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With the improvements in machine-to-machine (M2M) communication, ubiquitous computing, and wireless sensor networks, the Internet of Things (IoT) has become a notion that is constantly rising in importance. Using uniquely addressable IDs, the Internet of Things links diverse physical items and allows them to communicate with one another through the Internet. A general overview of the IoT in the context of the architecture and associated technologies is provided in this article. On the other hand, the Internet of Things does not follow a standardised architecture model. This is accomplished by describing widely recognised architectural concepts that are subsequently refined with the associated technology in various tiers. Also included are some solutions that have been developed and future directions for addressing the obstacles faced by the IoT paradigm. Finally, the article discusses several Internet of Things applications to demonstrate the viability of the IoT idea in real-world settings.

Along with research-based and state-of-the-art reviews, book reviews can be found in language teacher periodicals. However, less attention has been given to reviews on academic books emphasizing on the issues of cognitive engagement. As such, this paper seeks to review the book entitled, Fifty Strategies to Boost Engagement: Creating a Thinking Culture in the Classroom (50 Teaching Strategies to Support Cognitive Development) by Rebecca Stobaugh. Generally, this book adds to our understanding of how an educator needs to improve on the skill of engagement particularly through the cognitive domain.

The world is facing a massive challenge as the COVID-19 outbreak strikes across the globe. Many efforts have been made to detect, control and contain the coronavirus proactively and aggressively before a further catastrophe occurs. Indeed, ending the global COVID-19 pandemic is not a simple task. It requires adequate planning and implementation of sustainable strategies and interventions to control COVID-19 from keep spreading globally. One way to address this issue is using System Dynamics (SD). With this aim in mind, this paper presents an initial COVID-19 modelling work in the formulation stage of SD methodology. A literature review was carried out on published and unpublished papers to understand the essential outbreak model design structure. Within this process, a total of 15 COVID-19 models in SD were gathered and analysed. As the outcome, this paper highlights the components of the conceptual representation model for the COVID-19 outbreak, which later can serve as the core basis for modelling complex COVID19 outbreak dynamics and interventions for future development. As an implication, a comprehensive model can be developed to support decision making.

Previously, the black hole (BH) algorithm has been subjected to various fundamental enhancements. Among others, white hole operator and local search have been embedded in the BH algorithm to improve its performance significantly. This paper shows that combination of gravitational search, white hole operator, and local search also able to improve the performance of the BH algorithm significantly.

Optimal reactive power dispatch (ORPD) plays a significant role in the control and smooth operation of the power system through the enhancement of the network’s reliability, security, and economic aspects. This paper presents a bibliometric and visual analysis of ORPD-related research articles extracted from the Web of Science (WoS) database from its inception to October 29, 2019. A total of 263 articles drawn from 166 journals, published between 1989 and 2019, were retrieved and analysed using Excel, HistCite, and VOSviewer visualisation software. The total number of citations for the 263 articles ranges from 0 to 297. The top three journals with the most significant number of ORPD publications were the International Journal of Electrical Power and Energy Systems, Applied Soft Computing, and two journals qualified for the third place, IEEE Transaction on Power Systems and IET Generation Transmission and Distribution. The most active researcher is Provas Kumar Roy, with nine (9) articles from Kalyani Government Engineering College. The most trending/cited researcher is Yi Jia Cao, with 129 Total Local Citation Scores from Hunan University, Changsha. In terms of contribution by countries, India, China, Iran, and the United States were the most significant contributors with 27.8%, 20.9%, 11.8%, and 8% of the total articles, respectively. The top 5 most frequently used substantive keywords to identify the trending topic and research direction were Particle Swarm Optimisation, Genetic Algorithm, Gravitational Search Algorithm, Linear Programming, Evolutional Algorithm, and Hybrid Algorithm. This study provides a detailed outline and reveals the future research directions for both experienced and novice ORPD researchers to identify research topics, questions, and collaboration partners.

