3,361 research outputs found
Condition monitoring of an advanced gas-cooled nuclear reactor core
A critical component of an advanced gas-cooled reactor station is the graphite core. As a station ages, the graphite bricks that comprise the core can distort and may eventually crack. Since the core cannot be replaced, the core integrity ultimately determines the station life. Monitoring these distortions is usually restricted to the routine outages, which occur every few years, as this is the only time that the reactor core can be accessed by external sensing equipment. This paper presents a monitoring module based on model-based techniques using measurements obtained during the refuelling process. A fault detection and isolation filter based on unknown input observer techniques is developed. The role of this filter is to estimate the friction force produced by the interaction between the wall of the fuel channel and the fuel assembly supporting brushes. This allows an estimate to be made of the shape of the graphite bricks that comprise the core and, therefore, to monitor any distortion on them
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Integrated performance prediction and quality control in manufacturing systems
textPredicting the condition of a degrading dynamic system is critical for implementing successful control and designing the optimal operation and maintenance strategies throughout the lifetime of the system. In many situations, especially in manufacturing, systems experience multiple degradation cycles, failures, and maintenance events throughout their lifetimes. In such cases, historical records of sensor readings observed during the lifecycle of a machine can yield vital information about degradation patterns of the monitored machine, which can be used to formulate dynamic models for predicting its future performance. Besides the ability to predict equipment failures, another major component of cost effective and high-throughput manufacturing is tight control of product quality. Quality control is assured by taking periodic measurements of the products at various stages of production. Nevertheless, quality measurements of the product require time and are often executed on costly measurement equipment, which increases the cost of manufacturing and slows down production. One possible way to remedy this situation is to utilize the inherent link between the manufacturing equipment condition, mirrored in the readings of sensors mounted on that machine, and the quality of products coming out of it. The concept of Virtual Metrology (VM) addresses the quality control problem by using data-driven models that relate the product quality to the equipment sensors, enabling continuous estimation of the quality characteristics of the product, even when physical measurements of product quality are not available. VM can thus bring significant production benefits, including improved process control, reduced quality losses and higher productivity. In this dissertation, new methods are formulated that will combine long-term performance prediction of sensory signatures from a degrading manufacturing machine with VM quality estimation, which enables integration of predictive condition monitoring (prediction of sensory signatures) with predictive manufacturing process control (predictive VM model). The recently developed algorithm for prediction of sensory signatures is capable of predicting the system condition by comparing the similarity of the most recent performance signatures with the known degradation patterns available in the historical records. The method accomplishes the prediction of non-Gaussian and non-stationary time-series of relevant performance signatures with analytical tractability, which enables calculations of predicted signature distributions with significantly greater speeds than what can be found in literature. VM quality estimation is implemented using the recently introduced growing structure multiple model system paradigm (GSMMS), based on the use of local linear dynamic models. The concept of local models enables representation of complex, non-linear dependencies with non-Gaussian and non-stationary noise characteristics, using a locally tractable model representation. Localized modeling enables a VM that can detect situations when the VM model is not adequate and needs to be improved, which is one of the main challenges in VM. Finally, uncertainty propagation with Monte Carlo simulation is pursued in order to propagate the predicted distributions of equipment signatures through the VM model to enable prediction of distributions of the quality variables using the readily available sensor readings streaming from the monitored manufacturing machine. The newly developed methods are applied to long-term production data coming from an industrial plasma-enhanced chemical vapor deposition (PECVD) tool operating in a major semiconductor manufacturing fab.Mechanical Engineerin
๋ถํ์ค์ฑ ํ์์ ์์คํ ์ ์ ์ง ๋ณด์ ์ต์ ํ ๋ฐ ์๋ช ์ฃผ๊ธฐ ์์ธก
ํ์๋
ผ๋ฌธ (๋ฐ์ฌ)-- ์์ธ๋ํ๊ต ๋ํ์ : ๊ณต๊ณผ๋ํ ํํ์๋ฌผ๊ณตํ๋ถ, 2019. 2. ์ด์๋ณด.The equipment and energy systems of most chemical plants have undergone repetitive physical and chemical changes and lead to equipment failure through aging process. Replacement and maintenance management at an appropriate point in time is an important issue in terms of safety, reliability and performance. However, it is difficult to find an optimal solution because there is a trade-off between maintenance cost and system performance. In many cases, operation companies follow expert opinions based on long-term industry experience or forced government policy. For cost-effective management, a quantitative state estimation method and management methodology of the target system is needed. Various monitoring technologies have been introduced from the field, and quantifiable methodologies have been introduced. This can be used to diagnose the current state and to predict the life span. It is useful for decision making of system management.
