Sensors are crucial for the control of unmanned aerial vehicles (UAVs). However, sensor faults will inevitably appear over time. Therefore, it is important to develop a sensor fault detection approach for the reliability of UAV. This article presents a novel model-based UAV fault detection approach based on quadratic Kalman filter (QKF). First, an accurate kinematic and dynamic model of UAVs is established, where the model is linearized and discretized for Kalman filter (KF). Second, the first KF is used for denoising, the secondKF is used to detrend, and residuals are calculated for detection. It is worth mentioning that the second KF is a modified Sage–Husa adaptive KF, which can automatically estimate the measurement noise variance. Compared with traditional approaches, this approach has the advantages of noise reduction, self-adaptation, divergence avoidance, and high detection rate. Simulation and experimental results show the effectiveness of the proposed approach, which can accurately detect the abrupt and incipient fault of an inertial measurement unit (IMU) sensor. At the same time, it can get the higher fault detection rates (FDRs) compared with conventional KF. Furthermore, this approach also provides the leading information and foundation for UAV fault-tolerant control.

To maintain stable and reliable operations in smart grid, accurate state estimation is of paramount importance. However, synthesized false data injection attacks could wisely circumvent conventional bad data detection mechanisms by introducing arbitrary errors to state estimates to seriously affect the entire power system operation. To defend these attacks, phase measurement units are deployed in advance at various locations to reduce the chance of being attacked. However, when the budget of placement is not large enough so that the whole system cannot be covered by phase measurement units, the existing phase measurement unit placement algorithms based on greedy strategies are insufficient in some weak locations due to the nature of greedy strategies. In this paper, we propose a new hybrid attack, which can easily used by attackers to attack the buses with less connectivity and impose adverse impacts to state estimation with a low attack cost so that existing defenses based on greedy strategies become invalid. We future propose a pre-deployment phase measurement unit greedy algorithm for this new attack in which the most vulnerable buses are first protected, and then a greedy-based algorithm is used to deploy other phase measurement units until the whole system is observable. Experimental results on various IEEE standard systems demonstrate the effectiveness of our schemes.

State estimation plays a vital role to ensure safe and reliable operations in smart grid. Intelligent attackers can carefully design a destructive and stealthy false data injection attack (FDIA) sequence such that commonly used weighted least squares estimator combined with residual-based detection method is vulnerable to the FDIA. To effectively defend against an FDIA, in this paper, we propose a robust deviation-based detection method, in which an additional Kalman filter is introduced while retaining the original weighted least squares estimator, so that there are two state estimators. Moreover, an exponential weighting function is also applied to the introduced Kalman filter in our proposed method. When an FDIA occurs, the estimation results of weighted least squares estimator depend only on meter measurements at each time slot, but there is an adjustment process of estimated states for the Kalman filter based on historical states' transitions. Meanwhile, based on the exponential weighting function, estimated measurements in the Kalman filter can be adaptively suppressed for different attack strengths of FDIAs, and then the difference of the results of these two estimators increases. Subsequently, FDIAs can be effectively detected by checking the deviation of estimated measurements about the two estimators with a detection threshold. Experimental results validate the effectiveness of the proposed detection method against FDIAs. The impact of different attack strengths and noise on detection performance is also evaluated and analyzed.

This paper deals with the fault detection of a pneumatic control valve using canonical variate analysis (CVA). CVA can find the optimal linear combinations of p-window and f-window data, so that the correlation between these combinations can be maximized. Based on CVA, the p-window data is considered by traditional hotelling T ² statistic and squared prediction error (SPE) indicators, the corresponding fault detection rates (FDR) are low. In order to improve the FDR, a detection indicator based on SMD (square of the Mahalanobis distance) of the residual is proposed in this paper. The proposed indicator considers not only the information in the p-window data, but also that of the f-window data, which can improve the FDRs. The proposed techniques have been validated using a Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems (DAMADICS) benchmark. It concludes that 14 out of the 19 faults can be successfully detected using the proposed method (CVA-SMD). Simulation results have shown that the CVA-SMD can improve the FDR compared with existing CVA-T ² and CVA-SPE methods. Experiments based on real-world data have also demonstrated that the CVA-SMD has better performance than existing PCA-T ² , PCA-SPE, PCA-SMD, CVA-T ² and CVA-SPE methods.

With the knowledge of the measurement configuration and the topology structure of a power system, attackers can launch false data injection attacks (FDIAs) without detection by existing bad data detection methods in state estimation. The attacks can also introduce errors to estimated state variables, which are critical to grid reliability and operation stability. Existing protection methods cannot handle dynamic and variable network configurations. In this paper, to effectively defend against FDIAs, we propose a canonical variate analysis based detection method which monitors the variation of statistical detection indicators T ² and Q about projected canonical variables before and after attacks. Unlike most statistic models that only consider cross-correlation of discrete measurements constrained by Kirchhoff's Law at each independent sampling time, we also consider the auto-correlation of measurements caused by time series characteristics of varying loads. Experiment results on IEEE-14 bus system demonstrate the effectiveness and accuracy of our proposed method based on both synthetically generated data and real-world electricity data from the New York independent system operator.