Optimal IMC-PID controller design for large-scale power systems via EDE algorithm-based model approximation method

Abstract

In this paper, the authors propose an optimal IMC-PID controller design for the Load Frequency Control (LFC) of large-scale power system via model approximation method. The model approximation method uses the Enhanced Differential Evolution (EDE) algorithm to determine an optimal Reduced Order Model (ROM) for the considered large-scale power system by minimizing the performance measure called Integral Square Error (ISE) between their step responses. Later, the LFC design is carried out using an optimal ROM instead of processing with the large-scale power system model. Thus, this simplifies the design, reduces the computational efforts and also helps in determining the lower order controller. An optimal IMC design methodology is proposed by minimizing ISE between the actual output and the reference input responses of the large-scale power system using EDE algorithm. Further, PID controller gains are acquired by least square model matching with the optimal IMC transfer function. The proposed IMC-PID controller design allows a satisfied reference input tracking performance, robustness in disturbance rejection and improves the dynamic stability of the power system. The proposed method is validated by applying it to a single area power system of third-order SISO model and also extended to a centralized two-area thermal–thermal non-reheated power system of a seventh-order MIMO model. The simulation results and the comparison of error performance indices show the efficacy of the proposed method over the significant methods available in the literature.

Designing of 2‐degree of freedom load frequency controller for power system using novel improved pole clustering and genetic method of reduced‐order modelling

“…Example 3. Considering a sixth order discrete-time system is represented in its transfer function form as shown in Equation (31).…”

“…The two area thermal-thermal non-reheated power system is given by Vasu et al. 31 The block diagram of the system is shown in Figure 14. The nomenclature of all the parameters and their values can be noted from Table 8.…”

“…The proposed system possesses lesser number of oscillations and low value of undershoot. The comparative analysis performed in Figure 17 shows that the deviation in Vasu's 31 controller shows oscillatory behavior and these fluctuations can be sometimes dangerous and can cause damage in the system if load variation comes in between the operation. On the other hand, proposed controller does not exhibit oscillations, hence making the proposed controller more useful as compared to Vasu's controller.…”

“…The model order reduction (MOR) technique hence employed helps (i) in deriving the control law simply, (ii) in reducing the space and time complexity, (iii) in minimizing the efforts of controller designing, (iv) in simplifying the simulation for better understanding of the system behavior. Some researcher 7,22,31 designed LFC by employing an order reduction technique, but the research is required to be extended for obtaining better results by changing either the method of controller design or technique used for order reduction.…”

PID Tuning Method Based on IMC for Inverse-Response Second-Order Plus Dead Time Processes

“…It has resulted to be of particular interest in industry together with the PID algorithm [32], since the equations for the controller's parameters can be obtained from the transfer function of the process and the desired behavior of the closed-loop response; in most cases, only the closed-loop time constant is required as the user-defined tuning parameter, considering an appropriate trade-off between performance and robustness [20,[33][34][35][36][37]. Additional works, regarding IMC, that have been developed more recently can be found in [38][39][40][41][42][43][44].…”