David M. Greenwood

Bio: David M. Greenwood is an academic researcher from Newcastle University. The author has contributed to research in topics: Energy storage & Wind power. The author has an hindex of 12, co-authored 45 publications receiving 611 citations. Previous affiliations of David M. Greenwood include Durham University.

Study of weather and location effects on wind turbine failure rates

Abstract: Understanding the availability of wind turbines (WT) is vital to maximize WT energy production and minimize the capital payback period. Previous work on this subject concentrated on reliability and the location of WT failure modes rather than root causes. This paper concentrates on the influence of weather and WT location on failure rate and downtime, to try to understand root causes and the consequences of failure. The paper goes further than a previous study, which used Windstats data from the whole of Denmark, by considering a limited population of identical WTs at three locations on the German Nordzee, Ostzee and in western Germany, using data from WMEP and local weather stations. This new study focuses more precisely than the previous study by using more reliable data. The data were analysed to find the WT failures and weather conditions and then cross-correlate them. To confirm their representativeness, the reliability characteristics of these smaller WT populations followed the average trends of the overall WMEP survey. However, clear differences were observed in the failure behaviour of the WTs at the three locations. Annual periodicity was seen in the weather data, as expected, but not in individual WT population failure data. However, clear cross-correlations can be seen between WT failures and weather data, in particular wind speed, maximum temperature and humidity. These cross-correlations were more convincing than those found in the earlier, larger Danish study, vindicating the more focused approach. It is also clear from the analysis that Operation & Maintenance also has an impact on WT failure rates. These factors will be important for the operation of offshore WTs with the work indicating how weather conditions may affect offshore WT failure rates. Copyright © 2012 John Wiley & Sons, Ltd.

A Comparison of Real-Time Thermal Rating Systems in the U.S. and the U.K.

Abstract: Real-time thermal rating is a smart-grid technology that allows the rating of electrical conductors to be increased based on local weather conditions. Overhead lines are conventionally given a conservative, constant seasonal rating based on seasonal and regional worst case scenarios rather than actual, say, local hourly weather predictions. This paper provides a report of two pioneering schemes-one in the U.S. and one in the U.K.-where real-time thermal ratings have been applied. Thereby, we demonstrate that observing the local weather conditions in real time leads to additional capacity and safer operation. Second, we critically compare both approaches and discuss their limitations. In doing so, we arrive at novel insights which will inform and improve future real-time thermal rating projects.

Deadbands, Droop, and Inertia Impact on Power System Frequency Distribution

Abstract: Power system inertia is falling as more energy is supplied by renewable generators, and there are concerns about the frequency controls required to guarantee satisfactory system performance. The majority of research into the negative effect of low inertia has focused on poor dynamic response following major disturbances, when the transient frequency dip can become unacceptable. However, another important practical concern—keeping average frequency deviations within acceptable limits—was mainly out of the sight of the research community. In this manuscript, we present a method for finding the frequency probability density function (PDF) for a given power system. We pass from an initial stochastic dynamic model to deterministic equations for the frequency PDF, which are analyzed to uncover key system parameters influencing frequency deviations. We show that system inertia has little effect on the frequency PDF, making virtual inertia services insufficient for keeping frequency close to nominal under ambient load fluctuations. We establish that aggregate system droop and deadband width are the only parameters that have major influence on the average frequency deviations, suggesting that energy storage might be an excellent solution for tight frequency regulation. We also show that changing the governor deadband width does not significantly affect generator movement.

Investigating the Impact of Real-Time Thermal Ratings on Power Network Reliability

Abstract: Real-Time Thermal Rating (RTTR) is a smart-grid technology that allows electrical conductors to operate at an enhanced rating based on local weather conditions. RTTR also provides thermal visibility of the network, making system operators aware if the actual rating drops below the static seasonal rating. This paper investigates how using these enhanced variable ratings affects power network reliability. A methodology has been developed to assess network reliability with variable conductor ratings. The effect of failures and uncertainties in the RTTR system are also considered, and the effect of the correlation between conductor ratings due to common weather conditions is built into the model. State sampling and sequential Monte Carlo simulations are used to estimate the reliability of the RBTS 6-bus test network. At low loading levels, the RTTR appears to reduce network reliability but actually illustrates occasions when the existing ratings are being unknowingly infringed. For higher loading, the network reliability is significantly improved by the use of RTTR, with reductions in loss of load expectation of up to 67%.

