This study proposes a virgin structure of Fuzzy Logic Control (FLC) for Load Frequency Control (LFC) in a dual-area interconnected electrical power system. This configuration benefits from the advantages of fuzzy control and the merits of Fractional Order theory in traditional PID control. The proposed design is based on Fuzzy Cascade Fractional Order Proportional-Integral and Fractional Order Proportional-Derivative (FC FOPI-FOPD). It includes two controllers, namely FOPI and FOPD connected in cascade in addition to the fuzzy controller and its input scaling factor gains. To boost the performance of this controller, a simple and powerful optimization method called the Particle Swarm Optimization (PSO) algorithm is employed to attain the best possible values of the suggested controller’s parameters. This task is accomplished by reducing the Integral Time Absolute Error (ITAE) of the deviation in frequency and tie line power. Furthermore, to authenticate the excellence of the proposed FC FOPI-FOPD, a comparative study is carried out based on the obtained results and those from previously published works based on classical PID tuned by the Losi Map-Based Chaotic Optimization Algorithm (LCOA), Fuzzy PID Optimized by Teaching Learning-Based Optimization (TLBO) algorithm and Fuzzy PID with a filtered derivative mode tuned by PSO, which is employed in the same interconnected power system. The robustness of the suggested fuzzy structure is investigated against the parametric uncertainties of the testbed system. The simulation results revealed that the proposed FC FOPI-FOPD is robust, and it outperformed the other investigated controllers. For example, the drops in the frequency in area one and area two were improved by 89.785% and 97.590%, respectively, based on employing the proposed fuzzy configuration compared with the results obtained from the traditional PID.
Droop gains in direct current (DC) transmission grids are commonly studied using static indicators like V-I curves and power-sharing calculations. The dynamic studies of voltage source converters high-voltage DC have been challenging because of numerous control loops and complexities in DC-alternating current interactions, which is becoming even more challenging with converter to converter interactions in DC grids. This paper firstly presents a 126th-order multiple-input multiple-output small-signal dynamic linearized model of a five-terminal DC network, which includes all converter dynamics and controls in detail. The model accuracy is verified against a detailed benchmark model in PSCAD. The model is then employed to design DC voltage droop control at each of the four terminals considering the dynamics and transient behavior of the DC network. A root-locus study is used to find the optimum values of the droop gains and the cutoff frequency of the DC voltage feedback filters. The PSCAD model is employed to verify the design results and also to test large disturbances like converter tripping in the test DC grid. The study highlights the benefits of DC droop control and also points the possible dynamics instabilities with incorrectly tuned droop parameters.
Power Plant Heat exchanger is widely used in chemical and petroleum plants because it can sustain wide range of temperature and pressure. Heat exchanger is a high nonlinearity and poor dynamics plant; therefore it is complex to model and difficult to control its dynamics. In this paper two types of heat exchanger model and controller are applied for selecting suitable model and controller. First model is called (Physical model) and derived using real parameter of heat exchanger plant. Second, a Second Order Plus Dead Time (SOPDT model) that is derived from the response of heat exchanger. While the controllers are consisted of fuzzy proportional derivative (FPD) controller and proportional integral derivative (PID) controller and applied to the model and their responses are compared with the existing PID controller. The PID controller response based on Physical model gives similar response of existing PID controller based real heat exchanger plant in comparison with SOPDT model. That means the Physical model is able to represent the heat exchanger plant dynamics more accurately than SOPDT model. For the controller, the FPD control gives a slight enhancement based on SOPDT model. Therefore, FPD controller is more suitable than PID controller.
<span style="font-family: 'Times New Roman',serif; font-size: 12pt; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;" lang="EN-US">The recent </span><span style="font-family: 'Times New Roman',serif; font-size: 12pt; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: EN-GB; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;" lang="EN-GB">developments</span><span style="font-family: 'Times New Roman',serif; font-size: 12pt; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;" lang="EN-US"> in high power rated Voltage Source Converters (VSCs) and the control strategies have resulted in their successful application in HVDC transmission systems, which have become an attractive option for renewable energy applications or for distribution power in large metropolitan areas. A 153<sup>th</sup> order multiple-input multiple-output (MIMO) small-signal model of DC network model based on VSC-HVDC system and controls is developed in state-space form within MATLAB. The optimum values of the controller gains are selected by analyzing the root locus of the analytical model. The developed small-signal detailed models are linearized and implemented in MATLAB. The validity and accuracy of the proposed models are verified against nonlinear PSCAD/ EMTDC and a summary of the model structure and controls is presented in detailed. Confirmation of the effectiveness of optimization gains is done by simulating the modelled system in MATLAB and PSCAD software. There simulation results performed with very good matching is confirmed in the time domain. It is the most detailed model currently available.</span>
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.