George Balan

Industrial lighting audits may combine heterogeneous measurements into performance scores without demonstrating whether the underlying evidence is sufficiently complete, representative, and traceable. This study proposes an evidence-aware multicriteria assessment framework that separates performance evaluation from evidence coverage. Visual, energy, and operational indicators are evaluated only when supporting evidence is available and an applicable criterion has been declared, while evidence quality is reported separately through an evidence-coverage measure. The methodology is illustrated using field records from an industrial research hall under daylight-only and mixed daylight/electric-light conditions. Because no representative whole-installation operating state, applicable task-plane grid, or circuit-level electrical campaign was available, the case is treated as a methodological illustration rather than a compliance assessment or full validation of the installation. The framework prevents non-equivalent records from being combined into a single performance criterion, distinguishes missing evidence from undeclared targets, and converts evidence gaps into a prioritized measurement-completion plan. In the case study, weighted evidence coverage is 36.0%, indicating that measurement completion rather than global performance classification is the immediate audit priority. The proposed approach improves the transparency, traceability, and defensibility of industrial lighting audits conducted under incomplete evidence.

Digital Twin (DT) implementations in electrical and industrial systems are governed by fragmented ISO/IEC and IEC standards spanning terminology, architecture, interoperability, lifecycle management, and cybersecurity. This paper proposes a mathematical framework that integrates these standards into a unified compliance model. A layered DT architecture is defined as a finite set of functional abstractions, and standards are linked to layers through a multivalued mapping and an incidence matrix. Traceability, interoperability, fidelity, and security/governance indicators are normalized and aggregated through a bounded weighted functional to obtain a deterministic compliance score. The model is then extended by treating selected indicators as random variables, which enables probabilistic maturity classification and Monte Carlo-based robustness analysis. The resulting functional is bounded, monotone, and stable under bounded perturbations. Numerical experiments on a synthetic portfolio illustrate deterministic scoring and uncertainty effects. The framework provides a proof-of-concept basis for structured DT compliance assessment across heterogeneous electrical systems; however, broader empirical validation is still required before operational deployment.

Shading faults remain one of the most critical challenges affecting photovoltaic (PV) system efficiency, as they not only reduce power generation but also disturb maximum power point tracking (MPPT). To address this issue, this study introduces a hybrid optimization framework that combines Fuzzy Logic Control (FLC) with a Shading-Aware Particle Swarm Optimization (SA-PSO) method. The proposed scheme is designed to adapt dynamically to both partial shading (20%-80%) and complete shading events, ensuring reliable global maximum power point (GMPP) detection. In this approach, the fuzzy controller provides rapid decision support based on shading patterns, while SA-PSO accelerates the search process and prevents the system from becoming trapped in local minima. A comparative performance assessment with the conventional Perturb and Observe (P\&O) algorithm highlights the advantages of the hybrid model, showing up to an 11.8% improvement in power output and a 62% reduction in tracking time. These results indicate that integrating intelligent control with shading-aware optimization can significantly enhance the resilience and energy yield of PV systems operating under complex real-world conditions.

Overheating in low-voltage electrical distribution panels significantly reduces the reliability and lifespan of electronic components, especially as modern panels become more compact and densely populated. This paper presents the design and experimental validation of a low-cost, microcontroller-based temperature monitoring system for real-time detection of thermal anomalies in low voltage panels. The system employs K-type thermocouples and a MAX6675 converter, interfaced with an Arduino platform, to continuously monitor both internal and ambient temperatures. Temperature data are compared against operational thresholds derived from thermal modeling, enabling early warning of unsafe conditions. Experimental results demonstrate that the proposed solution accurately detects critical temperature increasing with fast response and high reliability. The system is cost-effective, easy to implement and provides a practical tool for predictive maintenance and enhanced operational safety in industrial electrical installations.

After an assessment of the economic and environmental impact that losses caused by Potential Induced Degradation (PID) can have, the main requirements of the IEC TS 62804 standard, dedicated to the test methods for the detection of potential induced degradation, are reviewed. The authors' steps for carrying out field measurements are then presented in detail, on the way in which ambient relative humidity and temperature influence the magnitude of the leakage current established between photovoltaic cells and the panel's grounded frame. Experimental setup solutions are presented that can be easily accessible to the maintenance team working in the field. The results of the tests performed can serve as alarm signs for the occurrence of PID contamination, allowing for effective countermeasures to be applied promptly.

