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A novel hybrid Luo-Cuk converter designed for LED lighting systems combines elements from both Luo and Cuk topologies. By integrating the input branch of both topologies, the converter, employing a single switch, can generate two distinct types of outputs simultaneously. One output follows a Luo-type, while the other adopts a Cuk-type . Through a series connection of the outputs from Luo and Cuk topologies, the voltage gain of the converter has been notably enhanced. The converter's output can be effectively regulated using a single controller. To validate the consistency between the topology's performance and theoretical analysis, simulation and prototype results have been provided.

This research aims to optimise performance in a Fuzzy Linear Quadratic Regulator controller by implementing 2 types of algorithm to stabilise the triple-link ‘Robogymnast’ robotic system. The Robogymnast recreates the movements of a human gymnast, with the hand/arm element of the system securely attached to a high bar with ball0bearing mountings, which can rotate freely. This article explores the complex factors involved in swing-up control in the underactuated Robogymnast and presents a linearisation of the mathematical modelling for the system, considering methods which apply Lagrangian equations in defining state space in such systems. Both the Teaching-Learning-Based Optimization (TLBO) and Particle Swarm Optimization (PSO) algorithm were used for tuning the hybrid Fuzzy Linear Quadratic Regulator controller for later application with the robot and assessment of response stability. In addition, MATLAB Simulink was used to simulate performance and show the effect of altering parameters including time for rising, settling and overshoot. This work aims primarily to explore how a Linear Quadratic Regulator/Fuzzy Logic controller can be applied with acrobatic robots.

An approach to controlling the three-link Robogymnast robotic gymnast and assessing stability is proposed and examined. In the study, a conventionally configured linear quadratic regulator is applied and compared with a fuzzy logic linear quadratic regulator hybrid approach for stabilising the Robogymnast. The Robogymnast is designed to replicate the movement of a human as they hang with both hands holding the high bar and then work to wing up into a handstand, still gripping the bar. The system, therefore has a securely attached link between the hand element and the ‘high bar’, which is mounted on ball bearings and can rotate freely. Moreover, in the study, a mathematical model for the system is linearised, investigating the means of determining the state space in the system by applying Lagrange’s equation. The fuzzy logic linear quadratic regulator controller is used to identify how far the system responses stabilise when it is implemented. This paper investigates factors affecting the control of swing-up in the underactuated three-link Robogymnast. Moreover, a system simulation using MATLAB Simulink is conducted to show the impact of factors including overshoot, rising, and settling time. The principal objective of the study lies in investigating how a linear quadratic regulator or fuzzy logic controller with a linear quadratic regulator (FLQR) can be applied to the Robogymnast, and to assess system behaviour under five scenarios, namely the original value, this value plus or minus ±25%, and plus or minus ±50%. In order to further assess the performance of the controllers used, a comparison is made between the outcomes found here and findings in the recent literature with fuzzy linear quadratic regulator controllers.

Grid-connected solar Photovoltaic (PV) systems are predicted to cause significant harmonic distortions in today’s power networks due to the increase utilization of power conversion systems widely recognized as harmonic sources. Estimating the actual harmonic emissions of a certain harmonic source can be a challenging task, especially with multiple harmonic sources connected, changes in the system’s characteristic impedance, and the intermittent nature of renewable resources. A method based on an Artificial Neural Network (ANN) system including the location-specific data is proposed in this paper to estimate the actual harmonic distortions of a solar PV inverter. A simple power system is modelled and simulated for different cases to train the ANN system and improve its prediction performance. The method is validated in the IEEE 34-bus test feeder with established harmonic sources, and it has estimated the individual harmonic components with a maximum error of less than 10% and a maximum median of 5.4%.

Cutting and punching of the steel used in power transformer core may cause edge burrs. This, along with the degradation of the lamination insulation, can lead to interlaminar short circuits. Analysing these faults helps understanding their effect on the transformer reliability and performance. In this light, the actual paper aims to experimentally simulate and analyse both faults using a 15 kVA three phase power transformer. Effects produced from both selected faults are experimentally investigated in this paper where different scenarios are considered such as the area of the affected regions and the number of short-circuited laminations. Various flux densities are considered ranging from 0.5 to 1.8 T. Of interest, the current at no load is recorded and the test is repeated for any given scenario. The obtained results are presented and discussed to study the effect of each fault on the transformer performance. Overall, the transformer current increases with the number of short-circuits between laminations for both faults. This increase is related to the flux density, which is dependent and sensitive to the short circuit location. Such findings represent a good indication of the severity of short circuits relative to their position in the transformer core, and can be exploited to discuss the power losses in the transformer core.

