Power system often suffers from low frequency oscillations (LFOs) which might result in instability in the long run, if allowed to sustain in the system for a long time. In order to mitigate these oscillations, power system stabilizers (PSS) are used through excitation control. Three recently developed meta-heuristic algorithms namely: Collective Decision Optimization (CDO), Grasshopper Optimization Algorithm (GOA) and Salp Swarm Algorithm (SSA) have been applied for the optimal tuning of PSS parameters for small signal stability analysis of a renewable integrated power network. This was done by designing a conventional speed-based lead-lag PSS in a multi-machine interconnected power system, whose parameters have been tuned using CDO, GOA and SSA in a way to shift all the eigenvalues associated to electromechanical modes to the left half of S plane. Comparison of the results obtained by the algorithms demonstrates the superiority of SSA over GOA and CDO to boost the overall system stability over a wide range of operating conditions. The PSS controller designed using SSA is observed to be more robust and efficient in damping out oscillations under different operating conditions.

中文翻译：

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分析 PSS 和可再生能源并网对区域间电网提高小信号稳定性的影响

电力系统经常受到低频振荡 (LFO) 的影响，如果允许在系统中持续很长时间，从长远来看可能会导致不稳定。为了减轻这些振荡，通过励磁控制使用电力系统稳定器 (PSS)。三种最近开发的元启发式算法，即：集体决策优化 (CDO)、Grasshopper 优化算法 (GOA) 和 Salp Swarm 算法 (SSA) 已应用于 PSS 参数的优化调整，用于可再生综合电网的小信号稳定性分析. 这是通过在多机互连电力系统中设计传统的基于速度的超前滞后 PSS 来完成的，其参数已使用 CDO 进行调整，GOA 和 SSA 以某种方式将与机电模式相关的所有特征值移动到 S 平面的左半部分。通过算法获得的结果的比较表明，SSA 优于 GOA 和 CDO，可在各种操作条件下提高整体系统稳定性。观察到使用 SSA 设计的 PSS 控制器在抑制不同操作条件下的振荡方面更加稳健和有效。