Mohammad Ashraf Hossain Sadi

As hyperscale and co-located data centers scale, the electric grid sees an increase in large, voltage-sensitive IT loads with these data center plant size ranging between 500 MW to 2 GW. A sudden loss of these loads as they switch to onsite UPS during grid voltage excursion events causes a grid frequency rise from generation and load imbalance, and a voltage rise because less power is flowing through the network. This paper proposes and theoretically demonstrates the use of high voltage circuit breaker operated braking resistors at data center transmission substations as an effective strategy in enhancing grid resilience under such large load loss scenarios. We developed a test bed to illustrate the dynamic behavior of the system with resistive braking on a gigawatt scale data center load cluster connected to a 345 kV network. The braking resistor(s), which in the case of inverter rich system comes in a multi-stage configuration, are connected or disconnected via high-speed circuit breaker(s). Results show that insertion for 0.25 to 0.85 seconds sufficiently reduce rate of change of frequency and provides time for primary governor response and capacitor switching to restore steady state. Sensitivity across different synchronous machines and inverter-based resource mix are tested and confirms robustness. We conclude circuit breaker controlled resistive braking is a practical means to enhance Bulk Electric System (BES) resilience for gigawatt scale data centers. The approach integrates with protection, needs no generator changes, and can be scaled with cluster size or growth of the data center facility load.

The utilization of data for generation output forecasting in combined cycle power plants (CCPPs) makes room for attackers to exploit and degrade the performance of machine learning models. This work investigates the potential impacts of cyberattacks on energy generation forecasting models for CCPPs. Attacks are implemented on the four top‐performing forecasting models: gradient boosting, extreme gradient boosting, Random Forest, and CatBoost, identified through a comparative analysis of 12 models, including various tree‐based methods, support vector machines, deep learning models, and linear regression techniques. Scaling, denial of service, fast gradient sign method, and basic iterative method attacks are employed with diverse attack volumes and perturbations to investigate the vulnerability of these models. To counteract these vulnerabilities, a two‐layer defence scheme employing ensemble adversarial training is proposed, aimed at mitigating the adverse effects of these cyberattacks. The findings underscore the significance of the proposed robust defence strategy in ensuring the reliability of forecasting models in the presence of cyberattacks.

This paper investigates the month-wise impact of COVID-19 conditions on residential load due to people’s presence at home during office hours in Memphis city, Tennessee, USA. The energy consumption patterns of four consumers are analyzed based on the data available from pre-COVID to continuing COVID-19 situations. It is observed that the energy consumptions from April 2020 to June 2021 for all families have increased during office hours than that of pre-COVID months. Therefore, the impacts of the increased loads on distribution transformer are analyzed in terms of hottest spot, top-oil temperature, and loss of transformer life. Moreover, an experimental setup is made to produce the harmonics in currents of non-linear residential load which has detrimental effects on temperature rise of distribution transformer. In addition, this work proposes implementation of harmonic filter at the load side considering the impact of harmonics produced by loads to reduce the temperature rise due to the increased load consumption and presence of harmonics in currents produced by the load. The rise in temperatures and the loss of life of distribution transformer with and without the proposed solutions are simulated in MATLAB to show the efficacy of the proposed solution. Moreover, cost value analysis among different methods, which can be implemented to reduce the adverse impact on distribution transformer, are provided to rank the available methods.

Smart inverters of distributed energy resources can enable cloud computing, condition monitoring, result visualization, remote control, and peer-to-peer energy trading in advanced power systems. However, the advent of data injection attacks in the communication architecture can alter measurement characteristics of power grids and have devastating consequences. In this article, we propose a time sequence machine learning-based anomaly detection methodology for detecting cyber intrusion into control signal setpoints and dc voltage signal measurement bias of the voltage source converter (VSC) in wind generators. We first investigated the effects of four types of denial of service, tampering signal, and stealthy-type data intrusion attacks on smart VSCs and overall wind farms. We then proposed a novel time sequence machine learning-based intrusion detection framework that can be implemented to detect different cyberattacks in the VSCs. The performance of the proposed framework has been compared with that of autoencoder and clustering-based intrusion detection framework. The proposed framework was validated by using the IEEE 39 bus power system in the presence of four wind farms in different locations. Using several metrics for intrusion detection performance, we validated the effectiveness of the proposed framework.