This thesis propose a methodology for lifetime prediction and management optimization in energy storage system and underground piping environment.
First part is about online state of health estimation algorithm for energy storage system. Lithium-ion batteries are widely used from portable electronics to auxiliary power supplies for vehicle and renewable power generation. In order for the battery to play a key role as an energy storage device, the state estimation, represented by state of charge and state of health, must be well established. Accurate rigorous dynamic models are essential for predicting the state-of health. There are various models from the first principle partial differential model to the equivalent circuit model for electrochemical phenomena of battery charge / discharge. It is important to simulate the battery dynamic behavior to estimate system state. However, there is a limitation on the calculation load, therefore an equivalent circuit model is widely used for state estimation. Author presents a state of health estimation algorithm for energy storage system. The proposed methodology is intended for state of health estimation under various operating conditions including changes in temperature, current and voltage. Using a recursive estimator, this method estimate the current battery state variable related to battery cell life. State of health estimation algorithm uses estimated capacity as a cell life-time indicator. Adaptive parameters are calibrated by a least sum square error estimation method based on nonlinear programming. The proposed state-of health estimation methodology is validated with cell experimental lithium ion battery pack data under typical operation schedules and demonstration site operating data. The presented results show that the proposed method is appropriate for state of health estimation under various conditions. The suitability of algorithm is demonstrated with on and off line monitoring of new and aged cells using cyclic degradation experiments. The results from diverse experimental data and data of demonstration sites show the appropriateness of the accuracy, robustness.
Second part is structural reliability model for quantification about underground pipeline risk. Since the long term usage and irregular inspection activities about detection of corrosion defect, catastrophic accidents have been increasing in underground pipelines. Underground pipeline network is a complex infrastructure system that has significant impact on the economic, environmental and social aspects of modern societies. Reliability based quantitative risk assessment model is useful for underground pipeline involving uncertainties. Firstly, main pipeline