• Energy Research

AbstractThis paper proposes a novel load frequency control (LFC) approach formulated on an optimal structure of the coefficient diagram method (CDM) in a two-area thermal power system. As part of a realistic analysis, nonlinearities related to governor dead band (GDB) and generation rate constraint (GRC) have been considered. In this article, a hybrid CDM method is combined with the optimization of its mathematical equations to achieve an innovative controller. Furthermore, a new metaheuristic optimization technique called the water cycle algorithm (WCA) is used to determine the optimal coefficients of the CDM controller. For the purpose of demonstrating the validity of the proposed scheme, a wide range of uncertainties in the dynamic parameters of a nonlinear power system were tested. In addition, a comparative study is presented between the results obtained from a classical integral, CDM alone, optimized fuzzy, optimized PID, and the suggested controller. In this new approach to improved control, algebraic support provides a robust and responsive controller that can provide fast and stable dynamic responses and effectively overcome the detrimental effects of nonlinearities and uncertainties in the parameters of the power system.

AbstractThis paper proposes a novel load frequency control (LFC) approach formulated on an optimal structure of the coefficient diagram method (CDM) in a two-area thermal power system. As part of a realistic analysis, nonlinearities related to governor dead band (GDB) and generation rate constraint (GRC) have been considered. In this article, a hybrid CDM method is combined with the optimization of its mathematical equations to achieve an innovative controller. Furthermore, a new metaheuristic optimization technique called the water cycle algorithm (WCA) is used to determine the optimal coefficients of the CDM controller. For the purpose of demonstrating the validity of the proposed scheme, a wide range of uncertainties in the dynamic parameters of a nonlinear power system were tested. In addition, a comparative study is presented between the results obtained from a classical integral, CDM alone, optimized fuzzy, optimized PID, and the suggested controller. In this new approach to improved control, algebraic support provides a robust and responsive controller that can provide fast and stable dynamic responses and effectively overcome the detrimental effects of nonlinearities and uncertainties in the parameters of the power system.

AbstractThere is a change in the traditional power system structure as a result of the increased incorporation of microgrids (MGs) into the grid. Multi‐area MGs will emerge as a result, and issues related to them will need to be addressed. Load frequency control (LFC) is a challenge in such structures, which are more complicated due to variations in demand and the stochastic characteristics of renewable energy sources. This paper presents a cascade fuzzy active disturbance rejection control technique to deal with the LFC problem. In order to tune different parameters of controllers, a newly developed heuristic algorithm called the Gazelle optimization algorithm (GOA) is also employed. Moreover, due to the fact that multi‐area MGs are regarded as cyber‐physical systems (CPSs), a relatively new concern for LFC problems is their resilience to cyberattacks such as false data injection (FDI) and denial of service (DoS) attacks. Therefore, this research also presents a novel machine learning approach called parallel attack resilience detection system (PARDS) to deal with the LFC problem in the presence of cyberattacks. The efficiency of the proposed strategy is investigated under different scenarios, such as non‐linearities in the power system or server cyberattacks.

AbstractThere is a change in the traditional power system structure as a result of the increased incorporation of microgrids (MGs) into the grid. Multi‐area MGs will emerge as a result, and issues related to them will need to be addressed. Load frequency control (LFC) is a challenge in such structures, which are more complicated due to variations in demand and the stochastic characteristics of renewable energy sources. This paper presents a cascade fuzzy active disturbance rejection control technique to deal with the LFC problem. In order to tune different parameters of controllers, a newly developed heuristic algorithm called the Gazelle optimization algorithm (GOA) is also employed. Moreover, due to the fact that multi‐area MGs are regarded as cyber‐physical systems (CPSs), a relatively new concern for LFC problems is their resilience to cyberattacks such as false data injection (FDI) and denial of service (DoS) attacks. Therefore, this research also presents a novel machine learning approach called parallel attack resilience detection system (PARDS) to deal with the LFC problem in the presence of cyberattacks. The efficiency of the proposed strategy is investigated under different scenarios, such as non‐linearities in the power system or server cyberattacks.

The traditional power system structure is constantly changing due to the application of renewable energy sources (RESs) and microgrids (MGs) into the power system network. Due to the variations in demand and renewable generation profiles, load frequency control (LFC) is one of the most important issues in MGs. As a consequence of the relatively low inertia of these systems, LFC is more challenging compared to absence of DERs and MGs. A comprehensive review of the LFC problem in MGs is the primary objective of this paper. The review process for this paper is undertaken from two perspectives. As a first step, different models for MG components and benchmarks for LFC studies have been described, which can ease the researchers' task of finding suitable models. The second step of this review is a discussion of various control strategies, considering the merits and limitations of each. It is helpful to researchers that this comprehensive literature review gives them an overview of previous well-researched papers in this respect and enables them to connect the dots between recent developments, challenges, and future trends in load frequency strategies for MGs.

The traditional power system structure is constantly changing due to the application of renewable energy sources (RESs) and microgrids (MGs) into the power system network. Due to the variations in demand and renewable generation profiles, load frequency control (LFC) is one of the most important issues in MGs. As a consequence of the relatively low inertia of these systems, LFC is more challenging compared to absence of DERs and MGs. A comprehensive review of the LFC problem in MGs is the primary objective of this paper. The review process for this paper is undertaken from two perspectives. As a first step, different models for MG components and benchmarks for LFC studies have been described, which can ease the researchers' task of finding suitable models. The second step of this review is a discussion of various control strategies, considering the merits and limitations of each. It is helpful to researchers that this comprehensive literature review gives them an overview of previous well-researched papers in this respect and enables them to connect the dots between recent developments, challenges, and future trends in load frequency strategies for MGs.

This paper presents a control method based on active disturbance rejection control (ADRC) for both the primary and secondary control layers in a hybrid DC/AC microgrid (MG). A DC bus inside MG includes a wind turbine generator, photovoltaic panels, a fuel cell and battery energy storage. Maintaining the DC link voltage under various scenarios is made possible by applying an energy management system (EMS) based on the deviation in the state of charge of the battery and using a fuel cell as a backup. Through a voltage source inverter, the DC bus of the MG is connected to the AC side, which supplies AC loads. Using the proposed ADRC-based control strategy for the inverter, the voltage, frequency, and power flow within the MG would be effectively controlled. In order to demonstrate the effectiveness of the proposed method, a comparison between the ADRC and PI controller has also been implemented in different scenarios, proving the superiority of the suggested controller over classical approaches.