Identification of unknown parameters of a single diode photovoltaic model using particle swarm optimization with binary constraints

Solar cell modelling primarily involves the formulation of the non-linear current versus voltage (I-V) curve. Determination of parameters plays an important role in solar cell accurate modelling. This paper presents an application of the hybrid Nelder-Mead simplex search method and modified Particle Swarm Optimization technique for identifying the parameters of solar cell and photovoltaic module models. The proposed technique is used to identify the unknown model parameters, namely, the generated photocurrent, saturation current, series resistance, shunt resistance, and ideality factor that govern the current-voltage relationship of a solar cell. A measurement data of 57 mm diameter commercial (R.T.C. France) silicon solar cell and a module consisting of 36 polycrystalline silicon cells (Photowatt-PWP 201) are used to test and verify the consistency of accurately estimating various parameters. Comparative study among different parameter estimation techniques is presented to demonstrate the effectiveness o...

Solar cell parameters identification using hybrid Nelder-Mead and modified particle swarm optimization

This paper explores the migration of adaptive filtering with swarm intelligence/evolutionary techniques employed in the field of electroencephalogram/event-related potential noise cancellation or extraction. A new approach is proposed in the form of controlled search space to stabilize the randomness of swarm intelligence techniques especially for the EEG signal. Swarm-based algorithms such as Particles Swarm Optimization, Artificial Bee Colony, and Cuckoo Optimization Algorithm with their variants are implemented to design optimized adaptive noise canceler. The proposed controlled search space technique is tested on each of the swarm intelligence techniques and is found to be more accurate and powerful. Adaptive noise canceler with traditional algorithms such as least-mean-square, normalized least-mean-square, and recursive least-mean-square algorithms are also implemented to compare the results. ERP signals such as simulated visual evoked potential, real visual evoked potential, and real sensorimotor evoked potential are used, due to their physiological importance in various EEG studies. Average computational time and shape measures of evolutionary techniques are observed 8.21E-01 sec and 1.73E-01, respectively. Though, traditional algorithms take negligible time consumption, but are unable to offer good shape preservation of ERP, noticed as average computational time and shape measure difference, 1.41E-02 sec and 2.60E+00, respectively.

EEG/ERP Adaptive Noise Canceller Design with Controlled Search Space (CSS) Approach in Cuckoo and Other Optimization Algorithms

Adaptive filtering of EEG/ERP through Bounded Range Artificial Bee Colony (BR-ABC) algorithm

In this paper, event related potential (ERP) generated due to hand movement is detected through the adaptive noise canceller (ANC) from the electroencephalogram (EEG) signals. ANCs are implemented with least mean square (LMS), normalized least mean square (NLMS), recursive least square (RLS) and evolutionary algorithms like particle swarm optimization (PSO), bacteria foraging optimization (BFO) techniques, genetic algorithm (GA) and artificial bee colony (ABC) optimization technique. Performance of this algorithm is evaluated in terms of signal to noise ratio (SNR) in dB, correlation between resultant and template ERP, and mean value. Testing of their noise attenuation capability is done on EEG contaminated with white noise at different SNR levels. A comparative study of the performance of conventional gradient based methods like LMS, NLMS and RLS, and swarm intelligence based PSO, BFO, GA and ABC techniques is made which reveals that PSO technique gives better performance in average cases of noisy environment with minimum computational complexity.

Adaptive filtering of EEG/ERP through noise cancellers using an improved PSO algorithm

A new modified particle swarm optimization algorithm for adaptive equalization

In recent years node localization in wireless sensor networks (WSNs) has attracted much attention due to the increase of usage and applications of WSNs. Many algorithms and techniques for locating sensor nodes have been proposed in the literature. A recent algorithm, which is based on a given set of pairwise distance estimates among nodes and the target, generates a map of node locations. This algorithm, known as FastMap, uses projections onto orthogonal hyperplanes to find the coordinates successively. In this paper, we analytically study the performance of the FastMap algorithm for 2D positioning. Moreover a modified version of FastMap with better performance is proposed and analyzed. The analysis shows that the optimum anchor placement should be at the edge of the network.

A modified fastmap algorithm for node localization in Wireless Sensor Networks

In this paper a weighted Fastmap (WFM) algorithm is proposed in which more than one pair of anchor nodes is used to evaluate the one-dimensional coordinates of the unknown nodes while in the original Fastmap (FM) algorithm only one pair of anchor nodes was employed. However, some nodes might be too far from the anchor nodes thus resulting in a high coordinate estimation error. This motivates the use of the WFM but at a slight increase in the computational complexity. The optimal WFM weights were determined via (constrained) minimization of the mean-squared error (MSE) of the estimated node coordinates. A simplification of the WFM is also introduced, called the averaged FM (AFM), where the complexity is reduced at the expense of the WFM performance. Both the WFM and AFM exhibit improved performance over the original FM algorithm.

A weighted Fastmap algorithm for wireless sensor nodes localization

In this paper, the idea of multi-split adaptive filter is applied to a Decision Feedback Equalizer (DFE). The feed-forward and feedback sections in the DFE are divided into parallel sub-filters by imposing separately the symmetry and antisymmetry conditions on the impulse responses of filters with the use of appropriate and distinct sets of linear constraints. The objective of this work is to obtain a new structure that gives better performance than the conventional structure and to study its convergence behavior. The performance of multi-split DFE is tested with different algorithms (LMS, RLS, and DCT-LMS) and compared with the performance of multi-split linear transversal Equalizer. We show also by simulation that the feedback section of DFE need not be split.

Convergence and Performance Analysis of Multi-Split Decision Feedback Equalizer

In recent years node localization in Wireless Sensor Networks (WSNs) has attracted much attention due to the increase of usage and applications of WSNs. Many algorithms and techniques for locating sens~r nodes .ha~e been proposed in the literature. A recent algonthm, whIch IS based on a given set of pairwise distance estimates among nodes and the target, generates a map ofnode locations. This algorithm, known as FastMap, uses proj ections onto orthogonal hyperplanes to find the coordinates successively. In this paper, we analytically study the performance o~ the FastMap algorithm for 2D positioning. Moreover a modIfied version of FastMap with better performance is proposed and analyzed. The analysis shows that the optimum anchor placement should be at the edge ofthe network.

https://ieeexplore.ieee.org/iel5/4634580/4641557/04641608.pdf

Waleed A. Saif

Papers

A new modified particle swarm optimization algorithm for adaptive equalization

A modified fastmap algorithm for node localization in Wireless Sensor Networks

A weighted Fastmap algorithm for wireless sensor nodes localization

Convergence and Performance Analysis of Multi-Split Decision Feedback Equalizer

A modified fastmapalgorithmfor node localizationin wireless sensornetworks