Engineering an ultrathin amorphous TiO2 layer for boosting the weatherability of TiO2 pigment with high lightening power

TiO2 pigments are typically coated with inert layers to suppress the photocatalytic activity and improve the weatherability. However, the traditional inert layers have a lower refractive index compared to TiO2, and therefore reduce the lightening power of TiO2. In the present work, a uniform, amorphous, 2.9-nm-thick TiO2 protective layer was deposited onto the surface of anatase TiO2 pigments according to pulsed chemical vapor deposition at room temperature, with Ti Cl4 as titanium precursor. Amorphous TiO2 coating layers exhibited poor photocatalytic activity, leading to a boosted weatherability. Similarly, this coating method is also effective for TiO2 coating with amorphous SiO2 and SnO2 layers. However, the lightening power of amorphous TiO2 layer is higher than those of amorphous SiO2 and SnO2 layers. According to the measurements of photoluminescence lifetime, surface photocurrent density, charge-transfer resistance, and electron spin resonance spectroscopy, it is revealed that the amorphous layer can prevent the migration of photogenerated electrons and holes onto the surface, decreasing the densities of surface electron and hole, and thereby suppress the photocatalytic activity.

Digital Weather Stations as a Part of Wind Power Station

This paper mainly studies Weather Stations part of the wind power station. The use of wind energy in practice is carried out using the facilities of the wind in which the kinetic energy of the windscreen flow is converted into mechanical energy wind speed, then electrical energy alternator. The effective operation of the wind turbine is dependent on the direction of the wind. Speed air density, which in turn depends on the temperature and humidity. Thus, the speed of the wind worked effectively in its composition must include the weather. Meteorological station also performs the role of prevention. When the sharp wind speed or increase wind speed above the maximum value, it sends a signal to the lock assembly wind to prevent wind turbine technology from damage. The work of the meteorological stations design as part of the Wind Energy Station is considered. The complex technical devices are used for its implementation. A set of technical means used to its implementation and designed system consists of a temperature, humidity, wind speed, wind direction and rain gauge sensors that are connected to PIC16f876A microcontroller.

基于中尺度模式的大气稳定性对风电场运行特性

为了了解大气稳定性决定的风速廓线分布对风电场运行特性的影响,基于耦合风电场参数化方案的数值天气预报模式,探索了不同大气稳定性下大型风电场的尾流效应和风力机功率输出规律.结果表明:风电场在稳定大气边界条件下具有明显的尾流效应,随着大气由非稳定性转为强稳定性,尾流呈现加强趋势.另外,上下游风电场的相互干扰在稳定大气边界层下较为明显;功率输出方面,稳定边界条件下风电场功率输出较平均功率高了约4%,而不稳定边界层下低于平均功率输出约9%.此外,地表切应力和大气稳定强度对风力机功率输出影响显著,在不稳定大气边界条件下,大气稳定度可以作为风功率的评估参数,因此传统上仅考虑风切变对功率的影响是片面的.

Weather index insurance for wind energy

As China vigorously promotes the development of new energy,photovoltaic power curtailment and wind power curtailment have been effectively resolved.At the same time,the yield from new energy power generation is becoming an important factor that affects the scale of investment in new energy.This paper focuses on the weather risks faced by wind power producers.By studying current research on weather index insurance in China and abroad,the functions and design methods for weather index insurance have been clarified.In addition,the feasibility of wind-power generation index insurance is discussed.The calculation methods for wind power generation index and the weather index insurance pricing methods for wind power enterprises are proposed.A weather index insurance model for wind power generation was established.The rationality and feasibility of the weather index insurance model proposed in this paper were verified using data from an existing power plant.The simulation results show that wind power enterprises can effectively avoid economic losses caused by weather risks through weather index insurance.

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.

