R&D DC Electrical Safety [Slides]
Objectives: Recognize DC and pulsed electrical hazards; Improve risk assessment skills; Concepts to improve worker safety; Engineered vs. administrative controls; tools of the trade.
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Objectives: Recognize DC and pulsed electrical hazards; Improve risk assessment skills; Concepts to improve worker safety; Engineered vs. administrative controls; tools of the trade.
Offshore wind (OSW) development activity is accelerating in the United States, with over 10 gigawatts (GW) of capacity likely to be installed on the Atlantic coast before 2030. In addition, states have committed to procure over 29 GW of offshore wind. Experienced European offshore wind energy developers are beginning to make large investments into offshore wind projects in American waters, and some U.S. developers have submitted key design documents for regulatory review. European offshore wind developers possess extensive experience in other geographic markets, which greatly increases confidence that the U.S. offshore wind industry will be successful. Nonetheless, existing U.S. electrical standards and European electrical standards are significantly different. If not addressed, these differences could potentially impact worker safety.
Anybody can design a system that works. Our mission is to design a system that, when it fails, will fail in an orderly manner that will result in a minimum of injury or loss of life There is no such thing as “safe,” or “working safely.”
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Explore the source record for details and available documents.
This report presents a non-contact approach to simultaneously obtain current-voltage (I-V) curves of photovoltaic (PV) substrings and modules in a string without the need of disconnecting the individual modules from the string. There are two types of I-V curve tracers currently available in the marketplace, capacitor-based and electronic load-based. The primary requirement of these conventional I-V tracers is the disconnection of individual modules in the string so the individual modules contacted through the connectors of the individual modules. These contact-tracers have three major limitations in the utility scale power plants: First limitation – Weather and Accuracy: The mass-produced commercial contact-tracers cannot obtain the I-V curves of both string and its modules (as high as 30 modules), almost simultaneously (within about 5 minutes) at, practically, a single irradiance level, a single module temperature, a single spectrum and a single AOI (angle of incidence). This inability of the contact-tracers forces the testing personnel to wait for an extended or multiple sunny (>800 W/m 2 ) duration(s) of a cloudy day. This is a serious limitation as waiting for the sunny conditions or days is a huge practical challenge in almost all locations, except desert locations. Also, since the I-V curves are obtained at different prevailing weather conditions, it becomes critical to translate all the measured I-V curves of 30 modules in the string to a single test condition, for example STC (standard test conditions), so the underperforming modules can be identified. The accuracy of translation equations is heavily influenced by the irradiance level and temperature, spectral and AOI ranges; Second limitation - Safety: The second limitation is related to the high voltage electrical safety of the test personnel during disconnecting and reconnecting of individual modules or cable connectors from the string under daylight conditions and damaging of the original module connectors (especially the field aged connectors) during the disconnecting and reconnecting process; Third limitation – Labor: The third limitation is related to the enormous amount of time and hardship for the test personnel under prevailing (often harsh) protracted field conditions. This project was executed by Arizona State University in collaboration with its industry partner, PV Measurements Inc. (PVM). To mitigate all the three challenges of the state-of-the-art equipment indicated above, we utilized a non-contact I-V (NCIV) tracer approach. In this approach, we utilized an electrostatic voltmeter (ESV) and voltage sensor/probe combination to obtain I-V curves. The ESV units are extensively used in the high voltage industry but not in the PV industry. To obtain the simultaneous I-V curves of the substrings and modules within a string, we utilized multiple commercial ESV-Probe sets. In this approach, we utilized a non-contact voltage sensor (called, Probe) placed on the glass surface of the module (above the last cell of the module). This probe senses the module voltage (with respect to ground) through measured capacitance which is dictated by the surface charges (which in turn is dictated by the module voltage) and transmits the sensed voltage to the voltmeter (called, ESV or NCV, non-contact voltmeter). The current is sensed by a non-contact hall sensor. In a 30-module string, the 30th probe obtains the entire string I-V along with the string I-V obtained by the electronic load. so that the I-V curves of the substrings and modules can be obtained by NCIV without the need of disconnecting the individual modules in the string. The string I-V curves obtained by the electronic load and NCIV can be compared for the accuracy determination. One can use 30 ESV units and 30 Probes to obtain 30 I-V curves of a 30-module string or use just 5 ESV units and 30 Probes in conjunction with 5 six-channel switchboxes (called, 6:1 switchboxes). To reduce the equipment cost, we utilized the 6:1 switchbox approach so the number of ESV units is reduced from 30 to 5. The approaches, achievements and challenges of this project are detailed in this report.
