This paper aims at analysis of an operation of a doubly-fed induction motor (DFIM). The paper mainly focuses on the soft start method for the DFIM using the conventional topology that is normally used in the case of a doubly-induction generator (DFIG). Likewise, the details about reactive power control is also discussed. With the proposed method, the DFIM can start from zero to any speed by controlling the machine-side converter (MSC). The computer simulation is performed to a 5 kW wound rotor induction motor using PLECS software. The simulation results validate that the DFIM can operate from the rest to any speed while operating only under the conventional topology.
Nowadays, it is desirable for the power industry to transmit power between different sites of the transmission system in the most cost-effective manner. Congestion management is one of the system operator's most challenging responsibilities in a deregulated context. Congestion would increase electricity costs and transmission loss and have a negative impact on the system's stability and security, so system operators work on it to reduce congestion in deregulated power systems. In this investigation, congestion is handled by considering three objective functions. The first objective is to minimize the generation cost and the second objective is to minimize the transmission loss of the system and third objective is to minimize the total congestion expanse. A water cycle algorithm is employed to mitigate the proposed congestion management and an IEEE 30 bus and 118 bus test system is employed to demonstrate the effectiveness of the suggested approach.
This paper proposes a novel optimization algorithm for designing power system stabilizers (PSSs). The prospect of attaining higher stability motivated the authors to creat a new optimization algorithm for this study. A novel algorithm named the Improved General. Relativity Search Algorithm (IGRSA) was also developed by cloud theory to improve the performance of GRSA. The supremacy of the proposed approach is tested by comparing it with an introduced objective function in a medium multi-machine power system. The nonlinear simulation results and eigenvalues analysis has demonstrated that the proposed approach in this study is highly effective in enhancing the dynamic stability of the power system.
This paper proposed and discussed a SRLSM with the segmental pole. The segmented SRLSM which known as SSRLSM was designed for domestic lift application. The SSRLSM was designed to fulfill the design target requirement where the lift must be able to transport a maximum 200 kg payload. A lab-scaled prototype of the SSRLSM was developed for experimental and measurement purpose. Beforehand, the SSRLSM was designed and simulated for different structure dimension to study the effect of structure’s reduction towards its performances. The structure dimensions involved are the number of coil number and the stack length where the number of coil is reduced from 6 coils to 3 coils. The results show that the thrust produced by the 6-coils and 3-coils SSRLSM is 2400 N and 1200 N, respectively. This shows that, the thrust is reduced by 50 % as the number of coil reduced by half. At the same time, the thrust produced also reduced by approximately 50 % as the stack length, z reduced by half from 400 mm to 200 mm. This shows that, the thrust, F of the SSRLSM is directly proportional to the number of coil as well the stack length, z.
Strategic Optimal Bidding of the data is a compulsory duty for Independent System Operator (ISO) which is the most complicated task that maximizes the profit of the supplier by handling bidding coefficient strategically. This paper endorses a strategy of optimal bidding coefficient data to improve the profit value by latest optimizing technique named hybrid Water Cycle Moth Flame Optimization Algorithm, which achieves a heuristic search thereby obtaining a global search of a stream using Levy flight movement. This method is applied and tested on an Indian-75 Bus system to test and investigate the new strategy whether receiving best solution of profit in comparison with other conventional techniques explained widely. On adding it evaluates the efficacy of the proposed method on the mentioned system through assessing total profit obtained, revenue, power generation, Market Clearing Price and cost of the individual GENCO. In order to show the Statistical Analysis the Box-plot is done to perform the visual data representation of the proposed and conventional methods.
This paper uses a reactive power control optimization algorithm to present an inverter-based photovoltaic generation system for increasing the distribution grid host capacity. The main objective of this study is to regulate bus voltages by providing sufficient minimal reactive power consumption, which is obtained by a Particle Swarm Optimization-based optimization algorithm. A modeling study of the system network is developed using DIgSILENT PowerFactory, and the control algorithm is implemented in MATLAB. The proposed approach is efficient for various scenarios of the IEEE 13-bus test system and practical distribution network. The analysis results are compared with outcomes from other control methods, including technical and economic assessments. The performance of the proposed optimization-based power system has shown that the proposed algorithm method yielded significantly superior mitigated voltage rises and increased hosting capacity compared to those achieved by existing control methods of a specific distribution network.
