As a result of the increase in penetration of inverter-based generation such as wind and solar, the dynamics of the grid are being modified. These modifications may threaten the stability of the power system since the dynamics of these devices are completely different from those of rotating generators. Protection schemes need to evolve with the changes in the grid to successfully deliver their objectives of maintaining safe and reliable grid operations. This paper explores the theory of traveling waves and how they can be used to enable fast protection mechanisms. It surveys a list of signal processing methods to extract information on power system signals following a disturbance. The paper also presents a literature review of traveling wave-based protection methods at the transmission and distribution levels of the grid and for AC and DC configurations. The paper then discusses simulations tools to help design and implement protection schemes. A discussion of the anticipated evolution of protection mechanisms with the challenges facing the grid is also presented.
In order to study the relative degradation between co-located PV modules and microinverters in an ACPV configuration, four 260 Watt PV modules and four 250 W microinverters purchased on the open market have been co-located in a thermal chamber set at a static temperature (69°C). Instantaneous electrical/thermal measurements have been taken on the microinverters with periodic dark IV measurements on the modules. After over 10,000 hours of testing, no failures or observable degradation have been seen in either the module or microinverter. Using average measured field-temperature data with Military Handbook analysis, this indicates an approximate field use of 44 years of operation lifetime for PV modules, and 13 years of operation for microinverters with reliability of 66.87% with a lower one-sided confidence level of 80%.
To determine risk of an electric shock to firefighter personnel due to contact with live parts of a damaged PV system, simulated PV arrays were constructed with multiple 'modules' connected to a central inverter. The results of this analysis demonstrate that ungrounded arrays are significantly safer than grounded arrays for reasonable module isolation resistances. Ungrounded arrays provide current hazards to personnel up to three orders of magnitude smaller than for a grounded array counterpart. While the size of the array does not affect the current hazard in grounded arrays for body resistances above 100,Ω, in ungrounded arrays, increased array size yields increased current hazards- considering that the overall fault current level is still significantly smaller than for grounded arrays. In both grounded and ungrounded arrays, the current hazard has a direct correlation to array voltage. Since the level of fault current in a grounded array can be significant, this work shows that the non- linearity of the array IV curve must be taken into account for body resistances below 600 Ω and array voltages above 1000V for accurate fault current determination. Although module and array isolation resistance is not a factor that modulates fault current in a grounded array, this resistance, Riso, has a significant effect on current hazard to the firefighter for ungrounded arrays.
2018 IEEE 7th World Conference on Photovoltaic Energy Conversion, WCPEC 2018 - A Joint Conference of 45th IEEE PVSC, 28th PVSEC and 34th EU PVSEC
Jones, C.B.; Hamzavy, Babak; Hobbs, William B.; Libby, Cara; Lavrova, Olga A.
Accelerated testing has been recognized as a valuable tool for identifying modules that are prone to early-life failures or excessive degradation. Yet, it is unclear how many of the tests, including IEC 61215, match with reality. The present work performed indoor aging tests based on the IEC standard and compared the current and voltage (I-V) curve results with fielded modules. The in-field modules were installed in a subtropical dessert climate about five years ago. The modules were recently outfitted with in situ I-V curve tracing devices that measured its characteristics at various environmental conditions. The IEC tests tended to decrease current, but the voltage increased, which caused power output to not change. The in-filed modules, on the other hand, experienced a drop in power that was greater than 5% over a 5 year period.
This project explored coupling modeling and analysis methods from multiple domains to address complex hybrid (cyber and physical) attacks on mission critical infrastructure. Robust methods to integrate these complex systems are necessary to enable large trade-space exploration including dynamic and evolving cyber threats and mitigations. Reinforcement learning employing deep neural networks, as in the AlphaGo Zero solution, was used to identify "best" (or approximately optimal) resilience strategies for operation of a cyber/physical grid model. A prototype platform was developed and the machine learning (ML) algorithm was made to play itself in a game of 'Hurt the Grid'. This proof of concept shows that machine learning optimization can help us understand and control complex, multi-dimensional grid space. A simple, yet high-fidelity model proves that the data have spatial correlation which is necessary for any optimization or control. Our prototype analysis showed that the reinforcement learning successfully improved adversary and defender knowledge to manipulate the grid. When expanded to more representative models, this exact type of machine learning will inform grid operations and defense - supporting mitigation development to defend the grid from complex cyber attacks! This same research can be expanded to similar complex domains.
The key objectives of this project were to increase meaningful stakeholder engagement in photovoltaic performance modeling and reliability areas. We did this by hosting six workshop over the past three years, giving conference and workshop presentations and contributing to technical standards committees. Our efforts have made positive contributions by increasing the sharing of information and best practices and by creating and sustaining a technical community in PV Performance Modeling. This community has worked together over the past three years and has improved its practice and decreased performance modeling uncertainties.
This report describes data collection and analysis of solar photovoltaic (PV) equipment events, which consist of faults and fa ilures that occur during the normal operation of a distributed PV system or PV power plant. We present summary statistics from locations w here maintenance data is being collected at various intervals, as well as reliability statistics gathered from that da ta, consisting of fault/failure distributions and repair distributions for a wide range of PV equipment types.
A hierarchical control algorithm was developed to utilize photovoltaic system advanced inverter volt-VAr functions to provide distribution system voltage regulation and to mitigate 10-minute average voltages outside of ANSI Range A (0.95-1.05 pu). As with any hierarchical control strategy, the success of the control requires a sufficiently fast and reliable communication infrastructure. The communication requirements for voltage regulation were tested by varying the interval at which the controller monitors and dispatches commands and evaluating the effectiveness to mitigate distribution system over-voltages. The control strategy was demonstrated to perform well for communication intervals equal to the 10-minute ANSI metric definition or faster. The communication reliability impacted the controller performance at levels of 99% and below, depending on the communication interval, where an 8-minute communication interval could be unsuccessful with an 80% reliability. The communication delay, up to 20 seconds, was too small to have an impact on the effectiveness of the communication-based hierarchical voltage control.
This paper describes efforts made by Sandia National Laboratories (SNL) and the National Renewable Energy Laboratory (NREL) to validate the SNL developed PV Reliability Performance Model (PV - RPM) algorithm as implemented in the NREL System Advisor Model (SAM). The PV - RPM model is a library of functions that estimates component failure and repair in a photovoltaic system over a desired simulation period. The failure and repair distributions in this paper are probabilistic representations of component failure and repair based on data collected by SNL for a PV power plant operating in Arizona. The validation effort focuses on whether the failure and repair dist ributions used in the SAM implementation result in estimated failures that match the expected failures developed in the proof - of - concept implementation. Results indicate that the SAM implementation of PV - RPM provides the same results as the proof - of - concep t implementation, indicating the algorithms were reproduced successfully.