Abstract: In this paper, an improved technique for contingency
ranking using artificial neural network (ANN) is presented. The
proposed approach is based on multi-layer perceptrons trained by
backpropagation to contingency analysis. Severity indices in dynamic
stability assessment are presented. These indices are based on the
concept of coherency and three dot products of the system variables.
It is well known that some indices work better than others for a
particular power system. This paper along with test results using
several different systems, demonstrates that combination of indices
with ANN provides better ranking than a single index. The presented
results are obtained through the use of power system simulation
(PSS/E) and MATLAB 6.5 software.
Abstract: This paper presents a novel approach for tuning unified power flow controller (UPFC) based damping controller in order to enhance the damping of power system low frequency oscillations. The design problem of damping controller is formulated as an optimization problem according to the eigenvalue-based objective function which is solved using iteration particle swarm optimization (IPSO). The effectiveness of the proposed controller is demonstrated through eigenvalue analysis and nonlinear time-domain simulation studies under a wide range of loading conditions. The simulation study shows that the designed controller by IPSO performs better than CPSO in finding the solution. Moreover, the system performance analysis under different operating conditions show that the δE based controller is superior to the mB based controller.
Abstract: In this paper, a new K-means clustering based
approach for identification of voltage control areas is developed.
Voltage control areas are important for efficient reactive power
management in power systems operating under deregulated
environment. Although, voltage control areas are formed using
conventional hierarchical clustering based method, but the present
paper investigate the capability of K-means clustering for the
purpose of forming voltage control areas. The proposed method is
tested and compared for IEEE 14 bus and IEEE 30 bus systems. The
results show that this K-means based method is competing with
conventional hierarchical approach
Abstract: Power flow (PF) study, which is performed to
determine the power system static states (voltage magnitudes and
voltage angles) at each bus to find the steady state operating
condition of a system, is very important and is the most frequently
carried out study by power utilities for power system planning,
operation and control. In this paper, a counterpropagation neural
network (CPNN) is proposed to solve power flow problem under
different loading/contingency conditions for computing bus voltage
magnitudes and angles of the power system. The counterpropagation
network uses a different mapping strategy namely
counterpropagation and provides a practical approach for
implementing a pattern mapping task, since learning is fast in this
network. The composition of the input variables for the proposed
neural network has been selected to emulate the solution process of a
conventional power flow program. The effectiveness of the proposed
CPNN based approach for solving power flow is demonstrated by
computation of bus voltage magnitudes and voltage angles for
different loading conditions and single line-outage contingencies in
IEEE 14-bus system.
Abstract: This paper presents Genetic Algorithm (GA) based
approach for the allocation of FACTS (Flexible AC Transmission
System) devices for the improvement of Power transfer capacity in an
interconnected Power System. The GA based approach is applied on
IEEE 30 BUS System. The system is reactively loaded starting from
base to 200% of base load. FACTS devices are installed in the
different locations of the power system and system performance is
noticed with and without FACTS devices. First, the locations, where
the FACTS devices to be placed is determined by calculating active
and reactive power flows in the lines. Genetic Algorithm is then
applied to find the amount of magnitudes of the FACTS devices. This
approach of GA based placement of FACTS devices is tremendous
beneficial both in terms of performance and economy is clearly
observed from the result obtained.
Abstract: This paper presents a new optimization technique based on quantum computing principles to solve a security constrained power system economic dispatch problem (SCED). The proposed technique is a population-based algorithm, which uses some quantum computing elements in coding and evolving groups of potential solutions to reach the optimum following a partially directed random approach. The SCED problem is formulated as a constrained optimization problem in a way that insures a secure-economic system operation. Real Coded Quantum-Inspired Evolution Algorithm (RQIEA) is then applied to solve the constrained optimization formulation. Simulation results of the proposed approach are compared with those reported in literature. The outcome is very encouraging and proves that RQIEA is very applicable for solving security constrained power system economic dispatch problem (SCED).
Abstract: The design of Automatic Generation Control (AGC) system plays a vital role in automation of power system. This paper proposes Hybrid Neuro Fuzzy (HNF) approach for AGC of two-area interconnected reheat thermal power system with the consideration of Generation Rate Constraint (GRC). The advantage of proposed controller is that it can handle the system non-linearities and at the same time the proposed approach is faster than conventional controllers. The performance of HNF controller has been compared with that of both conventional Proportional Integral (PI) controller as well as Fuzzy Logic Controller (FLC) both in the absence and presence of Generation Rate Constraint (GRC). System performance is examined considering disturbance in each area of interconnected power system.
