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Palm kernel and castor oils are two vegetable oils available in tropical climates which have been proposed as an alternative to mineral oils, and their methyl esters, also called monoesters, have been previously elaborated. As the insulation system of many power transformers is made up of oil and paper, it is essential to study the interaction between these methyl esters and insulating paper. In this paper, we present the results of thermal aging of insulating paper immersed in methyl esters of palm kernel (MEPKO) and castor (MECO) oils. A comparison is made with insulating paper immersed in mineral oil (MO) based on the total acid number, the kinematic viscosity, the quantity of dissolved decay products through Ultraviolet–Visible (UV–visible) spectroscopy, the paper breakdown voltage, and the physical appearance of the insulating oils. UV–visible spectroscopy and total acid number show that the quantity of decay dissolved products of methyl esters is greater than the quantity of decay dissolved products generated by MO during initial thermal aging. However, the viscosity of MEPKO is smaller than the viscosity of MO. In addition, the paper breakdown voltage when dipped in methyl esters is greater than when dipped in MO. Therefore, methyl esters better preserve the insulating paper than mineral oils during initial thermal aging. [ABSTRACT FROM AUTHOR]

Due to the difficulty in setting the extended Debye relaxation time constant, which can reflect the insulation state, the application of the voltage response (VR) method in the diagnosis of oil-paper insulation state is limited. In this paper, based on the extended voltage response (EVR) method of single-polarization multiple measurements, the relaxation time constant was determined according to the first and last initial slopes that, respectively, reflect the rapid polarization and slow polarization of the medium, so that by measuring the initial slopes of return voltages (Sr) after different discharging times, various time constant polarization can be investigated. The insulation resistance is directly determined by the initial slope of the decay voltage (Sd) which is directly proportional to the conductivity of the insulation. Based on the characteristic parameters of the dielectric response of moisture and aging samples, the coincidence degree between the simulation results and the measured values is higher than 91% and 98%, respectively. The state of insulation was analyzed with the identified parameters. With the increase of moisture and aging degree of insulation, the time constant τpi decreases gradually, and the polarizability αpi increases gradually. The method presented in this paper provides a reference for the application of the EVR method in the diagnosis of transformer oil-paper insulation state. [ABSTRACT FROM AUTHOR]

Frequency-domain dielectric spectroscopy (FDS) technology is commonly utilized for assessing the condition of power equipment with oil–paper insulation. Nonetheless, its application in practical field conditions is limited because of the constantly changing temperature in the measurement environment and the equipment under inspection. To address this issue, this paper presents a method for separating the conductance and polarization components in dielectric response, and conducts experiments to measure the dielectric curves of oil–paper samples at five different temperatures. Subsequently, the dielectric curves in the entire frequency range are decomposed into three distinct microscopic dielectric processes: low-frequency conductivity, low-frequency polarization, and high-frequency polarization. The impact of temperature variation on these microscopic dielectric processes is investigated to establish a connection between them and the physical meaning of the complex dielectric constant. This leads to proposing a new method for temperature translation normalization of dielectric curves. Experimental results reveal that each microscopic dielectric curve undergoes translation due to temperature changes, with varying amplitudes of translation. Based on the temperature translation characteristics of these microscopic processes, the suggested temperature translation normalization method for dielectric responses demonstrates excellent efficacy, aiding in the precise assessment of power equipment insulation status. [ABSTRACT FROM AUTHOR]

Establishing and optimizing a distributed parameter thermal path model for transformer windings is an important means to analyze the hot-spot temperature of the windings; furthermore, in the process of improving the thermal path model of transformers and improving the accuracy of their models, the thermal characteristics of the insulation paper are extremely important. A microscopic thermal contact resistance model was established for the contact interface of insulation paper and copper, according to the microscopic morphology parameters of copper and insulation paper observed by atomic force microscopy, the thermal contact resistance under different temperatures was calculated. A dual heat flow test platform was built to test the heat transfer behavior of four different thickness insulation papers under four temperatures, and the thermal contact resistance and thermal bulk resistance with temperature were analyzed. The results show that as the temperature increases, the thermal resistance of the insulation paper becomes larger and the thermal conductivity decreases. The average error between the experimental and theoretical values of the thermal contact resistance under four temperature points in the study is 6.6%. The results provide a data support for establishing and optimizing transformer heat generation and heat transfer models. [ABSTRACT FROM AUTHOR]

To support the safe operation of the novel three-element mixed insulation oil (TEMO) transformer, gas generation behaviors of the TEMO oil–paper insulation system under overheating faults and discharge faults were investigated. Results show that that the content of H2 and C2H6 generated by TEMO–paper insulation system is much higher than that in conventional mineral oil (MO)–paper insulation system under different overheating temperatures. Under partial discharge fault, the content of H2 dissolved in oil changes distinctly, while the content of C2H2 also increases with the discharge time. Under surface flashover breakdown, AC breakdown and lightning impulse breakdown fault, the content of H2 and C2H2 changes greatly compared to other gases, while the concentration of total hydrocarbon gases in TEMO–paper insulation system is smaller than that in MO–paper insulating system after the same breakdown times. The diagnosis model of overheating and discharge faults for the TEMO–paper insulation system was proposed based on the oil dissolved gas analysis, while H2, C2H2 and C2H4 were chosen as the characteristic gases and the boundary settings were determined. The verified result for the low-temperature overheating, lightning impulse and surface flashover fault for the TEMO–paper insulation system presents that the new diagnosis model is effective. [ABSTRACT FROM AUTHOR]

Inter-turn insulation plays a major role in the dielectric integrity of any transformer. Though the inter-turn insulation thickness is designed based on standard lightning impulse (LI) voltage distribution, the insulation fails due to various types of overvoltages from switching impulse (SI) to very fast transient overvoltages (VFTO). Moreover, the insulation gets degraded due to many factors such as service ageing, exposure to repeated applications of overvoltages due to various reasons, etc. The effect of the repeated impulses is characterized using the voltage–number (V–N) characteristics recommended by CIGRE Working group C4.302. Based on the design and rating of the transformer, the thickness of the inter-turn insulation is normally changed. To quantify and compare the impulse breakdown strength and degradation due to repeated impulses under SI, LI and VFTO, different thicknesses of oil impregnated paper, ranging from 0.25 to 0.75 mm are considered. Experiments are carried out to obtain the V–N characteristics for all the cases. V–N characteristics are mathematically modeled by enhancing the existing exponential model to incorporate the thickness of insulation and are then validated experimentally. Based on the model parameters, the effect of the various waveshapes and the degradation due to repeated impulses are quantified and compared with the standard LI. [ABSTRACT FROM AUTHOR]

