Nowadays, renewable energy is increasingly used in smart grids and microgrids to reduce the use of fossil fuels and improve network efficiency. Like all power system devices, microgrids are subject to transient and steady-state faults, such as short circuits. These faults impair reliability and consumer dissatisfaction. To accurately, automatically, and economically determine the location of a fault, a robust fault location method is needed to stabilize and repair the damaged part of the network. Given the access to the data of all nodes, the fault in these networks can be located based on the data on the two terminals. Accordingly, this paper proposes a method for determining fault distance and faulty section in the island and grid-connected microgrids. The proposed method uses Distributed Parameters Line Model and calculates the location of double-phase faults in the microgrid based on voltage and current data on both sides of each section, taking renewable energies and electric vehicles into account. At first, the measurement devices receive and store the current and voltage data at the beginning and end of each section. If a fault occurs, the fault distance is determined by calculating the difference between voltages and currents on both sides of the fault. According to the sampling rate, many voltage and current samples are obtained during the fault. The proposed method calculates a fault distance for each sample. As a result, many fault distances are obtained. These calculations are done for all sections. In the next step, the distances obtained for each section are plotted on the coordinate axis, and a curve is obtained for each section. Among the curves obtained, one curve has a global minimum, which indicates the faulty section. Other curves are ascending or descending. In addition, the global minimum point indicates the calculated distance of the fault from the beginning of the section. This method is not sensitive to electric vehicle Models and Distributed generation sources and uses only less than half-cycle data to execute the algorithm. The performance of the method is investigated with the simulation of a 9-bus microgrid in MATLAB/SIMULINK. The effects of changes in Line Parameters (two scenarios), different fault locations, fault resistance (0, 25, and 50 Ω), fault inception angles (36, 90, 180, and 270 degrees), different DGs operation modes (three scenarios), and measurements error (±3%) are studied. The maximum and minimum errors of this method are obtained to be 0.97% and 0.02%, respectively. The results indicate the high accuracy of the proposed method compared to other fault location methods.

Nowadays, renewable energy is increasingly used in smart grids and microgrids to reduce the use of fossil fuels and improve network efficiency. Like all power system devices, microgrids are subject to transient and steady-state faults, such as short circuits. These faults impair reliability and consumer dissatisfaction. To accurately, automatically, and economically determine the location of a fault, a robust fault location method is needed to stabilize and repair the damaged part of the network. Given the access to the data of all nodes, the fault in these networks can be located based on the data on the two terminals. Accordingly, this paper proposes a method for determining fault distance and faulty section in the island and grid-connected microgrids. The proposed method uses Distributed Parameters Line Model and calculates the location of double-phase faults in the microgrid based on voltage and current data on both sides of each section, taking renewable energies and electric vehicles into account. At first, the measurement devices receive and store the current and voltage data at the beginning and end of each section. If a fault occurs, the fault distance is determined by calculating the difference between voltages and currents on both sides of the fault. According to the sampling rate, many voltage and current samples are obtained during the fault. The proposed method calculates a fault distance for each sample. As a result, many fault distances are obtained. These calculations are done for all sections. In the next step, the distances obtained for each section are plotted on the coordinate axis, and a curve is obtained for each section. Among the curves obtained, one curve has a global minimum, which indicates the faulty section. Other curves are ascending or descending. In addition, the global minimum point indicates the calculated distance of the fault from the beginning of the section. This method is not sensitive to electric vehicle Models and Distributed generation sources and uses only less than half-cycle data to execute the algorithm. The performance of the method is investigated with the simulation of a 9-bus microgrid in MATLAB/SIMULINK. The effects of changes in Line Parameters (two scenarios), different fault locations, fault resistance (0, 25, and 50 Ω), fault inception angles (36, 90, 180, and 270 degrees), different DGs operation modes (three scenarios), and measurements error (±3%) are studied. The maximum and minimum errors of this method are obtained to be 0.97% and 0.02%, respectively. The results indicate the high accuracy of the proposed method compared to other fault location methods.

