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- Conventional Hydro
Real-Time Condition Health Monitoring and its Application to Hydro Turbines
Lead Companies
Colorado School of Mines
Lead Researcher (s)
- Samuel Dyas
Hydroelectric power has been the number one renewable energy source in the U.S. since the beginning of the industrial revolution and continues to be today. Hydroelectricity is a critical component in the power production grid to keep greenhouse gas emissions and pollution minimized. As such, it is crucial that unexpected shutdowns and unplanned maintenance of hydropower turbines be kept to a minimum, so as to maximize hydroelectricity production. This thesis aims to investigate condition health monitoring (CHM) methods specifically designed for non-intrusive cavitation detection within hydropower turbines. Cavitation is a highly damaging phenomenon common within turbines. When allowed to continue undetected over an extended period of time, cavitation can lead to severe and crippling effects for efficient operation. The application of CHM will lead to less downtime and ultimately more electrical production from hydropower turbines, resulting in the maximization of the U.S.’s number one renewable energy source’s potential. An instrumented cavitation inducing apparatus was designed and built for laboratory testing. The goal of the cavitation inducing apparatus was to produce both non-cavitating and cavitating flows within the available flow range. Also, it was critical for the apparatus to be simple and allow the instrumentation utilized to be placed as close as possible to the cavitation within the flow. Instrumentation including pressure transducers, accelerometers and acoustic emission sensors were used to non-intrusively record cavitation signals from the cavitation apparatus. Multiple signal processing techniques, spanning both the time and frequency domains were utilized to develop methods and metrics to quantify the cavitation monitoring data. Most of the techniques are well documented, including analyzing the root mean square values of the signals and utilizing the Fast Fourier Transform for frequency domain analysis. There were also some signal processing techniques developed throughout this project, specifically for cavitation monitoring. The metrics and methods developed proved successful at identifying volatile flow rates and subsequently the onset of cavitation state change with the flow. It was also determined that time domain signal processing techniques were more successful at cavitation characterization than frequency domain techniques. There is confidence the methods developed for non-intrusive cavitation monitoring through this thesis could be easy transferred to on-site operational test data received from a cavitating turbine and successfully diagnose the onset of cavitation with the flow range.
Technology Application
Conventional Hydro
Research Category
Powerhouse Equipment
Research Sub-Category
Asset Management
Status
complete
Completion Date
2013
- Conventional Hydro
Real-time Forecasting and Hydropower Optimization
Lead Companies
Cornell University
Lead Researcher (s)
- Jonathon Lamontagne
This report summarizes research that I conducted with Hydro Research Foundation (HRF) support. The general focus was hydropower operations optimization using dynamic programming (DP) algorithms. More specifically, there were three areas of concern. The first concern was near real-time operations optimization using a time decomposition algorithm with different representations of uncertainty. The second concern was the value of forecasts and forecast precision to operations optimization. The third concern is the reduction of the computational burden of high-dimensional DP using intelligent sampling of the state space. Idealized reservoir systems based on the Kennebec River in Maine are used as case studies. The objective of hydropower operations optimization is to maximize the expected benefit obtained from a reservoir system from the present time to the end of some planning horizon. In practice time is often broken into decision stages, so the problem becomes the selection of a sequence of releases which maximizes the expected benefit, subject to a set of constraints. The problem is challenging because the future availability of water is uncertain and the benefits and constraints are non-linear in the decision variable. Stochastic DP (SDP) algorithms are well suited to this type of problem. This research used the sampling SDP algorithm, which represents uncertain future flows as a range of flow time series scenarios rather than a Markov process. The first concern addressed in this report is use of a time decomposition algorithm to optimize operation of a reservoir with a sub-daily time step. This involves solving nested optimization models, each with a different planning horizon and time-step, where the longer-term planning models inform