This work compared the effectiveness of the Kalman Filter and Moving Average Filter methods in minimizing noise and improving the stability of signal readings on a load-cell sensor simulation. The two filtering methods were applied to process the sensor data, to enhance both the precision and stability of the signal readings. According to the test results, the Kalman filter produced a lower average error of 0.0236, compared to 0.0244 when no filter was used, demonstrating its strong ability to reduce noise and signal fluctuations. On the other hand, the Moving Average Filter recorded a slightly higher error of 0.0238. Although it effectively smooths the signal, its performance is less reliab... More >

The integration of renewable energy sources (RES) into modern power systems is transforming the traditional reliance on synchronous generators, leading to a greener energy portfolio while posing significant challenges to system stability due to reduced inertia. Diminished system inertia results in elevated rates of change of frequency (RoCoF) and larger frequency deviations, potentially culminating in blackouts. Accurate inertia estimation is paramount for implementing virtual inertia control and enhancing frequency support services. This study investigates curve-fitting techniques, with a focus on polynomial fitting, for inertia estimation. Simulations are conducted on a modified IEEE 9-bus... More >

The increasing greenhouse gas (GHG) emissions from fossil fuel-based energy systems have accelerated the global push toward cleaner technologies. Bi-facial photovoltaic (BPV) panels, capable of capturing solar irradiance from both sides, have emerged as a promising solution due to their higher energy yield and comparable costs to traditional PV systems. This paper explores the integration of BPV panels into a multi-connected grid comprising nano-, micro-, and main grid layers. A stochastic optimization framework is developed to address the uncertainties of solar irradiance. The problem is formulated as a Mixed-Integer Linear Programming (MILP) model and solved using the Augmented Epsilon Con... More >