共查询到20条相似文献，搜索用时 15 毫秒
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Coal-fired power plants are one of the most important targets with respect to reduction of CO2 emissions. The reasons for this are that coal-fired power plants offer localized large point sources (LPS) of CO2 and that the Indian power sector contributes to roughly half of all-India CO2 emissions. CO2 capture and storage (CCS) can be implemented in these power plants for long-term decarbonisation of the Indian economy. In this paper, two artificial intelligence (AI) techniques—adaptive network based fuzzy inference system (ANFIS) and multi gene genetic programming (MGGP) are used to model Indian coal-fired power plants with CO2 capture. The data set of 75 power plants take the plant size, the capture type, the load and the CO2 emission as the input and the COE and annual CO2 emissions as the output. It is found that MGGP is more suited to these applications with an R2 value of more than 99% between the predicted and actual values, as against the ~96% correlation for the ANFIS approach. MGGP also gives the traditionally expected results in sensitivity analysis, which ANFIS fails to give. Several other parameters in the base plant and CO2 capture unit may be included in similar studies to give a more accurate result. This is because MGGP gives a better perspective toward qualitative data, such as capture type, as compared to ANFIS. 相似文献
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Alireza REZVANI Ali ESMAEILY Hasan ETAATI Mohammad MOHAMMADINODOUSHAN 《Frontiers in Energy》2019,13(1):131
Photovoltaic (PV) generation is growing increasingly fast as a renewable energy source. Nevertheless, the drawback of the PV system is intermittent because of depending on weather conditions. Therefore, the wind power can be considered to assist for a stable and reliable output from the PV generation system for loads and improve the dynamic performance of the whole generation system in the grid connected mode. In this paper, a novel topology of an intelligent hybrid generation system with PV and wind turbine is presented. In order to capture the maximum power, a hybrid fuzzy-neural maximum power point tracking (MPPT) method is applied in the PV system. The average tracking efficiency of the hybrid fuzzy-neural is incremented by approximately two percentage points in comparison with the conventional methods. The pitch angle of the wind turbine is controlled by radial basis function network-sliding mode (RBFNSM). Different conditions are represented in simulation results that compare the real power values with those of the presented methods. The obtained results verify the effectiveness and superiority of the proposed method which has the advantages of robustness, fast response and good performance. Detailed mathematical model and a control approach of a three-phase grid-connected intelligent hybrid system have been proposed using Matlab/Simulink. 相似文献
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由于风力发电固有的间歇性和随机性特点,大容量风电机组直接接入高压输电网络,不仅对电网安全运营、电能质量是重大挑战,而且也严重影响了风力发电运营的经济性。在此背景下,设计了基于径向基函数和模糊技术的风能预测模型。该模型利用自组织神经网络模型进行数据分类、径向基函数网络模型进行初始预测以及模糊逻辑函数模型进行预测修正,再以数据预处理模型、数据归一化模型以及数据反归一化模型为辅助,预测目标风电机组未来72 h的发电功率。经过试验验证,证明本模型的预测精度较为理想,可以用于实际生产。 相似文献
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针对光伏系统的发电特性及影响光伏发电的因素,建立基于混沌自适应粒子群优化算法的反馈型神经网络短期发电量预测模型。该预测模型利用混沌自适应粒子群优化算法的全局优化能力初始化反馈性神经网络权值和阈值,可以克服反馈型神经网络收敛速度慢俄且易陷于局部最优等缺点。同时为提高预测精度,采用隶属度函数对温度进行模糊化处理。预测结果表明,建立的预测模型具有较高的精度。 相似文献
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针对BP神经网络收敛速度慢和易于陷入极小值的问题,采用将遗传算法全局寻优和BP神经网络局部寻优相结合的方法,提高了BP神经网络的计算精度和收敛速度.应用该模型对空调水系统进行了辨识,并以冷水机组和水泵能耗最小为目标进行优化,取得了满意的效果. 相似文献
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In this paper, three intelligent approaches were proposed, applied to direct torque control (DTC) of induction motor drive to replace conventional hysteresis comparators and selection table, namely fuzzy logic, artificial neural network and adaptive neuro-fuzzy inference system (ANFIS). The simulated results obtained demonstrate the feasibility of the adaptive network-based fuzzy inference system based direct torque control (ANFIS-DTC). Compared with the classical direct torque control, fuzzy logic based direct torque control (FL-DTC), and neural networks based direct torque control (NN-DTC), the proposed ANFIS-based scheme optimizes the electromagnetic torque and stator flux ripples, and incurs much shorter execution times and hence the errors caused by control time delays are minimized. The validity of the proposed methods is confirmed by simulation results. 相似文献
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Mehdi Amirinejad Naser Tavajohi‐Hasankiadeh Sayed Siavash Madaeni Maria Assunta Navarra Ezzat Rafiee Bruno Scrosati 《国际能源研究杂志》2013,37(4):347-357
