Adaptive discrete wavelet transform based technique for load frequency control

Otchere, Isaac Kofi; Ampofo, Desmond Okwabi; Frimpong, Emmanuel Asuming

doi:10.1109/powerafrica.2017.7991292

Cited by 6 publications

(3 citation statements)

References 9 publications

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“…The multi-resolution decomposition process is determined by wavelet transform concerning the image expansion onto a wavelet basis function set. In this, DWT (Otchere et al, 2017) has its own space-frequency localization property. The wavelet expansion of an image is considered as shown in equation ( 36).…”

Section: Subband Conversion By Adwtmentioning

confidence: 99%

A multi-objective opposition-based barnacles mating optimization for image super resolution using hyper-Spectral images

Bhasha

Reddy

2021

JEDT

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Purpose The problems of Super resolution are broadly discussed in diverse fields. Rather than the progression toward the super resolution models for real-time images, operating hyperspectral images still remains a challenging problem. Design/methodology/approach This paper aims to develop the enhanced image super-resolution model using “optimized Non-negative Structured Sparse Representation (NSSR), Adaptive Discrete Wavelet Transform (ADWT), and Optimized Deep Convolutional Neural Network”. Once after converting the HR images into LR images, the NSSR images are generated by the optimized NSSR. Then the ADWT is used for generating the subbands of both NSSR and HRSB images. The residual image with this information is obtained by the optimized Deep CNN. All the improvements on the algorithms are done by the Opposition-based Barnacles Mating Optimization (O-BMO), with the objective of attaining the multi-objective function concerning the “Peak Signal-to-Noise Ratio (PSNR), and Structural similarity (SSIM) index”. Extensive analysis on benchmark hyperspectral image datasets shows that the proposed model achieves superior performance over typical other existing super-resolution models. Findings From the analysis, the overall analysis of the suggested and the conventional super resolution models relies that the PSNR of the improved O-BMO-(NSSR+DWT+CNN) was 38.8% better than bicubic, 11% better than NSSR, 16.7% better than DWT+CNN, 1.3% better than NSSR+DWT+CNN, and 0.5% better than NSSR+FF-SHO-(DWT+CNN). Hence, it has been confirmed that the developed O-BMO-(NSSR+DWT+CNN) is performing well in converting LR images to HR images. Originality/value This paper adopts a latest optimization algorithm called O-BMO with optimized Non-negative Structured Sparse Representation (NSSR), Adaptive Discrete Wavelet Transform (ADWT) and Optimized Deep Convolutional Neural Network for developing the enhanced image super-resolution model. This is the first work that uses O-BMO-based Deep CNN for image super-resolution model enhancement.

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Section: Subband Conversion By Adwtmentioning

confidence: 99%

A multi-objective opposition-based barnacles mating optimization for image super resolution using hyper-Spectral images

Bhasha

Reddy

2021

JEDT

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“…In the extensive body of literature on Load Frequency Control, several methods have been proposed to effectively deal with the harsh power disturbances arising from the intermittencies of RES when integrated into the grid. These methods include state estimation techniques like Kalman filtering [1], Extended and Unscented Kalman Filter [16,17], data-driven modeling and system identification approaches [18], reinforcement learning based control [6,9,19,20], fuzzy logic control for rule-based adaptability [1,[21][22][23], and signal processing methods such as the wavelet transform [24]. Among these diverse methodologies, H-infinity (H∞) control stands out as a control theory approach that seeks to design controllers to minimize the worst-case effects of uncertainty and disturbances in a system [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Optimal Tuning of PID Controllers for LFC in Renewable Energy Source Integrated Power Systems Using an Improved PSO

Sekyere,

Effah,

Okyere

2024

J. Electron. Electric. Eng.

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The constant rise in energy demand and concerns about climate change have led to increased penetration of renewable energy sources (RES). Maintaining active power balance between generation and demand in power systems with significant penetration of these highly variable and intermittent renewable sources requires an efficient load frequency control (LFC) strategy. One such strategy that has gained the attention of researchers is optimal tuning of PID controllers of LFC using metaheuristic method. This paper presents a PSO variant for optimal tuning of PID controllers for load frequency control of power system integrated with renewable energy resources. The proposed PID tuning technique is tested on a two-area power system commonly used in the literature. Seven scenarios have been used to validate the effectiveness of the proposed Load Frequency Control. For more realistic evaluation, governor dead band and communication time delays have been incorporated in the test system in one of the scenarios. Simulation results obtained when compared with those of three well-known PID-tuning metaheuristic algorithms produced shorter settling time and smaller frequency and tie line power deviations.

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“…These methods both have to choose the appropriate wavelet basis function and the number of wavelet decomposition level according to the frequency component and sampling frequency of the signals to achieve good noise reduction effect [14]- [17], which can not achieve automatic signal noise reduction. [18]. Singular Value decomposition (SVD) was used to identify and remove laser-induced noise [19], but it's only useful to reduce laser-induced noise.…”

Section: Introductionmentioning

confidence: 99%

A Noise Reduction Method for Photoacoustic Imaging <italic>In Vivo</italic> Based on EMD and Conditional Mutual Information

et al. 2019

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Photoacoustic tomography is a technique to reconstruct the image of light energy absorption distribution in tissues based on the detected photoacoustic signals. In recent years, this research field has been greatly developed, and its application range is wide, including anatomy, functionality, and molecular imaging. However, the conversion efficiency of photoacoustic effect from light to sound is quite low, which leads to the low signal-to-noise ratio of photoacoustic signal and the poor quality of reconstructed photoacoustic image. The traditional method to improve the signal-to-noise ratio of photoacoustic signals is data averaging method, but it seriously limits the imaging speed due to multiple acquisition. Without sacrificing signal fidelity and imaging speed, an empirical mode decomposition (EMD) combined with conditional mutual information de-noising algorithm for photoacoustic tomography is proposed in this paper. The simulation results and experimental results of photoacoustic signal de-noising achieve significant improvement of signal-to-noise ratio of photoacoustic signal and the enhancement of contrast of the reconstructed image. The simulation results and experimental results show that EMD combined with mutual information method improves at least 2 dB and 3 dB, respectively, more than traditional wavelet threshold method and band-pass filter. The improvement of contrast-to-noise ratio is more than 2 dB and 3 dB, respectively, more than traditional wavelet threshold method and band-pass filter.

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Adaptive discrete wavelet transform based technique for load frequency control

Cited by 6 publications

References 9 publications

A multi-objective opposition-based barnacles mating optimization for image super resolution using hyper-Spectral images

A multi-objective opposition-based barnacles mating optimization for image super resolution using hyper-Spectral images

Optimal Tuning of PID Controllers for LFC in Renewable Energy Source Integrated Power Systems Using an Improved PSO

A Noise Reduction Method for Photoacoustic Imaging <italic>In Vivo</italic> Based on EMD and Conditional Mutual Information

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