Research on the IL-Bagging-DHKELM Short-Term Wind Power Prediction Algorithm Based on Error AP Clustering Analysis

With the continuous advancement of China’s“peak carbon dioxide emissions and Carbon Neutrality”process,the proportion of wind power is increasing.In the current research,aiming at the problem that the forecasting model is outdated due to the continuous updating of wind power data,a short-term wind power forecasting algorithm based on Incremental Learning-Bagging Deep Hybrid Kernel Extreme Learning Machine(IL-Bagging-DHKELM)error affinity propagation cluster analysis is proposed.The algorithm effectively combines deep hybrid kernel extreme learning machine(DHKELM)with incremental learning(IL).Firstly,an initial wind power prediction model is trained using the Bagging-DHKELM model.Secondly,Euclidean morphological distance affinity propagation AP clustering algorithm is used to cluster and analyze the prediction error of wind power obtained from the initial training model.Finally,the correlation between wind power prediction errors and Numerical Weather Prediction(NWP)data is introduced as incremental updates to the initial wind power prediction model.During the incremental learning process,multiple error performance indicators are used to measure the overall model performance,thereby enabling incremental updates of wind power models.Practical examples show the method proposed in this article reduces the root mean square error of the initial model by 1.9 percentage points,indicating that this method can be better adapted to the current scenario of the continuous increase in wind power penetration rate.The accuracy and precision of wind power generation prediction are effectively improved through the method.

Short-Term Power Load Forecasting with Hybrid TPA-BiLSTM Prediction Model Based on CSSA

Since the existing prediction methods have encountered difficulties in processing themultiple influencing factors in short-term power load forecasting,we propose a bidirectional long short-term memory(BiLSTM)neural network model based on the temporal pattern attention(TPA)mechanism.Firstly,based on the grey relational analysis,datasets similar to forecast day are obtained.Secondly,thebidirectional LSTM layermodels the data of thehistorical load,temperature,humidity,and date-type and extracts complex relationships between data from the hidden row vectors obtained by the BiLSTM network,so that the influencing factors(with different characteristics)can select relevant information from different time steps to reduce the prediction error of the model.Simultaneously,the complex and nonlinear dependencies between time steps and sequences are extracted by the TPA mechanism,so the attention weight vector is constructed for the hidden layer output of BiLSTM and the relevant variables at different time steps are weighted to influence the input.Finally,the chaotic sparrow search algorithm(CSSA)is used to optimize the hyperparameter selection of the model.The short-term power load forecasting on different data sets shows that the average absolute errors of short-termpower load forecasting based on our method are 0.876 and 4.238,respectively,which is lower than other forecastingmethods,demonstrating the accuracy and stability of our model.

System Outage Probability and Diversity Analysis of a SWIPT Based Two-Way DF Relay Network Under Transceiver Hardware Impairments

This paper investigates the system outage performance of a simultaneous wireless information and power transfer(SWIPT)based two-way decodeand-forward(DF)relay network,where potential hardware impairments(HIs)in all transceivers are considered.After harvesting energy and decoding messages simultaneously via a power splitting scheme,the energy-limited relay node forwards the decoded information to both terminals.Each terminal combines the signals from the direct and relaying links via selection combining.We derive the system outage probability under independent but non-identically distributed Nakagami-m fading channels.It reveals an overall system ceiling(OSC)effect,i.e.,the system falls in outage if the target rate exceeds an OSC threshold that is determined by the levels of HIs.Furthermore,we derive the diversity gain of the considered network.The result reveals that when the transmission rate is below the OSC threshold,the achieved diversity gain equals the sum of the shape parameter of the direct link and the smaller shape parameter of the terminalto-relay links;otherwise,the diversity gain is zero.This is different from the amplify-and-forward(AF)strategy,under which the relaying links have no contribution to the diversity gain.Simulation results validate the analytical results and reveal that compared with the AF strategy,the SWIPT based two-way relaying links under the DF strategy are more robust to HIs and achieve a lower system outage probability.

