Search Results (38)

In the event of a fault in a shipboard medium-voltage direct-current (MVDC) power system, a fault reconfiguration method issues control commands to the switchgear to execute switching actions, thereby redistributing power flow, isolating the faulted zone, and restoring power to the de-energized loads. Existing fault reconfiguration strategies mainly use classical optimization methods. These methods are usually centralized, and as the system scale increases, they suffer from the curse of dimensionality, which degrades real-time performance and reduces computational efficiency. This paper proposes a MADRL-based fault reconfiguration method for shipboard MVDC power systems. The proposed method considers load priority levels, maximizes total restored load, and improves load balancing. To this end, a QMIX-based method, Dependency-Corrected QMIX with Action Masking (Dep-QMIX-Mask), was developed, introducing a dependency correction mechanism to handle action dependencies during decentralized execution and applying action masking to rule out invalid switching actions under operational constraints. A shipboard MVDC power system model was established and used for validation. Across three representative fault cases, Dep-QMIX-Mask achieves served load rates of 0.88, 0.67, and 0.43, with SLR improvements of up to 19.6% over baseline methods. It consistently produces feasible switching sequences in all 20 independent runs per case, improving feasibility by 10 to 30 percentage points. In addition, Dep-QMIX-Mask improves zonal load balancing by reducing the PUR variance by 40.5% to 99.2% compared with baseline methods. These results indicate that Dep-QMIX-Mask can generate feasible sequential reconfiguration strategies while improving both load restoration and load balancing. Full article

In recent years, the increasing frequency of extreme weather events, the large-scale integration of distributed generation into distribution networks, and the widespread application of new power electronic devices have posed severe challenges to the security of power supply in distribution networks. To enhance the power supply reliability of the distribution network while considering its economic efficiency, this paper proposes a collaborative planning method for a flexible distribution network focused on power supply restoration and resilience enhancement In this method, a planning model for flexible distribution networks is established by optimally determining the siting and sizing of soft open point (SOP), with the objective of minimizing the annual comprehensive cost of the distribution network under multiple operational and planning constraints. Second-order cone programming (SOCP) relaxation and polyhedral approximation-based linearization techniques are employed to reformulate and solve the model, thereby obtaining the optimal siting and sizing Case for SOPs. Finally, simulations are conducted on a modified IEEE 33-bus test system to verify the effectiveness of the proposed method. The results show that, through appropriate siting and sizing of SOPs, outage loss costs can be significantly reduced, nodal voltage profiles can be improved, and load support can be provided to de-energized areas, leading to a reduction of more than 70% in the annual comprehensive cost of the distribution network and an improvement in the system reliability index from 99% to 99.999%, thus effectively enhancing both the economic efficiency and reliability of the distribution system. Full article

Electric induction motors are fundamental to industry, where reliability and continuous operation are critical. Though robust, they are prone to faults, particularly in demanding environments such as highway tunnels. Non-invasive diagnostic techniques are widely used for condition monitoring, yet most studies occur under controlled laboratory conditions, limiting their applicability in real-world scenarios. This research investigates the feasibility of applying Motor Current Signature Analysis (MCSA) for monitoring highway tunnel axial fan motors, aiming to determine its effectiveness for real-time diagnostics in industrial environments. Measurements were performed under actual operating conditions, highlighting practical challenges. Data acquisition was implemented remotely from electrical cabins feeding tunnel services, reducing installation complexity and costs compared to in-tunnel measurements. This approach enabled monitoring of all motors in a tunnel using minimal hardware (a single acquisition system equipped with Rogowski sensors) making the solution cost-effective and suitable for periodic measurements. Frequency domain analysis focused on harmonics associated with rotor bar defects and eccentricity, selected for their slow degradation and diagnostic relevance. The magnitude of these harmonics was tracked over time and compared across motors of the same model. Since most of the time the ventilators are de-energized, the periodic measurements can be seen almost as a real-time monitoring, at least for the faults considered, with much lower costs. Results were validated against maintenance reports, confirming bearing faults with eccentricity in two motors, while suspected rotor porosity remained unverified, as expected at low severity. Findings demonstrate that MCSA can provide operational insights for fault detection in tunnel environments, supporting predictive maintenance strategies. A key outcome of this study was selecting and implementing an effective measurement setup for industrial applications, while preparing the base for future machine learning integration to estimate Remaining Useful Life. Full article

