A grey wolf optimized fuzzy logic based MPPT for shaded solar photovoltaic systems in microgrids

As the solar PV system (SPVS) suffered from an unavoidable complication that it has nonlinearity in I–V curves, the optimum maximum power point (MPP) measurement is difficult under fluctuating climatic conditions. For maximizing SPVS output power, MPP tracking (MPPT) controllers are used. In this paper, a new adaptive fuzzy logic controller (AFLC) based MPPT technique is proposed. In this proposed AFLC, the membership functions (MFs) are optimized using the Grey Wolf Optimization (GWO) technique to generate the optimal duty cycle for MPPT. Four shading patterns are used to experiment with the performance of the proposed AFLC. The proposed approach tracks the global MPP for all shading conditions and also enhances the tracking speed and tracking efficiency with reduced oscillations. The effectiveness and robustness of proposed AFLC based tracker results over P&O and FLC are validated using Matlab/Simulink environment. The proposed AFLC overcome the drawbacks of the classical P&O, and FLC approaches.     

Adaptive neuro‐fuzzy inference system and artificial neural network modeling of proton exchange membrane fuel cells based on nanocomposite and recast Nafion membranes

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 Cs_2.5H_0.5PW₁₂O₄₀ 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.     

Intelligent hybrid power generation system using new hybrid fuzzy-neural for photovoltaic system and RBFNSM for wind turbine in the grid connected mode

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.     

Performance of off-grid residential solar photovoltaic power systems using five solar tracking modes in Kunming,China

This paper compares the performance of a 2.02 kW_p off-grid residential solar photovoltaic (PV) power system using PVSYST simulation software for a household in Kunming, Yunnan province, China. The monthly available solar energy; missing energy; array, final, and reference yields, performance ratio; and array capture and system losses were analyzed for five solar tracking modes: fixed tilted plane, seasonal tilt adjustment, horizontal axis tracking, vertical axis tracking, and dual axis tracking. Although there were some similar aspects across the five systems, minimum available solar energy (2461 kWh/y) and maximum missing energy (134.68 kWh/y) were obtained using the fixed tilted plane system (tilt angle = 25°, azimuth angle = 0°), whereas maximum available solar energy (3081 kWh/y) and minimum missing energy (48.53 kWh/y) in October were obtained using the dual axis tracking system. Average monthly performance ratio was maximal for the fixed tilted plane system (0.689), and minimal for the dual axis tracking system (0.596).     

Sizing of stand-alone photovoltaic power supply system based on genetic algorithm and neuro-fuzzy: application for isolated areas

This paper deals with the application of genetic algorithm (GA) and an adaptive neuro-fuzzy inference scheme (ANFIS), for the prediction of the optimal sizing coefficient of stand-alone photovoltaic supply (SAPVS) systems in remote areas. A database of total solar radiation data for 60 sites in Algeria has been used to determine the iso-reliability curves of a PVS system (C _A, C _S) for each site. Initially, the GA is used for determining the optimal coefficient (C _Aop, C _Sop) for each site by minimising the optimal cost (objective function). These coefficients allow the determination of the number of PV modules and the capacity of the battery. Subsequently, an ANFIS is used for the prediction of the optimal coefficient in remote areas based only on geographical coordinates. Therefore, 56 couples of C _Aop and C _Sop have been used for the training of the network and four couples have been used for testing and validation of the proposed technique. The simulation results have been analysed and compared with the alternative techniques. The technique has been applied and tested for Algeria locations, but it can be generalised for any location in the world.     

Optimization of turbine cold-end system based on BP neural network and genetic algorithm

The operation condition of the cold-end system of a steam turbine has a direct impact on the economy and security of the unit as it is an indispensible auxiliary system of the thermal power unit. Many factors influence the cold-end operation of a steam turbine; therefore, the operation mode needs to be optimized. The optimization analysis of a 1000 MW ultra-supercritical (USC) unit, the turbine cold-end system, was performed utilizing the back propagation (BP) neural network method with genetic algorithm (GA) optimization analysis. The optimized condenser pressure under different conditions was obtained, and it turned out that the optimized parameters were of significance to the performance and economic operation of the system.     