Stochastic Differential Equations

Abstract: We explore in this chapter questions of existence and uniqueness for solutions to stochastic differential equations and offer a study of their properties. This endeavor is really a study of diffusion processes. Loosely speaking, the term diffusion is attributed to a Markov process which has continuous sample paths and can be characterized in terms of its infinitesimal generator.

Lithium-Ion Battery Storage for the Grid—A Review of Stationary Battery Storage System Design Tailored for Applications in Modern Power Grids

Abstract: Battery energy storage systems have gained increasing interest for serving grid support in various application tasks. In particular, systems based on lithium-ion batteries have evolved rapidly with a wide range of cell technologies and system architectures available on the market. On the application side, different tasks for storage deployment demand distinct properties of the storage system. This review aims to serve as a guideline for best choice of battery technology, system design and operation for lithium-ion based storage systems to match a specific system application. Starting with an overview to lithium-ion battery technologies and their characteristics with respect to performance and aging, the storage system design is analyzed in detail based on an evaluation of real-world projects. Typical storage system applications are grouped and classified with respect to the challenges posed to the battery system. Publicly available modeling tools for technical and economic analysis are presented. A brief analysis of optimization approaches aims to point out challenges and potential solution techniques for system sizing, positioning and dispatch operation. For all areas reviewed herein, expected improvements and possible future developments are highlighted. In order to extract the full potential of stationary battery storage systems and to enable increased profitability of systems, future research should aim to a holistic system level approach combining not only performance tuning on a battery cell level and careful analysis of the application requirements, but also consider a proper selection of storage sub-components as well as an optimized system operation strategy.

A comprehensive review on uncertainty modeling techniques in power system studies

Abstract: As a direct consequence of power systems restructuring on one hand and unprecedented renewable energy utilization on the other, the uncertainties of power systems are getting more and more attention. This fact intensifies the difficulty of decision making in the power system context; therefore, the uncertainty analysis of the system performance seems necessary. Generally, uncertainties in any engineering system study can be represented probabilistically or possibilistically. When sufficient historical data of the system variables is not available, a probability density function (PDF) might not be defined, they must be represented in another manner i.e. using possibilistic theory. When some of the system uncertain variables are probabilistic and some are possibilistic, neither the conventional pure probabilistic nor pure possibilistic methods can be implemented. Hence, a combined solution is needed. This paper gives a complete review on uncertainty modeling approaches for power system studies making sense about the strengths and weakness of these methods. This work may be used in order to select the most appropriate method for each application.

Wind Turbine Gearbox Failure Identification With Deep Neural Networks

Abstract: The feasibility of monitoring the health of wind turbine (WT) gearboxes based on the lubricant pressure data in the supervisory control and data acquisition system is investigated in this paper. A deep neural network (DNN)-based framework is developed to monitor conditions of WT gearboxes and identify their impending failures. Six data-mining algorithms, the k- nearest neighbors, least absolute shrinkage and selection operator, ridge regression (Ridge), support vector machines, shallow neural network, as well as DNN, are applied to model the lubricant pressure. A comparative analysis of developed data-driven models is conducted and the DNN model is the most accurate. To prevent the overfitting of the DNN model, a dropout algorithm is applied into the DNN training process. Computational results show that the prediction error will shift before the occurrences of gearbox failures. An exponentially weighted moving average control chart is deployed to derive criteria for detecting the shifts. The effectiveness of the proposed monitoring approach is demonstrated by examining real cases from wind farms in China and benchmarked against the gearbox monitoring based on the oil temperature data.

Wind turbine reliability analysis

Abstract: Against the background of steadily increasing wind power generation worldwide, wind turbine manufacturers are continuing to develop a range of configurations with different combinations of pitch control, rotor speeds, gearboxes, generators and converters. This paper categorizes the main designs, focusing on their reliability by bringing together and comparing data from a selection of major studies in the literature. These are not particularly consistent but plotting failure rates against hours lost per failure reveals that problems with blades and gearboxes tend to lead to the greatest downtimes. New, larger wind turbines tend to fail more frequently than smaller ones so condition monitoring will become increasingly necessary if levels of reliability are to be improved.