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Effective fault detection, classification, and localization are vital for smart grid self-healing and fault mitigation. Deep learning has the capability to autonomously extract fault characteristics and discern fault categories from the three-phase raw of voltage and current signals. With the rise of distributed generators, conventional relaying devices face challenges in managing dynamic fault currents. Various deep neural network algorithms have been proposed for fault detection, classification, and location. This study introduces innovative fault detection methods using Artificial Neural Networks (ANNs) and one-dimension Convolution Neural Networks (1D-CNNs). Leveraging sensor data such as voltage and current measurements, our approach outperforms contemporary methods in terms of accuracy and efficiency. Results in the IEEE 6-bus system showcase impressive accuracy rates: 99.99%, 99.98% for identifying faulty lines, 99.75%, 99.99% for fault classification, and 98.25%, 96.85% for fault location for ANN and 1D-CNN, respectively. Deep learning emerges as a promising tool for enhancing fault detection and classification within smart grids, offering significant performance improvements. [ABSTRACT FROM AUTHOR]

Multilevel converters have received more attention recently. Multilevel converters can be used not only in high-voltage high-power applications, but also in energy storage systems, thanks to reduced harmonics and electromagnetic interferences. State-of-charge (SoC) balancing of the storage system cells within cascaded multilevel inverters, e.g., batteries, can be one of the challenges in practical applications. Different methods have been studied and reported in technical literature. However, the SoC balancing is still a challenging issue. In this paper, a new method is proposed to solve this problem by applying several clever but simple changes to the staircase modulation switching scheme without changing the output waveform properties. Validations for this simple method were performed through simulation and experimental prototype. Despite the simplicity of the method, the simulation and experimental results show that SoC balancing of the cells has significantly improved. [ABSTRACT FROM PUBLISHER]

Modern distribution systems are equipped with various distributed energy resources (DERs) because of the importance of local generation. These distribution systems encounter more and more uncertainties because of the ever-increasing use of renewable energies. Other sources of uncertainty, such as load variation and system components' failure, will intensify the unpredictable nature of modern distribution systems. Integrating energy storage systems into distribution grids can play a role as a flexible bidirectional source to accommodate issues from constantly varying loads and renewable resources. The overall functionality of these modern distribution systems is enhanced using communication and computational abilities in smart grid frameworks. Robust operation of these systems is effectively taken into consideration to manage the uncertainty, which offers an explicit way to control the desired conservativeness. This paper presents an optimal operating program for smart grids equipped with wind generators, controllable distributed generators, energy storage systems, and reactive power compensators. In order to make the studies more practical, uncertainty about wind generators and grid loads is taken into account. Furthermore, the presented operating program is robust in various conditions, i.e. there is no need to change the operating program in a wide range of probable states. The point estimation method and fuzzy clustering method are used for probabilistic assessment of the distribution system in the presence of uncertainties. The IEEE 37-node standard test system, which is a highly unbalanced system, is selected for the case study and the results are discussed comprehensively. [ABSTRACT FROM AUTHOR]

In this paper, a new structure for a nonisolated boost dc–dc converter is proposed. The proposed converter generates higher voltage gain than some conventional nonisolated boost dc–dc converters. In this paper, the voltage and current equations of the elements and voltage gain in continuous conduction mode and discontinuous conduction mode are extracted. Then, the critical inductance converter is extracted and the current stresses in the switches are calculated. To achieve high voltage gain, a generalized structure based on the proposed structure generates for dc–dc converters. Meanwhile, the root mean square current relations of devices are obtained for an extended structure. Finally, the results of PSCAD/EMTDC software and laboratory prototype are used to reconfirm theoretical concept. [ABSTRACT FROM AUTHOR]

In this paper, a new structure of nonisolated boost dc–dc converters based on voltage-lift technique is proposed. In comparison with conventional nonisolated boost dc–dc converters, the proposed converter generates higher voltage gain. In this paper, the relations between voltage and current of all elements in continuous conduction mode and discontinuous conduction mode are calculated as well as voltage gain in each mode. Then, the critical inductance and stress of switch current are extracted. Finally, the validity of given theories is examined by using the experimental results. [ABSTRACT FROM PUBLISHER]

In this paper, a new structure of nonisolated boost DC-DC converters is proposed. The operation of the proposed structure in continuous conduction mode (CCM) and discontinuous conduction mode (DCM) is presented. Then, the critical inductance relation between CCM and DCM is obtained. The voltage gain and current stress of switches are calculated. Finally, the validity of the presented theoretical issues is reconfirmed by using simulation results obtained from PSCAD/EMTDC software. [ABSTRACT FROM AUTHOR]

The subsynchronous resonance (SSR) phenomenon may occur when a steam turbine-generator is connected to a long transmission line with series compensation. Flexible AC transmission systems (FACTS) devices are widely applied to damp the SSR and low-frequency oscillation (LFO). A thyristor-controlled series capacitor (TCSC) is a commercially available FACTS device that was developed for damping the SSR and LFO. In this paper, 2 control methods for damping the SSR and LFO are added to the TCSC main controller in order to demonstrate that the SSR damping capability of the TCSC can be enhanced by proper modulation of the firing angle. The control methods are presented, namely the conventional damping controller (CDC) and fuzzy logic damping controller (FLDC). The particle swarm optimization (PSO) algorithm is used for searching optimized parameters of the CDC. Fast Fourier transform is carried out in order to evaluate the effect of the TCSC-based FLDC in damping the SSR and LFO. The study system was adopted from the IEEE second benchmark model by altering a part of the fixed series capacitor to the TCSC. The MATLAB/Simulink was used to verify the effectiveness of each control method. The simulation results show that the FLDC has an excellent ability in damping the SSR and LFO in the power system toward the CDC-optimized PSO algorithm. [ABSTRACT FROM AUTHOR]