Novel Bacteria Foraging Optimization for Energy-efficient Communication in Wireless Sensor Network

Optimization techniques based on Swarm-intelligence has been reported to have significant benefits towards addressing communication issues in Wireless Sensor Network (WSN). We reviewed the most dominant swarm intelligence technique called as Bacteria Foraging Optimization (BFO) to find that there are very less significant model towards addressing the problems in WSN. Therefore, the proposed paper introduced a novel BFO algorithm which maintains a very good balance between the computational and communication demands of a sensor node unlike the conventional BFO algorithms. The significant contribution of the proposed study is to minimize the iterative steps and inclusion of minimization of both receiving / transmittance power in entire data aggregation process. The study outcome when compared with standard energy-efficient algorithm was found to offer superior network lifetime in terms of higher residual energy as well as data transmission performance

Automated robust control system design for variable speed drives

Traditional PI controllers have been largely employed for the control of industrial variable speed drives due to the design ease and performance satisfaction they provide but, the problem is that these controllers do not always provide robust performance under variable loads. Existing solutions present themselves as complex control strategies that demand specialist expertise for their implementation. As a direct consequence, these factors have limited their adoption for the industrial control of drives. To counter this trend, the thesis proposes two techniques for robust control system design. The developed strategies employ particular Evolutionary Algorithms EA), which enable their simple and automated implementation. More specifically, the EA employed and tested are the Genetic Algorithms (GA), Bacterial Foraging (BF) and the novel Hybrid Bacterial Foraging, which combines specific desirable features of the GA and BF. The first technique, aptly termed Robust Experimental Control Design, employs the above mentioned EA in an automated trial-and-error approach that involves directly testing control parameters on the experimental drive system, while it operates under variable mechanical loads, evolving towards the best possible solutions to the control problem. The second strategy, Robust Identification-based Control Design, involves a GA system identification procedure employed in automatically defining an uncertainty model for the variable mechanical loads and, through the adoption of the Frequency Domain H-infinity Method in combination with the developed EA, robust controllers for drive systems are designed. The results that highlight the effectiveness of the robust control system design techniques are presented. Performance comparisons between the design techniques and amongst the employed EA are also shown. The developed techniques possess commercially viable qualities because they elude the need for skilled expertise in their implementation and are deployed in a simple and automated fashion

Automated robust control system design for variable speed drives

Traditional PI controllers have been largely employed for the control of industrial variable speed drives due to the design ease and performance satisfaction they provide but, the problem is that these controllers do not always provide robust performance under variable loads. Existing solutions present themselves as complex control strategies that demand specialist expertise for their implementation. As a direct consequence, these factors have limited their adoption for the industrial control of drives. To counter this trend, the thesis proposes two techniques for robust control system design. The developed strategies employ particular Evolutionary Algorithms EA), which enable their simple and automated implementation. More specifically, the EA employed and tested are the Genetic Algorithms (GA), Bacterial Foraging (BF) and the novel Hybrid Bacterial Foraging, which combines specific desirable features of the GA and BF. The first technique, aptly termed Robust Experimental Control Design, employs the above mentioned EA in an automated trial-and-error approach that involves directly testing control parameters on the experimental drive system, while it operates under variable mechanical loads, evolving towards the best possible solutions to the control problem. The second strategy, Robust Identification-based Control Design, involves a GA system identification procedure employed in automatically defining an uncertainty model for the variable mechanical loads and, through the adoption of the Frequency Domain H-infinity Method in combination with the developed EA, robust controllers for drive systems are designed. The results that highlight the effectiveness of the robust control system design techniques are presented. Performance comparisons between the design techniques and amongst the employed EA are also shown. The developed techniques possess commercially viable qualities because they elude the need for skilled expertise in their implementation and are deployed in a simple and automated fashion

Hybrid bacterial foraging sine cosine algorithm for solving global optimization problems

