Communication delays and false data injection attacks pose significant threats to the frequency control of automatic generation systems. This paper presents a Golden Jackal Optimizer (GJO)-enhanced frequency-shifted internal model control (FIMC) scheme aimed at addressing these challenges in a dual-area thermal power system. The FIMC approach employs a pole and zero shifting variable that acts as a system robustness indicator. The paper determines an analytical search range for this variable using the Routh-Hurwitz criteria, which were later utilized by GJO. The robustness and performance of the GJO-tuned FIMC are tested against random and step load disturbances, as well as system nonlinearities. The paper models various false data injection threats, assessing the effectiveness of the GJO-tuned FIMC in neutralizing these threats under inherent communication delays. Finally, the proposed strategy is verified in real-time through hardware, employing the OPAL-RT platform. The results are compared with a recent strategy, underscoring the advanced efficacy of the proposed approach.

The uncertainties associated with multi-area power systems comprising both thermal and distributed renewable generation (DRG) sources such as solar and wind necessitate the use of an efficient load frequency control (LFC) technique. Therefore, a hybrid version of two metaheuristic algorithms (arithmetic optimization and African vulture's optimization algorithm) is developed. It is called the ‘arithmetic optimized African vulture's optimization algorithm (AOAVOA)’. This algorithm is used to tune a novel type-2 fuzzy-based proportional–derivative branched with dual degree-of-freedom proportional–integral–derivative controller for the LFC of a three-area hybrid deregulated power system. Thermal, electric vehicle (EV), and DRG sources (including a solar panel and a wind turbine system) are connected in area-1. Area-2 involves thermal and gas-generating units (GUs), while thermal and geothermal units are linked in area-3. Practical restrictions such as thermo-boiler dynamics, thermal-governor dead-band, and generation rate constraints are also considered. The proposed LFC method is compared to other controllers and optimizers to demonstrate its superiority in rejecting step and random load disturbances. By functioning as energy storage elements, EVs and DRG units can enhance dynamic responses during peak demand. As a result, the effect of the aforementioned units on dynamic reactions is also investigated. To validate its effectiveness, the closed-loop system is subjected to robust stability analysis and is compared to various existing control schemes from the literature. It is determined that the suggested AOAVOA improves fitness by 40.20% over the arithmetic optimizer (AO), while frequency regulation is improved by 4.55% over an AO-tuned type-2 fuzzy-based branched controller.

A stealthy cyberattack (SCA) aims to gradually drive the system toward instability. Although research is continuing in attack detection algorithms, a resilient control strategy is vital if SCAs go undetected for a considerable time. Especially for a controller involved in automatic generation control (AGC), resiliency becomes more crucial as the controller has to sometimes work under abnormal conditions considering that shutdown of operation is not an option. This paper introduces an innovative approach to support dual-area thermal power systems in the face of network-related challenges such as transference latency (TL) and SCAs. We propose a new tri-parametric fractional controller (TFC) that merges the advantages of proportional-integral and proportional-derivative operations, eliminating the need for additional control loops and enhancing system efficiency. The TFC's efficacy in mitigating random and step load disturbances is established, outperforming existing controllers. We employ the complex root boundary method to define the optimal parameter search space, with an improved equilibrium optimizer algorithm determining the best controller settings. The inclusion of renewable energy sources, including solar and wind, is considered, and the robustness of the system is evaluated using uncertainty norms and complementary sensitivity functions. Our approach is validated through hardware-in-loop real-time verification on the OPAL-RT platform, demonstrating superior performance in maintaining frequency stability under SCAs and TL challenges.

This brief introduces a new fractional-order integral derivative ( ${\mathrm {FOI}}^{\lambda }\text{D}^{1-\lambda }$ ) controller for a class of integrating systems involving time delays. The stability region is explored through the complex root boundary (CRB) analysis which explicitly provides the search space of ${\mathrm {FOI}}^{\lambda }\text{D}^{1-\lambda }$ parameters for integrating time delay systems of any order. The optimal settings are obtained through a three-step optimization algorithm based on the CRB knowledge and the desired performance measures. Comparisons with other recent sophisticated tuning methods are also provided to demonstrate the effectiveness of the suggested control scheme through numerical study. Finally, a case study on a non-linear continuously stirred reactor under model uncertainties is presented to check the efficacy of the new method.