基于深度学习的电能配用优化算法在智能电网中的应用研究

随着国内电网规模不断扩大,借助前沿通信技术,智能电网得以实现对电力资源的实时监测,进而提升设备使用效率。为深化电能配用的智能化程度,加入深度学习技术,特别是长短期记忆(Long Short-Term Memory,LSTM)网络,旨在捕捉电力需求数据中潜藏的长期依赖关系,并精准预测未来电力需求走势。在高性能计算环境支持下,利用TensorFlow和Keras框架深入剖析海量的历史电力需求数据。为确保数据质量,对原始数据进行严格的预处理工作,构建适用于深度学习模型的高质量数据集。随后,按照科学的比例将数据集划分为训练集、验证集以及测试集,以便对深度学习模型进行训练和优化。实验结果表明,LSTM网络在测试集上的预测误差低至0.028,预测精度高达95.4%,与传统的循环神经网络(Recurrent Neural Network,RNN)和卷积神经网络(Convolutional Neural Network,CNN)模型相比表现更为优越。该方法有助于提升电力系统的运行效率,为智能电网的进一步发展提供有力支撑。

基于电力物联网的智能配用电信息采集技术研究

针对现有技术中配用电信息采集技术计算框架复杂、数据处理困难等问题,用NB-物联网技术实现对配电网用户用电信息采集和管理的智能化,并构建出智能用电信息采集系统。基于ADE7953电能计量芯片,配合STM32微处理器,实现对用电信息的采集,并通过优化电能计量算法提高采集系统的精准度,大大减少了采集时间。试验结果表明,研究系统的采集准确率提高了约10%,采集效率提高了约33%,智能用电信息采集系统具有高精准度和高效率。

Genetic Algorithm-Based Redundancy Optimization Method for Smart Grid Communication Network

This paper proposes a redundancy optimization method for smart grid Advanced Metering Infrastructure(AMI) to realize economy and reliability targets.AMI is a crucial part of the smart grid to measure,collect,and analyze data about energy usage and power quality from customer premises.From the communication perspective,the AMI consists of smart meters,Home Area Network(HAN) gateways and data concentrators;in particular,the redundancy optimization problem focus on deciding which data concentrator needs redundancy.In order to solve the problem,we first develop a quantitative analysis model for the network economic loss caused by the data concentrator failures.Then,we establish a complete redundancy optimization model,which comprehensively consider the factors of reliability and economy.Finally,an advanced redundancy deployment method based on genetic algorithm(GA) is developed to solve the proposed problem.The simulation results testify that the proposed redundancy optimization method is capable to build a reliable and economic smart grid communication network.