基于分层节点识别策略的中低压配电网同期线损优化系统的研究

作为电力工业的综合性经济指标之一,线损率可直接反映电网结构及其运行方式的合理性,同时也严重影响着电力企业的经济效益。随着智能电网的快速发展,对配电网的同期线损有了更高的要求。设计了10 kV中低压配电网同期线损优化系统,由数据采集单元、传输单元及分析单元三部分构成。其中,GPRS公网和230 M无线专网作为信息传输的通道;数据分析单元由数据读取、拓扑分析及线损计算构成。针对目前电量采集过程中数据缺失严重、同期线损取数及计算过程中误差较大、可靠性低等问题,在线损计算过程中,引入了分层式节点识别策略,提出了基于支持向量机的负荷节点智能识别方法,可有效、快速求取各种负荷节点的注入电流,从而降低了非同期数据所带来的线损误差。利用实际案例验证了配电网同期线损优化系统在线损计算中的可行性和有效性,该系统可为智能配网的规划设计、生产运行和运营管理等方面提供可靠的依据。

Design and Implementation of the Synchronous Line Loss Measurement System of Medium-voltage Distribution Network

As one of the comprehensive economic indicators of the power industry, the line loss can directly reflect the rationality of the power grid structure and its operation mode, and also seriously affect the economic benefits of power enterprises. With the rapid development of intelligent power grid, there are higher requirements for the synchronous line loss of distribution network. This paper designs a synchronous line loss optimization system of 10 kV middle and low voltage distribution system, which consists of data acquisition unit, transmission unit and analysis unit.Among them, GPRS public network and 230M wireless special network serve as the channel for information transmission. Data analysis unit consists of data reading, topology analysis and line loss calculation. In view of the serious loss of data in the current electricity collection process, the large error and low reliability in the process of acquiring line loss in the same period, in the process of line loss calculation, this paper introduces a hierarchical node identification strategy, puts forward the load node intelligent identification method based on support vector machine (SVM), which can effectively, fastly calculate various load node injection current, reduce the line loss error caused by non-synchronous data. Finally, the feasibility and effectiveness of the system in line loss calculation are verified in practical case. This system could be used to intelligent distribution network planning and design, production and operations management to provide reliable basis.