Electromagnetic pneumatic microvalves, widely used in knitting machines, typically operate based on a spring-return mechanism. When the coil is energized, the electromagnetic force overcomes the spring force to attract the armature, opening the valve. Upon de-energization, the armature returns to its original position under the restoring force of the spring, closing the valve. However, most existing electromagnetic microvalves adopt a radially asymmetric magnetic yoke design, which generates additional radial forces during operation, leading to armature misalignment or even sticking. Additionally, the inductance effect of the coil causes a significant delay in the armature release response, making it difficult to meet the knitting machine’s requirements for rapid response and high reliability. To address these issues, this paper proposes an improved electromagnetic microvalve design. First, the magnetic yoke structure is modified to be radially symmetric, eliminating unnecessary radial forces and preventing armature sticking during operation. Second, a permanent magnet assist mechanism is introduced at the armature release end to enhance release speed and reduce delays caused by the inductance effect. The effectiveness of the proposed design is validated through electromagnetic numerical simulations, and a multi-objective genetic algorithm is further employed to optimize the geometric dimensions of the electromagnet. The optimization results indicate that, while maintaining the fundamental power supply principle of conventional designs, the new microvalve structure achieves a pull-in time comparable to traditional designs during engagement but significantly reduces the release response time by approximately 80.2%, effectively preventing armature sticking due to radial forces. The findings of this study provide a feasible and efficient technical solution for the design of electromagnetic microvalves in textile machinery applications. Full article

Cables are critical to the safe and reliable operation of nuclear power plants (NPPs) since they are widely used as a connection medium for various safety-critical equipment. According to research data and operational experience (OPEX), cable materials can degrade with time, resulting in reduced dielectric strength and higher leakage current. Cables may degrade gradually over time under normal service conditions and fail unexpectedly as a result of sudden exposure to harsher environments, such as Secondary Steam Line Breaks (SSLBs), or when required to operate under the severe conditions of a design basis event, such as a Loss-of-Coolant Accident (LOCA). To assess the condition of medium- and low-voltage cables in Canadian nuclear power plants, numerous inspection methods and electrical testing techniques are employed. These techniques include dielectric spectroscopy, polarization/depolarization current analysis, reflectometry, dielectric standby tests, AC partial discharge, and very-low-frequency (VLF) Tan Delta assessments for medium-voltage (MV) cables. While these methods provide precise diagnostic insights, they require cables to be disconnected at both ends and de-energized, posing operational constraints. Consequently, on-line plant cable monitoring has garnered significant interest, particularly for new reactor developments and large-scale NPP refurbishments. This paper provides a comprehensive benchmarking of existing technologies and a state-of-the-art review of modern cable assessment methodologies. It examines commercially available solutions and ongoing research in power testing for low-voltage (LV) and MV cables, with a particular focus on their applicability in nuclear power settings. Full article

The adoption rate of battery electric vehicles (EVs) is rapidly increasing. Electric vehicles differ significantly from conventional internal combustion engine vehicles and vary widely across different manufacturers. Emergency responders (ERs) and recovery personnel may have less experience with EVs and lack timely access to critical information such as the extent of the stranded energy present, high-voltage safety hazards, and post-crash handling procedures in a user-friendly manner. This paper presents a software/hardware interactive tool named Electric Vehicle Information for Incident Response Solutions (EVIRS) to aid in the quick access to emergency response and recovery information. The current prototype of EVIRS identifies EVs using the VIN or Make, Model, and Year, and offers several useful features for ERs and recovery personnel. These features include integration and easy access to emergency response procedures tailored to an identified EV, vehicle structural schematics, the quick identification of battery pack specifications, and more. For EVs that are not severely damaged, EVIRS can perform calculations to estimate stranded energy in the EV’s battery and discharge time for various power loads using either EV dashboard information or operational data accessed through the CAN interface. Knowledge of this information may be helpful in the post-crash handling, management, and storage of an EV. The functionality and accuracy of EVIRS were demonstrated through laboratory tests using a 2021 Ford Mach-E and associated data acquisition system. The results indicated that when the remaining driving range was used as an input, EVIRS was able to estimate the pack voltage with an error of less than 3 V. Conversely, when pack voltage was used as an input, the estimated state of charge (SOC) error was less than 5% within the range of 30–90% SOC. Additionally, other features, such as retrieving emergency response guides for identified EVs and accessing lessons learned from archived incidents, have been successfully demonstrated through EVIRS for quick access. EVIRS can be a valuable tool for emergency responders and recovery personnel, both in action and during offline training, by providing crucial information related to assessing EV/battery safety risks, appropriate handling, de-energizing, transport, and storage in an integrated and user-friendly manner. Full article