This paper proposes a new hybrid algorithm between Bacterial Foraging Algorithm (BFA) and Sine Cosine Algorithm (SCA) called Hybrid Bacterial Foraging Sine Cosine Algorithm (HBFSCA) to solve global optimization problems. The proposed HBFSCA algorithm synergizes the strength of BFA to avoid local optima with the adaptive step-size and highly randomized movement in SCA to achieve higher accuracy compared to its original counterparts. The performances of the proposed algorithm have been investigated on a set of single-objective minimization problems consist of 30 benchmark functions, which include unimodal, multimodal, hybrid, and composite functions. The results obtained from the test functions prove that the proposed algorithm outperforms its original counterparts significantly in terms of accuracy, convergence speed, and local optima avoidance

Current Studies and Applications of Krill Herd and Gravitational Search Algorithms in Healthcare

Nature-Inspired Computing or NIC for short is a relatively young field that tries to discover fresh methods of computing by researching how natural phenomena function to find solutions to complicated issues in many contexts. As a consequence of this, ground-breaking research has been conducted in a variety of domains, including synthetic immune functions, neural networks, the intelligence of swarm, as well as computing of evolutionary. In the domains of biology, physics, engineering, economics, and management, NIC techniques are used. In real-world classification, optimization, forecasting, and clustering, as well as engineering and science issues, meta-heuristics algorithms are successful, efficient, and resilient. There are two active NIC patterns: the gravitational search algorithm and the Krill herd algorithm. The study on using the Krill Herd Algorithm (KH) and the Gravitational Search Algorithm (GSA) in medicine and healthcare is given a worldwide and historical review in this publication. Comprehensive surveys have been conducted on some other nature-inspired algorithms, including KH and GSA. The various versions of the KH and GSA algorithms and their applications in healthcare are thoroughly reviewed in the present article. Nonetheless, no survey research on KH and GSA in the healthcare field has been undertaken. As a result, this work conducts a thorough review of KH and GSA to assist researchers in using them in diverse domains or hybridizing them with other popular algorithms. It also provides an in-depth examination of the KH and GSA in terms of application, modification, and hybridization. It is important to note that the goal of the study is to offer a viewpoint on GSA with KH, particularly for academics interested in investigating the capabilities and performance of the algorithm in the healthcare and medical domains.Comment: 35 page

Mitigating unbalance using distributed network reconfiguration techniques in distributed power generation grids with services for electric vehicles: A review

© 2019 Elsevier Ltd With rapid movement to combat climate change by reducing greenhouse gases, there is an increasing trend to use more electric vehicles (EVs) and renewable energy sources (RES). With more EVs integration into electricity grid, this raises many challenges for the distribution service operators (DSOs) to integrate such RES-based, distributed generation (DG) and EV-like distributed loads into distribution grids. Effective management of distribution network imbalance is one of the challenges. The distribution network reconfiguration (DNR) techniques are promising to address the issue of imbalance along with other techniques such as the optimal distributed generation placement and allocation (OPDGA) method. This paper presents a systematic and thorough review of DNR techniques for mitigating unbalance of distribution networks, based on papers published in peer-reviewed journals in the last three decades. It puts more focus on how the DNR techniques have been used to manage network imbalance due to distributed loads and DG units. To the best of our knowledge, this is the first attempt to review the research works in the field using DNR techniques to mitigate unbalanced distribution networks. Therefore, this paper will serve as a prime source of the guidance for mitigating network imbalance using the DNR techniques to the new researchers in this field

Advanced Signal Processing Techniques Applied to Power Systems Control and Analysis

The work published in this book is related to the application of advanced signal processing in smart grids, including power quality, data management, stability and economic management in presence of renewable energy sources, energy storage systems, and electric vehicles. The distinct architecture of smart grids has prompted investigations into the use of advanced algorithms combined with signal processing methods to provide optimal results. The presented applications are focused on data management with cloud computing, power quality assessment, photovoltaic power plant control, and electrical vehicle charge stations, all supported by modern AI-based optimization methods