This paper presents a new optimization approach for solving the under-frequency load shedding (UFLS) problem in power systems. UFLS is a very important function in maintaining the power system within its safe operating limits.It is also the last resort in the event of frequency instability. This paper investigates the use of an adaptive neuro-fuzzy inference system (ANFIS)-based controller. The proposed ANFIS provides optimal response compared to conventional load shedding and other methods. The proposed approach is based on the simulation of the multi-zone load frequency control problem. Rate of change of frequency, voltage deviation and tie-line power transit are used as inputs to the ANFIS. The outputs in the first level are the optimal frequency threshold (Fth) values, and in the second level, they are the quantity of load shedding. Numerical results based on the IEEE 39 power system and the Algerian grid are used to demonstrate the effectiveness of the proposed approach in finding the optimal UFLS control compared with the conventional method as well as the results of the other intelligent method. The comparison showed that the proposed scheme gave the best response in all scenarios. [ABSTRACT FROM AUTHOR]

Finding the optimum conductor's pattern for unequally spaced grounding grids can be achieved by using empirical formulas or optimization techniques. Empirical formulas give an approximate solution depending on human experience and never cover the entire search space. On the other hand, the optimization techniques generally require a significant effort and time. To overcome the previous issues, this paper presents a closed-form solution based on optimized results. The main motivation behind this formula is the comprehensive study carried out by the authors to find optimum location of grounding grid conductors over different areas. In that work, particle swarm optimization technique (PSO) has been adapted with combined integration matrix method (CIMM) to facilitate the study. In this paper, the results obtained previously were summarized in the proposed formula. The proposed formula gives the optimum design of unequally grounding grids by specifying the conductor's locations as percentage of the side length for square and rectangular shapes. As long as the side length and the number of conductors are given, the location of each conductor as a percentage of the side length can be obtained. The effect of adding driven rods and the stratified soil conditions on the results of the optimized design were investigated. The validity of the proposed closed-form solution was verified experimentally. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid NBO–SDRN approach for a solar PV (SPV) array fed water pumping system utilizing a single-ended primary inductor converter (SEPIC) based BLDC motor drive. The proposed hybrid method combines Namib beetle optimization algorithm (NBO) and spiking deep residual networks (SDRN). Commonly, it is named the NBO–SDRN technique. The main goal of the NBO–SDRN method is enhanced energy efficiency, improved control algorithms, and optimal utilization of available solar energy resources. This paper focuses on integrating a SEPIC converter-based brushless DC motor drive in such systems to optimize energy conversion, enhance control, and promote environmentally friendly water supply. The BLDC (brushless direct current) motor drive and advanced control algorithms enable precise speed and torque control, improving overall energy efficiency and adaptability to varying water demand. By then, the NBO–SDRN method is done in the MATLAB working stage, and the execution is calculated with the present procedures. The proposed approach performs better outcomes in all existing singular spectrum analysis (SSA), heap-based optimization (HBO), and bee circle search algorithm (CSA) methods. From the result, the NBO–SDRN method-based efficiency is 95% higher than the existing methods. [ABSTRACT FROM AUTHOR]

The tracking of optimal current trajectories in an interior permanent magnet (IPM) synchronous machine is traditionally realized by a complex online tracking, inaccurate analytical equations, or tiresome offline calibration methods. This paper proposes a modified hybrid feed-forward/feedback flux-weakening algorithm for IPM synchronous machines. In this paper, the utilized power converter is a standard T-type multilevel inverter, and hence, the voltage and current harmonics contents of the conventional two-level inverter are improved. The control algorithm utilizes the optimal current profile for maximum torque per ampere (MTPA) operation and a voltage regulation (VR) feedback control for efficient flux-weakening operation. For infinite-speed IPM machine drives, the d-q current components are limited to follow the maximum torque per voltage (MTPV) trajectory. A low-complexity model predictive control (MPC) is employed to minimize the conflict that arises from using cascaded PI control loops for current and speed control. The performance of the drive is investigated based on the Prius 2004 IPM parameters. Extensive simulation scenarios were performed using the MATLAB/SIMULINK which validates the effectiveness of the proposed algorithm. Real-time simulations based on the dSPACE DS 1103 platform are conducted to confirm the system validity for real hardware implementation. The proposed IPM drive system proves simple structure, fast response, and low harmonic contents. [ABSTRACT FROM AUTHOR]

Due to increasing load demand and the energy crisis, microgrids (MGs) have attracted more attention. The idea and technology of microgrids (MGs) have undergone significant advancements largely aimed at enabling the automation of distribution systems and enhance the integration of renewable energy sources (RESs). In this paper, the MG is a combined form of various distributed generations (DGs), battery energy storage system (BESS), and plug-in hybrid electric vehicles (PHEVs). A novel approach is introduced to minimize the operational expenses of the grid-connected low-voltage microgrid by leveraging a probabilistic expert optimization technique. This methodology takes into account various factors such as power supply costs, power dissipation, PHEV charging requirements, and the optimal capacity of BESS. Three charging techniques are proposed in this paper to control the PHEVs' charging demand at homes and public charging stations. This study employs a comprehensive model to simulate a whole day, accounting for the stochastic charging requirements of PHEVs, load profiles, RESs outputs, and variations in energy price. To address the inherent nonlinearity of the optimization problem, the energy management within the MG is tackled using the slime mould algorithm, a population-based stochastic optimizer. The primary goal is to minimize operational costs in the microgrid by optimizing the efficient utilization of available resources. [ABSTRACT FROM AUTHOR]

The increasing integration of distributed energy resources such as photovoltaic (PV) systems into distribution networks introduces intermittent and variable power, leading to high voltage fluctuations. High PV integration can also result in increased terminal voltage of the network during periods of high PV generation and low load consumption. These problems can be solved by optimal utilization of the reactive power capability of a smart inverter. However, solving the optimization problem using a detailed mathematical model of the distribution network may be time-consuming. Due to this, the optimization process may not be fast enough to incorporate this rapid fluctuation when implemented in real-time optimization. To address these issues, this paper proposes a co-simulation-based optimization approach for optimal reactive power control in smart inverters. By utilizing co-simulation, the need for detailed mathematical modeling of the power flow equation of the distribution network in the optimization model is eliminated, thereby enabling faster optimization. This paper compares three optimization algorithms (improved harmony search, simplicial homology global optimization, and differential evolution) using models developed in OpenDSS and DigSilent PowerFactory. The results demonstrate the suitability of the proposed co-simulation-based optimization for obtaining optimal setpoints for reactive power control, minimizing total power loss in distribution networks with high PV integration. This research paper contributes to efficient and practical solutions for modeling optimal control problems in future distribution networks. [ABSTRACT FROM AUTHOR]

