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Abstract-One major challenge with integrating photovoltaic (PV) systems into the grid is that its power generation is intermittent and uncontrollable due to the variation in solar radiation. An accurate PV power forecasting is crucial to the safe operation of the grid connected PV power station. In this work, a combined model with three different PV forecasting models is proposed based on a rough set method. The combination weights for each individual model are determined by rough set method according to its significance degree of condition attribute. The three different forecasting models include a past-power persistence model, a support vector machine (SVM) model and a similar data prediction model. The case study results show that, in comparison with each single forecasting model, the proposed combined model can identify the amount of useful information in a more effective manner.

Disassembly sequence planning (DSP) is an important part of equipment maintenance in a hydropower station. In this paper, the generation of an excellent disassembly sequence (DS) for equipment is studied. Firstly, according to the characteristics of hydropower equipment, a combination node type is added to the directed graph analysis model, and the distance factor of components in space is added to the evaluation function of DS. Secondly, a DSP strategy including the grouping and minimization of the node's scope is adopted to reduce computational complexity. Thirdly, a novel team-based genetic algorithm (TBGA) combining teams, fast feasible solution generator (FFSG), precedence preservative crossover (PPX) mechanism, multi-point heuristic mutation (MHM) mechanism, and forward-and-backward optimization operator (FBOO) is designed for DSP. The proposed TBGA maintains global search capabilities through teams and enhances local search capabilities through individuals. In the evolutionary process, teams, MHM, and FBOO have good complementarity to improve the comprehensive performance of the algorithm. Finally, four experiments are conducted and the performance of TBGA is tested based on the comparison of a well-known genetic algorithm, simplified teaching-learning-based optimization, and simplified swarm optimization algorithm. The results show that the proposed method can get better search results in limited iterations and require only about 25% time of other algorithms.

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