Partial shading is a common and difficult problem to be solved in a photovoltaic (PV) system. Numerous efforts have been introduced to mitigate this problem. Some commonly used approaches are deploying some metaheuristic (MH) algorithm to track the multiple-peak P-V curve of partially shaded PV system. Cuckoo search (CS) is a new optimization algorithm based on the MH approach. It has been used to solve an optimization problem in many applications, including the maximum power point tracking (MPPT) problem. The CS algorithm performs well in tracking the global maximum power point (GMPP). However, just like any other MH algorithm, there is still a dilemmatic trading between their accuracy and the tracking time needed to find GMPP. This paper proposes a new MPPT algorithm by combining the CS algorithm with golden section search (GSS) to take beneficial features from both the algorithms. To validate the proposed algorithm, it is evaluated with various cases of partial shading. The simulation and experimental results show a noticeable performance improvement compared with the original CS algorithm and other MH algorithms.

A partial shading condition (PSC) is one of the most common problems in the photovoltaic (PV) system. It causes the output power of a PV system drastically decrease. Meta-heuristic algorithms (MHA) can track the maximum power point in a power-voltage (P-V) curve with multiple peaks. Grey wolf optimization (GWO) algorithm is a new optimization algorithm based on MHA. It has been used to solve optimization problems in many applications including MPPT for a PV system. However, the accuracy and tracking time in the original GWO (OGWO) can still be further improved for various PSCs. Therefore, there have been some modified grey wolf optimization (MGWO) algorithms proposed to improve the GWO. Nevertheless, only incremental improvement has been made. Therefore, a modified GWO, named enhanced grey wolf optimization (EGWO) is proposed in this paper. The proposed method adds the weighting average, the pouncing behavior and nonlinear convergence factor in the OGWO. In particular, since real wolves may engage in pouncing action when they are hunting, inclusion of pouncing completes the GWO algorithm and yields great improvements. As will be shown via simulation and experiment, the EGWO can drastically reduce the tracking time (up to 45.5% of the OGWO) and the dynamic tracking efficiency can be improved by more than 2%, compared to the OGWO. Moreover, the EGWO achieves the highest maximum power point compared to some of the existing GWO and other swarm based algorithms.

Four-wheel independent drive is an important research topic for the future development of pure electric vehicles due to its fast response and great flexibility of path tracking. One of the critical issue is to maintain the vehicle motion stability of the four-wheel independent drive electric vehicle (4WID EV) by appropriately distributing the torque of each wheel. The paper presents a hierarchical control structure to counteract the stability problems caused by the actuator faults and road friction uncertainties. Instead of using linear control at the top-level (i.e. the outer control loop), a novel sliding mode controller with adaptive PI sliding surface is designed to achieve the required virtual control signal. For the bottom-level controller (or the inner loop controller), dynamic control allocation is employed to optimally allocate the required actual torque for each wheel. The stability of the overall closed-loop system is successfully achieved and proved by using the Lyapunov method. The proposed controller has been tested via simulation and experiments with various actuator faults and road conditions. All the test cases showed the superiority of the proposed method compared to some of the currently existing 4WID control strategies.

This article presents a detailed and rigorous comparison between two vector current control (VCC) approaches for voltage source converters (VSC). A PLL-less VCC (also called the direct power control) has recently been proposed in several major publications with the assumption that it will improve the performance of conventional PLL-based VCC during weak grid conditions. In this article, first, we introduce an enhancement to both the PLL-based and PLL-less VCCs by using an optimal multivariable control design for both of them. Then, we show that both controllers perform equally during normal and weak grid conditions. However, the PLL-less VCC produces errors when the grid frequency variations occur and eventually becomes unstable when the grid is also weakened. Furthermore, the PLL-less VCC cannot produce a clean output current when unbalanced grid disturbances occur. This article proposes a method to address this latter issue. It also proves that the filters used in PLL-less VCC introduce similar dynamics to those of PLL. This article concludes that the PLL-based VCC is still the preferred choice for the grid-interfaced VSCs. Laboratory-scale experimental results are also included to validate the simulation results.

Solar modules under partial shading (PS) conditions will result in power and voltage characteristic curves (P-VCC) having multiple peaks. If the maximum power point cannot be obtained, the output power of the solar modules will be greatly reduced. Hence, there have been various maximum power point tracking (MPPT) control methods developed to address this problem. One alternative is to employ the meta-heuristic approach (MHA) to track the global maximum power point (GMPP). Recently, a new MHA called Bat Algorithm (BA) has performed well in the MPPT. Nevertheless, BA may fail to track the GMPP when there are some local maximum power points (LMPPs) close to the GMPP. Also, the tracking time needs to be further reduced to accommodate rapidly changing irradiance. Therefore, a combination of BA with the abandonment mechanism of Cuckoo Search (CS) is proposed to improve the tracking performance of the BA. Both simulation and experimental results show that the proposed method, as compared to BA, yields better accuracy and an improvement of convergence speed of about 35% for various P-VCCs can be achieved. Moreover, the MBA has also been tested against some of the state-of-the-art MPPT algorithms such as Particle Swarm Optimization and Grey Wolf Optimization (GWO), and the results showed the superiority of the proposed method.

Rail transport systems play a crucial role in public transport systems in that they are highly effective in mitigating traffic jams, maximizing transportation capacity, minimizing air pollution, and reducing energy consumption. This study optimized the operation of a single rapid transit train between stations for energy conservation. Past studies investigating energy conservation in train operation only considered the consumption of mechanical energy or assumed a constant efficiency value for the direct conversion of mechanical energy to electrical energy consumption. However, a train using minimal mechanical energy does not necessarily imply its input electrical energy is minimal. Therefore, only through direct minimization of electrical energy consumption can the goal of energy conservation in train operation be achieved. Simulation results from conventional mechanical model, the exact model, and the proposed model indicated that the conventional mechanical model could not reflect the actual electrical energy consumption and consequently could not yield the optimal train-speed curve. Simulation results from the exact and the proposed models revealed small differences between their energy consumption calculation results. The model this study proposed greatly reduced the simulation time. Moreover, when the coasting mode was added to the proposed model, the operational energy consumption was reduced by approximately 58%.