This article proposes an ANFIS-based control scheme for improved power quality and power management that will provide optimal and sustainable energy in a grid-connected renewable energy system. The system consists of a wind energy conversion system (WECS), a photo voltaic system (PVS), and a battery energy storage system (BESS). To increase the PVS's and WECS output powers, a unique control approach is suggested in this paper. The adaptive Neuro-Fuzzy Inference System (ANFIS) controller is incorporated into the proposed controller to generate three-phase reference current signals for harmonic elimination in the system during system dynamic conditions. The proposed control technique can work under voltage quality problems such as voltage sag, voltage swell, neutral currents, and reactive power. Renewable energy sources (PV and Wind) are interfaced to improve the DC-link overall performance by minimizing short-term and long-term voltage problems. The proposed controller regulates the energy flows between the renewable energy sources to the end users with a unity power factor. A supervisory control scheme is implemented for optimal power management in the system. Based on the load requirement, and how the renewable, battery, and grid sources are sharing the power, a detailed analysis is given in the paper. Simulation results from the MATLAB/Simulink platform under several test conditions at the grid side and load side illustrate the efficacy of the proposed control mechanisms in the environment of power optimization and energy management. The comparative analysis is performed to show the efficacy of the proposed system. Finally, the proposed system is validated and the THD content of the grid currents is found good.
Because of manufacturing constraints, designing analog active filters is highly challenging. Evolutionary computing is an effective method for automatically selecting the component values like resistors and capacitors. This work describes the partition-bound Particle Swarm Optimization (PB-PSO) for efficiently designing second-order active low-pass state variable filter (SVF) considering different manufacturing series. PB-PSO is responsible for efficiently picking components and minimizing total design error. The filter components are chosen to be compatible with the E12/ E24/ E96 series. Compared to earlier optimization strategies, the simulation findings show that PB-PSO reduces the overall design error.
This project aims at system implementation for the soft start operation of a doubly-fed induction motor (DFIM) based on stator flux vector control. In this paper, the theory is briefly discussed. The simulation is performed using PLECS software to validate the hypothesis. A 5 kW wound rotor induction motor (WRIM) mechanically coupled with a simulated load is setup for the experiment. The STM32F407 microcontroller is applied to control the experimental system. Moreover, the Modbus protocol is applied for communication between the microcontroller and the computer using RS485 standard. The problem about rotor angle correction before enabling the MSC, which does not appear in the simulation, is seriously discussed. The experimental results do substantiate the proposed method and can be practically applied to the real system.
Paper present a study enhancement of wire bonding process in the integrated circuit package with 2N AuPd coated Cu wire (2N-AuPdCu) for Automotive devices. Wire bonding is the electrical connection between pad and leadframe. In present, Au wire is recently use for interconnection while the gold price still exorbitant and caused concern to the wire bonding cost. Cu wire is a lower price and considered to be an alternative for the interconnection but still concern in term of corrosion and reliability. Recently, 4N AuPd coated Cu wire (4N-AuPdCu) material have been introduced for automotive device but still encounter the reliability problem. The 2N AuPd coated Cu wire was developed version for more reliability enhancement and consider to use for alternative wire of automotive device. In the experiment, Au wire, 4NAuPdCu wire and 2N AuPd coated Cu wire were used for wire bonding process on 8 leads Small Outline Integrated Circuit Package (8L-SOIC) and 14 leads Thin Shrink Small Outline Package (14L-TSSOP) to compare wire bond ability. Analysis, this package tested wire bond ability and reliability. The results performed well in wire bond ability and reliability for 2N AuPd coated Cu wire when comparing with Au wire and 4N AuPd coated Cu wire. Therefore, 2N AuPd coated Cu wire can enhance the quality and reliability for 8L-SOIC package and 14L-TSSOP package for automotive device.
This paper presents a novel target detection and identifying approach using polarimetric radar cross-section and matrix correlation coefficient. We have adopted a polarimetric radar cross-section matrix correlation strategy (PRMC) algorithm using a matrix correlation approach based on the polarimetric radar cross-section. It is projected as an inverse scattering problem under the electromagnetic scattering model using polarimetric Physical Optics approximation. The experimental measurements using canonical targets carried out under semicontrolled conditions verify the performance of the developed procedures. Finally, the identification strategies' effectiveness is demonstrated in free-space conditions and a scene with a brick and autoclaved aerated concrete wall.
