Hybrid Block Diagonalization Precoding for Multi-User Weighted Sum-Rate Maximization

This paper studies large-scale multi-input multi-output(MIMO)orthogonal frequency division multiplexing(OFDM)communications in a broadband frequency-selective channel,where a massive MIMO base station(BS)communicates with multiple users equipped with multi-antenna.We develop a hybrid precoding design to maximize the weighted sum-rate(WSR)of the users by optimizing the digital and the analog precoders alternately.For the digital part,we employ block-diagonalization to eliminate inter-user interference and apply water-filling power allocation to maximize the WSR.For the analog part,the optimization of the PSN is formulated as an unconstrained problem,which can be efficiently solved by a gradient descent method.Numerical results show that the proposed block-diagonal hybrid precoding algorithm can outperform the existing works.

Stability Prediction in Smart Grid Using PSO Optimized XGBoost Algorithm with Dynamic Inertia Weight Updation

Prediction of stability in SG(Smart Grid)is essential in maintaining consistency and reliability of power supply in grid infrastructure.Analyzing the fluctuations in power generation and consumption patterns of smart cities assists in effectively managing continuous power supply in the grid.It also possesses a better impact on averting overloading and permitting effective energy storage.Even though many traditional techniques have predicted the consumption rate for preserving stability,enhancement is required in prediction measures with minimized loss.To overcome the complications in existing studies,this paper intends to predict stability from the smart grid stability prediction dataset using machine learning algorithms.To accomplish this,pre-processing is performed initially to handle missing values since it develops biased models when missing values are mishandled and performs feature scaling to normalize independent data features.Then,the pre-processed data are taken for training and testing.Following that,the regression process is performed using Modified PSO(Particle Swarm Optimization)optimized XGBoost Technique with dynamic inertia weight update,which analyses variables like gamma(G),reaction time(tau1–tau4),and power balance(p1–p4)for providing effective future stability in SG.Since PSO attains optimal solution by adjusting position through dynamic inertial weights,it is integrated with XGBoost due to its scalability and faster computational speed characteristics.The hyperparameters of XGBoost are fine-tuned in the training process for achieving promising outcomes on prediction.Regression results are measured through evaluation metrics such as MSE(Mean Square Error)of 0.011312781,MAE(Mean Absolute Error)of 0.008596322,and RMSE(Root Mean Square Error)of 0.010636156 and MAPE(Mean Absolute Percentage Error)value of 0.0052 which determine the efficacy of the system.

考虑模糊聚类特性的电网运营风险自动预警系统

当前风险自动预警系统通常采用基于经验值的人工方法设置自动预警阈值,给出的是统一阈值,导致系统虚警率较高。因此,提出基于模糊聚类特性的电网运营风险自动预警系统。设计信息采集器和显示器,应用决策级融合原理和神经网络模型,整合实时采集的电网运营多源异构数据。从购电维度、输配电维度和售电维度入手,确定电网运营风险度量指标,得到风险度量结果。依托模糊聚类特性建立动态聚类图,对风险预警对象进行分类,分析电网运营风险的内容,作为自动预警阈值。根据预警阈值定义风险自动预警线,对比风险度量结果生成包含预警等级的自动预警信息。实验结果表明,所提系统的虚警率为2.67%,满足了电网运营风险自动预警要求。

Boundedness of Hausdorff operators on the power weighted Hardy spaces

In this paper, we consider the two-dimensional Hausdorff operators on the power weighted Hardy space H;（R;） （ -1 ≤α≤0）, defined by H;f（x）=∫R;Φ（u）f(A（u）x)du,where Φ∈L;oc;（R;）,A（u） = (α;（u）);is a 2×2 matrix, and each α;is a measurablefunction.We obtain that HΦ,A is bounded from H;（R;） （ -1≤α≤0） to itself, if∫R2|Φ（u）‖det A;（u）|‖A（u）‖;ln(1+‖A;（u）‖;/|det A;（u）|)du<∞.This result improves some known theorems, and in some sense it is sharp.

