Improved model predictive load frequency control of interconnected power system with synchronized automatic generation control loops

Abstract

Background Automatic generation control (AGC) of multi-area interconnected power system (IPS) is often designed with negligible cross-coupling between the load frequency control (LFC) and automatic voltage regulation (AVR) loops. This is because the AVR loop is considerably faster than that of LFC. However, with the introduction of slow optimal control action on the AVR, positive damping effect can be achieved on the LFC loop thereby improving the frequency control. In this paper, LFC synchronized with AVR in three-area IPS is proposed. Model predictive controller (MPC) configured in a dense distributed pattern, due to its online set-point tacking is used as the supplementary controller. The dynamics of the IPS subjected to multi-area step and random load disturbances are studied. The efficacy of the developed scheme is ascertained by simulating the disturbed system in MATLAB/Simulink. Results Based on the comparative analysis on the system responses, it is established that by cross-coupling the LFC loop with AVR, reductions of 66.45% and 59.09% in the frequency and tie-line power maximum deviations respectively are observed, while the respective settling times are found to be reduced by 29.68% and 22.77% when compared with the uncoordinated control scheme. In addition, the standard deviation and variance of the integral time absolute error of the system’s responses have reduced by 23.21% and 20.83% respectively compared to those obtained in a similar study. Conclusions The reduction in the maximum deviations and settling times in the system states indicates that introducing the voltage control via AVR loop has improved the frequency control significantly. While the lower standard deviation and variance of the integral time absolute error signify improvement in the robustness of the developed algorithm. However, this improvement is at the detriment of the controller size and computational complexity. In the uncoordinated control scheme, the control vector is one-dimensional, while in the coordinated scheme, the control vector is two-dimensional for each CA.

A Review of Non-Classical Load Frequecncy Control (Lfc) Schemesfor Multi-Area Interconnected Power Systems (Maips)

“…The proposed ANFIS Controller is simple to design and easy to implement and has the ability to adapt to disturbances, making it more effective. [16] Proposed a LFC synchronized with AVR in three-area IPS. Model predictive controller (MPC) configured in a dense distributed pattern, due to its online set-point tacking was used as the supplementary controller.…”

“…The many control approaches that have been suggested for LFC in interconnected power systems can be categorized (i) classical control approaches [22], [1]focus on designing proportional-integral-derivative (PID) controllers for controlling the frequency and tie-lines power flows; (ii) modern control approaches including optimal control method, sliding mode control schemes, and adaptive control systems; (iii) intelligent control schemes [22][1] such as fuzzy control systems; and (iv) soft computing-based approaches for controllers' parameter tuning which had a considerable attention from researchers in the last decade.In the reviewed literatures whereas [19], [7], [27] and [10] adopted modern control approaches to solving LFC problems, [14], [24] and [18] employed intelligent control schemes in their attempt to controlling the frequency and ensuring quality power output in a power system network. On the other hand, [13], [16], [25], [11], [6] and [20] utilized the more advanced and robust soft computing-based approaches in mitigating the effect of frequency deviations in interconnected power networks. From the foregoing it can be said that researchers are leaning more towards the use of soft computing-based approaches to solving load frequency control problems in interconnected power systems.…”

Distributed Control of an Ill-Conditioned Non-Linear Process Using Control Relevant Excitation Signals

“…It has the inherent capability to handle process coupling and interactions naturally. Recently, MPC has found application in a variety of industries outside of process and manufacturing [18,19]. Despite this feat, the presence of MPC in the industry is still not as expected, considering its benefits and potential to revolutionize the industry.…”

Hybrid MPC-Based Automatic Generation Control for Dominant Wind Energy Penetrated Multisource Power System

“…The authors proposed a Bayesian neural net-work identification model for typical ultra super critical thermal power unit. In paper [25], LFC synchronized with AVR in three-area interconnected power system is proposed. Model predictive controller (MPC) is configured in a dense distributed pattern due to its online set-point tracking, and it is used as the supplementary controller.…”