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Design of Multi-Machine Power System Stabilizers with Forecast Uncertainties in Load/Generation

机译:预测负荷/发电不确定性的多机电力系统稳定器的设计

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摘要

Oscillatory instability in modern power system has increased due to increasing complexity and integration of dynamic loads. Stability analysis of such interconnected power system is very prominent in the presence of uncertain load/generation. In this paper, a power system stabilizer (PSS) design approach, which aims at enhancing the oscillatory stability of the multi-machine power system over the specified uncertainty range in forecasted load/generation, is presented. With non-statistical uncertainty, problem of selecting design parameters of the PSS is formulated as an optimization problem with minimization of eigenvalues and damping ratios based multi-objective function. In order to account the non-statistical uncertainties, a boundary active power loss (BAPL) based objective function is proposed. This non-linear BAPL objective function is minimized for determining the optimal setting of the generators voltage and taps of the online tap changing transformers (OLTC) under various power system constraints. In this paper, both the objective functions are solved by a new metaheuristic technique known as gray wolf optimization (GWO). Boundary value-based approach is used to minimize the repeated load flows under uncertain load/generation scenarios. Improved small-signal stability (SSS) is achieved with optimal active power loss of uncertain power system. Eigenvalue and time domain analysis for New England system are carried out under wide range of disturbances to demonstrate the potential of the proposed approach.
机译:由于不断增加的复杂性和动态负载的集成,现代电力系统中的振荡不稳定性有所增加。在不确定的负载/发电情况下,这种互连电源系统的稳定性分析非常重要。本文提出了一种电力系统稳定器(PSS)设计方法,旨在在预测的负荷/发电量的指定不确定性范围内增强多机电力系统的振荡稳定性。在非统计不确定性的情况下,将选择PSS设计参数的问题作为基于多目标函数的特征值和阻尼比最小化的优化问题。为了解决非统计不确定性,提出了基于边界有功功率损耗(BAPL)的目标函数。为了确定在各种电力系统约束下发电机电压和在线抽头变换变压器(OLTC)的抽头的最佳设置,此非线性BAPL目标函数被最小化。在本文中,两个目标函数都通过一种称为灰狼优化(GWO)的新元启发式技术进行求解。基于边界值的方法用于最小化不确定负载/发电情况下的重复负载流量。通过不确定电源系统的最佳有功功率损耗,可以提高小信号稳定性(SSS)。在广泛的干扰下进行了新英格兰系统的特征值和时域分析,以证明该方法的潜力。

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