System Dynamics (SD) modelling is a highly complex process. Although the SD methodology has been discussed extensively in most breakthroughs and present literature, discussions on data collection methods for SD modelling are not explained in details in most studies. To date, comprehensive descriptions of knowledge extraction for SD modelling is still scarce in the literature either. In an attempt to fill in the gap, three primary groups of data sources proposed by Forrester: (1) mental database, (2) written database and (3) numerical database, were reviewed, including the potential data collections methods for each database by taking into account the advancement of current computer and information technology. The contributions of this paper come in three folds. First, this paper highlights the potential data sources that deserved to be acknowledged and reflected in the SD domain. Second, this paper provides insights into the appropriate mix and match of data collection methods for SD development. Third, this paper provides a practical synthesis of potential data sources and their suitability according to the SD modelling stage, which can serve as modelling practice guidelines.

To date, with the advancement in energy-related technology, the regulations prior to the environment and energy emission are also strictly increasing due to the impact of global warming. Hence, many electric vehicles are affected since one of their principles is closely related to the issue of short-range storage capacity and long charging time which are not in favor to most of the automotive customers. Thus, it embarks many kinds of research to establish development of an efficient energy management storage (EMS) to fulfill the objectives at the same time keeping the vehicle performance at its convincing standard. This work presents the Hyperheuristics Trajectory Based Optimization for Energy Management Storage (EMS) of The Split Plug-in Hybrid Electric Vehicle. The Split Plug-in Hybrid Electric Vehicle is discussed in different and recent perspective by zooming into other aspects of EMS point of view. A comprehensive discussion is elaborated by comparing various strength and weaknesses of this research concept. The performance results from this improved approach are compared with the conventional HEVs. The analytical modeling methods such as physics-based Resistive Companion Form technique and Bond Graph method are presented with some powertrain component and system modeling application examples.

The deep concern towards efficient and green energy has brought to the development of intermediate energy storage system. Electric vehicles (EVs), for example, is an application where the energy storage devices are vital. In EV, the bidirectional DC–DC converter is high in demand due to its nature EV of having two or more separate DC sources. A recent technology called the dual active bridge (DAB) converter has quickly become popular in DC–DC converter thanks to its inherent galvanic isolation via a high-frequency transformer. Furthermore, DAB topology allows for high-efficiency conversion and soft switching adaption. This paper presents the optimization of the phase shift angle controller of DAB using Particle Swarm Optimization (PSO). From the preliminary study, it was found out that the optimization performs well until there are abrupt changes in the system. The system is not able to respond to the reference voltage step change as the traditional one-time execution PSO have local optima entrapped in react to the changes. The paper proposes a reset function to allow PSO to identify that its current optimized value is not optimal anymore. This drives PSO to look for a new optimal value to improve DAB’s overall dynamic performance. Simulation of the modified PSO is done on a 200 kW DAB system with 20 kHz switching frequency is developed in MATLAB/Simulink environment. Simulation has been carried out with the objective to minimize the steady-state error as well as to improve the dynamic performance of the DAB. The DAB performance with the proposed solution is evaluated in terms of steady-state error and transient response by testing the system under various reference voltage and step-change input voltage. The self-excited re-exploration PSO also have been presented as to respond to the reference voltage step change as the basic PSO have local optima entrapped in react to the changes. In order to validate the simulation results, a hardware-in-the-loop (HIL) experimental circuit is built in Typhoon HIL-402 to verify the dynamic response and the steady state performance of the system.

In this paper, a newly surfaced nature-inspired optimization technique called moth-flame optimization (MFO) algorithm is utilized to address the optimal reactive power dispatch (ORPD) problem. MFO algorithm is inspired by the natural navigation technique of moths when they travel at night, where they use visible light sources as guidance. In this paper, MFO is realized in ORPD problem to investigate the best combination of control variables including generators voltage, transformers tap setting as well as reactive compensators sizing to achieve minimum total power loss and minimum voltage deviation. Furthermore, the effectiveness of MFO algorithm is compared with other identified optimization techniques on three case studies, namely IEEE 30-bus system, IEEE 57-bus system and IEEE 118-bus system. The statistical analysis of this research illustrated that MFO is able to produce competitive results by yielding lower power loss and lower voltage deviation than the selected techniques from literature.

