Meteorological Data Study and Forecasting Using Particle Swarm Optimization Algorithm
Weather systems use enormously complex
combinations of numerical tools for study and forecasting.
Unfortunately, due to phenomena in the world climate, such
as the greenhouse effect, classical models may become
insufficient mostly because they lack adaptation. Therefore,
the weather forecast problem is matched for heuristic
approaches, such as Evolutionary Algorithms.
Experimentation with heuristic methods like Particle Swarm
Optimization (PSO) algorithm can lead to the development of
new insights or promising models that can be fine tuned with
more focused techniques. This paper describes a PSO
approach for analysis and prediction of data and provides
experimental results of the aforementioned method on realworld
meteorological time series.
Weather, Climate, PSO, Prediction, Meteorological
Harmonic Analysis and Performance Improvement of a Wind Energy Conversions System with Double Output Induction Generator
Wind turbines with double output induction
generators can operate at variable speed permitting conversion
efficiency maximization over a wide range of wind velocities. This
paper presents the performance analysis of a wind driven double
output induction generator (DOIG) operating at varying shafts speed.
A periodic transient state analysis of DOIG equipped with two
converters is carried out using a hybrid induction machine model.
This paper simulates the harmonic content of waveforms in various
points of drive at different speeds, based on the hybrid model
(dqabc). Then the sinusoidal and trapezoidal pulse-width–modulation
control techniques are used in order to improve the power factor of
the machine and to weaken the injected low order harmonics to the
supply. Based on the frequency spectrum, total harmonics distortion,
distortion factor and power factor. Finally advantages of sinusoidal
and trapezoidal pulse width modulation techniques are compared.
DOIG, Harmonic Analysis, Wind.
Using Genetic Algorithm for Distributed Generation Allocation to Reduce Losses and Improve Voltage Profile
This paper presents a method for the optimal
allocation of Distributed generation in distribution systems. In this
paper, our aim would be optimal distributed generation allocation for
voltage profile improvement and loss reduction in distribution
network. Genetic Algorithm (GA) was used as the solving tool,
which referring two determined aim; the problem is defined and
objective function is introduced. Considering to fitness values
sensitivity in genetic algorithm process, there is needed to apply load
flow for decision-making. Load flow algorithm is combined
appropriately with GA, till access to acceptable results of this
operation. We used MATPOWER package for load flow algorithm
and composed it with our Genetic Algorithm. The suggested method
is programmed under MATLAB software and applied ETAP
software for evaluating of results correctness. It was implemented on
part of Tehran electricity distributing grid. The resulting operation of
this method on some testing system is illuminated improvement of
voltage profile and loss reduction indexes.
Distributed Generation, Allocation, Voltage Profile,losses, Genetic Algorithm.
Adaptive PID Controller based on Reinforcement Learning for Wind Turbine Control
A self tuning PID control strategy using reinforcement
learning is proposed in this paper to deal with the control of wind
energy conversion systems (WECS). Actor-Critic learning is used to
tune PID parameters in an adaptive way by taking advantage of the
model-free and on-line learning properties of reinforcement learning
effectively. In order to reduce the demand of storage space and to
improve the learning efficiency, a single RBF neural network is used
to approximate the policy function of Actor and the value function of
Critic simultaneously. The inputs of RBF network are the system
error, as well as the first and the second-order differences of error.
The Actor can realize the mapping from the system state to PID
parameters, while the Critic evaluates the outputs of the Actor and
produces TD error. Based on TD error performance index and
gradient descent method, the updating rules of RBF kernel function
and network weights were given. Simulation results show that the
proposed controller is efficient for WECS and it is perfectly
adaptable and strongly robust, which is better than that of a
conventional PID controller.
Wind energy conversion systems, reinforcementlearning; Actor-Critic learning; adaptive PID control; RBF network.
Load Modeling for Power Flow and Transient Stability Computer Studies at BAKHTAR Network
A method has been developed for preparing load
models for power flow and stability. The load modeling
(LOADMOD) computer software transforms data on load class mix,
composition, and characteristics into the from required for
commonly–used power flow and transient stability simulation
programs. Typical default data have been developed for load
composition and characteristics. This paper defines LOADMOD
software and describes the dynamic and static load modeling
techniques used in this software and results of initial testing for
BAKHTAR power system.
Load Modelling, Static, Power Flow.
A Predictive control based on Neural Network for Proton Exchange Membrane Fuel Cell
The Proton Exchange Membrane Fuel Cell (PEMFC)
control system has an important effect on operation of cell.
Traditional controllers couldn-t lead to acceptable responses because
of time- change, long- hysteresis, uncertainty, strong- coupling and
nonlinear characteristics of PEMFCs, so an intelligent or adaptive
controller is needed. In this paper a neural network predictive
controller have been designed to control the voltage of at the
presence of fluctuations of temperature. The results of
implementation of this designed NN Predictive controller on a
dynamic electrochemical model of a small size 5 KW, PEM fuel cell
have been simulated by MATLAB/SIMULINK.
PEMFC, Neural Network, Predictive Control..
