A feature-state observer and suppression control for generation-side low-frequency oscillation of thermal power units

Feng Hong, Yuzheng Zhao, Weiming Ji, Fang Fang, Junhong Hao, Zhenyong Yang, Jingqiu Kang, Lei Chen and Jizhen Liu

Applied Energy, 2024, vol. 354, issue PA, No S030626192301543X

Abstract: To construct a new energy system during the low-carbon transition, renewable energy sources (RESs) are increasingly being installed on a large scale worldwide which require thermal power units to operate at low loads and respond quickly to compensate for frequency fluctuations of the power grid during deep peak shaving and frequent load changes. While coal-fired thermal power plants operate under the deep-peak shaving process, low-frequency oscillation (LFO) accidents from the generation side have occurred constantly. According to our investigation, the reasons are the overlook of the nonlinearity of control valves is obviously highlighted under low load, and the preset parameters of the original control device can not match the flow characteristics, which may seriously cause a recurrence of LFOs. This paper focuses on generation-side LFO caused by the prime mover and its governor under all operating conditions constructing a novel nonlinear model and designing an oscillation feature observation matrix to identify the LFO modes, and a hierarchy feature-state observation classification method preventing LFO on the generation side is proposed. The results show that the proposed method can suppress the LFOs ahead, and this approach can weaken the oscillation amplitude by 85% and reduce the oscillation duration by 30 s.

Keywords: Low-frequency oscillation; Characteristic observation matrix; Security ranking evaluation; LFO control strategy (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (2)

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DOI: 10.1016/j.apenergy.2023.122179

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