Probabilistic Global Maximum Power Point Tracking Algorithm for Continuously Varying Partial Shading Conditions on Autonomous PV Systems

A photovoltaic (PV) string with multiple modules with bypass diodes frequently deployed on a variety of autonomous PV systems may present multiple power peaks under uneven shading. For optimal solar harvesting, there is a need for a control schema to force the PV string to operate at global maximum power point (GMPP). While a lot of tracking methods have been proposed in the literature, they are usually complex and do not fully take advantage of the available characteristics of the PV array. This work highlights how the voltage at operating point and the forward voltage of the bypass diode are considered to design a global maximum power point tracking (GMPPT) algorithm with a very limited global search phase called Fast GMPPT. This algorithm successfully tracks GMPP between 94% and 98% of the time under a theoretical evaluation. It is then compared against Perturb and Observe, Deterministic Particle Swarm Optimization, and Grey Wolf Optimization under a sequence of irradiance steps as well as a power-over-voltage characteristics profile that mimics the electrical characteristics of a PV string under varying partial shading conditions. Overall, the simulation with the sequence of irradiance steps shows that while Fast GMPPT does not have the best convergence time, it has an excellent convergence rate as well as causes the least amount of power loss during the global search phase. Experimental test under varying partial shading conditions shows that while the GMPPT proposal is simple and lightweight, it is very performant under a wide range of dynamically varying partial shading conditions and boasts the best energy efficiency (94.74%) out of the 4 tested algorithms.

Hybrid model based on K-means++ algorithm, optimal similar day approach, and long short-term memory neural network for short-term photovoltaic power prediction

Photovoltaic(PV) power generation is characterized by randomness and intermittency due to weather changes.Consequently, large-scale PV power connections to the grid can threaten the stable operation of the power system. An effective method to resolve this problem is to accurately predict PV power. In this study, an innovative short-term hybrid prediction model(i.e., HKSL) of PV power is established. The model combines K-means++, optimal similar day approach,and long short-term memory(LSTM) network. Historical power data and meteorological factors are utilized. This model searches for the best similar day based on the results of classifying weather types. Then, the data of similar day are inputted into the LSTM network to predict PV power. The validity of the hybrid model is verified based on the datasets from a PV power station in Shandong Province, China. Four evaluation indices, mean absolute error, root mean square error(RMSE),normalized RMSE, and mean absolute deviation, are employed to assess the performance of the HKSL model. The RMSE of the proposed model compared with those of Elman, LSTM, HSE(hybrid model combining similar day approach and Elman), HSL(hybrid model combining similar day approach and LSTM), and HKSE(hybrid model combining K-means++,similar day approach, and LSTM) decreases by 66.73%, 70.22%, 65.59%, 70.51%, and 18.40%, respectively. This proves the reliability and excellent performance of the proposed hybrid model in predicting power.

Analysis for Effects of Temperature Rise of PV Modules upon Driving Distance of Vehicle Integrated Photovoltaic Electric Vehicles

The development of vehicle integrated photovoltaics-powered electric vehicles (VIPV-EV) significantly reduces CO2 emissions from the transport sector to realize a decarbonized society. Although long-distance driving of VIPV-EV without electricity charging is expected in sunny regions, driving distance of VIPV-EV is affected by climate conditions such as solar irradiation and temperature rise of PV modules. In this paper, detailed analytical results for effects of climate conditions such as solar irradiation and temperature rise of PV modules upon driving distance of the VIPV-EV were presented by using test data for Toyota Prius and Nissan Van demonstration cars installed with high-efficiency InGaP/GaAs/InGaAs 3-junction solar cell modules with a module efficiency of more than 30%. The temperature rise of some PV modules studied in this study was shown to be expressed by some coefficients related to solar irradiation, wind speed and radiative cooling. The potential of VIPV-EV to be deployed in 10 major cities was also analyzed. Although sunshine cities such as Phoenix show the high reduction ratio of driving range with 17% due to temperature rise of VIPV modules, populous cities such as Tokyo show low reduction ratio of 9%. It was also shown in this paper that the difference between the driving distance of VIPV-EV driving in the morning and the afternoon is due to PV modules’ radiative cooling. In addition, the importance of heat dissipation of PV modules and the development of high-efficiency PV modules with better temperature coefficients was suggested in order to expand driving range of VIPV-EV. The effects of air-conditioner usage and partial shading in addition to the effects of temperature rise of VIPV modules were suggested as the other power losses of VIPV-EV.