The breakthroughs in power electronics semiconductor technology have led to the raised in popularity of Multi-level Voltage Source Inverters, which is the reason industries and researchers are relentlessly working towards extending it to both medium and high-power applications. Consequently, this paper presents a modified Selective Harmonic Elimination Pulse Width Modulation (SHE-PWM) controlled three-phase 5-level Multi-level Inverter (MLI) topology. The work is an extension of a single-phase topology proposed by the authors. The uniqueness of the proposed topology is that it comprises of six Cascaded H-bridge modules (two modules per phase) that are powered through a single direct current (DC) source, hence eliminating the need for multiple DC sources. In addition, conventional 5-level MLI has two switching angles (n = 2), which restricts its harmonic elimination capability to one (n = 1) lower order harmonics. This study removes that restriction by utilising the concept of multiple switching per step, resulting in the superimposing of 12 notches (n = 12) on the output voltage at the 3/9 distribution ratio. With the fundamental switching SHE-PWM, twelve non-linear, transcendental equations are generated, and they are optimally solved using the Hybrid Coded Genetic Algorithm to eliminate 11 (n − 1) lower-order harmonics. The low-switching frequency results in less electromagnetic interference as well as reduced switching loss, which improves the overall inverter efficiency. The topology has less output Total Harmonics Distortion, fewer components, and lower weight and cost than conventional topologies. The circuit was designed and validated through simulations performed in PSIM software.

This paper addresses optimization for hybrid electric vehicle (HEV) by using a single agent method to optimize the power losses and fuel consumption under a specific driving cycle base on Safe Experimentation Dynamics (SED) method. For optimization process, four gain are added in four main parts of the HEV system. Those main parts are engine, motor, generator and battery. These four gain are controlled the output for each components to give the minimum power losses. The design method is applied to free model of HEV by using Simulink/MATLAB software while M-File/MATLAB is used to apply the Safe Experimentation Dynamics (SED) method. The result from design method achieved minimum reduction of power losses and fuel consumption compared to original system. Thus, the comparison of the simulation results shown that the algorithm approach provides better performance.

This paper describes a study in maximum power point tracking (MPPT) technique for photovoltaic (PV) system using binary search algorithm (BSA). The aim of this study is to identify the effectiveness of BSA in performing MPPT under constant irradiance and rapid change irradiance conditions. The BSA MPPT model, together with a well-established particle swarm optimization (PSO) algorithm were developed and implemented with a DC-DC boost converter in MATLAB Simulink. Direct control strategy was implemented to simplify the development of the controller which generates the switching duty cycle of the power converter. In order to examine the performance of both algorithms, five different constant irradiance test cases and four rapid changing irradiance test cases were imposed to the PV system to examine the capability of the both algorithms. BSA exhibits a faster convergence speed with zero steady state oscillation. Both of the algorithms have shown the capability to adapt the rapid change irradiance condition effectively with tracking efficiency up to 99%.

This article proposes a new technique to solve optimization problems based on barnacles’ mating behavior namely Barnacles Mating Optimizer (BMO). Barnacles are micro-organisms existed since Jurassic times and they attach themselves to objects in the water and grow shell. They are also known as hermaphroditic micro-organisms and one of the special characters of barnacles is they have long penises which is the longest in animal kingdom relative to their body size. The Hardy-Weinberg principle is adopted in BMO to generate new off-springs and the mating process is performed randomly based on the length of the barnacle’s penis. The effectiveness of BMO is tested through a set of 23 benchmark functions in finding the optimal solutions. Some impactful and reliable results are presented compared with other techniques in this paper.

In line with high demand of renewable energy as well as the energy storage, the multiport DC–DC converters topology have recently received a lot of attention due to its own advantages. In this paper, a bidirectional quad active bridge (QAB) DC–DC converter with high frequency transformer is presented. Full bridge power converter is employed and the phase shift modulation is used in investigating the power transfer of QAB converter. In applying this proposed modulation, the changing or amount of delivering and receiving power in the QAB converter are influenced by the phase shift. Multiple cases of QAB configuration have been evaluated by (1) Multi-input single-output (balanced voltage source); (2) Multi-input single output (unbalanced voltage source); (3) Multi-input multi-output (balanced voltage source); and (4) Single-input multi-output (unbalanced load). The simulation results of a 2.5 kW system is analyzed through MATLAB/Simulink. Also, the power efficiency is discussed through this simulation. It is found out that maximum power can be achieved at 90° phase shift.

