Observing present scarcity of fossil fuel and emissions of greenhouse gases, electricity generated from Renewable Energy (RE) sources turns out to be the best alternative for generating the power. In RE system, the inverter is normally used to condition the DC power into AC to meet the requirements of load and transmission system. The inverter offers myriad benefits; however, the presence of harmonics (particularly low-order) in the output voltage affects the efficiency and performance of inverter, causes switching losses and decreases the lifetime of the system. In last three decades, significant research has been done to develop the efficient control technique for eliminating the unwanted harmonics. The preliminary review of existing control techniques revealed that the selective harmonic elimination pulse-width modulation (SHEPWM) is more proficient to eliminate the low-order harmonics. However, non-linear transcendental equations used in this technique pose a challenge to solve particularly for calculus-based methods. With the advent of powerful and low-cost computers, bio-inspired intelligent algorithms (BIAs) seem to be a better approach for solving these complex equations. This review paper presents the detailed principle operation of nine well-known BIAs and discusses their application in inverters for harmonic elimination (HE). Moreover, different objective functions are also discussed in this paper which is used by the researchers for HE. Additionally, the performance of five renowned BIAs, namely, Imperialist Competitive Algorithm, Particle Swarm Optimization, Differential Evolution, Bee Algorithm and Genetic Algorithm is critically evaluated. Their performance is analyzed in terms of accuracy, computational complexity, convergence speed, and a number of control parameters. The conclusion has been made on the basis of information extracted from the literature and evaluation results with future recommendations. This single paper covers all the essential information regarding HE in inverters, which will help researchers to design the efficient RE conversion system.

Selective harmonic elimination in inverters using bio-inspired intelligent algorithms for renewable energy conversion applications: A review

Induction motor (IM) drives, specifically the three-phase IMs, are a nonlinear system that are difficult to explain theoretically because of their sudden changes in load or speed conditions. Thus, an advanced controller is needed to enhance IM performance. Among numerous control techniques, fuzzy logic controller (FLC) has increasing popularity in designing complex IM control system due to their simplicity and adaptability. However, the performance of FLCs depends on rules and membership functions (MFs), which are determined by a trial-and-error procedure. The main objective of this paper is to present a critical review on the control and optimization techniques for solving the problems and enhancing the performance of IM drives. A detailed study on the control of variable speed drive, such as scalar and vector, is investigated. The scalar control functions of speed and V/f control are explained in an open- and closed-loop IM drive. The operation, advantages, and limitations of the direct and indirect field-oriented controls of vector control are also demonstrated in controlling the IM drive. A comprehensive review of the different types of optimization techniques for IM drive applications is highlighted. The rigorous review indicates that existing optimization algorithms in conventional controller and FLC can be used for IM drive. However, some problems still exist in achieving the best MF and suitable parameters for IM drive control. The objective of this review also highlights several factors, challenges, and problems of the conventional controller and FLC of the IM drive. Accordingly, the review provides some suggestions on the optimized control for the research and development of future IM drives. All the highlighted insights and recommendations of this review will hopefully lead to increasing efforts toward the development of advanced IM drive controllers for future applications.

Optimization techniques to enhance the performance of induction motor drives: A review

Influence of EFD viscosity on nanofluid forced convection in a cavity with sinusoidal wall

CVFEM for effect of Lorentz forces on nanofluid flow in a porous complex shaped enclosure by means of non-equilibrium model

Nature-inspired algorithms are a class of effective tools for solving optimization problems and these algorithms have good properties such as simplicity, flexibility and high efficiency. Despite their popularity in practice, a mathematical framework is yet to be developed to analyze these algorithms theoretically. This work intends to analyze nature-inspired algorithms both qualitatively and quantitatively. We briefly outline the links between self-organization and algorithms, and then analyze algorithms using Markov chain theory, dynamic system and other methods. This can serve as a basis for building a multidisciplinary framework for algorithm analysis.

