Located near the equator, Malaysia is a country with one of the highest lightning densities in the world. Lightning contributes to 70% of the power outages in Malaysia and affects power equipment, automated network systems, causes data losses and monetary losses in the nation. Therefore, consideration of insulator evaluation under lightning impulses can be crucial to evaluate and attempt to overcome this issue. This paper presents a new approach to increase the electrical performance of polymer insulators using a Room Temperature Vulcanisation (RTV) coating. The evaluation involves three different settings of polymer insulator, namely uncoated, RTV type 1, and RTV type 2 upper surface coatings. All the insulators were tested under three different conditions as dry, clean wet and salty under different impulse polarities using the even-rising test method. The voltage breakdown for each test was recorded. From the experiment, it was found that the effectiveness of the RTV coating application became apparent when tested under salty or polluted conditions. It increased the voltage withstand capabilities of the polymer insulator up to 50% from the basic uncoated insulator. Under dry and clean conditions, the RTV coating provided just a slight increase of the breakdown voltage. The increase in voltage breakdown capability decreased the probability of surface discharge and dry band arcing that could cause degradation of the polymeric material housing. The RTV type 1 coating was found to be more effective when performing under a lightning impulse. The findings might help the utility companies improve the performance of their insulators in order to increase power system reliability.
The reactive power control of a power system is discussed under two types of variables: continuous variables (e.g., generator bus voltages) and discrete variables (e.g., transformer taps and the size of switched shunt capacitors). This paper proposes a novel and powerful algorithm, named turbulent flow of water-based optimization (TFWO) as well as a new improved version of this algorithm, called θ-TFWO, for optimal reactive power distribution (ORPD) to reduce losses. The proposed method is applied to two large-scale IEEE 57-bus systems. Furthermore, to demonstrate the competitive performance of the suggested algorithm, its performance was compared to that of several other algorithms, including biogeography-based optimization (BBO), social spider algorithm (SSA), and optics inspired optimization (OIO), in terms of solving the ORPD problem. The results confirmed the robustness and effectiveness of the proposed method as a powerful optimizer applicable to optimal reactive power distribution in power systems.
Motion control involving DC motors requires a closed-loop system with a suitable compensator if tracking performance with high precision is desired. In the case where structural model errors of the motors are more dominating than the effects from noise disturbances, accurate system modelling will be a considerable aid in synthesizing the compensator. The focus of this paper is on enhancing the tracking performance of a wheeled mobile robot (WMR), which is driven by two DC motors that are subject to model parametric uncertainties and uncertain deadzones. For the system at hand, the uncertain nonlinear perturbations are greatly induced by the time-varying power supply, followed by behaviour of motion and speed. In this work, the system is firstly modelled, where correlations between the model parameters and different input datasets as well as voltage supply are obtained via polynomial regressions. A robust H ∞ -fuzzy logic approach is then proposed to treat the issues due to the aforementioned perturbations. Via the proposed strategy, the H ∞ controller and the fuzzy logic (FL) compensator work in tandem to ensure the control law is robust against the model uncertainties. The proposed technique was validated via several real-time experiments, which showed that the speed and path tracking performance can be considerably enhanced when compared with the results via the H ∞ controller alone, and the H ∞ with the FL compensator, but without the presence of the robust control law.
In this research work, numerical simulations are performed to correlate the photovoltaic parameters with various internal and external factors influencing the performance of solar cells. Single-diode modeling approach is utilized for this purpose and theoretical investigations are compared with the reported experimental evidences for organic and inorganic solar cells at various electrical and thermal conditions. Electrical parameters include parasitic resistances (Rs and Rp) and ideality factor (n), while thermal parameters can be defined by the cells temperature (T). A comprehensive analysis concerning broad spectral variations in the short circuit current (Isc), open circuit voltage (Voc), fill factor (FF) and efficiency (η) is presented and discussed. It was generally concluded that there exists a good agreement between the simulated results and experimental findings. Nevertheless, the controversial consequence of temperature impact on the performance of organic solar cells necessitates the development of a complementary model which is capable of well simulating the temperature impact on these devices performance.