Geomagnetically induced currents (GICs), although small in magnitude, can inflict detrimental effects on power systems. GICs damage transformers, generate unusual harmonics, trip reactive power support equipment, cause protective relays to malfunction, and may even collapse the entire power system if not mitigated. This paper studies the adverse effects of the negative-sequence harmonics produced by GICs flowing in power transformers. Our simulations show that a transformer’s susceptibility to GIC depends on core type, voltage level, associated harmonics order, and GIC magnitude. High-voltage and extra-high-voltage tests were carried out in SIMULINK. GIC was also emulated experimentally to validate the simulated excitation current and harmonics produced by the GIC. The simulation and experimental results show that the 2nd and the 5th harmonics are the most prominent harmonics associated with GIC and dc saturation. Even if a transformer is ungrounded, the negative-sequence harmonics produced by the GIC can still propagate through a transformer and cause harmful effects on the isolated side.

This paper proposes the novel application of a genetic algorithm optimized fuzzy logic controller as a nonlinear controller for capacitive bridge type fault current limiter (CBFCL) to improve the stability performance of the power systems. The proposed controller provides fast convergence for the system and uses data from the system as a feedback in the controller loops. The performance of the proposed genetic algorithm optimized fuzzy logic controlled CBFCL is compared with that of a static nonlinear controller based CBFCL and a static nonlinear controller-based bridge type fault current limiter (BFCL). The detail controller design and stability analysis are carried out on the IEEE 39 bus power system in MATLAB/SIMULINK. To capture a realistic system's response, a wind farm is connected to bus one in the IEEE 39 bus system. From the simulation results and several quantifying parameters, it is shown that the proposed genetic algorithm optimized fuzzy logic controlled CBFCL can effectively improve the stability and the performance of the power system as well as the grid connected wind farm. Further, the proposed controller performs better than the static nonlinear controller based CBFCL and the static nonlinear controller based BFCL.

High penetration of Doubly Fed Induction Generator (DFIG) into existing power grid can attribute complex issues as they are very sensitive to the grid faults. In addition, Fault Ride Through (FRT) is one of the main requirements of the grid code for integrating Wind Farms (WFs) into the power grid. In this work, to enhance the FRT capability of the DFIG based WFs, a Bridge-Type Flux Coupling Non-Superconducting Fault Current Limiter (BFC-NSFCL) is proposed. The effectiveness of the proposed BFC-NSFCL is evaluated through performance comparison with that of the Bridge-Type Fault Current Limiter (BFCL) and Series Dynamic Braking Resistor (SDBR). Moreover, a dynamic nonlinear controller is also proposed for controlling the operation of the BFC-NSFCL. Extensive simulations are carried out in the MATLAB/SIMULINK environment for both symmetrical and unsymmetrical temporary as well as permanent faults. Based on the simulation results and different numerical analysis, it is found that the proposed nonlinear controller based BFC-NSFCL is very effective in enhancing the FRT capability of the WF. Also, the BFC-NSFCL outperforms the conventional BFCL and SDBR by maintaining a near-seamless performance during various grid fault situations.

The important backbone of the Smart Grid is the cyber/information infrastructure, which is primarily used to communicate with different grid components. Smart grid is a complex cyber physical system containing a numerous and variety number of sources, devices, controllers and loads. Therefore, smart grid is vulnerable to the grid related disturbances. For such a dynamic system, disturbance and intrusion detection is a paramount issue. This paper presents a Simulink and Opnet based co-simulated platform to carry out a cyber-intrusion in a cyber-network for modern power systems and smart grid. The IEEE 30 bus power system model is used to demonstrate the effectiveness of the simulated testbed. The experiments were performed by disturbing the circuit breakers reclosing time through a cyber-attack. Different disturbance situations in the considered test system are considered and the results indicate the effectiveness of the proposed co-simulated scheme.

This paper proposes a fuzzy logic controlled bridge type fault current limiter (FCL) to enhance the transient stability of multi-machine power systems. The transient stability performance of the fuzzy logic controlled bridge type FCL is compared with that of another static nonlinear controlled bridge type FCL. The total kinetic energy (TKE) of the generators in the system is used to determine the transient stability enhancement index. Also, the critical clearing time has been presented as a stability limit. Instead of conventional reclosing, the optimal reclosing of circuit breakers is considered. Simulations are performed by using the Matlab/Simulink software. Simulation results of both permanent and temporary faults at different points of the IEEE 30-bus power system indicate that the fuzzy logic controlled bridge type FCL can enhance the transient stability of the system well. Also, the performance of the proposed fuzzy logic controller is better than that of the static nonlinear controller.