Extreme scenario extraction of a grid with large scale wind power integration by combined entropy-weighted clustering method

Large-scale integration of wind power into a power system introduces uncertainties to its operation and planning,making the power system operation scenario highly diversified and variable.In conventional power system planning,some key operation modes and most critical scenarios are typically analyzed to identify the weak and high-risk points in grid operation.While these scenarios may not follow traditional empirical patterns due to the introduction of large-scale wind power.In this paper,we propose a weighted clustering method to quickly identify a system’s extreme operation scenarios by considering the temporal variations and correlations between wind power and load to evaluate the stability and security for system planning.Specifically,based on an annual time-series data of wind power and load,a combined weighted clustering method is used to pick the typical scenarios of power grid operation,and the edge operation points far from the clustering center are extracted as the extreme scenarios.The contribution of fluctuations and capacities of different wind farms and loads to extreme scenarios are considered in the clustering process,to further improve the efficiency and rationality of the extreme-scenario extraction.A set of case studies was used to verify the performance of the method,providing an intuitive understanding of the extreme scenario variety under wind power integration.

Generalizations of mixed weighted power mean inequality

Tarnavas established mixed weighted power mean inequality in 1999. A separation of weighted power mean inequslity was derived in this paper. As its applications, some separations of other inequalities were given.

A Feature Weighted Mixed Naive Bayes Model for Monitoring Anomalies in the Fan System of a Thermal Power Plant

With the increasing intelligence and integration,a great number of two-valued variables(generally stored in the form of 0 or 1)often exist in large-scale industrial processes.However,these variables cannot be effectively handled by traditional monitoring methods such as linear discriminant analysis(LDA),principal component analysis(PCA)and partial least square(PLS)analysis.Recently,a mixed hidden naive Bayesian model(MHNBM)is developed for the first time to utilize both two-valued and continuous variables for abnormality monitoring.Although the MHNBM is effective,it still has some shortcomings that need to be improved.For the MHNBM,the variables with greater correlation to other variables have greater weights,which can not guarantee greater weights are assigned to the more discriminating variables.In addition,the conditional P(x j|x j′,y=k)probability must be computed based on historical data.When the training data is scarce,the conditional probability between continuous variables tends to be uniformly distributed,which affects the performance of MHNBM.Here a novel feature weighted mixed naive Bayes model(FWMNBM)is developed to overcome the above shortcomings.For the FWMNBM,the variables that are more correlated to the class have greater weights,which makes the more discriminating variables contribute more to the model.At the same time,FWMNBM does not have to calculate the conditional probability between variables,thus it is less restricted by the number of training data samples.Compared with the MHNBM,the FWMNBM has better performance,and its effectiveness is validated through numerical cases of a simulation example and a practical case of the Zhoushan thermal power plant(ZTPP),China.

WEIGHTED ESTIMATE FOR THE MULTILINEAR OSCILLATORY SINGULAR INTEGRAL OPERATORS

In this paper, the weighted Lp(Rn) boundedness for the multilinear oscillatory singular integral operators with polynomial phases is studied.

Properties, Inference and Applications of Inverse Power Two-Parameter Weighted Lindley Distribution

We proposed “a new extension of three-parametric distribution” called the inverse power two-parameter weighted Lindley (IPWL) distribution capable of modeling a upside-down bathtub hazard rate function. This distribution is studied to get basic structural properties such as reliability measures, moments, inverse moments and its related measures. Simulation studies are done to present the performance and behavior of maximum likelihood estimates of the IPWL distribution parameters. Finally, we perform goodness of fit measures and test statistics using a real data set to show the performance of the new distribution.

Randomly Weighted Averages on Order Statistics

We study a well-known problem concerning a random variable uniformly distributed between two independent random variables. Two different extensions, randomly weighted average on independent random variables and randomly weighted average on order statistics, have been introduced for this problem. For the second method, two-sided power random variables have been defined. By using classic method and power technical method, we study some properties for these random variables.