Distributed Denial of Service (DDoS) attacks are among the primary concerns in internet security today. Machine learning can be exploited to detect such attacks. In this paper, a multi-layer perceptron Model is proposed and implemented using deep machine learning to distinguish between malicious and normal traffic based on their behavioral patterns. The proposed Model is trained and tested using the CICDDoS2019 dataset. To remove irrelevant and redundant data from the dataset and increase learning accuracy, feature selection is used to select and extract the most effective features that allow us to detect these attacks. Moreover, we use the grid search algorithm to acquire optimum values of the Model’s hyperParameters among the Parameters’ space. In addition, the sensitivity of accuracy of the Model to variations of an input parameter is analyzed. Finally, the effectiveness of the presented Model is validated in comparison with some state-of-the-art works.

Conventional fault location methods using voltages and currents of one terminal are not applicable to multi-terminal transmission Lines. So far some techniques have been presented to solve fault location problem for multi-terminal transmission Lines. In these methods, lumped or Distributed frequency domain Models of transmission Lines have been used. In multi-terminal Lines, faulty section should be detected before fault location estimation. In this paper, a method based on time domain Model of transmission Line is presented to identify the faulty section and to determine the location of faults for three-terminal transmission Lines. In the suggested method, samples of voltages and currents at all three terminals are used for calculating the location of the fault. The proposed algorithm is independent of fault resistance, insensitive to load current and fault inception angle. Furthermore, in the method neither any knowledge about source impedance nor the fault type classification is required. Filtering of dc and high frequency components of voltages and currents signals are not necessary. The simulation results, using EMTP/ATP and MATLAB software, confirm the accuracy and precision of the proposed method.

To the extent of the usable bandwidth of the piezoelectric energy harvesters (PEH) and progress the harvesting proficiency, a 2-DOF bistable PEH (2D-BPEH) with an elastic substructure is developed to show the strengthened nonLinear large-amplitude periodic vibration performances. Introducing the substructure, which is demonstrated by the mass-spring sub-system added between the Distributed bimorph beam and exciting base, dynamic motions of the beam is expected to reproduce high energy trajectories and large deflections. Due to raising the accuracy of the Model and results, the key novelty of the present study is to consider the mathematical Model of composite smart bimorph beam with the aid of Distributed Parameters Model and Von Karman strain relations. With the help of Hamilton’, s principle, Electro mechanic Modeling of the 2-DOF system has been derived and three coupled equations are consequent utilizing the Galerkin method. Primarily deflection and voltage frequency response curves are calculated analytically,then, the Model has been compared and validated by the results of the 2-DOF PEH Model with lumped parameter beam in the literature. Numerical results indicate that accurate designing of 2-DOF piezoelectric energy harvester Parameters could intensely enhance the generating voltage and at a broader exciting frequency band. The results have shown that the 2-DOF bistable PEH coupled with elastic substructure as a magnifier harvests extra electrical power at specific input frequencies and operates at larger bandwidth than routine PEHs.

In the past, Geomorphologic Instantaneous Unit Hydrograph (GIUH) had been proposed as a tool to generation of flood hydrographs from storm rainfall data. Nowadays, Spatial Unit Hydrograph is used instead, with making use of Geographic Information Systems (GIS). First, watershed is divided into small square cells, and then routing of runoff from each cell to the basin outlet is calculated using the first passage time response function based on the mean and variance of the flow time distribution, which is derived from advection- dispersion transport equation. The flow velocity for ground surface cells is calculated by Overton’s equation and for channel flow cells by Manning’s equation based on the local slope, roughness coefficient and hydraulic radius. The total direct runoff at the basin outlet is obtained by superimposing all contributions from every grid cell. The Model is tested on kameh catchment in Khorasan Razavi state. Results are in excellent agreement with the measured hydrograph at the basin outlet. Sensitivity analysis shows that Parameters of channel roughness coefficient and minimum slope threshold have larger influence than parameter of thershold of drainage area in deLineating channel networks on peak discharge, time to peak and on the outflow hydrograph, Since the Model accounts for spatially Distributed hydrologic and geophysical characteristics of the catchments, it has great potential for study of the influence of changes in land use or soil cover on the hydrologic behavior of a river basin.