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This paper presents a novel data-driven framework to aid in system state estimation when the power system is under unobservable false data injection attacks. The proposed framework dynamically detects and classifies false data injection attacks. Then, it retrieves the control signal using the acquired information. This process is accomplished in three main modules, with novel designs, for detection, classification, and control signal retrieval. The detection module monitors historical changes in phasor measurements and captures any deviation pattern caused by an attack on a complex plane. This approach can help to reveal characteristics of the attacks including the direction, magnitude, and ratio of the injected false data. Using this information, the signal retrieval module can easily recover the original control signal and remove the injected false data. Further information regarding the attack type can be obtained through the classifier module. The proposed ensemble learner is compatible with harsh learning conditions including the lack of labeled data, concept drift, concept evolution, recurring classes, and independence from external updates. The proposed novel classifier can dynamically learn from data and classify attacks under all these harsh learning conditions. The introduced framework is evaluated w.r.t. real-world data captured from the Central New York Power System. The obtained results indicate the efficacy and stability of the proposed framework. [ABSTRACT FROM AUTHOR]

This paper applies a phasor-only state estimator (PSE) to a large power system consisting of two control regions, using synchrophasor data from 56 765/345/230 kV substations. Although the technique has been previously developed, this paper describes a topology processor to determine the redundant clusters of connected buses such that the PSE can be used in real time. This PSE allows corrections for phase biases, transformer taps, and current magnitude scaling. The ability to simultaneously solve the PSE for the two control regions is due to a PMU monitoring a tie-line between these two regions. The results show that with this method, the total vector errors of the measured voltage phasor data in these two control regions average to less than 0.5% under ambient conditions. The PSE also computes virtual phasor measurements on 70 buses that do not have PMUs, including large steam generator and wind-turbine generator substations. [ABSTRACT FROM AUTHOR]

Validation and data quality improvement of phasor data through state estimation is the first step in ensuring that the synchrophasor data is useful for applications in monitoring, visualization, and control. This paper presents a phasor-measurement-based state estimator (PSE) for improving data consistency by identifying angle biases and current scaling errors in the phasor data using the augmented state vector approach. These errors can arise from issues with the Global Positioning Signal (GPS), timing circuits, instrument channels, and/or data channel scaling. The PSE is demonstrated using several sets of disturbance data from the Central New York Power System. The PSE can also provide estimates of line parameters and transformer tap ratios with sufficient measurement redundancy. Finally, the PSE allows the computation of interface power flows for disturbance and stability monitoring. [ABSTRACT FROM AUTHOR]

This paper describes a new method for state estimation of a non-linear AC power system in a non-iterative manner. This method is based on the Kipnis–Shamir relinearization technique that is used to solve over-defined sets of polynomial equations. The technique transforms the equations to a higher dimensional linear space which allows the states to be solved in a non-iterative manner. Given accurate measurements, this new state estimation method provides the same results as traditional iterative state estimation methods, and the proposed method does not require an initial guess of system states nor does it have issues with convergence. [ABSTRACT FROM AUTHOR]