Observed that most setups have limitations in the number of RF nodes due to a limited number of measurements. However, it is well known that the main difficulty in radio tomographic imaging attributes to the uncertainties in the receive signal strength (RSS) measurements of transceivers due to multipath effects, especially, when the environment of interest is much cluttered, and requirements on the larger number of nodes for the performance improvements. However, no study has been conducted to solve the inverse problem and improve the quality of the reconstructed image using a reduced sensor model for Radio tomography system localization. This work focuses on the design and development of a Radio tomography system for human localization that will employ a transceiver sensor arrangement to increase the number of measurements, without making any changes to the hardware design as well as the number of pixels in the sensing domain. An image reconstruction technique namely, Adjacent Criterion Method (ACM) was proposed to enhance the image spatial resolution. A number of experiments were used to evaluate the performance of the system. The results showed that the proposed technique improves the spatial resolution and exhibits more accurate tomograms
Electric motors have revolutionized the way of human living and resulted in the modern lifestyle. These motors often operate in corrosive and dusty places and are exposed to a variety of undesirable conditions and situations that result in the failure of the motor. The faults occurring in Induction Motors (IM) need to be detected at a proper time for avoiding losses and further consequences. A well-designed fault detection scheme not only reduces motor failure but also increases productivity and even sometimes avoids accidents. This paper presents a review of fault detection and classification techniques in three-phase induction motors (TPIM). The main theme of this paper is to revisit the conventional methods for fault detection in TPIM and compare them with recently published methods based on parameters to be sensed, and the type of fault that can be detected, with their advantages and drawbacks. Around a hundred papers are critically reviewed and studied from old and new regimes. Attention is also given to fault detection methods based on artificial intelligence (AI) and machine learning (ML). This paper concludes with brief remarks which will be very useful for new researchers who are willing to research in the domain of fault detection and classification.
This research analyzes the impact of wireless transceiver subnet clustering on a hundred-core mesh-structured WiNoC architecture. The study aims to examine theeffects of distance-based wireless transceiver placements on transmission delay, network throughput, and energy consumption in a mesh Wireless NoC architecture with a hundred cores, particularly under the X-Y, West-First, Negative-First, and North-Last routing strategies. This research investigates the impact of positioning radio subnets at the farthest, farther, nearest, and closest positions within an architecture featuring four wireless transceivers. The Noxim simulator was used to simulate the analyzed wireless transceiver placements on the hundred-core mesh-structured WiNoC designs, with the objective of validating the results. The architecture with the wireless transceiver positioned at the midway proximity (nearer and further) delivers the best performance, as evidenced by the lowest latencies for all evaluated deterministic routing algorithms, corresponding to the simulation outcomes.
Plant factory artificial light (PFAL) is an effective technique for producing large amounts of crops per area and high-quality plant growth. This work aims to construct a semi-closed PFAL growth system based on NB-IoT using two types of LED arrays: phosphor-converted LED (pc-LED) and RB-LED. Next, while examining the features of the artificial light spectrum, compare the Curry leaf kale and Chinese kale in seedlings under various LED light sources. An NB-IoT module with the MAGELLAN platform monitored and controlled the temperature, humidity, and illumination of the semi-closed PFAL growing system. The results indicate that cos lettuce cultivated with PCLEDs is likely more photosynthesis-capable than cos lettuce grown with RBLEDs. Compared to RB-LED, the average fresh weight of the cos lettuce from PC-LED was significantly higher. The data gathered from the cloud system under the MAGELLAN platform during the 7-day trial, the control of lighting and watering in the semi-closed PFAL system, and the measurement results of environmental factors were all accurately completed. Organic veggies could be grown in a home or school using the semi-PFAL growing technique.
Due to the emerging deployment of cellular IoT, a network topology design appears to be one of the greatest challenges faced by mobile network operators, that is, both the capacity maximization and the overall network cost minimization have been considered as the objective of network planning. In this article, the topology design for cellular IoT is divided into two subproblems: gateway location and gateway connection problems. They are formulated as the integer linear programming problem. For the former subproblem, the best gateway locations and the optimal network cost can be obtained by the optimization approach to form multiple local networks. For the latter subproblem, a connection of selected gateways with the minimum connection cost can be presented by the Kruskal algorithm to form a backbone-like network. This results in a two-layered network with the minimum network cost. According to the results, a significant reduction in the network cost could be obtained with the optimal setting of system parameters. In addition to the optimization approach, the gateway location problem is examined by means of clustering algorithms. The fair gateway placement can be obtained by K-medoids clustering without the time complexity.
