- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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A Novel Modified Sine-Cosine Optimized MPPT Algorithm for Grid Integrated PV System under Real Operating Conditions
摘要： This research work presents a modified sine-cosine optimized Maximum Power Point Tracking (MPPT) algorithm for grid integration. Developed algorithm provides a maximum power (PV) panel and simplified extraction implementation with benefit high convergence velocity. Moreover, the performance and ability of Modified Sine-Cosine Optimized (MSCO) algorithm equated with recent Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) algorithms for comparative observation. Practical responses analyzed under steady state, dynamic and partial shading conditions by using dSPACE real controlling board laboratory scale hardware implementation. The MSCO based MPPT algorithm always shown the fast convergence rate, easy implementation, less computational burden and accurate to track the optimal PV power under varying weather conditions. Experimental results provided in the article clearly shown the validation of the proposed algorithm.
关键词： Maximum Power Point Tracking,Photovoltaic,Artificial Bee Colony,Particle Swarm Optimization,Sine-Cosine Optimized
更新于2025-09-23 15:23:52
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[IEEE 2018 10th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC) - Hangzhou (2018.8.25-2018.8.26)] 2018 10th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC) - SAR and Optical Image Registration Method Based on Quantum Particle Swarm Optimization
摘要： Abstract: In the most important step of GIS and optical image fusion, in order to improve the registration accuracy and efficiency of the strategic target, a new image registration method based on quantum particle swarm optimization (QPSO) with independent optical selection is presented. The proposed method consists of three steps: first, it decomposes the optical image into different frequency components with the wavelet transform; second, it extracts the feature corner points with the Harris corner detector; and finally, it constructs the similarity measure by combining the mutual information and the spatial distance, and uses the QPSO algorithm to search the optimal transformation parameters. The experimental results show that the proposed method is effective and feasible, it can achieve high accuracy and robustness for the low-frequency component of the optical image.
关键词： GIS Image,Image Registration,Independent Optical Selection,Quantum Particle Swarm Optimization,Optical Image
更新于2025-09-23 15:22:29
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[IEEE 2018 53rd International Universities Power Engineering Conference (UPEC) - Glasgow, United Kingdom (2018.9.4-2018.9.7)] 2018 53rd International Universities Power Engineering Conference (UPEC) - Optimal Siting of BESS in Distribution Networks under High PV Penetration
摘要： This paper focuses on the optimal siting of Battery Energy Storage Systems (BESS) in a Distribution Network with installed Photovoltaic Generation, in order to minimize the energy losses of the system. The bus voltage limit, as well as the ampacity level of the lines are taken into consideration as constraints, while the technical constraints of the BESSs have also been taken into account. Unified Particle Swarm Optimization is used as the solving optimization technique. Simulations are being carried out on IEEE-33 bus system regarding different scenarios and the results are presented and compared. A significant improvement in energy losses, voltage and line ampacity profile is achieved by the introduction of BESS units in a Distribution Network with high PV Penetration.
关键词： BESS,Particle Swarm Optimization,Distributed Generation,Battery Energy Storage Systems,PV,optimal sizing siting of ESS,PSO
更新于2025-09-23 15:22:29
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Vector quantization using the improved differential evolution algorithm for image compression
摘要： Vector quantization (VQ) is a popular image compression technique with a simple decoding architecture and high compression ratio. Codebook designing is the most essential part in vector quantization. Linde–Buzo–Gray (LBG) is a traditional method of generation of VQ codebook which results in lower PSNR value. A codebook affects the quality of image compression, so the choice of an appropriate codebook is a must. Several optimization techniques have been proposed for global codebook generation to enhance the quality of image compression. In this paper, a novel algorithm called IDE-LBG is proposed which uses improved differential evolution algorithm coupled with LBG for generating optimum VQ codebooks. The proposed IDE works better than the traditional DE with modifications in the scaling factor and the boundary control mechanism. The IDE generates better solutions by efficient exploration and exploitation of the search space. Then the best optimal solution obtained by the IDE is provided as the initial codebook for the LBG. This approach produces an efficient codebook with less computational time and the consequences include excellent PSNR values and superior quality reconstructed images. It is observed that the proposed IDE-LBG find better VQ Codebooks as compared to IPSO-LBG, BA-LBG and FA-LBG.
关键词： Improved differential evolution (IDE) algorithm,Improved particle swarm optimization (IPSO) algorithm,Bat algorithm (BA),Firefly algorithm (FA),Vector quantization,Image compression,Codebook,Linde–Buzo–Gray (LBG) algorithm
更新于2025-09-23 15:22:29
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Material distributive topology design of UWB antenna using parallel computation of improved BPSO with FDTD
摘要： In this article, the material distributive topology-based design optimization of ultra-wide band (UWB) antenna is proposed by using improved binary particle swarm optimization (BPSO) with finite difference time domain (FDTD) method. In the improved BPSO implementation, the velocity of each particle is calculated based on complete set of bits of particle position vector. The V-shaped transfer function is employed to transform all real values of velocities to values in the interval [0,1]. The fitness function of all the particles in BPSO algorithm are computed parallely by using FDTD simulation. The usage of FDTD and the parallel computation helps in analyzing the broadband frequency characteristics of the antenna with a single simulation run. The return loss of the optimized UWB antenna obtained from FDTD, Computer Simulation Technology (CST) simulation and practical measurement are in good agreement and show good impedance matching.
