The Luo converters family is a popular non-isolated DC-DC converter, which has achieved a high voltage gain with the voltage lift technique. The highest voltage gain in this family belongs to quadruple voltage lift converters. This study proposes three modified topologies with triple, quadruple, and hextuple voltage lift. All these converters use one switch in the power circuit. Additionally, the drive circuit of the switch is simple. The common ground of the Load and input source is the other bold point of these converters. It is good to note that the provided voltage gain has a one-degree relation according to the duty cycle, which decreases the increased loss rate by the duty cycle rising compared with quadratic and cubic topologies. Additionally, the provided voltage gain is as high as in the high-ordered conventional Luo converters without increasing the number of inductors. The theoretical relations of the converters are discussed, and the experimental results are discussed to validate them. The experiment results are for 150 W output power, 50 V input voltage, and 300-600V output voltage. The extracted efficiency is more than 90 % in the proposed topologies.

Please take note of the following text:The boost topology is the most popular non-isolated DC-DC converter with step-up capability. It’s worth noting that lower duty cycle percentages, which lead to higher efficiencies, can only provide a voltage gain from unity to two, which may not be suitable in some cases. This study introduces an improved topology of the boost converter. The provided voltage gain is six times that of a traditional boost converter. This means that a 50 % duty cycle provides a 12-times voltage gain. Similar to the boost converter, the input current is continuous, and only one switch with a simple drive circuit is used. The output voltage of the converter is divided among three capacitors, resulting in decreased output capacitor voltage stress due to the provided triple-stacked structure. The converter is discussed in both ideal and non-ideal modes. Finally, experimental results for a 150 W output power and 600 V output voltage are presented.

In this article, a multifunction control scheme is proposed for battery energy storage system (BESS) as an independent grid-supporting distributed energy resource in islanded/stand-alone microgrids (MGs) including synchronous generator. The proposed controller increases the utilization factor of the BESS to participate in electricity market ancillary services using its available unused capacity. The proposed controller incorporates adaptive frequency control, voltage regulation, as well as unbalanced load compensation. Additionally, an adaptive high-pass filter is proposed which enables the BESS to compensate for frequency deviations according to its state-of-charge for a longer effective life span of batteries. Furthermore, a voltage control loop is integrated to regulate the BESS point of common coupling voltage during low-voltage conditions by injecting reactive current. Finally, the available capacity of the BESS grid-connected converter is utilized to compensate for the unbalanced currents drawn by the loads. Simulation and experimental tests are compared under several scenarios to validate the proposed controller performance. The results reveal the efficacy of the proposed controller in maximizing the BESS utilization factor in islanded/stand-alone MGs.

This article proposes a distributed self-triggered mechanism (DSTM) as a novel secondary control approach for ac heterogeneous autonomous microgrids (MGs) with switching communication topologies, aiming to reduce computation, and communication demands. According to the presented DSTM, each distributed energy resource is equipped with a local algorithm enabling it to precalculate the next triggering time instant based on system states observed at the latest one. The parameters of DSTM are determined through Lyapunov stability analysis. Subsequently, the calculation of the next triggering time instant is achieved by solving a quadratic equation derived from the triggering condition, eliminating the need for continuous monitoring of the triggering condition and preventing Zeno behavior. The effectiveness of the proposed method is validated through simulations in MATLAB's Simulink, demonstrating its capability in regulating MG voltages and frequencies, achieving precise active power sharing, and balancing state of charge compared to existing methods.

Frequency response analysis (FRA) suffers from the interpretation of results despite its potential ability to detect faults related to the power transformer windings. This article presents a technique for interpreting frequency responses, which is based on image processing and a deep learning method called graph convolutional neural network (CNN). The proposed procedure transfers frequency responses into 2-D images through a visualization technique. The resulting images are aggregated into a dataset to be used as the CNN input. The proposed technique is applied on frequency responses of two different winding models with short circuit (SC) faults. The SC faults with different intensities are applied on different sections of a simulated ladder model winding and a 20 kV winding of a 1.6 MVA distribution transformer. After determining the frequency response for each faulty case and applying the visualization technique, the precise locating of the SC faults is performed by the CNN. Then, the results are analyzed by performance evaluation metrics. At this stage, the high performance of the CNN in the use of 2-D images instead of the conventional method is observed. Finally, by testing the high impedance SC faults in different sections of the simulated winding model and applying the suggested method step by step, early detection of the SC fault is also performed in this article. It should be noted that the suggested technique, in addition to its accuracy and high detection speed, can be considered as an important step in automatic interpretation of frequency responses for online monitoring of transformers.