This paper reports on a comparative experimental study of the current waveforms accompanying the propagation of creeping discharges over different liquid/pressboard interfaces under both negative and positive lightning impulse voltages; the investigated insulating liquids are mineral oil (MO), a synthetic ester (SE), a natural ester (NE) and an extra virgin olive oil (OO). Two different arrangements are used; in the first one, the point electrode was positioned in a perpendicular direction to the pressboard, and in the second one, the pressboard was positioned parallel to the axis of the point electrode. Due to the similarity of the recorded creeping discharge current shapes, it is hard to differentiate between the currents measured for each type of interface with time domain approaches. To circumvent this difficulty and identify the specific feature(s) associated to each type of interface, two techniques are used to analyze the signal of the discharge currents, namely discrete wavelet and fast Fourier-transform methods. It is shown that: (1) for the first arrangement, there is a particular spectral signature for each type of liquid/pressboard interface, and (2) for the second arrangement, the obtained results are in accordance to those obtained for the first arrangement only in some cases. [ABSTRACT FROM AUTHOR]

The escalating worldwide power demand necessitates innovative solutions to address the emerging power crisis. Grid-synchronized electric vehicles (EVs) have emerged as a promising avenue for mitigating this crisis. The vehicle-to-grid technology is unique advanced smart grid concept by which the exchange of energy is allowed among grid and EV. The integration of EV and grid affects an entire power system causing imbalance in supply–demand, voltage and frequency. To minimize these issues, feasible optimization techniques are introduced in this paper. Lithium-ion batteries are initially charged via smart homes, and convolutional neural networks (CNN) are used to ascertain a battery's state of charge (SOC). A bidirectional SEPIC–zeta converter is employed in this paper by which voltage is either increased or decreased. The gating sequence for the converter is generated by means of adaptive neuro fuzzy inference system which depends on an estimated SOC. If the SOC is greater than 80%, battery is discharged using a SEPIC mode; if the SOC is lower than 80%, battery is charged by grid using the ZETA mode. By using a PI controller, active and reactive power are also controlled. Simulation results using MATLAB showcase the efficacy of the suggested approach. The bidirectional SEPIC–Zeta converter exhibits an impressive efficiency of 96% and a voltage gain ratio of 1:8 when compared to other converters. CNN-based SOC tracking achieves remarkable tracking efficiency of 92.3%, outperforming other tracking methods. The grid current total harmonic distortion is notably curtailed at 2.3%, and a power factor of 0.9 is achieved using PI-based grid synchronization. [ABSTRACT FROM AUTHOR]

In the ship's electrical power system, the largest percentage of a load powered by an induction machine (IM) can be represented through the fan load characteristics. Likewise, it is well known that, from the point of view of the current load, the "most problematic" way of starting the induction machine is the direct start. This paper deals with a novel analytical modeling of the IM direct start-up speed–time curve under fan load. The derived analytical expressions have been used for modeling the start of induction machines of different powers and voltage levels. Also, the obtained results have been compared with the corresponding results provided by using the realized MATLAB/Simulink model of the induction machine coupled with fan load. Moreover, the results obtained have been compared with the methods presented in the literature as regards the modeling of start-up characteristics. The experimental validation of the proposed analytical expressions for modeling the direct start of IM driving fan load has also been carried out on a 300 W machine in a laboratory environment. Both the simulations and experimental verification have demonstrated an exceptional accuracy of the derived analytical expressions. The paper also provides the implemented MATLAB code for determining the speed–time curve of IM during direct start under fan-type load. [ABSTRACT FROM AUTHOR]

The rapid development of electric vehicles (EVs) has created more possibilities for their flexible participation in electric power dispatching. Considering the clustering and fast mobility of EVs coinciding with real-time market requirements for responsive demands, a bidding strategy is proposed in this paper to assist EV aggregators with submitting reasonable quotations for the real-time market. Based on Bayesian updating, the strategy in this paper considers relevant factors such as aggregator cost and expected return. First, a quotation strategy in electricity spot market is proposed for EV aggregators. Second, the applicable scope of the strategy is given under different scenarios. Then, the objective function of learning is proposed, and the specific process of Bayesian updating is demonstrated. Finally, a case study of the aggregator quotation strategy based on Bayesian updating is simulated to verify the effectiveness and feasibility of the proposed strategy, which helps improve the bidding success rate of EVs and peak-to-valley balance of the grid. [ABSTRACT FROM AUTHOR]

This paper presents the control strategies and performance analysis of doubly fed induction generator (DFIG) for grid-connected wind energy conversion system (WECS). The wind power produces environmentally sustainable electricity and helps to meet national energy demand as the amounts of non-renewable resources are declining. The development of the WECS can be seen with development in power electronics technology. Currently, two types of major generator used in WECS these are DFIG and the permanent magnet synchronous generator (PMSG). Both variable oprational speed are used in wind turbine. Because its high performance, reliability, controllability and high-power and voltage capabilities, multilevel converters have become a popular option for multi-MW WECSs. This paper focuses on the application of back-to-back modular multilevel converters in a DFIG and PMSG-based WECS. A comparative performance analysis of the two systems based on power quality, active, reactive power, and modular multilevel converter dynamics is presented. The comparison is based on simulation results created in MATLAB/Simulink software. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive review of mitigation approaches, with a specific focus on geometric modifications, to mitigate shaft voltage and bearing current in electric drives. While existing literature extensively covers inverter-based and coupling path-based methods, the potential of geometric modifications has been relatively underexplored. The geometric modification approach offers a cost-effective and weight-saving solution that can be incorporated during the early design phase of the machine. The review begins by outlining various mitigation techniques applied at the victim machine, including bearing insulation, ceramic ball bearings, shaft grounding, and more. Subsequently, it delves into the machine geometric modification approach, examining methods related to winding design, stator and rotor iron geometry, rotor surface insulation, and stator slot wedge modifications. The paper highlights the practical relevance of geometric modifications, emphasizing their advantages over conventional methods. The review paper aims to shed new light on the potential of geometric modifications in mitigating shaft voltage and bearing current in electric drives. By providing valuable insights into this approach, researchers and engineers can develop robust and efficient electric drive systems, advancing the field of electrical machine design and operation. [ABSTRACT FROM AUTHOR]

To achieve high precision and robust control of switched reluctance machines (SRMs), this paper proposes a novel rotor position estimation method based on gain-optimized sliding mode observer (SMO) with neural networks and whale optimization algorithm. For the SMO, this paper uses the easily measured phase current to constitute the error function of the SMO, which requires less pre-stored data. Using the neural networks' powerful nonlinear mapping capability, the relationship between the sliding mode gains and speed estimation error and the position estimation error is obtained, and then the whale optimization algorithm is used to find the optimal sliding mode gains in the restricted range. The simulation and experiment results show that the accuracy of the SMO control system is significantly improved. [ABSTRACT FROM AUTHOR]

