The Journal of Engineering

This article introduces a new method for induction motor speed control based on predictive control. The presented method is a modified type of model predictive control (MPC) to improve the performance and reduce the torque ripple of the induction motor. Its basis is to consider magnitude changes in the voltage vector to be applied to the motor in each sector. To validate the effectiveness of the proposed scheme, an experimental setup has been established on a prototype induction motor (IM) drive built on a Texas Instrument TMS320F28335 floating point Digital Signal Processor (DSP) board. The results of the proposed MPC demonstrates even more than 30% better performance on the IM compared to the conventional MPC in most cases.

Injection‐moulded products may have a variety of defects in production. Failing to detect and fix the defects may reduce product quality and lead to safety issues. An injection‐moulded product defect detection model, injection‐moulded product‐detection transformer (IMP‐DETR), is proposed to address the challenges of diversity, small size, and complex background in injection‐moulded products. The model constructs a feature extraction backbone network with the inverted residual mobile block module to extract key information and reduce interference from irrelevant backgrounds while maintaining lightweight. The small object fusion pyramid feature fusion network is used to capture rich texture information from small objects to improve the detection performance of fuzzy and small‐sized defects. Additionally, the Conv3XC‐Fusion module is designed to resolve the problem of integrating multi‐scale features, improving the stability of detection. Due to the lack of publicly available datasets for injection‐moulded product defects, custom dataset containing 2500 defect images was constructed. The experimental results indicate that the mean average precision of the IMP‐DETR model reaches 82.4%. Compared to other benchmark object detection models, IMP‐DETR demonstrates superior detection performance and a smaller model size, which is suitable for application in real scenarios.

Ensuring the operational reliability of substation relay protection systems through rapid defect diagnosis and state assessment is crucial for maintaining power system stability. This study introduces a new diagnostic framework that combines improved particle swarm optimization, K‐means clustering algorithms, support vector machine (SVM), and learning vector quantization neural networks to provide a comprehensive fault diagnosis and prediction model for relay protection systems. The model commences by identifying critical metrics for system state evaluation, employing an improved analytic hierarchy process to allocate weights to these indicators, and introducing variable weights theory to improve dependability of outcomes. The model enhances SVM with learning vector quantization for precise state prediction by utilizing operational data from substation relay protection systems. Improved particle swarm optimization optimizes key SVM parameters to improve accuracy. In order to effectively classify defect categories, the K‐means clustering algorithm is implemented. The model's efficacy, stability, and comprehensive applicational potential have been confirmed through experimental trials, which represent substantial progress in the field of substation fault management.

Synchronous reluctance motor has recently attracted the attention of researchers due to their characteristics such as higher efficiency compared to induction motors, lower cost in contrast to permanent magnet motors, robust rotor structure and limited rotor inertia. Also, research on five‐phase motors, which have more reliability and torque density than three‐phase motors, is of a great importance. In this essay, the optimization of the robust design of a five‐phase reluctance synchronous motor has been done through the design of experiments using the sequential Taguchi method based on fuzzy logic. To ensure that the performance of the motor is not sensitive to changes and uncertainties, the air gap manufacturing tolerances, the width of the rotor's tangential rib and the rotor's pitch control angle are considered as noise factors. In addition, to improve the motor's fault tolerance capacity, the main performance characteristics of the motor under single‐phase fault operation, such as average torque and ripple torque are considered as optimization targets, along with the main performance characteristics of the motor under normal operation. By comparing the optimization results of the proposed method with the conventional Taguchi optimization method, the effectiveness and superiority of the proposed method have been confirmed.

This paper reports the critical parameters in fitting the Al:HfO2 memristor and exploits the memristor symbol derived from this fitting to design filter circuits. Memristor‐based filters can obtain at least two different filter ranges with the same filtering effect as conventional filters. Multistate resistance can be achieved by controlling the filamentary gap; adjusting the initial gap in the high resistance state from 1 to 0.1 nm could vary the cut‐off frequency of the filters from 230 MHz to 1.54 GHz. Memristor‐based band‐pass filter is also simulated by cascading a low‐pass filter and a high‐pass filter; the results show that a cut‐off frequency of 5.46 to 22.22 GHz can be tuned by adjusting the initial gap of a low resistance state from 0.4 nm down to 0.2 nm. Replacing the resistor in a conventional filter with a memristor allows the filter to have a variable range; this would solve the limitation where the conventional analogue filters have only a single filter range due to the fixed values of their constituent elements.

