A Comprehensive Survey on State Estimation Techniques in Electrical Power Systems

Abstract

State estimation (SE) is a fundamental component of modern electrical power system operation, enabling accurate monitoring, control, and security assessment of the grid. It provides the best possible estimate of system states, such as bus voltage magnitudes and phase angles, using redundant and noisy measurements. With the increasing complexity of power networks, driven by renewable energy integration and smart grid technologies, the role of state estimation has become more critical than ever. This paper presents a comprehensive survey of state estimation techniques used in electrical power systems.
Classical approaches, including the Weighted Least Squares (WLS) and Least Absolute Value (LAV) methods, are discussed in detail. Their mathematical formulations, advantages, and limitations are analyzed to provide a strong theoretical foundation. Robust estimation techniques, such as Huber estimators and Least Median Squares, are also explored for their ability to handle bad data and measurement errors. The paper further examines dynamic state estimation methods based on Kalman filtering, including the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF). These methods are particularly useful for real-time monitoring and dynamic system analysis. The integration of Phasor Measurement Units (PMUs) has significantly enhanced the accuracy and speed of state estimation. PMU-based and hybrid state estimation techniques are reviewed for their ability to provide synchronized and high-resolution measurements. Distribution system state estimation (DSSE) is also discussed, considering the challenges of limited measurements and system uncertainties. In addition, the paper highlights the emergence of artificial intelligence and machine learning techniques in state estimation. These data-driven approaches offer improved performance in handling nonlinearities and large datasets. A comparative analysis of various techniques is presented based on accuracy, robustness, and computational complexity. The paper also addresses key challenges, including bad data detection, cyber-security threats, communication delays, and scalability issues. Special emphasis is given to the impact of renewable energy sources on state estimation accuracy and reliability. The survey further explores recent advancements in hybrid and distributed state estimation frameworks. Future research directions are identified, including AI-integrated estimation, blockchain-based energy systems, and real-time big data analytics. The need for secure and resilient state estimation methods in smart grids is also emphasized. This paper aims to serve as a valuable reference for researchers and practitioners working in the field of power system monitoring and control. It provides a structured overview of existing techniques while highlighting emerging trends and opportunities. The findings of this survey contribute to the development of more efficient and reliable state estimation methods. Ultimately, improved state estimation techniques will enhance the stability, efficiency, and sustainability of modern power systems