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In this article reduced switch count-based distributed static compensator (DSTATCOM) is presented. DSTATCOM is a reduced switch count-based multilevel inverter (MLI)-based DSTATCOM that is used to eliminate harmonics in the source current caused by a non-linear load. The reference current estimation is carried out with a sliding mode controller (SMC) based synchronous reference frame (SRF). The estimation of the harmonics real power and reactive power components is used for the subsequent calculation of desired currents of the DSTATCOM. In addition to this, DSTATCOM performance was also tested with an artificial neural fuzzy inference system-based control scheme. The training data is obtained from SMC-based SRF control schemes. The DSTATCOM is modeled with MATLAB/Simulink using Simpower system block sets. The simulation study of DSTATCOM with different control schemes is carried out to examine its harmonics elimination capabilities using MATLAB. The simulation results of the various DSTATCOM control method demonstrate the reduced harmonics current caused by the load. The ANFIS-SRF exhibits better compensation characteristics than the SMC-SRF scheme. The simulated results of DSTATCOM with an ANFIS-SRF scheme give reduced ripple content in the DC side voltage of the capacitor.

Abstract With the growing infrastructure for overhead lines, the demand for overhead‐line condition‐monitoring technologies has become increasingly urgent. Traditional current transformers used to measure three‐phase currents are bulky and difficult to install, which further increases the cost of monitoring. In order to meet the current state assessment needs of transmission and distribution networks, this study uses a novel magnetic‐field sensor array to realize nonintrusive overhead‐line current detection. Based on the magnetic fields measured by magnetic‐field sensors placed on the ground under the overhead line, the proposed algorithm can be used to calculate the three‐phase current values. The proposed method does not require on‐site calibration before the field measurements are conducted. With partially known overhead‐line geometry specifications from utilities, the algorithm can optimize the overhead‐line geometry and finally achieve accurate current detection. The simulation and experimental results verify that the proposed current‐detection technique can accurately calculate the three‐phase currents of the overhead line with only three three‐axis magnetic‐field sensors.

Monitoring the degradation of induction motors is an essential concern for industries, especially when there are high costs related to maintenance and their stops. Nowadays, there are many non-invasive ways to monitor the health status of induction motors, including sensors to measure temperature, magnetic field, vibration, current, and power. However, installing these sensors may not be a simple task since intrusive procedures such as engine disassembling, wire separation, and engine interruption are commonly observed. Thus, we propose MUCLA (Multiphase Clamp and Tracking - patent pending) as a new device and methodology for monitoring motor degradation by analyzing its stator current. MUCLA introduces a non-invasive current sensors array on a proposed clamp in the hardware perspective, while a spectral tracking methodology refers to our novelty in the software viewpoint. For end-users, the main MUCLA contribution relies on the combination of the simplicity of installation and the possibility of using the proposed device even when considering Variable-Frequency Drive powered engines. The results obtained with both simulation and practical tests show encouraging results over different input workloads and emulated degradation. Compared to the current initiatives, we can classify MUCLA as a better solution for industrial plants in terms of installation time, installation complexity, and cost.

Fuzzy-based control systems have demonstrated a remarkable ability to control nonlinear processes, a characteristic commonly observed in power systems, particularly in the context of power quality enhancement. Despite this, an updated and comprehensive literature review on the applications of fuzzy logic in the domain of power quality control has been lacking. To address this gap, this study critically examines published research on the effective and efficient use of fuzzy logic in resolving quality issues within power systems. Data sources included the Web of Science and academic journal databases, followed by an evaluation of target articles based on predefined criteria. The information was then classified into seven categories, including control system type, features of the fuzzy logic controller, fuzzy logic inference strategy, power quality issue, control device, implementation methodology (efficacy testing), and efficiency improvement. Our study revealed that fuzzy-based control systems have evolved from simple type-1 fuzzy controllers to advanced control systems (type-2 fuzzy and hybrid) capable of effectively addressing complex power quality issues. We believe that the insights gained from this study will be useful to both experienced and inexperienced researchers and industry engineers seeking to leverage fuzzy logic to enhance power quality control.

In the increasingly complex and dynamic electrical power system, forecasting harmonics is key to developing and ensuring a clean power supply. The traditional methods have achieved some degree of success. However, they often fail to forecast complex and dynamic harmonics, highlighting the serious need to improve the forecasting performance. Precise forecasting of electrical power system harmonics is challenging and demanding, owing to the increased frequency with harmonic noise. The occurrence of harmonics is stochastic in nature; it has taken a long time for the development of dependable and efficient models. Several machine learning and statistical methods have produced positive results with minimal errors. To improve the prognostic accuracy of the power supply system, this study proposes an organic hybrid combination of a convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) with the attention mechanism (AM) method (CNN-BiLSTM-AM) to forecast load harmonics. CNN models intricate non-linear systems with multi-dimensionality aspects. LSTM performs better when dealing with exploding gradients in time series data. Bi-LSTM has two LSTM layers: one layer processes data in the onward direction and the other in the regressive direction. Bi-LSTM uses both preceding and subsequent data, and as a result, it has better performance compared to RNN and LSTM. AM’s purpose is to make desired features outstanding. The CNN-BiLSTM-AM method performed better than the other five methods, with a prediction accuracy of 92.366% and a root mean square error (RMSE) of 0.000000222.