This paper proposes a modified ultra‐high gain DC–DC boost converter. The proposed converter has improved the performance of the conventional converter by reducing the total number of components (1 Capacitor and 1 Inductor) in the design structure. In addition, the continuous input current and high voltage gain features are retained in the proposed structure. The steady‐state modelling including the operating principles and the stress calculation along with the cost analysis of the components have been illustrated. The comparative performance analysis has been carried out to clarify the competitiveness of the proposed converter against the recent models. The proposed model has been simulated in Matlab/Simulink software and further, a 100 W laboratory prototype has been built to evaluate the performance of the proposed converter. The peak efficiency received was 90.5% while delivering 100 W power from the source. The results from the experiments have aligned well with the outcomes of the simulation and verify the correctness of the proposed converter.

Capacitive power transfer (CPT) has emerged as a promising alternative to traditional inductive methods. CPT offers advantages like cost‐effectiveness, reduced weight and volume, and greater tolerance to alignment errors. However, the high‐Q resonant circuits used as matching networks can be susceptible to high voltage stress, especially when transmitting substantial power. Consequently, designing matching networks for CPT systems necessitates consideration of multiple parameters, including practical constraints such as component losses and breakdown thresholds. In this work an innovative algorithm is presented for designing practical matching networks in CPT systems. The algorithm conducts a methodical search of potential solutions, and converges on component values that maximize power transfer efficiency whilst also minimizing component voltage stress. The proposed algorithm is demonstrated theoretically and experimentally.

In this article, the challenging problem of multi‐target cross localization within a novel distributed high‐frequency surface wave radar (HFSWR) is tackled. This system consists of two transmitter‐receiver pairs, each of which can correspondingly generate a set of Range‐Doppler measurements. By associating the Range‐Doppler measurements from the same target, the individual target location can be derived using the associated measurements. However, when multiple targets are involved, it is unknown which measurements originate from the same target, leading to ambiguities in the measurement‐to‐measurement association. False associations can result in ghosts, ultimately degrading the overall system performance. To deal with the ghosts, a three‐stage deghosting method is proposed that incorporates the geometric characteristics of the system, the velocity features of the targets, and the motion features of the targets across multiple frames. The simulation results demonstrate the effectiveness of the proposed method compared with the other investigated deghosting algorithms. The proposed approach exhibits robust target detection capability while significantly reducing both the OSPA error and the cardinality error.

Increasing the penetration of renewables on the demand side causes operational challenges in distribution grids due to their variable and uncertain behaviour. These resources have increased distribution grid net load fluctuation during recent years, which needs to be covered by demand side flexibility (DSF). DSF should be quantified and measured by a suitable index to show the level of a system DSF in different situations. In this paper, the flexibility area index of distribution systems as a suitable metric for prosumers’ DSF evaluation, especially in the presence of renewable sources, is calculated. This index is defined first for one prosumer and then extended to a distribution system by a combination of prosumer indices. The mentioned index is decomposed into two components, one for ramp‐up (negative flexibility) and another for ramp‐down (positive flexibility). To calculate and manage prosumers’ flexibility, a novel flexibility‐oriented stochastic framework for prosumers is suggested. The amount of load shedding is under control by the risk‐management strategy in the proposed framework, which can facilitate distribution system operator operation. The results obtained from the implementation of the case study validate the major features of the proposed framework more efficiently.

This paper presents a day‐ahead scheduling for multi‐energy entities. The deep load regulation involving pumped storages, which refers to deep peak regulation, is adopted to address the impact of wind power and photovoltaic (PV) uncertainties, thereby improving the economic efficiencies of day‐ahead dispatching. And the impact of different peak valley electricity price differences on the peak shaving effectiveness of pumped storage energy was studied. Firstly, the multi‐scenario random programming method is applied to solve the prediction uncertainties of wind power and PV output in the day‐ahead. Subsequently, the multi‐scenario set of day‐ahead wind power and PV output and the load forecasting curve are considered. A pumped storage scheduling model is then established integrating the hydropower, thermal power and pumped storage. The optimal generation scheduling of pumped storage and thermal units is determined by minimizing load fluctuations and peak shaving costs. Finally, a local power grid in the Hunan province of China is selected for verification. It is shown that the proposed model can effectively accommodate the fluctuation of renewable energy output, reduce the peak regulating pressure of thermal units, and improve the operational economy of the power system.

