863 research outputs found
An improved optimization technique for estimation of solar photovoltaic parameters
The nonlinear current vs voltage (I-V) characteristics of solar PV make its modelling difficult. Optimization techniques are the best tool for identifying the parameters of nonlinear models. Even though, there are different optimization techniques used for parameter estimation of solar PV, still the best optimized results are not achieved to date. In this paper, Wind Driven Optimization (WDO) technique is proposed as the new method for identifying the parameters of solar PV. The accuracy and convergence time of the proposed method is compared with results of Pattern Search (PS), Genetic Algorithm (GA), and Simulated Annealing (SA) for single diode and double diode models of solar PV. Furthermore, for performance validation, the parameters obtained through WDO are compared with hybrid Bee Pollinator Flower Pollination Algorithm (BPFPA), Flower Pollination Algorithm (FPA), Generalized Oppositional Teaching Learning Based Optimization (GOTLBO), Artificial Bee Swarm Optimization (ABSO), and Harmony Search (HS). The obtained results clearly reveal that WDO algorithm can provide accurate optimized values with less number of iterations at different environmental conditions. Therefore, the WDO can be recommended as the best optimization algorithm for parameter estimation of solar PV
Development of Hybrid PS-FW GMPPT Algorithm for improving PV System Performance Under Partial Shading Conditions
A global maximum power point tracking (MPPT) algorithm hybrid based on Particle Swarm Fireworks (PS-FW) algorithm is proposed which is formed with Particle Swarm Optimization and Fireworks Algorithm. The algorithm tracks the global maximum power point (MPP) when conventional MPPT methods fail due to occurrence of partial shading conditions. With the applied strategies and operators, PS-FW algorithm obtains superior performances verified under simulation and experimental setup with multiple cases of shading patterns
Swarm Intelligence and Metaphorless Algorithms for Solving Nonlinear Equation Systems
The simplicity, flexibility, and ease of implementation have motivated the
use of population-based metaheuristic optimization algorithms. By focusing
on two classes of such algorithms, particle swarm optimization (PSO)
and the metaphorless Jaya algorithm, this thesis proposes to explore the
capacity of these algorithms and their respective variants to solve difficult
optimization problems, in particular systems of nonlinear equations converted
into nonlinear optimization problems. For a numerical comparison to be
made, the algorithms and their respective variants were implemented and
tested several times in order to achieve a large sample that could be used
to compare these approaches as well as find common methods that increase
the effectiveness and efficiency of the algorithms. One of the approaches
that was explored was dividing the solution search space into several
subspaces, iteratively running an optimization algorithm on each subspace,
and comparing those results to a greatly increased initial population. The
insights from these previous experiments were then used to create a new
hybrid approach to enhance the capabilities of the previous algorithms, which
was then compared to preexisting alternatives.A simplicidade, flexibilidade e facilidade de implementa¸c˜ao motivou o uso
de algoritmos metaheur´ısticos de optimiza¸c˜ao baseados em popula¸c˜oes.
Focando-se em dois destes algoritmos, optimiza¸c˜ao por exame de part´ıculas
(PSO) e no algoritmo Jaya, esta tese prop˜oe explorar a capacidade destes
algoritmos e respectivas variantes para resolver problemas de optimiza¸c˜ao de
dif´ıcil resolu¸c˜ao, em particular sistemas de equa¸c˜oes n˜ao lineares convertidos
em problemas de optimiza¸c˜ao n˜ao linear. Para que fosse poss´ıvel fazer
uma compara¸c˜ao num´erica, os algoritmos e respectivas variantes foram
implementados e testados v´arias vezes, de modo a que fosse obtida uma
amostra suficientemente grande de resultados que pudesse ser usada para
comparar as diferentes abordagens, assim como encontrar m´etodos que
melhorem a efic´acia e a eficiˆencia dos algoritmos. Uma das abordagens
exploradas foi a divis˜ao do espa¸co de procura em v´arios subespa¸cos,
iterativamente correndo um algoritmo de optimiza¸c˜ao em cada subespa¸co,
e comparar esses resultados a um grande aumento da popula¸c˜ao inicial, o
que melhora a qualidade da solu¸c˜ao, por´em com um custo computacional
acrescido. O conhecimento resultante dessas experiˆencias foi utilizado na
cria¸c˜ao de uma nova abordagem hibrida para melhorar as capacidades dos
algoritmos anteriores, a qual foi comparada a alternativas pr´e-existentes
A novel fireworks factor and improved elite strategy based on back propagation neural networks for state-of-charge estimation of lithium-ion batteries.
