Cascaded fractional order automatic generation control of a PV-reheat thermal power system under a comprehensive nonlinearity effect and cyber-attack

Abstract

Integration of renewable energy systems to the existing power grid has become an inevitable issue all over the world. Besides its advantages, it brings serious problems such as system frequency and tie-line power fluctuations which may result in instability or undesired situations in a power system. On the other hand, cascaded controller structures have become so popular in the literature. Because they have more degree of freedom resulting in increased flexibility according to the non-cascaded ones. Within the above-given explanations, in this study, we aim to investigate the performances of cascaded fractional and integer order controllers TI-TD and PI-PD compared to the non-cascaded ones PID and TID controllers for a two area PV-Thermal reheat power system. The designed Article Title controllers are optimized by the Mayfly algorithm (MA) to minimize the integral of the time-weighted absolute error (ITAE) performance index. A detailed performance task is considered for the controllers. These are time domain analysis, robustness, random load change, progressive nonlinearities, and cyber attack issues. The nonlinearity analysis consists of governor dead band (GDB), generation rate constraints (GRC), boiler dynamics (BD), and time delay (TD). Also, two cyber-attack models called a resonance attack (ResA) and random attack (RndA) are applied to the controller systems. Simulation results show the superiority of the optimized and cascaded controllers, especially for TI-TD controller. The performance result of the controllers is also verified by comparing each other and some reported controllers in the literature.

Comparative Analysis of Various Controllers used for Automatic Generation Control in Electrical Power Systems

“…Each of these models vary widely in terms of their control strategies & efficiency levels. For instance, In [1], the research addresses the pressing need for stable and efficient Automatic Generation Control (AGC) in power systems, especially with the integration of renewable energy sources. This integration, while advantageous, introduces challenges like frequency oscillations and tie-line power fluctuations, leading to system instability.…”

“…This model employs Deep Reinforcement Learning for AGC in Ubiquitous Power IoT, harnessing the power of machine learning for optimal control. Work in [1] also deserves mention, utilizing Cascaded Fractional and Integer Order Controllers, which exhibit "High" efficiency. These controllers strike a balance between performance and computational complexity.…”

“…Fusing Parameters: To assess the models' overall performance when considering all parameters together, it's evident that Work in [25], which employs Deep Reinforcement Learning, emerges as a strong candidate due to its "Very High" efficiency and the moderate levels of complexity, delay, and scalability. Work in [1], utilizing Cascaded Fractional and Integer Order Controllers, also performs well when parameters are fused, as it combines "High" efficiency with moderate complexity, delay, and scalability. Work in [31] demonstrates promise by offering a high level of scalability while maintaining moderate levels in other parameters.…”

“…Efficiency, complexity, delay, and scalability were systematically assessed for each controller. Notably, controllers such as the PID and FOPID Controllers (Work in [1]) and the Distributed Coordination AGC with Adaptive Chaotic Gray Wolf Algorithm (Work in [2]) exhibited high levels of efficiency. The former harnesses fractional-order controllers to improve AGC performance, while the latter employs adaptive chaotic algorithms.…”

“…Work in [25], employing Deep Reinforcement Learning, showcased "Very High" efficiency and demonstrated resilience against disturbances. Work in [1], with its utilization of Cascaded Fractional and Integer Order Controllers, balanced "High" efficiency with moderate complexity, delay, and scalability.…”