the shorter-term models. This allows for rapid optimization of short-term operations, while efficiently considering seasonal objectives and constraints. A key consideration is how uncertainty is represented in each of the nested optimization models. By changing the probability of transitioning from one scenario to another, it is possible to generate a number of decision trees and the assessment of which is most advantageous. Three general transition cases were tested: the case which considers no transitions between scenarios, the case that assumes transitioning to any scenario is equally likely, and the case that bases the probability of transitioning on the flow forecast for the next period. Through testing on an idealized single reservoir system, it was discovered that use of forecasts to estimate transition probabilities is most useful in short-term optimization. In fact, long- and mid-term forecasts provide little improvement over configurations which allow no transitions between scenarios. In contrast, models that assume transition to any scenario is equally likely performed poorly, because they poorly reflect the persistence of flow when computing the long-term future value of water. Thus, it seems a successful algorithm configuration will use forecasts for short-term planning and a reasonable model of the persistence of flow for the long- and medium-term models (i.e. no transitions between scenarios or transitions based on forecasts). The second research concern is the value improved forecast precision to optimization performance, and how this might change depending on the planning horizon and time step considered. For example, does the precision of long-term forecasts significantly affect the performance of the time decomposition algorithm, or is the precision of short-term forecasts more important. This is examined by applying the same model configurations previously described, but with varying forecast precision. It was determined that for summer operations optimization, long- and mid-term forecast precision contributes very little to the efficient operation of the reservoir. As was discussed above, use of forecasts for long and mid-term planning is of little value, thus it is not surprising that forecast precision for longer-term planning is not important. In contrast, the performance of the algorithm is highly sensitive to the precision of the short-term forecast. The third research concern is the reduction of the computational burden of high-dimensional DP. In DP, the state of the system in any time is described by state variables. In reservoir optimization, the state variable is reservoir storage, so the addition of a reservoir to the system involves the addition of a state variable, and a new dimension to the state space. This exponentially increases the difficulty of numerically solving the optimization problem. However, many points in the state-space represent unreasonable combinations of storages. The proposed Corridor DP algorithm saves computational effort by focusing the optimization effort on the region of the state space which the system will likely visit. One challenge of implementing Corridor DP is that cubic spline interpolation can perform poorly when basis points are irregularly placed. Instead, this research used radial basis function (RBF) interpolation, which is not constrained by the placement of basis points. Because the Corridor DP optimizes over the entire state space, not just inside the corridor, a coarse spline is fit to a few points in the extremes of the state space. An RBF surface models the deviation from the spline in the corridor region. The Corridor DP algorithm is applied to an idealized four reservoir system. The corridor region was determined through simulation of the historic operation of the actual system. In numerical trials, an early implementation of the Corridor DP algorithm achieves a given relative error with about 1/2 the computational effort of DP with spline interpolation, and only 1/10 the computational effort of DP with linear interpolation. Theoretical results are presented which suggest how the radial basis function interpolation and corridor selection criteria might be improved in the future. This report focuses on the research conducted with HRF support. This research has focused on three concerns. First, the merit of various representations of uncertainty. Second, the value of forecast precision to the optimization effort. Finally, the reduction of the computational burden of solving highdimensional DP problems. Results addressing each concern are presented, as well as suggestions for future work, Before ending this executive summary, I’d like to express my gratitude to the Hydro Research Foundation for their support over the last three years. In addition to the financial support, they have provided many industry and government contacts who have proved invaluable as this research has evolved.
