• Energy Research

This paper presents a method to estimate the maximum level of variable energy resources that can be integrated into the grid based on the frequency security constraint. The method described uses the approximation of the frequency deviation extremum based on the sensitivity analysis. The approximated results are then employed to estimate the change of the maximum frequency deviation following the change of inertia and the equivalent regulation constant in the presence of variable energy resources. The frequency security constraint is then used to define the maximum level of variable energy resources penetration. The approximation model has been tested on two test systems and the obtained results are compared with those produced by the simulation. The comparison shows that the proposed method can be utilized to define the maximum level of variable energy resources penetration while securing the stability and reliability of the system without losing notable accuracy.

Fault-tolerant control systems (FTCS) are used in safety and critical applications to improve reliability and availability for sustained operation in fault situations. These systems may be used in process facilities to reduce significant production losses caused by irregular and unplanned equipment tripping. Internal combustion (IC) engines are widely used in the process sector, and efficient air–fuel ratio (AFR) regulation in the fuel system of these engines is critical for increasing engine efficiency, conserving fuel energy, and protecting the environment. In this paper, a hybrid fault-tolerant control system has been proposed, being a combination of two parts which are known as an active fault-tolerant control system and a passive fault-tolerant control system. The active part has been designed by using the genetic algorithm-based fault detection and isolation unit. This genetic algorithm provides estimated values to an engine control unit in case of a fault in any sensor. The passive system is designed by using the higher-order sliding mode control with an extra fuel actuator in the fuel supply line. The performance of the system was tested experimentally in MATLAB/Simulink environment. Based on the simulation results, the designed system can sustain the AFR despite sensor failures. A new method of managing the AFR of an IC engine has been demonstrated in this study, and it is highly capable, robust, reliable, and highly effective. A comparison with the existing works found in the literature also proves its superior performance. By inserting the fault in each sensor, it was clearly observed that proposed HFTCS was much better than the existing model as it was more fault-tolerant due to its ability to work in both online and offline modes. It also provided an exact value of 14.6 of AFR without any degradation.

Phasor measurement units (PMUs) have revolutionized power systems monitoring and control. By providing higher resolution measurements which provides better situational awareness, those time-synchronized measurements have enabled the development of better controls and operations for power systems. This paper discusses the basics of PMUs and their applications, mainly state estimation. The paper also presents a Kalman filter approach for state estimation. The NE 39-bus system is used under different circumstances to show the accuracy and robustness of the Kalman filter approach.

The power industry is in the process of grid modernization with the introduction of phasor measurement units (PMUs), advanced metering infrastructure (AMI), and other technologies. Although these technologies enable more reliable and efficient operation, the risk of cyber threats has increased, as evidenced by the recent blackouts in Ukraine and New York. One of these threats is false data injection attacks (FDIAs). Most of the FDIA literature focuses on the vulnerability of DC estimators and AC estimators to such attacks. This paper investigates FDIAs for PMU-based state estimation, where the PMUs are comparable. Several states can be manipulated by compromising one PMU through the channels of that PMU. A Phase Locking Value (PLV) technique was developed to detect FDIAs. The proposed approach is tested on the IEEE 14-bus and the IEEE 30-bus test systems under different scenarios using a Monte Carlo simulation where the PLV demonstrated an efficient performance.

Concerns have been voiced about the growing significance of cyber-threats, especially in light of the potentially dire repercussions of false data injection (FDI) assaults. This work investigates FDI detection in phasor measurement units (PMU), focusing on instances where an attack can be launched simply by compromising one unit. Simulated post-processing adversarial interventions i.e., noise and non-linearity were introduced to train and fortify the system against possible attacks and to render it resilient to perturbations. By learning complex non-linear patterns from the data, a deep de-noising auto-encoder model is used to de-noise and learn genuine feature representations, improving overall reliability. The suggested framework performs better than conventional machine learning and 1-D CNN models when it comes to precisely estimating intrusion, as shown by a comparison study. By using an integrated strategy, power system monitoring and control become more accurate and resilient, successfully tackling the changing issues faced by contemporary electrical grids. The proposed adversarially robust framework is evaluated using Monte-Carlos simulations and on varying load conditions to better comprehend the impact of adversarial interventions on the FDI detection accuracy under different load characteristics and attack scenarios. The proposed framework yielded an average 98.3% in Monte Carlo simulations and an average of 96.5% accuracy under varying load conditions. Surpassing the conventional ML and 1-D CNN algorithms in successfully identifying FDI attacks under adversarial vulnerability.

Cyber-threats are becoming a big concern due to the potential severe consequences of such threats is false data injection (FDI) attacks where the measures data is manipulated such that the detection is unfeasible using traditional approaches. This work focuses on detecting FDIs for phasor measurement units where compromising one unit is sufficient for launching such attacks. In the proposed approach, moving averages and correlation are used along with machine learning algorithms to detect such attacks. The proposed approach is tested and validated using the IEEE 14-bus and the IEEE 30-bus test systems. The proposed performance was sufficient for detecting the location and attack instances under different scenarios and circumstances.

Ahstract- Thyristor-controlled series capacitor (TCSC) devices have been utilized for enhancing transmission network loadability. The technical benefit of the TCSC devices comes at the expense of their high investment cost. In this paper, a new approach is proposed to provide a profound insight into the compromises between technical and economical aspects of installing TCSC devices in a transmission network. The proposed approach is a multiobjective optimization based algorithm used to maximize the loadability of the network and to minimize the investment cost of installing TCSCs under secured operation conditions. The results provide an efficient set of nondominated solutions to maximize the loadability at minimal cost for the decision-making process. The IEEE lIS-bus system is used as a test case to validate the effectiveness of the proposed method. Furthermore, the results are analyzed and compared with available results in the literature.

Phasor measurement units (PMUs) have offered significant advancements in power system operation and control. Despite these advantages, the industry has been slow in adopting PMU technology. This slow adoption is partially due to the high cost of installing PMUs, which motivates the optimal PMU placement (OPP) problem. The work presented in this paper is motivated by the idea that the value of a high cost asset can be increased by deploying it to improve network fault-tolerance. This strategy of deploying PMUs in the proximity of higher probability contingencies increases the likelihood of more effective remedial actions, both preventive and corrective. The proposed OPP therefore considers both the cost and the extent to which the PMU placements will benefit system observability in the presence of network vulnerabilities. A bi-level framework is used for optimizing the network vulnerability and the installation cost of PMUs. In the proposed framework, the installation cost of PMUs is treated as the primary objective, and the observability, which incorporates the vulnerability analysis, is treated as the secondary objective. The proposed approach can be used as a multistage installation process or as a single-stage installation process. This approach uses evolutionary algorithms to optimize and prioritize locations and installation cost of the PMUs. The proposed approach is tested on the IEEE 14-bus, IEEE 30-bus, and IEEE reliability test system.