The partial shading of a photovoltaic array repeatedly occurs in the natural environment, which can cause a failure of a conventional maximum power point tracking (MPPT) algorithm. In this paper, the convergence conditions of the standard particle swarm optimization (PSO) algorithm are deduced by the functional analysis, and then the influence of the random variables and inertia factor of the algorithm on the trajectory in the particle swarm optimization is analyzed. Based on the analysis results, an improved particle swarm optimization (IPSO) algorithm, which adopts both global and local modes to locate the maximum power point, is proposed. Compared to the standard PSO algorithm, in the improved PSO algorithm, many random and interfered variables are removed, and the structure is optimized significantly. The proposed algorithm is first simulated in MATLAB to ensure its capability. The feasibility of the approach is validated through physical implementation and experimentation. Results demonstrate that the proposed algorithm has the capability to track the global maximum power point within 3.3 s with an accuracy of 99%. Compared with five recently developed Global MPPT algorithms, the proposed IPSO algorithm achieved better performance in the maximum power tracking in the partial shading conditions. INDEX TERMS Maximum power point tracking, partial shade, particle swarm optimization, photovoltaic array.

Mobile cloud computing has the features of resource constraints, openness, and uncertainty which leads to the high uncertainty on its quality of service (QoS) provision and serious security risks. Therefore, when faced with complex service requirements, an efficient and reliable service composition approach is extremely important. In addition, preference learning is also a key factor to improve user experiences. In order to address them, this paper introduces a three-layered trust-enabled service composition model for the mobile cloud computing systems. Based on the fuzzy comprehensive evaluation method, we design a novel and integrated trust management model. Service brokers are equipped with a learning module enabling them to better analyze customers' service preferences, especially in cases when the details of a service request are not totally disclosed. Because traditional methods cannot totally reflect the autonomous collaboration between the mobile cloud entities, a prototype system based on the multi-agent platform JADE is implemented to evaluate the efficiency of the proposed strategies. The experimental results show that our approach improves the transaction success rate and user satisfaction. INDEX TERMS Mobile cloud computing, service composition, trust management, user preference learning, multi-agent technology.

The solar radiation near the surface is the main reason that affects photovoltaic power generation. Accurate ultra‐short‐term solar radiation prediction is the premise of photovoltaic power generation prediction. Here the cloud movement prediction method based on the ground‐based cloud images is presented. The cloud recognition, cloud matching, cloud area correction and cloud movement prediction are performed to predict the drift trajectory of the clouds that will block the sun. Then, using digital image technology, 13 kinds of feature information are extracted from the ground‐based cloud images. Then, these feature information are input into BP neural network, and the parameters of BP neural network are optimized by genetic algorithm. Through a large number of data training, a new ultra‐short‐term prediction model of solar radiation is established. Finally, through experimental comparison, the results show that the prediction accuracy of the model with the feature information of ground‐based cloud images can reach 96%, compared with the model without the feature information of ground‐based cloud images, the accuracy is improved by 5%. The proposed ultra‐short‐term solar radiation prediction model can effectively predict the radiation jumping process caused by cloud occlusion, and greatly improve the prediction accuracy, especially in cloudy weather.

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