In order to enhance the speed control performance of the permanent magnet synchronous motor (PMSM) with internal and external disturbances, a new adaptive terminal sliding mode reaching law (ATSMRL) is proposed with continuous fast terminal sliding mode control (CFTSMC). Firstly, the ATSMRL is presented with the aim of reducing the input control efforts, which can dynamically adopt all positive aspects in terms of the finite time convergence, high tracking precision, and reduction of the chattering in the control input of the system. Secondly, an extended sliding mode disturbance observer (ESMDO) is designed to estimate the total disturbances of the system, and then the estimated disturbance has been brought for the feed-forward compensation technique, which would enhance the disturbance rejection ability of the system. Afterwards, the close loop stability is validated by the Lyapunov function. Finally the comprehensive numerical and experimental analyses have been carried out to demonstrate the superiority of the ATSMRL method than that of terminal sliding mode reaching law (TSMRL).

The new continuous fast terminal sliding mode control (CFTSMC) is adopted in this paper for the speed regulation of permanent magnet synchronous motor (PMSM) drive applications. The CFTSMC method has a capability to converge the error faster, provide higher tracking accuracy and robust against the load disturbances. However, the CFTSMC method needs high switching gain during the strong disturbances, which introduces the chattering and steady state variation to the system. To address these issues, a composite CFTSMC technique based on the extended state observer (ESO) is proposed in this paper. The ESO is used to estimate the system disturbances for feed-forward compensation. The ESO based CFTSMC method needs smaller switching gain than the CFTSMC method. Moreover, the ESO based CFTSMC method has a strong robustness against load disturbances, better convergence rate and better tracking accuracy than the CFTSMC and conventional continuous sliding mode control (CSMC) method. The superiority of the proposed control method is verified by Lyapunov function and confirmed by the simulation and experimental results.

A novel sliding mode based extended state observer (SMESO) is proposed to improve thedisturbance rejection ability and enhance the dynamic performance of permanent magnet synchronous motor (PMSM) drive systems. Firstly, an extended state observer (ESO) based onthe fast terminal sliding mode control (FTSMC) method is developed for better comparison, which canimprove the robustness against load disturbances, finite time convergence, and get effective reduction of the chattering phenomenon. The SMESO based FTSMC is then developed by using the SMESO to estimate the system total disturbance, and input the signal to a feed-forward compensation controller to further improve the system dynamic performance. The closed loop stability analysis is conducted by using the Lyapunov stability function. The superior effectiveness of the proposed method is confirmed by comprehensive simulation and experiments.

Standalone rural microgrid (MG) systems are considered as a sustainable and economical solutions towards rural area electrifications. Specific control schemes are necessary to adopt for reliable and economic performance of these rural MGs. This study focuses on the optimal utilization of biomass potential considering specific applications of bio generator (BG) with BG-PV-WT-BSS and BG-PV-SMES based standalone rural MG systems. In the first case of the BG-PV-WT-BSS, the optimal sizing/selection of DGs of a rural MG has been proposed using the improved-MILP (I-MILP) approach. The objectives of this study were to minimize total net present cost (TNPC), the levelized cost of energy (LCOE) and GHG emissions. In the second case of BG-PV-SMES, the simulation model of the rural microgrid consisting of a variable speed bio generator (VSBG) and photovoltaic (PV) has been developed. Afterwards, a simplified EMS has been designed for the coordinated operation control of the distributed energy resources (DERs) in the rural MGs using MATLAB/Simulink ^® environment. For the DGs connected via power converter, FOSMC and FCS-MPC based coordinated control has been proposed in the simplified EMS. The purpose of the FOSMC and FCS-MPC based power converter is the improvement of the system performance (for instance power quality, regulated voltage and THD) under external disturbances. Simulation analysis shows the better operation of FOSMC and FCS-MPC under less THD and improved power quality.

This paper presents an optimized load frequency control (LFC) approach for interconnected power systems with conventional and renewable energy sources. A proportional integral derivative (PID) controller, tuned using particle swarm optimization (PSO) and genetic algorithm (GA) techniques, manages frequency in a thermal two-area tie-line IPS and a one-area multi-source power network. The integral time absolute error (ITAE) serves as the fitness function for optimization. Key contributions include developing sustainable power network models with renewable energy fluctuations, applying PSO and GA for robust PID tuning, and comparing controller performance under varying conditions. The PSO-PID controller shows superior frequency stability, achieving deviations of 59.97 Hz, 59.72 Hz, and 59.99 Hz for single-area multi-source, area 1, and area 2 in the two-area IPS. It outperforms the GA-PID controller in reducing undershoot, overshoot, and settling time, offering a more reliable LFC solution for interconnected power systems.

A suitable energy management scheme and integrating renewable energy resources (RERs) can significantly increase energy efficiency and the stability of future grids operation. This work modeled a household energy management comprising a microgrid (MG) system and demand response programs (DRPs). Residential loads with price-based tariffs are introduced to reduce peak load demands and energy costs. For incorporating the uncertainties in RERs, their stochastic nature is modeled with a probabilistic method. This paper proposes a joint optimization approach for the optimal planning and operation of grid-connected residential, rural MG integrated into renewable energy and electric vehicles (EVs) in view of DRPs. The investigation focuses on energy saving of residential homes under different DRPs and RERs integration. The EVs are integrated into MG by including photovoltaic (PV), wind turbine (WT), fuel cell (FC), and diesel engines (DEs). A multi-objective optimization problem has been formulated to minimize the operating cost, pollutant treatment cost, and carbon emissions cost defined as C1, C2, and C3, respectively. The load demand has been rescheduled because of three DRPs, i.e., critical peak pricing (CPP), real-time electricity pricing (RTEP), and time of use (TOU). Further, the EV load has also been analyzed in autonomous and coordinated charging strategies. Using a judgement matrix, the proposed multi-objective problem is transformed into a single-objective problem. The results of an artificial bee colony (ABC) algorithm are compared with the particle swarm optimization (PSO) algorithm. The simulation analysis was accomplished by employing ABC and PSO in MATLAB. The mathematical model of MG was implemented, and the effects of DRPs based MG were investigated under different numbers of EVs and load data to reduce different costs. To analyze the impact of DRPs, the residential, rural MG is implemented for 50 homes with a peak load of 5 kW each and EV load with 80 EVs and 700 EVs, respectively. The simulation results with different test cases are formulated while analyzing the tradeoff between ABC and PSO algorithms. The simulation analysis shows that multiple DRPs, EVs, and RERs offered a substantial trade-off.