State estimation is a vital function in modern power system operation and control, providing the system operator with accurate, real-time information about the network's operating condition. Conventional state estimation methods, such as Weighted Least Squares (WLS), often face challenges under dynamic conditions and in the presence of measurement noise. This paper presents a detailed investigation into the application of Kalman Filtering (KF) techniques for state estimation in power systems. Both standard Kalman Filter (KF) and Extended Kalman Filter (EKF) approaches are discussed in terms of their performance, accuracy, and computational efficiency. Simulation results demonstrate that Kalman filtering techniques offer significant improvements in accuracy and robustness compared to conventional methods, particularly under dynamic load variations and noisy measurement environments