灾害天气下计及一二次设备耦合故障的电网短时风险评估

灾害天气下电网发生短时多重故障有可能超出保护系统预设的反应能力,一二次设备耦合影响将助推多重故障风险发展和蔓延。为此,提出了计及一二次设备耦合故障的短时风险评估方法。首先,分析了一次设备与二次设备之间的交互作用及其对电网短时运行风险的影响。其次,构建了一次设备和二次设备的故障概率模型,重点分析了一二次设备耦合故障引发保护拒动的概率。然后,结合保护系统对超预期故障的反应能力,提出了灾害天气下电网短时多重故障风险评估方法。最后,采用改造的IEEE39节点系统对所提方法进行测试。算例结果表明:受外界气象环境影响,保护系统缺陷暴露导致非期望动作使得电网多重故障风险更为严重;综合考虑灾害天气对一二次设备影响下的短时运行风险,对于发现和消除保护系统薄弱环节及制定电网降风险运行策略具有一定参考价值。

台风灾害下提升电网韧性的实时优化调度策略

针对在台风灾害下输电线路停运可能引发的电网大停电事故,构建了提升电网韧性的实时优化调度策略。运用脆弱性曲线刻画风速与线路故障率关系,并考虑线路负载率对线路故障影响,获得台风灾害下的输电线路故障概率;通过蒙特卡罗抽样生成故障场景集,采用置信集法进行场景削减;在此基础上构建了综合考虑潮流均匀度、受灾区线路负载率和发电成本的多目标实时优化调度模型,将输电线路开断(Optimal Transmission Switching, OTS)引入到模型中以降低受台风灾害影响的线路数量。为提高优化调度模型求解的计算速度,采用Benders分解法进行求解。以IEEE 118节点系统为例进行仿真,验证了所提方法的有效性。

基于天气状态模式识别的SSA-BP神经网络光伏电厂功率及碳减排量预测

文章提出了一种基于天气状态模式识别并结合SSA-BP(Sparrow Search Algorithm-Back Propagation)预测光伏出力的方法。首先,在分析辐照度、温度、风速等参数变化规律基础上,基于高斯混合模型,针对专业天气类型开展分类,获得类晴、类雨和类阴3种典型的广义天气;然后,将数据作为SSA-BP神经网络输入,对光伏电厂出力分类进行预测;最后,结合碳核算方法学对光伏发电项目碳减排量进行核算。结果表明:利用分类识别和改进的SSA-BP神经网络,在3种天气类型预测中平均相对误差分别为0.195,0.243,0.310;SSA-BP与其他模型相比,平均相对误差降低了17.8%~66.7%。此外,预测CO_(2)减排量与实际核算值相对误差为3.37%,亦表现出良好预测效果。

考虑气候风险耦合影响的中长期电力资源规划

考虑气候风险对电力供应和需求的影响进行电力系统气候适应性规划研究十分重要且紧迫。文中从规划视角开展电力系统气候适应性研究,构建考虑气候风险耦合影响的中长期电力资源规划模型,通过电力资源优化配置提升电力系统气候适应水平。结果表明:考虑气候长期变化趋势的规划方案中可再生能源发电量占比在装机容量减少的情况下反而有所增加,可再生能源利用水平提升,系统年均总投资建设成本下降;而考虑极端天气气候风险的规划方案中可控电源年均装机容量和发电量增加,以支撑可再生能源消纳和系统安全稳定运行,可再生能源年均发电量略有减少,系统年均总投资建设成本增加。考虑气候风险耦合影响的电力规划方案能够统筹提升可再生能源利用和系统安全水平,推动可再生能源高质量发展。

Structural Characteristics and Mechanical Properties of Rock Mass in the Field of Tianwan Nuclear Power Plant, China

The structural characteristics and mechanical properties of the rock mass are important parts of the feasibility study on the nuclear power engineering field. In this study, by means of in situ investigation and statistics, the structural plane and joint fissure features of the rock mass were analyzed and discussed at different plots and different depth scopes in the Tianwan Nuclear Power engineering field, the rock mass integrality and its weathered degree were evaluated respectively, and especially, the unfavorable geological phenomena of strongly-weathered cystid existing in the field were studied. According to the results of indoor rock mechanical tests, in combination with drilling, the shallow seismic prospecting, sonic logging and point load tests, the statistical results of physical and mechanical indices of rocks at key plots of the field were analyzed, and the design parameters of the field were calculated. It provided scientific basis for the foundation design of the nuclear power plant.

