Optimization of Feedforward Neural Networks Using an Improved Flower Pollination Algorithm for Short-Term Wind Speed Prediction

Ren, Yidi; Li, Hua; Lin, Hsiung-Cheng

doi:10.3390/en12214126

Cited by 11 publications

(8 citation statements)

References 36 publications

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“…e current research on intelligent algorithms for either diagnosis or prediction has been attracting more attention in past years. However, the feature extractor and classifier of neural networks may not suit the load change for maintaining a highly accurate outcome [5][6][7][8]. For this reason, a combination of different neural network methods attempted to achieve a highly possible solution in a variety of disciplines.…”

Section: Introductionmentioning

confidence: 99%

Development of Deep Convolutional Neural Network with Adaptive Batch Normalization Algorithm for Bearing Fault Diagnosis

Lin

2020

Shock and Vibration

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It is crucial to carry out the fault diagnosis of rotating machinery by extracting the features that contain fault information. Many previous works using a deep convolutional neural network (CNN) have achieved excellent performance in finding fault information from feature extraction of detected signals. They, however, may suffer from time-consuming and low versatility. In this paper, a CNN integrated with the adaptive batch normalization (ABN) algorithm (ABN-CNN) is developed to avoid high computing resource requirements of such complex networks. It uses a large-scale convolution kernel at the grassroots level and a multidimensional 3 × 1 small convolution nuclear. Therefore, a fast convergence and high recognition accuracy under noise and load variation environment can be achieved for bearing fault diagnosis. The performance results verify that the proposed model is superior to Support Vector Machine with Fast Fourier Transform (FFT-SVM) and Multilayer Perceptron with Fast Fourier Transform (FFT-MLP) models and Deep Neural Network with Fast Fourier Transform (FFT-DNN).

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Section: Introductionmentioning

confidence: 99%

Development of Deep Convolutional Neural Network with Adaptive Batch Normalization Algorithm for Bearing Fault Diagnosis

Lin

2020

Shock and Vibration

Self Cite

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“…However, according to the operation of the FPA, the solution to the optimization problem is reliant upon interaction with pollen individuals. This has a detrimental consequence of being conveniently stuck at a local minimum [41]. Secondly, due to the application of population diversity especially with multimodal problems, FPA is highly pruned to being stuck in local optimum [42]- [45].…”

Section: B Research Gap and Motivationmentioning

confidence: 99%

“…where , , and are the fundamental operators, lower limit, upper limit, and the order of the operator. There is no unified definition of the fractional-order derivative, but there have been three generally accepted definitions, including Caputo, Grunwald-Letnikov (GL) and Riemann-Liouville (RL) [41]. Each of the three definitions has its individual properties.…”

Section: ) Fractional-order Calculus Preliminariesmentioning

confidence: 99%

Application of a New Fusion of Flower Pollinated With Pathfinder Algorithm for AGC of Multi-Source Interconnected Power System

2021

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As the world's population grows and energy demand increases, it is necessary to increase the scale of the electrical system, which is more complicated. Consequently, adopting automatic generation control (AGC) scheme to meet the demand becomes inevitable. In this article, the fusion of flower pollinated algorithm (FPA) and pathfinder algorithm (PFA), named hereafter as ℎFPAPFA, is proposed to achieve maximum control efficiency by combining the exploitation of FPA with the exploration capacity of PFA. The proposed ℎFPAPFA is meant to regulate two unequal multi-area interconnected power system with different generating units such as thermal, hydro, wind power and diesel plants. The proposed control scheme aims to achieve this by using the new algorithm to optimize the fractional-order set-point weighted PID (FOSWPID) parameters under time domain-based fitness functions namely, integral time square error (ITSE) and integral time absolute error (ITAE) while simultaneously minimizing the power losses. Employing the same interconnected power systems, a comparative study with some recent approaches in renowned journals is conducted. The performance of the proposed method is observed under diverse load conditions scenarios. Moreover, three nonlinearities including boiler dynamics, the governor dead band (GDB) and generation rate constraints (GRC) are further integrated into the system from a pragmatic context. Finally, sensitivity tests involving various parameter changes and the introduction of random step load perturbations are carried out. From the results, the proposed approach outperformed other approaches under different load condition scenarios, incorporation of nonlinearities and random load perturbation, demonstrating the proposed technique's efficacy and reliability.INDEX TERMS automatic generation control (AGC), flower pollinated algorithm, pathfinder algorithm integral time absolute error, governor dead band (GDB), generation rate constraints (GRC).

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“…As a representative of artificial methods, neural network has been successfully applied to state diagnosis and prediction in various fields, and it has excellent robustness in diagnosis and prediction of uncertain information [17][18][19]. For instance, Paya et al [20] used wavelet transformation to conduct a multi-layer neural network processing on pre-processed data and successfully detected single-and multi-class faults of such rotary machinery as transmissions.…”

Section: Literature Reviewmentioning

confidence: 99%

Gyro Motor State Evaluation and Prediction Using the Extended Hidden Markov Model

Dong

Wang²,

Tseng

et al. 2020

Symmetry

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This study extracted the featured vectors in the same way from testing data and substituted these vectors into a trained hidden Markov model to get the log likelihood probability. The log likelihood probability was matched with the time–probability curve from where the gyro motor state evaluation and prediction were realized. A core component of gyroscopes is linked to the reliability of the inertia system to conduct gyro motor state evaluation and prediction. This study features the vectors’ extraction from full life cycle gyro motor data and completes the training model to feature the vectors according to the time sequence and extraction to full life cycle data undergoing hidden Markov model training. This proposed model applies to full life cycle gyro motor data for validation, compared with traditional hidden Markov model predictive methods and health condition-trained data. The results suggest precise evaluation and prediction and provide an important basis for gyro motor repair and replacement strategies.

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Optimization of Feedforward Neural Networks Using an Improved Flower Pollination Algorithm for Short-Term Wind Speed Prediction

Cited by 11 publications

References 36 publications

Development of Deep Convolutional Neural Network with Adaptive Batch Normalization Algorithm for Bearing Fault Diagnosis

Development of Deep Convolutional Neural Network with Adaptive Batch Normalization Algorithm for Bearing Fault Diagnosis

Application of a New Fusion of Flower Pollinated With Pathfinder Algorithm for AGC of Multi-Source Interconnected Power System

Gyro Motor State Evaluation and Prediction Using the Extended Hidden Markov Model

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