A control method and system for network-constructed energy storage to resist voltage ride through and network attacks
CN122931734APending Publication Date: 2026-10-16GUANGDONG POLYTECHNIC NORMAL UNIV
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Patent Information
- Application Number
- CN202611011699.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-07-08
- Publication Date
- 2026-10-16
AI Technical Summary
Technical Problem
上述攻击均可导致GFM-BESS控制器基于错误或缺失的量测信息做出错误决策,在不造成物理扰动的前提下破坏系统正常运行
Benefits of technology
本发明提供一种构网型储能抵御电压穿越与网络攻击的控制方法及系统,包括:采集构网型储能系统的运行数据,基于所述运行数据,识别电压穿越扰动和网络攻击,并生成电压穿越扰动标识和网络攻击标识;基于所述运行数据,构建估算所述构网型储能系统的运行状态,得到估算运行状态,基于所述估算运行状态生成可信状态;基于所述可信状态、电压穿越扰动标识和网络攻击标识,构建弹性补偿项稳定构网型储能系统,并调整构网型储能控制参数;构建深度学习模型,将所述运行数据、可信状态、弹性补偿项、电压穿越扰动标识和网络攻击标识输入至所述深度学习模型中,输出原始控制指令;本发明能够同步识别构网型储能系统的电压穿越扰动与网络攻击,通过构建可信运行状态,结合弹性补偿项实现电压稳定和网络攻击抑制;本发明还构建深度学习模型生成控制指令并进行安全约束校验,有效提升高比例光伏场景下构网型储能运行的稳定性、抗干扰能力与运行安全性。
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Abstract
The application relates to a control method and system for resisting voltage ride-through and network attacks of a grid-forming energy storage, comprising the following steps: collecting operation data of a grid-forming energy storage system, identifying voltage ride-through disturbance and network attacks based on the operation data, and generating voltage ride-through disturbance identification and network attack identification; based on the operation data, constructing an unknown input observer group and estimating the operation state of the grid-forming energy storage system to obtain an estimated operation state, and generating a trusted state based on the estimated operation state; based on the trusted state, the voltage ride-through disturbance identification and the network attack identification, adjusting the parameters of the grid-forming energy storage, and constructing an elastic compensation term to stabilize the grid-forming energy storage system; constructing a deep learning model, inputting the trusted state, the voltage ride-through disturbance identification and the network attack identification into the deep learning model, and outputting an original control instruction; the application has the ability to resist high and low voltage ride-through, and also considers network security, thereby significantly improving the safety of the grid-forming energy storage.
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