Fingerprint database clustering and localization using k‐medoids and k‐nearest neighbour (k‐NN) algorithms respectively typically use distance‐based fingerprint similarity metrics, with their performances dependent on the type of distance metric used. This paper proposes employing a pattern‐based metric, the context similarity coefficient (CSC), for both algorithms instead of traditional distance‐based metrics. The CSC accounts for fingerprint behaviour and the non‐linear relationships among fingerprints during the similarity measurement. The performance of both algorithms with the CSC as the similarity metric is evaluated on four publicly available fingerprint databases, using position root mean square error (RMSE) and silhouette score as performance metrics. These results are compared to those of the same algorithms using five distance‐based metrics: Euclidean, square Euclidean, Manhattan, cosine, and Chebyshev distances. The k‐medoids algorithm with CSC shows moderate clustering performance compared to the five distance‐based metrics considered. However, when combined with the k‐NN algorithm also using CSC, it achieves the highest localization accuracy, with at least a 29% improvement in position RMSE across all four databases. The results indicate that while k‐medoids with CSC may not create well‐separated clusters, combining it with the k‐NN algorithm with CSC as its similarity metric significantly enhances localization accuracy compared to distance‐based metrics.

Amidst mounting global pressure, governments worldwide have embarked on a collective journey towards decarbonising their economies within the next 30 years. This ambitious goal necessitates the phasing out of naturally occurring hydrocarbons such as coal, natural gas, and oil. However, our analysis of data from academic papers, Australian Federal Government publications, and reports from energy industry bodies and manufacturers of electricity‐generating equipment suggests that such a complete elimination of fossil fuel use is not feasible. The data we've gathered, however, indicate that transitioning to a connected energy island power generation topology could at least create a sustainably robust energy supply capable of propelling Australia towards its environmental targets while bolstering its future economic well‐being.

This study presents an in‐depth overview of deep neural networks (DNN) and their hybrid applications for short‐term energy forecasting (STEF). It examines DNN‐based STEF from three perspectives: basics, challenges, and prospects. The study compares recent literature using metrics like mean absolute error (MAE), mean average percentage error (MAPE), and root mean square error (RMSE). Findings indicate that combining automated data‐driven models with enhanced DNNs effectively addresses forecasting challenges. It also highlights the role of DNNs in integrating energy prosumers, renewable energy systems, microgrids, big data, and smart grids to improve STEF.

Integrating non‐conventional renewable energy sources into distribution systems, alongside data science and enabling technological infrastructures, presents significant challenges, particularly in managing active demand. The rapid evolution of the electric energy system and increasing electricity demand highlight the need for reliable tracking and predictive methods to manage Distributed Energy Resources and digital infrastructure. These methods are essential for advancing carbon neutrality, democratizing environmental sustainability, and improving energy efficiency. Effective active demand monitoring requires understanding the transactional system concept, including digital infrastructure and decentralized demand. Although metaheuristic techniques are increasingly important in demand response integration, much research focuses on specific techniques rather than providing a comprehensive view of dynamic transaction integration for active demand. Technological advancements, like smart meters and communication systems, are shifting from basic consumption measurement to active customer participation. This article reviews key concepts in electrical distribution systems, such as active demand, DERs, and transactive systems. It examines prevalent metaheuristic techniques, emphasizing their role in integrating and predicting active demand and DER behaviors. Additionally, the study presents a methodology serving as a roadmap for efficient DER integration and the transition to active demand and transactive electricity systems, addressing gaps in the current literature.

This research contributes to the ongoing efforts to transition towards clean and renewable energy sources by providing a practical framework for decision‐making in energy resource planning. Hence, a recently developed multi‐criteria decision making technique tool is proposed for simultaneous evaluation of criteria and alternatives (SECA) to find an optimal site for establishing photovoltaic power stations in Iran. The proposed SECA method provides a systematic and comprehensive approach to evaluating renewable energy resources, taking into account technical, economic, environmental, and social factors. The data required for this research has also been collected from the Global Solar Atlas as well as the Iran Meteoritical Organization. The results show that despite the main influence of the solar radiation factor, it should be noted that among all the climatic and environmental indicators, “cloudiness factor” by weight of 0.2339 and “annual temperature” by weight of 0.2320 have been selected as the most important criteria. It was also found that among the ten candidate cities, Zahedan (w = 0.8939), Shahrekord (w = 0.8341) and Kerman (w = 0.8112) have the highest potential for building a solar power plant. The results of the study highlight the potential for solar energy resources to meet Iran's electricity needs in a sustainable manner.

