Yindi Jing

In centralized microgrids, secondary control signals are transmitted from the microgrid central controller (MGCC) to each distributed energy resource (DER) to restore voltage and frequency. However, these signals remain vulnerable to cyber attacks. Unlike unidirectional attacks, such as false data injection and denial-of-service attacks, Man-in-the-Middle (MITM) attacks enable bidirectional manipulation of both uplink and downlink communications, making detection and mitigation at the MGCC extremely challenging. To address these practical challenges, a novel autonomous identification and mitigation scheme is proposed in this paper for centralized microgrids, utilizing only the local measurements from each DER. In this scheme, each DER independently determines whether it is under attack by analyzing the trajectories of local measurements and calculated values at the point of common coupling. The attack signals are then reconstructed with the attack frequency and amplitude components estimated via Fast Fourier Transform and least squares estimation. Subsequently, the attacked DER implements mitigation actions by injecting the inverse of the reconstructed attack signals back into the local controller. Compared to existing schemes, the proposed scheme 1) utilizes local measurements only, enhancing resilience against MITM attacks in centralized microgrids; 2) identifies specific attacked secondary control signals; and 3) reconstructs and mitigates complex attack waveforms without relying on additional trustworthy communication links. The proposed scheme is evaluated in MATLAB/Simulink, with simulations confirming its effectiveness in identifying and mitigating MITM attacks.

This work studies a reconfigurable intelligent surface (RIS) equipped receiver for two-user systems, where the receiver is composed of an RIS and two antennas. The user signals incident at the RIS are first reconfigured via phase shifting before arriving at the receiver antennas. A low complexity closed-form reception design is proposed that combines partition-based beamforming at the RIS and zero-forcing (ZF) at the receiver antennas. The user achievable rate is derived through asymptotic statistical analysis and the results show the dependence of the rate on the receiver parameters. Simulation results demonstrate the performance promises of RIS and the reception design.

This article introduces an autonomous aerial vehicle (AAV)-enabled network slicing problem to provide content delivery, sensing data gathering, and mobile edge computing (MEC) services. Three tenants provide services to their clients by sharing a common infrastructure of a set of AAVs. The content delivery tenant needs to guarantee that each of its clients (users) receives the required content, the sensing tenant aims to gather an adequate amount of uncorrelated data, and the MEC tenant provides computing service to its clients. An energy consumption minimization framework is considered to meet the tenants’ requirements by optimizing the number of deployed AAVs, the deployment location of each AAV, the transmit power of each deployed AAV, the user–AAV association, and the transmission power as well as the computing resources of each AAV. Taking into account the spatial correlation among the sensing users, a subset of these users is activated to gather the required sensing information. A solution approach technique inherited from graph theory is presented, in which the Lagrange approach derives the transmission power and computing resource allocation expressions. Simulation results illustrate that the proposed framework significantly reduces the total energy consumption.

Incipient faults frequently occur in underground cables due to the widespread aging of power distribution infrastructure. These faults are self-clearing with short duration, posing significant challenges for accurately pinpointing their locations. This paper proposes a novel incipient fault location scheme for medium-voltage underground distribution systems in North America, leveraging synchronized waveform measurement units (WMUs). Initially, zero-sequence fault voltage models at the fault point are derived from upstream and downstream WMU measurements, incorporating system topology and parameters. Utilizing these zero-sequence fault voltage models, a maximum likelihood estimation (MLE) framework is developed to estimate the fault distance for each line segment, with a whitening process applied to mitigate the effects of correlated noise. Subsequently, a candidate fault line segment is identified for each path from the substation to the end of each branch. Finally, the fault location is pinpointed within one of the candidate fault line segments, using the corresponding fault distance estimation result and the depth between the substation and candidate fault line segments, along with a truncated mean square error (MSE). The performance of the proposed scheme is evaluated in PSCAD/EMTDC based on the IEEE 33-Bus and 123-Bus Test Feeders with various fault types, parameters and locations. The simulation results indicate that the proposed scheme achieves high accuracy in fault location.

This work is on the beamforming designs for the interference cancellation and mitigation in a system where multiple ground base stations equipped with uniform linear antenna arrays communicate with their associated unmanned aerial vehicle (UAV) users with the same time-frequency resource. Under the line-of-sight (LOS) channel condition, the interference between non-associated pairs of ground base stations and UAVs is a prominent issue that degrades the communication performance. For both the uplink and downlink communications, through identifying the beamforming vectors with polynomials, we derive beamforming solutions in closed-forms that can fully cancel the interference and have the highest SINR under the interference-free condition. The SINR expressions of the proposed interference-free designs are also obtained in closed-form, revealing the effect of the systems parameters and the UAV locations. For the uplink transmissions, the SINR-maximum receive beamforming design is also investigated, where the beamforming solution and the SINR result are obtained in closed-forms. Simulation results are provided for the sum-rate performance of the beamforming designs and for the validation of the theoretical analysis.

In power systems, disturbances often result from faults or operational events, making it crucial to accurately identify their sources to prevent system failures and maintain grid stability. Existing research primarily classifies disturbances based on waveform characteristics, such as sags, swells, and transients, without determining their root causes, including incipient faults, constant impedance faults, load switching, and capacitor switching events. This paper proposes a hypothesis testing-based scheme for classifying power distribution disturbances by their root causes, ensuring reliable and interpretable results without extensive datasets. The scheme uses discrete-time voltage and current measurements at substations to develop disturbance models for substation voltages, incorporating disturbance parameters and load impedance. Load impedance is estimated from recent normal cycles, and disturbance parameters are then derived using substation measurements and the estimated load impedance. By substituting these estimated parameters into the corresponding disturbance models, substation voltages for each disturbance type are estimated. The disturbance type is classified by selecting the one that minimizes the normalized mean square error between the estimated and measured substation voltages. The proposed method is evaluated using the IEEE 13-bus test feeder simulated in PSCAD/EMTDC and validated on a two-day real-world power system dataset collected by the IEEE Power & Energy Society Working Group on Power Quality Data Analytics.

In this paper, we investigate the problem of jamming detection and channel estimation during multi-user uplink beam training under random pilot jamming attacks in beamspace massive multi-input-multi-output (MIMO) systems. For jamming detection, we distinguish the signals from the jammer and the user by projecting the observation signals onto the pilot space. By using the multiple projected observation vectors corresponding to the unused pilots, we propose a jamming detection scheme based on the locally most powerful test (LMPT) for systems with general channel conditions. Analytical expressions for the probability of detection and false alarms are derived using the second-order statistics and likelihood functions of the projected observation vectors. For the detected jammer along with users, we propose a two-step minimum mean square error (MMSE) channel estimation using the projected observation vectors. As a part of the channel estimation, we develop schemes to estimate the norm and the phase of the inner-product of the legitimate pilot vector and the random jamming pilot vector, which can be obtained using linear MMSE estimation and a bilinear form of the multiple projected observation vectors. From simulations under different system parameters, we observe that the proposed scheme improves the detection probability by more than 25% compared to the baseline at medium-to-high channel correlation level. In addition, the proposed scheme effectively supports zero-forcing precoding, thus enabling reliable data transmission in jamming scenarios, specifically, achieving a symbol error rate (SER) of less than 10⁻³ at a jamming-to-signal ratio of 10 dB and a signal-to-noise ratio of 5 dB.