Non-intrusive efficiency estimation of inverter-fed induction machines

Motorised loads using induction machines use approximately 60% of the electricity globally. Most of these systems use three-phase induction motors due to their robustness and lower cost. They are often installed in continuously operating industrial plants/applications that require no operational interruptions. Whilst most of these induction machines are supplied from ideally sinusoidal supplies, applications are emerging where induction machines are fed from non-sinusoidal supplies. In particular, pulse width modulated inverters realize efficient control of induction machines in many automated industrial applications. From an energy management perspective, it is vital to continually assess the efficiency of induction machines in order to initiate replacement or economic repair. It is therefore of paramount importance that reliable and non-intrusive techniques for efficiency estimation of induction machines be investigated, that consider sinusoidal and non-sinusoidal supplies. This work proposes a non-intrusive efficiency estimation technique for inverter–fed induction motors that is based on harmonic regression analysis, harmonic equivalent circuit parameter estimation and harmonic loss analysis using limited measured data. Firstly, considerations for inverter-fed induction motor equivalent circuit modelling and parameter estimation techniques suitable for non-intrusive efficiency estimation are presented and the selection of one equivalent circuit for analysis is justified. Measured data is obtained from two different induction motors on a flexible 110kW test rig that utilises an HBM Gen 7i data acquisition system. By measuring voltage, current and input power at the supply terminals of the inverter-fed motor, the fundamental equivalent circuit parameters are estimated using population based incremental learning algorithm and compared with those obtained from the IEC 60034-2-1 Standard. The harmonic parameters are estimated using the bacterial foraging algorithm basing on the input impedance of the motor at each harmonic order. A finite harmonic loss analysis is carried out on the tested induction motors. The proposed techniques and harmonic loss analysis provide accurate efficiency estimates of within 1.5% error when compared to the direct method. Lastly, a related non-intrusive efficiency estimation technique is proposed that caters for a holistic loss contribution by all harmonics. The efficiency results from the proposed techniques are compared to those obtained from the IEC-TS 60034-2-3 Technical Specification and a direct method. The estimated efficiencies are comparable to those measured by the Technical Specification and a direct method within 2% error when tested on 37kW and 45kW PWM inverter-fed motors across the loading range. Furthermore, this work conducts a comprehensive non-intrusive rotor speed estimation comparative analysis in order to recommend the best technique(s), in terms of intrusiveness, accuracy and computational overhead. Errors of less than 1% have been reported in literature and experimental verification when using vibration analysis, Motor Current Signature Analysis (MCSA), Rotor Slot Harmonic (RSH) and Rotor Eccentricity Harmonic (REH) analysis techniques in inverter-fed IMs

Interline Unified Power Quality Conditioner for Enhancing Power Quality using FOFPID-based Interleaved CUK Converter

Electrical distribution systems face increased non-linear loads due to using power electronics for the converters. Due to these non-linear loads, the system exhibits PQ problems in the distributed feeders. To enhance PQ problems in the dual feeder, fractional order fuzzy proportional integral derivative controller (FOFPID) is introduced with interline unified power quality (IUPQC) conditioner. IUPQC conditioner includes a distribution static compensator (DSTATCOM), dynamic voltage restorer (DVR) and interleaved cuk converter (ICC). DSTATCOM and DVR are used for compensating the voltages and current in the dual feeders (feeder-1 and feeder-2). Also, ICC monitors the switching between the DSTATCOM and DVR compensators by providing proper power flow. Moreover, the FOFPID controller regulates an input supply from both feeders. The simulation is performed through MATLAB/Simulink platform, demonstrating the robustness of a proposed FOFPID with an IUPQC controller. The performance of a proposed controller is analyzed through two cases for both feeders. Furthermore, the total harmonic distortions (THD) are calculated for the feeder parameters. The proposed FOFPID with IUPQC controller also maintains stability in a dual feeder. Therefore, the entire response shows the functionality and feasibility of a proposed controller