failure threats and failure modes are defined. External corrosion is time-dependent factor and equipment impact is time-independent factor. The limit state function for each failure cause is defined and the accident probability is calculated by Monte Carlo simulation. Simplified consequence model is used for quantification about expected failure cost. It is applied to an existing underground pipeline for several fluids in Ulsan industrial complex. This study would contribute to introduce quantitative results to prioritize pipeline management with relative risk comparisons
Third part is maintenance optimization about aged underground pipeline system. In order to detect and respond to faults causing major accidents, high resolution devices such as ILI(Inline inspection), Hydrostatic Testing, and External Corrosion Direct Assessment(ECDA) can be used. The proposed method demonstrates the structural adequacy of a pipeline by making an explicit estimate of its reliability and comparing it to a specified reliability target. Structural reliability analysis is obtaining wider acceptance as a basis for evaluating pipeline integrity and these methods are ideally suited to managing metal corrosion damage as identified risk reduction strategies. The essence of this approach is to combine deterministic failure models with maintenance data and the pipeline attributes, experimental corrosion growth rate database, and the uncertainties inherent in this information. The calculated failure probability suggests the basis for informed decisions on which defects to repair, when to repair them and when to re-inspect or replace them. This work could contribute to state estimation and control of the lithium ion battery for the energy storage system. Also, maintenance optimization model helps pipeline decision-maker determine which integrity action is better option based on total cost and risk.ํํ๊ณต์ฅ ๋ด ์ฅ์น ๋ฐ ์๋์ง ์์คํ
์ ๋ฐ๋ณต์ ์ธ ์ฌ์ฉ์ผ๋ก ๋ฌผ๋ฆฌํํ์ ๋ณํ๋ฅผ ๊ฒช์ผ๋ฉฐ ๋
ธํํ๋๊ณ ์ค๊ณ ์๋ช
์ ๊ฐ๊น์์ง๊ฒ ๋๋ค. ์ ์ ํ ์์ ์ ์ฅ๋น ๊ต์ฒด์ ๋ณด์ ๊ด๋ฆฌ๋ ์์ ๊ณผ ์ ๋ขฐ๋, ์ ์ฒด ์์คํ
์ฑ๋ฅ์ ์ข์ฐํ๋ ์ค์ํ ๋ฌธ์ ์ด๋ค. ๊ทธ๋ฌ๋, ๋ณด์ ๋น์ฉ๊ณผ ์์คํ
์ฑ๋ฅ์ ์ ์งํ๋ ๊ฒ ์ฌ์ด์๋ ํธ๋ ์ด๋ ์คํ๊ฐ ์กด์ฌํ๊ธฐ ๋๋ฌธ์ ์ด์ ๋ํ ์ต์ ์ ์ ์ฐพ๋ ๊ฒ์ ์ด๋ ค์ด ๋ฌธ์ ์ด๋ค. ๋ง์ ๊ฒฝ์ฐ์ ์ด์ํ์ฌ์์๋ ๊ฒฝํ์ ๊ธฐ๋ฐํ ์ ๋ฌธ๊ฐ ์๊ฒฌ์ ๋ฐ๋ฅด๊ฑฐ๋, ์ ๋ถ์ฐจ์์ ์์ ๊ด๋ฆฌ ์ ์ฑ
์ต์ ๊ธฐ์ค์ ๋ง์ถ์ด ์งํํ๋ค. ๋น์ฉํจ์จ์ ๊ด๋ฆฌ๋ฅผ ์ํ์ฌ ์ ๋์ ์ธ ์ํ ์ถ์ ๊ธฐ๋ฒ์ด๋ ์ ์ง๋ณด์ ๊ด๋ฆฌ ๋ฐฉ๋ฒ๋ก ์ ํ์ํ๋ค. ๋ง์ ๋ชจ๋ํฐ๋ง ๊ธฐ์ ์ด ๊ฐ๋ฐ๋์ด์ง๊ณ ์๊ณ ์ ์ฐจ ์ค์๊ฐ ์ธก์ ๋ฐฉ๋ฒ์ด๋ ์ผ์ ๊ธฐ์ ์ด ๋ฐ๋ฌ ํ๊ณ ์๋ค. ๊ทธ๋ฌ๋, ์ฌ์ ํ ์ง์ ์ธก์ ๋ฐ ๊ฒ์ฌ ์ด์ ์ฅ๋น์ ์๋ช
์์ธก๊ณผ ์์คํ
๊ด๋ฆฌ์ ๋ํ ์์ฌ๊ฒฐ์ ์ ๋์ธ ๋ฐฉ๋ฒ๋ก ์ ๋ถ์กฑํ ์ค์ ์ด๋ค.
๋ณธ ๋
ผ๋ฌธ์์๋ ๋ฆฌํฌ ์ด์จ ๋ฐฐํฐ๋ฆฌ์ ์๋ช
์์ธก ๋ฐฉ๋ฒ๋ก ๊ณผ ์งํ๋งค์ค๋ฐฐ๊ด์ ๊ด๋ฆฌ ์ต์ ํ ๋ฌธ์ ๋ฅผ ๋ค๋ฃฌ๋ค.