Generation of synthetic daily global solar radiation data based on ERA-Interim reanalysis and artificial neural networks

Four variables (total cloud cover, skin temperature, total column water vapour and total column ozone) from meteorological reanalysis were used to generate synthetic daily global solar radiation via artificial neural network (ANN) techniques. The goal of our study was to predict solar radiation values in locations without ground measurements, by using the reanalysis data as an alternative to the use of satellite imagery. The model was validated in Andalusia (Spain), using measured data for nine years from 83 ground stations spread over the region. The geographical location (latitude, longitude), the day of the year, the daily clear sky global radiation, and the four meteorological variables were used as input data, while the daily global solar radiation was the only output of the ANN. Sixty five ground stations were used as training dataset and eighteen stations as independent dataset. The optimum network architecture yielded a root mean square error of 16.4% and a correlation coefficient of 94% for the testing stations. Furthermore, we have successfully tested the forecasting capability of the model with measured radiation values at a later time. These results demonstrate the generalization capability of this approach over unseen data and its ability to produce accurate estimates and forecasts.     

Optimization of fermentative hydrogen production process using genetic algorithm based on neural network and response surface methodology

A central composite design was carried out to investigate the effect of temperature, initial pH and glucose concentration on fermentative hydrogen production by mixed cultures in batch test. The modeling abilities of the response surface methodology model and neural network model, as well as the optimizing abilities of response surface methodology and the genetic algorithm based on a neural network model were compared. The results showed that the root mean square error and the standard error of prediction for the neural network model were much smaller than those for the response surface methodology model, indicting that the neural network model had a much higher modeling ability than the response surface methodology model. The maximum hydrogen yield of 289.8 mL/g glucose identified by response surface methodology was a little lower than that of 360.5 mL/g glucose identified by the genetic algorithm based on a neural network model, indicating that the genetic algorithm based on a neural network model had a much higher optimizing ability than the response surface methodology. Thus, the genetic algorithm based on a neural network model is a better optimization method than response surface methodology and is recommended to be used during the optimization of fermentative hydrogen production process.     

Equalization of series connected lithium-ion batteries based on back propagation neural network and fuzzy logic control

In this article, a nondissipative equalization scheme is proposed to reduce the inconsistency of series connected lithium-ion batteries. An improved Buck-Boost equalization circuit is designed, in which the series connected batteries can form a circular energy loop, equalization speed is improved, and modularization is facilitated. This article use voltage and state of charge (SOC) together as equalization variables according to the characteristics of open-circuit voltage (OCV)-SOC curve of lithium-ion battery. The second-order RC equivalent circuit model and back propagation neural network are used to estimate the SOC of lithium-ion battery. Fuzzy logic control (FLC) is used to adjust the equalization current dynamically to reduce equalization time and improve efficiency. Simulation results show that the traditional Buck-Boost equalization circuit and the improved Buck-Boost equalization circuit are compared, and the equalization time of the latter is reduced by 34%. Compared with mean-difference algorithm, the equalization time of FLC is decreased by 49% and the energy efficiency is improved by 4.88% under static, charging and discharging conditions. In addition, the proposed equalization scheme reduces the maximum SOC deviation to 0.39%, effectively reducing the inconsistency of batteries.     

Improvement of the three-dimensional fine-mesh flow field of proton exchange membrane fuel cell (PEMFC) using CFD modeling,artificial neural network and genetic algorithm

This study proposes a systematic methodology for improving PEMFC's performance combining computational fluid dynamic (CFD), artificial neural network (ANN), and intelligent optimization algorithms. Firstly, a three-dimensional (3-D) multiphase PEMFC CFD model with 3-D fine-mesh flow field is developed. Then the key structural features of the fine-mesh flow field are extracted as optimization decision variables, and the sampling points are selected by using the Latin hypercube sampling (LHS) experimental method. The power density and oxygen uniformity index of sampling points are calculated by CFD modeling to form the database, which is used to train the artificial neural network (ANN) surrogate model. Finally, the single-objective optimization (SOO) and multi-objective optimization (MOO) are implemented by using genetic algorithm (GA) and non-dominated sorting genetic algorithm (NSGA-II), respectively. It was found that using trained ANN surrogate models can get a high prediction precision. The maximum power density of SOO is increased by 7.546% than that of base case and is 0.562% larger than that of MOO case. However, the overall pressure drop in cathode flow field of SOO case is greater than that of MOO case and the base case. Furthermore, the oxygen concentration, the oxygen uniformity index and the water removal capacity of MOO case are better than that of SOO case. It is recommended that the improved flow field structure optimized by MOO is more beneficial to improve the overall performance of PEMFC.     