Abstract: Direct search methods are evolutionary algorithms used to solve optimization problems. (DS) methods do not require any information about the gradient of the objective function at hand while searching for an optimum solution. One of such methods is Pattern Search (PS) algorithm. This paper presents a new approach based on a constrained pattern search algorithm to solve a security constrained power system economic dispatch problem (SCED). Operation of power systems demands a high degree of security to keep the system satisfactorily operating when subjected to disturbances, while and at the same time it is required to pay attention to the economic aspects. Pattern recognition technique is used first to assess dynamic security. Linear classifiers that determine the stability of electric power system are presented and added to other system stability and operational constraints. The problem is formulated as a constrained optimization problem in a way that insures a secure-economic system operation. Pattern search method is then applied to solve the constrained optimization formulation. In particular, the method is tested using one system. Simulation results of the proposed approach are compared with those reported in literature. The outcome is very encouraging and proves that pattern search (PS) is very applicable for solving security constrained power system economic dispatch problem (SCED).
Abstract: This paper presents the development of an electricity simulation model taking into account electrical network constraints, applied on the Belgian power system. The base of the model is optimizing an extensive Unit Commitment (UC) problem through the use of Mixed Integer Linear Programming (MILP). Electrical constraints are incorporated through the implementation of a DC load flow. The model encloses the Belgian power system in a 220 – 380 kV high voltage network (i.e., 93 power plants and 106 nodes). The model features the use of pumping storage facilities as well as the inclusion of spinning reserves in a single optimization process. Solution times of the model stay below reasonable values.
Abstract: In this paper variation of spot price and total profits of
the generating companies- through wholesale electricity trading are
discussed with and without Central Generating Stations (CGS) share
and seasonal variations are also considered. It demonstrates how
proper analysis of generators- efficiencies and capabilities, types of
generators owned, fuel costs, transmission losses and settling price
variation using the solutions of Optimal Power Flow (OPF), can
allow companies to maximize overall revenue. It illustrates how
solutions of OPF can be used to maximize companies- revenue under
different scenarios. And is also extended to computation of Available
Transfer Capability (ATC) is very important to the transmission
system security and market forecasting. From these results it is
observed that how crucial it is for companies to plan their daily
operations and is certainly useful in an online environment of
deregulated power system. In this paper above tasks are demonstrated
on 124 bus real-life Indian utility power system of Andhra Pradesh
State Grid and results have been presented and analyzed.
Abstract: This paper proposes the application of a hierarchical fuzzy system (HFS) based on multi-input power system stabilizer (MPSS) and also Static Var Compensator (SVC) in multi-machine environment.The number of rules grows exponentially with the number of variables in a conventional fuzzy logic system. The proposed HFS method is developed to solve this problem. To reduce the number of rules the HFS consists of a number of low-dimensional fuzzy systems in a hierarchical structure. In fact, by using HFS the total number of involved rules increases only linearly with the number of input variables. In the MPSS, to have better efficiency an auxiliary signal of reactive power deviation (ΔQ) is added with ΔP+ Δω input type Power system stabilizer (PSS). Phasor model of SVC is described and used in this paper. The performances of MPSS, Conventional power system stabilizer (CPSS), hierarchical Fuzzy Multi-input Power System Stabilizer (HFMPSS) and the proposed method in damping inter-area mode of oscillation are examined in response to disturbances. By using digital simulations the comparative study is illustrated. It can be seen that the proposed PSS is performing satisfactorily within the whole range of disturbances.
Abstract: Photovoltaic power generation forecasting is an
important task in renewable energy power system planning and
operating. This paper explores the application of neural networks
(NN) to study the design of photovoltaic power generation
forecasting systems for one week ahead using weather databases
include the global irradiance, and temperature of Ghardaia city
(south of Algeria) using a data acquisition system. Simulations were
run and the results are discussed showing that neural networks
Technique is capable to decrease the photovoltaic power generation
forecasting error.
Abstract: Voltage flicker problems have long existed in several
of the distribution areas served by the Taiwan Power Company. In
the past, those research results indicating that the estimated ΔV10
value based on the conventional method is significantly smaller than
the survey value. This paper is used to study the relationship between
the voltage flicker problems and harmonic power variation for the
power system with electric arc furnaces. This investigation discussed
thought the effect of harmonic power fluctuation with flicker
estimate value. The method of field measurement, statistics and
simulation is used. The survey results demonstrate that 10 ΔV
estimate must account for the effect of harmonic power variation.
Abstract: This paper reviews the optimization available
transmission capability (ATC) of power systems using a device of
FACTS named SSSC equipped with energy storage devices. So that,
emplacement and improvement of parameters of SSSC will be
illustrated. Thus, voltage magnitude constraints of network buses,
line transient stability constraints and voltage breakdown constraints
are considered. To help the calculations, a comprehensive program in
DELPHI is provided, which is able to simulate and trace the
parameters of SSSC has been installed on a specific line.