The purpose of this paper is to fabricate organic field effect transistor and to investigate the effect of the thickness of the pentacene active layer and the thickness of gate insulator layer on MOSFET performance. The fabricated structure is top-contact. When the thickness of the insulator gate layer increases from 10 nm to 30 nm, the magnitude of the drain source current, when VGS = VDS = -4 V, decreases from 1813 nA to 214 nA and then the threshold voltage shifts from -1.4 V to -2.4 V. When the thickness of the pentacene increases from 9 nm to 40 nm, the threshold voltage voltage shifts slightly in the negative direction from -1.4 V to -1.6 V for SiO2 thickness of 10 nm. In case of SiO2 thickness of 20 nm, the threshold voltage voltage shifts from -1.9 V to -2.2 V. In case of SiO2 thickness of 30 nm, the threshold voltage voltage shifts from -2.4 V to -2.9 V. Besides that, the mobility decreases from around 0.31 cm2/(Vs) to 0.15 cm2/(Vs) when the pentacene thickness increases from 9 nm to 40 nm.
The Collision Avoidance System (CAS) is a safety system created to identify and prevent collisions, primarily on drones. The CAS comprises three processes: detection, prediction, and action. The predictive process is crucial as it determines whether a collision will occur, making it the core component of the system. Most drones are equipped with cameras. A visual-based prediction involves the use of a convolutional neural network (CNN). The CNN operates by autonomously learning and extracting hierarchical characteristics from input data through convolution, pooling, and fully connected layers. Currently, there are CNN models called pretrained models that are ready to use. However, not all pretrained models are suitable for compatibility with drones as they possess computational constraints. Our objective is to establish a suitable model selection from a variety of pre-trained CNN models with lightweight architectures. The transfer learning technique is applied to customize these models with the ColANet dataset. Subsequently, we evaluate these models regarding their accuracy, model size, inference time, and power consumption. Finally, the selected model is deployed in real time on a Raspberry Pi 3B+ with data input from a DJI Tello drone camera, and the prediction performance is evaluated.
In this paper, an enhanced 0.5 kW Z-source inverter (ZSI) model is used in the design of a wind energy generating system (WEGS) that uses a 1.2 kW wind turbine to overcome the operating restrictions of the traditional ZSI model. To achieve a constant line-to-line voltage with varying loads at the output side of the WEGS, a closed-loop control technique is applied at the load side. A proportional-integral (PI) controller is utilised with the ZSI for closed-loop control since it is the least complicated in terms of tuning and operation. Also, ZSI systems have nonlinear behaviour, which precludes direct application of the PI controller technique to them. As a result, the novelty of this article is the optimisation of stabilised PI coefficients (Kp, Ki) with a modified ZSI model. Particle swarm optimisation (PSO), sine-cosine algorithm (SCA), and whale optimisation algorithm (WOA)-based optimisation techniques are used to handle PI tuning for closed-loop modified ZSI. In terms of the stability of the closed-loop modified ZSI with WEGS, the WOA performs better. The outcomes show that the suggested controller can control the variation in AC output voltage of ZSI with variable load exactly.
Electric power distribution system planning is a key area of concentration for developing more efficient, trustworthy, and environmentally friendly energy sources in the future. While distributed generations (DGs) are excellent at reducing system actual power losses, shunt capacitors can supplement their effectiveness when utilized in tandem. It is feasible to achieve even higher reductions in power losses and enhanced system efficiency by combining DGs with shunt capacitors. The Loss Sensitivity Factor (LSF) measures how sensitive real power loss in a network is to changes in power injection (active or reactive power) at a single bus. The methodology analyses LSFs to assess the ideal sites for placing DGs and capacitors in radial distribution networks (RDNs). The Golden Jackal Optimization (GJO) strategy is adopted to discover the ideal sizing and allocation of DGs and capacitors. The strategy is tested using two RDNs, one with 33 buses and the other with 69 buses. In addition, five major technical indices are explored and examined, used as an evaluation criterion to distinguish between the optimal and baseline performances. These are the voltage deviation index (VDI), power loss index (PLI), fast voltage stability index (FVSI), line stability factor (LQP), and novel line stability index (NLSI). Voltage deviation, power loss, and voltage stability analysis-related indices are among the technical characteristics addressed.