Power Control for Fading Channels in Cognitive Radio Networks with Outage-Probability Minimization

Cognitive radio allows Secondary Users(SUs) to dynamically use the spectrum resource licensed to Primary Users(PUs),and significantly improves the efficiency of spectrum utilization and is viewed as a promising technology.In cognitive radio networks,the problem of power control is an important issue.In this paper,we mainly focus on the problem of power control for fading channels in cognitive radio networks.The spectrum sharing underlay scenario is considered,where SUs are allowed to coexist with PUs on the condition that the outage probability of PUs is below the maximum outage probability threshold limitation due to the interference caused by SUs.Moreover,besides the outage probability threshold which is defined to protect the performance of PUs,we also consider the maximum transmit power constraints for each SU.With such a setup,we emphasize the problem of power control to minimize the outage probability of each SU in fading channels.Then,based on the statistical information of the fading channel,the closed expression for outage probability is given in fading channels.The Dual-Iteration Power Control(DIPC) algorithm is also proposed to minimize the outage probability based on Perron-Frobenius theory and gradient descent method under the constraint condition.Finally,simulation results are illustrated to demonstrate the performance of the proposed scheme.

OPTIMAL POWER ALLOCATION FOR COGNITIVE RADIO UNDER INTERFERENCE OUTAGE CONSTRAINT

Cognitive radio is able to share the spectrum with primary licensed user,which greatly improves the spectrum efficiency.We study the optimal power allocation for cognitive radio to maximize its ergodic capacity under interference outage constraint.An optimal power allocation scheme for the secondary user with complete channel state information is proposed and its approxi-mation is presented in closed form in Rayleigh fading channels.When the complete channel state in-formation is not available,a more practical transmitter-side joint access ratio and transmit power constraint is proposed.The new constraint guarantees the same impact on interference outage prob-ability at primary user receiver.Both the optimal power allocation and transmit rate under the new constraint are presented in closed form.Simulation results evaluate the performance of proposed power allocation schemes and verify our analysis.

Investigating Practical Measures to Reduce Power Outages and Energy Curtailments

This work attempts to investigate some practical measures that may reduce severe power outages that lead to energy curtailments. The first step of this attempt is to explore, from the consumer’s perspective, the adverse effects of the energy curtailments that reflect enormous damages (tangible and intangible) to the residential sector in the city of Riyadh (the capital of the Kingdom of Saudi Arabia). The second step is to propose, analyze, and employ energy conservation strategies that lead to both energy conservation and costs savings. The study results show that some customers will suffer enormous tangible and intangible losses should these outages occur during specific times, seasons, and for prolonged durations. In order to reduce these power outages and hence mitigate their adverse effects and consequences, the study proposes proper practical measures and solutions without compromising the consumers’ needs, satisfaction, and convenience.

Modelling Complete Power Outage Data Using Reliability

Data on time between complete power outages, Time between Failure (TBF) in Uyo were considered. Trend test and serial correlation test were conducted graphically for the data. The tests proved that the data were identically and independently distributed (iid). Summary statistics of the data showed that complete power outage occurred 416 times between the year 2014 and 2018. The maximum likelihood estimation method was used to estimate the parameters of Weibull 2-parameter, Normal, Lognormal 2-parameter and exponential distributions. The values of Kolmogorov-Smirnov, Anderson Darling and Chi-Square statistics were used to determine the best fit distributions. A model for the computation of reliability of electric power was then proposed.

Outage Probability Minimization for Cognitive Relay Network Under Interference Power Constraints

A cognitive relay network model is proposed, which is defined by a source, a destination, a cognitive relay node and a primary user. The source is assisted by the cognitive relay node which is allowed to coexist with the primary user by imposing severe constraints on the transmission power so that the quality of service of the primary user is not degraded by the interference caused by the secondary user. The effect of the cognitive relay node on the proposed cognitive relay network model is studied by evaluating the outage probability under interference power constraints for different fading environments. A relay transmission scheme, namely, decode-and-forward is considered. For both the peak and average interference power constraints, the closed-form outage expressions are derived over different channel fading models. Finally, the analytical outage probability expressions are validated through simulations. The results indicate that the proposed model has better outage probability than direct transmission. It is also found that the outage probability decreases with the increase of interference power constraints. Meanwhile, the outage probability under the average interference power constraint is much less than that under the peak interference power constraint when the average interference power constraint is equal to the peak interference power constraint.

Theory Study and Application of the BP-ANN Method for Power Grid Short-Term Load Forecasting

Aiming at the low accuracy problem of power system short-term load forecasting by traditional methods, a back-propagation artificial neural network （BP-ANN） based method for short-term load forecasting is presented in this paper. The forecast points are related to prophase adjacent data as well as the periodical long-term historical load data. Then the short-term load forecasting model of Shanxi Power Grid （China） based on BP-ANN method and correlation analysis is established. The simulation model matches well with practical power system load, indicating the BP-ANN method is simple and with higher precision and practicality.