This paper presents the study of the effect of transformer’s turn ratio mismatch on the performance of dual active bridge (DAB) converter. DAB converter was part of the components of a solid state transformer (SST) where it provides galvanic isolation as well as interface systems with different voltage level. In this study, a DAB circuit was developed and the performance was examined in both MATLAB Simulation and hardware-in-the-loop (HIL) simulation. The DAB system was examined under five different transformer ratio. The behavior of the DAB shows a comparable results under both test platform. The real-time performance of DAB converter has shown an efficiency up to 95.91%.

One of the serious problems with modern electrical loads is harmonics, which is generated from nonlinear loads. Harmonics can lead to excessive heat and noise in the loads and create a large amount of energy losses either in transmission systems or distribution systems. Compensation of these harmonics substantially improves the power factor and reduces the total harmonic distortion index (THD). This means that the system can transfer more active power without having to increase the capacity. Traditionally, passive filters have been used to remove harmonics, but for their intrinsic downsides, they have been replaced by active power filter (APF). APF has superior filtering characteristics and dynamic response compared to passive filters. Synchronous Reference Frame (SRF) has been widely adopted for harmonics extraction in APF, although it has been found out to have a slow response. Discrete Wavelet Transform (DWT) on the other hand, is a good candidate for harmonics extraction. The new controller is thoroughly developed and rigorously simulated in MATLAB-Simulink environment. The harmonics source is a 5.5 kW nonlinear load mimicking a real-life load from previous practical studies. As a conclusion, DWT is a better candidate for harmonics extraction in APF, as compared with SRF.

This paper presents a novel bio-inspired optimization algorithm namely the Barnacles Mating Optimizer (BMO) algorithm to solve optimization problems. The proposed algorithm mimics the mating behaviour of barnacles in nature for solving optimization problems. The BMO is first benchmarked on a set of 23 mathematical functions to test the characteristics of BMO in finding the optimal solutions. It is then applied to optimal reactive power dispatch (ORPD) problem to verify the reliability and efficiency of BMO. Extensive comparative studies with other algorithms are conducted and from the simulation results, it is observed that BMO generally provides better results and exhibits huge potential of BMO in solving real optimization problems.

A new evolutionary algorithm called Barnacles Mating Optimizer (BMO) to solve optimization problems is presented in this paper. BMO is inspired from the behavior of barnacle’ mating in nature. They are known as micro-organisms that existed since Jurassic times and classified as hermaphroditic micro-organisms. They have a unique feature which is they own long penises that can be said as the longest among microorganisms, relatively to size of their body. To show the effectiveness of proposed BMO in solving optimization problems, a set of 23 mathematical functions are used to test the characteristic of BMO in finding the optimal solutions especially in unimodal, multimodal and composite test functions. Comparisons with other evolutionary and swarm algorithms also will be presented.

The power system is a complex network which is expected to operate with consumption of minimal resources giving the maximum security and reliability. The optimal power flow (OPF) problem is an important tool to help the operator to achieve these goals by providing the optimal setting of all control variables. Due to its significant influence on security and economic operation of power systems, one of the OPF problems namely optimal reactive power dispatch (ORPD), has received an ever-increasing interest from electric utilities. This paper proposes a recent nature inspired algorithm called moth-flame optimizer (MFO) to solve the ORPD problem. MFO is utilized to find the best combination of control variables such as the real power generation to be generated, generator voltages, tap changing transformers' ratios and the size of reactive power compensation devices so that the total minimum loss in the network can be obtained. The effectiveness of MFO is tested on IEEE-30 bus system with 25 control variables and the comparison with other recent algorithms such as grey wolf optimizer (GWO), ant-lion optimizer (ALO) and multi-verse optimizer (MVO) also will be presented in this paper.

A novel bio-inspired optimization algorithm is proposed in this paper namely barnacles mating optimizer (BMO) algorithm. The main inspiration of BMO is originated from the mating behavior of barnacles in nature. Barnacles are hermaphroditic micro-organisms which have both male and female sex reproductions. To create new off-springs, they must be fertilized by a neighbor. They are well-known for their long penises, about seven times the length of their bodies to cope with the changing tides and sedentary lifestyle. In BMO, the selection of barnacle's parents is decided randomly by the length of barnacle's penis to create new off-springs. The exploitation and exploration processes are the generation of new off-springs inspired by the Hardy-Weinberg principle and sperm cast situation, respectively. The effectiveness of proposed BMO is tested through a set of benchmark multi-dimensional functions which the global and local minimum are known. Comparisons with other recent algorithms also will be presented in this paper.