Technology Application
Conventional Hydro
Research Category
Interconnect Integration and Markets
Research Sub-Category
Hydraulic Forecasting
Status
complete
Completion Date
2013
- Conventional Hydro
Reduced Order Description of Experimental Two-Phase Pipe Flows: Characterizations of Flow Structures and Dynamics via Proper Orthogonal Decomposition
Lead Companies
University: Portland State University
Lead Researcher (s)
- Bianca Viggiano
Multiphase pipe flow is investigated using proper orthogonal decomposition for tomographic X-ray data, where holdup, cross-sectional phase distributions and phase interface characteristics within the pipe are obtained. Six cases of stratified and mixed flow with water content of 10%, 30% and 80% are investigated to gain insight into effects of velocity and proportion of water on the flow fields. Dispersed and slug flows are separately analyzed to consider the added interface complexity of the flow fields. These regimes are also highly applicable to industry operational flows. Instantaneous and fluctuating phase fractions of the four flow regime are analyzed and reduced order dynamical descriptions are generated. Stratified flow cases display coherent structures that highlight the liquid-liquid interface location while the mixed flow cases show minimal coherence of the eigenmodes. The dispersed flow displays coherent structures for the first few modes near the horizontal center of the pipe, representing the liquidliquid interface location while the slug flow case shows coherent structures that correspond to the cyclical formation and break up of the slug in the first 5 modes. The low order descriptions of the high water content, stratified flow field indicates that main characteristics can be captured with minimal degrees of freedom. Reconstructions of the dispersed flow and slug flow cases indicate that dominant features are observed in the low order dynamical description utilizing less than 1% of the full order model. POD temporal coefficients a1, a2 and a3 show a high level of interdependence for the slug flow case. The coefficients also describe the phase fraction holdup as a function of time for both dispersed and slug flow. The second coefficient, a2, and the centerline holdup profile show a mean percent difference below 9% between the two curves. The mathematical description obtained from the decomposition will deepen the understanding of multiphase flow characteristics and is applicable to long distance multiphase transport pipelines, fluidized beds, hydroelectric power and nuclear processes to name a few
Technology Application
Conventional Hydro
Research Category
Water Conveyance
Research Sub-Category
Penstock
Status
complete
Completion Date
2017
- Conventional Hydro
Refining Quagga Habitat Suitability Models
Lead Companies
Bureau of Reclamation
Lead Researcher (s)
- Yale Passamaneck
As introductions of invasive freshwater mussels continue to be detected across the Western United States there is significant interest in understanding what waters are most at risk of infestation. In the absence of extensive laboratory studies on the physiological tolerances of invasive dreissenid mussels, correlative studies comparing mussel distribution and environmental parameters remains the best tool available for understanding the risk of mussel establishment in new waters. Previous efforts to define such habitat suitability parameters for dreissenid mussels have drawn primarily on data from waters in the Eastern US and Europe. Hydrological regimes in these regions are often less dynamic than in Reclamation waters in the arid Western US. We hypothesize that such factors may play an as yet unrecognized role in determining a waterbody's potential risk of invasive mussel establishment and infestation, and may serve to limit the spread of infestations in Reclamation waters. The proposed work will draw on a decade of early detection research conducted at the Reclamation Detection Laboratory for Exotic Species (RDLES), as well as publicly available data on water quality and hydrology to understand what factors may control the establishment of mussels in the Western US. Of particular interest will be waterbodies where RDLES has identified evidence of dreissenid mussel introductions, but populations have not proceeded to establishment and infestation. These data suggest initial habitat suitability but that some environmental features limited population expansion and survival. This is significant because for waters where no detection has occurred, it is not necessarily possible to distinguish if this is due to unsuitable environmental conditions or simply a lack of any introduction. This project will assess how current habitat suitability models may be refined to more accurately inform risk assessment in Reclamation waters.
Technology Application
Conventional Hydro
Research Category
Environmental and Sustainability
Research Sub-Category
Fish and Aquatic Resources
Status
ongoing
Completion Date
2021
- Conventional Hydro
Renewable Energy Production from Water Distribution Systems
Lead Companies
Georgia Institute of Technology
Lead Researcher (s)
- Ilker Telci
Water quality monitoring and search for environment friendly energy sources is becoming two of the most popular engineering research topics as we better understand the limits of our planet. In this thesis, first an optimal design methodology for water quality monitoring networks in river systems is developed. Next, a data interpretation approach is proposed to identify pollution source locations utilizing the water quality measurements supplied by the monitoring network. As the third topic, the thesis introduces an optimal design technique for energy recovery systems in water distribution networks. In the first part of this thesis, an optimization algorithm is developed for the water quality monitoring system. In this process, the best monitoring locations are determined by utilizing the outcomes of a simulation model. The results of the simulation model is an essential component of this approach since they incorporate the unsteady and