Nondestructive inspection of electrical insulators subjected to the high electrical stress and environmental damage is fundamental for reliable operation of a transmission lines. The breakage and defect of the insulator have great influence on the safe of transmission lines, and insulator defect detection with difference types is a complex work. This paper proposed an insulator defect detection method inspired by human receptive field model, which meets the requirements for detecting defect insulator in a simple background. In this method, the defect detection combined human receptive field model of human visual system is constructed and applied on the different insulators, so as to achieve accurate detection of the insulator defected parts. Experimental results show that the method can accurately and robustly detect the defect (such as cracks and damage) of electrical insulator in case of noise affect.

The diversity of power generation provides an important guarantee for the electric reliability of human society. The forecasting of power generation is an important topic in the electrical industry. However, most of recent work are focus on some special type power generation, overall electric load forecasting is lacking attention. In order to improve practical applications, this paper proposes a power generation predication method based on one of popular machine learning algorithm that is support vector machine, so as to predict both overall power generation and some special types of power generation. The nonlinear relation of electric net power generation is explored by historical monthly recorded data, this relation can help the predication of net electric generation for the next month. Experimental results show that our proposed electric generation forecasting method based on support vector machine can get suitable predication model and achieve high predicted precision, which is in accordance with the real data in the record. ARTICLE HISTORY

With the rapid development of industry and technology, the electrical power system becomes more complex and the electrical equipment becomes more diverse. Defective equipment is often the cause of industrial accidents and electrical injuries, which can result in serious injuries, such as electrocution, burns, and electrical shocks. In some cases, electrical equipment fault may result in death. However, in some special situation, some fault is very small even invisible, such as equipment aging, holes, and cracks, so the detection of these incipient faults is difficult or even impossible. These potential incipient faults become the biggest hidden danger in the electrical equipment and electricity power system. For these reasons, this paper proposes a superresolution reconstruction method for electrical equipment incipient fault to ensure complete detection in electrical equipment, which aims to guarantee the security of electrical power system operation and industry production. Experimental results show that this method can get a state-of-the-art reconstruction effect of incipient fault, so as to provide reliable fault detection of electrical power system.