The incorporation of information and communication technologies in the power grids has greatly enhanced efficiency in the management of demand-responses. In addition, smart grids have seen considerable minimization in energy consumption and enhancement in power supply quality. However, the transmission of control and consumption information over open public communication channels renders the transmitted messages vulnerable to numerous security and privacy violations. Although many authentication and key agreement protocols have been developed to counter these issues, the achievement of ideal security and privacy levels at optimal performance still remains an uphill task. In this paper, we leverage on Hamming distance, elliptic curve cryptography, smart cards and biometrics to develop an authentication protocol. It is formally analyzed using the Burrows-Abadi-Needham (BAN) logic, which shows strong mutual authentication and session key negotiation. Its semantic security analysis demonstrates its robustness under all the assumptions of the Dolev-Yao (DY) and Canetti- Krawczyk (CK) threat models. From the performance perspective, it is shown to incur communication, storage and computation complexities compared with other related state of the art protocols.
The fabrication of lightweight, ultra-thin, low power and intelligent body-borne sensors leads to novel advances in wireless body area networks (WBANs). Depending on the placement of the nodes, it is characterized as in/on body WBAN; thus, the channel is largely affected by body posture, clothing, muscle movement, body temperature and climatic conditions. The energy resources are limited and it is not feasible to replace the sensor's battery frequently. In order to keep the sensor in working condition, the channel resources should be reserved. The lifetime of the sensor is very crucial and it highly depends on transmission among sensor nodes and energy consumption. The reliability and energy efficiency in WBAN applications play a vital role. In this paper, the analytical expressions for energy efficiency (EE) and packet error rate (PER) are formulated for two-way relay cooperative communication. The results depict better reliability and efficiency compared to direct and one-way relay communication. The effective performance range of direct vs. cooperative communication is separated by a threshold distance. Based on EE calculations, an optimal packet size is observed that provides maximum efficiency over a certain link length. A smart and energy efficient system is articulated that utilizes all three communication modes, namely direct, one-way relay and two-way relay, as the direct link performs better for a certain range, but the cooperative communication gives better results for increased distance in terms of EE. The efficacy of the proposed hybrid scheme is also demonstrated over a practical quasi-static channel. Furthermore, link length extension and diversity is achieved by joint network-channel (JNC) coding the cooperative link.
For closed-loop controlled DC-AC inverter system, the performance is highly influenced by load variations and online current measurement. Any variation in the load will introduce unwanted periodic error at the inverter output voltage. In addition, when the current sensor is in faulty condition, the current measurement will be imprecise and the designed feedback control law will be ineffective. In this paper, a sensorless continuous sliding mode control (SMC) scheme has been proposed to address these issues. The chattering effect due to the discontinuous switching nature of SMC has been attenuated by designing a novel boundary-based saturation function where the selection of the thickness of boundary is dependent to the PWM signal generation of the inverter. In order to remove the dependency on the current sensor, a particle swarm optimization(PSO) based modified observer is proposed to estimate the inductor current in which the observer gains are optimized using PSO by reducing the estimation errors cost function. The proposed dynamic smooth SMC algorithm has been simulated in MATLAB Simulink environment for 0.2-kVA DC-AC inverter and the results exhibit rapid dynamic response with a steady-state error of 0.4V peak-to-peak voltage under linear and nonlinear load perturbations. The total harmonic distortion (THD) is also reduced to 0.20% and 1.14% for linear and non-linear loads, respectively.
Matched MeSH terms: Electric Power Supplies/standards*
This study investigates the effects of stirring duration on the synthesis of graphene oxide (GO) using an improved Hummers' method. Various samples are examined under different stirring durations (20, 40, 60, 72, and 80 h). The synthesized GO samples are evaluated through X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), energy dispersive spectroscopy (EDX), Fourier transform infrared spectroscopy (FTIR), and Raman spectroscopy. The GO sample with 72 h stirring duration (GO72) has the highest d-spacing in the XRD results, highest atomic percentage of oxygen in EDX (49.57%), highest intensity of oxygen functional group in FTIR spectra, and highest intensity ratio in Raman analysis (ID/IG = 0.756). Results show that GO72 with continuous stirring has the highest degree of oxidation among other samples. Electrochemical impedance spectroscopy analysis shows that GO72-titanium dioxide (TiO2) exhibits smaller charge transfer resistance and higher electron lifetime compared with the TiO2-based photoanode. The GO72 sample incorporating TiO2 nanocomposites achieves 6.25% photoconversion efficiency, indicating an increase of more than twice than that of the mesoporous TiO2 sample. This condition is fully attributed to the efficient absorption rate of nanocomposites and the reduction of the recombination rate of TiO2 by GO in dye-sensitized solar cells.
