Mathematical model of small wind turbines

Bolte, Ekkehard; Landwehr, Matthias

doi:10.1109/ever.2014.6844151

Cited by 5 publications

(3 citation statements)

References 4 publications

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“…Modeling of wind generation is non-trivial due to the non-linear response of wind turbines and quasi deterministic nature of wind production. From [12], [14], a model of wind energy production is derived where P w is the power contained in wind impinging on the surface of a micro wind turbine and is described as…”

Section: B Wind Energy Harvestingmentioning

confidence: 99%

Accurate Models of Energy Harvesting for Smart Environments

Jackson

Kartakis

McCann

2017

2017 IEEE International Conference on Smart Computing (SMARTCOMP)

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Over the last decade, the energy optimization of resource constrained sensor nodes constitutes a major research topic in smart environments. However, state of the art energy optimization algorithms make strong and unrealistic assumptions of energy models, both in simulations and during the operation of smart systems. For instance, simplistic energy models for energy harvesting leads to inaccurate representation and prediction of the true dynamics of energy. Consequently, systems for smart environments are unable to meet expected performance criteria. In this paper, we propose innovative models to overcome the drawbacks of simplistic energy representations in smart environments. We provide the insights of how to generate precise lightweight energy models. Using the physical properties of solar and flow energy harvesting as case studies, the trade-off between energy harvesting inference and real-time measurement of energy generation is explored. To evaluate our proposed energy models against the simplistic versions, we use real measured data from our environmental micro-climate monitoring deployment in an urban park and a 103% improvement is seen. Additionally, to define the trade-offs between inferred and measured energy generation, experiments are conducted utilizing solar and smart water testbeds.

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Section: B Wind Energy Harvestingmentioning

confidence: 99%

Accurate Models of Energy Harvesting for Smart Environments

Jackson

Kartakis

McCann

2017

2017 IEEE International Conference on Smart Computing (SMARTCOMP)

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“…For solar energy generation, the methodologies in [16], [17] are employed to transform incoming energy from the sun, solar zenith and temperature to calculate accurate energy generation metrics. Similarly, the methods in [17], [18] are utilised to derive accurate wind energy production from wind speed, direction and atmospheric pressure. Fig.…”

Section: A Data Sourcesmentioning

confidence: 99%

Long Term Sensing via Battery Health Adaptation

Jackson

Qin

McCann

2017

2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)

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Energy Neutral Operation (ENO) has created the ability to continuously operate wireless sensor networks in areas such as environmental monitoring, hazard detection and industrial IoT applications. Current ENO approaches utilise techniques such as sample rate control, adaptive duty cycling and data reduction methods to balance energy generation, storage and consumption. However, the state of the art approaches makes a strong and unrealistic assumption that battery capacity is fixed throughout the deployment time of an application. This results in scenarios where ENO systems over allocate sensing tasks, therefore as battery capacity degrades it causes the system to no longer be energy neutral and then fail unexpectedly. In this paper, we formulate the problem to maximise the quality-ofservice in terms of duty cycle and the battery capacity to extend the deployment lifetime of a sensing application. In addition, we develop a lightweight algorithm to solve the formulated problem. Moreover, we evaluate the proposed method using real sensor energy consumption data captured from micro-climate sensors deployed in Queen Elizabeth Olympic Park, London. Results show that a 307% extension of deployment lifetime can be achieved when compared to a traditional ENO solution without a reduction in the duty cycle of the sensor.

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“…The solar panels are assumed to be 5 parallel connected PV modules and modelled using a standard PV model [44]. The wind turbine is modelled as a locally manufactured Piggott Turbine [45] using the model in [46]. The pico-hydro turbine has a constant head of 3.5 m. The irradiance of the PV array starts at 500 W/m² and increases to 1000 W/m² after 10 seconds, simulating the panel moving out of shade.…”

Section: Solar Pv Interface Modificationsmentioning

confidence: 99%

Modeling and simulation of a pico-hydropower off-grid network

Williamson¹,

Griffo

Stark

et al. 2017

Modeling and Dynamic Behaviour of Hydropower Plants

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This chapter will cover the system arrangement, modelling and control of such a system, with implementation of simulated example and expansion of the concept to include solar PV and wind turbine sources. 1.2. System Overview The proposed pico-hydropower off-grid network is built up from a number of identical picohydropower generator units, with one or more installed at each turbine site dependent on the environmental conditions. As each site may have different environmental conditions, each unit may have different power available at the turbine, which may vary over time. The generator is interfaced onto the AC network through a power electronic AC-DC-AC interface, which allows a distributed grid, shown in Fig. 2. There is no communication between each unit, increasing the system reliability, so each unit must use local measurements to control the generator unit. This topology provides flexibility and redundancy in the network, making it suitable for a scalable and expandable system.

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Mathematical model of small wind turbines

Cited by 5 publications

References 4 publications

Accurate Models of Energy Harvesting for Smart Environments

Accurate Models of Energy Harvesting for Smart Environments

Long Term Sensing via Battery Health Adaptation

Modeling and simulation of a pico-hydropower off-grid network

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