In the construction of a new power system, the identification and evaluation of power consumption status of power customers will become an important basis for them to participate in the emerging businesses such as demand response and virtual power plants. In order to ensure the power safety of important power customers, a new evaluation of power consumption status of important power customers based on the AHP (analytic hierarchy process)‐TOPSIS (technique for order preference by similarity to an ideal solution) algorithm is proposed by fully mining and applying the power big data. Firstly, a power consumption big data analysis platform based on the Hadoop architecture is built to provide a high‐performance platform support for big data analysis. Secondly, nine evaluation indexes are constructed from the three dimensions of voltage, load and synthesis, which objectively and scientifically describes the power consumption status of important power customers. Finally, the AHP‐TOPSIS algorithm is used to evaluate and analyse the voltage, load and comprehensive indicators respectively, thus, obtaining the evaluation values of three kinds of indicators. The power consumption status scores of important power customers are determined by the variable weight weighted summation. The rationality and feasibility of the method and algorithm are proved by example analysis and field verification. This method helps to promote the transformation from post fault emergency repair to warning beforehand. It has the multiple effects of ensuring safe power consumption, supporting accurate patrolling and active emergency repair serving.

Sustainable development requires focusing on renewable energy, such as solar power, due to the limited availability of fossil fuels. Solar energy, available only during daylight, can be stored in thermal batteries using phase change materials (PCM). However, PCM has low thermal conductivity, slowing its melting process. Fins have been added to enhance heat diffusion, but their optimal design requires further study. This research examines an annular chamber with fixed inner and outer wall temperatures and explores fin configurations. Two series of evenly spaced fins (four and five) were analyzed, with melting times calculated using CFD simulations. Larger fins reduce melting time but limit PCM storage. Effectiveness, defined as the PCM space over melting time, was used to evaluate performance. Higher boundary temperatures decreased melting time but did not always increase effectiveness. The study also used artificial neural networks (ANN) combined with a genetic algorithm to predict optimal conditions. The configuration with five fins, a 90°C boundary temperature, a fin length of 45 mm, and a thickness of 5.4 mm achieved the highest effectiveness of 4.7, showing that smaller fins at lower temperatures are more efficient.

This article presents the new hybrid switched‐capacitor (SC)‐based transformerless DC–DC converter with a high step‐up capability and common ground features. The proposed hybrid SC‐based converter provides low voltage stress across semiconductor devices to utilize MOSFETs with lower Rdson and low voltage ratings. In addition, the diodes in SC cells with low voltage stress can reduce the reverse recovery power loss of the converter. Thus, the overall efficiency of the proposed hybrid SC‐based converter can be increased. The converter circuit, operating principle, and design guidelines of the proposed hybrid SC‐based converter are discussed. To confirm the performance of the proposed hybrid SC‐based converter, a detailed comparison study with the conventional hybrid SC converter is also presented. Finally, a laboratory prototype with 200 W and 25 to 200 V is set up to verify the effectiveness of the proposed hybrid SC‐based converter.

For citrus trees cultivated by dwarf dense planting, the fruits are randomly distributed in space, which poses difficulties for mechanized picking. To improve picking efficiency, a citrus picking system based on dual robot collaboration is designed and a global scanning picking scheme that scans the entire fruit tree to achieve orderly fruit picking is proposed in this research. The dual‐robot calibration with the iterative method and closed‐form method is completed in the research. The picking problem is attributed to the single traveling salesman problem, and the trajectory planning and the picking task are completed with genetic algorithm in the research. The picking experiments are designed in the research. As a result, in the picking experiments, the average time for planning the picking sequence of citrus was 0.1184 s, the longest time is 0.1560 s. In each group of citrus picking experiments, the total picking time averaged 158.9 s, the longest group took 170 s, the picking success rate is 82%. The picking results show that the built dual‐machine system can effectively complete the picking task and meet the real‐time requirement of picking. The proposed dual‐robot picking system can provide a reference for the establishment of other picking robot system.

This research presents a machine learning (ML)‐based model that determines the DC and RF characteristics of InGaAs sub‐channel double gate high electron mobility transistors (DG‐HEMTs) to optimize the device structure. We employ technology computer‐aided design (TCAD) simulations to analyze the DC and RF performance of InGaAs sub‐channel DG‐HEMTs, generating a range of datasets by varying the material composition, layer width, and thickness of different layers in the device structure. We then train and optimize support vector regression (SVR) models using 5‐fold cross‐validation, varying the kernel function and degree parameters, and achieve better performance with the radial basis function (RBF) kernel. The simulated results indicate that the ML model predicts physical parameters more effectively than experimental analysis, offering a compact modeling solution that requires fewer computing resources than traditional methods.