This paper investigates the challenges associated with constructing wireless chargers at the megawatt level using inductive power transfer (IPT) technology for marine energy storage systems. For marine applications, charging can only take place at a quay, meaning that only static charging is possible. In this paper, the specifications of the MF Ampere ferry have been used for the feasibility study. Through analytical calculations and PSIM simulation, it was determined that several low-power (100 kW) IPT systems need to be paralleled to achieve 1 MW power transfer. Building a single unit of 1 MW is not feasible due to the voltage and current stress across the components and limited space available onboard and onshore. However, a 100-kW power level can be implemented, despite the involved misalignments, if the primary system is kept stationary with respect to the secondary. Two proposed solutions for implementing a fast charger at the 1 MW level are presented in this paper: 10 units of 100 kW without mechanical mooring assistance or 5 units of 200 kW with mechanical mooring assistance. [ABSTRACT FROM AUTHOR]

This paper introduces a novel index for static eccentricity (SE) fault diagnosis in synchronous reluctance motors (SynRMs). Although SynRMs with rotor barriers under SE have been modeled in a few papers, any indices for the fault detection have not yet been reported. The proposed index is a specific frequency pattern in the motor current, which can also determine the fault severity. A novel analytical magnetic field investigation is applied to ascertain the proposed index. An accurate nonlinear numerical method is proposed for the motor inductances calculation. The model considers the rotor flux barriers, magnetic saturation, and the stator slots effect. The air gap flux density and the motor current are then achieved. The fast Fourier transform is exploited as a signal-processing tool to calculate motor current spectra. Then, a two-dimension time-stepping finite element method is used to attest of the proposed numerical model. Effectiveness of the proposed index is verified by simulation and experimental tests. [ABSTRACT FROM AUTHOR]

This paper presents a three-port isolated hybrid converter (3PIHC) with extended phase-shift modulation (EPM) to reduce voltage and current stress in the converter for DC microgrid applications. A high-frequency transformer with three independent windings is employed for isolating three ports in the converter. A wide variation in voltage transformation ratio is one of the main concern in DC microgrid, which need to be addressed for effective power conversion and utilization of the energy generation systems. Traditional phase-shift modulation (TPM) in 3PIHC leads to high conduction and core losses in the converter under wide variation in voltage transformation ratio. This paper proposes an extended phase-shift modulation in 3PIHC for different sequence of voltage transformation ratios between port 1 and port 2 (m 12) and between port 1 and port 3 (m 13) , which is a cost-effective solution for reducing losses in the converter. The peak current at all ports and power equations in 3PIHC are deduced, and the computational method for obtaining the phase-shift values with minimum current stress is explained in detail. A comparative evaluation study proves that the proposed three-port converter with EPM outperforms to TPM in the aspects like low current stress, reduced semiconductor switching losses and transformer losses for wide variation in voltage transformation ratios. A hardware prototype of the proposed converter is built, and the results are evaluated and presented. [ABSTRACT FROM AUTHOR]

The capacitor voltage unbalancing is the common problem in modular multilevel converter. This problem occurs even at the fundamental frequency due to the modulation technique which generates the unequal periods of the gate signals. To reduce this problem, this paper introduced a new submodule (SM)-based modular multilevel converter (MMC). The proposed MMC can generate the 2N + 1 level at the load side. By properly controlling the arm current of the converter, DC capacitor voltage in each submodule is balanced at its base voltage and improves the circulating current during unequal periods of the gate signals without using controller. This paper presents the performance of new submodule-based modular multilevel converter. Finally, the proposed converter is compared with the conventional modular multilevel converter topologies with and without third harmonic injection method. Further the proposed structure and presented techniques have been simulated in the MATLAB/SIMULINK, and the practical implementation has been done using hardware-in-the-loop (HIL) test. [ABSTRACT FROM AUTHOR]

Automated and accurate detection of power quality (PQ) events is important from the point of view of safety as well as maintaining the reliability of the power transmission and distribution network. However, detection of multiple PQ events in a noisy environment is challenging task. Another important issue is the choice of meaningful features that can directly influence the accuracy of PQ detection. Considering these two aforesaid facts, this paper presents a novel framework for automated classification of PQ signals in a noisy environment employing cross Stockwell Transform (XST). The XST proposed in this paper has better noise suppression capability compared to conventional Stockwell Transform. Here, XST was used to convert 1D PQ signals to 2D time–frequency (T–F) images. To improve the accuracy of PQ detection, an automated feature extraction method employing deep learning is implemented in this work. The noise free T–F images obtained using XST were fed as inputs to several pre-trained convolutional neural networks (CNNs) for deep feature extraction. Transfer learning technique was implemented to reduce the computational cost. The extracted deep features were further undergone selection using one-way analysis of variance test followed by false discovery rate correction. The statistically significant deep features were subsequently fed to three benchmark machine learning classifiers for classification of PQ signals. In addition, tests were also carried out on real-life PQ signals to verify the practicability of the proposed framework. Investigations revealed that the proposed method returned mean accuracy of 99.72% and 96.45% for classification of simulated and real-life PQ signals, respectively. [ABSTRACT FROM AUTHOR]

The fast-growing influence of grid-interfaced photovoltaic (PV) networks makes it necessary to adhere to grid-code (GC) regulations. These regulations mandate that PV systems inject active power both during and after the grid fault occurrence, as well as provide reactive current to the grid during voltage dips, in order to prevent power system stability concerns. In this context, two novel control schemes have been proposed in this paper for grid-connected photovoltaic networks that can support low-voltage ride-through (LVRT). The proposed control techniques have been demonstrated to be simple and efficient in the event of severe voltage dips, i.e., work up to a voltage drop of 90% of the nominal grid voltage. Our first approach involves an enhanced reactive current injection strategy that provides reactive power support to the power grid during voltage dips without any additional devices. The control structure is comprised of a voltage level estimator and a reactive current controller. The voltage level estimator obtains power grid voltage, while the reactive current controller computes active−reactive current commands. It is particularly noteworthy that the proposed scheme generates active current as much as possible according to the voltage dips and tracks the active current reference correctly, where conventional strategies fail to cope. The second control method for LVRT presented in this research work involves using hardware, specifically a crowbar. The use of a crowbar is the most straightforward method to provide support to the power system, bypassing over-current during voltage dips and keeping the current within the normal converter rating. The control strategies proposed in this paper are validated in MATLAB/Simulink under symmetrical and unsymmetrical severe voltage dips as well as grid voltage harmonics, and a detailed comparison study with a state-of-the-art PQ-based fault-ride-through controller has been conducted. [ABSTRACT FROM AUTHOR]

