This work focuses on a mitigating harmonic filter. It investigates the effect of mitigating harmonic filters in the textile industry with innovative energy conservation strategies for energy bill reduction, which covers a pathway to climate change mitigation. Here, the effect of the harmonic filter is found out by the systematic energy audit methodology (Preliminary, Detailed and Post-Audit phase). From the energy auditing, it has been found that the textile industry needed a passive harmonic filter for harmonic mitigation. Since, third, fifth, and seventh order of harmonic predominantly exists in the system. The high stability at higher current, known tuning frequency, low cost, and low power consumption make the passive filter the best fit for the system. The voltage and current Total Harmonic Distortion Factor (THDF) have been measured using the class ‘A’ power quality and energy analyzer. The harmonic filter’s effect in harmonics mitigation is prominent; 66.45% of the reduction of current harmonics is achieved after installing the passive filter at the Point of Common Coupling (PCC) of the system. Also, the reduction of harmonics ensures energy conservation by reducing additional losses (joule, copper, and eddy current losses). The techno-economic analysis with payback period calculation is carried out and reported. Also, the effect of harmonics like mechanical anomalies (temperature rise) is carefully studied using an infrared thermographic technique in the textile industry’s motor loads. The energy conservation and their carbon emission reduction are calculated and reported.

Predicting the need for modeling and solutions is one of the largest difficulties in the electricity system. The static-constrained solution, which is not always powerful, is provided by the Gradient Method Power Flow (GMPF). Another benefit of using both dynamic and transient restrictions is that GMPF will increase transient stability against faults. The system is observed under contingency situations using the Dynamic Stability for Constrained Gradient Method Power Flow (DSCGMPF). The population optimization technique is the foundation of a recent algorithm called Training Learning Based Optimization (TLBO). The TLBO-based approach for obtaining DSCGMPF is implemented in this work. The total system losses and the cost of the individual generators have been optimized. Analysis of the stability limits under contingency conditions has been conducted as well. To illustrate the suggested approaches, a Standard 3 machine 5-bus system is simulated using the MATLAB 2022B platform.