Short-circuit resistances are transformer parameters that characterize the electrical load losses and correct operation of these machines. However, the traditional concept of short-circuit resistance, independent of the harmonic frequencies, has been superseded by present transformer standards. Hence, new expressions for short-circuit resistances of three-phase transformers have been developed in this article based on the IEEE Standard C57.110-2018 and are presented jointly with the losses that these resistances characterize. These refer to the secondary effective short-circuit resistance of each phase (Rcc,z), of each harmonic (Rcc,h), and the non-fundamental frequency combined harmonics (Rcc,Hz). Likewise, the harmonic loss factor (HLFz%) has been established to determine the importance of the harmonics in each phase’s load losses. The application of these short-circuit resistances to the calculation of the load losses for a 630 kVA transformer from an actual residential distribution network has shown that the same values are obtained as with the IEEE Standard C57.110-2018, and they are 48.75% higher than those recorded with the traditional short-circuit resistances when the current distortion rates are 36.47%.

This article proposes a modular system for prosumer installations composed of photovoltaic (PV) panels and energy storage (ES) integrated with the low voltage (LV) network through a common 4-wire AC/DC inverter. The novel idea is a control strategy for the inverter in which additional functionalities are incorporated. Apart from transmitting an active power generated by the PV source, the same inverter is used to manage energy generated by the PV and to compensate for the current unbalance, harmonics (including subharmonics and interharmonics) and reactive power of the prosumer loads. As a result of the algorithm operation, the currents flowing to the prosumer installation are sinusoidal, symmetrical and purely active, which results in voltage balancing and improving voltage waveforms at the point of common coupling (PCC). In this way, with the widespread use of this solution among prosumers, the impact of the prosumer installation on the distribution network is minimized, and power quality (PQ) disturbances such as unacceptable voltage rises, voltage unbalance and harmonics are avoided. The presented approach may be a solution to the problems network operators face nowadays due to the uncontrolled connection of PV sources. The proposed modular system is also beneficial for the prosumer as the instances of unacceptable overvoltage and, consequently, shutdowns of prosumer installations are reduced. The features of the proposed method are shown in relation to other means applied for PQ improvement in the networks with distributed generation. A principle of the control and the involving algorithm for the inverter is presented. The efficiency of the control strategy was tested in a simulation developed in the PSCAD/EMTDC program. The results of simulations are presented, and the proposed solution is concluded.

This paper presents a preliminary study of a 25 kV–50 Hz railway substation power supply system. The control of a back-to-back converter based on modular multilevel converter (MMC) technology was investigated to fit with the power quality requirements of the application. One of the main challenges is the presence of constraining load conditions, under which the train circulation variability, low-frequency harmonics and critical power transients can notably decrease the power quality and lead to instability. In order to address this, cascaded controllers based on resonant controllers are proposed to ensure the desired performance. Furthermore, balancing voltage algorithms are added to avoid stress phenomena and additional losses in the studied power conversion interface. The paper presents the design of the control stages and demonstrates the robust performance of the system using a realistic loading condition of a railway substation.

Metrological characterisation of static energy meters under realistic low power quality conditions is a basic requirement for proper grid control and fair energy billing. The paper reports about a new proposed methodology, where the meters are tested under conditions directly recorded at installation sites. The waveforms of voltages and currents are sampled using a portable instrument; they are reproduced in laboratory conditions with a phantom power generator, with a bandwidth covering up to the 40th harmonic. The recording site is a photovoltaic energy production facility, having a a nominal power of 50 kW, at the coupling section to the grid. These waveforms were then reproduced in the laboratory, and tested on different models of single- and three-phase commercial static energy meters; the models chosen represent both energy meters used by energy providers at the point of common-coupling, and also meters typically used for in-line monitoring by end users. The quantity of interest is the reading error of the measured energy, when tested with the conditions reproduced from the on-field measurements, in comparison with a reference meter. All tested energy meter models comply with the present international documentary standards, which require tests under low power quality conditions; nevertheless, there are models that show unacceptable errors (up to 25%) in the measurement of active energy when tested with the on-field recorded waveforms. This suggests that the standardised testing waveforms might, in some cases, be not fully representative of the actual conditions encountered in the field.

The design strategies for flood risk reduction in coastal towns must be informed by the likelihood of flooding resulting from both precipitation and coastal storm surge. This paper discusses various bivariate extreme value methods to investigate the joint probability of the exceedance of thresholds in both precipitation and sea level and estimate their dependence structure. We present the results of the dependence structure obtained using the observational record at Bridgeport, CT, a station with long data records representative of coastal Connecticut. Furthermore, we evaluate the dependence structure after removing the effects of harmonics in the sea level data. Through this comprehensive analysis, our study seeks to contribute to the understanding of the joint occurrence of sea level and precipitation extremes, providing insights that are crucial for effective coastal management.

The need to monitor the power network is leading to a significant increase in the number of measurement points. These points consist of intelligent electronic devices and instrument transformers (or more in general sensors). However, as the number of devices increases, so does the demand for their characterization and testing. To this end, the authors formulated a new characterization procedure that offers numerous benefits for manufacturers and system operators. These benefits include: (i) reducing testing time (thus lowering costs), (ii) simplifying the existing procedures, and (iii) increasing the number of tested devices. In this study, to complete the validation of the proposed characterization procedure, the authors performed a comprehensive uncertainty evaluation. This included the identification and analysis of the uncertainty sources, the implementation of the Monte Carlo method to obtain the statistical parameters of the quantities of interest, and the final method assessment according to the obtained results. Each step is described in detail, and the results allow one to (i) replicate the uncertainty analysis on other types of instrument transformers and (ii) implement the proposed harmonic characterization procedure with the confidence that the method is accurate, flexible, and scalable.

