrate that the method is particularly fast, and that it
respects all operational constraints in the electrical network.
Vale et al. [6] adopt an ANN to schedule distributed energy
resources in an isolated renewable power grid. Mixed-integer
linear programming is applied to obtain the ANN training set.
Although the tests carried out show that the approach is not
highly effective, it is nevertheless good enough for small
players. Ankalikiet al. [7] employ a multilayer feed forward
neural network (FFNN) for contingency operation planning,
using training data obtained from a Newton-Raphson (N-R)
method under different system topologies and at a series of
load levels. Backpropagation (BP) algorithm is used to train
multilayer FFNN, until the error reaches a prescribed tolerance.
The method is useful to assess the ability of a power system to
support a suite of peak demands in all foreseeable
contingencies.
III. AI IN REACTIVE POWER AND VOLTAGE STABILITY
CALCULATION
Because of power system deregulation and transmission
facilities expansion, electric power utilities are being forced to
operate close to the voltage stability limit. The calculation of
reactive power and voltage information under contingencies is
essential for power system stability and security.
Ozdemir and Lim [8] formulate post-outage reactive power
flow analysis as a nonlinear constrained optimization problem
of a bounded network to be solved by GA. Load bus voltage
magnitudes are revised by minimizing the effect of fictitious
sources on the network reactive power distribution using GA.
One advantage of this method is that it adopts and improves a
linear power flow model for the bounded network, which leads
to modest computational costs. The other important advantage
is that the application of GA improves the accuracy of the
adopted linear power-flow model. Rahiet al. [9] consider a
multilayer FFNN trained by BP learning for voltage stability
assessment. A voltage stability index is derived from the root
discriminant analysis of quadratic equations of distribution
load flow. This method is helpful in identifying the weak areas
of a system. However, it is difficult to scale up the problem as
the input patterns increase dramatically with problem size.
Bahmanyar and Karami [10] employ an multilayer perceptron
(MLP) ANN for voltage stability margin (VSM) calculation.
The Gram-Schmidt orthogonalization process [11] is adopted
for input set reduction. The MLP ANNs are comparably
trained using a BP algorithm with all the inputs and with a
reduced set of inputs from the New England 39-bus test
system. The input number reduces from 67 to 5, with only a
slight increase in the root mean-squared error. The method
performs better when scaling up to a large southern/eastern
Australian system. In that case, 11 training inputs are selected
from a set of 114 inputs to learn the voltage stability of the
large system. Under various system configurations and
operation conditions, the inputs-reduced MLP ANNs are able
to estimate the actual VSM with higher accuracy than when
using the full set of inputs. One disadvantage of the proposed
method is that the network training time for inputs reduced
increases slightly because limited inputs information is
available for training. However, compared with the traditional
continuation power flow (CPF) method [12], the proposed
input-reduction prevails due to its higher accuracy and
relatively shorter calculation time.
IV. EXAMPLES OF AI TECHNIQUES FOR CONTINGENCY
SELECTION
The development of AI makes it possible to solve the
contingency clustering and ranking problems with higher speed
and accuracy.
A. AI in Contingency Clustering
Matos et al. [13] use a fuzzy logic technique for multi-
contingency clustering. The well-known F-measure method, is
applied for feature selection to reduce set of variables that are
to be used later for classification. Bezdek’s Fuzzy C-Mean
Algorithm [14] and a trial-and-error procedure [15] are
simultaneously applied to select the most proper number of
security clusters in an automatic fashion. Two kinds of fuzzy
classification techniques are aggregated to achieve global
evaluations for the selected set of contingencies. This
classification approach only achieves feasibility for a given
topological configuration, as it is trained offline and then
applied online. The possibility of extending the proposed
method in an adaptive online fashion for all system topologies
is aspirational future work.
