Modelling and Tracking of the Global Maximum Power Point in Shaded Solar PV Systems Using Computational Intelligence

Solar Photovoltaic (PV) systems are renewable energy sources that are environmentally friendly and are now widely used as a source of power generation. The power produced by solar PV varies with temperature, solar irradiance and load. This variation is nonlinear and it is difficult to predict how much power will be produced by the solar PV system. When the solar panel is directly coupled to the load, the power delivered is not optimal unless the load is properly matched to the PV system. In the case of a matched load the variation of irradiance and temperature will change this matching so a maximum peak power point tracking is therefore necessary for maximum efficiency. The complete PV system with a maximum power point tracking (MPPT) includes the solar panel array, MPPT algorithm and a DC-DC converter topology. Each subsystem is modelled and simulated in MATLAB/Simulink environment. The components are then combined with a DC resistive load to assess the overall performance when the PV panels are subjected to different weather conditions. The PV panel is modelled based on the Shockley diode equation and is used to predict the electrical characteristic curves under different irradiances and temperatures. In this dissertation, five MPPT algorithms were investigated. These algorithms include the standard Perturb and Observe (PnO), Incremental conductance (IC), Fuzzy Logic (FL), Particle Swarm Optimisation (PSO) and the Firefly Optimisation (FA). The algorithms are tested under different weather conditions including partial shading. The Particle Swarm and Firefly algorithm performed relatively the same and were chosen to be the best under all test conditions as they were the most efficient and were able to track the global maximum power point under partial shading. The PnO and IC performed well under static and varying irradiance, the PnO was seen to lose track of the MPP under rapid increasing irradiance. The PnO was tested under partial shaded conditions and it was seen that it is not reliable under these conditions. The Fuzzy logic performed better than the PnO and IC but was not as good as the PSO and FA. Since the fuzzy logic requires extensive tuning to converge it was not tested under partial shaded conditions. A DC-DC boost converter interface study between a DC source and the DC load are performed. This includes the steady state and dynamic analysis of the Boost converter. The converter is linearised about its steady state operating point and the transfer function is obtained using the state space averaged model. The simulation results of the complete PV system show that PSO and Firefly algorithm provided the best results under all weather conditions compared to other algorithms. They provided less oscillations at steady state, high efficiency in tracking (99%), quick convergence time at maximum power point and where able to track global power under partial shaded weather conditions for all partial shaded patterns. The Fuzzy logic performed well for what it was tested for which are static irradiance and rapid varying irradiance. The PnO and IC also performed relatively well but showed a lot of ringing at steady state. The PnO failed to track the MPP at certain instances under rapid increasing irradiance and the IC was shown to be unstable at low irradiance. The PnO was not reliable in tracking the global maximum power point under partial shaded conditions as it converged at local maximum power points for some partial shaded patterns

Self-Synchronization in Duty-cycled Internet of Things (IoT) Applications

In recent years, the networks of low-power devices have gained popularity. Typically these devices are wireless and interact to form large networks such as the Machine to Machine (M2M) networks, Internet of Things (IoT), Wearable Computing, and Wireless Sensor Networks. The collaboration among these devices is a key to achieving the full potential of these networks. A major problem in this field is to guarantee robust communication between elements while keeping the whole network energy efficient. In this paper, we introduce an extended and improved emergent broadcast slot (EBS) scheme, which facilitates collaboration for robust communication and is energy efficient. In the EBS, nodes communication unit remains in sleeping mode and are awake just to communicate. The EBS scheme is fully decentralized, that is, nodes coordinate their wake-up window in partially overlapped manner within each duty-cycle to avoid message collisions. We show the theoretical convergence behavior of the scheme, which is confirmed through real test-bed experimentation.Comment: 12 Pages, 11 Figures, Journa

Attraction and diffusion in nature-inspired optimization algorithms

Nature-inspired algorithms usually use some form of attraction and diffusion as a mechanism for exploitation and exploration. In this paper, we investigate the role of attraction and diffusion in algorithms and their ways in controlling the behaviour and performance of nature-inspired algorithms. We highlight different ways of the implementations of attraction in algorithms such as the firefly algorithm, charged system search, and the gravitational search algorithm. We also analyze diffusion mechanisms such as random walks for exploration in algorithms. It is clear that attraction can be an effective way for enhancing exploitation, while diffusion is a common way for exploration. Furthermore, we also discuss the role of parameter tuning and parameter control in modern metaheuristic algorithms, and then point out some key topics for further research

MPPT control of PV array based on PSO and adaptive controller

In general, photovoltaic (PV) array is not able to generate maximum power automatically, because some partial shading caused by trees, clouds, or buildings. Irradiation imperfections received by the PV array are overcome by applying maximum power point tracking (MPPT) to the output of the PV array. In order to overcome these partial shading problems, this system is employing particle swarm optimization (PSO) as MPPT method. It optimizes the output power of the solar PV array by Zeta converter. Output voltage of MPPT has high rate such that it needs stepdown device to regulate certain voltage. Constant voltage will be the input voltage of buck converter and controlled using adaptive PID. Adaptive control based on model reference adaptive control (MRAC) has design that almost same as the conventional PID structure and it has better performance in several conditions. The proposed system is expected to have stable output and able to perfectly emulate the response of the reference model. From the simulation results, it appears that PSO have high tracking accuracy and high tracking speed to reach maximum power of PV array. In the output voltage regulation, adaptive control does not have a stable error status and consistently follows the set point value