Copyright of Guangdong Architecture Civil Engineering is the property of Guangdong Architecture Civil Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

It is well understood that geomagnetically induced currents (GICs) flow through the grounded neutrals of wye-connected transformers, generating harmonic currents that flow along frequency-dependent transmission lines. Harmonic currents affect protection systems and cause harmonic voltage drops that disturb power system voltage stability. In the literature, GICs are modelled as dc-currents for harmonic analysis. However, GICs are multi-frequency currents having both positive and negative polarities. Therefore, a representative harmonic analysis of power systems is only possible when GICs are modelled as multi-frequency AC currents. In this paper, the authors subjected a multi-machine power system model to real GICs from the 2003 halloween storm. Representative time intervals within the storm were selected, and a harmonic analysis of transmission line and generator stator currents was carried out. The results showed that the harmonic spectrum of currents fluctuated with a real GIC instead of reaching a steady state with a DC model for GIC. Moreover, harmonics induced on generator windings increase the risk of dynamic voltage and power system instability. The results confirm the susceptibility of power system generators to GICs and validate the use of synthetic GIC for modelling the effect of power system stability.

Abstract Microcantilevers are at the heart of atomic force microscopy (AFM) and play a significant role in AFM‐based techniques. Recent advancements in multifrequency AFM require the simultaneous excitation and detection of multiple eigenfrequencies of microcantilevers to assess more data channels to quantify the material properties. However, to achieve higher spatiotemporal resolution there is a need to optimize the structure of microcantilevers. In this study, the architecture of the cantilever with gold nanoparticles using a dip‐coating method is modified, aiming to tune the higher eigenmodes of the microcantilever as integer multiples of its fundamental frequency. Through the theoretical methodology and simulative model, that integer harmonics improve the coupling in multifrequency AFM measurements is demonstrated, leading to enhanced image quality and resolution. Furthermore, via the combined theoretical‐experimental approach, the interplay between induced mass and stiffness change of the modified cantilever depending on the attached particle location, size, mass, and geometry is found. To validate the results of this predictive model, tapping‐mode AFM is utilized and bimodal Amplitude Modulation AFM techniques to examine and quantify the impact of tuning higher‐order eigenmodes on the imaging quality of a polystyrene‐polymethylmethacrylate (PS‐PMMA) block co‐polymer assembly deposited on a glass slide and Highly Ordered Pyrolytic Graphite (HOPG).

In current electrical power industry, a great number of power electronic devices produce harmonics. Moreover, the fundamental system frequency of a power line can deviate in the cases of fluctuating and periodically varying loads and transients. This frequency deviation introduces further undesirable frequency components in the spectrum. Correct measurement of harmonics under such system frequency deviations has prominent importance for the protection of power electronics devices. For this goal, this paper proposes a new iterative optimization-based algorithm for the estimation of harmonics in these circumstances. The method is based on time-domain Gauss-Newton optimization of distorted power line signal which attains its parameters directly from the spectrum of the signal by employing local extrema of the corresponding spectrum. In the first phase of the algorithm, the deviated system frequency is estimated. In the second phase, the harmonic components are estimated and fine-tuned correspondingly. Case studies show that the proposed method is robust and precisely estimates deviations in the system’s fundamental frequency and measure the amplitudes of harmonic components very accurately. It requires just a stoppage criterion. The computational assessment of the method also shows its time efficiency in identifying harmonics. Thus, it is a very good candidate for real-time harmonic measurements.

An emerging type of photovoltaic (PV) system for which maximum power point tracking (MPPT) algorithms are scarce in the literature is the PV water heating system (PWHS). The application of existing PWHS-specific MPPT algorithms presents a significant difficulty in that none of them is able to provide, concurrently, low-complexity implementations and high tracking efficiencies. This paper addresses such challenge by proposing a novel impedance matching-based MPPT algorithm that has O(1)-complexity iterations and high efficiency for typical PWHS loads. Such efficiency improvement is achieved via generalization of a previous impedance matching algorithm, which is obtained by taking into account the effect of current harmonics into the impedance matching computations. The proposed algorithm is validated by means of simulation experiments, whose results confirm that it yields superior performance when compared to other low-complexity MPPT methods in the PWHS literature. [ABSTRACT FROM AUTHOR]

Over the past two decades, there has been a surge in digitalization across industries. This tremendous growth of industries has brought many challenges in the field of semiconductor technologies for controlling non-linear loads, resulting in distorted voltages, currents, and power factor, which frequently disturb other loads that are connected in parallel. Many types of passive filters have been designed by power engineers to moderate or reduce the degree of harmonic content in the system; nevertheless, manual tuning is required, which takes time and energy. To lower Total Harmonic Distortion %THD, optimization technique approaches play an important role in tuning the filter components as well as the Quality Factor, increasing the filter's lifetime, and confirming compliance with standards. The Particle Swarm Optimization is proposed for non-linear AHUs, Un-Interrupted Power Supply, and Lighting loads from the IT industry, each with a different dominant harmonic. This paper presents the passive filters for the dominant harmonic which were simulated using MATLAB Simulink software, as well as the accompanying waveforms and findings. [ABSTRACT FROM AUTHOR]

Electrical machines generate unwanted flux and current harmonics. Harmonics can be suppressed using various methods. In this paper, the harmonics are significantly reduced using Iterative Learning Control (ILC) and Neural Networks (NNs). The ILC can compensate for the harmonics well for operation at constant speed and current reference values. The NNs are trained with the data from the ILC and help to suppress the harmonics well even in transient operation. The simulation model is based on flux and torque maps, depending on d q -currents and the electrical angle. The maps are generated from FEM simulation of an interior permanent magnet synchronous machine (IPM) and are published with the paper. They are intended to serve other researchers for direct comparison with their own methods. Simulation results in this paper verify that by using ILC and NNs together, current harmonics in transient operation can be eliminated better than without NNs. [ABSTRACT FROM AUTHOR]

