Search Results (85)

Ensuring the reliability and temporal accuracy of real-time data transmission in industrial systems presents significant challenges. This study evaluates the performance of a Siemens Programmable Logic Controller (PLC) transmitting data to a MongoDB database via Node-RED over 24 h and 14-day intervals. Key issues observed include time drift, timestamp misalignment, and forward/backward time jumps, mainly resulting from Node-RED’s internal timing adjustments. These anomalies compromised the integrity of time-sensitive data. A significant disruption on day 8 due to a power outage introduced data gaps and required manual system recovery. Additional spikes in missing data were observed after day 12. The Predictive Missing Value (PMV) model addressed these gaps. The model achieved strong accuracy at larger intervals (e.g., 5 min) but showed reduced performance at finer resolutions (1–2 min) due to the irregularity of data patterns. This research highlights the difficulty of maintaining temporal consistency in long-term, real-time systems. It also evaluates the PMV model’s effectiveness in mitigating data loss while acknowledging its limitations under complex timing disruptions. Full article

This paper proposes an interpretable optimization method for power grid outage scheduling based on reinforcement learning. An outage scheduling optimization model is proposed, considering the convergence of power flow calculation, voltage violations, and operational economic behavior as objectives, while considering constraints such as simultaneous outage constraints, mutually exclusive constraints, and maintenance windows. Key features of the outage schedule are selected based on Shapley values to construct a Markov optimization model for outage scheduling. A deep reinforcement learning agent is established to optimize the outage schedule. The proposed method is applied to the IEEE-39 and IEEE-118 bus system for validation. Experimental results show that the proposed method outperforms existing algorithms in terms of voltage violation, total power losses, and computational time. The proposed method eliminates all voltage violations and reduces active power losses up to 5.7% and computation time by 6.8 h compared to conventional heuristic algorithms. Full article

Accurate and reliable localization is crucial for autonomous systems operating in dynamic and semi-structured environments, such as precision agriculture and outdoor robotics. Advances in Global Navigation Satellite System (GNSS) technologies, particularly Differential GPS (DGPS) and Real-Time Kinematic (RTK) positioning, have significantly enhanced position estimation precision, achieving centimeter-level accuracy. However, GNSS-based localization continues to encounter inherent limitations due to signal degradation and intermittent data loss, known as GNSS outages. This paper proposes a novel complementary RTK-like position increment prediction model with the purpose of mitigating challenges posed by GNSS outages and RTK signal discontinuities. This model can be integrated with a Dual Extended Kalman Filter (Dual EKF) sensor fusion framework, widely utilized in robotic navigation. The proposed model uses time-synchronized inertial measurement data combined with the velocity inputs to predict GNSS position increments during periods of outages and RTK disengagement, effectively substituting for missing GNSS measurements. The model demonstrates high accuracy, as the total aDTW across 180 s trajectories averages at 1.6 m while the RMSE averages at 3.4 m. The 30 s test shows errors below 30 cm. We leave the actual Dual EKF fusion to future work, and here, we evaluate the standalone deep network. Full article

In recent years, there has been an increase in the frequency of severe weather events (like hurricanes). These events are responsible for most power outages in power distribution systems (PDSs). Particularly susceptible to storms are overhead PDSs. In this study, the dynamic Bayesian network (DBN)-based failure model was developed for different hurricane scenarios to predict the line failure of overhead lines. Based on the outcomes of the DBN model, a service restoration model was formulated to maximize restored loads and minimize power losses using Particle Swarm Optimization (PSO)-based distributed generation (DG) integration and system reconfiguration. Three different case studies based on the IEEE 33 bus system were conducted. The overhead line failure prediction and service restoration model findings were further used to calculate resilience metrics. With reconfiguration the load restored from 90.3% to 100% for Case 1 and from 34.994% to 80.35% for Case 2. However, for Case 3, reconfiguration alone was not sufficient to show any improvement in performance. On the other hand, DG integration successfully restored load to 100% in all three cases. These results demonstrated that the combined DBN-based failure modeling and PSO-driven optimal restoration strategy under hurricane-induced disruptions can effectively strengthen system resilience. Full article

