This study investigates the vulnerability of DC microgrid systems to cyber threats, focusing on false data injection attacks (FDIAs) affecting sensor measurements. These attacks pose significant risks to equipment, generation units, controllers, and human safety. To address this vulnerability, we propose a novel solution utilizing a Non-Linear Autoregressive Network with Exogenous Input (NARX) observer. Trained to differentiate between normal conditions, load changes, and cyber-attacks, the NARX network estimates DC currents and voltages. The system initially operates without FDIAs to collect data for training NARX networks, followed by online deployment to estimate output DC voltages and currents of Distributed Energy Resources (DER). An attack mitigation strategy using a PI controller aligns NARX output with actual converter output, generating a counter-attack signal to nullify the attack impact. Comparative analysis with other AI-based methods is conducted, demonstrating the effectiveness of our approach. MATLAB simulations validate the method's performance, with real-time validation using OPAL-RT further confirming its applicability.

This paper adopts a Direct Current (DC) Microgrid with a consensus-based secondary control strategy to efficiently manage the microgrid, ensuring precise voltage regulation and fair power distribution among Distributed Energy Resources (DERs). The consensus approach employs a communication network to establish a Cyber layer for DER information exchange and harmonize the entire system to achieve a common goal. However, this cyber layer’s vulnerability could destabilize the interconnected control system and lead to the whole system being unsynchronized. The paper explores replay attacks conducted in the path of the communication link between interconnected Distributed Generation Units (DGUs), demonstrating their impact on secondary control using simulations and real-time experiments. A method leveraging Distributed Kalman Filter observers for attack detection is proposed which requires low computational demand to suit the real-time application, while an attack mitigation technique using observer-generated residues with the optimal condition is implemented. Validation is conducted through both MATLAB simulations and real-time OPAL-RT experiments, confirming the proposed system’s effectiveness.

Dynamic wireless charging systems (DWCS) for electric vehicles is an effective solution to overcome driving range anxiety, high volume, and cost of battery by offering continuous charging for electric vehicles in motion. However, this technology suffers from some challenges associated with the high cost of infrastructure and power pulsation at the battery side, hindering the widescale commercial adoption of the technology. As the electric vehicle moves along the transmitter coils, power pulsation arises due to fluctuation in the mutual coupling, which deteriorates the battery’s lifetime and the overall system’s efficiency. In recent years, a significant volume of research methodologies have been proposed to address power fluctuation in DWCS. These methods can be categorized into three main areas, which include developing magnetic coil structures to suppress the coupling variation, optimizing the compensation network for coping with the coupling fluctuation, and applying controllers for output power stabilization. This paper presents a comprehensive review of the current literature on magnetic coil structures, compensation network topologies, and control schemes aiming to address the impacts of power variation in DWCS. This paper gives a new and holistic insight into the challenges and the corresponding solutions to address the power pulsation issues in DWCS.

The rapidly increasing distributed energy resources (DERs) in power systems are now getting interconnected to set community grid structures, where power quality will be a major concern. The grid-to-grid (G2G) bidirectional power transfer among the distribution microgrid will not be considered commercially feasible unless the upstream harmonics are under the limits. The aggregation of such harmonics, measured as total harmonic distortion (THD), is feared to be beyond tolerable limits with the progression of rooftop grid-tied PV-like installations. Hence, this THD needs to be characterized with DER generation end variables. In this work, the photovoltaic (PV) DERs’ dependency on environment variables such as irradiance was profiled in the context of generating and injecting harmonics into the grid. A mathematical model of a grid-tied three-phase PV DER was developed as part of this correlation characterization, matching the fundamental unit structure of a 1.4 MW solar canopy located on the Florida International University (FIU) Miami campus. To determine the qualitative association with produced THD patterns, the model was evaluated with various irradiance settings. A real-time digital simulation (RTDS) platform was used to verify it. Following this confirmation, sets of data from power quality meters at the point of common coupling and FIU field sensors were utilized to validate further the correlation model. The results showed that the grid current’s THD exhibited a high correlation with the irradiance profile and its variation over time. The early morning and late afternoon periods of the day, associated with a low irradiance, constantly had higher harmonics generated from the PV DER. The midday THD was rather rational with partial shadings, hence a geolocation-dependent factor. These findings were verified by an RTDS and validated by real field data. In quantifying the THD injected by a single DER at a high-frequency (2–150 kHz) supraharmonics (SH) level, a 3% peak increment in magnitude was observed from the high-fixed to the low-fixed irradiance profile. The correlation characteristics depicted that the hybrid microgrid suffered from a daytime-dependent harmonic insertion from the grid-tied DER. This is a global problem unless specific measures are taken to mitigate the harmonics. The electrically notorious higher-frequency SH was found to increase proportionally. The G2G power transfer can be limited because of the higher THD in the early morning and late afternoon, which will also worsen because the numbers of grid-tied PV DERs (i.e., rooftop solar and industrial solar) are likely to increase rapidly soon. The community grid structure can thus have a controlled harmonics filtration setup purposefully designed to address the findings of this work, which also fall within the scope of our future research.

This paper addresses the challenge of improving the efficiency of solar photovoltaic systems, which often suffer from intermittent energy harvesting and suboptimal power extraction methods. Solar energy, a promising renewable source, can replace fossil fuels, but its efficiency is hindered by fluctuating power output. To tackle this issue, the study introduces a novel approach utilizing wavelet-based ripple extraction. By employing a ten-level wavelet decomposition process, this method aims to obtain smooth, ripple-free power from solar canopies. The research examines the tracking efficiency of maximum power point tracking (MPPT) and the power oscillation issue. Notably, the study compares the Perturb and Observe method with the Artificial Neural Network MPPT approach, assessing ripple power during wavelet decomposition stages. Results reveal that the proposed wavelet and signal analyzer-based technique achieves a remarkable tracking efficiency of nearly 99.9% while minimizing power fluctuations, surpassing prior studies. This innovative approach enhances the performance and efficiency of solar PV systems, ultimately contributing to the broader adoption of solar energy. Real-time validation through OPAL-RT further validates its effectiveness.

In commercial applications, the operation of DC/DC converters significantly impacts overall system performance and long-term reliability. This study introduces a data-driven digital twin ( DT ) approach for estimating critical degradation parameters of DC/DC BUCK converter under steady-state condition. Initially, a circuit-level MATLAB/Simulink digital model ( DM_C ) is refined against a hardware prototype’s switching model dataset using offline particle swarm optimization. The optimized digital model’s steady-state response is then verified with its average model response while varying the duty and load. Subsequently, degradation profiles are imposed on the inductor, capacitor, MOSFET in the DM_C . A large dataset is generated from this model, allowing training, validation, and testing of machine learning (ML) models for component health regression tasks. The proposed method employs random forest ML models, achieving impressive regression results with a squared R value as high as 0.99978 and a root mean square error of 4.2× 10 ⁻⁶ . The method is further validated on a medium power level DC/DC BUCK prototype with varying load conditions, and includes the analysis of MOSFET’s on-resistance under degradation conditions. This data-driven DT method shows promise for identifying parasitic degradation and ohmic loss parameters, enhancing converter reliability assessments in a non-invasive, generalized, and computationally efficient manner.