Particle Swarm Optimization (PSO) is applied to design a
pattern recognition system in the event of unforeseen
contingencies in [16]. A Fisher Linear Discriminant approach
[17] and a single ranking method are combined to select the
most dominant features. A linear first-order function, using
selected features as variables, is defined as the classifier of the
pattern recognition system. The weighting coefficients of the
classifier are trained using PSO. Although the PSO algorithm
achieves better classification accuracy with the least squares or
other equivalent methods, it nevertheless suffers from a high
training computational cost. Multi-Class SVM with SFS
(Sequential Forward Selection) feature selection is applied to
design a static security classifier for a power system in [18].
Several algorithms, such as Grid Search [19], PSO, GA, and
Differential Evolution (DE) are adopted to select parameters of
the proposed SVM classifier. Multi-Class SVM with DE based
parameter selection has proven to be of high accuracy with
enhanced performance.
B. AI in Contingency Ranking
Contingency ranking is low computational-cost approach
for identifying most severe outages in an electrical network. AI
techniques based on various performance indices (PIs) are
widely and commonly adopted for contingency ranking
purposes.
1) PI based contingency ranking
Sachan and Gupta propose a PI contingency selection
method in [20] based on radial-basis function (RBF) ANNs.
First a conventional N-R load flow is applied to obtain the
training data under several loading levels and generation
scenarios. The active power flow performance index, PIp, and
the reactive power flow performance index, PIv, are selected as
the outputs of the RBF ANN. The results show the PIs
calculated by trained RBF ANNs could evaluate the severity of

3. line outages accurately and rapidly. A cascade neural network
(CNN) contingency ranking approach is proposed in [21] using
BP trained MLP ANN, while adopting the same indices as
[20]. The study includes a very large number of scenarios
under various load patterns. However, both of the methods
discussed above have not pay much attention on feature
selection techniques for ANNs or training speed improvement
approaches.
Much research has been recently conducted on input
reduction and calculation-speed improvement for the training
of ANNs. Performance index values following a set of
contingencies are predicted by RBF ANNs fitted by linear
regression in [22], where a method based on mutual
information is proposed for feature selection. A BP trained
MLP is applied for automatic contingency selection in [23].
The power system is separated into different local areas
through a system decomposition method to reduce the input
dimension. In contrast with other methods, the PIp and PIv
indices are independently selected as the output of two
different MLP ANNs in each given area. This strategy reduces
the input number that can be used for training the MLP neural
network, while preserving the necessary information for
classification task. In [24], a CNN approach trained by a
modified BP algorithm is proposed by Singh and Srivastava for
contingency selection and ranking using PIp. N-R load flow is
used to generate the training patterns for different load flow
and topologies. The proposed CNN includes a three-layered
FFNN filter module and a four-layered FFNN ranking module.
The filter module is trained by a modified BP algorithm to
filter out critical contingencies from all contingency cases. The
selected critical contingencies are passed to the ranking module
for further ranking. To avoid excessively large input
dimensions for ANN training, angular distance clustering is
applied for input feature selection. Chauhan proposes a fast
real power contingency ranking method using a modified
counter propagation network with a neuro-fuzzy (NF) feature
selector [25]. The proposed NF selector effectively reduces the
size of the input pattern, which not only ensures savings in
training time but also improves the estimation accuracy and
reduces the execution time. Baghaee and Abedi employ a
weighted PI for contingency ranking of a power system in [26].
A fuzzy logic analytical hierarchy process is applied to adjust
the weighting factors of the PI calculation equations. The
appropriately adjusted and unequal PI values make the ranking
process become more accurate, realistic and more similar to the
natural behavior of existing power systems. Contingency
monitoring and evaluation using three types of neural networks
are proposed by García-Lagos and coauthors in [27]. The
training data is obtained by active (reactive) power flow for
different load patterns. An unsupervised Kohonen's linear self-
organizing map [28] is adopted to monitor the system’s state
evolution, which includes both the instantaneous system state
and the evolutionary trend followed by the system towards a
possibly severe operation point related to a particular
contingency. Both MLP and RBF are employed separately to
calculate the PIp and PIv indices for each contingency. The
RBF method proves to be not as promising as MLP for the
purpose of contingency ranking. The same authors later
improve their study through the application of principal
component analysis to reduce the input vector dimension [29].
