This paper proposes an on-chip integrated power management system with a Distributed Maximum Power Point Tracking (DMPPT) controller for photovoltaic (PV) cells to enhance energy extraction efficiency in partial shading and inhomogeneous conditions.Each PV cell is allocated a single MPPT unit to achieve localized power maximization and loss reduction in contrast to centralized tracking systems. The proposed DMPPT controller is realized in 0.18 µm CMOS and integrates Ripple Correlation Control (RCC) and a synchronous boost converter for efficient cell-level tracking. Cadence Virtuoso simulations were carried out using a single-diode PV model at irradiance values from 100 W/m² to 1200 W/m² and a constant temperature of 25°C. The converter runs with a 100 kHz switching frequency, achieving 92% peak efficiency and stable voltage regulation.The suggested scheme achieves a mean output voltage of 12.3 V, 986.6 mA of current, and offers nearly twice the normalized power compared to centralized MPPT techniques under partial shading. The chip occupies an area of approximately 1.73 mm². The results verify the engineering feasibility and high efficiency of cell-level Distributed MPPT (DMPPT) for maximizing energy output and operational reliability of photovoltaic systems under non-uniform irradiance.
With the rapid growth of enterprise, 5G/6G, and IoT infrastructures generating massive, heterogeneous traffic, conventional signature-based IDSs increasingly fail to keep pace. To address this gap, we conducted a systematic literature review of unsupervised machine-learning approaches—particularly those supporting on-the-fly adaptation and memory-prediction capabilities—that can detect zero-day attacks without the overhead of labeled data. Following PRISMA 2020 guidelines, we screened 2847 records (2015–2025) and retained 127 primary studies, then used VOSviewer bibliometrics to identify four research clusters: (i) classical clustering and one-class SVM , (ii) deep autoencoders and GANs, (iii) concept-drift and continual-learning algorithms, and (iv) memory-augmented or brain-inspired methods—where autoencoder-based solutions dominated 63% of publications. Our gap analysis revealed that fewer than 15% of studies genuinely evaluated streaming or on-the-fly learning, only three implemented explicit cortical-inspired memory-prediction, and emerging telemetry (e.g., Quick UDP Internet Connections— QUIC/Transport Layer Security—TLS, 5G slices) remains largely unaddressed by existing datasets. Building on these findings, we introduce AMNAD (Adaptive, Memory-augmented, Near real-time Anomaly Detection), which couples a lightweight edge profiler with a continual, prototype-memory autoencoder to deliver millisecond-latency detection on 10Gbps links while maintaining robust accuracy under concept drift. By releasing our review, dataset taxonomy, and reference architecture under an open-source license, we aim to accelerate reproducible research in unsupervised network security analytics.
This study presents a scalable, decentralised approach to optimised large-scale Demand Response (DR) in electrical distribution networks with significant renewable energy integration, focusing on user comfort. By modelling thermostatically controlled Electric Water Heaters (EWHs) as flexible loads in residential installations, the proposed direct control DR scheme coordinates activation signals through local hubs, optimising both renewable energy utilisation and user comfort. A multi-objective genetic algorithm (GA) was used to iteratively optimise EWH activation time and duration, balancing curtailment reduction against comfort impact. Simulation results on a representative network indicate that aggressive control strategies significantly reduce renewable energy curtailment, up to 37%, though this comes at the cost of user comfort, with up to 7% of cold-water events compared to a 0.12% reference. In contrast, less aggressive strategies deliver lower curtailment benefits but with a reduced impact on comfort. The proposed decentralised optimisation framework enhances scalability, communication efficiency, and computational feasibility compared to a centralised alternative. The findings highlight the potential for coordinated DR to reduce reliance on traditional energy storage, improve grid flexibility, and maintain end user comfort, with practical insights for implementation and future research directions addressing forecast uncertainties and extended evaluation horizons.
High-voltage outdoor insulators are continuously exposed to environmental stressors such as moisture, pollution, high temperatures, and ultraviolet (UV) radiation. These conditions can significantly increase surface conductivity, elevate leakage current, and ultimately lead to flashover or long-term degradation of surface performance. Room temperature vulcanized (RTV) silicone rubber (SiR) is often used as a coating material to address the issue. However, maintaining the electrical and surface integrity under prolonged exposure to such environmental conditions remains a challenge. This study focuses on enhancing the performance of RTV SiR coatings by incorporating nano-sized boron nitride (nBN) and aluminium oxide (nAl₂O₃) fillers. Various nanofiller compositions were evaluated to optimize the material's dielectric, thermal, and surface properties. The samples were first characterized through surface resistivity, relative permittivity, dielectric loss factor (tan delta), and hydrophobicity measurements to find the optimized composition. Additional material characterizations, including Thermogravimetric Analysis (TGA), Scanning Electron Microscopy (SEM), and Transmission Electron Microscopy (TEM), were conducted to assess thermal stability and filler dispersion. Among the tested formulations, RTV SiR with 2 wt.% nAl₂O₃ and 1 wt.% nBN demonstrated the most optimal improvement in electrical, thermal, and surface performance. TGA results revealed that the addition of nBN and nAl₂O₃ improved the thermal stability of the RTV SiR coating compared to the unfilled RTV SiR, delaying the material degradation by approximately 20°C. Flashover voltage tests were carried out to verify the performance of the insulator coated with an optimized nanofiller composition, compared with an uncoated insulator and an unfilled RTV SiR-coated insulator. The tests were conducted under extremely harsh conditions, specifically in a salt-fog environment with a highly polluted surface. Compared with the unfilled RTV SiR coating, incorporating nAl₂O₃ and nBN increased the flashover voltage by 40.5%, demonstrating improved performance under polluted conditions. The improvement is even more apparent compared to the uncoated insulator, with a 75.1% increase in flashover voltage. These findings confirm that the addition of nAl₂O₃ and nBN effectively enhances the electrical and thermal performance of RTV SiR coatings for high-voltage outdoor insulators.© 2017 Elsevier Inc. All rights reserved.