์ฒซ ์ฅ์์๋ ์๋์ง ์ ์ฅ์์คํ
์ด์ ํจํด์ ์ ํฉํ ๋ฐฐํฐ๋ฆฌ SOH ์ถ์ ๋ฐฉ๋ฒ๋ก ์ ๋ํ ๊ฒ์ด๋ค. ๋ฆฌํฌ ์ด์จ ๋ฐฐํฐ๋ฆฌ๋ ์ด๋๊ฐ๋ฅ ์ ์์ฅ์น์์๋ถํฐ ์๋์ฐจ ๋ฐ ์ ์ฌ์๋ฐ์ ๋ฑ์ ๋ณด์กฐ ์ ๋ ฅ ์ ์ฅ์ฅ์น๋ก์ ํ์ฉ์ด ์ด๋ฃจ์ด์ง๊ณ ์๋ค. ๋ฐฐํฐ๋ฆฌ๊ฐ ์ ์์ ์ธ ์ญํ ์ ํ๊ธฐ ์ํ์ฌ SOC์ SOH์ ์ ํํ ์ถ์ ์ด ์ค์ํ๋ค. ์ ํํ ๋์ ๋ชจ๋ธ์ SOH ์์ธก์ ์ํ์ฌ ํ์์ ์ด๋ค. BMS์๋ ๊ณ์ฐ ๋ก๋์ ํ๊ณ๊ฐ ์๊ธฐ ๋๋ฌธ์ ์ํ ์ถ์ ์ ์ํ์ฌ ๊ณ์ฐ ๋ถํ๊ฐ ๋น๊ต์ ์ ์ ๋ฑ๊ฐํ๋ก ๋ชจ๋ธ์ด ์ฌ์ฉ๋๋ค. ๋ณธ ๋
ผ๋ฌธ์์๋ SOH ์์ธก ์๊ณ ๋ฆฌ์ฆ์ ์ ์ํ๊ณ , ์
๋ฐ ์ค์ฆ ์ฌ์ดํธ ๋ฐ์ดํฐ๋ก ๊ฒ์ฆํ๋ค. ๋ฐ๋ณต ์์ธก๊ธฐ์ ๊ด์ธก๊ธฐ ๊ธฐ๋ฒ์ ํ์ฉํ์ฌ SOH๋ฅผ ์ถ์ ์ ํตํ์ฌ ํ์ฌ์ ๋ฐฐํฐ๋ฆฌ ์ํ๋ฅผ ์ ์ํ๋ค. SOH ์์ธก ์๊ณ ๋ฆฌ์ฆ์ ์ฉ๋์ ์ค์ ์ํ๋ณ์๋ก ํ์ฌ ์์ธก๋๋ค. ์ ์ ์๊ณ ๋ฆฌ์ฆ์์๋ SOH๋ฅผ ์ ํํ ์ถ์ ํ๊ธฐ ์ํ์ฌ ํ์ฅ์นผ๋งํํฐ๋ฅผ ๋์
ํ์ฌ ๋ฐฐํฐ๋ฆฌ ์ํ๋ณ์๋ค์ ์ ํํ ์์ธกํ๊ณ ์ด๋ฅผ ๊ธฐ๋ฐ์ผ๋ก SOH๋ฅผ ์ถ์ ํ๋ ์๊ณ ๋ฆฌ์ฆ์ ์ ์ํ๋ค.
