SYNAPS (Synchronous Analysis and Protection System)

SYNAPS是一个创新项目，汇集了来自电力工程、电力线通信和统计信号处理领域的专家，针对所谓的能源三难困境，即提高能源安全、减少碳排放和降低成本的挑战。SYNAPS的目标是开发一个低压网络的网络配电自动化平台，该平台将提供故障检测，故障分类和故障定位，以及智能保护和重新配置，成本比以前可能的低得多。实际上，该项目将在国家电网中增加一个具有成本效益的智能层，这不仅将解决长期存在的全行业挑战，而且还将为我们的城市和基础设施变得更加智能并向物联网的未来发展，开辟无数其他稳定的、面向未来的增长机会。由于低压电网最初是用于单向能源分配的，因此以前很少有人对其进行监测。然而，由于消费者运营的可再生分布式发电设备、电动汽车的部署不断增加，对网络稳定性的影响，现在出现了一个新的当务之急——更不用说“爆炸的人行道”问题了。目前，分布式发电仅占电网总发电容量的一小部分，因此其对低压电网性能的影响可以忽略不计。然而，对于分布式发电和电动汽车安装数量的增加所带来的影响，特别是当这些设施集中在同一地点的集群时，行业存在重大担忧。低压电网需要能够支持双向电流和实时通信。每年约有9%的电力在配电网中损失，据报道，45%的配电网运营商总网络成本和50%的客户分钟损失是由于低压电缆故障造成的。管理这些新的低碳技术面临着巨大的挑战，但智能电网的早期准备和引入应该会使过渡更容易，并降低总体成本。该项目将利用机器学习方法自动监控低压网络，并检测和定位已知和异常的问题事件。此外，算法也将得到改进，以支持基于软件的网络保护和重新配置。预计这种智能传感器网络将在网络效率和未来证明方面做出重大贡献，并为消费者和欧盟/英国的环境和能源政策目标带来巨大利益。

SYNAPS is an innovative project which brings together experts from thepower engineering, powerline communications, and statistical signalprocessing communities to target the so-termed energy trilemma, namelythe challenge to improve energy security, reduce carbon emissions, andreduce costs.SYNAPS aims to develop a networked distribution automation platform forlow-voltage networks which will provide fault detection, classificationand location of faults, together with smart protection andreconfiguration, at a significantly lower cost than has previously beenpossible. In effect, this project will add a cost-efficient smart layeracross the national power grid which will not only solve long-standing,industry-wide challenges but will also open up countless otheropportunities for stable, future-proofed growth as our cities and infrastructure become smarter and progress to the internet-of-thingsfuture.Since the low-voltage network was originally intended for one-waydistribution of energy, there has been little previous interest inmonitoring it. However, there is now a new imperative created by theimpact on network stability due to the growing deployment of consumeroperated renewable distributed generation equipment, electricvehicles--- not to mention the 'exploding pavements' issue.Currently, distributed generation amounts to only a small proportion ofthe total network generating capacity, hence its impact on low-voltagenetwork performance is negligible. However, there is significantindustry concern about the effects of increased numbers of distributedgeneration and electric vehicle installations, especially when these areconcentrated in co-located clusters.The low-voltage electricity network needs to be able to support two wayelectrical flow and real-time communication. About 9% of electricity islost in the distribution network, annually, and it has been reportedthat 45% of Distribution Network Operator total network costs and 50% ofcustomer minutes lost are due to low-voltage cable faults.Managing these new low carbon technologies present significantchallenges but early preparation and introduction of a Smart Grid shouldmake the transition easier and reduce overall costs. This project willdraw upon machine learning methodology to automatically monitorlow-voltage networks and detect and localise both known, and anomalous,problem events. Furthermore, algorithms will also be progressed tosupport software-based protection and reconfiguration of the network.It is anticipated that such smart sensor networks will make asignificant contribution in network efficiency and future-proofing, andhave immense benefits for both consumers and EU/UK environmental andenergy policy targets.