Nature-Inspired Algorithms and Applied Optimization

Improved firefly algorithm is applied to determine the optimum switching angles for the 11-level cascaded H bridge multilevel inverter with non-equal DC sources.Firefly algorithm takes least estimation time and surpasses all other 11 metaheuristic algorithms.The algorithm and the model are developed using MATLAB and the validity of the simulation is confirmed by an experimental setup using FPGA Spartan 6A DSP.Results are compared with the results obtained using Particle swarm optimization and artificial bee colony algorithm and it is proved that the proposed method offers reduced THD with less computation period. Programmed pulse width modulation is an optimized pulse width modulation which is particularly applicable for high-power applications where the power losses must be kept below firm limits. Based on the offline estimation, it is capable of pre programming the harmonic profile of the output waveform over a range of modulation indices by eliminating some lower order harmonics. In this paper, improved firefly algorithm (FA) is applied to determine the optimum switching angles for the 11- level cascaded H bridge multilevel inverter (MLI) with adjustable DC sources in order to eliminate pre specified lower order harmonics and to achieve the desired fundamental voltage. Though number of optimization algorithms is available for the estimation of switching angles, Firefly algorithm takes least computation time and surpasses all other 11 metaheuristic Algorithms. The algorithm and the model are developed using MATLAB and the validity of the simulation is confirmed by an experimental setup using FPGA Spartan 6A DSP. Results are compared with the results obtained using particle swarm optimization (PSO) and artificial bee colony algorithm (ABCA) and it is proved that the proposed method offers reduced total harmonic distortion (THD) with less computation period.

Application of improved firefly algorithm for programmed PWM in multilevel inverter with adjustable DC sources

Investigation on effects of different types of nanoparticles on critical parameters of nano-liquid insulation systems

Failure analysis of transformer liquid — solid insulation system under selective environmental conditions using Weibull statistics method

Increased power densities drive researchers to enhance and improve the life of insulation system employed in transformers. In order to reduce the oxidation process and enhance the life of liquid insulation, Anti-Oxidant (AO) additives are usually added with Liquid Insulation (LI). The main aim of this research work is to understand the behaviour of LIs with AO additives under thermally aged condition. Here, Butylated Hydroxy Toluene (BHT) AO and natural esters like Sunflower oil (SFO) and Rapeseed oil (RSO) are used for analyses and then subjected to an accelerated ageing test. Important life determining properties of inhibited and uninhibited Mineral Oil (MO), SFO and RSO samples are examined and compared. From the experimental results, it is concluded that the degradation rate of inhibited samples are lesser than that of uninhibited samples. Also, inhibited SFO samples show similar or better performance compared to MO and RSO samples.

Comparison of antioxidant influence on mineral oil and natural ester properties under accelerated aging conditions

Professor & Head, Electrical & Electronics Engineering Department, Thiagarajar College of Engineering, Madurai, Tamilnadu, India Abstract : In this paper, an efficient method has been proposed for transmission line over load alleviation in deregulated power system using real coded genetic algorithm (RCGA). For secure operation of power system, the network loading has to be maintained within specified limits. Transmission line congestion initiates the cascading outages which forces the system to collapse. Accurate prediction and alleviation of line overloads is the suitable corrective action to avoid network collapse. In this paper an attempt is made to explore the use of real coded genetic algorithm to find the optimal generation rescheduling for relieving congestion. The effectiveness of the proposed algorithm has been analyzed on IEEE 30 bus test system. The results obtained by the proposed method are found to be quite encouraging when compared with Simulated Annealing (SA) and hence it will be useful in electrical restructuring.

Sujatha Balaraman

Papers

Application of improved firefly algorithm for programmed PWM in multilevel inverter with adjustable DC sources

Investigation on effects of different types of nanoparticles on critical parameters of nano-liquid insulation systems

Failure analysis of transformer liquid — solid insulation system under selective environmental conditions using Weibull statistics method

Comparison of antioxidant influence on mineral oil and natural ester properties under accelerated aging conditions

Congestion management in deregulated power system using real coded genetic algorithm