This study focuses on understanding and addressing the degradation of photovoltaic (PV) panels, particularly through Potential-Induced Degradation (PID). Given the significant investment in renewable energy and the push to increase photovoltaic capacity, controlling panel degradation is crucial for efficiency. The study outlines various degradation mechanisms, including Light-Induced Degradation (LID), hot spot formation, and delamination, while emphasizing the predominant role of electrochemical processes associated with potential differences in PID. The article describes a method for simulating and accelerating PID effects using high-voltage stress tests under laboratory conditions. Tests were conducted on two 245 W polycrystalline panels to gauge performance degradation, utilizing electroluminescence (EL) imaging to identify defective cells. It highlights methods to enhance shunt resistance and control leakage current as strategies to mitigate the impact of PID, suggesting the maintenance of high shunt resistance. The study highlights the significance of electroluminescence in evaluating the performance of panels, as demonstrated through experiments that correlate voltage stress with the visual degradation of cells. These tests promote proactive measures for identifying and ameliorating PID in operational PV systems.

Shading faults remain one of the most critical challenges affecting photovoltaic (PV) system efficiency, as they not only reduce power generation but also disturb maximum power point tracking (MPPT). To address this issue, this study introduces a hybrid optimization framework that combines Fuzzy Logic Control (FLC) with a Shading-Aware Particle Swarm Optimization (SA-PSO) method. The proposed scheme is designed to adapt dynamically to both partial shading (20%-80%) and complete shading events, ensuring reliable global maximum power point (GMPP) detection. In this approach, the fuzzy controller provides rapid decision support based on shading patterns, while SA-PSO accelerates the search process and prevents the system from becoming trapped in local minima. A comparative performance assessment with the conventional Perturb and Observe (P&O) algorithm highlights the advantages of the hybrid model, showing up to an 11.8% improvement in power output and a 62 % reduction in tracking time. These results indicate that integrating intelligent control with shading-aware optimization can significantly enhance the resilience and energy yield of PV systems operating under complex real-world conditions.

The increasing complexity of modern automotive lighting systems requires advanced validation strategies that ensure both functional performance and regulatory compliance. This study presents a structured integration of Software-in-the-Loop (SiL) and Hardware-in-the-Loop (HiL) testing within a digital twin (DT) framework for validating headlamp systems. A gated validation process (G10–G120) is proposed, aligning each development phase with corresponding simulation stages from early requirements and concept validation to real-world scenario testing and continuous integration. A key principle of this approach is the adoption of a framework built upon the V-Cycle, adapted to integrate DT technology with SiL and HiL workflows. This architectural configuration ensures a continuous data flow between the physical system, the digital twin, and embedded software components, enabling real-time feedback, iterative model refinement, and traceable system verification throughout the development lifecycle. The paper also explores strategies for effective DT integration, such as digital twin-as-a-service, which combines virtual testing with physical validation to support earlier fault detection, streamlined simulation workflows, and reduced dependency on physical prototypes during lighting system development. Unlike the existing literature, which often treats SiL, HiL, and DTs in isolation, this work proposes a unified, domain-specific validation framework. The methodology addresses a critical gap by aligning simulation-based testing with development milestones and regulatory standards, offering a foundation for industrial adoption.

In modern automotive lighting systems, efficiency, adaptability, and reliability of each component are essential for ensuring optimal performance. However, the implementation of LED MATRIX (multiple LEDs are organized in a grid and independently controlled to adjust the light output) often faces challenges due to high costs and complex integration processes. To address these issues, the paper proposes and validates a method that uses a simulated model as a cost-effective alternative for testing and optimizing various lighting scenarios before real-world deployment. The method allows for the transformation of the simulated model into a digital twin, which replicates the functionality of the hardware, including LED MATRIX, in a simulation environment. Virtual testing platforms like CANoe (Controller Area Network Environment) are used within this method to evaluate system performance evaluation under extreme conditions, ensuring both safety and reliability. Furthermore, the incorporation of STM32 microcontrollers enables real-time diagnostics and adaptive functionality, supporting the development of efficient and scalable lighting solutions for modern vehicles.