Multi-Layer Optimization for Load Scheduling to Manage Unreliable Grid Outages in Developing Countries

This paper describes the significant cost saving opportunities for consumers in developing countries by the use of computational intelligence and demand-side-management techniques to mitigate the massive use of diesel back-up during grid outages. Application of load scheduling optimization is investigated during scheduled power outages, for residential consumer in India. The specific load shifting approaches explored include a day ahead predicted load schedule which is generated by performing a DSM referring to the forecasted day ahead outage. Whereas in reality the predicted may not match the actual outage, thus in these cases a fuzzy logic rule base is referred on real time basis to take corrective action & reach the best optimal load schedule possible to attain the lowest cost. The load types modeled include passive loads and schedulable, i.e. typically heavy loads. It is found that this multi-level DSM schemes show excellent benefits to the consumer. The maximum diesel savings for the consumer due to load shifting can be approximately ranging from 45% to as high as 75% for a flat-tariff grid. The study also showed that the actual savings potential depends on the timing of power outage, duration and the specific load characteristics.

Outage Performance of Non-Orthogonal Multiple Access Based Unmanned Aerial Vehicles Satellite Networks

With rapid development of unmanned aerial vehicles(UAVs), more and more UAVs access satellite networks for data transmission. To improve the spectral efficiency, non-orthogonal multiple access(NOMA) is adopted to integrate UAVs into the satellite network, where multiple satellites cooperatively serve the UAVs and mobile terminal using the Ku-band and above. Taking into account the rain fading and the fading correlation, the outage performance is first analytically obtained for fixed power allocation and then efficiently calculated by the proposed power allocation algorithm to guarantee the user fairness. Simulation results verify the outage performance analysis and show the performance improvement of the proposed power allocation scheme.

A Levenberg–Marquardt Based Neural Network for Short-Term Load Forecasting

Short-term load forecasting (STLF) is part and parcel of theefficient working of power grid stations. Accurate forecasts help to detect thefault and enhance grid reliability for organizing sufficient energy transactions.STLF ranges from an hour ahead prediction to a day ahead prediction. Variouselectric load forecasting methods have been used in literature for electricitygeneration planning to meet future load demand. A perfect balance regardinggeneration and utilization is still lacking to avoid extra generation and misusageof electric load. Therefore, this paper utilizes Levenberg–Marquardt(LM) based Artificial Neural Network (ANN) technique to forecast theshort-term electricity load for smart grids in a much better, more precise,and more accurate manner. For proper load forecasting, we take the mostcritical weather parameters along with historical load data in the form of timeseries grouped into seasons, i.e., winter and summer. Further, the presentedmodel deals with each season’s load data by splitting it into weekdays andweekends. The historical load data of three years have been used to forecastweek-ahead and day-ahead load demand after every thirty minutes makingload forecast for a very short period. The proposed model is optimized usingthe Levenberg-Marquardt backpropagation algorithm to achieve results withcomparable statistics. Mean Absolute Percent Error (MAPE), Root MeanSquared Error (RMSE), R2, and R are used to evaluate the model. Comparedwith other recent machine learning-based mechanisms, our model presentsthe best experimental results with MAPE and R2 scores of 1.3 and 0.99,respectively. The results prove that the proposed LM-based ANN modelperforms much better in accuracy and has the lowest error rates as comparedto existing work.

Very Short-Term Generating Power Forecasting for Wind Power Generators Based on Time Series Analysis

In recent years, there has been introduction of alternative energy sources such as wind energy. However, wind speed is not constant and wind power output is proportional to the cube of the wind speed. In order to control the power output for wind power generators as accurately as possible, a method of wind speed estimation is required. In this paper, a technique considers that wind speed in the order of 1 - 30 seconds is investigated in confirming the validity of the Auto Regressive model (AR), Kalman Filter (KF) and Neural Network (NN) to forecast wind speed. This paper compares the simulation results of the forecast wind speed for the power output forecast of wind power generator by using AR, KF and NN.

