A New Damped Double-Tuned Filter to Improve Power Quality and System Performance for Nonlinear Household Loads

The growing use of nonlinear household appliances, such as LED lighting and inverter-based devices, has led to significant power quality problems. This is mainly due to harmonic currents altering the shape of voltage waveforms. Such distortions can lead to increased system losses, transformer overheating, and reduced equipment lifespan. Therefore, this paper proposes an optimized model of a new damped double-tuned filter (DDTF) designed to accommodate dynamic variations in household loads. The particle swarm optimization (PSO) algorithm is used to enhance the design by determining the optimal values for the filter’s constituent parts. Additionally, an artificial neural network (ANN) model is developed to validate and predict filter performance based on experimental data. The DDTF is specifically designed to mitigate dominant harmonics at the 3rd, 5th, and 7th orders. Both simulation and experimental validation were conducted using MATLAB Simulink under realistic household load scenarios. At peak load (2100 W), the unfiltered system exhibited a total harmonic distortion of voltage (THDv) of 155.1%, a total harmonic distortion of current (THDi) of 204.41%, and a power factor of 0.55. After using the new six-stage DDTF at various load levels (from 350 W to 2100 W), the THDv dropped to 7.98%, the THDi fell to 3.57%, and the power factor increased to 0.8089. The ANN-based performance evaluation achieved 94% prediction accuracy, with an error margin of 2% to 6%. These results demonstrate that the designed DDTF is a viable, efficient, and cost-effective approach to mitigating harmonics and enhancing power quality in residential electrical systems.