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The failure progression of wind turbine bearings comprises of multiple degraded health states due to applied load by varying operating conditions (VOC). Therefore, determining the VOC impact on the failure dynamics severity is an essential task for bearing failure prognostics. This article introduces a hybrid prognosis method using real-time supervisory control and data acquisition (SCADA) and vibration signals to predict remaining useful life (RUL) for wind turbine bearings. The SCADA data are utilized to define the role of environmental conditions such as wind speed and ambient temperature in bearing failure dynamics. Afterward, for each environmental condition, failure dynamics are identified by the vibration signal. Finally, RUL of the faulty bearings is forecast via an adaptive Bayesian algorithm using the failure dynamics, conditional to the VOC. The efficacy of the method is validated using experimental data, and test results indicate a higher RUL accuracy compared to the Bayesian algorithm.

As wind energy is becoming a significant utility source, minimizing the operation and maintenance (O&M) expenses has raised a crucial issue to make wind energy competitive to fossil fuels. Wind turbines (WTs) are subject to unexpected failures due to operational and environmental conditions, aging, and so on. An accurate estimation of time to failures assures reliable power production and lower maintenance costs. In recent years, a notable amount of research has been undertaken to propose prognosis techniques that can be employed to forecast the remaining useful life (RUL) of wind farm assets. This article provides a recent literature review on modeling developments for the RUL prediction of critical WT components, including physics-based, artificial intelligence (AI)-based, stochastic-based, and hybrid prognostics. In particular, the pros and cons of the prognosis models are investigated to assist researchers in selecting proper models for certain applications of WT RUL forecast. Our comprehensive review has revealed that hybrid methods are now the leading and most accurate tools for WT failure predictions over individual hybrid components. Strong candidates for future research include the consideration of variable operating environments, component interaction, physics-based prognostics, and the Bayesian application in the development of WT prognosis methods.

A 30-year financial model and business case is presented for implementing a microgrid at an Ontario school to meet their energy needs and net-zero carbon emissions. The project considers several fa...

In North America, many utility-scale turbines are approaching the half-way point of their anticipated initial lifespan. Accurate remaining useful life (RUL) estimation can provide wind farm owners insight into how to optimize the life and value of their farm assets. An improved understanding of the RUL of turbine components is particularly essential as many owners consider retiring, life-extending, or repowering their farms. In this article, an integrated prognosis method based on signal processing and an adaptive Bayesian algorithm is proposed to predict the RUL of various faulty bearings in wind turbines. The signal processing leverages feature extraction, feature selection, and signal denoising to detect the dynamics of various failures. Then, RUL of the faulty bearings is forecast via the adaptive Bayesian algorithm using the extracted features. Finally, a new fusion method based on an ordered weighted averaging (OWA) operator is applied to the RUL obtained from the features to improve accuracy. The efficacy of the method is evaluated using data from historical failures across three different Canadian wind farms. Experimental test results indicate that the OWA operator delivers a higher accuracy for RUL prediction compared to the other feature-based methods and Choquet integral fusion technique.

Gear faults contribute to a significant portion of failures in wind turbine system. As such, condition monitoring and fault detection of these components assist in maintenance scheduling; hence, preventing catastrophic failures of the gearbox. This paper introduces a new hybrid fault detection approach to detect gear faults in wind turbines. to accomplish this task, vibration signals are collected and used to extract various time-domain features. Next, a Dynamic Principle Component Analysis (DPCA) is adaptively employed to identify failure dynamics by reducing the time-domain feature dimension. Following that, a Support Vector Machine (SVM) is implemented to detect and isolate gear faults. Experimental test studies with ten-year historical data of three wind farms in Canada are conducted. Test results indicate that the proposed hybrid approach performs superior compared to DPCA using Multilayer Perceptron (MLP) Neural Networks (NNs).

The energy sector is fast-evolving. Carbon policies, distributed energy systems, net-zero construction, electric vehicles, and non-utility procurement are some examples that have the potential to impact conventional energy systems. There is a need to further the understanding of how systems will respond to simultaneous and extreme increases in these energy system modifiers to prepare energy stakeholders for the significant changes that are likely to occur. This paper provides an overview of the impacts that renewable energy and disruptive energy technologies can have on traditional operation and management of electricity grids. Impacts such as stranded assets, price volatility, demand curve changes, and GHG reductions, of both probable and improbable levels of change in energy system variables will be presented. Furthermore, this research will begin to consider how simultaneous changes can impact energy system dynamics and how systems can be designed to handle, and even benefit from disruptive system changes. This research will serve to increase awareness of the numerous potential variables that will influence energy systems and assist in system planning to increase the likelihood of success for energy sector stakeholders.