The identification and analysis of harmonics, frequency, and transient events are essential today. It is necessary to have available data relating to harmonics, frequency, and transient events to understand power systems and their proper control and analysis. Power quality monitoring is the first step in identifying power quality disturbances and reducing them and, as a result, improving the performance of the power system. In this paper, while presenting different methods for measuring these quantities, we have made some corrections to them. These reforms have been obtained through the analysis of power network signals. Finally, we introduce a new monitoring system capable of measuring harmonics, frequency, and transient events in the network. In addition, these values are provided for online and offline calculations of harmonics, frequency, and transient events. In this paper, two new and practical methods of the "algebraic method" are used to calculate network harmonics and wavelet transform to calculate transient modes in the network. Furthermore, the proposed monitoring system is able to reduce the amount of data-storage memory. The results of the simulations performed in this article show the superiority of using the new method presented for online and offline monitoring of power quality in electric power systems. [ABSTRACT FROM AUTHOR]

Multilevel inverter technology has become the most significant method of energy conversion from DC to AC for uninterrupted power supply. The quality of the power supply depends on the appearance of harmonics, which is considered a problem that needs to be overcome. This paper demonstrates the use of a new modulation technique to reduce the harmonics in both diode-clamped and cascaded multilevel inverters, examining its Total Harmonic Distortion (THD) compared to other PWM methods. Simulations were carried out in MATLAB/Simulink for five-level diode-clamped and cascaded multilevel inverters. The simulation results of both multilevel inverters using the sinusoidal PWM, modified reference PWM, modified carrier PWM, and modified reference and modified carrier PWM methods showed that the latter had the best THD performance for both inverter types, especially for the cascaded multilevel inverter. [ABSTRACT FROM AUTHOR]

Due to nonlinear components in the charging piles of electric vehicles, harmonics and nonstationary signals in the electric vehicle charging load bring voltage and current distortion, seriously affecting the accuracy of the power-related calculation in nonsinusoidal environments. This paper proposed a new approach to calculate the active power and root mean square values from decomposed components using the adaptive chirp mode decomposition (ACMD) method on voltage and current. The advantage of the ACMD-based method is that it correctly provides the power-related quantities of harmonics or nonstationary components for the electric vehicle charging load. The performance of the proposed method was verified using synthetic signals and simulation tests. The experimental results presented better estimations for each quantity defined in IEEE Standard 1459-2010, compared with the discrete wavelet transform approach. [ABSTRACT FROM AUTHOR]

Ultrasonic welding of plastics has been used in a wide range of industries and improving welding quality and production speed are still important issues. This paper introduces a method to evaluate the welding condition during welding. In this method, vibrations propagated during welding were acquired using a piezoelectric element. By fast fourier transform analysis, tracking analysis, and calculation of the data, the total harmonic distortion with the welding time was obtained. The welding condition can be determined by comparing the welding strength against the welding time obtained from the 180° peeling test. As a result, it was found that the propagated vibration has characteristics that indicate the completion of welding, making it possible to efficiently evaluate the quality of the weld. [ABSTRACT FROM AUTHOR]

The high penetration of renewable energy sources has necessitated the use of more energy-storage devices in Smartgrids. The proposed work addresses the development and implementation of an Instantaneous Discharge Controller (IDC) for a hybrid energy storage system. The discharge control algorithm manages the discharge of the battery and supercapacitor and protects the battery from transient currents. Hybrid energy storage systems (HESSs) are well known for providing ideal attributes such as high-power density and high-energy density for many application areas, including electric vehicles and renewable energy-supported microgrids. However, the application of HESSs for supporting shunt active filters and protecting low power density storage systems from fast variations in load has not been proposed yet. In this context, a hybrid energy storage system (HESS) is proposed here to eliminate harmonics and to support the grid by providing real and reactive power supervened by varying load conditions. This paper proffers an innovative controller for a shunt active filter unified with an HESS to effectively manage storage devices for a microgrid connected to a grid. [ABSTRACT FROM AUTHOR]

This paper proposed a simplified modeling approach for a power quality (PQ) assessment of Electric Submersible Pumps (ESP) systems supplied by the two-level, the neutral-point-clamped three-level, and the cascaded H-bridge (CHB) multilevel inverter VFD topologies. The VFD switching function models and their analytical expressions are proposed to understand how they can create high-frequency components that might excite the resonance mode in a transmission cable or a rotating shaft system. Voltage, current, and motor airgap torque harmonics induced by each VFD topology in a balanced operation mode are derived and correlated to the PWM carrier and motor operating frequencies. The motor airgap harmonics are calculated based on Concordia's transformation of voltages and currents in αβ-plan. These harmonic components are represented in the form of Campbell diagrams. An analysis of harmonics under unbalanced conditions was also conducted in a CHB VFD topology-powered ESP system with failed and bypassed cells. The investigated modulation technique is a neutral-shift PWM method that enables the system to operate balanced line-line voltages even if the line-neutral voltages are unbalanced. The effects of modifying the electrical spectrum using the neutral-shift PWM method on electrical and mechanical spectra are analyzed. The results of the Matlab/Simulink-based simulation show that the proposed full ESP system model is highly accurate in both normal and failure modes. The results are consistent with theoretical predictions and are graphically shown in the time and frequency domains for easy analysis. Hybrid experimental–numerical results on a reduced-scale laboratory setup are also discussed to confirm the correctness of the suggested developments. [ABSTRACT FROM AUTHOR]