Fast and accurate detection of soft short circuits (SCs) in the battery packs of damaged electric vehicles is needed by first responders and mechanics to mitigate the potential risk from battery fires that may occur hours, days, or weeks after an accident. Here, this paper presents an SC-detection algorithm for potentially damaged lithium-ion batteries that works quickly and without a priori knowledge of the battery-pack chemistry, capacity, state of charge, or state of health. The proposed universal SC-detection algorithm is designed to be implemented on an inexpensive handheld device that can connect to and monitor the voltages of all cells in a pack. Transient filtering and linear-quadratic state observation provide estimates of normalized SC current for every cell in the pack. Cells with SC-current estimates outside a sigma-based threshold are detected. Simulations, experiments, and electric vehicle (EV) crash data are used to verify the speed, sensitivity, and accuracy of the method, demonstrating 96% accurate detection of 0.0027 C SCs in under 1 h for 5S cell groups in the lab and no false positives for crashed Volkswagen, Chevrolet, and Tesla vehicles without SCs.
The manufacturers of Nationally Recognized Testing Laboratory (NRTL) listed, switch-rated, plug and receptacle combinations tout their convenience, reliability, efficiency, and compliance with both the National Fire Protection Association (NFPA) 70®, National Electric Code (NEC), and National Fire Protection Association 70E®, The Standard for Electrical Safety in the Workplace, as advantages to using these products in lieu of traditional metal-enclosed disconnect switches. The purpose of this paper is to raise awareness of several unintended consequences that can result when replacement involves high energy cord and plug connected equipment. This paper describes several possible issues in complying with the NFPA 70 Articles 110 and 400 and NFPA 70E Articles 110 and 130 that should be considered when using load rated plug/receptacle combinations. This paper is intended to address high energy circuits typically associated with 480 volt pin and sleeve type connections in applications where incident energy levels may exceed 1.2 calories/centimeter² (cal/cm²).
This Distributed Wind (DW) Certification Best Practices Guideline describes the typical approach for certification of distributed wind turbines above and below 150 kilowatts (kW) in size based on the conformity assessment requirements in the United States. The purpose of the guideline is to clarify and consistently describe the path to certification for various systems and components by helping the user navigate the complex path to certification compliance. This is done via clarification of both the required turbine type certification elements, as well as third-party electrical safety listing of turbine system components and subassemblies. In the United States specifically, there is no wind turbine certification scheme that governs or maintains a consistent set of conformity assessment requirements, and this can lead to wide ranging interpretations of the standards and required elements for certifications. This guideline attempts to simplify the path by organizing the information and guiding the user to the applicable set of requirements. Any wind turbine manufacturer or designer of wind turbines used in distributed generation applications in the United States would find value in the conformity assessment guidance in this guideline. Users are expected to be involved in the technical development of the product and supporting documentation, as the details provided are geared towards electrical and mechanical engineering of the system and components.
Renewable energy systems continue to be one of the fastest growing segments of the energy industry. This article focuses on the understanding of how photovoltaic (PV) technology behaves under dc arc conditions. Emphasis is placed on the electrical safety aspect of dc arc-flash incident energy (IE) evaluation. Because of the fast proliferation of PV systems and lack of formal equivalent calculation guidelines, such as IEEE 1584 for ac systems, it has been necessary to rely on different equations and models presented by various researchers over the last few years. This article discusses the behavior of PV systems under arc conditions and presents the results of the available methods to estimate the dc arc-flash IE. It provides a comparative analysis of a proposed arc-flash IE calculation method against different laboratory tests, including those performed for this article at the National Renewable Energy Laboratory (NREL). Detailed explanations are provided regarding the effect of the PV module current-voltage (I-V) and power-voltage (P-V) curves under arcing conditions. Examples of the application of the proposed calculation method to the test measurements are included.
In monitoring the power grid, an ability to differentiate between fault types is essential to ensuring electrical safety. Accordingly, this study introduces a fault detection and classification method by considering different machine learning (ML) and feature extraction (FE) methods combinations. Specifically, the proposed method is established in two classification layers; the first layer determines the fault, and the second layer distinguishes the type of fault. Based on the proposed system model, this study seeks to determine the influential data attributes in a power grid signal using FE methods, including fast Fourier transform, power spectral density (PSD), auto-correlation, and wavelet transform (WT). A cross-comparison of the effectiveness of the Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF) is also performed to accomplish the classification layers of the proposed method. The designed algorithm is analyzed under the various combinations of FE and ML methods, and outcomes are presented by considering the trade-off between computational complexity and prediction accuracy. The results reveal that the RF-based ML algorithm shows the most accurate classification performance with PSD, and the most time-saving of the models is the DT WT. Also, SVM emerges superior on a subsequent test of the simulated models on real-world signals.
