Sheng-Uei Guan

Parameter Estimation of Photovoltaic Models via Cuckoo Search

Since conventional methods are incapable of estimating the parameters of Photovoltaic (PV) models with high accuracy, bioinspired algorithms have attracted significant attention in the last decade. Cuckoo Search (CS) is invented based on the inspiration of brood parasitic behavior of some cuckoo species in combination with the Levy flight behavior. In this paper, a CS-based parameter estimation method is proposed to extract the parameters of single-diode models for commercial PV generators. Simulation results and experimental data show that the CS algorithm is capable of obtaining all the parameters with extremely high accuracy, depicted by a low Root-Mean-Squared-Error (RMSE) value. The proposed method outperforms other algorithms applied in this study.

Approximate Single-Diode Photovoltaic Model for Efficient I-V Characteristics Estimation

Precise photovoltaic (PV) behavior models are normally described by nonlinear analytical equations. To solve such equations, it is necessary to use iterative procedures. Aiming to make the computation easier, this paper proposes an approximate single-diode PV model that enables high-speed predictions for the electrical characteristics of commercial PV modules. Based on the experimental data, statistical analysis is conducted to validate the approximate model. Simulation results show that the calculated current-voltage (I-V) characteristics fit the measured data with high accuracy. Furthermore, compared with the existing modeling methods, the proposed model reduces the simulation time by approximately 30% in this work.

DEM: Direct Estimation Method for Photovoltaic Maximum Power Point Tracking

Abstract Since the electrical characteristics of a Photovoltaic (PV) Module vary with the changing atmospheric conditions, re- searchers have shown an increasing interest in Maximum Power Point Tracking (MPPT) approaches. This paper presents a direct Maximum Power Point (MPP) estimation method derived from the mathematical expressions of the Current-Voltage (I-V) characteristics of a PV module. Simulation results demonstrate that the proposed approach is sufficiently accurate for practical applications.

Design, analysis, tools and applications for programmable high-speed and power-aware 4G processors

Data rate traffic and communication capacity demand have been increased continuously. Therefore, a highly advanced 4G wireless system is required to meet a high demand for modern mobile terminals. For getting a further improvement for 4G communication systems, new paradigms of design, analysis tools and applications for 4G communication processors are necessary. In this paper, some of these new paradigms are discussed. Furthermore, a single-step discrete cosine transform truncation (DCTT) method is proposed for the modeling-simulation in signal integrity verification for high-speed communication processors.

Maximum Power Point Tracking (MPPT) via Weightless Swarm Algorithm (WSA) on cloudy days

The atmospheric conditions such as environmental temperature T and irradiance G are inputs to PhotoVoltaic (PV) arrays. Both inputs change the P-V characteristic curve and thereby shift the operating point from the Maximum Power Point (MPP). In order to adjust such a shifted point to optimal, a MPPT method based on our Weightless Swarm Algorithm (WSA) is proposed in this paper and implemented on the Solarex MSX60 PV module. The optimization model is formulated and presented in this paper. Simulation results show that conventional methods such as P&O and IncCond fail to track the MPP efficiently on cloudy days whereby irradiance G changes abruptly. On the other hand, our proposed WSA is able to overcome this limitation with a little extra processing time.

Weightless Swarm Algorithm (WSA) for Dynamic Optimization Problems

In this work the well-known Particle Swarm Optimization (PSO) algorithm is applied to some Dynamic Optimization Problems (DOPs). The PSO algorithm is improved by simplification instead of introducing additional strategies into the algorithm as done by many other researchers in the aim of improving an algorithm. Several parameters (w, V max , V min and c 2) are being excluded from the conventional PSO. This algorithm is called Weightless Swarm Algorithm (WSA) as the prominent parameter, inertia weight w does not exist in this proposed algorithm. Interestingly, WSA still works effectively via swapping strategy found from countless trials and errors. We then incorporate the proven clustering technique from literature into the framework of the algorithm to solve the six dynamic problems in literature. From the series of tabulated results, we proved that WSA is competitive as compared to PSO. As only one parameter exists in WSA, it is feasible to carry out parameter sensitivity to find the optimal acceleration coefficient, c 1 for each problem set.