In this study, a proton exchange membrane fuel cell (PEMFC) is modeled by multilayer perceptron neural network (MLPNN), RBF neural network (RBFNN), and adaptive neuro‐fuzzy inference system (ANFIS). Experimental data are obtained on the basis of the fabricated membrane‐electrode assembly (MEA) responses using prepared nanocomposite and recast Nafion membranes in the PEMFC. Four parameters including cell temperature, inlet gas temperature, current density, and inorganic additive percent are used as inputs, and the cell voltage is considered as the output. The results show that there is no considerable discrepancy between the RBFNN accuracy (R = 0.99554) and the MLPNN accuracy (R = 0.99609) for the performance prediction. The required time for developing the RBFNN model is significantly lower than the MLPNN model. A variety of ANFIS structure is explored to approximate the behavior of the system. The effect of cell and inlet gas temperatures on the PEMFC performance is investigated by the ANFIS developed model. Predicted polarization and power–current behavior by the ANFIS for the MEA prepared by the recast Nafion and the nanocomposite membranes at the cell temperatures 50 °C to110°C are in high agreement with the experimental data. Predicted data by the ANFIS show that because of the property of Cs2.5H0.5PW12O40 additive for retaining water, much higher current density and power density at the same voltage are achieved for the nanocomposite membrane compared with the recast Nafion membrane in the PEMFC. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
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为了提高燃气轮机故障诊断的效果,提出了一种基于自适应模糊神经网络(Adaptive Network-based Fuzzy Inference System,ANFIS)和改进的人工蜂群算法(Improved artificial bee colony algorithm,IABC)的故障诊断方法:基于自适应模糊神经网络构建燃气轮机故障诊断模型。针对自适应模糊神经网络受聚类参数影响较大的问题,采用手榴弹爆炸原理改进的人工蜂群算法对这些参数进行优化。仿真结果表明,与未优化的ANFIS模型和ABC-ANFIS模型相比,IABC-ANFIS可以更稳定、准确地识别故障,为燃气轮机故障诊断提供实际参考。 相似文献
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指出了模糊优选BP神经网络模型的缺点，在模糊优选BP神经网络模型的基础上，引入加速遗传算法，提出融入遗传算法的模糊优选神经网络智能决策模型。并将其应用于某流域蓄滞洪区优选决策逆命题的目标权重计算，结果表明，该模型能够明显加快网络的收敛速度，改善网络的全局寻优能力，集成了模糊优选BP神经网络和遗传算法的优点。 相似文献
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Because wind has a high volatility and the respective energy produced cannot be stored on a large scale because of excessive costs, it is of utmost importance to be able to forecast wind power generation with the highest accuracy possible. The aim of this paper is to compare 1‐h‐ahead wind power forecasts performance using artificial intelligence‐based methods, such as artificial neural networks (ANNs), adaptive neural fuzzy inference system (ANFIS), and radial basis function network (RBFN). The latter was implemented using three different learning algorithms: stochastic gradient descent (SGD), hybrid, and orthogonal least squares (OLS). The application dataset is the injected wind power in the Portuguese power systems throughout the years 2010–2014. The network architecture optimization and the learning algorithms are presented. An initial data analysis showed data seasonality; therefore, the wind power forecasts were performed according to the seasons of the year. The results showed that ANFIS was the best performer method, and ANN and RBFN‐OLS also showed strong performances. RBFN‐Hybrid and RBFN‐SGD performed poorly. In general, all methods outperformed persistence. 相似文献
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Over the past few decades, the world demand for energy has risen steadily, forcing the world communities to look for alternative sources. Photovoltaic (PV) is seen as the most promising solution for this demand. However, the PV system is popularly known to suffer from low‐energy harvesting due to the change of environment conditions. An inexpensive and practical solution to extract the energy from the PV is by improving the maximum power point tracking (MPPT) controller technique. An ideal MPPT should be able to track the true maximum power operating point accurately under all circumstances and overcome all nonlinearities in the characteristic I‐V curves. This paper presents an updated review of the techniques based on the perturbative MPPT methods, both using the conventional and soft computing methods. The working principles of the techniques, parameter effects, and their limitations are discussed. The focus of this review is to direct the readers to the new direction of MPPT using the artificial intelligence and evolutionary computation techniques. Besides serving as a comprehensive source of information, the paper also provides a critical review on the relative performance of the selected MPPT methods. This includes the module dependency, tracking performance, and the ability to handle the partial shading conditions. Copyright © 2015 John Wiley & Sons, Ltd. 相似文献
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基于神经网络和混合遗传算法的凝汽器真空优化控制 总被引：1，自引：0，他引：1
利用人工神经网络进行凝汽器真空建模,然后采用混合遗传算法对运行工况寻优,以获得各种工况下凝汽器的最佳运行方式。通过对某电厂的300MW机组现场热态试验与计算,表明该方法可以指导运行人员进行凝汽器真空的优化调整。 相似文献
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混合遗传算法在贮灰坝监测系统上的应用 总被引：1，自引：2，他引：1
BP网络能较好地解决贮灰坝监测系统的预报问题，但容易陷入局部极小值；遗传算法能寻求到全局最优解，因此，将二者结合的突出优点在于可以克服前者经常得出局部极小值解的缺点，而取得全局最优解。将BP算法用于加强遗传算法的局部搜索，并且应用到贮灰坝的监测系统上，取得了较好的效果。 相似文献
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在应用神经网络来判断透平在线状态的工作中,面对众多的相关过程参数及环境参数,如何从中为神经网络选择适当的输入参数是非常重要的。根据透平热力性能在线诊断的需要,本文研究了如何用遗传算法为神经网络寻找优化的输入组合以期达到少输入、快训练和准确回忆的目的。结果表明,由遗传算法选定的较少的参数作为诊断网络的输入即可判断透平性能状态的好坏。 相似文献
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根据交流电弧炉在冶炼周期的不同阶段对电极调节系统的不同要求,提出了基于复合智能控制系统的集成系统控制方案。在熔化期采用基于遗传模拟退火算法(GSA)的BP神经网络控制器进行控制,在氧化期采用模糊控制器进行控制,在还原期采用数字式PID控制器进行控制,并进行了控制器设计。 相似文献