Analysis and Modeling of Time Output Characteristics for Distributed Photovoltaic and Energy Storage

Due to the unpredictable output characteristics of distributed photovoltaics,their integration into the grid can lead to voltage fluctuations within the regional power grid.Therefore,the development of spatial-temporal coordination and optimization control methods for distributed photovoltaics and energy storage systems is of utmost importance in various scenarios.This paper approaches the issue from the perspective of spatiotemporal forecasting of distributed photovoltaic(PV)generation and proposes a Temporal Convolutional-Long Short-Term Memory prediction model that combines Temporal Convolutional Networks(TCN)and Long Short-Term Memory(LSTM).To begin with,an analysis of the spatiotemporal distribution patterns of PV generation is conducted,and outlier data is handled using the 3σ rule.Subsequently,a novel approach that combines temporal convolution and LSTM networks is introduced,with TCN extracting spatial features and LSTM capturing temporal features.Finally,a real spatiotemporal dataset from Gansu,China,is established to compare the performance of the proposed network against other models.The results demonstrate that the model presented in this paper exhibits the highest predictive accuracy,with a single-step Mean Absolute Error(MAE)of 1.782 and an average Root Mean Square Error(RMSE)of 3.72 for multi-step predictions.

Gaussian Kernel Based SVR Model for Short-Term Photovoltaic MPP Power Prediction

Predicting the power obtained at the output of the photovoltaic(PV)system is fundamental for the optimum use of the PV system.However,it varies at different times of the day depending on intermittent and nonlinear environmen-tal conditions including solar irradiation,temperature and the wind speed,Short-term power prediction is vital in PV systems to reconcile generation and demand in terms of the cost and capacity of the reserve.In this study,a Gaussian kernel based Support Vector Regression(SVR)prediction model using multiple input variables is proposed for estimating the maximum power obtained from using per-turb observation method in the different irradiation and the different temperatures for a short-term in the DC-DC boost converter at the PV system.The performance of the kernel-based prediction model depends on the availability of a suitable ker-nel function that matches the learning objective,since an unsuitable kernel func-tion or hyper parameter tuning results in significantly poor performance.In this study for thefirst time in the literature both maximum power is obtained at max-imum power point and short-term maximum power estimation is made.While evaluating the performance of the suggested model,the PV power data simulated at variable irradiations and variable temperatures for one day in the PV system simulated in MATLAB were used.The maximum power obtained from the simu-lated system at maximum irradiance was 852.6 W.The accuracy and the perfor-mance evaluation of suggested forecasting model were identified utilizing the computing error statistics such as root mean square error(RMSE)and mean square error(MSE)values.MSE and RMSE rates which obtained were 4.5566*10-04 and 0.0213 using ANN model.MSE and RMSE rates which obtained were 13.0000*10-04 and 0.0362 using SWD-FFNN model.Using SVR model,1.1548*10-05 MSE and 0.0034 RMSE rates were obtained.In the short-term maximum power prediction,SVR gave higher prediction performance according to ANN and SWD-FFNN.

Equivalent Method of Integrated Power Generation System of Wind, Photovoltaic and Energy Storage in Power Flow Calculation and Transient Simulation

针对工程实际开展风光储联合发电系统在潮流计算和机电暂态仿真中的等值方法研究，旨在为大容量风光储联合发电系统的并网仿真分析奠定基础。将潮流计算的等值分为单元机组和集电系统2部分来研究。单元机组等值采用根据不同控制模式选取不同节点类型的方法，针对集电系统等值提出基于损耗不变原则的方法。等值模型和详细模型的算例结果表明，潮流计算等值方法具有较好的精度。在机电暂态仿真动态等值中，基于实际工程计算的最严重工况分析原则，提出运行在满出力点的单机“倍乘”等值模型，为工程计算中的风光储联合发电系统动态等值提供了一种解决方案。