关键词： UWB antenna,finite difference time domain (FDTD) method,topology optimization,Binary particle swarm optimization (BPSO)
更新于2025-09-23 15:21:21
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Added Value of Multitemporal Polarimetric UAVSAR Data for Permanent Scatterers Detection
摘要： In the last decades, differential synthetic aperture radar (SAR) interferometric (InSAR) (DInSAR) techniques have been used to estimate the Earth’s surface deformation with high resolution. In this paper, we present an approach for increasing the quantity of permanent scattered pixels. These pixels are selected for DInSAR processing based on polarimetric information prepared by new sensors. The objective of this paper is then to test existing algorithms that confirm the contribution of polarimetric data for improving persistent scatterers (PS) detection. These algorithms are formulated based on two different selection criteria: amplitude dispersion index and mean coherence. Different approaches are analyzed to optimize both selection criteria in terms of pixels’ quantity and density and finally their results are quantitatively compared. Experimental results with exploiting quad-pol UAVSAR dataset over an urban area in CA, provide the expected improvement. Comparing the number of PSs between quad-pol with dual-pol and single-pol cases illustrate remarkable improvement in both selection criteria. For quad-pol case, we achieve an increase of 50% and 60% with respect to dual-pol and single-pol data, respectively, when using average coherence and over 6 times more for amplitude dispersion index. The results of our study demonstrates the added value of polarimetric SAR observations (dual pol and quad-pol) for improved permanent scatterers detection monitored areas.
关键词： Polarimetry,particle swarm optimization,Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR),Permanent Scatterer
更新于2025-09-23 15:21:21
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[Advances in Intelligent Systems and Computing] Soft Computing for Problem Solving Volume 817 (SocProS 2017, Volume 2) || Temperature Resolution and Spatial Resolution Improvement of BOCDR-Based DTS System Using Particle Swarm Optimization Algorithm
摘要： Temperature resolution and spatial resolution are major performance metrics in any distributed temperature sensing (DTS) system. In this paper, we have presented a detailed analysis on the performance of a Brillouin optical correlation domain reflectometry (BOCDR)-based DTS (BOCDR-DTS) system. Particle swarm optimization (PSO) evolutionary algorithm is being used in this paper to improve the performance of the proposed BOCDR-DTS system. Using this optimization algorithm, we minimized the Brillouin frequency shift (BFS) error in sensing system. As a consequence of this, the achieved temperature and spatial resolution are ~0.839 °C and ~43 cm, respectively. The results were simulated using MATLAB version 15.0.
关键词： Spontaneous Brillouin scattering,Distributed temperature sensing,Particle swarm optimization (PSO),Temperature resolution and spatial resolution,Brillouin frequency shift,Optical correlation domain reflectometry (OCDR)
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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Optimal location identification for aggregated charging of electric vehicles in solar photovoltaic powered microgrids with reduced distribution losses
摘要： The battery-powered electric vehicle finds an alternative for fossil fuel-based vehicles in the transportation sector. The charge-discharge power profiles of the battery storage systems (BSS) contribute toward distribution losses, which can be minimized by proper scheduling. Such scheduling gives better results if the charging stations are optimally placed in the solar photovoltaic (PV) powered microgrid. This paper proposes a methodology to identify the optimal location to charge the electric vehicle in the microgrid. The proposed methodology has been developed using particle swarm optimization (PSO)-based optimal power flow (OPF) with an integrated power management (IPM) algorithm. The novelty of the IPM algorithm is the coordinated charging-discharging of the multiple numbers of aBSS of the EVs to reduce the overall distribution losses of the microgrid. The proposed methodology is tested in a standard solar PV powered microgrid network, where the optimal locations to charge the electric vehicles are identified. The daily distribution loss of the network is computed for all possible charging locations of the electric vehicle in the microgrid, and it is found that the distribution loss is minimum for the identified optimal locations. Also, to evaluate the effectiveness of the proposed methodology, the distribution loss analysis is carried out for three test cases; i) un-optimized power flow, ii) PSO based-OPF, and iii) PSO-based OPF with IPM. The case study shows that the PSO-based OPF gives 84% reduction in daily distribution loss compared to the conventional un-optimized power flow test case. The daily distribution loss is further reduced by 8% by incorporating the IPM algorithm in the PSO-based OPF. The utility can thereby encourage the electric vehicle (EV) owners to park their EVs at the optimal locations to reduce the distribution losses.
关键词： microgrid,particle swarm optimization,Battery storage systems,renewable generation,electric vehicle,optimal location
更新于2025-09-23 15:21:01
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Improved cooperative artificial neural network <scp>a??</scp> particle swarm optimization approach for solar photovoltaic systems using maximum power point tracking
摘要： Photovoltaic (PV) energy represents one of the most important renewable energies, but its disadvantage resides in its maximum power point, which varies according to meteorological changes that make the efficiency low. Intelligent techniques, using the maximum power point tracking (MPPT) method, can achieve an efficient real-time tracking of this point in order to ensure optimal functioning of the system. The output power of the PV system is removed from solar irradiation and cell temperature of the PV panel type SOLON 55W. Therefore, it is essential to harvest the generated power of the PV system and optimally exploit the collected solar energy. For this objective, this work treats on a new artificial neural network-particle swarm optimization approach (ANN-PSO). The ANN is used to predict the solar irradiation level and cell temperature followed by PSO to optimize the power generation and optimally track the solar power of the PV panel type SOLON 55W based on various operation conditions under changes in environmental conditions. The simulation results of the proposed approach give a minimum error with a relevant efficiency, that is, the power provided by ANN-PSO approach is optimal and closer to the PV power. Consequently, this novel approach ANN-PSO shows its major capability to extract the optimal power with excellent efficiency up of 97%. For this objective, this work treats a new hybrid ANN-PSO approach.
关键词： photovoltaic system,particle swarm optimization,maximum power point tracking,artificial neural network
更新于2025-09-23 15:21:01