Issues related to stable microgrid (MG) operation often pose challenges for engineers and researchers, including energy management, power quality maintenance, and the effect of perturbations. Therefore, continuous measurement and monitoring of the system's stability level require special attention. Thus, motivated and focused on addressing these issues, this paper presents a novel decision-making methodology for determining the MG stability index (MGSI) to comprehensively measure the MG stability level. The proposed index is based on continuously measuring fundamental MG parameters, including voltage, frequency, battery state of charge (SOC), and total harmonic distortion. The influence of these parameters on system stability is considered using 256 rules in a Mamdani-based fuzzy inference system. The proposed methodology is evaluated through seven case studies considering different modes of MG operation, types of loads, and availability of power sources and simulated using MATLAB Simulink software. The results demonstrate variations in MGSI for different investigated scenarios, such that a grid-connected MG system with high renewable output and high battery SOC, along with critical/noncritical load, exhibits the best MGSI compared to an islanded MG system with low renewable output, low battery SOC, and an inductive load that shows least MGSI. Additionally, to demonstrate the credibility of the fuzzy-based controller in complex decision-making, a comparative analysis is performed with the analytic hierarchy process under the multi-criteria decision-making technique. The paper also includes sensitivity analysis that reflects the system's sensitivity to different input sources and robustness analysis of the controller. [ABSTRACT FROM AUTHOR]

One of the most prevalent and destructive types of cyber-attacks on power systems is the false data injection (FDI) attack. In a false data injection attack, the attacker inflicts large damages on the network by manipulating the measurements. The pivotal solution to opposing this type of cyber-attack is to use phasor measurement units (PMUs). In this paper, a new method is presented to confront the FDI attack by using the optimal placement of PMU instruments. In the proposed algorithm at the beginning, all PMUs placements that achieve network observability are determined using the tabu search (TS) algorithm. Then, from the observable placement vectors, the placements that minimize the possibility of a cyber-attack on the network is identified. For this purpose, a new attack criterion is presented, which is obtained from the adversary strategy in the attack scheme. Since the measurements obtained from the PMUs must be transferred to a phasor data concentrator (PDC) center, the PDC placement also must be determined. In this paper, the optimal placement of PDC is presented by considering the cost of communication infrastructure, because the cost of communication infrastructure between PMUs and PDC is significant. For this purpose, we have used the Kruskal algorithm. The simulations performed on the IEEE 30-bus and 118-bus test system confirm the effectiveness of the proposed method for opposing cyber-attacks and reducing the cost of communication infrastructure. [ABSTRACT FROM AUTHOR]

Single-phase converters are commonly used in small and medium power supply systems, but their inherent 2ω-ripple power has a significant impact on system performance, including maximum power point fluctuations in photovoltaic systems, low-frequency light flicker in light-emitting diode lighting systems, and the efficiency and lifetime of fuel cell systems. In this paper, we review the single-phase converter topologies with 2ω-ripple suppress and provide a comprehensive summary and comparison of power decoupling topologies. Power decoupling topology can be divided into passive power decoupling topology (PPDT) and active power decoupling topology (APDT). Among them, the PPDT does not need to add switching devices and has simple structure and convenient control. However, due to the volume and life of the energy storage element, the application place is also limited. The APDT needs to add an active power buffer to suppress the 2ω-ripple power. According to the different working modes of the active power buffer switch, the APDT can be divided into independent APDT and dependent APDT. The former operates independently in the original converter, while the latter needs to share switching devices or energy storage elements with the original converter. This paper systematically summarizes the existing single-phase converter circuit topologies for suppressing 2ω-ripple and their development process, aiming to provide classification and comparison for researchers and engineers to select the circuit topologies suitable for their specific applications. [ABSTRACT FROM AUTHOR]

Increasing the integration of distributed generation (DG) into distribution networks provides many technological benefits, including improving system security, performance, and reliability. The intermittent nature of renewable DGs poses certain difficulties for this integration. Moreover, the large integration of DGs will lead to islanding conditions in the power system. In the islanded operation, the microgrid keeps power injection into the network. The islanding event can occur intentionally or unintentionally; the former is controllable and required for maintaining the main utility, whereas the latter is uncontrollable, caused by regular faults. However, islanding detection is important for ensuring the system's reliability and operation. Hence, a comprehensive review is made in this paper to examine different methods for islanding detection in the power system. Unlike the previous review papers, the proposed review paper focused on different types of islanding detection methods, including active, reactive, hybrid active–passive methods, deep learning and machine learning techniques. All of these methods have their advantage and disadvantages. Moreover, the complexities in power systems increased with the increasing penetration of DGs. Thus, the rapid islanding detection technique is necessary for improving the system's performance. This review has provided some recent literature for signal processing, which includes the recent feature selection and advance finding for islanding occurrences. From the comparative analysis, it is found that the Non-detection zone (NDZ) is more in the passive method is higher than in the active and hybrid active–passive methods. At the same time, the remote islanding detection methods are NDZ free, but it has computational complexities when compared with existing methods. Moreover, the DL based methods have higher computational time due to large training and testing data. It is found that hybrid methods are more feasible for providing accurate results in islanding detection. In addition to that, a feasible and economical solution in terms of recent research trends is provided. [ABSTRACT FROM AUTHOR]

In PMSM drive systems, sensor-less vector control of permanent magnet synchronous motors (PMSM) over the entire speed range poses considerable challenges. Through the design and simulation of the sensor-less vector control system of PMSM, this paper proposes a novel method for sensor-less field-oriented control (FOC) for PMSM applications. To solve this intricate control problem, FOC combines gh-reference frame space vector pulse width modulation (SVPWM) with cubature Kalman filter (CKF). Implementing CKF to estimate rotor position and speed accurately provides the basis for this sensor-less control technique. Furthermore, the integrated control system using gh-SVPWM aims to reduce controller board computing resources utilization while minimizing torque ripple and stator current harmonic distortion. The effectiveness of CKF-based sensor-less FOC integrated with gh-SVPWM is verified in various operating environments using an extensive simulation and experimental framework, confirming its potential to enhance PMSM driving performance and efficiency in different applications. [ABSTRACT FROM AUTHOR]

In today's context of power generation from wind, the demand for reliable and high-performing inverters is on the rise. The quasi-Z-source inverters (q-ZSI) are gaining attention in grid-tied wind power generation systems (WPGS) when compared to conventional inverters for their inherent capability of single-stage power conversion and maximum power point tracking (MPPT) performance. However, the erratic nature of wind speed creates a constant challenge for engineers seeking to optimize the performance of wind turbines (WT). Therefore, this paper suggests a new grey wolf-optimized MPPT (GWO-MPPT) controller for q-ZSI-based grid-tied WPGS, which holds great potential for maximizing the power output of WTs. The conventional MPPT controller used in q-ZSI-based WPGS cannot guarantee stable operation during large variations in wind speed. The GWO-MPPT controller can immediately respond during wind speed variations and trace the maximum power point (MPP) without fluctuations around it, thus improving the MPPT performance. The GWO algorithm estimates the optimum rectifier DC voltage for each wind speed to locate the MPP and subsequently determines the shoot-through duty ratio for the q-ZSI. In addition, this work introduces a decoupling current control strategy supported by dual frequency modulation (DFM) for managing the grid's active and reactive power flows. The functionality of the proposed controllers is rigorously examined using the MATLAB/Simulink tool through extensive simulations. Finally, their practicality is evaluated through an experimental hardware setup. This integrated approach thoroughly investigates the controllers' effectiveness and suitability for practical implementation in WPGS. [ABSTRACT FROM AUTHOR]