In power system networks, automatic fault diagnosis techniques of switchgears with high accuracy and less time consuming are important. In this work, classification of abnormal location in switchgears is proposed using hybrid gravitational search algorithm (GSA)-artificial intelligence (AI) techniques. The measurement data were obtained from ultrasound, transient earth voltage, temperature and sound sensors. The AI classifiers used include artificial neural network (ANN) and support vector machine (SVM). The performance of both classifiers was optimized by an optimization technique, GSA. The advantages of GSA classification on AI in classifying the abnormal location in switchgears are easy implementation, fast convergence and low computational cost. For performance comparison, several well-known metaheuristic techniques were also applied on the AI classifiers. From the comparison between ANN and SVM without optimization by GSA, SVM yields 2% higher accuracy than ANN. However, ANN yields slightly higher accuracy than SVM after combining with GSA, which is in the range of 97%-99% compared to 95%-97% for SVM. On the other hand, GSA-SVM converges faster than GSA-ANN. Overall, it was found that combination of both AI classifiers with GSA yields better results than several well-known metaheuristic techniques.
Matched MeSH terms: Electric Power Supplies/standards*
Audio forgery is any act of tampering, illegal copy and fake quality in the audio in a criminal way. In the last decade, there has been increasing attention to the audio forgery detection due to a significant increase in the number of forge in different type of audio. There are a number of methods for forgery detection, which electric network frequency (ENF) is one of the powerful methods in this area for forgery detection in terms of accuracy. In spite of suitable accuracy of ENF in a majority of plug-in powered devices, the weak accuracy of ENF in audio forgery detection for battery-powered devices, especially in laptop and mobile phone, can be consider as one of the main obstacles of the ENF. To solve the ENF problem in terms of accuracy in battery-powered devices, a combination method of ENF and phase feature is proposed. From experiment conducted, ENF alone give 50% and 60% accuracy for forgery detection in mobile phone and laptop respectively, while the proposed method shows 88% and 92% accuracy respectively, for forgery detection in battery-powered devices. The results lead to higher accuracy for forgery detection with the combination of ENF and phase feature.
Ingested foreign bodies (Fb) in children usually pass out in stools without any sequels. However, Fbs like open safety pins, button batteries, and super absorbent polymer (SAP) / jelly toys are associated with complications such as intestinal obstruction, and bowel perforation. Intestinal obstruction in children of uncertain nature should raise a suspicion of foreign body ingestion such as SAP, which may not be obvious at the initial presentation. The study reports two such children, outlining the presentation and management. Key Words: Children, Intestinal obstruction, Foreign body, Health hazard, Bilious vomit, Super absorbent polymer beads.
Generalized adaptive resonance theory (GART) is a neural network model that is capable of online learning and is effective in tackling pattern classification tasks. In this paper, we propose an improved GART model (IGART), and demonstrate its applicability to power systems. IGART enhances the dynamics of GART in several aspects, which include the use of the Laplacian likelihood function, a new vigilance function, a new match-tracking mechanism, an ordering algorithm for determining the sequence of training data, and a rule extraction capability to elicit if-then rules from the network. To assess the effectiveness of IGART and to compare its performances with those from other methods, three datasets that are related to power systems are employed. The experimental results demonstrate the usefulness of IGART with the rule extraction capability in undertaking classification problems in power systems engineering.