The journey towards sustainable transportation has significantly increased the grid penetration of electric vehicles (EV) around the world. The connection of EVs to the power grid poses a series of new challenges for network operators, such as network loading, voltage profile perturbation, voltage unbalance, and other power quality issues. This paper presents a coalescence of knowledge on the impact that electro-mobility can impose on the grid, and identifies gaps for further research. Further, the study investigates the impact of electric vehicle charging on the medium-voltage network and low-voltage distribution network, keeping in mind the role of network operators, utilities, and customers. From this, the impacts, challenges, and recommendations are summarized. This paper will be a valuable resource to research entities, industry professionals, and network operators, as a ready reference of all possible power quality challenges posed by electro-mobility on the distribution network. [ABSTRACT FROM AUTHOR]

Summary: This paper discusses the performance of the Bernoulli Polynomial‐based artificial neural network (BePANN) for the control of a single‐phase grid‐integrated solar photovoltaic (PV) system. A single‐phase, single‐stage topology of a grid‐integrated PV system is utilized to feed nonlinear loads at the point of common coupling. The fundamental load component is extracted from distorted load current using a single‐layer neural network. There are several control techniques mentioned in the literature for load compensation. However, many techniques show slower convergence response, higher oscillations, and a large computational burden. The designed BePANN controller attenuates harmonic components from nonlinear current and improves the power quality (PQ) under normal and distorted grid conditions. The single‐layer BePANN control has a simplified structure based on several polynomial terms; this reduces the computational burden and complexity of the controller. The objective of the designed controller is to fulfill the load's active power demand from the generated solar PV power and feed the excess power back to the grid when surplus. When solar PV is not integrated with the grid, the voltage source converter acts as a distribution static compensator, improving the system's utilization factor. The proposed control technique is simulated in MATLAB Simulink, and results are experimentally verified through laboratory prototype under normal and abnormal grid conditions. The application of higher order polynomials for PV‐integrated systems with multifunctional PQ capabilities has been implemented for the first time in this article. The main outcomes of the proposed control technique are solving current‐related PQ issues and reactive power compensation and providing improved power factor under both normal and distorted polluted grids. [ABSTRACT FROM AUTHOR]

One of the best ways to address urgent sustainability issues like global warming, depletion of fossil fuel reserves, and emissions of greenhouse gases is to use electric vehicles (EVs). By reducing environmental damage and lowering emissions that contribute to climate change, incorporating electric vehicles into the distribution system will benefit public health. A microgrid is a tiny power network that collaborates with groups of loads as well as distribution generators, powerful software solutions, and other elements utilizing devices connected to the grid that function as a single controlled entity. Last but not least, the study states various ways to mitigate the effects of electric vehicles on distribution system power quality and get rid of harmonics. [ABSTRACT FROM AUTHOR]

This paper presents a study of the impact of connecting two-phase loads on High Voltage (HV) transmission networks. The case study chosen for this purpose is the connection of the substation supplying the High-Speed Rail (HSR). The power supply of this type of train is ensured by a two-phase system, which can affect the quality of the electrical energy by the appearance of disturbances, such as harmonics, unbalances, and flickers. Two 225-kV lines supply the HSR of Morocco. In this paper, the power quality recordings of SICAM Q80 installed in one of the substations supplying the Moroccan HSR are exposed and analyzed. The objective of this article is to study and understand the connection mode of two-phase loads and their impact on the transmission network in terms of power quality, as well as the disturbances due to the power supply network and those generated by the railway installation. [ABSTRACT FROM AUTHOR]

Copyright of Journal Renewable Energy is the property of ECORFAN-Mexico S.C. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Conventional deadbeat predictive current control (DBPCC) based on space vector modulation (SVM) shows quick dynamic responses and a good steady‐state performance in induction motor (IM) drives. However, the motor parameters change during operation due to temperature and saturation changes, leading to inaccurate reference voltage vectors and a degraded performance. Furthermore, conventional DBPCC uses a fixed vector sequence of 0127 over the entire speed range, which results in large current harmonics at high modulation indices. To address the above issues, this paper proposes a robust deadbeat predictive current control (RDBPCC) for IM drives. Based on an ultra‐local model, the proposed method updates the input voltage gain and unknown system components online according to the voltage and current of the previous two control cycles. Because the final control expression contains only the measured stator current and voltage values, the model shows a strong robustness. The steady‐state performance is significantly improved by selecting the optimal vector sequence according to the modulation index based on the principle of current harmonic minimization. The experimental results confirm that, compared with conventional vector control and conventional DBPCC, the proposed method achieves a strong parameter robustness and reduces the current total harmonic distortion (THD) by more than 10% and 20% at high‐modulation indices. [ABSTRACT FROM AUTHOR]

This study identified time-varying harmonic characteristics in a high-density plasma (HDP) chemical vapor deposition (CVD) chamber by depositing low-k oxide (SiOF). The characteristics of harmonics are caused by the nonlinear Lorentz force and the nonlinear nature of the sheath. In this study, a noninvasive directional coupler was used to collect harmonic power in the forward and reverse directions, which were low frequency (LF) and high bias radio frequency (RF). The intensity of the 2nd and 3rd harmonics responded to the LF power, pressure, and gas flow rate introduced for plasma generation. Meanwhile, the intensity of the 6th harmonic responded to the oxygen fraction in the transition step. The intensity of the 7th (forward) and 10th (in reverse) harmonic of the bias RF power depended on the underlying layers (silicon rich oxide (SRO) and undoped silicate glass (USG)) and the deposition of the SiOF layer. In particular, the 10th (reverse) harmonic of the bias RF power was identified using electrodynamics in a double capacitor model of the plasma sheath and the deposited dielectric material. The plasma-induced electronic charging effect on the deposited film resulted in the time-varying characteristic of the 10th harmonic (in reverse) of the bias RF power. The wafer-to-wafer consistency and stability of the time-varying characteristic were investigated. The findings of this study can be applied to in situ diagnosis of SiOF thin film deposition and optimization of the deposition process. [ABSTRACT FROM AUTHOR]