This study presents a multi-objective solving indicator-based evolutionary algorithm (IBEA) to solve the optimal power flow (OPF) problem with multiple and competing objectives. The objective functions for the multi-objective OPF (MOOPF) are active power loss, aggregate voltage deviation, total generation cost, and emission pollution. This algorithm combines the shift-based density estimation method with a weighted sum approach to produce a set of non-dominated solutions on each objective space. Moreover, an S-shaped fuzzy membership approach is used to extract the best compromise solution from the obtained non-dominated solutions. To validate the IBEA's performance, standard IEEE 30-bus and IEEE 57-bus test systems with nine different cases are being used. This paper also presents a stochastic optimal power flow problem for two-objective optimization with load demand and wind power uncertainty. [ABSTRACT FROM AUTHOR]

Industries rely on the early and efficient detection of bearing faults to enhance the service life of three-phase induction motors. Various bearing health condition monitoring techniques have been proposed for years, majorly involving sound and/or vibration sensors. This paper compares sound- and vibration-based approaches for automated and real-time bearing health diagnosis, evolving a fusion of wavelet scattering transform and a hybrid two-dimensional convolutional neural network architecture with a multi-class support vector machine classifier. In this work, data are collected from our own laboratory-based sample process control application operated through a supervisory control and data acquisition system under harsh industrial environmental conditions, including dynamicity in motor load, speed, and externally induced noise levels from a nearby induction motor. Multiple parameters, such as image resolution, color channel, and intelligent architecture, are optimized for cost-effective real-time fault detection. This study also suggests that the proposed hybrid model, driven by normalized time–frequency feature images, can work with the reduced signal sampling frequency and analog-to-digital converter bit resolution while accomplishing the performance. The robustness of the proposed model is verified on publicly available databases. Rigorously, it is observed that the individual sound and vibration sensor-based measurement systems perform equally well for bearing fault diagnosis with the help of quintessential specification tuning in the data acquisition system. [ABSTRACT FROM AUTHOR]

Power system simulation is vital for designing and evaluating the performance of electrical system protection and control devices. Although several commercial simulators exist, most are expensive and not open-source code. It is essential to develop educational and research simulators that can prepare students, allow researchers to develop new methodologies, and assist system operators in making decisions. This paper aims to present a computer simulation platform called AnaDyn (dynamic analysis), a non-commercial and open-source platform. AnaDyn is an educational/research platform that allows dynamic (transient stability), quasi-steady-state simulation, modal, and power flow analysis. Short- and long-term studies of voltage, frequency, and rotor angle instability problems can be performed. The main characteristics of AnaDyn are quasi-steady-state simulation, in which the equations are not simplified; easy integration with other tools and toolboxes, since AnaDyn is developing in MATLAB; flexible modeling that allows the inclusion of new devices and controller tests. The detailed modeling of various power system devices, the description of the numerical solution method, and the interface are also presented in this work. The platform is validated by comparing a test system's results with those obtained with Brazil's most popular commercial software. The main potentials of quasi-steady-state simulation are also presented. The results indicate that AnaDyn is efficient for educational and research studies on the stability of short- and long-term electrical systems. [ABSTRACT FROM AUTHOR]

The permanent magnet claw pole machine (PMCPM) with soft magnetic composite (SMC) cores has shown better performance than the traditional transverse flux machine, and its performance can be improved by developing its stator with hybrid silicon sheet and SMC cores, and this kind of PMCPM is named as HPMCPM. Due to its complex 3D magnetic structure, analyzing its performance by using the traditional finite element method (FEM) is time-consuming. To overcome this constraint, this paper proposes a magneto-electric-thermal coupling network (METCN) model with bidirectional data transmission to calculate the electromagnetic performance and temperature distribution and the calculation results are verified by the FEM method and experimental measurement results. Compared with the 3D FEM, the proposed METCN method has the advantage of fast calculation speed and similar accuracy. [ABSTRACT FROM AUTHOR]

This paper uses an improved switching table-based direct torque control space vector selection technique with a nine-level hysteresis band to reduce torque ripple in a PMBLDC drive. The commutation torque ripple is mitigated by different rates of correction of torque depending upon the error quantum. Multi-rates of correction are applied using three sets of lookup tables corresponding to low, medium and high torque errors. In general, the existing state of the art incorporates an observer-based controller that optimizes complex objective functions. This increases computational cost and complexity. The proposed method is based on modified switching tables. This decreases the computational cost and improves the dynamic response. Frequency-domain analysis of torque response establishes the minimization of high-frequency components. The experimental validation is obtained on 1 kw BLDC motor using DSPACE 1104 real-time controller. [ABSTRACT FROM AUTHOR]

This paper proposes a design scheme for a wireless power transfer (WPT) system based on multi-relay coils to solve the power supply demand of 500 kV transmission line online monitoring equipment. The relationship between the operating frequency and the coil loss is established to analyze the influence of the number of relay coils and the coil distance on the transmission performance. The particle swarm optimization model is established with transmission efficiency as the optimization objective. The distribution spacing between coils, the working frequency, and the number of coils are optimized. Based on the equidistant arrangement of relay coils, an optimization scheme of non-equidistant arrangement is proposed. Under the condition of the same number of relay coils, the transmission efficiency of the system is increased by 20.56% and has been verified by experiments. In addition, finite element analysis software has simulated the surrounding high-voltage field strength of the WPT system added to the insulators. The results show that the insulators still meet the insulation standards. The simulation and experimental results show that the proposed scheme can effectively meet the power demand of high-voltage line monitoring equipment. [ABSTRACT FROM AUTHOR]

Many distribution system operators are installing PQ monitoring systems in their medium-voltage networks. Installation of PQ monitors in all the nodes of the network is uneconomical, and power quality monitors are rather expensive. Therefore, the optimal placement of PQ monitors has to be found, while minimising cost and maximising observability. Researches published so far have presented PQ placement algorithms, but only from the aspect of one PQ disturbance (either dips or harmonics). In this paper, an algorithm for PQ monitor placement is presented, and its novelty is that it is the first research work accounting for several key PQ disturbances at once: harmonics, flicker, unbalance and dips. The chosen PQ disturbances are also modelled using novel modelling techniques, with the PQ disturbance behaviour modelled for a longer time period of one week or month. PQ disturbance behaviour is modelled for a real study case, where known real network topology, customer structure and measurements are used to develop a model validated by PQ measurements. Obtained synthetic PQ data, which are validated by PQ measurements, are used to optimise the PQ monitor locations. To choose the optimal PQ monitor locations, multiple linear regression was used, with the coefficient of determination R2 being the selection criterion. The results show that only two PQ monitors are necessary to monitor the analysed large real medium-voltage distribution grid, with high confidence. The methodology is straightforward and relatively easy to apply, and can be implemented for other medium-voltage distribution networks. [ABSTRACT FROM AUTHOR]