๋๋ฒ์งธ ์ฅ์ ๊ตฌ์กฐ ์ ๋ขฐ๋ ๋ถ์์ ํตํ์ฌ ์งํ๋ฐฐ๊ด์ ์ ๋์ ์ํ์ฑ ๋ชจ๋ธ์ ์๋ฆฝํ๋ค. ๋ฐฐ๊ด์ ์ฅ๊ธฐ ์ฌ์ฉ๊ณผ ๋ถ๊ท์นํ ๊ฒ์ฌ/๋ณด์ ํ๋์ ๋ํ ๋ถํ์ค์ฑ์ ์งํ๋ฐฐ๊ด ์์ ์ฌ๊ณ ์ ์ํ์ฑ์ ์ฆ๋์ํค๋ ์์ธ์ด๋ค. ์ฐ์
๋จ์ง ๋ด์ ์งํ๋ฐฐ๊ด ๋คํธ์ํฌ๋ ๋ณต์กํ ์ธํ๋ผ๋ฅผ ๊ฐ์ถ๊ณ ์๊ธฐ ๋๋ฌธ์ ์ฌ๊ณ ๋ฐ์์ ๊ฒฝ์ ์ , ํ๊ฒฝ์ , ์ฌํ์ ์ผ๋ก ํฐ ์ํ์์๊ฐ ๋๋ค. ์ ๋ขฐ๋ ๊ธฐ๋ฐ ์ ๋์ ์ํ๋ ๋ชจ๋ธ์ ์งํ๋ฐฐ๊ด์ ํฐ ๋ถํ์ค์ฑ ์์๋ฅผ ๊ณ ๋ คํ๋๋ฐ ์ ์ฉํ ๋ฐฉ๋ฒ๋ก ์ด๋ค. ๋ฐฐ๊ด ์ฌ๊ณ ์ํ์์ธ๊ณผ ์ฌ๊ณ ๋ชจ๋๋ฅผ ์ ์ํ๊ณ , ๋ถ์๊ณผ ํ๊ณต์ฌ์ ์ด๋ฅด๋ ์๊ฐ ์์กด์ , ๋น์์กด์ ์์๋ฅผ ๊ณ ๋ คํ์ฌ ํ๊ณ์ํํจ์๋ฅผ ๊ฒฐ์ ํ๋ค. ๋ชฌํ
์นด๋ฅผ๋ก ์๋ฎฌ๋ ์ด์
์ ํตํ์ฌ ์ฐ๊ฐ ์ฌ๊ณ ํ๋ฅ ์ด ์ ์ถ๋๋ฉฐ ์ฌ๊ณ ์ํฅ๊ฑฐ๋ฆฌ ๋ฐ ๋์ถ๋ ๊ณ์ฐ ๋ชจ๋ธ๊ณผ ํฉํ์ฌ ์ ๋์ ์ํ์ฑ ๋ถ์์ ํ ์ ์๋ค. ๋ฐฐ๊ด์ ์กด์ฌํ๋ ๋ค์ํ ๋ฌผ์ง๋ค์ ๋ํ์ฌ ์ผ์ด์ค ์คํฐ๋๋ฅผ ์งํํ์ฌ ์ ๋ํ๋ ์ํ๋์ ๊ทผ๊ฑฐํ์ฌ ๋ฐฐ๊ด๊ด๋ฆฌ ์ฐ์ ์์๋ฅผ ์ ํ๋ ์์ฌ๊ฒฐ์ ์ ๋ฐ์ํ ์ ์๋ค.
