智能电网广域测量数据融合算法研究
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摘要
针对WAMS海量数据问题,该文以同步相量测量单元(PMU)为基础,提出了4类网内数据融合算法。构建了高速网络层、中间件-服务层、数据融合物理层和高级应用层的体系结构,提出了智能电网广域测量数据融合系统。以电压稳定性监测指标(VSI)为例,设计了适用于该应用的数据融合算法,并对网内数据融合算法进行了仿真验证。结果表明在不影响VSI计算准确性的同时,提出的4种算法均能降低信息开销,实现50%～60%数据传输量的减少,有效提高了网络传输效率。
Based on the massive data of WAMS,based on the synchronous phasor measurement unit(PMU),four types of intra-network data fusion algorithms are proposed,including high-speed network layer,middleware-service layer,data fusion physical layer and advanced application layer. The architecture proposes a smart grid wide-area measurement data fusion system. Taking the voltage stability monitoring index(VSI) as an example,the data fusion algorithm suitable for this application is designed,and the data fusion algorithm in the network is simulated and verified. The results show that the VSI calculation is not affected. At the same time of accuracy,the proposed four algorithms can reduce the information overhead,reduce the data transmission by 50%～60%,effectively improve the network transmission efficiency.
Based on the massive data of WAMS,based on the synchronous phasor measurement unit(PMU),four types of intra-network data fusion algorithms are proposed,including high-speed network layer,middleware-service layer,data fusion physical layer and advanced application layer. The architecture proposes a smart grid wide-area measurement data fusion system. Taking the voltage stability monitoring index(VSI) as an example,the data fusion algorithm suitable for this application is designed,and the data fusion algorithm in the network is simulated and verified. The results show that the VSI calculation is not affected. At the same time of accuracy,the proposed four algorithms can reduce the information overhead,reduce the data transmission by 50%～60%,effectively improve the network transmission efficiency.
引文
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[2] Rahul Gore,Simi P.Valsan.Big data challenges in smart grid IoT(WAMS)deployment[J].2016 8th international conference on communication systems and networks,2016,5-10.
[3]马智远,周凯,许中,等.基于数据融合技术的电能质量扰动关联分析[J].电器与能效管理技术,2019(3):56-63.
[4] Sun K,Hur K,Zhang P.A new unified scheme for controlled power system separation using synchronized phasor measurements[J].IEEE Transactions on Power Systems,2011,26(3):1544-1554.
[5] Darudi A,Bashari M,Javidi M H.Electricity price forecasting using a new data fusion algorithm[J].Iet Generation Transmissicn&Distribution,2015,9(12):1382-1390.
[6]阎博,张昊,郭子明,等.基于多源数据融合的电网故障综合分析与智能告警技术研究与应用[J].中国电力,2018,51(2):39-46.
[7]黄良,高正浩,曹洪,等.一二次系统融合的电网风险评估实用化计算方法及数据建模研究[J].电力系统保护与控制,2016,44(17):104-110.
[8] T. Yang,R. Zhao,W. On the modeling and analysis of communication traffic in intelligent electric power substations[J].IEEE Transactions on Power Delivery,2017,32(3):1329-1338.
[9] Eriksson R,Soder L.Wide-area measurement system-based subspace identification for obtaining linear models to centrally coordinate controllable devices[J].IEEE Transactions on Power Delivery,2011,26(2):988-997.
[10]刘道伟,张东霞,孙华东,等.时空大数据环境下的大电网稳定态势量化评估与自适应防控体系构建[J].中国电机工程学报,2015,35(2):268-276.