In multi-area interconnected power systems (MAIPS), the measurement of all system states is difficult due to the lack of a sensor or the fact that it is expensive to measure. In order to solve this limitation, a new load frequency controller based on the second-order sliding mode is designed for MAIPS where the estimated state variable is used fully in the sliding surface and controller. Firstly, a model of MAIPS integrated with disturbance is introduced. Secondly, an observer has been designed and used to estimate the unmeasured variables with disturbance. Thirdly, a new second-order sliding mode control (SOSMC) law is used to reduce the chattering in the system dynamics where slide surface and sliding mode controller are designed based on system states observer. The stability of the whole system is guaranteed via the Lyapunov theory. Even though state variables are not measured, the experimental simulation results show that the frequency remains in the nominal range under load disturbances, matched and mismatched uncertainties of the MAIPS. A comparison to other controllers illustrates the superiority of the highlighted controller designed in this paper.

Daily peak load forecasting is critical for energy providers to meet the loads of grid-connected consumers. This study proposed a Seasonal Trend decomposition using Loess combined with Long Short-Term Memory (STL-LTSM) method and compared its performance on peak forecasting of electrical energy demand with Convolutional Neural Network and LSTM (CNN-LSTM), Wavenet, and the classic approaches Artificial Neural Network (ANN) and LSTM. The study evaluated the models using demand data from the power system in Tien Giang province, Vietnam, from 2020 to 2022, considering historical demand, holidays, and weather variables as input characteristics. The results showed that the proposed STL-LSTM model can predict future demand with lower Base Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). Therefore, the proposed method can help energy suppliers make smart decisions and plan for future demand.

The implementation of the sliding mode control (SMC) for load frequency control of power networks becomes difficult due to the chattering phenomenon of high-frequency switching. This chattering problem in SMC is extremely dangerous for actuators used in power systems. In this paper, a continuous control strategy by combining a second-order mode and integral siding surface is proposed as a possible solution to this problem. The proposed second-order integral sliding mode control (SOISMC) law not only rejects chattering phenomenon in control input, but also guarantees the robustness of the multi-area power network, which has an effect on parametric uncertainties such as the load variations and the matched or mismatched parameter uncertainties. Moreover, the reporting of the simulation indicates that the proposed controller upholds the quality requirement by controlling with operating conditions in the larger range, rejects disturbance, reduces the transient response of frequency, eliminates the overshoot problem, and can better address load uncertainties compared to several previous control methods. The simulation results also show that the proposed SOISMC can be used for practical multi-area power network to lessen high parameter uncertainties and load disturbances.

To provide a more practical method of controlling the frequency and tie-line power flow of a multi-area interconnected power system (MAIPS), a state observer based on sliding mode control (SOboSMC) acting under a second-order time derivative is proposed. The proposed design is used to study load frequency control against load disturbance, matched and mismatched uncertainty and parameter measurement difficulties of power systems that exist in the modern power plant, such as multi-area systems integrated with wind plants. Firstly, the state observer is designed to optimally estimate system state variables. The estimated states are applied to construct the model of the MAIPS. Secondly, a SOboSMC is designed with an integral switching surface acting on the second-order time derivative to forcefully drive the dynamic errors to zero and eliminate chattering, which can occur in the first-order approach to sliding mode control. In addition, the stability of the total power system is demonstrated with the Lyapunov stability theory based on a new linear matrix inequality (LMI) technique. To extend the validation of the proposed design control for practical purposes, it was tested in a New England system with 39 bus power against random load disturbances. The simulation results confirm the superiority of the proposed SOboSMC over other recent controllers with respect to overshoot and settling time.

This paper proposes a synchronous sliding mode control (SSMC) method for a 4-DOF parallel robot model in practice with the influence of uncertain dynamics such as friction, external noise, and model error. A closed kinematic chain structure characterizes the parallel robot. The moving platform is controlled by four kinematic chains working together to follow a predetermined trajectory. However, traditional control methods only control each joint individually; if one of the actuators fails or has an external force, it can lead to system breakdown. Therefore, synchronous control to coordinate the operation of the arms is necessary. The synchronization algorithm is characterized by the cross-coupling error, which is the combination of tracking and synchronization errors for the system to achieve the synchronization goal. Cross-coupling error is used to design the sliding surface of the SSMC. The advantages of sliding mode control (SMC) are that it resists the influence of uncertain dynamics and it is combined with the synchronization algorithm. A control law is developed based on the sliding surface to ensure simultaneous convergence of both tracking and synchronization errors towards zero. The stability of the system is proven using the Lyapunov theory. In order to verify the effectiveness of the proposed approach, a 4-DOF parallel robot testbench is constructed. The SSMC controller results are compared with the testbench’s proportional-integral-derivative (PID), synchronous proportional-integral-derivative (SPID), and SMC controllers.