Radiation therapy (RT) is the cornerstone treatment for prostate cancer; however, it frequently induces gastrointestinal and genitourinary toxicities that substantially diminish the patients’ quality of life. While many individuals experience transient side effects, a subset endures persistent, long-term complications. A promising strategy to mitigate these toxicities involves enhancing tumor radiosensitivity, potentially allowing for lower radiation doses. In this context, mito-lonidamine (Mito-LND), an antineoplastic agent targeting the mitochondrial electron transport chain’s complexes I and II, emerges as a potential radiosensitizer. This study investigated Mito-LND’s capacity to augment RT efficacy and reduce adverse effects through comprehensive in vitro and in vivo assessments using hormone-sensitive and hormone-refractory prostate cancer models. Employing a Seahorse analysis and ¹H/³¹P magnetic resonance spectroscopy (MRS), we observed that Mito-LND selectively suppressed lactate production, decreased intracellular pH, and reduced bioenergetics and oxygen consumption levels within tumor cells. These findings suggest that Mito-LND remodels the tumor microenvironment by inducing acidification, metabolic de-energization, and enhanced oxygenation, thereby sensitizing tumors to RT. Our results underscore the potential of Mito-LND as a therapeutic adjunct in RT to improve patient outcomes and reduce radiation-associated toxicities in early-stage prostate cancer. Full article

This paper aims to propose inductive wireless power transfer (IWPT) technology for pinpointing fault locations in LV distribution underground cables following the use of other pre-location methods. The proposed device is portable, hence battery-powered, and operates by scanning for faults above ground via inductive coupling with the de-energized cable. This primarily relies on impedance changes in the cable due to permanent faults as the device scans the length of the cable. A detailed frequency domain mathematical model for the system is deduced and circuit design/parameters affecting the inductive coupling are investigated. An optimal design strategy for the portable device is demonstrated to achieve high fault-locating sensitivity with a minimum device VA rating. The device is tested under multiple fault scenarios (including shunt and open-circuit (cable break) faults) using a MATLAB/Simulink circuit model, and the results are validated against the mathematical model. The device’s performance with single-core and multi-core cables is examined. Finally, a critical comparative evaluation of the IWPT method with existing fault pinpointing techniques is conducted that highlights both the advantages and limitations of the proposed technique. The research shows that the proposed technology provides a promising new solution for LV network operators to minimize excavations for underground cable faults by pinpointing locations where a considerable deflection in induced cable current occurs when passing a fault point. Full article

Unlike traditional tap changers, which require transformers to be de-energized before making changes, On-Load Tap Changers (OLTCs) can adjust taps while the transformer is in service, ensuring continuous power supply during voltage regulation. OLTCs enhance grid reliability and support load balancing, reducing strain on the network and optimizing power quality. Their importance has grown as the demand for stable voltage and the integration of renewables has increased, making them vital for modern and resilient power systems. While enhanced OLTCs often incorporate stronger materials and improved designs, mechanical components like contacts and diverter switches can still experience wear over time. This can result in longer maintenance intervals. In the era of digitalization, advanced diagnostic techniques capable of detecting early signs of wear or malfunction are essential to enable preventive maintenance for these important components. This contribution introduces a novel method for detecting faults and irregularities in OLTCs, leveraging vibroacoustic signals to enhance OLTC diagnostics. This paper proposes a tolerance-based approach using the envelope of vibroacoustic signals to identify faults. A significant challenge in this field is the limited availability of faulty signal data, which hinders the performance of machine learning algorithms. To address this, this study introduces a nonlinear model utilizing amplitude modulation with a Gaussian carrier to simulate faults by introducing controlled distortions. The dataset used in this study, with data recorded under real operating conditions from 2016 to 2023, is free of anomalies, providing a robust foundation for the analysis. The results demonstrate a marked improvement in the robustness of detecting simulated faults, offering a promising solution for enhancing OLTC diagnostics and preventive maintenance in modern power systems. Full article

We describe enhancements to an operational forecast system based on the Weather Research and Forecasting (WRF) model for the prediction of high-impact weather events affecting power utilities, particularly conditions conducive to wildfires. The system was developed for Pacific Gas and Electric Corporation (PG&E) to forecast conditions in Northern and Central California for critical decision-making such as proactively de-energizing selected circuits within the power grid. WRF forecasts are routinely produced on a 2 km grid, and the results are used as input to wildfire fuel moisture, fire probability, wildfire spread, and outage probability models. This forecast system produces skillful real-time forecasts while achieving an optimal blend of model resolution and ensemble size appropriate for today’s computational resources afforded to utilities. Numerous experiments were performed with different model settings, grid spacing, and ensemble configuration to develop an operational forecast system optimized for skill and cost. Dry biases were reduced by leveraging a new irrigation scheme, while wind skill was improved through a novel approach involving the selection of Global Ensemble Forecast System (GEFS) members used to drive WRF. We hope that findings in this study can help other utilities (especially those with similar weather impacts) improve their own forecast system. Full article

