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The permanent magnet synchronous motor (PMSM) control is demanding task because, which is a nonlinear multivariable coupling system. The vector control has been widely used in the control of PMSM system, where the rotor position information is needed for close loop control system. The mechanical sensors are used for rotor position, which increased the cost of the system. These sensors can be eliminated by adopting the sensorless control methods. In this paper a new continuous terminal sliding mode observer (CTSMO) based speed sensorless control method is proposed, which is substitute of the conventional SMO (CSMO). The suggested the CTSMO is robust against parametric change and load disturbances, which is used to estimate the speed and rotor position from the back EMF (BEMF) of the PMSM system. The conventional SMO needs the low pass filter (LPF), position compensation techniques and sigmoid function instead of sign function to decrease the chattering phenomena. To eliminate the chattering phenomena effectively the new CTSMO based speed sensorless method is designed in this paper, which has also ability of minimization of the speed and rotor position error. The stability of the proposed technique is analyzed by Lyapunov function, the simulation have been carried out for the validation of the suggested technique.

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.

AC microgrids are a contemporary adaptation of traditional power distribution networks, propelled by the rapid integration of renewable energy resources. Yet, their dynamic operational nature poses distinct protection & control challenges. This paper introduces a protection approach for AC microgrids utilizing the Particle Filter Algorithm (PFA). The method involves initial measurement and state estimation of current and voltage signals at the designated bus using PFA. Then, the dual index is computed via PFA encompasses 1). per-phase particle residuals (PPPR), which are derived as an index for fault detection and classification, essentially capturing the difference among estimated & measured current. 2). also, a second index is generated named fifth and seventh root means square (RMS) harmonics distortion (F&SRHD) from the estimated current signals, computed by slight modification in conventional total harmonic distortion. Variances in any of the PPPRs and F&SRHD more than the threshold level indicate the fault within the AC microgrids. Fault localization is accomplished by analyzing the directional patterns of non-fundamental components of 3-phase active energy (3-pRE). Rigorous MATLAB/Simulink 2022b simulations authenticate the efficacy of the presented strategy. Both the high-impedance faults (HIF) and low-impedance solid faults (LISF) are successfully detected across radial and meshed scenarios, with 99% accuracy.

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.

In this paper, a novel fast terminal sliding mode reaching law (FTSMRL) based sliding mode control (SMC) is proposed to improve the speed dynamic performance of the permanent magnet synchronous motor (PMSM) and drive. The proposed FTSMRL contains two power terms, which can take the leading role in the SMC process, when sliding state is near or far from the selected sliding trajectory. The proposed SMC based FTSMRL can not only suppress the chattering phenomenon effectively, but also improve the reaching velocity of the sliding state variable. In order to further improve the system disturbance rejection ability of the control system, an extended state observer (ESO) is also developed in this work. The proposed observer estimates the disturbances and inputs the signal to a feed-forward compensation controller considering the chattering phenomenon caused by the high switching gain. The proposed method has been fully confirmed by mathematical stability check, numerical analysis and experiments, separately.

The smart grid has revolutionized the conventional electricity grid with the proposition of demand-side management (DSM). A DSM program enables the user to schedule its energy consumption in compliance with any pricing signal. This scheduling helps the grid operator to reduce the peak load demand and jointly benefits the user to reduce its electricity costs. Despite that, while doing so, it jeopardizes the user’s comfort. In the present paper, the authors have investigated the impact of communal DSM programs on the consumption patterns of users, including single as well as multiple households. The objective is to simultaneously minimize the electricity costs and user discomfort to make a win-win situation for both the grid operator and the user. Therefore, a multi-objective optimization problem (MOOP) has been formed to simultaneously minimize the daily electricity cost, peak to average ratio (PAR) of load demand, user discomfort, environmental emission, and total net present cost (TNPC). In order to evaluate the best scheduling method, sizing scenarios for a residential microgrid in a Southern Pakistani metropolis surrounded by rural areas are presented in this paper. The originality of this article comes from a comparison of the techno-economic and environmental performance of several sizing options for a residential load powered by renewable energy. The artificial bee colony (ABC) algorithm has been selected to solve the MOOP. The DSM programs are based upon different pricing signals, including real-time electricity pricing (RTEP), critical peak pricing (CPP), time of use (TOU), and day-ahead pricing (DAP) pricing. The results of the proposed ABC algorithm are compared with GA and standard algorithms, and they reveal the effectiveness of the proposed method. When demand response is used, the suggested optimization technique shows that the SH spring with PV/WG/grid-connected microgrid is the most investable-reliable sizing option with a minimal TNPC of $\$ $ 1405.18 for DAP tariff with SH spring. Additionally, with a reduction in emissions of 6699 kg/yr, DAP tariff with SH spring shows that PV/WG/DG/grid-connected system has the greatest impact on the environment. For DAP tariff with SH spring, optimal sizes of PV, WG and converter are 26.7 kW, 30 kW and 6.67 kW, respectively.

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.

The robust nonlinear speed control technique has been proposed for the speed regulation of permanent magnet synchronous motor (PMSM) by adopting a new fast terminal sliding mode control (FTSMC) based on the finite time sliding mode observer (FTSMO). The proposed technique can ensure the robustness against load disturbances, which has the capability of high tracking precision, fast finite time convergence and smooth dynamic operation in both transient and steady state conditions. Moreover, the requirement of high switching gain to reach the upper bound limit of total disturbances may produce the steady state error and chattering. To address this issue, one robust FTSMO based FTSMC is designed to alleviate those limitations aforementioned. The FTSMO can successfully estimate the total disturbances of the system, then the estimated disturbances can be compensated by the feed-forward compensation technique. The FTSMO based FTSMC should require the smaller switching gain to meet upper bound limit of the disturbances compared with the conventional SMC (CSMC) and FTSMC. The stability of the close loop control system is fully verified by the Lyapunov theory. Comprehensive simulation and experimental results have fully demonstrated that the proposed method is robust against load disturbances.