物联网技术在智能电网中的应用与优势

需积分: 10 9 下载量 63 浏览量 更新于2024-08-10 收藏 457KB PDF 举报
"物联网技术在智能电网的作用-framework for improving critical infrastructure cybersecurity" 物联网技术在智能电网的应用扮演着至关重要的角色,其主要目标是提高电力系统的效率、可靠性和可持续性。智能电网是物联网技术的一个重要应用场景,它能够实现电力设备的状态监测、电力生产的精细化管理以及电力资产的全生命周期管理。以下是对物联网在智能电网中应用的详细说明: 1. **电力设备状态检测**:物联网技术通过安装传感器和设备,能够实时监测电力设施的工作状态,及时发现并预防故障,确保电网稳定运行。 2. **电力生产管理**:物联网技术使得电力公司可以实时掌握发电、输电和配电的详细情况,优化能源分配,降低运营成本。 3. **电力资产全寿命周期管理**:通过物联网,可以追踪设备的使用情况,预测维护需求,从而延长设备寿命,减少不必要的维修费用。 4. **智能用电**:物联网技术促进用户与电网之间的双向交互,比如智能电表可以实现远程读取和控制,帮助用户更好地管理用电,实现节能和效率提升。 5. **用电信息采集**:实时采集和分析用户用电数据,帮助电网公司了解负荷分布,优化资源配置,防止过载或欠载。 6. **智能家居**:物联网技术可以集成到智能家居系统中,实现自动化控制,如自动调整空调、照明等电器的使用,进一步提高能效。 7. **家庭能效管理**:通过物联网,用户可以获得关于能耗的详细报告,鼓励节能行为,如合理安排电器使用时间,减少非必要能耗。 8. **分布式电源接入**:支持分布式能源(如太阳能、风能)的接入和管理,使电网更加灵活,适应可再生能源的波动性。 9. **电动汽车充放电管理**:物联网技术协调电动汽车的充电,平衡电网负荷,避免充电高峰期对电网造成的压力。 10. **实时网络架构**:物联网应用于智能电网的网络架构如图所示,显示了从数据采集到决策控制的整个过程,强调了信息的实时流动和处理。 物联网技术通过集成RFID(射频识别)、无线传感器和定位技术,实现了对电网的全面感知。通过云计算、模糊识别、数据挖掘等智能计算技术,可以对收集到的大量数据进行分析,为电网决策提供依据,提升电网的智能化水平。 在智能电网的发展中,物联网技术是不可或缺的一部分,它不仅解决了传统电网中如储能问题等难题,还为提高供电可靠性、提升用电效率以及节能减排提供了强有力的技术支持。物联网技术的应用有望在智能电网领域实现原创性的突破,引领全球智能电网技术的发展方向。

Compared with homogeneous network-based methods, het- erogeneous network-based treatment is closer to reality, due to the different kinds of entities with various kinds of relations [22– 24]. In recent years, knowledge graph (KG) has been utilized for data integration and federation [11, 17]. It allows the knowledge graph embedding (KGE) model to excel in the link prediction tasks [18, 19]. For example, Dai et al. provided a method using Wasser- stein adversarial autoencoder-based KGE, which can solve the problem of vanishing gradient on the discrete representation and exploit autoencoder to generate high-quality negative samples [20]. The SumGNN model proposed by Yu et al. succeeds in inte- grating external information of KG by combining high-quality fea- tures and multi-channel knowledge of the sub-graph [21]. Lin et al. proposed KGNN to predict DDI only based on triple facts of KG [66]. Although these methods have used KG information, only focusing on the triple facts or simple data fusion can limit performance and inductive capability [69]. Su et al. successively proposed two DDIs prediction methods [55, 56]. The first one is an end-to-end model called KG2ECapsule based on the biomedical knowledge graph (BKG), which can generate high-quality negative samples and make predictions through feature recursively propagating. Another one learns both drug attributes and triple facts based on attention to extract global representation and obtains good performance. However, these methods also have limited ability or ignore the merging of information from multiple perspectives. Apart from the above, the single perspective has many limitations, such as the need to ensure the integrity of related descriptions, just as network-based methods cannot process new nodes [65]. So, the methods only based on network are not inductive, causing limited generalization [69]. However, it can be alleviated by fully using the intrinsic property of the drug seen as local information, such as chemical structure (CS) [40]. And a handful of existing frameworks can effectively integrate multi-information without losing induction [69]. Thus, there is a necessity for us to propose an effective model to fully learn and fuse the local and global infor- mation for improving performance of DDI identification through multiple information complementing.是什么意思

2023-06-11 上传