Particle Swarm (PSO) and Bacterial Foraging (BF) Optimizers are two widely used optimization techniques. A proper combination of these two algorithms would improve their search capability while minimizing their shortcomings, such as parameter dependency and premature convergence. This paper presents a hybrid optimization algorithm that combines PSO and BF (HPSBF) to ensure security and the system’s stability following faults and disturbances. The formulated objective function is claimed to be innovative and straightforward.

The set objectives are to minimize the dropped load by shedding relays while maximizing the lowermost swing frequency. The optimal operation of Under-Frequency Load-Shedding (UFLS) Relays is driven by the HPSBF technique as a bounded optimization with bounds representing the limits of the system’s state variables. The viability of the HPSBF is verified against conventional-, PSO-, and BF-UFLS approaches. The standard IEEE 9-bus and IEEE 39-bus systems are exploited to examine the response of the developed UFLS techniques. The tested systems are exposed to various operational scenarios such as loss of power plants and a considerable abrupt load increase. The DigSilent power factor software is used to simulate the IEEE 9- and 39-bus systems, while MATLAB code was implemented to obtain optimal operational points for the implemented algorithms. The HPSBF accomplished the uppermost swing frequency and the lowermost quantity of the disconnected load. Furthermore, the computational times of HPSBF are equivalent to those of the PSO.

粒子群 (PSO) 和细菌觅食 (BF) 优化器是两种广泛使用的优化技术。这两种算法的适当组合将提高它们的搜索能力，同时最大限度地减少它们的缺点，例如参数依赖性和过早收敛。本文提出了一种结合 PSO 和 BF (HPSBF) 的混合优化算法，以确保安全性和系统在故障和扰动后的稳定性。公式化的目标函数据称具有创新性和直接性。

设定的目标是通过去除继电器同时最大化最低摆动频率来最小化下降的负载。欠频减载 (UFLS) 继电器的最佳操作由 HPSBF 技术驱动，作为有界优化，边界表示系统状态变量的限制。HPSBF 的可行性已通过常规、PSO 和 BF-UFLS 方法进行验证。标准 IEEE 9 总线和 IEEE 39 总线系统被用来检查开发的 UFLS 技术的响应。被测试的系统暴露于各种操作场景，例如发电厂的损失和负载的突然增加。DigSilent 功率因数软件用于模拟 IEEE 9 和 39 总线系统，同时实施 MATLAB 代码以获得所实施算法的最佳操作点。HPSBF 完成了断开负载的最高摆频和最低数量。此外，HPSBF 的计算时间与 PSO 的计算时间相同。