Furthermore, the provided program is able to compute ATC, TTC
and maximum value of their enhancement after using SSSC.
Abstract: The main objective of this paper is a comparative
investigate in enhancement of damping power system oscillation via
coordinated design of the power system stabilizer (PSS) and static
synchronous series compensator (SSSC) and static synchronous
compensator (STATCOM). The design problem of FACTS-based
stabilizers is formulated as a GA based optimization problem. In this
paper eigenvalue analysis method is used on small signal stability of
single machine infinite bus (SMIB) system installed with SSSC and
STATCOM. The generator is equipped with a PSS. The proposed
stabilizers are tested on a weakly connected power system with
different disturbances and loading conditions. This aim is to enhance
both rotor angle and power system stability. The eigenvalue analysis
and non-linear simulation results are presented to show the effects of
these FACTS-based stabilizers and reveal that SSSC exhibits the best
effectiveness on damping power system oscillation.
Abstract: Static Var Compensator (SVC) is a shunt type FACTS
device which is used in power system primarily for the purpose of
voltage and reactive power control. In this paper, a fuzzy logic based
supplementary controller for Static Var Compensator (SVC) is
developed which is used for damping the rotor angle oscillations and
to improve the transient stability of the power system. Generator
speed and the electrical power are chosen as input signals for the
Fuzzy Logic Controller (FLC). The effectiveness and feasibility of
the proposed control is demonstrated with Single Machine Infinite
Bus (SMIB) system and multimachine system (WSCC System)
which show improvement over the use of a fixed parameter
controller.
Abstract: The security of power systems against malicious cyberphysical
data attacks becomes an important issue. The adversary
always attempts to manipulate the information structure of the power
system and inject malicious data to deviate state variables while
evading the existing detection techniques based on residual test. The
solutions proposed in the literature are capable of immunizing the
power system against false data injection but they might be too costly
and physically not practical in the expansive distribution network.
To this end, we define an algebraic condition for trustworthy power
system to evade malicious data injection. The proposed protection
scheme secures the power system by deterministically reconfiguring
the information structure and corresponding residual test. More
importantly, it does not require any physical effort in either microgrid
or network level. The identification scheme of finding meters being
attacked is proposed as well. Eventually, a well-known IEEE 30-bus
system is adopted to demonstrate the effectiveness of the proposed
schemes.
Abstract: The main objective of Automatic Generation Control (AGC) is to balance the total system generation against system load losses so that the desired frequency and power interchange with neighboring systems is maintained. Any mismatch between generation and demand causes the system frequency to deviate from its nominal value. Thus high frequency deviation may lead to system collapse. This necessitates a very fast and accurate controller to maintain the nominal system frequency. This paper deals with a novel approach of artificial intelligence (AI) technique called Hybrid Neuro-Fuzzy (HNF) approach for an (AGC). The advantage of this controller is that it can handle the non-linearities at the same time it is faster than other conventional controllers. The effectiveness of the proposed controller in increasing the damping of local and inter area modes of oscillation is demonstrated in a two area interconnected power system. The result shows that intelligent controller is having improved dynamic response and at the same time faster than conventional controller.
Abstract: The identification and elimination of bad
measurements is one of the basic functions of a robust state estimator
as bad data have the effect of corrupting the results of state
estimation according to the popular weighted least squares method.
However this is a difficult problem to handle especially when dealing
with multiple errors from the interactive conforming type. In this
paper, a self adaptive genetic based algorithm is proposed. The
algorithm utilizes the results of the classical linearized normal
residuals approach to tune the genetic operators thus instead of
making a randomized search throughout the whole search space it is
more likely to be a directed search thus the optimum solution is
obtained at very early stages(maximum of 5 generations). The
algorithm utilizes the accumulating databases of already computed
cases to reduce the computational burden to minimum. Tests are
conducted with reference to the standard IEEE test systems. Test
results are very promising.
Abstract: This paper proposes a Particle Swarm Optimization
(PSO) based technique for the optimal allocation of Distributed
Generation (DG) units in the power systems. In this paper our aim is
to decide optimal number, type, size and location of DG units for
voltage profile improvement and power loss reduction in distribution
network. Two types of DGs are considered and the distribution load
flow is used to calculate exact loss. Load flow algorithm is combined
appropriately with PSO till access to acceptable results of this
operation. The suggested method is programmed under MATLAB
software. Test results indicate that PSO method can obtain better
results than the simple heuristic search method on the 30-bus and 33-
bus radial distribution systems. It can obtain maximum loss reduction
for each of two types of optimally placed multi-DGs. Moreover,
voltage profile improvement is achieved.