In a wind farm, the grounding circuit is a complex electrical system made up of several interconnected groundings. Its role is very important for the protection of the electrical equipment during a direct lightning strike. To contribute to the optimization of the conception of this complex grounding system in his real configuration (wind turbine towers, grounding grids, and several hundred meters of buried conductor wires ...), we propose in this paper a realistic modeling. Realistic because, to model in all rigors using an electromagnetic field formalism a problem of propagation of electromagnetic transients in electrical device of large geometrical dimensions where the main environment (soil) is not homogeneous does not bring an appreciable advantage and consumes a very high calculation time. Also, the ideal for the engineer is to have a model that is easy to understand, accessible in its theory and obviously preserving the accuracy of the results and with very low computation time. In this work, to study the lightning surges in a wind farm, mainly to know the Ground Potential Rise and the distribution of the currents in the conductors, we use a modeling based on the transmission line equations. Our modeling is carried out by considering the effect of the frequency on the electrical characteristics (resistivity and dielectric permittivity) of the soil; the results we obtain are without loss of precision and with a very short calculation time. [ABSTRACT FROM AUTHOR]

The ability to predict and preempt insulator failures holds the potential to enhance the reliability of electrical power grids. The increase in insulator leakage current is an indication that failures may occur. By harnessing historical data and employing time series forecasting models, it is possible to identify potential faults before they escalate into disruptive failures. In this paper, a hybrid model for time series prediction is proposed by combining the Christiano–Fitzgerald random walk filter for signal denoising with an ensemble bootstrap aggregation model for leakage current forecasting. A comparison between bootstrap aggregation, boosting, random subspace, and stacked generalization ensemble learning models is presented. With a root mean square error of 7.62 × 10 - 4 (in a statistical evaluation), the ensemble bootstrap aggregation model with Christiano–Fitzgerald random walk filter proved to be a promising approach to be applied for time series fault forecasting. The proposed method was shown to be more promising than the original ensemble bootstrap aggregation model and the long short-term memory. [ABSTRACT FROM AUTHOR]

As the percentage of renewable energy source within the energy production mix has expanded, it is become gradually difficult to determine the appropriate values for controller gains and model parameters. Controlling system frequency of interconnected power system during sudden load disturbance requires careful consideration of the controller gain values and small signal stability model parameters. This study proposes an innovative methodology for ascertaining controller gain values and model parameters for plug-in electric vehicles (PEVs) in load frequency control (LFC) applications. Proposed method uses the root locus (RL) approach to find the suitable controller gain values and model parameters for PEVs. This paper offers a thorough mathematical description of the proposed RL approach. Routh approximation method is used for reduced-order modelling (ROM), which comprises thermal and PEV systems, to reduce complexity of higher-order system while designing controllers. Fractional-order proportional-integral-derivative controller (FO-PID) is proposed, and its parameters are adjusted using particle swarm optimization (PSO) tool. To validate the efficacy of suggested method, a comprehensive comparison of time response parameters and performance indices (PI) is carefully carried out. Also, the various PEVs state of charge (SOC) levels is investigated, and effects of these levels are studied in LFC with robustness analysis of controller. The proposed method for determining gain value is highly reliable and efficient, outperforming existing methodologies in the literature. [ABSTRACT FROM AUTHOR]

Renewable energy sources (RES) are currently being substantially integrated into the power grid because of the recent liberalization of the energy market and associated benefits to both the environment and the economy. A significant amount of renewable energy sources are stochastic and weather dependent, being integrated into the power system that causes a mismatch between the generation of power and the load demand, which creates major issues like variations in frequency and power transmission between the regions. So it is necessary to maintain the frequency and tie-line power variations within a defined range. This is known as automatic generation control (AGC) or load frequency control (LFC). This paper uses brown bear optimization algorithm (BOA) to tune the PID and cascaded PI-PDN controllers for LFC of the renewable integrated two-area power system. The ITAE objective function is utilized to obtain optimal gains of the PID controller. The performance of LFC with BOA-tuned PID controller is compared with other optimization techniques named grey wolf optimization technique (GWO) and particle swarm optimization (PSO) on a RES integrated two-area power system under various scenarios. Further, the effectiveness of the BOA is verified by comparing performance of the PID and cascaded PI-PDN controllers. According to simulation results, BOA offers superior performance in comparison with the GWO and PSO technique. [ABSTRACT FROM AUTHOR]

This paper presents a novel forecasting methodology based on neural networks and second-generation wavelets, and an example of application of such methodology for forecasting the oil temperature of an actual power transformer. The proposed method consists on selecting the variables with the highest correlation concerning the magnitude to be predicted (in situations featuring multiple variables), selecting the most appropriate second-generation wavelet from a set of candidate lifting wavelet transforms to smooth each variable's data and using the values smoothed by the selected second-generation wavelet to train the considered neural network. The authors applied the proposed methodology to predict the oil temperature of an existing transformer for four days and for one month using field data collected during four years of equipment operation. The obtained results indicate that selecting the most appropriate second-generation wavelet considering different selection techniques and using them to smooth the input variables significantly reduce the forecasting errors. The application of the proposed approach to forecast the oil temperature from the field-collected data using a long short-term memory neural network considering different batch sizes, neuron numbers and second-generation wavelet selection techniques decreased the average forecasting errors presented by using just the considered long short-term memory network by up to eleven times. The innovations of this proposal about literature are the use of correlation-based variable selection techniques given improving neural network forecasting accuracy, which is unprecedented in transformer insulating oil temperature forecasting applications, and the employment of SGWs to smooth the network's inputs to increase the accuracy of the model significantly. For the application case study presented in this research, the minor average forecasting errors in percentage to predict the four-day and one-month future temperature were, respectively, 0.012 and 0.326%. Also, for one month of forecasting, the average error in degrees Celsius was less than 0.2. [ABSTRACT FROM AUTHOR]

Globally, and especially in developing nations, the increasing demand for energy, coupled with transmission and consumption inefficiencies, poses significant challenges. As the proliferation of household appliances and electric vehicles (EVs) rises, dependency on electricity surges, further straining the existing power infrastructure. While renewable energy resources (RERs) such as solar and wind gain traction, the reliance on conventional utilities persists, especially in homes. This reliance, especially on fossil fuels, intensifies environmental concerns due to greenhouse gas emissions. This paper presents the smart household energy management system (SHEMS), designed to optimize domestic energy consumption. Capitalizing on the Internet of Things (IoT), SHEMS offers real-time energy monitoring and management, providing homeowners an adaptable architectural framework to regulate their energy use. Fundamental component of SHEMS is a controller interfacing with all household loads and the main meter, ensuring timely data on power consumption. Notably, during peak times, the system can autonomously adjust appliance settings, balancing energy savings without compromising user convenience. Paired with a user-friendly application, SHEMS provides individuals comprehensive oversight of their consumption patterns, fostering more sustainable energy use in an increasingly electrified world. [ABSTRACT FROM AUTHOR]