์ธ๋ฒ์งธ ์ฅ์ ๋
ธํํ๋ ๋ฐฐ๊ด ์์คํ
์ ๊ด๋ฆฌ ์ต์ ํ์ ๋ํ ๋ด์ฉ์ด๋ค. ์ฌ๊ณ ์ ์ํ์ฑ์ ๋ฏธ์ฐ์ ๋ฐฉ์งํ๊ธฐ ์ํ์ฌ ๋ค์ํ ๊ฒ์ฌ, ๋ณด์ ๋ฐฉ๋ฒ๋ก ์ด ์ฌ์ฉ๋๋ค. ๊ทธ๋ฌ๋, ์ด์ ๋ํ ํจ๊ณผ๊ฐ ์ํ์ฑ๊ณผ ์ด๋ป๊ฒ ๊ด๋ จ๋์ด์ ๋ํ๋๋์ง ์๊ธฐ ์ด๋ ต๋ค. ๋๋ถ๋ถ ๊ฒฝํ์ ์ผ๋ก ํน์ ์ ๋์ ์ธ ๋ฐฉ์์ ํตํ์ฌ ๋ณด์์ ์ธ ์์ ๊ด๋ฆฌ๋ฅผ ์งํํ๋ ํ๊ณ์ฑ์ด ์๋ค. ์ ์๋ ๋ฐฉ๋ฒ๋ก ์ ํ ๋๋ก ํ์ฌ ์์ ๊ด๋ฆฌ ๋ฐฉ๋ฒ์ ๋ํ ์ค์ ์ ์ธ ๋ถ์ ๊ด๋ฆฌ์ ์ํฅ ์ ๋๋ฅผ ์ ๋ํ ํ๋ค. ์ ๋ขฐ๋ ๋ชฉํ์ ์ ์ ๋์ด์ง ์์ฐ ๋ฑ์ ์ ํ์กฐ๊ฑด์ผ๋ก ํ๋ ์ต์ ํ๋ฅผ ์ค์ํ์ฌ ์ต์ ์ ๊ฒ์ฌ ์ฃผ๊ธฐ, ์ต์ ์ ๊ฒ์ฌ ๋ฐฉ๋ฒ๋ก ์ ํ์ธํ๋ค.
์ ์ฐ๊ตฌ๋ฅผ ํ ๋๋ก ๊ฐ์ ๋ ๋ฆฌํฌ์ด์จ ๋ฐฐํฐ๋ฆฌ์ ์จ๋ผ์ธ ์ํ์ถ์ ์๊ณ ๋ฆฌ์ฆ ์ ์ํ๊ณ ์ํ๋ ํ์ฐ ๋น์ฉ์ ๊ฒฐํฉํ ๊ตฌ์กฐ ์ ๋ขฐ๋ ๋ชจ๋ธ๋ก ์งํ๋ฐฐ๊ด ๊ด๋ฆฌ ์ต์ ํ ๋ฐฉ๋ฒ๋ก ์ ์ ์ํ๋ค.Abstract i
Contents vi
List of Figures ix
List of Tables xii
CHAPTER 1. Introduction 14
1.1. Research motivation 14
1.2. Research objectives 19
1.3. Outline of the thesis 20
CHAPTER 2. Lithium ion battery modeling and state of health Estimation 21
2.1. Background 21
2.2. Literature Review 22
2.2.1. Battery model 23
2.2.2. Qualitative comparative review of state of health estimation algorithm 29
2.3. Previous estimation algorithm 32
2.3.1. Nonlinear State estimation method 32
2.3.2. Sliding mode observer 35
2.3.3. Proposed Algorithm 37
2.3.4. Uncertainty Factors for SOH estimation in ESS 42
2.4. Data acquisition 44
2.4.1. Lithium ion battery specification 45
2.4.2. ESS Experimental setup 47
2.4.3. Sensitivity Analysis for Model Parameter 54
2.5. Result and Discussion 59
2.5.1. Estimation results of battery model 59
2.5.2. Estimation results of proposed method 63
2.6. Conclusion 68
CHAPTER 3. Reliability estimation modeling for quantitative risk assessment about underground pipeline 69
3.1. Introduction 69
3.2. Uncertainties in underground pipeline system 72
3.3. Probabilistic based Quantitative Risk Assessment Model 73
3.3.1. Structural Reliability Assessment 73
3.3.2. Failure mode 75
3.3.3. Limit state function and variables 79
3.3.4. Reliability Target 86
3.3.5. Failure frequency modeling 90
3.3.6. Consequence modeling 95
3.3.7. Simulation method 101
3.4. Case study 103
3.4.1. Statistical review of Industrial complex underground pipeline 103
3.5. Result and discussion 107
3.5.1. Estimation result of failure probability 107
3.5.1. Estimation result validation 118
CHAPTER 4. Maintenance optimization methodology for cost effective underground pipeline management 120
4.1. Introduction 120
4.2. Problem Definition 124
4.3. Maintenance scenario analysis modeling 126
4.3.1. Methodology description 128
4.3.2. Cost modeling 129
4.3.3. Maintenance mitigation model 132
4.4. Case study 136
4.5. Results 138
4.5.1. Result of optimal re-inspection period 138
4.5.2. Result of optimal maintenance actions 144
CHAPTER 5. Concluding Remarks 145
References 147Docto
Condition Monitoring of Wind Turbines Using Intelligent Machine Learning Techniques
Wind Turbine condition monitoring can detect anomalies in turbine performance which have the potential to result in unexpected failure and financial loss. This study examines common Supervisory Control And Data Acquisition (SCADA) data over a period of 20 months for 21 pitch regulated 2.3 MW turbines and is presented in three manuscripts. First, power curve monitoring is targeted applying various types of Artificial Neural Networks to increase modeling accuracy. It is shown how the proposed method can significantly improve network reliability compared with existing models. Then, an advance technique is utilized to create a smoother dataset for network training followed by establishing dynamic ANFIS network. At this stage, designed network aims to predict power generation in future hours. Finally, a recursive principal component analysis is performed to extract significant features to be used as input parameters of the network. A novel fusion technique is then employed to build an advanced model to make predictions of turbines performance with favorably low errors
