Accurate fault classification and detection for the microgrid (MG) becomes a concern among the researchers from the state-of-art of fault diagnosis as it increases the chance to increase the transient response. The MG frequently experiences a number of shunt faults during the distribution of power from the generation end to user premises, which affects the system reliability, damages the load, and increases the fault line restoration cost. Therefore, a noise-immune and precise fault diagnosis model is required to perform the fast recovery of the unhealthy phases. This paper presents a review on the MG fault diagnosis techniques with their limitations and proposes a novel discrete-wavelet transform (DWT) based probabilistic generative model to explore the precise solution for fault diagnosis of MG. The proposed model is made of multiple layers with a restricted Boltzmann machine (RBM), which enables the model to make the probability reconstruction over its inputs. The individual RBM layer is trained with an unsupervised learning approach where an artificial neural network (ANN) algorithm tunes the model for minimizing the error between the true and predicted class. The effectiveness of the proposed model is studied by varying the input signal and sampling frequencies. A level of considered noise is added with the sample data to test the robustness of the studied model. Results prove that the proposed fault detection and classification model has the ability to perform the precise diagnosis of MG faults. A comparative study among the proposed, kernel extreme learning machine (KELM), multi KELM, and support vector machine (SVM) approaches is studied to confirm the robust superior performance of the proposed model.

In this work, the evaluation of the design and optimization of proposed offgrid hybrid microgrid systems for different load dispatch strategies is presented by assessing the component sizes, system responses and different cost analyses of the proposed system. This study optimizes the sizing of the Barishal and Chattogram (two popular divisions in Bangladesh) hybrid microgrid systems consisting of wind turbine, storage unit, solar PV, diesel generator and a load profile of 27.31 kW for five dispatch techniques: (i) generator order, (ii) cycle charging, (iii) load following, (iv) HOMER predictive dispatch and (v) combined dispatch strategy. The considered microgrids are optimized for the least CO ₂ gas emission, Net Present Cost, and Levelized Cost of Energy. The two microgrids are analyzed for the five dispatch techniques using HOMER software, and subsequently, the power system performance and feasibility study of the microgrids are performed in MATLAB Simulink. The results in this research provide a guideline to estimate different component sizes and probable costing for the optimal operation of the proposed microgrids under various load dispatch conditions. The simulation results suggest that ‘Load Following’ is the best dispatch strategy for the proposed microgrids having a stable power system response with the lowest net present cost, levelized cost of energy, operating cost, and CO ₂ emission rate. Additionally, the combined dispatch strategy is determined to be the worst dispatch technique for the proposed off-grid hybrid microgrid design having the maximum levelized cost of energy, net present cost, operating cost and CO ₂ emission.

Although the use of a series capacitor increases the transmission line (TL) capacity, it inverts the voltage/current and creates a sub-harmonic frequency that results in unintended operation of the protection devices, which further decreases the transient stability under fault conditions. Restoring the transient stability requires an accurate diagnosis of faults in TLs, which further requires exploration of in-depth features. This imposes challenges for the existing approaches as they demand a large amount of data. In this paper, a self-attentive weight-sharing capsule network (WSCN) is proposed to achieve robust and high classification performance while utilizing a limited amount of data. The capsule network, with the weight-sharing mechanism delivers robust performance in detecting and classifying the faults in the TL domain. Simultaneously, the self-attention layer highlights the more dominant features that make the network work upon the limited data effectively. A Western-System-Coordinating-Council WSCC 9-bus and 3-machine test model, which was modified with the series capacitor was studied to quantify the robustness of the self-attention WSCN. The results were compared with those of others networks to confirm the explicit classification ability of the proposed network. A study on real-world data obtained from power network was also conducted to validate the proposed self-attention WSCN.

The interdependence of power and electrified transportation systems introduces new challenges to the reliability and resilience of charging infrastructure. With the increasing prevalence of electric vehicles (EVs), power system attacks that can lower customers charging satisfaction rates are on the rise. The existing false data injection attacks (FDIAs) detection strategies are not suitable for protecting the power-dependent transportation infrastructure since (a) these detectors are primarily optimized for power grids alone, and (b) they overlook the impact of attacks on the quality-of-service of EVs and charging stations (CSs). In response to these challenges, this paper aims to develop an FDIA detection strategy that takes advantage of the data correlations between power and transportation systems, ultimately enhancing the charging satisfaction rate. To achieve this goal, we propose a graph autoencoder-based FDIA detection scheme capable of extracting spatio-temporal features from both power and transportation data. The input features of power systems are active and reactive power while those for transportation systems are the hourly traffic volume in CSs. The proposed model undergoes comprehensive training and testing on various types of FDIAs, showcasing improved generalization abilities. Simulations are conducted on the 2,000-bus power grid of the state of Texas, featuring 360 active CSs. Our investigations reveal an average detection rate of 98.3%, representing a substantial improvement of 15-25% compared to state-of-the-art detectors. This underscores the effectiveness of our proposed approach in addressing the unique challenges posed by power-dependent electrified transportation systems.

This paper proposes a hand free human-machine interaction (HMI) system to establish a novel way for communication between humans and computers. A regular interaction system based on the computer mouse puts the user’s hand for too long in a pronation posture that increases inflammation in the wrist and hand. Additionally, the need for hand obstructs the use of computers for handicap people. In this paper, we develop a new pointing device for differently able people based on open and closed human eyes with inertia measurement that restrict to deal with carpal tunnel syndrome (CTS) for regular people and enables a novel way to interact with computers for the handicap people. The proposed system carries the human head gesture and eyes to perform the movement and clicking event of the mouse cursor. A combined three-axis accelerometer and gyroscope is used to detect the head gesture and translate it into the position of the mouse cursor on the computer monitor. To perform the left and right-clicking event, the user needs to shut down the left and right eye for a moment while opening another eye. This paper is also carried out the design of a deep learning approach to classify the individual openness and closeness of both human eyes with quite a high accuracy of 95.36% that ensures the comprehensive control over the clicking performance. The use of complementary filter removes the noise and drift from the obtained performance and confirms the smooth and accurate operation of the proposed device. An experimental validation is added to show the effectiveness of the proposed HMI system. The experimental details along with the performance evaluation prove that the proposed HMI system has extensive control over its performance for differently able people.