Air accumulator is a critical component in Underwater Compressed Air Energy Storage systems and Buoyancy Energy Storage systems. In order to bridge the gap between the CFD simulations and experiment verification of flow around air accumulator, in this study, the force characteristics and flow structures around a balloon-shaped air accumulator bluff body model have been investigated using a computational-experimental dual prong method for the first time. The three-dimensional Unsteady Reynolds Averaged Navier-Stokes (URANS) closure is achieved through the k-ω SST turbulence model in FLUENT. The force characteristics and flow structures are measured in a low-speed closed-loop wind tunnel to verify the simulation results. The free end and shape effects are investigated by comparing the flow structure of three different but interconnected bluff bodies at the equivalent Reynolds number of 7.0 × 104. The results show that the k-ω SST turbulence model can correctly predict the time-averaged force characteristics of the balloon-shaped bluff body and flow structures in the wake. Besides, the distinct vortex structures are induced due to the free end and tapered shape effects.

With the rapid development of marine renewable energy technologies, the demand to mitigate the fluctuation of variable generators with energy storage technologies continues to increase. Offshore compressed air energy storage (OCAES) is a novel flexible-scale energy storage technology that is suitable for marine renewable energy storage in coastal cities, islands, offshore platforms, and offshore renewable energy farms. For deep-water applications, a marine riser is necessary for connecting floating platforms and subsea systems. Thus, the response characteristics of marine risers are of great importance for the stability and safety of the entire OCAES system. In this study, numerical models of two kinds of flexible risers, namely, catenary riser and lazy wave riser, are established in OrcaFlex software. The static and dynamic characteristics of the catenary and the lazy wave risers are analyzed under different environment conditions and internal pressure levels. A sensitivity analysis of the main parameters affecting the lazy wave riser is also conducted. Results show that the structure of the lazy wave riser is more complex than the catenary riser; nevertheless, the former presents better response performance.

As utility scale wind farms age, maintenance and contingency planning become increasingly important. Decisions about when and how to repair or replace major turbine components can critically influence profitability. Condition monitoring and prognostic reliability modelling are sometimes used to support these decision-making processes. These often resource intensive, sophisticated techniques are frequently administered by third parties and can be black boxes to wind farm stakeholders. Early experience from the YR21 Investment Decision Support Program has highlighted the importance of broad engagement across wind farm teams in maintenance and planning discussions. The utilization of geographic information systems to illustrate data trends across wind farms proved to be a valuable tool in fostering fundamental understanding of an operation’s signature performance characteristics. This graphical representation of the farm provides a useful visualization of the operation’s best and worst performers in terms of power produced, wind speeds experienced, total revolutions, or highest gear box temperature. These transparent representations of the data represent valuable starting points for discussion of performance or potential maintenance issues across farms. In some cases, it can reveal unexpected trends that may raise bigger questions about how the farm is operating in general. Finally, these simple figures can serve as complementary inputs to larger, more complex data-driven decision systems. Geographic information system plots are presented for three wind farms to demonstrate the potential utility in simple, transparent, and accessible data visualization.

In North America, many utility-scale 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 optimise the life and value of their farm assets. In this study, data records from a wind farm have been used to estimate the reliability of wind turbine (WT) generators. For this study, non-parametric life data analysis, Weibull Standard Folio life data analysis, and ALTA Standard Folio life data analysis have been used to predict the reliability of the generators. The naive prediction interval procedure also has been used here to provide an approximate range for the remaining life of each generator. This study provides some insight into how reliable a subset of WT generators is and the lifetime distribution of individual generators. These outcomes may be leveraged further by the research community for companion applications like prognostic maintenance and investment decision support systems. This study also begins to investigate how electrical loads may influence turbine generator reliability. The work also illustrates a valuable example of how to estimate component remaining useful life based on truncated/limited data records.

A data driven exergy analysis has been conducted for the first known grid connected Underwater Compressed Air Energy Storage facility, located in Toronto, Canada. Further to examining the plant through conventional exergy analysis, results were enhanced by splitting exergy destruction rates into avoidable and unavoidable, as well as endogenous and exogenous parts via advanced exergy analysis. The conventional exergy analysis showed that under real operational conditions, the exergy destruction ratio was 47.1%, while under the theoretical unavoidable operational conditions it could be reduced to 15.9%. The overall outcome of the conventional exergy analysis was confirmed by the advanced exergy analysis, the details, however, were quite different. The results of advanced exergy analysis assigned the improvement priority to heat exchanger 4, followed by the turbine and the stage 3 compressor. Conversely, the conventional exergy analysis indicated that the total exergy destruction of the turbine was higher than that for heat exchanger 3. The advanced exergy analysis also revealed that 76.4% of the exergy destruction was avoidable, highlighting the significant potential of the system for performance improvement.

There is growing interest in multi-purpose offshore floating platforms that: harvest energy from the sun, wind, water, and waves, desalinize water, host agriculture and aquaculture, and house residents. While there are some basic commonalities with well established, oil and gas platforms, lighter variants are functionally different with little wind research coverage. Here, we investigate a floating, multi-purpose, light duty platform under 1:150 scaled straight atmospheric boundary layer wind (ABL), tornado like vortices (TLV), and downburst (DB) conditions. The experiments examined the movement of a 1:150 geometrically scaled platform with six degrees of freedom and two mooring Configurations. Four Configurations are studied, (1) Loosely moored platform, (2) Tightly moored platform, (3) Platform with ballast, and (4) Platform with ballast and weight on the deck. DB winds produced the greatest movement, followed by the TLV winds. Little movement was seen under the ABL winds. Loosely moored platforms moved more than tightly moored.