Failure Prognosis of Wind Turbine Components
Wind energy is playing an increasingly significant role in the World\u27s energy supply mix. In North America, many utility-scale wind turbines are approaching, or are beyond the half-way point of their originally anticipated lifespan. Accurate estimation of the times to failure of major turbine components can provide wind farm owners insight into how to optimize the life and value of their farm assets. This dissertation deals with fault detection and failure prognosis of critical wind turbine sub-assemblies, including generators, blades, and bearings based on data-driven approaches. The main aim of the data-driven methods is to utilize measurement data from the system and forecast the Remaining Useful Life (RUL) of faulty components accurately and efficiently. The main contributions of this dissertation are in the application of ALTA lifetime analysis to help illustrate a possible relationship between varying loads and generators reliability, a wavelet-based Probability Density Function (PDF) to effectively detecting incipient wind turbine blade failure, an adaptive Bayesian algorithm for modeling the uncertainty inherent in the bearings RUL prediction horizon, and a Hidden Markov Model (HMM) for characterizing the bearing damage progression based on varying operating states to mimic a real condition in which wind turbines operate and to recognize that the damage progression is a function of the stress applied to each component using data from historical failures across three different Canadian wind farms
A two-layer control architecture for operational management and hydroelectricity production maximization in inland waterways using model predictive control
This work presents the design of a combined control and state estimation approach to simultaneously maintain optimal water levels and maximize hydroelectricity generation in inland waterways using gates and ON/OFF pumps. The latter objective can be achieved by installing turbines within canal locks, which harness the energy generated during lock filling and draining operations. Hence, the two objectives are antagonistic in nature, as energy generation maximization results from optimizing the number of lock operations, which in turn causes unbalanced upstream and downstream water levels. To overcome this problem, a two-layer control architecture is proposed. The upper layer receives external information regarding the current tidal period, and determines control actions that maintain optimal navigation conditions and maximize energy production using model predictive control (MPC) and moving horizon estimation (MHE). This information is provided to the lower layer, in which a scheduling problem is solved to determine the activation instants of the pumps that minimize the error with respect to the optimal pumping references. The strategy is applied to a realistic case study, using a section of the inland waterways in northern France, which allows to showcase its efficacy.Peer ReviewedPostprint (author's final draft
Real options and investment under uncertainty: What do we know?
No abstract available for this paper.
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