Tuning of Power System Stabilizer Using Cascade Forward Backpropagation

Aribowo, Widi; Muslim, Supari; Munoto,; Suprianto, Bambang; Buditjahjanto, I Gusti Putu Asto

doi:10.1109/icvee50212.2020.9243204

Cited by 6 publications

(6 citation statements)

References 11 publications

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“…Neural networks are machine learning devices inspirited by biological neural networks and capable of processing with mimic as the human brain [21]. Artificial neural networks (ANN) could construct model linear and nonlinear algorithms.…”

Section: Feed-forward Neural Networkmentioning

confidence: 99%

Improving neural network using a sine tree-seed algorithm for tuning motor DC

Aribowo

Suprianto

Joko

2021

IJPEDS

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A DC motor is applied to delicate speed and position in the industry. The stability and productivity of a system are keys for tuning of a DC motor speed. Stabilized speed is influenced by load sway and environmental factors. In this paper, a comparison study in diverse techniques to tune the speed of the DC motor with parameter uncertainties is showed. The research has discussed the application of the feed-forward neural network (FFNN) which is enhanced by a sine tree-seed algorithm (STSA). STSA is a hybrid method of the tree-seed algorithm (TSA) and Sine Cosine algorithm. The STSA method is aimed to improve TSA performance based on the sine cosine algorithm (SCA) method. A feed-forward neural network (FFNN) is popular and capable of nonlinear issues. The focus of the research is on the achievement speed of DC motor. In addition, the proposed method will be compared with proportional integral derivative (PID), FFNN, marine predator algorithm-feed-forward neural network (MPA-NN) and atom search algorithm-feed-forward neural network (ASO-NN). The performance of the speed from the proposed method has the best result. The settling time value of the proposed method is more stable than the PID method. The ITAE value of the STSA-NN method was 31.3% better than the PID method. Meanwhile, the ITSE value is 29.2% better than the PID method.

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Section: Feed-forward Neural Networkmentioning

confidence: 99%

Improving neural network using a sine tree-seed algorithm for tuning motor DC

Aribowo

Suprianto

Joko

2021

IJPEDS

Self Cite

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“…The network between the input and the hidden layer is trained. Hidden units are added and stored on the network [23]. The weighting between the hidden unit and the output can be adjusted.…”

Section: Cascade-forward Backpropaga-tion Neural Networkmentioning

confidence: 99%

An Improved Neural Network Based on Parasitism – Predation Algorithm for an Automatic Voltage Regulator

et al. 2021

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The parasitism – predation algorithm (PPA) is an optimization method that duplicates the interaction of mutualism between predators (cats), parasites (cuckoos), and hosts (crows). The study employs a combination of the PPA methods using the cascade-forward backpropagation neural network. This hybrid method employs an automatic voltage regulator (AVR) on a single machine system, with the performance measurement focusing on speed and the rotor angle. The performance of the proposed method is compared with the feed-forward backpropagation neural network (FFBNN), cascade-forward backpropagation neural network (CFBNN), Elman recurrent neural network (E-RNN), focused time-delay neural network (FTDNN), and distributed time-delay neural network (DTDNN). The results show that the proposed method exhibits the best speed and rotor angle performance. The PPA-CFBNN method has the ability to reduce the overshoot of the speed by 1.569% and the rotor angle by 0.724%.

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“…The artificial neural networks are machine learning systems inspired by the biological neural networks and able to perform processing as similar to the human brain [12], [13]. A linear and non-linear algorithm models, artificial neural networks (ANNs) can build.…”

Section: Neural Network Architecturementioning

confidence: 99%

Integration of artificial neural networks for multi-source energy management in a smart grid

Jarmouni

Mouhsen

Lamhammedi

et al. 2021

IJPEDS

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<span lang="EN-US">Among the most widespread renewable energy sources is solar energy; Solar panels offer a green, clean, and environmentally friendly source of energy. In the presence of several advantages of the use of photovoltaic systems, the random operation of the photovoltaic generator presents a great challenge, in the presence of a critical load. Among the most used solutions to overcome this problem is the combination of solar panels with generators or with the public grid or both. In this paper, an energy management strategy is proposed with a safety aspect by using artificial neural networks (ANNs), in order to ensure a continuous supply of electricity to consumers with a maximum solicitation of renewable energy.</span>

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Tuning of Power System Stabilizer Using Cascade Forward Backpropagation

Cited by 6 publications

References 11 publications

Improving neural network using a sine tree-seed algorithm for tuning motor DC

Improving neural network using a sine tree-seed algorithm for tuning motor DC

An Improved Neural Network Based on Parasitism – Predation Algorithm for an Automatic Voltage Regulator

Integration of artificial neural networks for multi-source energy management in a smart grid

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