太阳能PV/T光储直驱热电联产系统性能

面对居民日益增长的生活热水和电能需求,光伏/光热(photovoltaic/thermal,PV/T)技术的应用可以降低建筑运行时的能源消耗。本文介绍了一种太阳能PV/T光储直驱热电联产(combined heat and power,CHP)系统,为了减少系统运行过程中的能量损失,采用直流压缩机和储能电池,并在兰州地区对系统的运行性能开展了实验测试。研究结果表明,PV/T系统的光伏板温度相比传统PV组件温度平均降低12.26℃,平均发电效率相对提升8.1%。在将24.4~27.2℃的水加热到50.1~50.7℃的过程中,平均性能系数(coefficient of performance,COP)可达到5.48,相比传统空气源热泵热水器提高82.1%~106.8%。平均集热效率和综合效率分别为37.30%和71.24%,PV/T系统的发电量和耗电量分别为3.33kWh和1.69kWh,发电量相比PV系统提高5.7%。太阳能PV/T光储直驱热电联产系统可以减少建筑部门的能源消耗,并提升PV/T系统的发电效率和综合效率,在晴天条件下可以实现离网运行。

Weather Prediction With Multiclass Support Vector Machines in the Fault Detection of Photovoltaic System

Since the efficiency of photovoltaic(PV) power is closely related to the weather,many PV enterprises install weather instruments to monitor the working state of the PV power system.With the development of the soft measurement technology,the instrumental method seems obsolete and involves high cost.This paper proposes a novel method for predicting the types of weather based on the PV power data and partial meteorological data.By this method,the weather types are deduced by data analysis,instead of weather instrument A better fault detection is obtained by using the support vector machines(SVM) and comparing the predicted and the actual weather.The model of the weather prediction is established by a direct SVM for training multiclass predictors.Although SVM is suitable for classification,the classified results depend on the type of the kernel,the parameters of the kernel,and the soft margin coefficient,which are difficult to choose.In this paper,these parameters are optimized by particle swarm optimization(PSO) algorithm in anticipation of good prediction results can be achieved.Prediction results show that this method is feasible and effective.

A PV powered shunt active power filter for power quality improvement

This paper deals with power quality improvement using a three-phase active power filter(APF) connected to a PV power system. A direct power control(DPC) approach is proposed to eliminate harmonic current caused by any nonlinear loads and at the same time guarantees the delivery of a part of the load request from the same PV source. A boost converter is used for maximum power point(MPP) tracking purposes under various climate conditions through a fuzzy logic technique. The suggested study is tested under a MATLAB/Simulink environment. The obtained results depict the efficacy of the proposed procedures to meet the IEEE 519-1992 standard recommendation on harmonic levels.

Comparative assessment of maximum power point tracking procedures for photovoltaic systems

The fast growing demands and increasing awareness for the environment, PV systems are being rapidly installed for numerous applications.However, one of the important challenges in utilizing a PV source is the maximum power harnessing using various maximum power point tracking techniques available. With the large number of MPPT techniques, each having some merits and demerits, confusion is always there for their proper selection. Discussion on various proposed procedures for maximum power point tracking of photovoltaic array has been done. Based on different parameters analysis of MPPT techniques is carried out. This assessment will serve as a suitable reference for selection, understanding different ways and means of MPPT.

Overview of Maximum Power Point Tracking Control Methods for PV Systems

Maximum power point tracking (MPPT) controllers play an important role in photovoltaic systems. They maximize the output power of a PV array for a given set of conditions. This paper presents an overview of the different MPPT techniques. Each technique is evaluated on its ability to detect multiple maxima, convergence speed, ease of implementation, efficiency over a wide output power range, and cost of implementation. The perturbation and observation (P & O), and incremental conductance (IC) algorithms are widely used techniques, with many variants and optimization techniques reported. For this reason, this paper evaluates the performance of these two common approaches from a dynamic and steady state perspective.