Minimising power losses and their consequences is a significant matter in electrical steel applications. Increasing the resistivity of the steel strips has been confirmed as a successful method to overcome the problem of power losses. To increase the resistivity of the strip, different effective methods have been proposed and tested. In this work, a new material has been proposed to achieve the objective of increasing the resistivity of the steel samples by adding Manganese (IV) oxide based on a diffusion technique. The surface of the samples is to be coated with the proposed Manganese oxide. This should guarantee an increase in the resistivity of the samples, which in turn reduces the power losses caused by the eddy current. The samples tested were of non-oriented electrical steels containing 2.4 wt% Si-Fe (with a thickness of 0.305 mm*300 mm*30 mm). It was measured for losses and permeability before and after treatment by a Single Strip Tester (SST) at 0.5–1.7 T using an Alternating Current magnetic properties measurement system under controlled sinusoidal at different frequencies. The obtained results revealed that the depth of Manganese oxide diffusion is inversely proportional to the increase in the temperature. It was demonstrated that the best amount of diffusion of the element into the strips was achieved at 525°C, which was 60 weight % in comparison with 700°C which was 20 wt%. Likewise, at 800°C it was 7 wt%. However, the depth of diffusion of the manganese was the same at those tested temperatures, which were equal to 200 µm deep on each of the side strips. The diffusion of the material was investigated using Scanning Electron Microscope (SEM) coupled with Energy Dispersive X-ray Spectroscopy (EDS). Furthermore, from the results, it was concluded that the power losses in the coating samples were improved by 9% as compared with uncoated samples.

Electrical steels play a crucial role in modern electrical devices and power systems due to their exceptional magnetic properties. This comprehensive review delves into the advancements in the field of electrical steels, focusing on key aspects such as microstructure, loss analysis, magnetic properties, alloying elements, and the influence of coatings. The microstructural characteristics of electrical steels are explored in relation to their impact on magnetic behaviour and overall performance. Loss analysis techniques are discussed, highlighting the importance of minimizing energy dissipation in applications. The intricate relationship between magnetic properties and material composition, including the role of alloying elements, is examined to elucidate the mechanisms governing magnetic behaviour. Furthermore, the influence of coatings on the performance of electrical steels is investigated, considering both protection against environmental factors and their impact on magnetic properties. Through a comprehensive synthesis of recent research, this review provides valuable insights into the multifaceted nature of electrical steels and sets the stage for further advancements in their design and application.

This paper describes the design and modelling of a DC linear stepper motor (DLSM), to investigate the air-gap magnetic flux density and the static features of the thrust force under the DC current excitation condition, while taking the nonlinear properties of iron materials used into account. A Finite Element Method (FEM) study utilizing software called a “MagNet” was employed to model and construct a linear DC motor. The thrust force, air-gap flux density, and static thrust force at various airgaps were compared in simulation and real time. The result showed that the magnetic flux density presents a sinusoidal shape in both the simulation and measurement, where the peak error value was around 7.14%. A comparison was made with thrust force, as the armature current has different values; the results show that the difference between the experimental and computed peak value was 8.07%, while the error between the measured and simulated value was 5.02% when the thrust force was measured at different air gap lengths.