Power outages pose considerable risks to the reliability of electric grids, affecting both consumers and utilities through service disruptions and potential economic losses. This study analyzes a historical outage dataset from a Regional Transmission Organization (RTO) to reveal key patterns and trends that suggest outage management strategies. By integrating exploratory data analysis, predictive modeling, and a Large Language Model (LLM)-based interface integration, as well as data visualization techniques, we identify and present critical drivers of outage duration and frequency. A random forest regressor trained on features including planned duration, facility name, outage owner, priority, season, and equipment type proved highly effective for predicting outage duration with high accuracy. This predictive framework underscores the practical value of incorporating planning information and seasonal context in anticipating outage timelines. The findings of this study not only deepen the understanding of temporal and spatial outage dynamics but also provide valuable insights for utility companies and researchers. Utility companies can use these results to better predict outage durations, allocate resources more effectively, and improve service restoration time. Researchers can leverage this analysis to enhance future models and methodologies for studying outage patterns, ensuring that artificial intelligence (AI)-driven methods can contribute to improving management strategies. The broader impact of this study is to ensure that the insights gained can be applied to strengthen the reliability and resilience of power grids or energy systems in general. Full article

This work quantifies downtime caused by cyberattacks for eight critical urban services in Milan by coupling sectoral Continuous-Time Markov Chains (CTMCs) with an approximately equal-area H3 hexagonal grid of the city. The pipeline ingests OpenStreetMap infrastructure, simulates coupled failure/repair dynamics across sectors (power, telecom, hospitals, ambulance stations, banks, ATMs, surveillance, and government offices), and reports availability, outage burden (area under the infected/down curve, or AUC), and multi-sector distress probabilities. Cross-sector dependencies (e.g., power→telecom) are modeled via a joint CTMC on sector up/down states; uncertainty is quantified with nested bootstraps (inner bands for stochastic variability, and outer bands for parameter uncertainty). Economic impacts use sector-specific cost priors with sensitivity analysis (PRCC). Spatial drivers are probed via hotspot mapping (Getis–Ord $G_i^{*}$, local Moran’s I) and spatial regression on interpretable covariates. In a baseline short decaying attack, healthcare remains the most available tier, while power and banks bear a higher burden; coupling increases P(≥$k\mathrm{sectors}\mathrm{down})$ and per-sector AUC relative to an independent counterfactual, with paired-bootstrap significance at $\alpha =0.05$ for ATMs, banks, hospitals, and ambulance stations. Government offices are borderline, and telecom shows the same direction of effect but is not significant at $\alpha =0.05$. Under a persistent/adaptive attacker, citywide downtime and P(≥2) rise substantially. Costs are dominated by telecom/bank/power under literature-informed penalties, and uncertainty in those unit costs explains most of the variance in total loss. Spatial analysis reveals statistically significant hotspots where exposure and dependency pressure are high, while a diversified local service mix appears protective. All code and plots are fully reproducible with open data. Full article

In this paper, we design, implement, and empirically evaluate a tamper-evident, blockchain-secured solar energy logging system for resource-constrained edge Internet of Things (IoT) devices. Using a Merkle tree batching approach in conjunction with threshold-triggered blockchain anchoring, the system combines high-frequency local logging with energy-efficient, cryptographically verifiable submissions to the Ethereum Sepolia testnet, a public Proof-of-Stake (PoS) blockchain. The logger captured and hashed cryptographic chains on a minute-by-minute basis during a continuous 135 h deployment on a Raspberry Pi equipped with an INA219 sensor. Thanks to effective retrial and daily rollover mechanisms, it committed 130 verified Merkle batches to the blockchain without any data loss or unverifiable records, even during internet outages. The system offers robust end-to-end auditability and tamper resistance with low operational and carbon overhead, which was tested with comparative benchmarking against other blockchain logging models and conventional local and cloud-based loggers. The findings illustrate the technical and sustainability feasibility of digital audit trails based on blockchain technology for distributed solar energy systems. These audit trails facilitate scalable environmental, social, and governance (ESG) reporting, automated renewable energy certification, and transparent carbon accounting. Full article