Comparison between Batteries and Fuel Cells for Photovoltaic System Backup
Batteries and fuel cells contain a great potential to back up severe photovoltaic power fluctuations under inclement weather conditions. In this paper comparison between batteries and fuel cells is carried out in detail only for their PV power backup options, so their common attributes and different attributes is discussed. Then, the common and different attributes are compared; accordingly, the fuel cell is selected as the backup of Photovoltaic system. Finally, environmental evaluation of the selected hybrid plant was made in terms of plant-s land requirement and lifetime CO2 emissions, and then compared with that of the conventional fossilfuel power generating forms.
Fuel cell, PV cell, hybrid power plant.
A Wind Farm Reduced Order Model Using Integral Manifold Theory
Due to the increasing penetration of wind energy, it is
necessary to possess design tools that are able to simulate the impact
of these installations in utility grids. In order to provide a net
contribution to this issue a detailed wind park model has been
developed and is briefly presented. However, the computational costs
associated with the performance of such a detailed model in
describing the behavior of a wind park composed by a considerable
number of units may render its practical application very difficult. To
overcome this problem integral manifolds theory has been applied to
reduce the order of the detailed wind park model, and therefore
create the conditions for the development of a dynamic equivalent
which is able to retain the relevant dynamics with respect to the
existing a.c. system. In this paper integral manifold method has been
introduced for order reduction. Simulation results of the proposed
method represents that integral manifold method results fit the
detailed model results with a higher precision than singular
Wind, Reduced Order, Integral Manifold.
An Interval-Based Multi-Attribute Decision Making Approach for Electric Utility Resource Planning
This paper presents an interval-based multi-attribute
decision making (MADM) approach in support of the decision
process with imprecise information. The proposed decision
methodology is based on the model of linear additive utility function
but extends the problem formulation with the measure of composite
utility variance. A sample study concerning with the evaluation of
electric generation expansion strategies is provided showing how the
imprecise data may affect the choice toward the best solution and
how a set of alternatives, acceptable to the decision maker (DM),
may be identified with certain confidence.
Decision Making, Power Generation, ElectricUtilities, Resource Planning.
Dynamic Load Modeling for KHUZESTAN Power System Voltage Stability Studies
Based on the component approach, three kinds of
dynamic load models, including a single –motor model, a two-motor
model and composite load model have been developed for the
stability studies of Khuzestan power system. The study results are
presented in this paper. Voltage instability is a dynamic phenomenon
and therefore requires dynamic representation of the power system
components. Industrial loads contain a large fraction of induction
machines. Several models of different complexity are available for
the description investigations. This study evaluates the dynamic
performances of several dynamic load models in combination with
the dynamics of a load changing transformer. Case study is steel
industrial substation in Khuzestan power systems.
Dynamic load, modeling, Voltage Stability.
A Neuro Adaptive Control Strategy for Movable Power Source of Proton Exchange Membrane Fuel Cell Using Wavelets
Movable power sources of proton exchange
membrane fuel cells (PEMFC) are the important research done in the
current fuel cells (FC) field. The PEMFC system control influences
the cell performance greatly and it is a control system for industrial
complex problems, due to the imprecision, uncertainty and partial
truth and intrinsic nonlinear characteristics of PEMFCs. In this paper
an adaptive PI control strategy using neural network adaptive Morlet
wavelet for control is proposed. It is based on a single layer feed
forward neural networks with hidden nodes of adaptive morlet
wavelet functions controller and an infinite impulse response (IIR)
recurrent structure. The IIR is combined by cascading to the network
to provide double local structure resulting in improving speed of
learning. The proposed method is applied to a typical 1 KW PEMFC
system and the results show the proposed method has more accuracy
against to MLP (Multi Layer Perceptron) method.
Adaptive Control, Morlet Wavelets, PEMFC.
Adaptive PID Control of Wind Energy Conversion Systems Using RASP1 Mother Wavelet Basis Function Networks
In this paper a PID control strategy using neural
network adaptive RASP1 wavelet for WECS-s control is proposed.
It is based on single layer feedforward neural networks with hidden
nodes of adaptive RASP1 wavelet functions controller and an infinite
impulse response (IIR) recurrent structure. The IIR is combined by
cascading to the network to provide double local structure resulting
in improving speed of learning. This particular neuro PID controller
assumes a certain model structure to approximately identify the
system dynamics of the unknown plant (WECS-s) and generate the
control signal. The results are applied to a typical turbine/generator
pair, showing the feasibility of the proposed solution.
Adaptive PID Control, RASP1 Wavelets, WindEnergy Conversion Systems.
Decoupled, Reduced Order Model for Double Output Induction Generator Using Integral Manifolds and Iterative Separation Theory
In this paper presents a technique for developing the
computational efficiency in simulating double output induction
generators (DOIG) with two rotor circuits where stator transients are
to be included. Iterative decomposition is used to separate the flux–
Linkage equations into decoupled fast and slow subsystems, after
which the model order of the fast subsystems is reduced by
neglecting the heavily damped fast transients caused by the second
rotor circuit using integral manifolds theory. The two decoupled
subsystems along with the equation for the very slowly changing slip
constitute a three time-scale model for the machine which resulted in
increasing computational speed. Finally, the proposed method of
reduced order in this paper is compared with the other conventional
methods in linear and nonlinear modes and it is shown that this
method is better than the other methods regarding simulation
accuracy and speed.
DOIG, Iterative separation, Integral manifolds,Reduced order.