风电系统高影响天气的时空格局分析

以风电系统为例,基于已有标准和研究,定义和识别了与风速和气温等基本气象要素相关的风电系统高影响天气,并利用近30年气象站观测资料,总结了风电系统高影响天气的时空格局。研究发现,这些高影响天气的区域特征明显:新疆、四川盆地(“三北”、川渝西部和沿海地区)等地多出现影响风电出力的小风天气(大风天气),影响电力设备安全的极大风多出现在我国西部高原山地和东部沿海地区;夏季高温频发(冬季低温频发和电线积冰风险较高)的气象站主要分布在新疆东部、川渝东部、华东中部(东北、蒙西和新疆北部)等地。这些高影响天气的年代际变化也存在差异:我国大部分地区的大风日数和小风日数,以及东北、蒙西和新疆等地的低温日数和电线积冰风险日数在近10年都维持较低水平,这对风电出力和电力系统安全都是有利的;我国大部分区域的极端高温日数近30年来不断增加,这将给我国夏季电力设备的稳定运行和能源保供带来更多风险。

天气雷达站电磁环境影响监测研究

天气雷达站产生的超标电磁辐射会对人体造成严重的损伤。为此,提出天气雷达站电磁环境影响监测与分析。分析天气雷达站电磁影响因素,结合发射天线的方向函数,构建雷达功率密度模型。测试结果表明,对于S波、C波段、X波段,真实值与计算值呈现随距离增加而降低的趋势。对于三个波的频段,当接近天线的远场区时,计算值与真实值逐渐接近,相对误差均较小,说明模型在远场区的适用性更强;研究算法与实测值的拟合程度较高;在模型的应用中,真实反映了某市区的电磁辐射情况。

考虑极端天气冲击过程的风电机组竞争失效可靠性评估

针对风电机组的运行可靠性易受极端天气影响的问题,提出了一种考虑极端天气冲击过程的风电机组竞争失效可靠性评估方法。同时考虑风电机组自然退化、极端天气冲击导致的瞬时退化以及极端天气持续过程产生的加速退化,构建极端天气下的风电机组综合退化模型;考虑风电机组退化过程对其耐冲击性能的影响,建立风电机组竞争失效可靠度模型以评估风电机组可靠性;基于我国北方某风电场工程数据和极端天气历史数据评估了某2 MW风电机组的可靠性并进行了参数灵敏度分析。结果显示,到第5年时,仅考虑自然退化的方法评估风电机组可靠度为0.84,而考虑极端天气冲击过程的方法评估风电机组可靠度为0.178,后者更加符合工程实际。通过模型参数的灵敏度分析发现,运行时间超过3年后,退化失效开始逐渐影响风电机组的可靠性。

Bootstrapped Multi-Model Neural-Network Super-Ensembles for Wind Speed and Power Forecasting

The bootstrap resampling method is applied to an ensemble artificial neural network (ANN) approach (which combines machine learning with physical data obtained from a numerical weather prediction model) to provide a multi-ANN model super-ensemble for application to multi-step-ahead forecasting of wind speed and of the associated power generated from a wind turbine. A statistical combination of the individual forecasts from the various ANNs of the super-ensemble is used to construct the best deterministic forecast, as well as the prediction uncertainty interval associated with this forecast. The bootstrapped neural-network methodology is validated using measured wind speed and power data acquired from a wind turbine in an operational wind farm located in northern China.

融合精细化气象因素与物理约束的深度学习模型在短期风电功率预测中的应用

现有基于深度学习方法的风电功率预测是一种以气象数据为输入的间接预测,其预测精度依赖于气象预报的准确率,然而现有气象预报资料普遍存在分辨率低,预报模式不稳定的问题。同时,深度学习模型完全依赖数据驱动,缺乏物理规律的指导,预测精度难以进一步提升。因此,提出一种精细化气象因素与物理深度学习相结合的方法。首先,通过降尺度与多模式集成技术,对数值天气预报数据进行处理,改善气象预报产品的低分辨率和准确率问题;其次,基于风电场尾流效应和功率曲线两种物理模型,一方面将物理模型嵌入神经网络损失函数作为正则化项,引入物理约束指导学习过程,以构建物理深度学习网络;另一方面,利用物理模型产生预训练样本,解决观测数据不足的情况,构建预训练模型,为后续有监督学习任务提供支持。最后,通过对某市近海风电场的实际数据进行仿真分析,验证了所提出方法的有效性和优越性。

