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This paper deals with the detection and classification of two types of lamination faults (i.e., edge burr and lamination insulation faults) in a three-phase transformer core. Previous experimental results are exploited, which are obtained by employing a 15 kVA transformer under healthy and faulty conditions. Different test conditions were considered such as the flux density, number of the affected laminations, and fault location. Indeed, the current signals were used where four features (Average, Fundamental, Total Harmonic Distortion (THD), and Standard Deviation (STD)) were extracted. Elaborating A total of 328 samples, these features are utilized as input vectors to train and test classification models based on SVM, KNN, and DT algorithms. Based on the selected features, the results confirmed that the transformer current can be used for the detection of lamination faults. An accuracy rate of more than 84% was obtained using three different classifiers. Such findings provided a promising step toward fault detection and classification in electrical transformers, helping to prevent the system and avoid other related issues such as the increase in power loss and temperature.

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.

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.

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.

This paper introduces analysis, control, and comparison of two benchmarking optimization approaches called Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for Direct Torque Control (DTC) of a three-phase Induction Motor (IM). This study aims to determine the most efficient and robust of the two different metaheuristic optimization techniques including PID-PSO and PID-GA for DTC of IM. The purpose of the proposed control technique that has been presented is to get over the most significant drawback of DTC, which is a high level of torque output. The issue of torque ripples needs to be reduced to a significant amount using the two proposed control methods PSO-DTC and GA-DTC. As a result, PSO-DTC is the most applicable scheme. The proposed PID-PSO of DTC provided an excellent work performance for IM system drive. The comparison results of the suggested control methods showed a significant improvement of the control system compared to the classical DTC. The result is a high fidelity estimate of electromagnetic torque and speed for computation of motor parameters. A high ripple suppression capability was achieved by the PSO-DTC, which was measured at 22.5 % out of 47.28 % for the traditional approach. Both proposed control schemes were implemented using MATLAB/Simulink platform.

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%.

This article presents an investigation of a PV solar system based on Maximum Power Point Tracking (MPPT) using one of the artificial intelligence control techniques. To avoid the problems of the traditional P&O method, the optimization tool of Particle Swarm Optimization (PSO) is proposed to be employed with a Perturbation & Observation (P&O) technique with added a Proportional Integral (PI) controller (PSO + PI + P&O). This proposed method achieved the optimal operating variables of a photovoltaic (PV) module in terms to mitigating the issue of partial shading weather conditions. A DC–DC boost converter is designed to be switched to obtain the desired MPP based on the corresponding duty cycle according to the parameters provided by the IV characteristics. It is observed from the results that the proposed PSO + PI + P&O showed excellent improvement. Moreover, better performance was achieved in terms of extracting the maximum unique power point between both variables of current and voltage generated from the PV. The proposed MPPT control method was implemented in MATLAB/Simulink. The testbed of the suggested method was thorough and highlighted its high efficiency compared to the conventional P&O as well as the PI controller based on P&O methods.

Of a number of ML (Machine Learning) algorithms, k-nearest neighbour (KNN) is among the most common for data classification research, and classifying diseases and faults, which is essential due to frequent alterations in the training dataset, in which it would be expensive using most methods to construct a different classifier every time this happens. Therefore, KNN can be used effectively as it does not require a residual classifier to be constructed in advance. KNN offers ease of use and can be applied across a broad variation spectrum. Here, a novel KNN classification approach is put forward using the Bayesian Optimization Algorithm (BOA) for optimisation. This paper seeks to make classification more accurate and suggest alterations of nearest neighbour K value to use information about dataset structure and the similarity measure of distance. The findings of experimental work based on the University of California Irvine (UCI) repository datasets in general shows improved performance of classifiers compared with conventional KNN and give greater reliability without a significant time cost to speed.

The reliability and availability of induction motors is significant which can be achieved by monitoring the performance of the motor regularly in the industry. Knowledge-based approaches can be able efficiently to deal with the sensor data for ensuring the reliability with high motor performance. Recently, deep learning networks based on machine learning structures have provided an accurate and faster framework for fault diagnosis by ignoring feature extraction process ...

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.

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.

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.