This paper presents a recent swarm intelligence technique viz. Cuckoo Search Algorithm (CSA) for solving the Optimal Chiller Loading (OCL) problem for energy conservation. Multi-chillers system has been widely used by commercial and industrial facilities to provide cooling energy. The main problem of multi-chillers system is that it conserved huge amount of energy. In this study, Partial Load Ratio (PLR) of the chiller is used as the variables to be optimized while the power consumption in kW is selected as the objective function to be minimized. On the other hand, CSA is a one of well-known swarm intelligence techniques that has been used to solve many optimization problems. In order to show the effectiveness of CSA in solving OCL problem, a case study with six-chiller system is considered. Results obtained are compared with other techniques available in literatures.

This paper presents an approach to determine the maximum power point (MPP) in photovoltaic (PV) system under partial shaded conditions using Binary Search Algorithm (BSA). The BSA perform MPP tracking by changing the search boundaries to eliminate the region where the MPP cannot lie. In this study, the BSA and particle swarm optimization (PSO) based MPPT were developed in Matlab Simulink with a DC/DC boost converter. Both algorithms were simulated under ten different test cases of partial shading conditions to study the accuracy, speed and tracking efficiency. BSA exhibits a fast convergence with zero steady state oscillation along with the capability of performing MPP tracking under partially shaded PV system.

Brushless Direct Current (BLDC) motors has gained popularity in recent years due to their performance, efficiency and power density. Many type of speed controller has been developed and Proportional Integral Derivative (PID) controller has been the most prominent due to simplicity. However, lack of evidence presented in literature regarding the bidirectional speed control of such motor. In this paper, a PID bidirectional BLDC motor speed controller using Direct Commutation Switching Scheme is proposed. The controller is developed and tested using MATLAB/Simulink. It is found that the controller performed efficiently for all the test cases.

A new all-digital current control technique is presented in this paper. The current control is based on Ramptime current control technique, where multisampling is chosen as the sampling strategy due to its ability to provide higher accuracy. The resultant current control is called Digital Ramptime current control. The performance of the new current control is compared with the original Ramptime in an active power filter experiment. Results show that with sufficient sampling, the performance of Digital Ramptime is satisfactory.

Photovoltaic (PV) system has been widely used as one of the renewable energy resources. Due to the non-linear I-V characteristic and changes of the external environment and load, the output power of the PV array varies according to different situation. Hence, the maximum power point (MPP) tracking technology is necessary to be implemented in order to track for the only MPP on the P-V curve to increase the energy harness from the PV array. In this study, two commonly used MPPT algorithm, perturb and observe and incremental conductance were developed in Matlab Simulink with a boost DC-DC converter to study the accuracy, speed and tracking efficiency under STC, slow changing irradiance condition and step changing irradiance condition. Comparative studies shows that modified incremental conductance is able to track MPP faster and more efficiently under fast changing of irradiance.

This paper proposes an application of a recent nature inspired optimization technique namely Moth-Flame Optimization (MFO) algorithm in solving the Optimal Reactive Power Dispatch (ORPD) problem. In this paper, loss minimization is used as objective function of ORPD problem where the best combination of control variables such as voltage magnitude at generator buses, transformer-tap setting and injected MVAR will be obtained by MFO. To show the effectiveness of proposed algorithm, the IEEE 30 bus system is utilized and compared with other nature inspired algorithms. The results show that MFO perform better in obtaining less total system loss compared to those other selected algorithms.

This paper proposes an application of a recent nature inspired optimization technique namely Moth-Flame Optimization (MFO) algorithm in solving the Economic Dispatch (ED) problem. In this paper, the practical constraints will be included in determining the minimum cost of power generation such as ramp rate limits, prohibited operating zones and generators operating limits. To show the effectiveness of proposed algorithm, two case systems are used: 6-units and 15-units systems and then the performance of MFO is compared with other techniques from literature. The results show that MFO is able to obtain less total cost than those other algorithms.