The state of charge (SOC) of Lithium-ion battery is one of the key parameters of the battery management system. In the SOC estimation algorithm, the Back Propagation (BP) neural network algorithm is easy to converge to the local optimal solution, which leads to the problem of low accuracy based on the BP network. It is proposed that the Fireworks Elite Genetic Algorithm (FEG-BP) is used to optimize the BP neural network, which can not only solve the problem of the traditional neural network algorithm that is easy to fall into the local maximum optimal solution but also solve the limitation of the traditional neural network algorithm. The searchability of the improved algorithm has been significantly enhanced, and the error has become smaller and the propagation speed is faster. Combining the experimental data of charging and discharging, the proposed FEG-BP neural network is compared with the traditional genetic neural network algorithm (GA-BP), and the results are analyzed. The results show that the standard BP neural network genetic algorithm predicts error within 7%, while FEG-BP reduces the error to within 3%
Modeling Dynamic Swarms
This paper proposes the problem of modeling video sequences of dynamic swarms
(DS). We define DS as a large layout of stochastically repetitive spatial
configurations of dynamic objects (swarm elements) whose motions exhibit local
spatiotemporal interdependency and stationarity, i.e., the motions are similar
in any small spatiotemporal neighborhood. Examples of DS abound in nature,
e.g., herds of animals and flocks of birds. To capture the local spatiotemporal
properties of the DS, we present a probabilistic model that learns both the
spatial layout of swarm elements and their joint dynamics that are modeled as
linear transformations. To this end, a spatiotemporal neighborhood is
associated with each swarm element, in which local stationarity is enforced
both spatially and temporally. We assume that the prior on the swarm dynamics
is distributed according to an MRF in both space and time. Embedding this model
in a MAP framework, we iterate between learning the spatial layout of the swarm
and its dynamics. We learn the swarm transformations using ICM, which iterates
between estimating these transformations and updating their distribution in the
spatiotemporal neighborhoods. We demonstrate the validity of our method by
conducting experiments on real video sequences. Real sequences of birds, geese,
robot swarms, and pedestrians evaluate the applicability of our model to real
world data.Comment: 11 pages, 17 figures, conference paper, computer visio
A Lite Fireworks Algorithm with Fractal Dimension Constraint for Feature Selection
As the use of robotics becomes more widespread, the huge amount of vision
data leads to a dramatic increase in data dimensionality. Although deep
learning methods can effectively process these high-dimensional vision data.
Due to the limitation of computational resources, some special scenarios still
rely on traditional machine learning methods. However, these high-dimensional
visual data lead to great challenges for traditional machine learning methods.
Therefore, we propose a Lite Fireworks Algorithm with Fractal Dimension
constraint for feature selection (LFWA+FD) and use it to solve the feature
selection problem driven by robot vision. The "LFWA+FD" focuses on searching
the ideal feature subset by simplifying the fireworks algorithm and
constraining the dimensionality of selected features by fractal dimensionality,
which in turn reduces the approximate features and reduces the noise in the
original data to improve the accuracy of the model. The comparative
experimental results of two publicly available datasets from UCI show that the
proposed method can effectively select a subset of features useful for model
inference and remove a large amount of noise noise present in the original data
to improve the performance.Comment: International Conference on Pharmaceutical Sciences 202
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