This paper describes the design and development of harvesting system for the gantry system to harvest eggplants. For this purpose, the harvesting robot was successfully designed and fabricated for the gantry system to harvest eggplants. The operation of the harvester was controlled by Programmable Logic Controller (PLC). Basically, the limit switches, DC motor, and relay are connected to the PLC. Meanwhile, a PLC ladder diagram was designed and developed to control the operation of the eggplant harvester. A visual basic programme was developed to interface the harvester with a greenhouse gantry control system. A videogrammetry method was employed to calculate the distance between the stems of eggplants and the cutter of robot end effector. The end effector used electric as its power source and it was controlled via Programmable Logic Controller (PLC). Visual Basic Programme was developed to interface the harvester with the gantry control system. The accuracy of the videogrammetry was tested to be 67.2% for X-axis, 88.2% for Y-axis and 84.7% for Z-axis. Meanwhile, the speed of the end effector for harvester is 2.4 km/h and it could lift up to 55 cm. In order to determine detachment force of eggplant, 16 samples of mature eggplants were tested in a greenhouse, and as a result, more than 22.76 N force was needed to detach a mature eggplant inside the gantry system.
Electrical Discharge Machining, EDM is one of the technologies used for surface
texturing such as the embedded micro-dimples on the metallic acetabular cup. During
the machining process, changes in the gap distance may lead to load changes from
open to short circuit. Limiting the load current under short circuit conditions and load
voltage under open circuit conditions is the requirement in this system. Power supply
is one of the elements that controls the process parameters which is related to improve
the machining condition as well as Material Removal Rate (MRR). A considerable
number of power supply design method were proposed for various EDM application.
This research proposed a Switch Mode Power Supply method implementing new
design of Flyback power supply which can stabilize the voltage during open circuit
condition as well as during discharge condition. Experimental studies were conducted
to verify the capability of Flyback power supply by machining eight micro-dimples in
lower position and twelve micro-dimples in upper position, both in circular
arrangement on metallic acetabular cup. Research conducted shows that the Flyback
power supply improve the consistency of MRR when compared to Linear power supply.
This may help to predict the machining time, thus improving the production of microdimples
in required time.
Recently, global interest in organizing the functioning of renewable energy resources (RES) through microgrids (MG) has developed, as a unique approach to tackle technical, economic, and environmental difficulties. This study proposes implementing a developed Distributable Resource Management strategy (DRMS) in hybrid Microgrid systems to reduce total net percent cost (TNPC), energy loss (Ploss), and gas emissions (GEM) while taking the cost-benefit index (CBI) and loss of power supply probability (LPSP) as operational constraints. Grey Wolf Optimizer (GWO) was utilized to find the optimal size of the hybrid Microgrid components and calculate the multi-objective function with and without the proposed management method. In addition, a detailed sensitivity analysis of numerous economic and technological parameters was performed to assess system performance. The proposed strategy reduced the system's total net present cost, power loss, and emissions by (1.06%), (8.69%), and (17.19%), respectively compared to normal operation. Firefly Algorithm (FA) and Particle Swarm Optimization (PSO) techniques were used to verify the results. This study gives a more detailed plan for evaluating the effectiveness of hybrid Microgrid systems from a technical, economic, and environmental perspective.
Today, the whole world faces a great challenge to overcome the environmental problems related to global energy production. Most of the islands throughout the world depend on fossil fuel importation with respect to energy production. Recent development and research on green energy sources can assure sustainable power supply for the islands. But unpredictable nature and high dependency on weather conditions are the main limitations of renewable energy sources. To overcome this drawback, different renewable sources and converters need to be integrated with each other. This paper proposes a standalone hybrid photovoltaic- (PV-) wave energy conversion system with energy storage. In the proposed hybrid system, control of the bidirectional buck-boost DC-DC converter (BBDC) is used to maintain the constant dc-link voltage. It also accumulates the excess hybrid power in the battery bank and supplies this power to the system load during the shortage of hybrid power. A three-phase complex vector control scheme voltage source inverter (VSI) is used to control the load side voltage in terms of the frequency and voltage amplitude. Based on the simulation results obtained from Matlab/Simulink, it has been found that the overall hybrid framework is capable of working under the variable weather and load conditions.