In this study, energy, exergy, and environmental (3E) assessments have been conducted on a proposed combined-cycle power plant (CCPP) with three pressure levels of the HRSG and reheating process. 3E design approaches cross-link mechano-electric and environmental objectives. Herewith, the suggested combined-cycle is formed by a gas unit, condenser, steam turbines, triple-pressure heat recovery steam generator (HRSG) and also utilizes reheat facilities and auxiliary components. It is observed that more than 56% of total exergy destruction occurs in the combustor, followed by HRSG (15.29%), steam turbines (roughly 15.02%), gas turbine (8.93%), air compressor (1.79%), and condenser (0.66%). A parametric study is also presented that examines the sensitivity of performance indicators to various environmental states, steam pressures, pinch points, and steam mass flow rates. Moreover, it is presented that the implementation of Siemens SGT-100-1S over other GT configurations can considerably reduce deficiency of the overall cycle. The effects of each contaminant mass flowrate (NOx, CO, UHC, and CO2) and adiabatic flame temperature (AFT) are also studied when the gas unit operates under partial power and incomplete combustion conditions. In conclusion, a number of potential causes of irreversibilities and corrective optimization guidance are offered for each main equipment of the CCPP.

This work deals with the efficiency of cavern-based compressed air energy storage (CAES) facilities. Due to the complex interactions of the components of cavern-based CAES plants, a small deviation from designed for operating conditions can significantly alter the efficiency of the system. In this work, the sensitivity of individual system components to off-design working conditions are investigated, and components with the highest effect on both the exergy efficiency and the system efficiency are identified. The interaction between system components is also studied to identify the weak points of the system. The results show that the overall system efficiency is most sensitive to deviations in approach temperature in heat exchangers, energy leakage (as heat or compressed air) while the system is in a standby state, and leakage from compressors. For instance, a change of 10 °C in the approach temperature in the heat exchangers can lower system efficiency by 3.5%. System standby for 18 h with an air leakage rate of 4% per day from the cavern drops system efficiency by 2.1%. Additionally, a leakage rate of only 0.06% in the compressors can lower the system efficiency by 1.7%. This work also reveals that the combined effect of having multiple system components working at off-design conditions is more than the linear sum of their individual effects because of the exogenous influence these components have on peripheral components. The results from this study can help to improve the efficiency of CAES facilities.

Corporate power purchase agreements (CPPA)s made up the majority of new wind energy PPAs signed in the U.S. in the last several years. Companies such as Google and Amazon have realized the environmental and economic benefits of partnering with renewable energy (RE) developers, directly, virtually, or through their utility, to bring new projects into the markets in which they operate. CPPAs allow companies to meet sustainability goals, reduce carbon costs, and achieve stable energy prices. What about the rest of North America? As carbon pricing comes into effect and technology costs decrease, RE CPPAs can efficiently lower environmental costs while addressing climate change goals. Some North American wind farm owners are approaching the end of their original utility PPA. A new CPPA to replace an expiring PPA enables the maximization of profit over the lifetime of the assets. There are challenges associated with these agreements such as term length, complex and poly-aggregated contracts, and policy barriers. This works focuses on delineating the barriers and potential benefits of CPPAs in North American markets to inform prospective stakeholders and provide insights for policy development in this space. The methodology consists of (1) jurisdictional scans to uncover lessons learned elsewhere and (2) gathering stakeholders through workshops to gather insights and educate developers, corporations, utilities, and policy makers. The workshops will also identify available projects, interested corporations, and potential collaborations. The main findings from this work demonstrate that developers and corporations are interested in pursuing CPPAs, with additional revenue (developers) and sustainability goals (corporations) being the main drivers. While interest is high, several barriers were identified including lack of appropriate market for implementation, policy uncertainty, complexity of negotiations and contract lengths. The workshops served as a critical first step outlining the barriers and towards informing policy development to enable CPPAs.

The present investigation deals with the flow behind a torus, placed normal to the flow direction in three different aspect ratios (defined as the main diameter to the cross-sectional diameter of the torus) of 2, 3 and 5 at a constant Reynolds number of 9000. The Large Eddy Simulations (LES) are carried out to model the three-dimensional flow field around the torus. Wake structure, turbulence properties and force characteristics are investigated. Three shedding frequencies are detected at the Reynolds number studied. The highest frequency is attributed to the small-scale instability of the separating shear layer, the second one is a vortex shedding frequency that increases with aspect ratio, and the lowest one is due to the pulsation of the inner shear layer that can be observable for aspect ratios of 2 and 3. The torus has a blockage effect on the flow which is dependent on the center hole size. For the higher aspect ratio, the flow structure develops quickly, the wake flow is influenced by both inner and outer shear layers, thus forming the regular vortex ring patterns. For the lower aspect ratios, the flow pattern is mainly governed by the interaction of the outer shear layers. The results align well with documented experiments in the literature.