Prediction of IGBT junction temperature using improved cuckoo search-based extreme learning machine

Liu, Boying; Chen, Guolong; Lin, Hsiung-Cheng; Zhang, Weipeng; Liu, Jiaqi

doi:10.1016/j.microrel.2021.114267

Cited by 18 publications

(13 citation statements)

References 45 publications

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“…Over the past decade, various ELM studies have been performed to improve generalization performance [49], [50], [47]. The ELM architecture has a very similar topological structure to other popular neural networks such as Back Propagation Neural Network and radial basis neural network.…”

Section: Meta-extreme Learning Machine Methodsmentioning

confidence: 99%

Offshore wind speed short-term forecasting based on a hybrid method: Swarm decomposition and meta-extreme learning machine

Dokur

Erdoğan

Salari

et al. 2022

Energy

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Section: Meta-extreme Learning Machine Methodsmentioning

confidence: 99%

Offshore wind speed short-term forecasting based on a hybrid method: Swarm decomposition and meta-extreme learning machine

Dokur

Erdoğan

Salari

et al. 2022

Energy

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“…In addition, the hidden nodes could be set up before the training samples are gained. There are two reasons behind the slow learning process of SLFN [27]. These are:…”

Section: Extreme Learning Machinementioning

confidence: 99%

An Extreme Learning Machine Based Adaptive VISMA for Stability Enhancement of Renewable Rich Power Systems

et al. 2022

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Maintaining power system stability in renewable-rich power systems can be a challenging task. Generally, the renewable-rich power systems suffer from low and no inertia due to the integration of power electronics devices in renewable-based power plants. Power system oscillatory stability can also be affected due to the low and no inertia. To overcome this problem, additional devices that can emulate inertia without adding synchronous machines can be used. These devices are referred to as virtual synchronous machines (VISMA). In this paper, the enhancement of oscillatory stability of a realistic representative power system using VISMA is proposed. A battery energy storage system (BESS) is used as the VISMA by adding an additional controller to emulate the inertia. The VISMA is designed by using Fruit Fly Optimization. Moreover, to handle the uncertainty of renewable-based power plants, the VISMA parameters are designed to be adaptive using the extreme learning machine method. Java Indonesian Power Grid has been used as the test system to investigate the efficacy of the proposed method against the conventional POD method and VISMA tuning using other methods. The simulation results show that the proposed method can enhance the oscillatory stability of the power system under various operating conditions.

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“…By substituting equation (10) with Eq. ( 24) and other parameter values, then, 𝐶 𝑚𝑖𝑛 = 0.7 (1.544)(0.01)10,000 (25) 𝐶 𝑚𝑖𝑛 = 4.54 𝑥 10 −3 𝐹 (26)…”

Section: Non-isolated Bidirectional DC Converter Modellingmentioning

confidence: 99%

“…In addition, the hidden nodes could be set up before the training samples are gained. There are two reasons why SLFN has low speed learning process [25]:…”

Section: Extreme Learning Machinementioning

confidence: 99%

Adaptive Controller of Bidirectional DC-DC Converter Based on Extreme Learning Machine for Electric Vehicle

2022

IJIES

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This paper proposed a novel adaptive controller of a non-isolated bidirectional coupled inductor DC converter based on an extreme learning machine (ELM) for electric vehicles. MATLAB and SIMULINK are used as the software platform to simulate the proposed method. Different operating conditions are considered to show the efficacy of the extreme learning machine. From the simulation results, it is found that by designing the controller based on an extreme learning machine the controller can automatically adjust the value depending on the operating conditions (motor moving forward, backward, and regenerative braking). In addition, the performance of the motor with DC converter based on ELM is compared with DC converter based on grey wolf optimization and based on conventional PI controller. This is indicated by the overshoot and the settling time is much better when using controller based on an extreme learning machine (overshoot 221.6 and settling time 3.8 second).

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Prediction of IGBT junction temperature using improved cuckoo search-based extreme learning machine

Cited by 18 publications

References 45 publications

Offshore wind speed short-term forecasting based on a hybrid method: Swarm decomposition and meta-extreme learning machine

Offshore wind speed short-term forecasting based on a hybrid method: Swarm decomposition and meta-extreme learning machine

An Extreme Learning Machine Based Adaptive VISMA for Stability Enhancement of Renewable Rich Power Systems

Adaptive Controller of Bidirectional DC-DC Converter Based on Extreme Learning Machine for Electric Vehicle

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