A Novel Hybrid Algorithm for Function Optimization: Particle Swarm Assisted Incremental Evolution Strategy

Summary. This chapter presents a new algorithm for function optimization problems, particle swarm assisted incremental evolution strategy (PIES), which is designed for enhancing the performance of evolutionary computation techniques by evolving the input variables incrementally. The whole evolution consists of several phases and one more variable is focused in each phase. The number of phases is equal to the number of variables in maximum. Each phase is composed of two stages: In the single-variable evolution (SVE) stage, a population is evolved with respect to one independent variable in a series of cutting planes; in the multivariable evolving (MVE) stage, the initial population is formed by integration. The integration integrates the solutions found by the SVE stage in the current phase and the solutions found by the MVE stage in the last phase. Subsequently the population is evolved with respect to the incremented variable set in a series of cutting hyperplanes. To implement this incremental optimization, a combination of evolution strategy (ES) and particle swarm optimization (PSO) is used. ES is applied to searching optima in the cutting planes/hyperplanes, while PSO is applied to adjust the cutting planes (in SVE stages) or hyperplanes (in MVE stages). The experiment results show that PIES generally outperforms three other evolutionary algorithms, improved normal GA, PSO, and SADE_CERAF, in the sense that PIES can find solutions closer to the true optima both in the variable space and in the objective space.

High-speed area-efficient and power-aware multiplier design using approximate compressors along with bottom-up tree topology

Estimating arithmetic is a design paradigm for DSP hardware. By allowing structurally incomplete arithmetic circuits to occasionally perform imprecise calculations, higher performance can be achieved in many different electronic systems. By means of approximate compressor design and bottom-up tree topology, this paper presents a novel approach of implementing high-speed, area-efficient and power-aware multipliers. Experimental results are given to show the applicability and effectiveness of our proposed approach.

Ordered Incremental Multi-Objective Problem Solving Based on Genetic Algorithms

Many Multiple Objective Genetic Algorithms (MOGAs) have been designed to solve problems with multiple conflicting objectives. Incremental approach can be used to enhance the performance of various MOGAs, which was developed to evolve each objective incrementally. For example, by applying the incremental approach to normal MOGA, the obtained Incremental Multiple Objective Genetic Algorithm (IMOGA) outperforms state-of-the-art MOGAs, including Non-dominated Sorting Genetic Algorithm-II (NSGA-II), Strength Pareto Evolutionary Algorithm (SPEA) and Pareto Archived Evolution Strategy (PAES). However, there is still an open question: how to decide the order of the objectives handled by incremental algorithms? Due to their incremental nature, it is found that the ordering of objectives would influence the performance of these algorithms. In this paper, the ordering issue is investigated based on IMOGA, resulting in a novel objective ordering approach. The experimental results on benchmark problems showed that the proposed approach can help IMOGA reach its potential best performance.

Hierarchical Meta-Learning in Time Series Forecasting for Improved Interference-Less Machine Learning

The importance of an interference-less machine learning scheme in time series prediction is crucial, as an oversight can have a negative cumulative effect, especially when predicting many steps ahead of the currently available data. The on-going research on noise elimination in time series forecasting has led to a successful approach of decomposing the data sequence into component trends to identify noise-inducing information. The empirical mode decomposition method separates the time series/signal into a set of intrinsic mode functions ranging from high to low frequencies, which can be summed up to reconstruct the original data. The usual assumption that random noises are only contained in the high-frequency component has been shown not to be the case, as observed in our previous findings. The results from that experiment reveal that noise can be present in a low frequency component, and this motivates the newly-proposed algorithm. Additionally, to prevent the erosion of periodic trends and patterns within the series, we perform the learning of local and global trends separately in a hierarchical manner which succeeds in detecting and eliminating short/long term noise. The algorithm is tested on four datasets from financial market data and physical science data. The simulation results are compared with the conventional and state-of-the-art approaches for time series machine learning, such as the non-linear autoregressive neural network and the long short-term memory recurrent neural network, respectively. Statistically significant performance gains are recorded when the meta-learning algorithm for noise reduction is used in combination with these artificial neural networks. For time series data which cannot be decomposed into meaningful trends, applying the moving average method to create meta-information for guiding the learning process is still better than the traditional approach. Therefore, this new approach is applicable to the forecasting of time series with a low signal to noise ratio, with a potential to scale adequately in a multi-cluster system due to the parallelized nature of the algorithm.