Interpreting results of a transformer frequency response analysis (FRA) is quite challenging. One of the common methods to summarize FRA data is to employ statistical indicators (SIs) over FRA spectra. However, SI-specific boundary conditions for various operational modes of transformers are left unexplored. The lack of such boundary conditions renders interpretation of SIs difficult and subjective. In this article, in an attempt to find data-driven boundary conditions, first the conventional measurement setup of FRA technique is modified to emulate interturn winding short-circuit. Then, the boundary conditions of various SIs for normal, suspicious, and critical operational modes of transformers under fault are obtained. Nevertheless, the price of moving subjective boundaries to their objective data-driven counterparts is paid in an intrinsic uncertainty introduced by the process of data collection per se. In order to capture and quantify this uncertainty, a novel solution inspired by bolstered error estimation used in pattern recognition is proposed. In particular, the proposed method allows reporting the level of confidence that an observed magnitude of SI belongs to a specific operational mode. Having this confidence level is also warranted from an operational perspective because it enables utility operators to enhance the decision-making process and estimate the severity of transformer faulty conditions.

Recently, axial displacement detection of transformer winding has been performed using synthetic-aperture radar (SAR) imaging, as an online method, in ultra-wideband frequency band. Another important problem in power transformers is partial discharge (PD), which emits signal in the ultra-high-frequency (UHF) frequency band. If detection of axial displacement using SAR imaging can be applied to the UHF band, both defects can be detected using only one set of antennas and application of these methods can be more economic. However, as shown in this paper, using the same frequency band for detection of both defects leads to wrong conclusions about winding conditions, if PD is occurred during the application of SAR imaging. As a solution, UHF stepped-frequency imaging method and generalized likelihood ratio test method are proposed for detection of axial displacement. Finally, on a three-phase real transformer both defects are simultaneously detected using designed monitoring system.

For transient stability studies of power systems with high penetration of microgrids, models of the connected microgrids must be used. In this paper, a novel dynamic equivalent model is proposed for grid-connected microgrids using measurement data of point of common coupling (PCC). The proposed equivalent model includes electrical components, such as synchronous generator (SG), voltage source converter (VSC), and active and reactive loads. The proposed equivalent model contains different controllers, such as excitation system, governor, and VSC's controllers. All SG-based distributed generation (DG) units of the microgrid are represented by one equivalent SG, while an equivalent VSC represents converters-based DGs. Similarly, a parallel active and reactive static load is considered in the equivalent model to represent the static loads of the microgrid. In order to make the equivalent model more efficient, accurate, and valid at different operating points, a series resistance and reactance are utilized to connect the equivalent generator to the PCC. The final goal is to identify the parameters of the equivalent components such that the equivalent model behaves as like as the detailed microgrid facing different disturbances. The identification procedure is carried out by means of a genetic algorithm using measurement data at the PCC. To show the effectiveness and accuracy of the proposed model, the identified equivalent model is studied at different operating points and the results are compared with the original detailed model.

In this paper, the mechanical faults of transformers including the winding radial deformation and axial displacement on 1.6 MVA transformer winding are investigated. Then, by estimating the parameters of the detailed model of this transformer winding in MATLAB software and changing these parameters in a manner that is proportional to the mechanical defects in electro-magnetic transients program software, the sampled differential current of the transformer is extracted for each disturbance. Next, the internal and external electrical faults and inrush current of the transformer are simulated. Afterwards, these signals are analyzed using maximal overlap discrete wavelet transform with Daubechies4 wavelet function, and their features are extracted. These extracted features are considered for training the classifiers of Decision Tree and artificial neural network. According to the simulation results, the proposed procedure is capable of classifying and discriminating among winding mechanical defects, internal and external electrical faults, and inrush current with a good accuracy that is the main novelty of this paper in comparison to other published works, which are limited to classifying only some of the mentioned faults.