高寒地区纯电动汽车电池热管理系统实用技术探究

新能源汽车动力电池在低温环境下的能量衰减,制约了纯电动汽车的发展。针对电池热管理系统寒冷天气的技术性能,主要基于保温、预热、电池热管理系统管控策略等测试进行分析与研究,为改善动力电池低温性能和对动力电池低温热管理技术的进一步研究提供指导。

基于多模式增量更新的短期光伏功率预测方法

针对传统短期预测模型在特殊天气下准确率低以及未考虑光伏运行环境后续变化的问题,提出一种基于多模式增量更新的短期光伏功率预测方法。在分析气象特征的基础上,根据历史情况预测广义天气类型,在日前根据预测天气类型制定相应的训练函数与数据增强方法,最后基于参数冻结技术对模型进行增量更新,提升了模型对特殊天气的刻画能力以及对后续环境的适应能力。在真实的光伏数据集上进行实验,结果证明该更新方法能有效提高预测准确性。

考虑长周期供需不平衡风险的新型电力系统规划方法

受高比例新能源并网带来的波动性和间歇性影响,新型电力系统的长周期供需不平衡矛盾日益突出。该文将电力系统的长周期供需不平衡风险分为两部分:连续多日无风无光的极端天气场景和月电量供需不平衡风险。首先,选取连续多日无风无光的极端天气场景,提出基于条件风险价值理论(conditional value at risk,CvaR)的月电量不平衡风险评估模型。在此基础上,提出考虑长周期供需不平衡风险的新型电力系统规划方法,通过季节性储能等灵活性资源的优化配置,可有效提升电力系统的长周期平衡能力。最后,基于IEEE RTS-79算例分析论证了所提方法的有效性,并初步讨论季节性储能在平抑长周期供需不平衡风险方面的作用。

Wind Power Forecasting Using Grey Wolf Optimized Long Short-Term Memory Based on Numerical Weather Prediction

Wind power generation is among the most promising and eco-friendly energy sources today. Wind Power Forecasting (WPF) is essential for boosting energy efficiency and maintaining the operational stability of power grids. However, predicting wind power comes with significant challenges, such as weather uncertainties, wind variability, complex terrain, limited data, insufficient measurement infrastructure, intricate interdependencies, and short lead times. These factors make it difficult to accurately forecast wind behavior and respond to sudden power output changes. This study aims to precisely forecast electricity generation from wind turbines, minimize grid operation uncertainties, and enhance grid reliability. It leverages historical wind farm data and Numerical Weather Prediction data, using k-Nearest Neighbors for pre-processing, K-means clustering for categorization, and Long Short-Term Memory (LSTM) networks for training and testing, with model performance evaluated across multiple metrics. The Grey Wolf Optimized (GWO) LSTM classification technique, a deep learning model suited to time series analysis, effectively handles temporal dependencies in input data through memory cells and gradient-based optimization. Inspired by grey wolves’ hunting strategies, GWO is a population-based metaheuristic optimization algorithm known for its strong performance across diverse optimization tasks. The proposed Grey Wolf Optimized Deep Learning model achieves an R-squared value of 0.97279, demonstrating that it explains 97.28% of the variance in wind power data. This model surpasses a reference study that achieved an R-squared value of 0.92 with a hybrid deep learning approach but did not account for outliers or anomalous data.

基于电量-功率的多模态映射的长预见期光伏集群功率预测

现有光伏功率预测方法的时间尺度大多短于7 d,长预见期时间尺度下建立的时序预测模型的拟合能力难以满足需求。提出一种基于电量-功率多模态映射的长预见期(8~15 d)光伏集群功率预测方法。对天气进行分型并提取粗粒度下的光伏出力特性,在此基础上对细粒度电量进行预测;基于出力特性构建约束,将预测电量还原为功率,实现长预见期下光伏功率的有效预测。将所提方法应用于甘肃某光伏集群,预测精度提升了6.58个百分点,在实现长预见期预测的同时,提高了可靠性。