Power transformers are critical components in electrical power systems, where failures can cause significant outages and economic losses. Traditional maintenance strategies, typically based on offline inspections, are increasingly insufficient to meet the reliability requirements of modern digital substations. This work presents an integrated multi-sensor monitoring framework that combines online frequency response analysis (OnFRA^® 4.0), capacitive tap-based monitoring (FRACTIVE^® 4.0), dissolved gas analysis, and temperature measurements. All data streams are synchronized and managed within a SCADA system that supports real-time visualization and historical traceability. To enable automated fault diagnosis, a Random Forest classifier was trained using simulated datasets derived from laboratory experiments that emulate typical transformer and bushing degradation scenarios. Principal Component Analysis was employed for dimensionality reduction, improving model interpretability and computational efficiency. The proposed model achieved perfect classification metrics on the simulated data, demonstrating the feasibility of combining high-fidelity monitoring hardware with machine learning techniques for anomaly detection. Although no in-service failures have been recorded to date, the monitoring infrastructure is already tested and validated through laboratory conditions, enabling continuous data acquisition. Full article

This paper explores cybersecurity risk modeling for open data in Smart City environments, with a specific case study focused on the Hradec Kralove Region. The goal is to establish a cybersecurity baseline through automated analysis using extended BPMN modeling, complemented by Business Impact Analysis (BIA). The approach identifies critical data flows and quantifies the impact of disruptions in terms of Recovery Time Objective (RTO), Maximum Tolerable Period of Disruption (MTPD), and Maximum Tolerable Data Loss (MTDL). A framework for automated risk mitigation selection is proposed. Results demonstrate the effectiveness of combining process mapping with security requirements to prioritize protections for Smart City data. As an example from the open data domain, the visualization-publishing process was found to tolerate an outage of up to one week, but required high confidentiality and integrity. The maximum tolerable data loss (MTDL) was set at 24 h, leading to the selection of measures such as encryption, access control, and regular backups. This structured methodology enhances data availability and integrity, supporting resilient urban digital infrastructure. Full article

Visible light communication (VLC) is considered to be one of the key technologies for advancing the next-generation intelligent transportation systems (ITSs). However, in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) VLC, the line-of-sight (LOS) link for communication is often obstructed by vehicle mobility. To address this issue and enhance system performance, a novel V2I–VLC system is proposed and analyzed in this study. The system targets mountain road traffic scenarios employing optical reflecting intelligent surfaces (RISs). To emphasize the practicality of the study, the effects of atmospheric turbulence (AT) and weather conditions are also considered in the channel modeling. Further, the closed-form expressions for average path loss, channel capacity, and outage probability are derived. Furthermore, a novel closed-form expression is also derived for the properties of RIS, which can be used to calculate the required number of RIS elements to achieve a target energy efficiency. In the performance analysis, the accuracy of the derived theoretical expression is validated by numerical simulation, and the effectiveness of the RIS-aided V2I–VLC system is evaluated. Moreover, with a reasonable number of required RIS elements, the system performance in terms of path loss is improved by more than 23.5% on average over the existing studies. Full article

As the final stage of electrical energy delivery, distribution networks play a vital role in ensuring reliable power supply to end users. In regions with limited distribution automation, reliance on operator experience for fault handling often prolongs outage durations, undermining energy sustainability through increased economic losses and carbon-intensive backup generation. Remote-controlled switches (RCSs), as fundamental components of distribution automation, enable remote operation, rapid fault isolation, and load transfer, thereby significantly enhancing system reliability. In the process of intelligent distribution network upgrading, this study targets scenarios with sufficient line capacity and constructs a reliability-oriented analytical model for optimal RCS allocation by traversing all possible faulted lines. The resulting model is essentially a mixed-integer linear programming formulation. To address bilinearities, the McCormick envelope method is applied. Multi-binary products are decomposed into bilinear terms using intermediate variables, which are then linearized in a stepwise manner. Consequently, the model is transformed into a computationally efficient mixed-integer linear programming problem. Finally, the proposed method is validated on a 53-node and a 33-bus test system, with an approximately 30 to 40 times speedup compared to an existing mixed-integer nonlinear programming formulation. By minimizing outage durations, this approach strengthens energy sustainability through reduced socioeconomic disruption, lower emissions from backup generation, and enhanced support for renewable energy integration. Full article

