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Harmonic pollution and reactive power consumption are the two most important factors affecting power quality. Power electronics, as the largest harmonic source, will produce a large number of harmonics while running, and will also lose reactive power, reduce the power factor of the system and affect power quality. Firstly, the current situation of reactive power and harmonic suppression based on fuzzy control is briefly introduced. Then, the harmonic component and power factor of the power grid are taken as two objective functions to model the power grid. The improved particle swarm optimization (PSO) genetic algorithm is used to establish the harmonic suppression and reactive power compensation model. To improve the design efficiency, aiming at the complexity of the algorithm and the field measurement data, the reactive power compensation device is quickly solved on the Hadoop cloud computing platform to achieve the global optimal compensation effect, to improve the convergence speed and global search ability of the particle swarm optimization algorithm. Thus, based on ensuring the power factor, the harmonic component is restrained and the safety and reliability of power grid operation are improved.

Energy is an indispensable material resource for human production and life. It is a powerful engine and an important guarantee for human survival, economic and social sustainable development and world change. The economy is developing rapidly, the demand for energy continues to grow, energy consumption has increased sharply in a short period, and the security of energy supply and demand has also shown a severe trend. Predicting energy demand is especially important. However, due to the many influencing factors and the lack of energy data, the energy demand prediction has great uncertainty in the prediction results. Because of the above problems, this paper proposes an energy big data demand prediction model based on a fuzzy rough set model. Firstly, according to the data, the factors affecting the energy demand are determined, and the fuzzy C-means clustering algorithm is used to discretize the data according to the characteristics of the fuzzy rough set. Then the decision table is established and the attribute importance is calculated, and then the neighborhood rough set is used for attribute reduction. Then extract the correlation rules to establish a prediction model. Compare the prediction model proposed in this paper with the existing gray prediction method and energy elasticity coefficient method. The results show that this method can more scientifically predict the changes in energy big data demand. Finally, based on the experimental results, the corresponding strategies for optimizing the energy structure are proposed to provide reference for the optimization and development of energy demand.

Sine cosine algorithm (SCA) is a recently developed optimization technique, which uses sine function and cosine function as operators to find the global optimal solution. However, proper parameter setting is a challenging task. Only using the number of iterations to adjust the algorithm parameters cannot fully reflect the convergence information in the evolution process, so SCA lacks the adaptability in solving different optimization problems. To address this issue, a cloud model based sine cosine algorithm (CSCA) is proposed. In CSCA, the cloud model is used to adjust the control parameter adaptively while keeping SCA algorithm framework unchanged. The performance of the presented CSCA method is evaluated using 13 benchmark test functions with different dimensions. Experimental results demonstrate that the proposed algorithm is superior to other SCA variants in terms of robustness and scalability.