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A Network-cognizant Aggregate-frequency Reduced-order Power System Dynamical Model

机译:网络认知的集频降阶电力系统动力学模型

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This paper presents a reduced second-order power-system dynamical model that accounts for locational effects of load disturbances on system frequency dynamics over time scales corresponding to inertial and primary-frequency response. The locational aspects are retained in the proposed model by incorporating linearized power-flow balance into differential equations that describe synchronous-generator dynamics. Individual synchronous-generator speed dynamics are then combined into a single aggregate frequency state via weighting factors that can be tuned to maximize the accuracy of the reduced-order model. The proposed reduced-order model is general in the sense that its parameters are related to those of the original full-order model in analytical closed form, so that it can be constructed easily for different systems. Time-domain simulations demonstrate the accuracy of the reduced-order model with various choices of weighting factors and highlight the effect of load disturbance location on aggregate-frequency dynamics.
机译:本文提出了一种简化的二阶电力系统动力学模型,该模型考虑了负载扰动在与惯性和一次频率响应相对应的时间尺度上对系统频率动力学的位置影响。通过将线性化的潮流平衡纳入描述同步发电机动力学的微分方程中,位置方面在建议的模型中得以保留。各个同步发电机的速度动力学然后通过加权因子组合成单个集合频率状态,这些加权因子可以进行调整以最大程度地降低降阶模型的准确性。所提出的降阶模型是通用的,因为它的参数与解析封闭形式的原始全阶模型的参数有关,因此可以轻松地为不同的系统构造它。时域仿真证明了加权因子的各种选择下降阶模型的准确性,并强调了负载扰动位置对集合频率动态的影响。

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