Dynamic Equivalent Method of Motor Loads for Power Systems Based on the Weighted

Dynamic equivalence can not only largely reduce the system size and the computation time but also stress the dominant features of the system [1]-[3]. This paper firstly recommends the basic concept of dynamic equivalent and the status of both domestic and abroad development in this area. The most existing equivalent methods usually only deal with static load models and neglect the dynamic characteristics of loads such as induction motors. In addition, the existing polymerization method which is based on the frequency domain algorithm of induction electric machines parameters takes a long time to equivalent for the large system, then the new method based on the weighted is proposed. Then, the basic steps for dynamic equivalence with the weighted method are introduced as follows. At first, the clustering criterion of motor loads based on time domain simulation is given. The motors with similar dynamic characteristics are classified into one group. Then, the simplication of the buses of motors in same group and network is carried out. Finally, parameters of the equivalent motor are calculated and the equivalent system is thus obtained based on the weighted. This aggregation method is applied to the simple distribution system of 4 generators. Simulation results show that the method can quickly obtain polymerization parameters of generator groups and the aggregation model retains the dynamic performance of the original model with good accuracy, the active and reactive power fitting error is smaller as well.

可再生能源电制氢合成氨系统的并/离网运行方式与经济性分析

可再生能源电制氢合成氨(renewablepowerto ammonia,Re Pt A)是风力发电、光伏发电等可再生能源的大规模消纳方式之一,也是化工行业实现低碳清洁发展的重要技术路线之一。利用风电光伏电解水制氢合成氨系统模型,分析电网调峰型系统、电网友好型系统、工艺离网型系统的绿氨成本,并对电化学储能设备规模、储氢设备规模、电解水制氢设备规模、电解水制氢设备效率等影响因素进行敏感性分析。基于3种绿氨系统的设备配置方案和运行方式评估不同类型系统所需的电网调峰电量,提出3种系统所适合的应用场景。研究结论是近期应该发展电网友好型RePtA系统,远期应该发展工艺离网RePtA系统。

基于灰色系统理论的电网工程绿色建造模糊综合评价模型

为贯彻落实绿色发展理念,提升现有电网工程绿色建造评价方法的全面性、系统性以及客观性,提出了一种基于灰色系统理论的模糊综合评价模型。通过研究影响电网工程设计、施工与交付等环节的重要绿色评价因子,构建了多层次的绿色建造评价指标体系。并基于灰色系统理论确定指标权重,建立了多层级模糊综合评价模型。同时选取电网工程案例来进行验证,证明了该模型能够科学、合理地确定各指标的权重,从而为电网工程绿色建造评价及不断优化提供了支撑。

A Weighted Combination Forecasting Model for Power Load Based on Forecasting Model Selection and Fuzzy Scale Joint Evaluation

To solve the medium and long term power load forecasting problem,the combination forecasting method is further expanded and a weighted combination forecasting model for power load is put forward.This model is divided into two stages which are forecasting model selection and weighted combination forecasting.Based on Markov chain conversion and cloud model,the forecasting model selection is implanted and several outstanding models are selected for the combination forecasting.For the weighted combination forecasting,a fuzzy scale joint evaluation method is proposed to determine the weight of selected forecasting model.The percentage error and mean absolute percentage error of weighted combination forecasting result of the power consumption in a certain area of China are 0.7439%and 0.3198%,respectively,while the maximum values of these two indexes of single forecasting models are 5.2278%and 1.9497%.It shows that the forecasting indexes of proposed model are improved significantly compared with the single forecasting models.

INVERSE POWER METHOD AND WEIGHTED SOBOLEV SPACES

1. Introduction This paper is a continuation of the paper [5]. Now we want to treat the problem

含电动汽车和光伏的配电网演化动态博弈调度策略

针对大规模电动汽车的无序充电行为可能导致电网出现的三相不平衡问题,基于演化动态博弈模型,提出一种考虑三相负荷平衡和光伏消纳的电动汽车充电调度策略。首先,基于电动汽车接入时段的主观性以及光伏出力和负荷的不确定性,引入游牧集群和定居集群之间的演化动态博弈方程;然后,考虑用户侧和电网侧双方利益关系,设计以最小化电动汽车充电费用、三相负荷不平衡度和无功需求为目标的调度优化模型;最后,通过对某商业区配电网算例进行仿真求解,对比分析三相有功曲线、三相无功曲线以及荷电状态曲线的变化趋势。算例结果表明,该调度策略可有效降低三相不平衡度,满足无功补偿的需求以及改善用户充电成本。

