Power versus voltage curves of partial shading photovoltaic (PV) systems contain several local peaks (LPs) and one global peak (GP). Most conventional maximum power point tracker (MPPT) techniques may not follow the GP under partial shading conditions (PSC). The use of metaheuristic techniques such as the bat algorithm (BA) and particle swarm optimization (PSO) can overcome these obstacles. All problems inherent in the using of BA as MPPT of PV systems has been discussed and solved in this paper. The first problem is the random initial values of bats that may cause premature convergence. Therefore, the initial values of bats were modified to be close to the anticipated positions of peaks to reduce the convergence time and improve the chance of capturing the GP. The second problem occurs when shading pattern changes the value and position of the GP which is not configurable because all bats are concentrated at the previous GP; this can be resolved by BA re-initialization. The the third problem is the GP memorized in the execution of the BA code forces the PV system to work at the duty ratio of the highest GP ever seen, which may not be the real GP. This problem is solved by updating the memorized GP. This paper also proposes a new criterion for selecting the optimal swarm size against number of peaks to reduce the convergence time and improve the chance of capturing the GP. To the authors’ knowledge, most of these problems inherent in the BA have hitherto not been addressed in the literature. The simulation and experimental results obtained from the proposed modified BA (MBA) with re-initialization have been compared to the PSO and grey wolf optimization (GWO) techniques which show the superiority of using MBA strategy in the MPPT of partial shading PV systems.

Partial shading of PV systems generates many peaks in the P–V curve. These peaks have one global peak (GP), the remaining being local peaks (LPs). Metaheuristic techniques such as PSO have proven superiority in capturing the GP and avoiding entrapment in an LP in comparison to conventional techniques. In case of partial shading conditions (PSC), the GP may change its position and value in the P–V curve and the PSO is unable to capture the GP unless they reinitialize. Reinitialization of PSO particles spends a long time for convergence; and it may cause premature convergence. This paper proposes a novel strategy for scanning the new position of the GP in case of PSC changes without a need for reinitialization. The proposed strategy sends a particle to the anticipated places of peaks to search for any peak with power greater than the current GP and when it locates this new GP it will move the PSO particles directly to the new GP. This strategy reduced the reinitialization time by 650% as compared to the time required for the random reinitialization of the conventional PSO technique. Moreover; this proposed strategy completely avoids the premature convergence associated with conventional PSO techniques.

The high relying of electric vehicles on either in-vehicle or between-vehicle communications can cause big issues in the system. This paper is going to mainly address the cyber attack in electric vehicles and propose a secured and reliable intelligent framework to avoid hackers from penetration into the vehicles. The proposed model is constructed based on an improved support vector machine model for anomaly detection based on the controller area network (CAN) bus protocol. In order to improve the capabilities of the model for fast malicious attack detection and avoidance, a new optimization algorithm based on social spider (SSO) algorithm is developed which will reinforce the training process at offline. Also, a two-stage modification method is proposed to increase the search ability of the algorithm and avoid premature convergence. Last but not least, the simulation results on the real data sets reveal the high performance, reliability and security of the proposed model against denial-of-service (DoS) hacking in the electric vehicles.

The partially shaded photovoltaic (PSPV) condition reduces the generated power and contributes to hot spot problems that may lead to breakdown of shaded modules. PSPV generates multiple peak, one global one and many other local peaks. Many efficient, accurate and reliable maximum power point tracker (MPPT) techniques are used to track the global peak instead of local peaks. The proposed technique is not limited to global peak tracking, but rather it is capable of tracking the sum of all peaks of the PV arrays using an interleaved boost converter (IBC). The proposed converter has been compared with the state of the art conventional control method that uses a conventional boost converter (CBC). The converters used in the two PSPV systems are interfaced with electric utility using a three-phase inverter. The simulation findings prove superiority of the PSPV with IBC compared to the one using CBC in terms of power quality, reliability, mismatch power loss, DC-link voltage stability, efficiency and flexibility. Also, IBC alleviates partial shading effects and extracts higher power compared to the one using CBC. The results have shown a remarkable increase in output generated power of a PSPV system for the three presented scenarios of partial shading by 61.6%, 30.3% and 13%, respectively, when CBC is replaced by IBC.

The Global Maximum Power (GMP) of photovoltaic (PV) systems changes its location on the power–voltage (P–V) curve as the shading pattern (SP) changes over time. Although the original Particle Swarm Optimization (PSO) technique can catch the GMP easily under the same SP, once it changes its location, it cannot catch the new GMP because the particles search around the first GMP caught. Therefore, conventional PSO is a time-invariant GMP tracker that cannot follow the dynamic GMP under variant SP. The novelty in this study is the modification of the conventional PSO technique to become a time-variant GMP technique. This has been achieved through dispersing the particles based on two new reinitialization methodologies for searching for the variant GMP. The first methodology depends on dispersing the PSO particles at a certain predefined time (PDT) in order to look for the new GMP of the new SP. The latter depends on continually monitoring any changes in the SP to disperse the particles to follow the new GMP. A detailed comparison between the improved PSO with two new reinitialization methodologies and the conventional PSO is introduced. The improved PSO with SP change reinitialization methodology tracked the dynamic GMP efficiently and accurately compared the conventional PSO and the improved PSO with PDT reinitialization. Also, no hardware modification in the existing PV system is required, which makes it an excellent option to improve the performance of new and existing PV systems.

This paper presents an improved control strategy for a doubly-fed induction generator (DFIG) during unbalanced grid voltage conditions. The proposed strategy was applied in both synchronization and grid-connected conditions. The synchronization process is carried by controlling the extracted positive and negative sequence components of the stator q-axis voltage to follow the grid q-axis voltage. This strategy can be accomplished by controlling the positive and negative sequence components of the rotor d-axis current. By perturbing the rotor d-axis current, the stator EMF builds up and follows the grid voltage accurately. The stator frequency and the phase difference between the stator and grid voltage are compensated by adjusting the stator d-axis positive and negative voltage components to zero. After synchronization, the proposed control strategy focuses on regulating the average stator active and reactive power control by controlling the positive components of q and d-axis currents, respectively. The second target is to minimize the generator torque ripple by controlling the rotor negative sequence components. At the same time, the grid side converter is controlled to minimize the grid power pulsations to reduce the impact of the unbalanced grid voltage. This study focuses on enhancing the dynamics of DFIG during the unbalanced grid voltage by using Multivariable State Feedback (MSF) current controllers. Experiments are carried out to validate the performance improvement by using the proposed method. The simulation and experimental results showed superior performance of the proposed control strategy.