# Application of stochastic fractal search algorithm in novel harmonic blocking filter design for optimizing harmonic mitigation and hosting capacity in electric distribution systems

## TL;DR

This paper uses a new algorithm to design filters that reduce power grid distortions caused by renewable energy sources, improving grid stability and performance.

## Contribution

The novel use of the Stochastic Fractal Search algorithm for optimizing Harmonic Blocking Filters in electric distribution systems.

## Key findings

- SFS-optimized HBF reduces THDV by 78% and TDDI by 90% in TS1 with minimal impact on PLOSS.
- In TS2, the SFS-optimized HBF achieves a 99.96% reduction in TDDI and a 27.6% decrease in PLOSS.
- MOAOS-optimized HBF increases Harmonic-Constrained Hosting Capacity by up to 16.4% in TS3.

## Abstract

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.