TS2 parameters.

Power networks are being transformed by the incorporation of renewable energy sources (RES), such as photovoltaic systems and wind turbines, which also promote sustainability and lower carbon emissions. However, the widespread use of inverter-based RES threatens power quality and grid stability, with harmonic distortion being a key issue. System performance is compromised by harmonic distortion, elevating the risk of resonance and overheating equipment and increasing power losses. In this study, the Stochastic Fractal Search (SFS) algorithm is used to develop Harmonic Blocking Filters (HBF), an optimized passive power filter for reducing harmonic distortion and minimizing the system active power losses (PLOSS) in electric distribution systems. Two multi-objective optimization algorithms: Multi-Objective Artificial Hummingbird (MOAHA) and Multi-Objective Atomic Orbital Search (MOAOS) efficiently determine the ideal HBF design to maximize the system’s Harmonic-Constrained Hosting Capacity (HCHC) and minimize PLOSS to support RES while minimizing voltage and current total demand distortion (THDV and TDDI). Three test systems (TS) are used to validate the HBF’s superiority in mitigating the harmonics, minimizing PLOSS, and maximizing HCHC. In TS1, the SFS-optimized HBF decreases THDV by 78% and TDDI by 90% while maintaining PLOSS almost the same as compared to the previously obtained results in the literature. In TS2, the SFS-optimized HBF decreases THDV by 16.2%, TDDI by 99.96%, and PLOSS by 27.6% compared to the uncompensated case with no filter connected. In TS3, the SFS-optimized HBF decreases THDV by 45.71%, TDDI by 99.96%, and PLOSS by 33.26% compared to the uncompensated case. For the HCHC enhancement application, MOAOS has proven superior to MOAHA and the MOAOS-optimized HBF increases the system’s HCHC by 4.18% and in TS3, this value is increased by 16.4% compared to the literature.

随着光伏系统、风力涡轮机等可再生能源（RES）的接入，电力网络正经历深刻变革，此举同时助力可持续发展并降低碳排放。然而，基于变流器的可再生能源大规模应用，对电能质量与电网稳定性构成威胁，其中谐波畸变是核心问题之一。谐波畸变会削弱系统性能，提升谐振风险与设备过热概率，同时增加功率损耗。本研究采用随机分形搜索（SFS）算法，开发谐波阻断滤波器（HBF）——一种优化型无源电力滤波器，用于降低配电系统中的谐波畸变，并最小化系统有功功率损耗（PLOSS）。为最大化系统的谐波约束并网容量（HCHC）、最小化PLOSS以支撑可再生能源并网，同时最小化电压与电流总需求畸变（THDV与TDDI），研究采用两种多目标优化算法：多目标人工蜂鸟（MOAHA）算法与多目标原子轨道搜索（MOAOS）算法，以高效确定HBF的最优设计方案。本研究采用三个测试系统（TS）验证HBF在抑制谐波、降低PLOSS以及提升HCHC方面的优越性。在测试系统TS1中，经SFS优化的HBF相较于已有文献结果，可将THDV降低78%、TDDI降低90%，同时保持PLOSS基本不变。在TS2中，相较于未安装滤波器的无补偿工况，经SFS优化的HBF可使THDV降低16.2%、TDDI降低99.96%、PLOSS降低27.6%。在TS3中，相较于无补偿工况，经SFS优化的HBF可使THDV降低45.71%、TDDI降低99.96%、PLOSS降低33.26%。针对谐波约束并网容量提升应用场景，MOAOS的性能优于MOAHA；经MOAOS优化的HBF可使系统HCHC提升4.18%，而在TS3中，该值相较于已有研究文献提升了16.4%。