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

Modelling and Analysis of Photovoltaic System under Partially Shaded Conditions using Improved Harmony Search Algorithm

With the increasing penetration of solar electricity in residential, institutional and commercial centres around the globe, the phenomenon of partial shading (PS) in Photovoltaic (PV) power generation is gaining attention. Under Partial shading condition (PSC), cells that are shaded tends to have an equivalent current with cells that are unshaded in series-connection, due to this, the shaded cells operates in reverse bias and consequently becomes load and consumes the generated power. This causes a serious problem known as hotspot. This is characterized by the presence of excessive heat which consequently reduces the total generated power. Recently, researchers use the technique of bypass diodes across the PV cells so that the problem of partial shading can be reduced, but this solution taken alone, has made the nonlinearity and complexity of the system to increase. The shaded cells generate multiple peaks with only one global peak. Conventional Maximum Power Point Tracking (MPPT) algorithms do not differentiates the global peak from local peaks which may end up tracking local peak as global peak, this results in serious power loss. This paper seeks to solve this problem by modelling a PV system under PSC and through the application of Improved Harmony Search algorithm (IHSA) and variable step Perturb & Observe (P&O) to track the global peak instead of local peaks. Simulation was done in MATLAB/Simulink 2018a environment, and the results under standard test condition (STC) and PSC showed that the proposed IHSA had an improvement of 25%, 3.17% and 2.27%, 3.07% and 2.21%, 3.26% and 2.26% when compared with the improved particle swarm optimization (IPSO) under STC and PSC conditions respectively, which had a better advantage of minimizing power oscillation and improving the efficiency of the system, improved MPPT tracking, reduced error and a better tracking efficiency in both conditions. Keywords: MPPT, photovoltaic system, partial shading, tracking efficiency, Harmony search algorith

The Emergence of Canalization and Evolvability in an Open-Ended, Interactive Evolutionary System

Natural evolution has produced a tremendous diversity of functional organisms. Many believe an essential component of this process was the evolution of evolvability, whereby evolution speeds up its ability to innovate by generating a more adaptive pool of offspring. One hypothesized mechanism for evolvability is developmental canalization, wherein certain dimensions of variation become more likely to be traversed and others are prevented from being explored (e.g. offspring tend to have similarly sized legs, and mutations affect the length of both legs, not each leg individually). While ubiquitous in nature, canalization almost never evolves in computational simulations of evolution. Not only does that deprive us of in silico models in which to study the evolution of evolvability, but it also raises the question of which conditions give rise to this form of evolvability. Answering this question would shed light on why such evolvability emerged naturally and could accelerate engineering efforts to harness evolution to solve important engineering challenges. In this paper we reveal a unique system in which canalization did emerge in computational evolution. We document that genomes entrench certain dimensions of variation that were frequently explored during their evolutionary history. The genetic representation of these organisms also evolved to be highly modular and hierarchical, and we show that these organizational properties correlate with increased fitness. Interestingly, the type of computational evolutionary experiment that produced this evolvability was very different from traditional digital evolution in that there was no objective, suggesting that open-ended, divergent evolutionary processes may be necessary for the evolution of evolvability.Comment: SI can be found at: http://www.evolvingai.org/files/SI_0.zi

Digital controller design of an industrial sewing station using evolutionary algorithm

Mestrado em Engenharia IndustrialThe PID controller is one of the most used systems in the industry due to its simplicity and efficiency. However, the main problem to implement this controller is to set the correct gain to get the desired system response. The purpose of this thesis is to get the values of proportional, integral and derivative gain of the controller to obtain an adequate response to an industrial sewing workstation. The sewing station consists of a sewing machine installed into a cart, which is moved by an induction motor guided by a rail. When the user accelerates the sewing machine, the motor must move the car in the speed as the sewing machine sews the fabric. Thereby, the controller needs to send the correct control signal to the motor system, to move the car at the set-point speed. From this system, a mathematical model was obtained, that is used to develop the controller, which will be implemented in a digital microcontroller. The controller was designed using the Genetic Algorithm, using the step response characteristics (e.g. overshoot, rise time, steady-state error) and the control signal to calculate the fitness function. Also was used the Particle Swarm Optimization to compare the results. With the PID gains found as a result of the evolutionary algorithms, that with the better performance was implemented for tests on the company FactoryPlay, which design and produces inflatable structures for theme parks, based on the district of Bragança.O controlador PID é um dos sistemas mais utilizados na indústria devido à sua simplicidade e eficiência. No entanto, o principal problema para implementar este controlador é definir o ganho correto para obter a resposta do sistema desejada. O objetivo desta tese é obter os valores de ganho proporcional, integral e derivativo do controlador para obter uma resposta adequada a uma estação de trabalho de costura industrial. A estação de costura consiste em uma máquina de costura instalada em um carrinho, que é movida por um motor de indução guiado por um trilho. Quando o usuário acelera a máquina de costura, o motor deve mover o carro na velocidade em que a máquina de costura costura o tecido. Assim, o controlador precisa enviar o sinal de controle correto ao sistema do motor, para mover o carro na velocidade do ponto de ajuste. A partir desse sistema, foi obtido um modelo matemático, usado para desenvolver o controlador, que será implementado em um microcontrolador digital. O controlador foi projetado usando o algoritmo genético, usando as características da resposta ao degrau (e.g. overshoot, rise time, steady-state error) e o sinal de controle para calcular a função objetivo. Também foi utilizada a otimização por enxame de partículas para comparar os resultados. Utilizando os ganhos do PID encontrados como resultado dos algoritmos evolutivos, foi implementado para testes na empresa FactoryPlay, que projeta e produz estruturas infláveis, situada no distrito de Bragança

Parameter identification of induction motor

Numerous recent techniques of induction motor parameters calculating are hard to be done and expensive. Accurate calculations of the parameters of these motors would allow savings in different prospective like energy and cost. The major problem in calculating induction motor parameters is that it\u27s hard to measure output power precisely and without harm during the operation of the machines. It will be better to find other way to find out the output power with certain amount of inputs like input voltage and current.;Particle swarm optimization (PSO) and genetic algorithms (GAs) are often used to estimate quantities from limited information. They belong to a class of weak search procedures, that is, they do not provide the best solution, but one close to it. It is a randomized process in which follows the principles of evolution.;In this thesis genetic algorithm and partial swarm optimization are used to identify induction motor parameters. The inputs used to estimate electrical and mechanical parameters are measured stator voltages and currents. The estimated parameters compare well with the actual parameters. Data Acquisition (DAQ) is used to obtain these variable with the help of LABVIEW software. The induction motor used is a 7.5-hp with a constant frequency and in free acceleration. IEEE standard test of 7.5-hp induction motor is used to compare with performance of the simulated and measured data obtained. According to the output results, method of optimizing induction machine can be used in different models of induction motor

Home Energy Management System and Internet of Things: Current Trends and Way Forward

Managing energy in the residential areas has becoming essential with the aim of cost saving, to realize a practical approach of home energy management system (HEMS) in the area of heterogeneous Internet-of-Thing (IoT) devices. The devices are currently developed in different standards and protocols. Integration of these devices in the same HEMS is an issue, and many systems were proposed to integrate them efficiently. However, implementing new systems will incur high capital cost. This work aims to conduct a review on recent HEMS studies towards achieving the same objectives: energy efficiency, energy saving, reduce energy cost, reduce peak to average ratio, and maximizing user's comfort. Potential research directions and discussion on current issues and challenges in HEMS implementation are also provided