Dynamic energy recovery in electric and hybrid electric vehicles can assist in restoring kinetic energy during descending and braking operations. The recouped energy can be stored in various storage units, such as a flywheel energy storage (FES) units, ultra-capacitors (UCs) or batteries, thus conserving energy for later use during acceleration condition. The FES is used in a variety of modern applications; for instance, it tends to be associated with the motor shaft to assist in starting the vehicle from rest. Several different components have been utilized to execute the motoring and braking mechanism in a structured test rig. However, there is an urgent need to represent this application within the limits of the laboratory facilities while being able to simulate the natural system. Consequently, this paper will examine the effect of rotating inertia at a different rotating speed in the drive system on propelled energy. It will also evaluate the stored energy in the UC storage unit and the braking time in the regenerative braking (RB) process. Moreover, it will demonstrate the experimental probing and mathematical modeling of the motor/flywheel (lumped into an equivalent inertial mass), which is used in different drive cycle schemes to simulate real system behavior. The analysis is accomplished with experimental verification, highlights the advantage of the new approach for evaluating storage capacity. Also, it gives the static and dynamic (time dependency) equations to show an accurate system representation as a ready-made (black box) models, which will be used to determine the overall system performance.

The Robogymnast is a complex system formed from a triple-inverted pendulum and mimics the action of a gymnast as they hang by their hands from a high bar and perform progressive upswings to eventually rotate completely around the bar. The three links of the Robogymnast can be compared to the lower limbs, the torso, and the upper limbs of a gymnast, with a single passive joint and two further stepper-motor powered joints. There is sensor equipment attached to the different links to gather data and control signals. While this system has been physically constructed, the current paper describes the approach to automating the 3-link pendulum and describes the system, its parts and setup. Following this, STM32 is applied for programming, system operation and presentation of results.

This paper proposes a robust and effective method of direct torque and rotor flux control (DTRFC) strategy for an induction motor (IM). The described scheme ensures the elimination of uncontrollable angles (UCAs) over the entire speed range. This means that each voltage vector (VV) produces the required effect of both torque and flux without any counteracting effect. First, the behaviour of the DTRFC algorithm was analysed at low and high speeds in terms of determining the values of UCAs. Through the analysis, it was found that the basic scheme suffered from UCAs at medium and high speeds. Accordingly, a special strategy for medium and high speeds with 18 sub-sectors (SSs) was proposed while maintaining the basic 6 sectors strategy for low speed. The transition speed between the two strategies was determined to ensure the absence of UCAs over the whole speed range. The simulation results of the proposed method were obtained in the MATLAB/Simulink environment. Furthermore, to verify the effectiveness of this method, a dSPACE-based experimental induction motor DTRFC drive system was accomplished.

This paper presents an experimental investigation on the influence of shading on mono-crystalline (mono-Si) solar cell parameters. The variations of equivalent circuit parameters with shading were determined and then used in modelling a mono-Si solar cell and a mono-Si photovoltaic (PV) module under partial shading. It was found that the simulation by considering the parameter variations with shading in the single cell model did not lead to a noticeable improvement in modelling accuracy. However, for the PV module, a significant improvement in modelling accuracy in the reverse bias region was achieved when considering all parameter variations in the model. A further investigation was performed to identify the key parameters that are responsible for the improvement. The results revealed that in addition to the photo-generated current, the shunt resistance also has a significant effect on the model accuracy. A modelling approach was thus proposed, which includes the variation of the shunt resistance with shading, in addition to the variation of the photo-generated current. This approach was experimentally validated using a mono-Si PV module. The results show that the proposed approach is more accurate, compared to the approach that considers only the variation of the photo-generated current, without the need to include an avalanche breakdown term.

In this paper, a combined structure of algorithms based on field-oriented control (FOC) and direct torque control (DTC) is proposed for improving the performance of a three-phase induction motor (IM) over a wide range of torque loads. The objective of this study is to investigate the state-of-the-art research performed on the resilient intersection control management between FOC and DTC algorithms for IM drive. Three algorithms are employed in the proposed scheme: Direct Torque Stator Flux Control (DTSFC), Direct Torque Rotor Flux Control (DTRFC), and the FOC algorithm. The main aim of the control law is to launch the drive system by selecting DTSFC or DTRFC during the transient state while FOC is switched smoothly to the steady-state. The proposed method had several advantages, including the ability to produce torque beyond the pull-out torque range at 340%, high torque dynamics during transient state within 0.5 ms, low chattering up to 0.05 N.m, and fixed frequency at steady-state operation. Furthermore, a comprehensive comparison study has been conducted, including the most recent research, to verify the effectiveness and superiority of the proposed technique. Results were obtained using both MATLAB/Simulink environment and also were experimentally performed by using a dSPACE DS1103 digital signal processing board to verify the concept of the proposed control scheme.

