Modern factories still struggle with unexpected machine failures because traditional maintenance systems depend on fixed rules and threshold-based alerts. These older approaches often overlook subtle or complex patterns in multimodal sensor data, causing them to miss early signs of wear and leading to late or incorrect maintenance decisions. As a result, production can slow down, costs increase and equipment reliability suffers. To address this challenge, this study introduces a smart and interpretable fault diagnosis and predictive maintenance framework designed to detect wear, degradation and potential failures before they disrupt operations. The proposed framework integrates multiscale feature extraction, multimodal sensor fusion and cross-sensor correlation analysis with advanced temporal modeling using a Temporal Convolutional Network (TCN). By jointly performing tool-health classification and Remaining Useful Life (RUL) estimation, the framework provides a comprehensive assessment of machine condition. When evaluated on the NASA Ames milling dataset, the model achieved an overall accuracy of 86%, correctly classifying healthy and failed tools in more than 88% of cases and worn tools in over 75%, demonstrating consistent performance across different stages of tool wear. Explainable artificial intelligence (XAI) techniques, including attention-based visualizations and SHAP-based feature attribution, reveal that electrical and vibration signals are the most influential early indicators of tool degradation. The proposed framework exhibits low computational latency and minimal memory requirements, making it suitable for real-time fault diagnosis and deployment on industrial edge devices. Overall, the framework balances predictive accuracy, interpretability and practical applicability, enabling proactive and reliable maintenance decisions that enhance machine uptime and support efficient smart manufacturing operations.

Superimposed quantities, also known as delta quantities, are the transient signatures of fault currents and voltages that can be utilized by protective relays to achieve fast fault detection for electric power grid applications. With the installation of superconducting fault current limiters (SFCLs) on power transmission lines, the profiles of superimposed quantities in power system faults are affected due to the altered impedance properties at transmission line terminals, which may further affect protective relay performance. In this article, we investigate the impact of a resistive SFCL on superimposed quantity profiles. The equivalent circuit of a resistive SFCL connected to an electric power transmission system is first illustrated based on which the simulation model is established in an electromagnetic transients program. Various types of power transmission line faults are simulated. The superimposed current and voltage quantities at transmission line terminals are obtained. The simulation results demonstrate that the acquired superimposed quantities are significantly affected by the SFCL. Possible consequences to protection functions are discussed.

This paper proposes a time-domain-based protection scheme for radial and loop microgrid systems with inverter-based resources (IBRs), such as solar photovoltaic (PV) and type-4 wind turbines. The protection scheme is designed to function during both grid-interconnected and grid-isolated modes. The proposed scheme provides an ultra-high-speed sub-cycle directional element aided with low bandwidth communication between relays. Like directional comparison schemes, relays identify whether faults are in-zone or out-zone. The directional element is based on time-domain superimposed quantities and Park’s transformation algorithms. More specifically, the element calculates the superimposed positive-sequence direct component of transient energy during faults. Superimposed voltage and current quantities are calculated using delta filters and decoupled double synchronous reference frame (DDSRF) filters. The proposed filtering method improves the reliability of the superimposed directional element when IBRs are present. The protection scheme is evaluated on a modified IEEE 34-bus distribution system simulated using an electromagnetic transients program.

This paper develops a multi-agent protection scheme for electric power distribution systems and microgrids with aggregated electronically coupled distributed energy resources (DER). The proposed protection scheme uses both voltage and current measurements to sense faults, and then trips only the faulted feeders, and broadcasts fault events to the main microgrid control system, and adjacent protective agents. The objective of this paper is to improve distribution system resilience by using a minimal communication protection scheme, and reducing power outages. Two study cases are presented. The EMTP-RV program is used to model the power system and the control systems of a PV based DER unit. The IEEE 34 bus test system is chosen for demonstration.

Modern power distribution networks are increasingly challenged with nonlinear operating conditions, the high penetration of distributed energy resources, and conflicting operational objectives such as loss minimization and voltage regulation. Existing load-flow optimization approaches often suffer from slow convergence, premature stagnation in non-convex search spaces, and limited robustness when handling conflicting multi-objective performance criteria under fixed network constraints. To address these challenges, this paper proposes a Fractional Multi-Objective Load Flow Optimizer (FMOLFO), which integrates a fractional-order numerical regularization mechanism with an adaptive Pareto-based Differential Evolution framework. The fractional-order formulation employed in FMOLFO operates over an auxiliary iteration domain and serves as a numerical regularization strategy to improve the sensitivity conditioning and convergence stability of the load-flow solution, rather than modeling the physical time dynamics or memory effects of the power system. The optimization framework simultaneously minimizes physically consistent active power loss and voltage deviation within existing network operating constraints. Extensive simulations on IEEE 33-bus and 69-bus benchmark distribution systems demonstrate that FMOLFO achieves an up to 27% reduction in active power loss, improved voltage profile uniformity, and faster convergence compared with classical Newton–Raphson and metaheuristic baselines evaluated under identical conditions. The proposed framework is intended as a numerically enhanced, optimization-driven load-flow analysis tool, rather than a control- or dispatch-oriented optimal power flow formulation.

This study optimizes the placement of μ-PMUs using the BPSO and BGWO algorithms for the IEEE 33-bus and 69-bus systems, with a focus on minimizing deployment costs while ensuring robust system observability. Three case studies are analysed: Case 1 (normal conditions), Case 2 (single μ-PMU outage), and Case 3 (Zero Injection Buses, ZIBs). In Case 1, both algorithms identified 24 μ-PMUs as the optimal placement for the IEEE 69-bus system, achieving the minimum PMUs required for full observability. For Case 2, redundancy requirements increased the μ-PMU count to 24 μ-PMUs for the IEEE 33-bus system and 51 μ-PMUs for the IEEE 69-bus system, ensuring full observability even under a single μ-PMU failure. Case 3, leveraging Zero Injection Buses (ZIBs), reduced the μ-PMU count to 20 μ-PMUs for both BPSO and BGWO, optimizing the system configuration while maintaining observability. A trade-off analysis was performed to examine the trade-off between redundancy and PMU count, showing that increasing the number of μ-PMUs improves system resilience. Voltage and current channels were measured from the optimized placements to ensure accurate voltage measurement in all case studies. Subsequently, the Weighted Least Squares algorithm was applied for voltage estimation, serving as a peripheral to the main objective of the optimal μ-PMU placement. Voltage estimation was conducted under three noise levels: 0.01 STD for basic analysis and 0.02 and 0.04 STD to observe the impact of varying measurement noise. The results highlight that higher μ-PMU placements improve voltage estimation accuracy, particularly under higher noise levels. Statistical analysis confirms that BGWO outperforms BPSO in terms of computational efficiency, stability, and convergence, especially in large-scale systems. By enhancing grid monitoring and state estimation, this research directly contributes to the development of more resilient and efficient power networks, which is a fundamental prerequisite for integrating renewable energy sources and advancing overall power system sustainability. This research emphasizes the balance between cost and reliability in μ-PMU placement and provides a comprehensive methodology for state estimation in modern power systems.