Permanent magnet synchronous motors (PMSMs) are known as highly efficient motors and are slowly replacing induction motors in diverse industries. PMSM systems are nonlinear and consist of time-varying parameters with high-order complex dynamics. High performance applications of PMSMs require their speed controllers to provide a fast response, precise tracking, small overshoot and strong disturbance rejection ability. Sliding mode control (SMC) is well known as a robust control method for systems with parameter variations and external disturbances. This paper investigates the current status of implementation of sliding mode control speed control of PMSMs. Our aim is to highlight various designs of sliding surface and composite controller designs with SMC implementation, which purpose is to improve controller’s robustness and/or to reduce SMC chattering. SMC enhancement using fractional order sliding surface design is elaborated and verified by simulation results presented. Remarkable features as well as disadvantages of previous works are summarized. Ideas on possible future works are also discussed, which emphasize on current gaps in this area of research.

In this article, a hybrid asynchronous particle swarm optimization-genetic algorithm (APSO-GA) is proposed for the removal of unwanted lower order harmonics in the cascaded H-bridge multilevel inverter (MLI). The APSO-GA is applicable to all levels of MLI. In the proposed method, ring topology based APSO is hybrid with GA. APSO is applied for exploration and GA is used for the exploitation of the best solutions. In this article, optimized switching angles are calculated using APSO-GA for seven-level and nine-level inverter, and results are compared with GA, PSO, APSO, bee algorithm (BA), differential evolution (DE), synchronous PSO, and teaching–learning-based optimization (TLBO). Simulation results show that APSO-GA can easily find feasible solutions particularly when the number of switching angles is high; however, the rest of all stuck at local minima due to less exploration capability. Also, the APSO-GA is less computational complex than GA, BA, TLBO, and DE algorithms. Experimentally, the performance of APSO-GA is validated on a single-phase seven-level inverter.

Growing electricity demand, the deployment of renewable energy sources and the widespread use of smart home appliances provide new opportunities for home energy management systems (HEMSs), which can be defined as systems that improve the overall energy production and consumption of residential buildings by controlling and scheduling the use of household equipment. By saving energy, reducing residential electricity costs, optimizing the utilization rate and reliability of utility companies’ power systems, and reducing air pollution for society, HEMSs lead to an enhancement in the socioeconomic development of low-carbon economies. This review aims to systematically analyze and summarize the development trends and challenges of HEMSs in recent years. This paper reviews the development history of the HEMS architecture and discusses the characteristics of several major communication technologies in the current HEMS infrastructure. In addition, the common objectives and constraints related to scheduling optimization are classified, and several optimization methods in the literature, including various intelligent algorithms, have been introduced, compared, and critically analyzed. Furthermore, experimental studies and challenges in the real world are also summarized and recommendations are given. This paper reveals the trend from simple to complex in the architecture and functionality of HEMSs, discusses the challenges for future improvements in modeling and scheduling, and shows the development of various modeling and scheduling methods. Based on this review, researchers can gain a comprehensive understanding of current research trends in HEMSs and open up ideas for developing new modeling and scheduling approaches by gaining insight into the trade-offs between optimum solutions and computational complexity.

This study presents selective harmonic elimination pulse width modulation technique‐based hybrid asynchronous PSO‐Newton‐Raphson (APSO‐NR) algorithm for the elimination of undesired harmonics in cascaded H‐bridge multilevel inverter. The proposed algorithm is applicable to all levels of MLI having equal and non‐equal DC sources. In the proposed method, ring topology‐based APSO algorithm is hybrid with NR method. APSO worked as a global search technique and NR is used for the refinement of best solutions. APSO‐NR is applied to the seven‐level inverter to eliminate fifth and seventh harmonics. In simulations, the performance of the proposed algorithm is compared with genetic algorithm, bee algorithm and particle swarm optimisation. The results proved that the proposed algorithm is efficient, and gives more precise firing angles in less number of iterations with high capability of tackling local optima. For the 48% of modulation index range, APSO‐NR minimised the fitness function value lower than (10 ⁻²⁵ ). The proposed algorithm is validated through the experimental implementation of the three‐phase seven‐level inverter.

Photovoltaic (PV) systems provide a reliable green energy solution by improving maximum power point tracking (MPPT) methods to achieve maximum power under various weather conditions. Among MPPT algorithms, the perturbation and observation (P&O) algorithm is widely used in PV systems due to its simple implementation. However, the algorithm has limitations in terms of tracking convergence speed, steady-state oscillation, and drift problem during sudden irradiance variations. This paper proposes a modified P&O algorithm for optimal MPPT under different weather conditions. A new strategy is used to control the duty cycle perturbation step size, which provides faster tracking convergence. The algorithm detects the attainment of the steady-state oscillation in a simple way and then stops the perturbation process. This eliminates the oscillation around the MPP. Moreover, the algorithm can detect the change in solar irradiance immediately and solve the drift problem without additional sensors. The proposed method was simulated using MATLAB/Simulink and then validated experimentally under different irradiances utilizing a boost converter with a 0.05 s sampling time. The experimental results showed that the proposed strategy could be successfully implemented, with an average tracking time of 0.5 s and an average steady-state efficiency of 99.8%. Moreover, the results showed that the proposed method was superior to the standard P&O algorithm, with a 50% improvement in tracking time. In this study, a real-time test was conducted to verify the effectiveness of the proposed algorithm under different solar irradiances and temperatures.

Solar energy exposed its prominence to diminish the growing energy demands. But the formation of multiple peaks under partial shading conditions causes the conventional maximum power point tracking controllers to fail to track the global maximum power point (GMPP). Scanning the whole power–voltage curve to locate the GMPP takes extreme time and reduces algorithm effectiveness. Thus, the proposed method devotes the skipping and scanning mechanism incorporated with the perturb and observe (P&O) algorithm to reduce the scanning area drastically. The skipping mechanism skips over the scanning areas between successive peaks based on a specific formula that narrows down the scanning zone. Along with a buck–boost converter, the system includes a voltage control loop controlled by a proportional–integral controller. The proposed algorithm's effectiveness is tested in both simulation and experimental environments on discretized partial shading patterns, while GMPP is deployed at different locations. According to the experimental outcomes, the proposed algorithm outperforms the modified maximum power trapezium, skipping fast GMPP, modified cuckoo search, and the conventional P&O algorithm with an average convergence time of 1.02 s and average efficiency of 99.44%.