In this research, based on the sandwich sandwich theory, a new finite element model for satellite panels is constructed, which can numerically express the multi‐layer structure, material properties and their interactions of satellite panels, and more accurately predict and simulate the behaviour of satellite panels under various conditions. In the process of model building, Lanczos method is used to analyse the modal of the satellite solar panel model, and the frequency coincidence analysis between the analysis results and the real mode is carried out. The results show that the frequency error of the first five modes is less than 5%, which indicates that the established finite element model of satellite solar panel has high accuracy. It can well capture the modal characteristics of satellite solar panel in the vibration process. Using modal confidence and modal assurance criteria (MAC) to assess, conform to the degree of its MAC value is above 0.9, conform to the modal correlation standard. To sum up, the satellite panels finite element modelling method is appropriate, has the high accuracy and reliability, can better meet the needs of design and manufacture of satellite, promote the sustainable development of satellite technology and progress. image

The non‐reciprocity of electromagnetic waves is a technology garnering significant attention for its applications in electromagnetic security and the mitigation of electromagnetic interference, serving to control radio waves in space. This article outlines a comprehensive design approach for a novel non‐reciprocal structure that integrates ferrite and metal patches. Additionally, it presents experimental results utilizing two types of ferrites. The experiments demonstrated the attainment of 15 dB isolation at both 6.25 GHz and 6.5 GHz.

The effective management of microgrids is important towards transition to sustainable energy paradigm. By optimizing the utilization of different energy sources, such as solar photovoltaic panels and energy storages, it improves the reliability of the grid and develops resiliency in dealing with of challenges of unexpected variations in demand. To this end, the proposed paper presents DeepEMS, a system developed to manage the energy of microgrids through the incorporation of diverse intelligent algorithms. DeepEMS provides dynamic microgrid management through the utilization of Bidirectional Long Short‐Term Memory (BiLSTM) networks, Sliding Linear Programming (SLP), and Random Forest (RF). By implementing these methodologies, DeepEMS can optimize energy consumption throughout the microgrid by dynamically identifying and coordinating the needs of various energy sources. DeepEMS achieves precise multimodal optimization and facilitates integration of storage systems, grid interactions, and renewable energy sources (RES), as demonstrated by simulations and data analytics. DeepEMS presented performance in control, resource allocation, management, and grid utilization. Furthermore, in a comparative analysis with alternative intelligent models including XGBoost, Light GBM, RF, and Decision Trees, DeepEMS consistently demonstrated higher performance as measured by several key performance indicators (KPIs).

High altitude platform station (HAPS) is one of the promising solutions to many of the developing and less developed countries. To a great extent, HAPS is relatively cost‐effective and can be easily moved on demand while observing the required quality of service. For tropical countries, rain attenuation is the main factor that affects HAPS performance. This study investigated HAPS performance in Tanzania under heavy rain conditions. Rain rate, rain attenuation, and carrier‐to‐noise ratio were used to quantify the system's performance. In this study, the rain rate exceeds by 0.01% (R0.01)$( {{R}_{0.01}} )$ of an average, was obtained using the Chebil model. Based on Tanzania Meteorological Agency data, the R0.01${R}_{0.01}$ registered a high amount of rainfall for the five selected regions: Dar es Salaam, Kagera, Tanga, Unguja, and Pemba. By using ITU‐R model, rain attenuation for the downlink was predicted, assuming horizontal polarisation, for 31.0–31.3 GHz. Finally, the HAPS link budget was analysed with a platform altitude of 22 km at frequency of 31.0–31.3 GHz for different elevation angles. Results showed that the performance of the system was promising in the given rainfall scenarios.

A two‐stage Class A‐AB operational transconductance amplifier with low power consumption, high slew rate, and high bandwidth is introduced for handling large capacitive loads. Unlike the conventional two‐stage operational transconductance amplifiers that use a Miller capacitor, compensation is provided by the load capacitor (CL) at the output node. The proposed two‐stage amplifier maintains a 45° phase margin (PM) over any load capacitance. This is achieved through a MOSFET‐based RC network at the output node. Dual nMOS/pMOS differential stages drive output directly, improving both SR⁺ and SR⁻. Post‐layout simulation results with a capacitive load of 100 pF (CL) demonstrate that the proposed operational transconductance amplifier has a DC gain of 60.1 dB, an excellent average slew rate of 20.3 V/µs, a gain‐bandwidth product of 10.6 MHz, an average 1% settling time of 122.2 ns and a PM of 76.6°, while consuming only 103.5 µW. Reducing the CL to 10 pF reduces the PM to 47.6°, while increasing the gain‐bandwidth and average slew rate to 82.9 MHz and 142.6 V/µs respectively.