The integration of the cyber-network with the physical power grid makes it prone to cyber-attacks disrupting the normal operation of the grid and therefore critical to detect. This paper compares how the detection of Distributed Denial of Service (DDOS) attacks, one of the most common types of cyber-attack, on smart grids varies depending on the Machine Learning (ML) method used for detection, the different datasets used for the training, and the features of the dataset incorporated in the training. The most commonly used datasets namely KDDCup'99 and CICIDS'17 datasets are adapted for the sake of testing. The different ML methods used for these experiments are Decision Tree, Random Forest, Quadratic Discriminant Analysis, Support Vector Machine, Naïve Bayes, and Extreme Gradient Boosting. With extensive comparison analyses among the ML models based on accuracy, computation time, and storage usage, the paper demonstrates the applicability of the models in smart grids
Geomagnetically induced current (GIC) is lower in the low and middle latitude areas than in the auroral and sub-auroral areas. However, this may not be the case during intense solar storms. Long high voltage transmission lines may be severely affected by GIC. Using another grounded winding transformer, it enters the grid and back to the Earth through the grounded winding transformer. Transformers' reactive power consumption may also be significant if severe GMD occurs. The Bangladesh power system network is expanding. Several 400 kV transmission lines are in operation. The 400 kV grid of the Bangladesh power system is taken as a good test case for studying GIC effects in the low latitude region. This paper deals with the simulated response of the 400 kV power grid of BPSN during GMD events.
This paper demonstrates the threshold voltage extraction method of silicon carbide (SiC) n-channel MOSFETs over temperatures. The presence of high-density traps at the SiC/SiO2 interface affects the change of threshold voltage and the channel region mobility of SiC device. To design complex integrated circuits (ICs) in a SiC process and meet extensive application requirements, accurate measurement of threshold voltage with temperature is of paramount importance. The linear extrapolation method cannot be implemented for SiC because of the difference in the transconductance curve compared to silicon (Si). The extracted threshold voltages obtained with the second derivative method are also not consistent with the increasing temperature. A slope-based approach on the logarithmic transfer curve has been proposed here to extract the threshold voltage. Multiple dies have been measured after examining different 4H-SiC CMOS wafers fabricated at Fraunhofer IISB. The 1 µm technology node is a triple well process, with a single poly and a single metal layer. Characterization results for NMOS devices with different lengths and widths are shown for temperatures 25°C, 150°C, and 300°C. The transfer characteristics of the n-channel SiC MOSFETs are measured utilizing a Keysight B1500A Semiconductor Device Parameter Analyzer. The findings of this work can be used in further investigation of the threshold voltage drift of SiC CMOS devices and the design of ICs capable of operating in harsh environments.
This paper proposes the optimized design of an interior permanent magnet synchronous machine (IPMSMs) using widely used low-priced ferrite magnets. Four types of IPMSM analyzed in this paper which has been named with different tags according to changing air gap length and width of permanent magnets (PM) of the machine. The analysis simply shows how efficiency and performances of IPMSM changes by varying the air-gap length and width of PM. I-shaped PM was considered in this thesis. To analyze the efficiency and torque ripple, Finite Element Analysis (FEA) was used and simulation done by Lua scripting which is compatible in the MATLAB environment.
<p>Smart grid technologies have been transforming the power grid operation paradigms by integrating smart sensing devices, advanced communication networks, and powerful computing resources. In addition, data-driven applications have significantly increased in recent years, accelerating the use of smart sensors, such as phasor measurement units (PMU), in power grid monitoring. It necessitates a well-functioning communication network (CN) for PMU measurement data transfer to the control center even in the event of failures. This paper proposes a PMU network routing algorithm to ensure data transfer for control center's resilient observability to the power grid. The interdependent roles of PMUs in power grid observability is first identified based on the power grid topology. Then, a failure-tolerant routing algorithm is proposed to find data transfer paths in the CN that meets the power grid monitoring needs. The resultant routing paths ensure resilience against single link failure, where the resilience is defined in terms of grid observability. Besides, a cost metric is defined to minimize end-to-end delay in the network to facilitate real-time data transfer. Simulation results verify the superiority of the proposed routing algorithm compared with conventional fault-tolerant routing algorithms that are agnostic to the domain knowledge of power grid observability.</p>
This letter proposes a three-phase phase-locked loop (PLL) algorithm relying on an adaptive Clarke transform (CT) under amplitude and/or phase angle unbalanced condition. Analytical expressions for coefficients of matrix associated with the adaptive CT are derived and used to generate orthogonal signals. An auxiliary algorithm is also proposed to track the actual amplitudes and phase angle deviations. Unlike the conventional CT relying on constant coefficients, the adaptive CT can generate accurate orthogonal signals from the three-phase voltages including phase angle and/or amplitude imbalances. As a result, the proposed PLL is not affected by the unbalanced phase angles with/without amplitude imbalances. The advantages of the proposed adaptive CT-based PLL method is illustrated by both simulation and experimental results.
Power distribution networks are evolving due to the integration of DERs and increased customer participation. To maintain optimal operation, minimize losses, and meet varying load demands, frequent network reconfiguration is necessary. Traditionally, the reconfiguration task relies on optimization software and expert operators, but as systems grow more complex, faster and more adaptive solutions are required without expert intervention. Data-driven reconfiguration is gaining traction for its accuracy, speed, and robustness against incomplete network data. LLMs, with their ability to capture complex patterns, offer a promising approach for efficient and responsive network reconfiguration in evolving complex power networks. In this work, we introduce LLM4DistReconfig, a deep learning-based approach utilizing a fine-tuned LLM to solve the distribution network reconfiguration problem. By carefully crafting prompts and designing a custom loss function, we train the LLM with inputs representing network parameters such as buses, available lines, open lines, node voltages, and system loss. The model then predicts optimal reconfigurations by outputting updated network configurations that minimize system loss while meeting operational constraints. Our approach significantly reduces inference time compared to classical algorithms, allowing for near real-time optimal reconfiguration after training. Experimental results show that our method generates optimal configurations minimizing system loss for five individual and a combined test dataset. It also produces minimal invalid edges, no cycles, or subgraphs across all datasets, fulfilling domain-specific needs. Additionally, the generated responses contain less than 5% improper outputs on seen networks and satisfactory results on unseen networks, demonstrating its effectiveness and reliability for the reconfiguration task.
Bottom vanes are vortex generating devices that are mounted on the river bed at an angle to the prevailing flow direction. They can be used effectively for sediment management and training of alluvial rivers. We tested the three-dimensional flow field generated by bottom vanes in a 45.6 m long and 2.45 m wide straight flume at the open-air physical modelling facility of BUET in Dhaka, Bangladesh, for all combinations of 4 vane heights, 5 vane angles and 2 bed topographies. Both topographies were moulded in concrete. The two bed topographies consisted of a flat bed and a bed with scour holes recorded in preceding mobile-bed experiments. Vanes at an angle of 30° to the flow were found to generate the strongest vortices. The scour holes did not weaken the vortices appreciably. We conclude therefore that local scour does not jeopardize the effectiveness of bottom vanes.
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