Fundamentals of parameter estimation of synchronous generator (SG) have already been presented in different standards such as IEEE Std. 115. The first proposed methods require short circuit tests and/or some tests for which the synchronous generator should be out of service. In recent reports, however, to avoid the shortcomings of former methods, partial load rejection tests on$d- q$axes have been recommended to estimate the electrical parameters of SG such as different reactances and time constants. In this paper, it is first shown that the standard well-known methods are valid when there is no saturation effect. Therefore, a new method is proposed to improve SG parameters estimation taking into account the saturation effect. The proposed method uses saturation curve parameters, rotor angle, and analytical equations of the SG alongside the load rejection tests results. To show the accuracy and precision of the proposed method, it is applied to experimental data of an 80 MVA gas turbine unit, and the results are discussed.

In this paper, disk space variation, radial deformation, short circuit, and axial displacement as four common transformer winding faults, in different locations and with various extents, were practically applied to 20 kV winding of a 1.6 MVA distribution transformer. For classification of the fault type, its location, and extent, a new calculation method for transfer function (TF) comparison indices called windowed calculation was proposed. The whole frequency range of the TF is scanned in this method. It was shown that the presented method increases the visual fault detection ability even for fault-type detection and significantly enhances the accuracy of the classification winding faults type, location, and extent. In this study, Fisher discriminant analysis (FDA) was utilized for dimension reduction and feature extraction. The ability of the FDA for maximizing the between-class separability while minimizing their within-class variability was utilized for this application so as to decrease the dimension and extract appropriate features from a lot of calculated features by the proposed method.

We propose a novel series transformer based diode-bridge-type solid state fault current limiter (SSFCL). To control the fault current, a series RLC branch is connected to the secondary side of an isolation series transformer. Based on this RLC branch, two current limiting modes are created. In the first mode, R and C are bypassed via a paralleled power electronic switch (insulated-gate bipolar transistor, IGBT) and L remains connected to the secondary side of the transformer as a DC reactor. In the second mode, the series reactor impedance is not enough to limit the fault current. In this case, the fault current can be controlled by selecting a proper on-off duration of the parallel IGBT, across the series damping resistor ( R . and capacitor, which inserts high impedance into the line to limit the fault current. Then, by controlling the magnitude of the DC reactor current, the fault current is reduced and the voltage of the point of common coupling (PCC) is kept at an acceptable level. In addition, in the new SSFCL, the series RC branch, connected in parallel with the IGBT, serves as a snubber circuit for decreasing the transient recovery voltage (TRV) of the IGBT during on-off states. Therefore, the power quality indices can be improved. The measurement results of a built prototype are presented to support the simulation and theoretical studies. The proposed SSFCL can limit the fault current without any delay and successfully smooth the fault current waveform.

In this paper, a novel method based on image processing is proposed for detecting the presence and location of mechanical deformations on an actual power transformer winding. A vertical imaging setup is used to obtain a two-dimensional (2-D) image of the transformer winding using synthetic aperture radar (SAR) imaging method and Kirchhoff migration algorithm. The main goal of the image processing method is to detect the location of the radial deformation on the transformer high-voltage (HV) winding. Two different deformations are applied on the winding under the test in this paper. The first one is a modeled bulgy mechanical deformation and the second one is an actual concave buckling made on the actual transformer HV winding. The experimental results for different cases show the effectiveness of the proposed method to detect the presence and location of the deformation on the transformer winding.

Multicell converters are an interesting alternative for medium voltage and high power applications, because of the increased number of output voltage levels and apparent frequency. The two most significant types of multicell converter are the flying capacitor multicell (FCM) converter and its derivative, stacked multicell (SM) converter. Balancing flying capacitor voltages is an important constraint to the proper performance of FCM and SM converters. Thus, observation of the flying capacitor voltages used in active control is valuable, but using voltage sensors for observation increases cost and size of the converter. This paper deals with a new strategy to estimate the flying capacitor voltages of both FCM and SM converters. The proposed strategy is based on a discrete time model of the converter and uses only a load current sensor. The circuit was simulated using PSCAD/EMTDC software and simulation results were presented to validate the effectiveness of the proposed estimation strategy in observing the flying capacitor voltages. Simplicity is the most significant advantage of the proposed strategy, its performance being based on simple equations.