Renewable and energy storage resources for enhancing transient stability margins: A PDE-based nonlinear control strategy

Obuz, Serhat; Ayar, Muharrem; Trevizan, Rodrigo D.; Ruben, Cody; Bretãs, Arturo S.

doi:10.1016/j.ijepes.2019.105510

Cited by 18 publications

(5 citation statements)

References 18 publications

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“…Offset center SMA heat engine is another example of an SMA crank heat engine. Ridgway M Bank first invented this type of engine in 1973 at Lawrence Berkeley Laboratory, University of California [3]. In this design, the engine has a flywheel with 20 Nitinol SMA wire loops attached to the spoke of an offset wheel-hub.…”

Section: Crank Enginementioning

confidence: 99%

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A review on Nitinol shape memory alloy heat engines

Abubakar

Wang

2020

Smart Mater. Struct.

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Nitinol (NiTi) shape memory alloy (SMA) heat engine is a very promising candidate application of SMA since its invention, but yet to be put as commercial due to its low efficiency and performance. In this paper, a review of different types of NiTi SMA heat engine is presented. The review is done based on the following headings: conceptual design, constitutive driving model, the performance of the engine, and engine limitations. Factors like temperature, cooling rate, size of NiTi SMA element, and the stretch ratio of NiTi SMA spring were identified as determinants of final output power and efficiency of SMA heat engines. It is found that a crankshaft NiTi SMA heat engine produced the highest power output value of 4 watt, an unsynchronized pulley NiTi SMA heat engine with 11.3% of engine efficiency has the highest engine performance so far recorded. Some engine's drawbacks like an improper driving model, drag loss, backsliding were identified as the major problem affecting the engine performance, and which, if solved, will increase the overall performance and efficiency for future development toward its commercialization.

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Section: Crank Enginementioning

confidence: 99%

“…For this reason, many sources of energy are explored to achieve this goal. These energy sources range from the use of firewood, coal, petroleum, solar, nuclear, to mention just a few [1][2][3][4][5]. However, some of these energy sources, like fossil fuel [6], are hazardous due to carbon footprint [7] and are nonrenewable.…”

Section: Introductionmentioning

confidence: 99%

A review on Nitinol shape memory alloy heat engines

Abubakar

Wang

2020

Smart Mater. Struct.

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“…Due to the application of new energy technologies 1 , the construction and development of smart grids, and the widespread adoption of power electronic devices, the power system structure has changed dramatically, which makes the safe and stable operation of modern power system faces many challenges. Therefore, fast and accurate TSA method need to be explored urgently so that operators can take emergency control measures in time based on the assessment results 2 .…”

Section: Introductionmentioning

confidence: 99%

Power system transient stability assessment based on improved graph attention broad learning system

Ni,

Zhao

2024

Fifteenth International Conference on Signal Processing Systems (ICSPS 2023)

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A fast and accurate transient stability assessment method is essential for the safe and stable operation of power systems. Existing deep learning algorithms have achieved good results in transient stability assessment (TSA), but the training time of these models is extended as the number of network layers continues to increase. There-fore, in order to improve the accuracy of power system transient stability and the efficiency of operation, the improved graph attention broad learning system (IGAT-BLS) model is proposed in this paper, which combines broad learning system with graph attention network. This model leverages the deep feature extraction capability of graph attention networks to enhance its feature representation, and utilizes the flat structure of broad learning system (BLS) to reduce the complexity of the network model. Simulation experiment of the New England 39-bus benchmark system show that, compared with other TSA methods based on deep learning, the proposed method can make more accurate transient stability judgments with shorter training time.

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“…Energy demand is increasing every day, and the energy depends on is mostly non-renewable and depleting every day. These energy sources include the following: petroleum, firewood, solar (Abdelsalam et al 2020;Giostri et al 2020), biomass (Chao et al 2017;Qiao et al 2018;Zhao et al 2020), hydropower (Ahmad and Hossain 2020;Gil-gonzález, Montoya, and Garces 2020), nuclear, coal, wind (Cao et al 2020;Intessar Al-Iedani and Zoran 2020) and many more (Huang et al 2020;Obuz et al 2020;Poul et al 2020;Rahman and Eswaran 2020;Salehi, Mostafa, and Fasihfar 2019). These energy sources (Emeksiz and Demirci 2019) are dangerous to our environment due to harmful carbon footprint emissions (Urbanucci and Daniele 2019).…”

Section: Introductionmentioning

confidence: 99%

Thermal Energy Conversion Using Niti Shape Memory Alloy Material

Abubakar,

Nuhu

2024

Preprint

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In this paper, a NiTi SMA heat engine is proposed. It converts thermal energy from a source of temperature different to mechanical energy. The Assumed temperature difference is (70 oC). The temperature is obtainable when the engine is used in a geothermal source. But if the engine is used for ocean thermal energy, incorporated solar or heat pumped will be used. The engine behavior is simulated, and the model is compared to experimental data. The engine developed a maximum driving force of 17.3N, a torque of 0.9Nm, and an efficiency of 4.51%.

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Renewable and energy storage resources for enhancing transient stability margins: A PDE-based nonlinear control strategy

Cited by 18 publications

References 18 publications

A review on Nitinol shape memory alloy heat engines

A review on Nitinol shape memory alloy heat engines

Power system transient stability assessment based on improved graph attention broad learning system

Thermal Energy Conversion Using Niti Shape Memory Alloy Material

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