Here, we report that long-term stable and efficient organic solar cells (OSCs) can be obtained through the following strategies: i) combination of rapid-drying blade-coating deposition with an appropriate thermal annealing treatment to obtain an optimized morphology of the active layer; ii) insertion of interfacial layers to optimize the interfacial properties. The resulting devices based on poly[4,8-bis(5-(2-ethylhexyl)thiophen-2-yl)benzo[1,2-b;4,5-b']dithiophene-2,6-diyl-alt-(4-(2-ethylhexyl)-3-fluorothieno[3,4-b]thiophene-2-carboxylate-2,6-diyl)] (PBDTTT-EFT):[6,6]-phenyl C71 butyric acid methyl ester (PC71 BM) blend as the active layer exhibits a power conversion efficiency (PCE) up to 9.57 %, which represents the highest efficiency ever reported for blade-coated OSCs. Importantly, the conventional structure devices based on poly(3-hexylthiophene) (P3HT):phenyl-C61 -butyric acid methyl ester (PCBM) blend can retain approximately 65 % of their initial PCE for almost 2 years under operating conditions, which is the best result ever reported for long-term stable OSCs under operational conditions. More encouragingly, long-term stable large-area OSCs (active area=216 cm2 ) based on P3HT:PCBM blend are also demonstrated. Our findings represent an important step toward the development of large-area OSCs with high performance and long-term stability.
Smart grid (SG) application is being used nowadays to meet the demand of increasing power consumption. SG application is considered as a perfect solution for combining renewable energy resources and electrical grid by means of creating a bidirectional communication channel between the two systems. In this paper, three SG applications applicable to renewable energy system, namely, distribution automation (DA), distributed energy system-storage (DER) and electrical vehicle (EV), are investigated in order to study their suitability in Long Term Evolution (LTE) network. To compensate the weakness in the existing scheduling algorithms, a novel bandwidth estimation and allocation technique and a new scheduling algorithm are proposed. The technique allocates available network resources based on application's priority, whereas the algorithm makes scheduling decision based on dynamic weighting factors of multi-criteria to satisfy the demands (delay, past average throughput and instantaneous transmission rate) of quality of service. Finally, the simulation results demonstrate that the proposed mechanism achieves higher throughput, lower delay and lower packet loss rate for DA and DER as well as provide a degree of service for EV. In terms of fairness, the proposed algorithm shows 3%, 7 % and 9% better performance compared to exponential rule (EXP-Rule), modified-largest weighted delay first (M-LWDF) and exponential/PF (EXP/PF), respectively.
Manufacturing an array of high-quality metallic pattern layers on a dielectric substrate remains a major challenge in the development of flexible and 3-D frequency selective surfaces (FSS). This paper proposes an improved fabrication solution for the 3-D FSS based on water transfer printing (WTP) technology. The main advantages of the proposed solution are its ability to transform complicated 2-D planar FSS patterns into 3-D structures while improving both manufacturing quality and production costs. WTP technology makes use of water surface tension to keep the thin metallic patterns of the proposed FSS floating flat with the absence of a solid planar substrate. This feature enables these metallic FSS patterns to be transferred onto 3-D structures through a dipping process. To test the effectiveness of the proposed technique, the FSS was designed using computer simulation software Microwave Studio to obtain the numerical performance of the FSS structure. The WTP technology was then used to fabricate the proposed FSS prototype before its performance was tested experimentally. The measurement results agreed well with the numerical results, indicating the proposed manufacturing solution would support the development of complicated 3-D electronics devices, such as conformal antenna arrays and metamaterials.
State of charge (SOC) is a crucial index used in the assessment of electric vehicle (EV) battery storage systems. Thus, SOC estimation of lithium-ion batteries has been widely investigated because of their fast charging, long-life cycle, and high energy density characteristics. However, precise SOC assessment of lithium-ion batteries remains challenging because of their varying characteristics under different working environments. Machine learning techniques have been widely used to design an advanced SOC estimation method without the information of battery chemical reactions, battery models, internal properties, and additional filters. Here, the capacity of optimized machine learning techniques are presented toward enhanced SOC estimation in terms of learning capability, accuracy, generalization performance, and convergence speed. We validate the proposed method through lithium-ion battery experiments, EV drive cycles, temperature, noise, and aging effects. We show that the proposed method outperforms several state-of-the-art approaches in terms of accuracy, adaptability, and robustness under diverse operating conditions.