This booklet has been prepared for KEK users, primarily working on the Belle II experiment, with useful information for maintaining a safe working environment. In it, topics from initiating and fulfilling a work plan to radiological and electrical safety to fall prevention are introduced with instructions and hints for keeping your workplace safe.
The development of a functional hydrogen detection system is a multifaceted process that integrates hardware, deployments strategies, and analytics which can be supported by the NREL Sensor Laboratory: 1. Support of the design, validation and optimization of sensing prototypes; 2. Guide optimized sensing element development, including control electronics; 3. Laboratory testing to validate/optimize metrological performance (measurement range, detection limit, etc.); 4. Provide test sites for field deployments representative of real-world scenarios with controlled hydrogen releases; 5. Develop sensor placement and operation guidance; 6. Provide guidance on electronics to accommodate facility integration; 7. Electrical safety designs to allow for operation within restricted zones; 8. Integration into facility monitoring and control systems; 9. Guide incorporation of cyber security elements to protect facilities from malicious attacks; 10. Modeling and application of advanced analytics to detect and quantify emissions; 11. Higher Order dispersion models to guide sensor placement for reliable detection; 12. Advanced analytics for improved metrological performances, and to inform inverse modeling; 13. Market support and commercialization (national and international markets); 14. Commercial deployments in H2@SCALE markets (e.g., HUBs and other large-scale hydrogen markets); and 15. Leverage off international collaborations/partnerships (e.g., NREL is on the advisory board for the European initiative "pre-Normative Research on Hydrogen Releases Assessment"-NHyRA).
This Distributed Wind (DW) Certification Best Practices Guideline describes the typical approach for certification of distributed wind turbines above and below 150 kilowatts (kW) in size based on the conformity assessment requirements in the United States. The purpose of the guideline is to clarify and consistently describe the complex path to certification for various systems and components. This is done via clarification of both the required turbine type certification elements, as well as third-party electrical safety listing of turbine system components and subassemblies.
Renewable energy systems continue to be one of the fastest growing segments of the energy industry. This paper focuses on the understanding of how photovoltaic (PV) technology behaves under dc arc conditions. Emphasis is placed on the electrical safety aspect of DC arc flash incident energy evaluation. Because of the fast proliferation of PV systems and the lack of formal equivalent calculation guidelines such as IEEE 1584 for AC systems, it has been necessary to rely on different equations and models presented by various researchers over the last few years. This paper discusses the behavior of PV systems under arc conditions and presents the results of available methods to estimate the dc arc flash incident energy. This paper provides a comparative analysis of a proposed arc-flash incident energy calculation method against different laboratory tests including those performed by NREL. Detailed explanations are provided regarding the effect of PV module I-V and P-V curves under arcing conditions. Examples of the application of the proposed calculation method to the test measurements are included.
Eocycle's Competitiveness Improvement Project (CIP) award will fund certification testing of the company's EOX S-16 25-kW turbine to make sure it meets UL electrical safety standards. In addition to validating the turbine's reliability and anticipated reduced maintenance requirements, UL certification will also boost consumer confidence in the technology, with the potential to increase the market for the entire small-wind industry.
This help sheet provides an overview of physical safety and security design elements for public EV charging stations and general best practices that can be considered for the safety and comfort of charging station customers.
Mitigating thermal runaway and cell-to-cell propagation is essential for improving the safety of electric and hybrid vehicles. Enhancing digital twin capabilities to predict battery mechanical abuse is particularly critical for automotive and aerospace applications, where crashworthiness is a key concern. Understanding failure conditions and propagation in battery modules during mechanical abuse is complex due to interactions between structural deformation, heat transfer, electrochemical processes, exothermic reactions and mechanical fracture. While prior studies have focused on modeling cell-level behavior, extending these models to module or pack level is necessary for a system level understating of electric vehicle safety. This study develops coupled large deformation finite element models that simultaneously solve for electrochemistry, material failure, internal short circuit and thermal runaway propagation. The models account for mechanical and thermal interactions between lithium-ion cells and other battery components while the contact interfaces are evolving with time. Model-predicted voltage, temperature and force responses are compared with experimental data for validation. The results demonstrate that the approach captures key failure mechanisms, including thermal propagation through heat transfer, electrical propagation from short circuits in parallel-connected cells, and mechanical propagation via penetration and crack formation. These findings show that computational models are valuable tools for understanding battery module failure and providing insight that can reduce the need for extensive experimental testing.