Forecasting Model of Photovoltaic Power Based on KPCA-MCS-DCNN

Accurate photovoltaic(PV)power prediction can effectively help the power sector to make rational energy planning and dispatching decisions,promote PV consumption,make full use of renewable energy and alleviate energy problems.To address this research objective,this paper proposes a prediction model based on kernel principal component analysis(KPCA),modified cuckoo search algorithm(MCS)and deep convolutional neural networks(DCNN).Firstly,KPCA is utilized to reduce the dimension of the feature,which aims to reduce the redundant input vectors.Then using MCS to optimize the parameters of DCNN.Finally,the photovoltaic power forecasting method of KPCA-MCS-DCNN is established.In order to verify the prediction performance of the proposed model,this paper selects a photovoltaic power station in China for example analysis.The results show that the new hybrid KPCA-MCS-DCNN model has higher prediction accuracy and better robustness.

Research and practice of designing hydro/ photovoltaic hybrid power system in microgrid

Small-hydro power station is often used in remote areas beside a river,but it doesn't match electricity demand so well,especially in dry seasons. A photovoltaic (PV) system with battery is a suitable option to complement the electricity gap. In this paper,a new structure of megawatt-class PV system integrating battery at DC-bus (DC: direct current) is proposed to be used in hydro/PV hybrid power system,and 4 main designing considerations and several key equipments are discussed. In 2011,a 2 MWp PV station with the proposed structure was built up in Yushu,China. From stability analysis,the station shows a strong stability under load cut-in/off and solar irradiance's fluctuation.

风冷式PV/T空调系统日间冷电联产性能实验研究

提出了一种风冷式PV/T空调系统,对系统在夏季日间的冷电联产性能进行了实验测试,分析了环境因素、运行参数等对系统发电和制冷性能的影响。结果表明:光伏板温度随太阳辐照度的升高而升高,光伏板输出电效率随光伏板温度的升高而降低;太阳辐照度、室外温度和室外风速都对系统制冷性能有影响,其中室外温度对制冷量和COP的影响最大,当室外温度从23.6℃升高到32.8℃时,制冷量增大1.6倍,COP降低38.8%。该风冷式PV/T空调系统在夏季日间具有一定的冷电联产性能,可进一步优化以拓展其使用时间和应用范围。

考虑分布式光伏发电特性的CHPV组合优化调度

近年来光伏装机容量逐年提升,为进一步提高光伏的消纳能力,满足居民的电热需求,本文首先分析分布式光伏的发电特性,提出一种由微型热电联供机组和光伏机组的组合机组,简称CHPV组合,构建以运行成本最低为目标的CHPV组合优化模型,采用K-means聚类方法对某地一年的光伏数据进行聚类,生成3个典型的光伏出力场景并代入CHPV组合优化模型中,采用Cplex对本文所提模型进行仿真验证。结果表明,本文所构建的CHPV组合可有效满足居民不同季节下的电热需求和提高光伏的消纳能力。

A Review on Photovoltaic Systems: Mechanisms and Methods for Irradiation Tracking and Prediction

Solar energy is the raw material and main source for several applications of renewable energy systems;thus, knowledge about the intensity of solar irradiation is essential for efficiency of these systems. Electric energy sources capable of meeting the growing demands of society with minimal impacts to the environment and high efficiency have been object of research in the last decade. In this context, the conversion of sunlight into electricity through photovoltaic cells has become one of the most encouraged and used resources in the world. However, the most unpredictable factor, which hampers capturing solar irradiation, preventing a proper conversion of sunlight into electricity, is the presence of clouds in the sky. Many methods of tracking and prediction of irradiation were proposed to increase efficiency in the production of energy by photovoltaic cells. This article presents an updated review on the mechanisms used for tracking and irradiation prediction, and their respective methods. It begins with a brief review on photovoltaic systems and classification of its mechanisms. Then, it presents a detailed overview on the evolution of mechanisms and their corresponding methods for tracking and irradiation prediction. Finally, the authors conclude with an analysis of performance efficiency of the mechanisms and their corresponding methods presented, describing the pros and cons of the most significant proposals for tracking and irradiation prediction.