The ever-growing modern smart grid with more distributed energy resources is providing efficient energy supply while facing several challenges that include harmonics induced among many. Previous and present literature shows that various machine and deep learning models are superior and accurate as compared to the traditional and conventional signal processing techniques. Obtaining accurate results becomes extremely important especially the fact that harmonics are essentially nonlinear, nonparametric, and adaptive in nature. This paper proposes a novel forecasting model that aggregates two deep learning models: convolutional neural network (CNN) and long short term memory (LSTM) recurrent neural network (RNN) detect and forecast harmonics in a power system. CNN-LSTM hybrid forecasting model for harmonics in the power grid system has achieved significantly superior performance in collaborative data mining on spatiotemporal measurement data. Sample features are extracted using CNN before they are passed through LSTM for prediction. To show the superiority of the hybrid CNN-LSTM deep neural prediction network model, it is compared with CNN, LSTM and NARX (Non-Linear Autoregressive with External (Exogenous) Input). CNN-LSTM forecasting performance is superior as compared to the other four models. MSE and RMSE for CNN-LSTM are 0.00038 (〖3.8 × 10〗^ (−4)) and 0.0000014917 (1.4917 × 10^(−6)) respectively. [ABSTRACT FROM AUTHOR]

Diode bridge front-end Adjustable Speed Drives (ASDs) generally have an inductor either in the DC side (DC choke) or in the AC side (AC choke). Since both the types of ASDs are commercially widely used, a comparison of their behavior under normal and abnormal grid conditions is of interest. It is identified in this work that there exists a difference in the ripple current through the DC bus capacitor between the two variants. Further, the difference in the ripple current varies depending on the type of the disturbance and its level. As an outcome of the study, it is identified that the stress levels arising in the DC choke variant on the DC bus capacitor are lesser compared to the corresponding AC choke variant under balanced, unbalanced, and distorted grid voltage conditions, for the same value of the equivalent inductance in the AC and DC side. In order to match the stress levels arising in the DC bus capacitor between the ASDs with DC and AC choke, it is found that a 50% higher value of inductance is required in an AC choke variant. The effects are analyzed by simulations and validated using an experimental setup. [ABSTRACT FROM AUTHOR]

This paper presents a detailed performance analysis of multilevel inverter for both stand-alone and grid connected PV systems. Here, converter circuit is not only tested for parameters like total harmonic distortion (THD), power output and system efficiency by connecting the non-linear load but the variations of power factor is also considered which is not found commonly. First the simulated results for THD, power output and efficiency are tabulated with variation of load power factor individually for both systems and best operating power factor is found with respect to distortions and efficiency respectively. For Stand-alone system, circuit efficiency as high as 96.8% is achieved when operated at 0.85 power factor. Then the analytical analysis has been carried out for grid connected system where efficiency of 84.3% is achieved for 60o grid phase angle and 75.8% for 10o and thus proposing operation at higher grid phase angle. Harmonic components have been further reduced and brought below 5% level by means of passive filters. Finally, to validate the proposed outcome, simulated results have been compared with analytical results and corresponding plots have been shown. [ABSTRACT FROM AUTHOR]

Electric vehicles and the charging stations and their operation require a thorough examination to evaluate the effects on the electrical network. This becomes particularly challenging in the case of high photovoltaic penetration, due to the variability of the solar resource and vehicle connection patterns, which cater to individual user preferences. The current study investigates the impact of harmonics generated by charging stations and electric vehicles on different photovoltaic penetration scenarios within an electrical distribution system. DC and AC charging stations are analyzed. The findings reveal a third harmonic magnitude increase exceeding 300% compared to other cases. Furthermore, this study demonstrates the effects of current and voltage variations on end-users and substation transformers. The impact of harmonics on the hosting capacity of the network is also analyzed, resulting in a 37.5% reduction in the number of vehicles. [ABSTRACT FROM AUTHOR]

Solar and Wind energy based Renewable Energy Systems (RES) are one of the most rapidly growing technologies as a means of producing clean electrical energy. Grid integration of RES involves various types of power electronics-based converters and inverters. These electronic devices produce harmonics at their terminals, which are transferred to the grid. Harmonics forecasting is one of the techniques used to design harmonics mitigation devices in order to reduce harmonics. The core objective of this work is to develop a hybrid forecasting model to produce accurate and reliable harmonics forecasts for RES. Six novel hybrid forecasting models are proposed in this work to perform harmonics forecasting. These models are based on different combinations of multi-layered Artificial Neural Networks (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS). The forecasting models proposed are two-staged architecture. Three hybrid forecasting models (model-1, 2 & 3) use ANN in the first stage and ANFIS in the second while the other three models (model-4, 5 & 6) are designed vice versa of prior. Two renewable generators are used to generate harmonics. The first generator combines Double-Fed Induction Generator (DFIG) driven by wind turbine with solar photovoltaic (PV) panels whereas, the second generator combines wind turbine driven Permanent Magnet Synchronous Generator (PMSG) with solar panels. The purpose of these generators is to produce voltage and current waveforms using real-world data (Wind Speed & Solar Irradiation). Harmonics are extracted from these waveforms which are used to create training and testing datasets for the forecasting models. Harmonics are forecasted using the six forecasting models proposed and results are validated by comparing them to benchmark work done in the literature. The results show that model-3 and model-6 are the best and most consistent performing models.