计及短路比提升与暂态过电压抑制的含高比例风电送端电网两阶段式调相机优化配置

随着送端电网中大规模风电机组的接入及常规同步机组的置换淘汰,电网短路比水平下降,电压支撑能力削弱,故障时易导致新能源机组暂态过电压而发生脱网事故。面向含高比例风电的送端电网,研究综合计及短路比提升和暂态过电压抑制的调相机优化配置方法。首先,利用新能源多场站短路比(multiple renewable energy stations’short-circuit ratio,MRSCR)分析了调相机接入对风电场短路比的提升作用,并基于调相机暂态快速无功响应特性揭示了其对风电机组暂态过电压的抑制机理;然后,提出一种同时计及短路比提升和暂态过电压抑制的两阶段式调相机优化配置策略,第一阶段以风电场短路比提升为核心约束优化配置调相机位置与基础容量,第二阶段以暂态过电压安全为关键约束进行调相机配置容量修正,并引入量子遗传算法(quantum genetic algorithm,QGA)进行优化求解。最终,基于PSD-BPA在含高比例风电的送端电网进行算例分析,验证了所提出的调相机优化配置方法能够显著提升风电机组多场站短路比水平并有效抑制风电机端暂态过电压。

基于柔直控制的南方电网异步联网频率同步运行控制器设计与应用

电网分区异步运行将成为未来南方电网发展趋势,针对分区异步电网间频率失去了同步性和一致性问题,开展分区异步电网频率同步运行控制策略研究,提出了分区异步电网频率同步运行控制器结构,优化确定运行关键控制参数并通过仿真验证其有效性。鲁西柔直工程作为试点实施分区异步电网频率同步运行,现场实施效果表明该控制能够有效保证分区异步运行电网间实际运行中频率的一致性和同步性,且与电网AGC策略具有良好的适配性,实施中未出现两侧电网功率振荡和频率振荡发散情况,有效改善分区电网频率稳定性和提升分区电网间功率的灵活性支援性能,同时也实现全网调频资源的实时、自动共享,推动构建全网统一的调频辅助服务市场。

弹性电网关键技术研究的进展与展望

现代电网发展日益复杂、新的供电威胁与日俱增,特别是在处于大量新能源系统并网发电的发展潮流中,传统的电力系统在遭遇突发冲击或扰动时会显得格外脆弱,已不能满足用户或电力企业愈发严格的用电质量要求。为了使电网在小概率-高损失的事件中仍能保持基本的运行功能,具备抵御性、适应性以及快速恢复能力的“弹性电网”得到了众多学者的青睐,其可有效提高系统的弹性恢复力,增强系统的抗灾抗扰动能力,进而提高系统的整体运营能力。着眼于弹性电网的优化策略,基于弹性恢复力理论阐述了弹性电网的基本架构,根据系统弹性提升的方法分析了发展弹性电网所用到的关键技术,从传统电网到未来电网的演化过程中展望了弹性电网的发展趋势。

基于幂加权最小二乘拟合的红外相机辐射定标方法

分析了现有线性度计算方法的不足,提出基于偏差/测量值的新线性度评价方式,通过该方式最小二乘拟合的低端温度偏差比高端大一个数量级,并采用加权拟合提高系统响应线性度。在分析不同光谱的温度和辐亮度拟合函数基础上,提出一种基于辐亮度倒数幂加权最小二乘线性拟合的地面和星上辐射定标方法,确认最佳权重幂次,建立了幂次n=1的加权最小二乘拟合外黑体线性定标方程、内黑体线性定标方程、内外黑体辐亮度转换模型。针对星上内黑体高精度两点定标建立了基于加权最小二乘拟合内外黑体辐亮度转换模型;针对星上黑体发射率退化提出了基于恒星定标的红外相机星上内黑体辐亮度定标修正方法,建立了外黑体辐亮度、内黑体辐亮度和恒星辐亮度转换模型。采用加权线性拟合后,积分时间10 ms的低温端外黑体反演偏差由1.63 K下降到0.52 K,内黑体偏差反演温度偏差由0.83 K下降到0.39 K,系统响应线性度明显提升。