Recently, free-space optical (FSO) communication systems utilizing unmanned aerial vehicle (UAV) relays have garnered significant attention. Integrating UAV relays into FSO communication and employing cooperative diversity techniques not only fulfill the need for long-distance transmission but also enable flexible adjustments of relay positions based on the actual environment. This paper investigates the performance of a parallel-UAV-relay-based FSO communication system. In the considered system, the channel fadings include atmospheric loss, atmospheric turbulence, pointing errors, and angle-of-arrival fluctuation. Using the established channel model, we derive a tractable expression for the probability density function of the total channel gain. Then, we derive closed-form expressions of the system outage probability (OP) and average bit error rate (ABER). Moreover, we also derive the asymptotic OP and ABER for a high-optical-intensity regime. Our numerical results validate the accuracy of the derived theoretical expressions. Additionally, the effects of the number of relay nodes, the field of view, the direction deviation, the signal-to-noise ratio threshold, the atmospheric turbulence intensity, the transmit power, and the transmission distance on the system’s performance are also discussed. Full article

Wind power ramping events refer to sustained unidirectional and large-magnitude fluctuations in wind power output over short durations, exhibiting distinct temporal characteristics and imposing significant impacts on power balance. To address the strong temporal dependency of wind power ramping events, a time-sequential outage model for conventional generators was derived and system operational states were sampled using non-sequential Monte Carlo simulation. Considering the frequency dynamics caused by active power imbalances, dynamic frequency security constraints were formulated. An optimal power flow model was developed to minimize wind curtailment and load shedding comprehensive losses, incorporating these dynamic frequency constraints. The optimal power flow model was employed to solve line power flows for sampled system states and compute comprehensive loss risk indices. Case studies on the IEEE RTS-79 system evaluated and compared operational risks across multiple scenarios, validating the effectiveness of the proposed methodology. Full article

This study investigates the performance of a mixed dual-hop free-space optical/underwater wireless optical communication (FSO-UWOC) system employing a decode-and-forward (DF) relay protocol, particularly under a comprehensive hybrid channel fading model. The FSO link is assumed to experience Gamma–Gamma atmospheric turbulence fading, combined with air path loss and pointing errors. Meanwhile, the UWOC link is modeled with generalized Gamma distribution (GGD) oceanic turbulence fading, along with underwater path loss and pointing errors. Based on the proposed hybrid channel fading model, closed-form expressions for the average outage probability (OP) and average bit error rate (BER) of the mixed dual-hop system are derived using the higher transcendental Meijer-G function. Similarly, the closed-form expression for the average ergodic capacity of the mixed relay system is obtained via the bivariate Fox-H function. Additionally, asymptotic performance analyses for the average outage probability and BER under high signal-to-noise ratio (SNR) conditions are provided. Finally, Monte Carlo simulations are conducted to validate the accuracy of the derived theoretical expressions and to illustrate the effects of key system parameters on the performance of the mixed relay FSO-UWOC system. Full article

In recent years, extreme natural disasters, such as typhoons, have become increasingly frequent, leading to persistent power outages in urban distribution grids. These outages pose significant challenges to the stability of urban power supply systems. With the growing number of electric vehicle (EV) users and the expanding EV industry, and considering the potential of EVs as flexible load storage resources, this paper proposes a post-disaster power supply restoration strategy that takes into account the potential of coordinated scheduling of EVs and mobile energy storage. First, a compression method based on the Minkowski addition is proposed for the EV cluster model in charging stations, which establishes an EV dispatchable model. Second, the spatiotemporal matrix of failure rates for distribution network elements is calculated using the Batts wind field model, enabling the generation of distribution network failure scenarios under typhoon conditions. Finally, the power supply restoration strategy of multi-source coordination with the participation of EV cluster and mobile storage is formulated with the objective of minimizing the loss of the distribution network side. Simulation results demonstrate that the proposed strategy effectively utilizes the load storage potential of EVs and mobile energy storage, enhances recovery performance, ensures cost-effectiveness, and explicitly solves the islanding operation stability problem. Full article