Today’s power distribution networks are predicted to incorporate more Power Electronics (PE)-based power conversion systems, widely acknowledged as harmonic sources. Concerns about power harmonic severity in the distribution networks can arise, especially with the growing numbers of Medium Voltage Direct Current (MVDC) systems, which are also facilitated by such power converters. Yet, an accurate harmonic model of the MVDC converter is required to investigate its harmonic emissions, propagations, effects, and solutions in today’s distribution networks. This article is devoted to the development of a detailed model of a cascaded Neutral Point-Clamped (NPC)-based MVDC converter for accurate harmonic analysis studies. An appropriate control system with a simple Proportional Integral (PI) controller tuned using the loop-shaping technique is developed. An interleaved Sinusoidal Pulse-Width Modulation (SPWM) scheme aiming to improve the harmonic performance of such an application is introduced. The detailed model of the MVDC system was developed using the Simulink/MATLAB simulation environment, for which the concept of operation was validated, control system performance was investigated, and the effectiveness of the harmonic reduction method was analysed. The key finding is that the interleaved SPWM technique has significantly reduced the Total Harmonic Distortion (THD) to 2% with no significant even-order harmonic components in comparison to the reported models.

A novel hybrid machine learning technique is proposed for the protection of three-phase power transformers in this study. Here, the developed model was tested across several types of current signal fault cases from different fault conditions and examined based on a laboratory-constructed transformer system, in which internal and external faults were created. The data gathered on signals were used to develop a novel hybrid model. A process for optimal feature identification was put forward, with machine learning classifiers being trained to classify faults. The methods used included orthogonal matching pursuit and discrete wavelet transform for extraction of statistical characteristics from unprocessed data. Following this, the bees algorithm (BA) was used to create an optimized subset, minimizing the amount of data needed and making the model more accurate. In order to distinguish normal operational conditions (inrush current) from faults, an optimized feature set was used as an input for three classification algorithms: the k-nearest neighbour, support vector machine, and artificial neural network. Training was conducted via k-fold cross-validation. Comparisons were made between the proposed approach and a comparable approach, which used the genetic algorithm (GA). This model was analysed based on specificity, accuracy, precision, recall, and F1 score. The findings from the experiment suggest that the model proposed here is suitable for fault identification in a range of conditions and faults.

The design of Passive Power Filters (PPFs) has been widely acknowledged as an optimization problem. This paper addresses the PPF parameters design problem using the novel Manta Ray Foraging Optimization (MRFO) algorithm. Moreover, an analytical method based on Monte Carlo Simulation (MCS) is proposed to investigate the harmonic performance of such an optimally designed PPF with variations in power networks. The MRFO algorithm has shown a superior solution-finding ability, but a relatively higher computational effort in comparison with other recently proposed algorithms. The harmonic performance of the optimal PPF solution with uncertainties was analyzed using the proposed method. The results imply that the optimally designed PPF can effectively attenuate the high-order harmonics and improved the system performance parameters over different operating conditions to continually comply with the standard limits. The proposed MCS method showed that the optimally designed PPF reduced the voltage and current distortions by roughly 54% and 30%, respectively, and improved the network hosting capacity by 10% for the worst-case scenario.

Fault diagnosis of induction motor anomalies is vital for achieving industry safety. This paper proposes a new hybrid Machine Learning methodology for induction-motor fault detection. Some of the motor parameters such as the stator currents and vibration signals provide a great deal of information about the motor’s conditions. Therefore, these signals of the motor were selected to test the proposed model. The induction motor was assessed in a laboratory under healthy, mechanical, and electrical faults with different loadings. In this study a new hybrid model was developed using the collected signals, an optimal features selection mechanism is proposed, and machine learning classifiers were trained for fault classification. The procedure is to extract some statistical features from the raw signal using Matching Pursuit (MP) and Discrete Wavelet Transform (DWT). Then, the Invasive Weed Optimization algorithm (IWO)-based optimal subset was selected to reduce the data dimension and increase the average accuracy of the model. The optimal subset of features was fed into three classification algorithms: k-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest (RF), which were trained using k-fold cross-validation to distinguish between the induction motor faults. A similar strategy was performed by applying the Genetic Algorithm (GA) to compare with the performance of the proposed method. The suggested fault detection model’s performance was evaluated by calculating the Receiver Operation Characteristic (ROC) curve, Specificity, Accuracy, Precision, Recall, and F1 score. The experimental results have proved the superiority of IWO for selecting the discriminant features, which has achieved more than 99.7% accuracy. The proposed hybrid model has successfully proved its robustness for diagnosing the faults under different load conditions.

