State Estimation Techniques in Power Systems

State estimation in power systems entails the reconstruction of voltage magnitudes and phase angles across a network from a combination of measurements. Traditionally founded upon supervisory control and data acquisition (SCADA) data, state estimation has evolved to incorporate phasor measurement units (PMUs), advanced metering infrastructure (AMI) and other intelligent electronic devices. Static methods infer a snapshot of system variables, whereas dynamic state estimation tracks time‐varying phenomena, such as generator rotor angles and frequency oscillations. In transmission networks, high observability and robust redundancy permit centralised approaches, often employing variations of the weighted least squares and Kalman filtering families. In contrast, distribution system state estimation (DSSE) contends with sparse measurements, high penetration of renewable generation and unbalanced loading. To address these challenges, researchers have explored decentralised and robust estimators, hybrid data‐driven and model‐based frameworks, and machine learning algorithms that generate pseudo‐measurements to enhance observability. Recent advances have focused on co-optimising sensor placement, managing asynchronous data streams and integrating historical and real-time data for improved accuracy. Global deployment of these techniques underpins reliable grid operation, facilitates integration of intermittent renewables, and supports real-time control, making state estimation indispensable for the modern smart grid.

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State Estimation Techniques in Power Systems publication trend

The graph below shows the total number of articles in state estimation techniques in power systems across all publications each year (not limited to Nature Index journals).

Technical terms

State Estimation (SE): Algorithmic process to infer system voltage magnitudes and phase angles from measurements.

Phasor Measurement Unit (PMU): High-speed, time-synchronised device measuring electrical waveform magnitude and phase.

Supervisory Control and Data Acquisition (SCADA): Infrastructure for collecting slower, asynchronous power system measurements.

Pseudo-measurement: Synthetic data generated to supplement real measurements and improve network observability.

Distribution System State Estimation (DSSE): SE tailored for low-voltage networks with limited sensor coverage and unbalanced loads.

Compressive Sensing: Signal processing technique reconstructing high-dimensional states from sparse measurements using sparsity assumptions.

References

1. Distribution network state estimation based on attention-enhanced recurrent neural network pseudo-measurement modeling. Protection and Control of Modern Power Systems (2023).
2. A Survey of Power System State Estimation Using Multiple Data Sources: PMUs, SCADA, AMI, and Beyond. IEEE Transactions on Smart Grid (2023).
3. Comparisons on Kalman-Filter-Based Dynamic State Estimation Algorithms of Power Systems. IEEE Access (2020).
4. Hybrid State Estimation for Distribution Systems With AMI and SCADA Measurements. IEEE Access (2019).
5. Decentralized Robust Dynamic State Estimation in Power Systems Using Instrument Transformers. IEEE Transactions on Signal Processing (2018).
6. Sparsity Based Approaches for Distribution Grid State Estimation - A Comparative Study. IEEE Access (2020).