stochastic nature of hydrodynamics and the contaminant fate and transport processes in rivers into the optimization model. In this approach, the ideal monitoring locations are determined through a multi-objective optimization technique. One of the objectives of the monitoring system is specified as the early detection of the contaminants and the other as the reliability of the monitoring network. The methodology developed was first applied to a simple hypothetical river system to demonstrate the importance of the unsteady hydrological properties of the watershed on the optimal locations of the monitoring stations. Then, it is tested on a realistic river system. The results show that the design technique developed can be effectively used for the optimal design of monitoring networks in river systems. In the second part of the study, a methodology for rapid identification of contaminant source locations is introduced. Since this is an ill posed problem which has non-unique solutions, a classification routine which correlates candidate spill locations with the measurements at the water quality monitoring stations is developed. For this purpose, the breakthrough curve of a contaminant measured at monitoring site is parameterized using its statistical moments. Then, a large number of spill scenarios are simulated for the training of the monitoring system. After the training process, the method is ready for sequential elimination of the candidate locations which leads to the identification of spill location for a breakthrough curve observed at the monitoring station. The model developed is applied to real river system and the results show that this technique can be a reliable starting point for the contaminant source investigation projects The third part of the thesis is devoted to renewable energy production from water distribution systems. The main idea behind this study is to harvest as much available excess energy as possible by utilizing micro turbines. The energy production at these turbines is constrained by the minimum pressure limit set by the management. Moreover, the unsteady nature of the flow in the network results in variations in the available excess energy. These aspects of the water distribution systems necessitate operation schedules for the micro turbines. In this study, a simulation-optimization method is developed which maximizes the energy recovered at the micro turbine(s). This simulationoptimization model is based on Genetic Algorithms (GA). A smart seeding of the GA is introduced to lower the computational burden. The algorithm tests several energy recovery system configurations which has different turbine locations and turbine types. Then the best configuration which has the highest energy production is selected. The methodology is first applied to a real pump driven network. Then, this network is converted into a hypothetical gravity driven system and the optimization model is tested on this new system. The results show that the energy recovery systems in water distribution networks can provide significant economic and environmental benefits and the methodology introduced is not only an optimal design tool but also an effective means of assessing the renewable energy potential in water distribution systems.
Technology Application
Conventional Hydro
Research Category
Water Conveyance
Research Sub-Category
Canal
Status
complete
Completion Date
2012
- Conventional Hydro
Representing Hydropower in Power Flow and Stability Models
Lead Companies
Pacific Northwest National Laboratory
Lead Researcher (s)
- Nadar Samaan
Identification of modeling gaps for existing and new hydropower replacements/installations including, but not limited to the following: • Governor dead-band issues • Response exceeding generator nameplate template • Forced oscillations, while operating in rough zones • Secondary control loops • Frequency trip settings • Water-hammer effect and water inertia • Over/underestimation of water availability • Static head-water values that are not adjusted to represent current conditions • Over/underestimation of available generation capacity
Technology Application
Conventional Hydro
Research Category
Technology
Research Sub-Category
Status
ongoing
Completion Date
TBD
- Conventional Hydro
Research, Monitoring, and Evaluation of Emerging Issues and Measures to Recover the Snake River Fall Chinook Salmon ESU
Lead Companies
U.S. Geological Survey
Lead Researcher (s)
- Kenneth Tiffan
- Russell Perry
In this report, USGS scientists and partners illustrate how a life-cycle model of intermediate complexity can be used to understand population dynamics and factors affecting different life stages of Snake River basin fall Chinook salmon.
Technology Application
Conventional Hydro
Research Category
Environmental and Sustainability
Research Sub-Category
Fish and Aquatic Resources
Status
complete
Completion Date
2020
- Conventional Hydro
Review and Recommendation of Hydrologic Forecast Verification Strategies and Methods
Lead Companies
CEATI International
Lead Researcher (s)
- #0432
Provide a verification framework that can be implemented on hydrologic parameters at a variety of levels of sophistication.
Technology Application
Conventional Hydro
Research Category
Interconnect Integration and Markets
Research Sub-Category
Hydraulic Forecasting
Status
ongoing
Completion Date
Expected 2020
- Conventional Hydro
Review of Data Screening Methods for Discharge/Inflow Time Series
Lead Companies
CEATI International
Lead Researcher (s)
- #0425
The main objectives of this project are to review/describe/classify data screening methods, identify easily applicable and robust techniques, develop a reference guide to assist users in the selection of data screening methods, and identify emerging techniques for discharge/inflow data screening.
Technology Application
Conventional Hydro
Research Category
Interconnect Integration and Markets
Research Sub-Category
Hydraulic Optimization
Status
complete
Completion Date
2020
Don’t see your waterpower research?
Have questions about WaRP?
Contact Marla Barnes at: marla@hydro.org