The main disadvantage of PI based contingency ranking is
a masking effect that occurs when the index is used up to only
a first-order formulation. In [30], a three layer MLP ANN with
BP learning is applied for line flow contingency analysis. A
severity index and a margin index are defined to eliminate the
undesirable masking effect. A regression-based correlation
technique is adopted for ANN input and output feature
selection. Fuzzy-logic based PIs are employed to alleviate the
masking effect for contingency ranking in [31]. Each PI is
calculated for m = 1, 2 and 3 (where m is a positive integer of
performance index equation). Fuzzy logic is applied to
combine all three calculated PIs to obtain a single severity
index which can eliminate the masking effect. At an ensuing
state, the severity indices of bus voltage magnitude and
apparent power are united using fuzzy logic for further ranking
of contingencies.
A PI based multiple-contingencies selection method is
modeled as a combinatorial optimization problem solved by
GA in [32]. The PIp index in branches and the PIv metric in
buses are separately defined as fitness functions in two
different applications of GA. Fast decoupled load flow is
proposed as a basis for calculating both indices. Double
branch contingency tests carried out on two networks reveal
that the proposed method is suitable for online security
assessment. The approach proposed in [33] employs least
squares support vector machine (LS-SVM) with RBF Kernel
function to rank the contingencies and predict the severity level
for a power system using a voltage reactive performance index
(PIVQ). Tests on standard IEEE-39 bus power system show that
the proposed LS-SVM method is vastly superior to several
other AI approaches on classifier performance.
2) Voltage based contingency ranking
Voltage based contingency ranking is a low computational
cost venue for detecting the voltage security of the power
system. Multiple AI techniques have been applied for the
purpose of voltage contingency ranking.
Srivastava and her research group have contributed several
works on voltage contingency ranking using ANN along with
numerous improved methods. In [34], RBF ANN is applied for
fast voltage contingency screening using PIv as a metric. An
approach based on the class separability index and correlation
coefficient [35] is used to select appropriate training features
for the RBF ANN. A hybrid neural network, including filter
and ranking modules, is proposed for voltage contingency
screening and ranking in [36]. Both the filter module and
ranking module adopt three-layered FFNNs trained by a
modified BP algorithm. The masking effect of PIv is
considered using m = 4. In [37], a cascade fuzzy neural
network that includes filter and ranking modules is employed
for voltage contingency screening and ranking. A fuzzified
MLP trained by a BP algorithm is proposed for voltage
contingency ranking in [38]. The BP algorithm is modified by
an adaptive learning rate. In contrast to [37], the output vector
is defined only in terms of fuzzy membership values of PIv,
and there is no filter module included before the ranking step.
In [39], a contingency ranking method for voltage collapse is
proposed using parallel self-organizing hierarchical neural
network (PSHNN). A novel fuzzy PSHNN method, which
combines the advantages of [37] and [39], is proposed for

4. contingency ranking in [40]. Once trained, the resulting model
produces a fast and accurate ranking of contingencies for
various load patterns.
Voltage contingency selection is modeled as a nonlinear
constrained optimization problem solved by GA in [41]. Load
bus voltage magnitudes, first calculated from linear models, are
later revised by using GA to minimize all reactive power
mismatches. The accuracy of this GA optimization method is
better relative to that of the simple linear reactive power model.