Although increasing the number of switches increases the switch losses, most designed controllers focus on controlling an inverter circuit with more than six switches. The paper aims to address this issue that arises in implementation of the voltage source inverter (VSI) for brushless DC (BLDC) motors. It optimises the sinusoidal pulse width modulation (PWM) controller, minimising total harmonic distortion (THD) while keeping the VSI’s circuit at six switches to avoid increased switching losses. This was achieved by applying an artificial neural network (ANN) to generate a signal, which combines with the already existing reference and carrier signals. The addition of the new signal to the existing signals contributed to generating more pulses compared with the conventional sinusoidal PWM. Simulink was used to design the system and analyse its performance with the conventional and neutral point clamped (NPC) VSI systems. Results indicated that the proposed system performs better when controlled with an LCC filter. Compared with the control experiments, its output waveform has the lowest THD value, which is 6.04%. The switching losses of all the systems were also computed. Results from the computation indicated that the proposed system is capable of reducing the switching losses by 0.6 kW compared with the NPC VSI brushless DC motor (BLDCM) system. BLDCM speed was tested across various conditions; the results are reported in Section 5.

This paper introduces a novel non-integral method for calculating AC capacitor voltage in shunt hybrid active power filters (SHAPFs). These filters commonly use passive power filters (PPFs) that can introduce voltage containing harmonics into the AC capacitor, leading to overvoltage situations that can be dangerous. To avoid accidents due to overvoltage, it is necessary to monitor the AC capacitor voltage continuously. The proposed method enables the calculation of the voltage across the AC capacitor, using only the sensor available in the SHAPF, without requiring additional sensors. By avoiding the use of integration to calculate the voltage, the method effectively mitigates errors due to DC offset inflows. The effectiveness of the proposed method was verified through simulations and actual experiments. Results demonstrate that the proposed voltage calculation method improves the voltage calculation accuracy by approximately 1.93[%] when compared to the general voltage calculation method. The proposed non-integral AC capacitor voltage calculation method represents a significant advancement for SHAPFs, providing an effective means for reducing overvoltage risks, without requiring additional sensors or costly equipment.

Grid integration of Renewable Energy Systems (RES) involves various types of power electronics-based converters and inverters. The use of these electronic-based devices results in inducing both current and voltage harmonics to the grid. In order to reduce harmonics, especially for large-scale integration of RES, harmonics forecasting is one of many techniques used to design harmonics mitigation devices. The core objective of this work is to develop a novel forecasting model for accurate and reliable harmonics estimation for RES. To achieve this; two hybrid generator models are used. First model consists of wind turbine coupled with Doubly Fed Induction Generator (DFIG) combined with Solar Photo Voltaic (PV) based power generator which are connected to common grid. The second model uses Permanent Magnet Synchronous Generator (PMSG) with wind turbine in conjunction with Solar-PV generator. With real world meteorological data (wind speed and solar irradiation) as inputs, these generators simulate and produce output power with current and voltage waveforms. Harmonics are extracted from these waveforms to record, analyze, arrange and forecast future harmonics. Three parameters namely, Total Harmonics Distortion (THD) and the dominant individual harmonics contents, 11th harmonic (h11) and 13th harmonic (h13) are forecasted for both voltage and current waveforms. Artificial Neural Networks (ANN) and Adaptive Neuro Fuzzy Inference Systems (ANFIS) are the prominent methods used for forecasting. Three types of three-layered ANN structures namely Cascaded Neural Network with Recurrent Local feedback (3LCRNNL), Cascaded Neural Network with Recurrent Global feedback (3LCRNNG) and Cascaded Neural Network with Local and Global feedback (CRNNGL) have been proposed and utilized in this work with hyperbolic tangent as transfer function to adjust weights and scaled conjugate gradient method as optimizer to reduce training error. ANFIS is also employed with subtractive clustering method to improve adaptability and accuracy of forecasts. The results are compared and presented which shows that ANFIS recorded the best performance for THDV and h13 and 3LCRNNGL for h11 for the Wind DFIG-PV model voltage harmonics forecast. For forecasts of the current harmonics, 3LCRNNGL performed best for THDI and h11, whereas ANFIS performed best for h13. For Wind PMSG-PV generator model, ANFIS yields the best results in every scenario involving voltage and current harmonics.

Pulse Width Modulation (PWM) driven power converters are widely used in realizing the function of rectification and inversion in renewable and emerging power applications. They play a key role in tying renewable energy sources of solar and wind to conventional energy grid systems. Such emergying styles of power systems are referred to as Distributed Generation (DG) or energy mix. The PWM-based switching devices are also sources for injecting harmonics to degrade the quality level in this emerging style of DG systems causing steady-state waveform distortion. Thus power converters operated by finely tuned PWM play an important role in mitigating the voltage waveform distorting harmonics and consequently improving the stability of the system. This paper presents the mitigation of harmonic contents by reproducing as a novel space vector-based switching pattern of SVPWM. In this work, the proposed switching pattern will have a DC link RMS current equal to or less than that of a conventional SVPWM current under the same switching frequency (SF), load, L-C filter, DC bus voltage, and Modulation Index (MI). The results include a 13.43% improvement in Total Harmonic Distortion (THD) at relatively high modulation indices. The Weighted Total Harmonic Distortion (WTHD) has also improved by 47.37% and the number of thyristors has been reduced lowering switching losses. The Matlab-Simulink simulation results have been experimentally verified with an Advanced Digital Analog Learning Module (ADALM M2k)-based experimental setup, improving the proposed SVPWM THD by 13.49%. The conventional and proposed SVPWM performance comparison is done by obtaining Fast Fourier Transform (FFT) by driving a two-level three-phase voltage source inverter.

