T. O. Ting

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.

Hybrid Metaheuristic Algorithms: Past, Present, and Future

Hybrid algorithms play a prominent role in improving the search capability of algorithms. Hybridization aims to combine the advantages of each algorithm to form a hybrid algorithm, while simultaneously trying to minimize any substantial disadvantage. In general, the outcome of hybridization can usually make some improvements in terms of either computational speed or accuracy. This chapter surveys recent advances in the area of hybridizing different algorithms. Based on this survey, some crucial recommendations are suggested for further development of hybrid algorithms.

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.

Exponential inertia weight for particle swarm optimization

The exponential inertia weight is proposed in this work aiming to improve the search quality of Particle Swarm Optimization (PSO) algorithm. This idea is based on the adaptive crossover rate used in Differential Evolution (DE) algorithm. The same formula is adopted and applied to inertia weight, w. We further investigate the characteristics of the adaptive w graphically and careful analysis showed that there exists two important parameters in the equation for adaptive w; one acting as the local attractor and the other as the global attractor. The 23 benchmark problems are adopted as test bed in this study; consisting of both high and low dimensional problems. Simulation results showed that the proposed method achieved significant improvement compared to the linearly decreasing method technique that is used widely in literature.

A hybrid MPPT method for Photovoltaic systems via estimation and revision method

Maximum Power Point Tracking (MPPT) methods can be classified into direct and indirect approaches. They are used to improve the efficiency of power conversion in Photovoltaic (PV) systems. However, a review of present literature implies that the indirect methods never produce accurate results. Meanwhile, the conventional direct Perturb and Observe (P&O) method has two problems: oscillations at steady state and slow dynamic response under changing environment conditions. Estimation and Revision (ER) method is proposed in this paper to overcome these limitations by the alternative use of MPP estimation and MPP revision process. The efficiency of the ER method is verified in an MPPT system implemented with a specific DC-DC converter and an adopted PV module.

Computational Intelligence and Metaheuristic Algorithms with Applications

Nature-inspired metaheuristic algorithms have become powerful and popular in computational intelligence and many applications. There are some important developments in recent years, and this special issue aims to provide a timely review of such developments, including ant colony optimization, bat algorithm, cuckoo search, particle swarm optimization, genetic algorithms, support vector machine, neural networks, and others. In addition, these algorithms have been applied in a diverse range of applications, and some of these latest applications are also summarized here. Computational intelligence and metaheuristic algorithms have become increasingly popular in computer science, artificial intelligence, machine learning, engineering design, data mining, image processing, and data-intensive applications. Most algorithms in computational intelligence and optimization are based on swarm intelligence (SI) [1, 2]. For example, both particle swarm optimization [1] and cuckoo search [3] have attracted much attention in science and engineering. They both can effectively deal with continuous problems [2] and combinatorial problems [4]. These algorithms are very different from the conventional evolutionary algorithms such as genetic algorithms and simulated annealing [5, 6] and other heuristics [7]. Many new optimization algorithms are based on the so-called swarm intelligence (SI) with diverse characteristics in mimicking natural systems [1, 2]. Consequently, different algorithms may have different features and thus may behave differently, even with different efficiencies. However, It still lacks in-depth understanding why these algorithms work well and exactly under what conditions, though there were some good studies that may provide insight into algorithms [2, 8]. This special issue focuses on the recent developments of SI-based metaheuristic algorithms and their diverse applications as well as theoretical studies. Therefore, this paper is organized as follows. Section 2 provides an introduction and comparison of the so-called infinite monkey theorem and metaheuristics, followed by the brief review of computational intelligence and metaheuristics in Section 3. Then, Section 4 touches briefly the state-of-the-art developments, and finally, Section 5 provides some open problems about some key issues concerning computational intelligence and metaheuristics.

Analysis of quality-of-service aware orthogonal frequency division multiple access system considering energy efficiency

The increasing demand of high-speed and secure wireless broadband networks has generated significant interests in the optimisation and the analysis of energy-efficiency in orthogonal frequency division multiple access (OFDMA) system. In this study, the authors present an in-depth mathematical analysis of the maximisation of energy-efficiency by taking into consideration the quality of service (QoS). By using optimality conditions, they have shown that the optimal solutions can be obtained analytically. Furthermore, it has been proved in this study that this optimisation problem is strictly concave with the existence of a global maximum. Case studies with multiple subchannels validate the consistency of numerical results with the results obtained from derivative-free method like genetic algorithm. Graphical illustrations also validate and confirm the numerical values obtained from the mathematical analysis. Therefore the solution is optimal with respect to the OFDMA model adopted in this study. The authors proposed approach can be used for practical applications because of its simplicity and efficacy with QoS guaranteed for efficient energy consumption.

Multicores and GPU utilization in parallel swarm algorithm for parameter estimation of photovoltaic cell model

HighlightsBetter estimation of parameters, on two models.In this work here, we successfully identified the relevant parameters of two photovoltaic models. To prove the efficacy of the proposed method, we included a comparison study.Utilization of multicores and GPU facilities.We have implemented the parallel swarm algorithm utilizing the multicores and GPU computing capabilities of a computer. Bio-inspired metaheuristic algorithms have been widely applied in estimating the extrinsic parameters of a photovoltaic (PV) model. These methods are capable of handling the nonlinearity of objective functions whose derivatives are often not defined as well. However, these algorithms normally utilize multiple agents in the search process, and thus the solution process is extremely time-consuming. In this regard, it takes much time to search the possible solutions in the whole search domain by sequential computing devices. To overcome the limitation of sequential computing devices, parallel swarm algorithm (PSA) is proposed in this work with the aim of extracting and estimating the parameters of the PV cell model by utilizing the power of multicore central processing unit (CPU) and graphical processing unit (GPU). We implement this PSA in the OpenCL platform with the execution on Nvidia multi-core GPUs. Simulation results demonstrate that the proposed method significantly increases the computational speed in comparison to the sequential algorithm, which means that given a time requirement, the accuracy of a solution from the PSA can be improved compared to that from the sequential one by using a larger swarm size.

EEG Eye State Identification Using Incremental Attribute Learning with Time-Series Classification

Eye state identification is a kind of common time-series classification problem which is also a hot spot in recent research. Electroencephalography (EEG) is widely used in eye state classification to detect humans cognition state. Previous research has validated the feasibility of machine learning and statistical approaches for EEG eye state classification. This paper aims to propose a novel approach for EEG eye state identification using incremental attribute learning (IAL) based on neural networks. IAL is a novel machine learning strategy which gradually imports and trains features one by one. Previous studies have verified that such an approach is applicable for solving a number of pattern recognition problems. However, in these previous works, little research on IAL focused on its application to time-series problems. Therefore, it is still unknown whether IAL can be employed to cope with time-series problems like EEG eye state classification. Experimental results in this study demonstrates that, with proper feature extraction and feature ordering, IAL can not only efficiently cope with time-series classification problems, but also exhibit better classification performance in terms of classification error rates in comparison with conventional and some other approaches.