The results have shown that the proposed method is very
effective for capturing all serious voltage magnitudes. A hybrid
decision tree (DT) approach is presented in [42] for fast voltage
contingency screening and ranking. The model combines a
filter and a ranking module. All the contingencies are first
presented to the DT filter module to reduce the burden on the
DT ranking module. In the design of the DT ranking module,
the K-class problem is converted into K two-class problems
which are trained separately. This is helpful in reducing the
problem size and improving the accuracy of the DT ranking
module. Once trained, the proposed hybrid DT method is able
to screen and rank the voltage contingencies under unknown
load conditions.
V. AI IN ONLINE CONTINGENCY ANALYSIS
Online contingency analysis seeks to identify component
failures and collapse cases in real-time using data reported by a
Supervisory control and data acquisition system.
A real-time ANN line outage contingency analysis is
proposed in [43] to recognize the vulnerable load buses in the
system. A variety load patterns are generated for training the
ANNs, and a reduced Jacobian matrix is applied to identify the
vulnerabilities. The trained ANN model has a superior
performance relative to that of offline ranking. However, real-
time contingency analysis is still not widely available because
of its very large computational costs, and consequent slow
execution time. A main trend followed to realize practical real-
time contingency analysis is online contingency selection.
Verma and Niazi [44] propose an FFNN online contingency
screening and ranking approach for the static security
assessment of a power system. This method provides fast
computation of contingency rankings that are in close
agreement with the N-R method, and can analyze unknown
load patterns. In [45] a FFNN pattern classifier is adopted to
study online dynamic security contingency screening. It is
found that composite indices extracted from feature selection
algorithm are highly correlated to the system stability, and are
only loosely correlated to system structure. This observation
allows the ANN classifier to perform accurately with a low
dimension feature space. The approach is suitable for extensive
power system operation conditions.
To avoid contingencies that may lead to power system
cascading failures, it is necessary to know the complete
voltages information in all nodes. Online voltage stability
prediction is an effective way to detect the security of the
power system. In [46], an ANN method is developed for online
voltage stability prediction. A BP trained MLP is adopted to
elucidate the linear relationship between the power system
operating state and the corresponding voltage stability margin.
The selected input features for ANN training are bus voltages
and phase angles. The P margin for each input data generated
from CPF is selected as target outputs for ANN training. The
input data is obtained from power-management units (PMUs).
The best locations for the installation of PMUs are approached
using an optimal technique. In [47], an FFNN predicted L-
index is applied for online voltage stability assessment and
monitoring. Once trained, the L-indices for all the buses in the
power system can be calculated using the trained network at
each monitoring instant. Moreover, the trained FFNN
algorithms allow the simultaneous assessment of the stability
margin and voltage profile for individual buses, the global
stability margin, as well as possible improvement measures of
the power system during both normal and contingent situations.
The integration of wind power, with its stochastic
characteristics, has accentuated the challenges for power
system stability assessment. Instead of using a conventional
worst-case scenario approach to represent the stochastic
uncertainty associated with wind, Hua et al. [48]adopt a five-
minute stochastic wind model derived from capturing the
characteristics of real wind power fluctuation data using a
Wiener process. To guarantee realizability for multi-machine
systems, the influence of a control device on the stability
boundary is also considered. The performance of the algorithm
has been illustrated on a simplified Australian dynamic system,
and results show the short term 5-min ahead probabilistic
prediction increases the total transfer capacity and the stability
margin. Besides these achievements, a concomitant
improvement in computational efficiency makes this short-
term online contingency assessment feasible for
implementation in multi-machine systems.
VI. CONCLUSIONS
This paper has mentioned numerous current artificial
intelligence methodologies applied for contingency analysis for
power system security assessment. The adoption of AI
experienced in relatively recent years provides opportunities to
overcome several shortcomings of traditional contingency
analysis methods. With the expected increase of possible
sources for cascading failures in power systems, multi
contingencies need to be considered with an ever-increasing
emphasis for the purpose of maintaining adequate power
system security levels in the future. In addition, online
contingency analysis tools that could supply outages
information to the control room in real-time are expected by
the authors to be a subject of critical interest for future
research.
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