Inference of Tampered Smart Meters with Validations from Feeder-Level Power Injections

Due to the high cost and large error of traditional UAV big data tracking and automatic measurement technology, a method of big data tracking and automatic measurement for UAV trajectory based on MEMS sensor was put forward. The iterative learning control algorithm was used to estimate the repetitive disturbance and modeling error of system based on the simplified dynamics model of four-rotor helicopter and the optimal estimation characteristics of Kalman filter. The discrete equation of quadratic performance function in time domain was selected to compensate the estimated model error disturbance, and then the big data tracking was completed. Based on the data of gyroscope, the quaternion differential equation was established. The differential equation was solved by first-order Picard method, and a set of quaternion data was obtained. The gradient descent method was used to process the acceleration data and magnetic data, and thus to get the optimal quaternion. Finally, the measurement results were obtained by fusing the two quaternions with MEMS sensors. Simulation results prove that the proposed method can obtain the trajectory tracking data and measurement information of UAV accurately.

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“…Then we follow the same approach after adding wind energy profiles. We investigate the detector's performance with a single source of training data, energy generation profiles [12]. We aim to determine which DL models presented with different training settings offer the best detection performance.…”

Section: Et Detection Model Developmentmentioning

confidence: 99%

“…SG has resulted in new forms of energy theft wherein malicious customers endeavor to execute cyberattacks rather than tap on the line or tamper with meters like in the case of TEG. [12].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fine-Tuned RNN-Based Detector for Electricity Theft Attacks in Smart Grid Generation Domain

Eddin

¹

,

Albaseer

²

,

Abdallah

³

et al. 2022

IEEE Open J. Ind. Electron. Soc.

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In this paper, we investigate the problem of electricity theft attacks on smart meters when malicious customers (i.e., adversaries) claim injecting more generated energy into the grid to get more profits from utility companies. These attacks can be applied by accessing the smart meters monitoring renewable-based distributed generation (DG), and manipulating the reading. In this paper, we propose approaches that: (1) rely on data sources with only a single generator (i.e., solar only) and multi-fuel type and (2) address the crucial effects of slight perturbations that the attacker can add, which can deceive the detector. In particular, this paper introduces an efficient multi-task DL-based detector that offers a higher detection rate, copes with different fuel types, and uses only single data sources. The proposed detector incorporates months and days as two additional features to boost the performance and properly guide the model to successful detection. The proposed method is then extended to consider small perturbations that attackers may use to apply successful attacks. We conduct extensive simulations for two different detectors, one for solar DG and the other for multiple fuel types (i.e., solar and wind).Using a realistic dataset, the results reveal that the proposed RNN-based detectors identify adversaries at a higher rate than the existing solutions, even with minimal perturbations and different fuel types.

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“…The multiplication symbol of quaternion is denoted by  , the multiplication rule of quaternion can be expressed as Formula (8): According to the quaternion, the corresponding direction cosine matrix can be obtained by Formula (9) (Tang et al 2019).…”

Section: Big Data Tracking Of Uav Trajectorymentioning

confidence: 99%

Big Data Tracking and Automatic Measurement Technology for Unmanned Aerial Vehicle Trajectory Based on MEMS Sensor

Xing¹,

Wang²,

Dong³

2021

Preprint

0

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Due to the high cost and large error of traditional UAV big data tracking and automatic measurement technology, a method of big data tracking and automatic measurement for UAV trajectory based on MEMS sensor was put forward. The iterative learning control algorithm was used to estimate the repetitive disturbance and modeling error of system based on the simplified dynamics model of four-rotor helicopter and the optimal estimation characteristics of Kalman filter. The discrete equation of quadratic performance function in time domain was selected to compensate the estimated model error disturbance, and then the big data tracking was completed. Based on the data of gyroscope, the quaternion differential equation was established. The differential equation was solved by first-order Picard method, and a set of quaternion data was obtained. The gradient descent method was used to process the acceleration data and magnetic data, and thus to get the optimal quaternion. Finally, the measurement results were obtained by fusing the two quaternions with MEMS sensors. Simulation results prove that the proposed method can obtain the trajectory tracking data and measurement information of UAV accurately.

show abstract

Inference of Tampered Smart Meters with Validations from Feeder-Level Power Injections

Cited by 10 publications

References 31 publications

RETRACTED ARTICLE: Big data tracking and automatic measurement technology for unmanned aerial vehicle trajectory based on MEMS sensor

RETRACTED ARTICLE: Big data tracking and automatic measurement technology for unmanned aerial vehicle trajectory based on MEMS sensor

Fine-Tuned RNN-Based Detector for Electricity Theft Attacks in Smart Grid Generation Domain

Big Data Tracking and Automatic Measurement Technology for Unmanned Aerial Vehicle Trajectory Based on MEMS Sensor

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