Qualities of welding play an important part in industry especially in manufacturing field. Post-welding non-destructive test is one of the importance process to ensure the quality of welding but it is time consuming and costly. To reduce the chance of defects, online monitoring had been utilized by continuously sense some of welding parameters and predict welding quality. One of the parameters is welding current, which is rich of information but lack of study focus on extract them at signal analysis level. This paper presents the analysis of welding current using Short Time Energy (STE) signal processing to quantify the pattern of the current. GMAW set with carbon steel specimens are used in this experimental study with high-bandwidth and high sampling rate oscilloscope capturing the welding current. The results indicate welding current as signatures have high correlation with the welding process. Continue with STE analysis, the value below 5000 is declare as good welding, meanwhile the STE value more than 6000 is contained defect.

This research implements a PI controller based on harmony search (HS) optimization algorithm for voltage source inverter to improve the output performance under step load change conditions. The HS algorithm aims to handle the trial and error procedure used in finding the PI parameters and then apply the proposed control algorithm via the eZdsp TMS320F28355 board to link the inverter prototype with the Matlab Simulink. The mean absolute error (MAE) is used as an optimization problem to minimize the output voltage error for the developed controller (PI-HS) as compared to the PI controller based particale swarm optimization algorithm (PI-PSO). Based on the experimental results obtained, it is noted that the proposed controller (PI-HS) provides a good dynamic performance, robustness, constant voltage amplitude, and fast response in terms of overshoot, transient, and steady-state.

With increasing concern over the environment and ever stringent emissions regulations, the electric vehicle has been investigated as an alternative form of transportation. However, the electric vehicle suffers from relatively short range and long charging times and consequently has not become an acceptable solution to the automotive consumer. The addition of an internal combustion engine to extend the range of the electric vehicle is one method of exploiting the high efficiency and lack of emissions of the electric vehicle while retaining the range and convenient refuelling times of a conventional gasoline powered vehicle. The term that describes this type of vehicle is a hybrid electric vehicle. Many configurations of hybrid electric vehicles have been designed and implemented, namely the series, parallel and power-split configurations. This paper discusses the comparison between Split Plug-in Hybrid Electric Vehicle(SPHEV) and Hybrid Electric Vehicle(HEV). Modelling methods such as physics-based Resistive Companion Form technique and Bond Graph method are presented with powertrain component and system modelling examples. The modelling and simulation capability of existing tools such as ADvanced VehIcle SimulatOR (ADVISOR) is demonstrated through application examples. Since power electronics is indispensable in hybrid vehicles, the issue of numerical oscillations in dynamic simulations involving power electronics is briefly addressed.

This paper presents a modified Firefly Algorithm (MFA) for solving economic dispatch (ED) problems. ED is one of the most challenging problems of power system since it is difficult to determine the optimum generation scheduling to meet the particular load demand with the minimum fuel costs while all constraints are satisfied. In addition the practical ED problems which are involving objective functions with quality and inequality constraints including the practical operation constraints of generators such as ramp rate limit, prohibited operating zones and generation limits make it harder to find the global optimum results of ED. To demonstrate the effectiveness and feasibility of MFA in solving ED, two well-known ED test systems with non-convex solution features have been tested and compared with some of the most recently published ED solution methods in literatures. The results of this research show that MFA is able to find more economical solution than those determined by other methods.

This paper presents an experimental comparison of three current control techniques, namely Hysteresis, PI and Ramptime Current Control Techniques. The testing platform is a 300 W shunt active power filter with diode rectifier load. Results are analysed both in the time domain and the frequency domain. The pros and cons of each of the above mentioned current control techniques are discussed. It is found that Ramptime Current Control is capable of incorporating the advantages of the two other control techniques with negligible shortcomings.

Delta robot is a type of parallel robot. It consists of three arms connected to universal joints at the base. In this project, 3 degree of freedom Delta robot use of parallelograms in the arms, this maintains the orientation of the end effectors. The development of the Delta robot corresponds to the current requirement of having a robot able to transfer amount of light object in the least time. In this paper involves forward kinematics calculation using S-S (spherical-spherical) joint pair and compared with real position of Delta robot.