基于BP神经网络与遗传算法风电场超短期风速预测优化研究

风速预测对于风力发电并网调度至关重要。基于BP神经网络建立了风速预测模型,并从BP算法及遗传算法自身特点出发,针对BP网络结构确定困难、收敛速度慢等问题,提出创建多种群遗传算法,实现对BP神经网络的结构和权值初始值的同步优化。通过具体算例表明,经优化后的BP算法的收敛步数和计算时间明显减少,预测精度更高,网络整体性能有了显著提高。     

Applications of artificial neural networks for refrigeration, air-conditioning and heat pump systems—A review

In this paper, an attempt has been made to review the applications of artificial neural networks (ANN) for energy and exergy analysis of refrigeration, air conditioning and heat pump (RACHP) systems. The studies reported are categorized into eight groups as follows: (i) vapour compression systems (ii) RACHP systems components, (iii) vapour absorption systems, (iv) prediction of refrigerant properties (v) control of RACHP systems, (vi) phase change characteristics of refrigerants, (vii) heat ventilation air conditioning (HVAC) systems and (viii) other special purpose heating and cooling applications. More than 90 published articles in this area are reviewed. Additionally, the limitations with ANN models are highlighted. This paper concludes that ANN can be successfully applied in the field of RACHP systems with acceptable accuracy.     

A simplified model for generating sequences of global solar radiation data for isolated sites: Using artificial neural network and a library of Markov transition matrices approach

The purpose of this work is to develop a hybrid model which will be used to predict the daily global solar radiation data by combining between an artificial neural network (ANN) and a library of Markov transition matrices (MTM) approach. Developed model can generate a sequence of global solar radiation data using a minimum of input data (latitude, longitude and altitude), especially in isolated sites. A data base of daily global solar radiation data has been collected from 60 meteorological stations in Algeria during 1991–2000. Also a typical meteorological year (TMY) has been built from this database. Firstly, a neural network block has been trained based on 60 known monthly solar radiation data from the TMY. In this way, the network was trained to accept and even handle a number of unusual cases. The neural network can generate the monthly solar radiation data. Secondly, these data have been divided by corresponding extraterrestrial value in order to obtain the monthly clearness index values. Based on these monthly clearness indexes and using a library of MTM block we can generate the sequences of daily clearness indexes. Known data were subsequently used to investigate the accuracy of the prediction. Furthermore, the unknown validation data set produced very accurate prediction; with an RMSE error not exceeding 8% between the measured and predicted data. A correlation coefficient ranging from 90% and 92% have been obtained; also this model has been compared to the traditional models AR, ARMA, Markov chain, MTM and measured data. Results obtained indicate that the proposed model can successfully be used for the estimation of the daily solar radiation data for any locations in Algeria by using as input the altitude, the longitude, and the latitude. Also, the model can be generalized for any location in the world. An application of sizing PV systems in isolated sites has been applied in order to confirm the validity of this model.     

H2-selective mixed matrix membranes modeling using ANFIS,PSO-ANFIS,GA-ANFIS

The novel contribution of the current study is to employ adaptive neuro-fuzzy inference system (ANFIS) for evaluation of H₂-selective mixed matrix membranes (MMMs) performance in various operational conditions. Initially, MMMs were prepared by incorporating zeolite 4A nanoparticles into polydimethylsiloxane (PDMS) and applied in gas permeation measurement. The gas permeability of CH₄, CO₂, C₃H₈ and H₂ was used for ANFIS modeling. In this manner, the H₂/gas selectivity as the output of the model was modeled to the variations of feed pressure, nanofiller contents and the kind of gas, which were defined as input (design) variables. The proposed method is based on the improvement of ANFIS with genetic algorithm (GA) and particle swarm optimization (PSO). The PSO and GA were applied to improve the ANFIS performance. To determine the efficiency of PSO-ANFIS, GA-ANFIS and ANFIS models, a statistical analysis was performed. The results revealed that the PSO-ANFIS model yields better prediction in comparison to two other methods so that root mean square error (RMSE) and coefficient of determination (R²) were obtained as 0.0135 and 0.9938, respectively. The RMSE and R² values for GA-ANFIS were 0.0320 and 0.9653, respectively, and for ANFIS model were 0.0256 and 0.9787, respectively.