Phasor-Aided Distributed State Estimation for Bad Data Detection

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Solution Overview

Problem

Conventional centralized state estimation (CSE) in large-scale power systems faces scalability issues due to rising data management and processing costs, and decentralized state estimation (DSE) methods like ADMM-based DSE require global observability, while hybrid state estimation (HSE) using SCADA and PMU data lacks effective bad data processing under multiple bad data conditions.

Innovation Solution

A decentralized phasor-aided state estimation (DPHASE) method integrates SCADA and PMU measurements using ADMM-based DSE, with local state vector extension and covariance matrices, performing parallel DSE and cross-validation through phasor-aided normalized residual tests to identify and correct bad data.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If centralized state estimation (CSE) is used to manage and process all big data from large-scale power systems, then comprehensive system monitoring is achieved, but data management and processing costs increase significantly

Engineering Contradiction:

Improvesystem monitoring comprehensivenessVSAvoiddata processing cost

Core Design Contradiction:

ReliabilityVSLoss of energy

Solution Approach 1:

The patent divides the centralized state estimation problem into multiple decentralized sub-problems by partitioning the power system into different areas. Each area performs independent state estimation using local measurements and exchanged information with neighboring areas, eliminating the need for a single centralized processor to handle all data, thus reducing data management and processing costs while maintaining comprehensive system monitoring capability

Inventive Principle:

Principle #1Segmentation

2Productivity

If decentralized state estimation (DSE) is implemented to reduce data processing burden, then computation efficiency improves, but bad data identification accuracy decreases under multiple bad data conditions

Engineering Contradiction:

Improvecomputation efficiencyVSAvoidbad data identification accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The patent combines SCADA-based state estimation and PMU-based state estimation in a hybrid framework. The SCADA-based estimation provides comprehensive coverage using algebraic relationships, while the PMU-based estimation provides precise phasor measurements with time synchronization. By merging these two approaches, the system maintains high computation efficiency through decentralized processing while improving bad data identification accuracy through cross-validation between the two estimation methods

Inventive Principle:

Principle #5Merging (Combining)

3Measurement precision

If hybrid state estimation (HSE) using both SCADA and PMU data is performed, then estimation accuracy improves, but the complexity of bad data processing increases

Engineering Contradiction:

Improvestate estimation accuracyVSAvoidbad data processing complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent segments the bad data processing into two independent stages: first, SCADA-based state estimation identifies bad data using normalized residual tests with algebraic relationships; second, PMU-based state estimation identifies bad data using phasor measurements with time synchronization. This segmentation allows each method to process bad data independently according to its strengths, reducing the overall complexity compared to attempting to process all bad data simultaneously in a unified framework

Inventive Principle:

Principle #1Segmentation

4Productivity

If fully distributed approaches are used to avoid central coordination, then data transmission efficiency improves, but requirements for local observability increase

Engineering Contradiction:

Improvedata transmission efficiencyVSAvoidlocal observability requirement

Core Design Contradiction:

ProductivityVSAdaptability or versatility

Solution Approach 1:

The patent creates a hybrid framework that works with both SCADA measurements (which provide comprehensive coverage but lack time synchronization) and PMU measurements (which provide precise phasor data but have limited coverage). This multi-functional approach allows the decentralized system to achieve effective local observability by combining information from both measurement types, maintaining data transmission efficiency while reducing the strict observability requirements that would be needed if only a single measurement type were used

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20250357785A1Method for distribution of phasor-aided state estimation to monitor operating state of large scale power system and method for processing defect data in mixed distributed state estimation by using same

Publication Date: 2025.11.20 POSTECH ACADEMY INDUSTRY FOUNDATION

AI summary

Disclosed are a method for distribution of PHASE to monitor the operating state of a power system by using heterogeneous data obtained from measurement of SCADA and a time synchronized PMU and a method for processing defect data in mixed DSE by using same. The method for distribution of PHASE to monitor the operating state of a large scale power system includes the steps of: defining an extended state variable and an extended state variable set for each region; performing a SCADA-based DSE by using a SCADA measurement value for each region and a covariance matrix thereof, and parallelly performing a PMU-based DSE by using a PMU measurement value for each region and a covariance matrix thereof; and mixing the estimation results of the SCADA-based and the PMU-based DSE algorithms so as to perform a phasor-aided normalized residual test and a general normalized residual test.