“拉闸限电”事件原因和影响解析:公众评论大数据驱动视角

不同于现有定性理论研究与计量建模分析,本文以2021年“拉闸限电”事件为对象,基于群体智慧理论提出大数据驱动研究框架,集成社会网络分析、主题建模和情感分析等方法研究公众对事件的评论大数据,深入解析突发事件的原因和影响。研究表明:(1)公众对事件的评论聚焦在限电影响、奥论走向、限电原因和解决措施4个方面。(2)通过构建事件内外部归因体系,发现电力需求不断增加与煤炭供应紧张和可再生能源发电不足导致的电力供应减少之间的缺口是主因。(3)识别事件多维影响后发现高耗能企业面临转型压力,政府“一刀切”执行方式和多种误导性信息是引发负面情绪的诱因。本研究为制定突发事件典论引导策略提供了参考。

初始转速与导叶控制策略对变速抽水蓄能机组泵断电过程影响规律研究

在可变速抽水蓄能机组泵断电暂态过程中,因导叶关闭引起的压力波动、回流、转速双峰等不利现象,极大影响了机组及压力管道系统的安全运行;初始转速作为影响机组动态特性的重要参数,其对泵断电过程中转速、流量的影响规律尚不明晰。针对以上问题,基于一维瞬变流理论特征线法和改进Suter变换法,构建可变速抽水蓄能机组调节系统模型。通过对可变速抽水蓄能机组泵断电过程进行模拟,分析了导叶关闭时间对特征参数的影响规律,阐明了初始运行转速与倒流流量、反转转速极值之间的内在联系。结果表明,正转正流阶段和正转逆流阶段的初期各特征参数时域响应趋势极为相近;与此同时,对比不同导叶关闭时间的结果,得出由反转逆流阶段进入停机能够增强机组及压力管道系统安全性的结论;此外,在同一扬程和入力条件下,采用不同初始运行转速对泵断电过程进行模拟,得出流量、转速极值特性与初始运行转速呈负相关的结论。

协同双小区下行协作NOMA-SWIPT中断性能分析

本文研究了在毫微微蜂窝网络(femtocell network,FN)中,协同双小区系统的非正交多址接入(non-orthogonal multiple access,NOMA)与无线携能通信(simultaneous wireless information and power transfer,SWIPT)下行协作通信的中断性能,提出了一种边缘用户在邻基站及源基站随机中心用户共同协作的下行接入方案。所提方案共分为两个时隙:第一时隙内由两基站向所有用户广播叠加信号,提供中继服务的中心用户对其所接收的叠加信号逐级解码并收集能量。第二时隙,中心用户将其第一时隙内所收集的能量作为额外功率资源,在优先保证自身通信质量的前提下对成功解码的边缘用户信息进行再编码转发。基于空间均质泊松点过程(Poisson point process,PPP)中心用户的位置模型,推导了中心用户与边缘用户平均中断概率的表达式,进行了蒙特卡罗仿真验证,同时分析了各仿真参数(中心用户分布半径、用户阈值速率、路径损耗指数等)与中心用户、边缘用户平均中断概率的关系。结果表明:所提方案可以改善边缘用户的下行接入中断性能和系统吞吐量。

基于DF-NOMA辅助的PLC系统安全性能分析

为了探求影响电力通信系统数据安全传输的关键因素,构建基于解码转发(decode-and-forward,DF)中继和非正交多址接入(non-orthogonal multiple access,NOMA)技术辅助的电力线通信(power line communication,PLC)系统,并研究其安全传输性能.针对外部窃听和内部窃听两种情况,联合考虑背景噪声和脉冲噪声的影响,分析系统的可达速率、遍历安全速率和安全中断概率等性能,并利用高斯-切比雪夫求积方法获得其相应的闭合表达式.结果表明:较高的脉冲噪声会降低系统的频谱效率和安全传输性能;功率分配系数以及源用户到中继用户的距离均对系统安全传输产生显著影响.进一步地,通过蒙特卡罗仿真实验验证了理论分析的正确性.

医院周期性电工倒闸对医疗设备的危害分析及应对策略

目的:制定医疗设备配电安全目标与应对策略,最大程度降低电工倒闸对医疗设备的危害。方法:基于危害分析与风险评估(HARA)方法,从医疗设备安全管理角度对2018-2023年北京儿童医院的电工倒闸进行医疗设备危害因素分析与风险评估,采用戴明循环(PDCA)管理法的闭环管理流程,制定精细化的医疗设备配电安全目标与应对策略。结果:2023年医院配电室开展的2次周期性电工倒闸操作共发生6台次设备故障问题,显著低于2018-2022年电工倒闸导致的20.4台次故障平均值,且一级负荷科室等高风险级区域故障为零。结论:制定的医疗设备配电安全目标与应对策略针对性强、执行度高,是实施医疗设备全生命周期精细化管理的有效方法。