Attraction and diffusion in nature-inspired optimization algorithms

Nature-inspired algorithms usually use some form of attraction and diffusion as a mechanism for exploitation and exploration. In this paper, we investigate the role of attraction and diffusion in algorithms and their ways in controlling the behaviour and performance of nature-inspired algorithms. We highlight different ways of the implementations of attraction in algorithms such as the firefly algorithm, charged system search, and the gravitational search algorithm. We also analyze diffusion mechanisms such as random walks for exploration in algorithms. It is clear that attraction can be an effective way for enhancing exploitation, while diffusion is a common way for exploration. Furthermore, we also discuss the role of parameter tuning and parameter control in modern metaheuristic algorithms, and then point out some key topics for further research

Improving the Response Time of M-Learning and Cloud Computing Environments Using a Dominant Firefly Approach

© 2013 IEEE. Mobile learning (m-learning) is a relatively new technology that helps students learn and gain knowledge using the Internet and Cloud computing technologies. Cloud computing is one of the recent advancements in the computing field that makes Internet access easy to end users. Many Cloud services rely on Cloud users for mapping Cloud software using virtualization techniques. Usually, the Cloud users' requests from various terminals will cause heavy traffic or unbalanced loads at the Cloud data centers and associated Cloud servers. Thus, a Cloud load balancer that uses an efficient load balancing technique is needed in all the cloud servers. We propose a new meta-heuristic algorithm, named the dominant firefly algorithm, which optimizes load balancing of tasks among the multiple virtual machines in the Cloud server, thereby improving the response efficiency of Cloud servers that concomitantly enhances the accuracy of m-learning systems. Our methods and findings used to solve load imbalance issues in Cloud servers, which will enhance the experiences of m-learning users. Specifically, our findings such as Cloud-Structured Query Language (SQL), querying mechanism in mobile devices will ensure users receive their m-learning content without delay; additionally, our method will demonstrate that by applying an effective load balancing technique would improve the throughput and the response time in mobile and cloud environments

Social Algorithms

This article concerns the review of a special class of swarm intelligence based algorithms for solving optimization problems and these algorithms can be referred to as social algorithms. Social algorithms use multiple agents and the social interactions to design rules for algorithms so as to mimic certain successful characteristics of the social/biological systems such as ants, bees, bats, birds and animals.Comment: Encyclopedia of Complexity and Systems Science, 201

A Novel RSSI Prediction Using Imperialist Competition Algorithm (ICA), Radial Basis Function (RBF) and Firefly Algorithm (FFA) in Wireless Networks

This study aims to design a vertical handover prediction method to minimize unnecessary handovers for a mobile node (MN) during the vertical handover process. This relies on a novel method for the prediction of a received signal strength indicator (RSSI) referred to as IRBF-FFA, which is designed by utilizing the imperialist competition algorithm (ICA) to train the radial basis function (RBF), and by hybridizing with the firefly algorithm (FFA) to predict the optimal solution. The prediction accuracy of the proposed IRBF–FFA model was validated by comparing it to support vector machines (SVMs) and multilayer perceptron (MLP) models. In order to assess the model’s performance, we measured the coefficient of determination (R2), correlation coefficient (r), root mean square error (RMSE) and mean absolute percentage error (MAPE). The achieved results indicate that the IRBF–FFA model provides more precise predictions compared to different ANNs, namely, support vector machines (SVMs) and multilayer perceptron (MLP). The performance of the proposed model is analyzed through simulated and real-time RSSI measurements. The results also suggest that the IRBF–FFA model can be applied as an efficient technique for the accurate prediction of vertical handover

Simultaneous Parameter Tuning of PSS and Wide-Area POD in PV Plant using FPA

In future power grid scenario, large-scale renewable energy based on power plant will be one of the main generations. Among renewable based power plant type, large-scale photovoltaic (PV) plant becoming more popular as they could provide zero emission and sustainable energy. However, even though PV plant could contribute positive impact to the environment, they could also contribute negatively to the power system. Large-scale PV generation came with different dynamic and zero inertia characteristic due to the application of the power electronic devices. Furthermore, PV plant has also drawback in terms of intermittent power output due to the uncertainty of the sources. Those handicaps could deteriorate the stability performance of power system especially oscillatory stability. Adding power system stabilizer (PSS) to the systems is one of the approaches for handling the oscillatory stability. However, with integration of PV plant in the systems, PSS alone is not enough to handle the oscillatory problems coming from various sources such us from PV plant dynamic. Hence, utilizing wide-area power oscillation damping (POD) as PV plant supplementary controller is inevitable. Hence, this paper proposes simultaneous parameter tuning between PSS and wide-area POD in PV plant using flower pollination algorithm (FPA) as the optimization method. The two-area power system is employed to evaluate the performance of PSS and POD using FPA. From the results, it is found that the proposed method could enhance the oscillatory stability of the system