期刊
IET GENERATION TRANSMISSION & DISTRIBUTION
卷 14, 期 16, 页码 3250-3259出版社
INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/iet-gtd.2020.0260
关键词
power system state estimation; phasor measurement; smart power grids; Kalman filters; distributed power generation; expectation-maximisation algorithm; demand side management; smart meters; interpolation; compensation; robust distribution system state estimation; distributed energy resources; demand side response technologies; smart meters; SMs; advanced network automation; advanced metering infrastructures; three-phase unbalanced distribution system state estimation; noisy distribution system measurements; bad data attacks; delayed measurements; hybrid sources; microphasor measurement units; delayed SM measurements; noisy data; IEEE 24 bus system; real-time measurement devices; hybrid measurement devices; network automation; cyber attack; DSSE; SCADA; mu PMUs; Kalman smoother; expectation-maximisation based forecasting; delay compensation; common timestamp; IIT Kanpur smart grid; IEEE 13 bus systems; IEEE 123 bus systems; IEEE 37 bus systems; Hypersim
资金
- Ministry of Power, Govt. of India [MPIIT/EE/2014297]
- IIT Kanpur [MPIIT/EE/2014297]
- UK-India JUICE project [DST/EE/2017092, EP/P003605/1]
- EPSRC [EP/P003605/1] Funding Source: UKRI
With growing connection of distributed energy resources, availability of demand side response technologies, deployment of smart meters, the distribution system needs advanced network automation for running the system efficiently. State estimation is the core driver of network automation. While the output from SMs will make the state estimation more accurate, advanced metering infrastructures come with several challenges such as noisy, erroneous measurement including lost or missed measurements, exposure to cyber attack and so on. This study proposes a three-phase unbalanced distribution system state estimation which is robust against noisy distribution system measurements, bad data attacks and missing or delayed measurements. This method considers measurement from hybrid sources such as SCADA, micro-phasor measurement units (mu PMUs) and SMs. Kalman smoother is used to fill the missing measurements and expectation-maximisation based forecasting is used to interpolate the hybrid measurements to a common timestamp and compensate for the delay in SM measurements. Extensive numerical comparisons are made on IEEE 13, 37 and 123 bus systems to test the robustness of the proposed DSSE against delayed SM measurements and bad or noisy data. An IEEE 24 bus system is modelled and real-time measurement devices are interfaced to it in Hypersim. The data from the hybrid measurement devices of IIT Kanpur smart grid is also used to test the robustness of the proposed method.
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