摘要
电力系统惯性高效连续追踪本质上涉及两个环节,1)低计算代价辨识数据匹配度最高的模型;2)限时提高惯性时间常数提取值与实际值契合率。因此,采用何种算法实现上述两个环节,直接决定了惯性评估与追踪的效果。针对环节1,采用递归思想大幅降低模型辨识耗时和空间占用;针对环节2,采用基于最大似然估计的贝叶斯网络提高每一时步惯性时间常数提取值与实际值契合率;同时,在算法设计上采用多线程并行计算,极大降低计算耗时。IEEE 39节点系统仿真验证结果表明,所提方法估计的惯性时间常数相对误差均小于5%;耗时较已有方法降低97%;单次模型更新所需通信量仅为16字节,较已有方法大大降低通信成本。
The efficient tracking of power system inertia involves two key processes, namely, first, to identify the best-fit model at a low computational cost;second, to improve the fit probability between the estimated and the actual inertia in time. Accordingly, the effectiveness of the inertia evaluation and its online tracking are determined by whether the above two processes are achieved. In this paper, as for process 1, the recursive algorithm is applied in order to substantially reduce the temporal and space cost of the model identification;as for process 2, Bayesian-net with maximum likelihood estimation is used to improve the fit probability between the extracted and the actual value of the inertia. Besides, the multi-thread parallel computing method is developed to lower the calculation cost substantially. Simulation results in the IEEE 39-Bus System show that, by using the proposed method, the relative errors of the identified inertia are less than 5%,the time consumption is reduced by 97%, and the communication memory is 16 bytes/per update, which are much lower than those of the previous methods.
作者
黄思维
张俊勃
曾繁宏
HUANG Siwei;ZHANG Junbo;ZENG Fanhong(School of Electric Power,South China University of Technology,Guangzhou 510641,China;Guangzhou Power Supply Company,Guangdong Power Grid Corporation,Guangzhou 510620,China)
出处
《高电压技术》
EI
CAS
CSCD
北大核心
2021年第10期3519-3527,共9页
High Voltage Engineering
基金
广东省自然科学基金(2018B030306041)
中央高校基础研究基金(2019SJ01)。
关键词
电力系统惯性的快速追踪和实时监控
惯性识别
类噪声信号
贝叶斯网络
递归法
多线程并行计算
fast-tracking and real-time monitoring of power system inertial
inertial identification
ambient signals
Bayesian-net
recursive method
multi-thread parallel computing