This study develops and implements a design of the Fuzzy Proportional Integral Derivative with filtered derivative mode (Fuzzy PIDF) for Load Frequency Control (LFC) of a two-area interconnected power system. To attain the optimal values of the proposed structure’s parameters which guarantee the best possible performance, the Bees Algorithm (BA) and other optimisation tools are used to accomplish this task. A Step Load Perturbation (SLP) of 0.2 pu is applied in area one to examine the dynamic performance of the system with the proposed controller employed as the LFC system. The supremacy of Fuzzy PIDF is proven by comparing the results with those of previous studies for the same power system. As the designed controller is required to provide reliable performance, this study is further extended to propose three different fuzzy control configurations that offer higher reliability, namely Fuzzy Cascade PI − PD, Fuzzy PI plus Fuzzy PD, and Fuzzy (PI + PD), optimized by the BA for the LFC for the same dual-area power system. Moreover, an extensive examination of the robustness of these structures towards the parametric uncertainties of the investigated power system, considering thirteen cases, is carried out. The simulation results indicate that the contribution of the BA tuned the proposed fuzzy control structures in alleviating the overshoot, undershoot, and the settling time of the frequency in both areas and the tie-line power oscillations. Based on the obtained results, it is revealed that the lowest drop of the frequency in area one is −0.0414 Hz, which is achieved by the proposed Fuzzy PIDF tuned by the BA. It is also divulged that the proposed techniques, as was evidenced by their performance, offer a good transient response, a considerable capability for disturbance rejection, and an insensitivity towards the parametric uncertainty of the controlled system.

This paper proposes a design of Sliding Mode Control (SMC) for Load Frequency Control (LFC) in a two-area electrical power system. The mathematical model design of the SMC is derived based on the parameters of the investigated system. In order to achieve the optimal use of the proposed controller, an optimisation tool called the Bees Algorithm (BA) is suggested in this work to tune the parameters of the SMC. The dynamic performance of the power system with SMC employed for LFC is studied by applying a load disturbance of 0.2 pu in area one. To validate the supremacy of the proposed controller, the results are compared with those of recently published works based on Fuzzy Logic Control (FLC) tuned by Teaching–Learning-Based Optimisation (TLBO) algorithm and the traditional PID optimised by Lozi map-based Chaotic Optimisation Algorithm (LCOA). Furthermore, the robustness of SMC-based BA is examined against parametric uncertainties of the electrical power system by simultaneous changes in certain parameters of the testbed system with 40% of their nominal values. Simulation results prove the superiority and the robustness of the proposed SMC as an LFC system for the investigated power system.

This paper focuses on using the Bees Algorithm (BA) to tune the parameters of the proposed Fuzzy Proportional–Integral–Derivative with Filtered derivative (Fuzzy PIDF), Fractional Order PID (FOPID) controller and classical PID controller developed to stabilize and balance the frequency in the Great Britain (GB) power system at rated value. These controllers are proposed to meet the requirements of the GB Security and Quality of Supply Standard (GB-SQSS), which requires frequency to be brought back to its nominal value after a disturbance within a specified time. This work is extended to employ the proposed fuzzy structure controller in a dual-area interconnected power system. In comparison with controllers tuned by Particle Swarm Optimization (PSO) and Teaching Learning-Based Optimization (TLBO) used for the same systems, simulation results show that the Fuzzy PIDF tuned by BA is able to significantly reduce the deviation in the frequency and tie-line power when a sudden disturbance is applied. Furthermore, the applied controllers tuned by BA including the Fuzzy PIDF prove their high robustness against a wide range of system parametric uncertainties and different load disturbances.