The objective of this paper is to adequately model a doubly-fed induction generator (DFIG)-based type-3 wind turbine under grid unbalance by considering not only positive and negative-sequence circuits but also the 3rd harmonic circuit. This 3rd harmonic is a positive sequence component caused by frequency coupling with the negative-sequence 60-Hz component. It is not a zero-sequence component and cannot be got rid of by delta-connected transformers. Hence, accurate modeling is necessary to capture this harmonic component. In addition to modeling, we design an efficient algorithm for steady-state analysis by formulating the steady-state analysis problem as an optimization problem. A set of equality constraints has been formed to reflect the relationship of voltage, current, and power in the ac circuits, the dc circuit, and the different frames. This formulation is defined in YALMIP, a MATLAB interface for optimization problems. The optimization problem is then solved by a nonlinear optimization solver. The results of the steady-state analysis are phasors of harmonic components at steady state. They have been validated by the phasors obtained from Fourier transforms of electromagnetic transient (EMT) simulation results. The paper contributes to both the sophisticated phasor model of DFIG with consideration of grid unbalance and the efficient computing procedure of steady-state analysis by use of advanced solvers.

In this paper, a new accurate and comprehensive analytical model of harmonic and interharmonic distortion produced by a single-phase AC/DC diode bridge rectifier (DBR) is presented. Its main and new characteristic is the ability to consider the presence of interharmonics in addition to harmonics in the voltage at the terminals of DBR due to the background distortion. Analytical expressions able to predict DC voltage and AC current either in time or frequency domains are obtained. Several numerical and experimental tests have been performed showing very accurate results. The proposed model presents all the advantages of analytical models (e.g., fastness); therefore, it can be easily integrated with iterative harmonic and interharmonic analysis procedures. Subsequent applications are the possibility to perform parametric analyses and probabilistic studies, to derive harmonically and interharmonically coupled admittance matrices, to help in introducing standard limits for interharmonics.

This study presents a method for monitoring the voltage across the AC capacitor in a grid-connected hybrid active power filter without the use of additional voltage sensors. In the case of an AC capacitor used in a hybrid, active power filter, there is a risk of burnout caused by exceeding the allowable voltage owing to the harmonic current and voltage generated by the inverter. Therefore, this study presents a method to monitor the voltage of the AC capacitor using the current from which the DC component has been removed without any additional voltage sensors to prevent the burn-out of the AC capacitor. The proposed sensorless AC capacitor voltage monitoring method is verified through simulations and experiments. The difference between the magnitude of the voltage measured in the simulations and experiments and the magnitude of the voltage predicted with the proposed method was approximately 0.526[%] on average.

Abstract This paper reports an algorithm for quick and precise estimation of the single‐phase grid frequency under DC offset and both odd and even harmonics. The proposed single‐phase grid frequency estimation method is derived from a three‐phase relation. It relies on a recursive discrete Fourier transform‐based orthogonal signal generator and a two samples‐based algorithm. The proposed approach is simple, demands low computational effort and generates the single‐phase grid frequency at a cost of only one fundamental period as the transient response time under the DC offset and both odd and even harmonics condition. It also produces enhanced performance when compared to several single‐phase grid frequency estimation methods documented in the technical literature. Computer simulations and laboratory experiments are conducted to demonstrate the usefulness of the proposed single‐phase grid frequency estimation method.

The progressive application of non-linear loads in distribution systems (DS) increases current harmonics flow in DS's apparatuses, especially distribution transformers (DTs). Since DTs' operating temperature rises due to the harmonics flow, their loading should be reduced such that the hot spot temperature (HST) is preserved under its permissible value. This means that DTs' available capacity is influenced by load harmonic content. In this paper, a novel formulation for DTs' failure rate in the presence of harmonics is presented as a function of load harmonic contents. Using the suggested equivalent failure rate, DTs' available capacity in harmonic polluted DS is mathematically formulated. Additionally, the presence of the harmonic increases the HST, leading to DTs' aging acceleration. Therefore, the impact of harmonic components on DTs' aging is arithmetically modeled. To evaluate the efficacy of the suggested reliability model, it is applied to three distinct DTs having respectively industrial, commercial, and residential loads. The obtained results indicate that the available capacity of DTs with the same rated capacity would be different regarding to their load harmonic contents. On the other hand, it is comprehended from the achieved results that the aging acceleration factor (Faa) of the DTs increases owing to their load harmonic contents.

Due to the significant share of the induction motors (IMs) in global electricity consumption, the study of the IM in different power supply regimes is the aim of this work. In practice, these electrical receivers are not supplied from ideal voltage systems, therefore the power quality (PQ) is an important aspect in analysing the operation of this type of motors. We proposed a methodology for analysing the effects of unbalanced distorted voltages not only on the technical parameters of the IM but also on the power supply as well as on the driven mechanical system. We identified some dependencies, in analytical forms, between the sizes of interest and the considered PQ disturbances. The results showed that the supply voltage disturbances have significant effects on the induction motor, both electrically and mechanically. These effects are amplified if the IM is running at loads below 50% of the rated load.