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- 实验方案
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GWDWT-FCM: Change Detection in SAR Images Using Adaptive Discrete Wavelet Transform with Fuzzy C-Mean Clustering
摘要： Change detection in remote sensing images turns out to play a significant role for the preceding years. Change detection in synthetic aperture radar (SAR) images comprises certain complications owing to the reality that it endures from the existence of the speckle noise. Hence, to overcome this limitation, this paper intends to develop an improved model for detecting the changes in SAR image. In this model, two SAR images captivated at varied times will be considered as the input for the change detection process. Initially, discrete wavelet transform (DWT) is employed for image fusion, where the coefficients are optimized using improved grey wolf optimization (GWO) called adaptive GWO (AGWO) algorithm. Finally, the fused images after inverse transform are clustered using fuzzy C-means (FCM) clustering technique and a similarity measure is performed among the segmented image and ground truth image. With the use of all these technologies, the proposed model is termed as adaptive grey wolf-based DWT with FCM (AGWDWT-FCM). The similarity measures analyze the relevant performance measures such as accuracy, specificity and F1 score. Moreover, the performance of the AGWDWT-FCM in change detection model is compared to other conventional models, and the improvement is noted.
关键词： Filter coefficient,Adaptive discrete wavelet transform,Grey wolf optimization,Synthetic aperture radar,Fuzzy C-means clustering
更新于2025-09-23 15:21:21
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Dynamic global maximum power point tracking of the PV systems under variant partial shading using hybrid GWO-FLC
摘要： Maximum power point tracker (MPPT) techniques have been used to extract the maximum power available form photovoltaic (PV) energy systems. Conventional MPPT techniques like perturb and observe (P&O), hill climbing (HC), incremental conductance etc. were good enough to track the maximum power for the unshaded PV systems because it has only one power peak in the P-V curve. In the case of partial shading conditions (PSC), many peaks are created; one global maximum power point (GMPP) and many local maximum power points (LMPPs). Most of conventional MPPT techniques may stick to one of the LMPPs, which reduce the MPPT efficiency of PV systems. Soft computing techniques like particle swarm optimization (PSO), gray wolf optimization (GWO), and Cuckoo search optimization (CSO) etc. can catch the GMPP of PV system under the same PSC. These latter techniques suffer from two problems, the first problem is the high oscillations around the GMPP, the second problem is that, they cannot follow the new GMPP once it changed its position due to the searching agents will be busy around old GMPP caught. The solution of these two problems are the motivation of this research. GWO has been used to catch the GMPP and the problem of oscillations around the GMPP has been solved by hybridizing this technique with fuzzy logic controller (FLC) for soft tune the output generated power at the GMPP. The FLC characterizes by accurate GMPP catching with almost zero oscillations. The second problem is solved in this paper by re-initializing the GWO with two new initialization techniques. The results obtained from GWO-FLC with two different re-initialization techniques have been compared to the results of PSO without reinitializing its particles. The results obtained from this work prove the superior performance of the new proposed technique in terms of dynamic GMPP catching and MPPT power efficiency in case of time variant PSCs.
关键词： Global maximum power point,Fuzzy logic controller,MPPT energy efficiency,Partial shading condition,Particle swarm optimization,Grey wolf optimization
更新于2025-09-23 15:21:01
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Electro-Hydrodynamic Design of an Intelligent Balloon Water Gate Controlled by an Efficient Maximum-Power-Seeking Controller for a Solar Generation System
摘要： The intelligent balloon water gate (IBWG) invention is a hydraulic gate model made of reinforced plastic that controls the water level (WL) downstream or upstream of a barrage. The IBWG automatically in?ates and de?ates by compressed air to close and open the water passage, respectively. The whole design consists of a balloon, a waterway, sensors, an air compressor, a control panel, an electrical circuit and a photovoltaic generation (PVG) system. The Tyass barrage in Iraq was considered as a case study. The Tyass barrage was built with concrete and four sliding steel water gates and redesigned using the IBWG. The originality of the current research resides in the combination of the IBWG mechanism with an ef?cient maximum power point (MPP) seeking controller for a photovoltaic generation system, which is one of the most promising sources of renewable energy in the world. To the best of our knowledge, in this ?eld, this scenario has not yet been discussed in detail. Upper and lower water sensors are used to control the IBWG. The upper sensor sends a signal to the control panel when the downstream water level reaches its maximum value to open the air inlet valve and close the outlet valve, in?ating 14 IBWGs with a volume of 3.5 m3 under 122 psi of pressure and closing the water passage. When the WL decreases below the minimum level, the lower sensor initiates the opposite procedure. The air compressor automatically ?lls the air tank to 181 psi and is supplied by a 24 VDC AGM rechargeable battery with a capacity of 40-60 Ah, which is charged by four solar panels connected in parallel and exposed to an average of 8.8 hrs/day of sunshine. The proposed MPP-seeking controller was implemented by a backstepping design coupled with the grey wolf mechanism. The solar irradiance data were observed 39 years ago. The proposed controller is capable of following the MPP with minimum oscillations under an external irradiance variation. The IBWG system is veri?ed at night or during the early morning when the sun is not active. Nevertheless, it is possible to store compressed air in an auxiliary tank to avoid emergencies such as partial shading conditions.
关键词： PVG system,backstepping technique,lyapunov stability,grey wolf optimization,Electro-hydrodynamic rubber water gate
更新于2025-09-16 10:30:52
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A Hybrid Intelligent Approach for Solar Photovoltaic Power Forecasting: Impact of Aerosol Data
摘要： The penetration of solar photovoltaic (PV) power in distributed generating system is increasing rapidly. The increased level of PV penetration causes various issues like grid stability, reliable power generation and power quality; therefore, it becomes utmost important to forecast the PV power using the meteorological parameters. The proposed model is developed on the basis of meteorological data as input parameters, and the impacts of these parameters have been analyzed with respect to forecasted PV power. The main focus of this research is to explore the performance of optimization-based PV power forecasting models with varying aerosol particles and other meteorological parameters. A newly developed intelligent approach based on grey wolf optimization (GWO) using multilayer perceptron (MLP) has been used to forecast the PV power. The performance of the GWO-based MLP model is evaluated on the basis of statistical indicators such as normalized mean bias error (NMBE), normalized mean absolute error (NMAE), normalized root-mean-square error (NRMSE) and training error. The results of the developed model show the values of NMBE, NMAE and NRMSE as 2.267%, 4.681% and 6.67% respectively. To validate the results, a comparison has been made with particle swarm optimization, Levenberg–Marquardt algorithm and adaptive neuro-fuzzy approach. The performance of the model is found better as compared to other intelligent techniques. The obtained results may be used for demand response applications in smart grid environment.
关键词： Solar power forecasting,Artificial neural network,Distributed power generation,Grey wolf optimization,Solar PV
更新于2025-09-16 10:30:52