This article proposed an off-board bidirectional battery charger for electric vehicles (EVs) that have been designed to perform various modes of operation of EVs like grid-to-vehicle (G2V) and vehicle-to-grid (V2G) while improving the grid power quality (PQ). During the charging process, the charger operates in the G2V mode. In this mode, power flows from the utility grid to the EV, ensuring a smooth and efficient transfer, which has the ability to maintain a sinusoidal grid current waveform and a unity power factor throughout the operation. This not only ensures efficient power transfer but also minimizes disruptions and distortions in the grid. On the other side of the operational spectrum lies the discharging phase, governed by the V2G mode. This mode allows the charger to deliver power from the EV's batteries back to the utility grid. This capability is a significant step toward establishing a two-way relationship between EVs and the grid, enabling EVs to act as energy reservoirs and support grid stability during peak demand or emergencies. This paper presents the proposed adaptive direct power control (ADPC) theory for the proposed electric vehicle charger (EVC), which consists of a grid-side converter and an EV side converter. The proposed control algorithm on both sides of the converters plays a significant role in various operating modes. The ADPC employs a control strategy referred to as the second-order generalized integrator to estimate the actual synchronizing voltage templates specifically designed for single-phase grid-side converters. Consequently, the appropriate current reference used by the charger is to ensure that the power factor remains unity throughout various operating modes. Similarly, for the EV side converter, the integration of the power delivery error further enhances steady-state responses with the proposed ADPC by generating actual phase shifted pulses for the DAB. The overall performance under similar conditions is studied with virtual direct power control to further check the robustness of the proposed ADPC. The adopted topology and control technique further implemented in the prototype of an off-board EVC to verify the various operating conditions to demonstrate its effectiveness and justified as per the IEEE 519-2022 standard. [ABSTRACT FROM AUTHOR]

To overcome the jolt vibration impact of marine circuit breakers, this paper studies the relationship between the influence parameters and the contact performance of the contact system. A parametric approach is utilized to explore the effects of load size, pulse width, and different waveform loads on the vibration response of the contact system. The results show that the vibration increases with the increase in acceleration at peak contact acceleration. The ultimate upsetting acceleration peak of the contact system is determined to be 9.2 g. Large acceleration impacts should be avoided. As far as pulse width is concerned, the vibration response of the system with narrow pulse width is more sensitive than that with broad pulse width. So, monitoring of narrow pulses should be increased. Additionally, under various forms of load, the range of safe contact areas is triangular load, half-sine load, and step load in that order. Thus, circuit breakers need to increase the detection of step loads to prevent faults. The findings provide a theoretical foundation and guidance for the design of low-vibration marine circuit breakers and the improvement of their contact performance. [ABSTRACT FROM AUTHOR]

This paper presents the transient behavior of an earthing system under the influence of lightning current injected in to it. Vertical rods and horizontal electrodes buried in homogeneous soil are considered in this analysis. State-space representation (SSR)-based transmission line methodology (TLM) is adopted with nonlinear parameters for modeling the earthing electrodes in the time domain. Soil ionization phenomenon is taken up with the presence of residual resistivity. In this method, soil resistivity variation is adopted to evaluate the soil ionization phenomenon that occurs due to changes in the electric field. This study incorporates mutual coupling between the electrode segments for low and high soil resistivity. Earthing electrodes are subjected to high-magnitude lightning current impulse with different peaks. Transient voltage and impulse impedance are calculated to analyze the transient behavior of earthing electrodes with soil ionization. The impulse impedance of vertical earthing rods and horizontal electrodes is calculated and compared with the power frequency impedance. Percentage ohmic reduction between them is also reported. The influences of a critical electric field, soil resistivity and lightning magnitude on the transient performance of earthing electrodes are calculated. Also, an effect of the front time of lightning current impulse for a fixed peak on the impedance of the earthing electrode is evaluated and significant changes are observed. Simulated results are validated with the experimental and theoretical results reported in the literature, and adequate agreements are achieved. [ABSTRACT FROM AUTHOR]

This paper presents a new method to estimate the parameters of multi-cage models of induction motors using only the information provided by manufacturers available in technical data sheets. The parameters are obtained through the solution of an optimization problem solved through the particle swarm optimization algorithm. We tested and validated the model using models with two, three, four, and five cages in the rotor. Besides, experimental results of 200 induction motors with power ranging from 22 to 90 kW proved that the method provides reliable estimates of the parameters that can correctly reproduce both the dynamic and steady-state behavior of induction motors. The accurate knowledge of the estimated parameters is essential to several control schemes, particularly those based on vector field orientation. [ABSTRACT FROM AUTHOR]

In this paper, a novel order reduction technique is suggested for constructing an accurate and less complex reduced order controller of a higher order system model. This reduction strategy is based on a mixed integrated Routh and differential approach that can produce the reduced-order model with the same stability properties as the real system. In this procedure, a simplified Automatic Voltage Regulator (AVR) plant's reduced transfer function (RTF) is created by reducing the numerator and denominator using a mixed Routh-differential technique. A controller for the large-order system is subsequently developed using the calculated lower-order model. The higher order AVR system's PID controller is developed using the lower-order model of the original system. Different case studies are used to study both the original higher order and the newly designed lower order AVR systems with and without the reduced controller. With the use of step responses, various error indices, time-domain responses, and sinusoidal input, the models' performances for each of the case studies are assessed. Additionally, the proposed reduced order model and developed controller's efficacy and accuracy are contrasted with those of other existing methods. [ABSTRACT FROM AUTHOR]

In recent years, Model Predictive Torque Control (MPTC) has gained popularity as a powerful method of controlling Induction Motors (IMs). There are, however, some factors such as the weighted coefficient that must be taken into account to attain satisfactory performance at different operation points for stator flux. As of now, an empirical procedure has been used for tuning the weighting factor in MPTC, which is not as effective. Model Predictive Flux Control (MPFC) is proposed in this paper, which eliminates complicated process of predicting the stator current by using the flux vectors as control variables. In order to get over these obstacles, a novel Model Predictive Control (MPC) method utilizing a Cascaded Artificial Neural Network (ANN) is proposed in this research. As a result, the control complexity is diminished and weighting factor in standard MPTC is eliminated. In-depth evaluations and comparisons of MPTC and MPFC are conducted, covering low-speed function, dynamic response, and steady-state performance. In contract to traditional approaches, the preferred method has much lower computational cost, virtuous dynamic response, and superior steady-state performance. The overall proposed work is simulated using MATLAB/SIMULINK platform and experimental tests performed on 3Φ VSI fed IM drives are used to verify the efficacy of proposed approach. [ABSTRACT FROM AUTHOR]