Maximum Power Point Tracking Control Using Neural Networks for Stand-Alone Photovoltaic Systems

The employment of maximum power point tracking techniques in the photovoltaic power systems is well known and even of immense importance. There are various techniques to track the maximum power point reported in several literatures. In such context, there is an increasing interest in developing a more appropriate and effective maximum power point tracking control methodology to ensure that the photovoltaic arrays guarantee as much of their available output power as possible to the load for any temperature and solar radiation levels. In this paper, theoretical details of the work, carried out to develop and implement a maximum power point tracking controller using neural networks for a stand-alone photovoltaic system, are presented. Attention has been also paid to the command of the power converter to achieve maximum power point tracking. Simulations results, using Matlab/Simulink software, presented for this approach under rapid variation of insolation and temperature conditions, confirm the effectiveness of the proposed method both in terms of efficiency and fast response time. Negligible oscillations around the maximum power point and easy implementation are the main advantages of the proposed maximum power point tracking (MPPT) control method.

Parasitic Effects on the Performance of DC-DC SEPIC in Photovoltaic Maximum Power Point Tracking Applications

This paper presents an analysis of the effect of parasitic resistances on the performance of DC-DC Single Ended Pri- mary Inductor Converter (SEPIC) in photovoltaic maximum power point tracking (MPPT) applications. The energy storage elements incorporated in the SEPIC converter possess parasitic resistances. Although ideal components significantly simplifies model development, but neglecting the parasitic effects in models may sometimes lead to failure in predicting first scale stability and actual performance. Therefore, the effects of parasitics have been taken into consideration for improving the model accuracy, stability, robustness and dynamic performance analysis of the converter. Detail mathematical model of SEPIC converter including inductive parasitic has been developed. The performance of the converter in tracking MPP at different irradiance levels has been analyzed for variation in parasitic resistance. The converter efficiency has been found above 83% for insolation level of 600 W/m2 when the parasitic resistance in the energy storage element has been ignored. However, as the parasitic resistance of both of the inductor has increased to 1 ohm, a fraction of the power managed by the converter has dissipated;as a result the efficiency of the converter has reduced to 78% for the same insolation profile. Although the increasing value of the parasitic has assisted the converter to converge quickly to reach the maximum power point. Furthermore it has also been observed that the peak to peak load current ripple is reduced. The obtained simulation results have validated the competent of the MPPT converter model.

A Survey of the Researches on Grid-Connected Solar Power Generation Systems and Power Forecasting Methods Based on Ground-Based Cloud Atlas

Photovoltaic power generating is one of the primary methods of utilizing solar energy resources,with large-scale photovoltaic grid-connected power generation being the most efficient way to fully utilize solar energy.In order to provide reference strategies for pertinent researchers as well as potential implementation,this paper tries to provide a survey investigation and technical analysis of machine learning-related approaches,statistical approaches and optimization techniques for solar power generation and forecasting.Deep learning-related methods,in particular,can theoretically handle arbitrary nonlinear transformations through proper model structural design,such as hidden layer topology optimization and objective function analysis to save information that can increase forecasting accuracy while filtering out irrelevant or less affected data for forecasting.The research’s results indicate that RBFNN-AG performed the best when applying the predetermined number of days,with an NRMSE value of 4.65%.RBFNN-AG performs better than sophisticated models like DenseNet(5.69%),SLFN-ELM(5.95%),and ANN-k-means-linear regression correction(6.11%).Additionally,scenario application and PV system investment techniques are provided to evaluate the current condition of new energy development and market trends both domestically and internationally.

基于光储混合配置的级联H桥型光伏逆变器功率平衡策略

级联H桥(cascade H-bridge,CHB)变换器由于其模块化结构,已成为大规模光伏(photovoltaic,PV)并网逆变器的优选方案。然而,在不同光照强度和温度下,不同区域PV组件之间发电功率不同,导致CHB变换器相间输出功率不均衡、不稳定等问题。为此,文中提出一种基于CHB结构的光储混合电能路由,在三相CHB光伏逆变器中配置少量的储能模块,利用光储协同控制,维持CHB内部功率稳定,消除分布式PV在相间产生的不均衡功率,且变换器输出功率能够时刻满足网侧上层功率调度,提高了光伏电站的稳定性。针对系统安全区域,在载波移相调制策略下,全桥子模块传输的功率受到约束,并对光伏和储能模块的配置作详细分析。最后,通过仿真以及实验验证所提拓扑及控制策略的正确性和有效性。