The frequency domain measurement of radiated emissions from electric railways (from 9 kHz up to 150 kHz) has been omitted from the main part of the relevant standard (lack of repeatability and reproducibility of the results is mentioned as the reason). This paper describes the radiated emissions measured from three electric trains to emphasize the importance of the suitable time length selection (by comparing specific durations of the recorded data: 1 min and 5 min) and the influence of calculation methods of the resultant spectrum in frequency domain (RMS, mean, and Max of spectrum). The results revealed the requirement of unique definitions for pre-, during-, and post-measurement factors so that the repeatable and reproducible results could be achieved. The prerequisites for having less uncertain results are as follows: (1) pure background measurement (in energized and de-energized state of the catenary); (2) precoordinated operation mode, speed, and power of the train during the measurement; (3) precise details of the analysis step. A unique analysis method is required (to be clearly elaborated in the relevant standards) to obtain comparable results between different working groups engaged with the radiated-emission measurements from a train in a frequency range of 9 kHz to 150 kHz. Full article

This article covers the analysis of voltages induced on the conductors of a de-energized circuit of a multi-circuit, multi-voltage HVAC transmission line. As a result of the multiplied interactions between the circuits in such lines, the expected electrical shock currents (touch currents) to which a lineman performing live work on such a line may be exposed are determined. A number of supporting structures of three- and four-circuit lines with various degrees of geometric asymmetry are analyzed. Analyses have shown that in multi-circuit lines in which circuits of different voltages are carried on a common structure, despite the outage of one of the circuits, touch voltages and electrical shock currents (touch currents) exceeding the permissible values can be expected on its conductors, endangering the safety of the lineman. The arrangements of s in such lines that provide the smallest values of touch currents are indicated. Full article

Dynamic wireless charging (DWC) has emerged as a viable approach to mitigate range anxiety by ensuring continuous and uninterrupted charging for electric vehicles in motion. DWC systems rely on the length of the transmitter, which can be categorized into long-track transmitters and segmented coil arrays. The segmented coil array, favored for its heightened efficiency and reduced electromagnetic interference, stands out as the preferred option. However, in such DWC systems, the need arises to detect the vehicle’s position, specifically to activate the transmitter coils aligned with the receiver pad and de-energize uncoupled transmitter coils. This paper introduces various machine learning algorithms for precise vehicle position determination, accommodating diverse ground clearances of electric vehicles and various speeds. Through testing eight different machine learning algorithms and comparing the results, the random forest algorithm emerged as superior, displaying the lowest error in predicting the actual position. Full article

Prostate cancer is a multi-focal disease that can be treated using surgery, radiation, androgen deprivation, and chemotherapy, depending on its presentation. Standard dose-escalated radiation therapy (RT) in the range of 70–80 Gray (GY) is a standard treatment option for prostate cancer. It could be used at different phases of the disease (e.g., as the only primary treatment when the cancer is confined to the prostate gland, combined with other therapies, or as an adjuvant treatment after surgery). Unfortunately, RT for prostate cancer is associated with gastro-intestinal and genitourinary toxicity. We have previously reported that the metabolic modulator lonidamine (LND) produces cancer sensitization through tumor acidification and de-energization in diverse neoplasms. We hypothesized that LND could allow lower RT doses by producing the same effect in prostate cancer, thus reducing the detrimental side effects associated with RT. Using the Seahorse XFe96 and YSI 2300 Stat Plus analyzers, we corroborated the expected LND-induced intracellular acidification and de-energization of isolated human prostate cancer cells using the PC3 cell line. These results were substantiated by non-invasive ³¹P magnetic resonance spectroscopy (MRS), studying PC3 prostate cancer xenografts treated with LND (100 mg/kg, i.p.). In addition, we found that LND significantly increased tumor lactate levels in the xenografts using ¹H MRS non-invasively. Subsequently, LND was combined with radiation therapy in a growth delay experiment, where we found that 150 µM LND followed by 4 GY RT produced a significant growth delay in PC3 prostate cancer xenografts, compared to either control, LND, or RT alone. We conclude that the metabolic modulator LND radio-sensitizes experimental prostate cancer models, allowing the use of lower radiation doses and diminishing the potential side effects of RT. These results suggest the possible clinical translation of LND as a radio-sensitizer in patients with prostate cancer. Full article

The well-known methods of scene extraction on video are focused on analyzing the similarity between frames. However, they do not all analyze the composition of the image scene, which may remain the same during maintenance. Therefore, this paper proposes an algorithm for equipment maintenance scene detection based on human hand tracking. It is based on the assumption that, when servicing technological equipment, it is possible to determine the change in repair action by the position of the service engineer’s hands. Thus, certain information and the algorithm that processes these changes allow us to segment the video into actions performed during the service. We process the time series obtained by moving the hand position using spectral singular value decomposition for multivariate time series. To verify the algorithm, we performed maintenance on the control cabinet of a mining conveyor and recorded the work on a first-person video, which was processed using the developed method. As a result, we obtained some scenes corresponding to opening the control cabinet, de-energizing the unit, and checking the contacts with a multimeter buzzer test. A third-person video of motor service was similarly processed. The algorithm demonstrated the results in separate scenes of removing screws, working with a multimeter, and disconnecting and replacing motor parts. Full article