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Based on cloud model and matter element theory, and combining the uncertain reasoning characteristics of the cloud model and qualitative and quantitative analysis can be carried out at the same time by matter element theory, a power transformer fault diagnosis method is proposed, which effectively solves the problem of fewer data samples, especially fewer fault data samples. Taking the actual data as an example, the improved matter-element theory model and correlation calculation data are compared, and the results show that the improved matter element theory model has higher diagnostic accuracy than traditional methods. Example analysis verifies the correctness and effectiveness of the method.

With the continuous growth of China's power grid scale and load, more and more attention is paid to the security and stability of regional power grid. Since the proportion of power generation, transmission and substation planning in some areas is not coordinated, grid structure is not reasonable, this will lead to the frequent fault of safety and stability of the power grid, so some measures must be taken to optimize the transformation. In this paper, it use operation of the power grid in northern Chenggu area as the research object, which belong to Hanzhong, Shaanxi Province. Based on the analysis of the general methods and the steps of regional power network calculation, combined with the characteristics of the grid structure in the area, this paper analyses the typical operation mode of the regional power grid, based on the analysis results, the reactive power optimization is optimized. Aiming at the stability problem of regional power network, and according to different operation mode, this paper puts forward the corresponding measures to improve the level of power grid stability.

Scraper conveyor is one of main electrical and mechanical equipment in the mining face, and its reliability has an important significance to production safety. In scraper conveyor reliability life analysis, the linear parameter method is often used to estimate reliability life parameters in traditional Weibull distribution model. But the error of prediction results is larger due to the nonlinear characteristics of fault data. To solve this problem, a simulated annealing algorithm is proposed to optimize the model parameters for the scraper conveyor reliability life prediction. Research results show that the simulated annealing algorithm has small life prediction error, and raises the accuracy of the scraper conveyor reliability life prediction.

Support vector machine is a machine learning algorithm developed by Vapnik from the statistical learning theory for data classification via study from a small sample of fault data. For fault data it can isolate the fault categories accurately even though only has the small sample of data. In the present work, support vector machine's classification mechanism and its application in mechanical fault diagnosis are introduced. Therefore, give an instance the support vector machine makes fault classification for the coal mine scraper conveyor's faults. Last but not the least, put forward some of the shortcomings of the support vector machine and look forward to the direction of development of the support vector machine fault diagnosis in the future.

In order to avoid losses, which is caused by electromechanical failure in the coal large transport equipments, this article introduced a model that integrated by breakdown extraction, failure diagnosis and fault analysis in the large-scale scraper chain conveyor breakdown. The weak signal of mechanical vibration detection by ANFIS is adopted. And it is also used for Troubleshooting effectively and accurately. Lastly, an effective diagnosis suggestion is given through the instrumentation KS-2000. And by this way, we not only proved the feasibility and superiority of this plan, but also achieved predictive maintenance in the true sense.

A novel fault diagnosis method for 10KV rural electrical power grids based on Petri net theory is proposed in the paper. The structure of the 10KV rural power system network is built, the fault reasons are analyzed, the fault diagnosis models are established using the Petri net matrix analysis method, the decision-making strategies based on Petri net theory are presented for different fault types in order to ensure the stability of power grids and avoid accidents expanding. The feasibility using the Petri net theory in 10kv rural distribution power system for fault diagnosis is demonstrated with the practice case.

As a new research field on controlling science and engineering, fractional-order control gets more and more concerning. This article provides an overview of fractional-order control of background and basic knowledge of mathematics, giving different definitions and numerical algorithms of fractional calculus, finally establishing a mathematical model of fractional-order systems which used for analyzing and researching image signal processing of fractional-order systems.

Based on multi-sensor information fusion theory, a Gas fire-extinguishing control panel is introduced in this paper. The intelligent algorithm of neural network and fuzzy consequence is used to get the fire probability. According this principle, we designed an information fusion module which would send the probability to the main control panel to carry out the alarm and linkage. The practical application results indicate that the proposed method can effectively decrease the mistake rate of the fire detector and fire extinguisher.

In coal mine the forecast on fire is mainly based on the smoke, gas and temperature parameters for recognizing, and sometimes it has leak check and wrong check, therefore a novel method for mine fire based on image processing is presented. First the data are obtained by infrared CCD, then the blaze characters are extracted and entered into the fuzzy neural network model after being quantization, finally the fire can be detected. The experiment results show that this method can recognize fire signals and it reduced leak forecast, and also it is more reliable and has stronger antijamming ability. It will inevitably play an important role in coal mine safety production.

According to main distribution regions of coal resource and the characteristic of gangues, a novel image process method of separating gangues from coal is proposed with wavelet analysis in this article. Under the different frequency ranges, a series of image processing approaches are exerted on coal gangue images with wavelet transform. Images of gangues and coal are first collected by means of the measurement system, and then are denoised. The image edges are detected by fast multi-scale edge detection. Images are segmented by algorithm of self-adaptive threshold. Using the multi-resolution merit of wavelet analysis, gangues images decomposition are especially stressed on, which provides the important data for selecting gangues from the coal. The coal gangues automatic separation system is realized by means of a newly ARM microprocessor, which has characteristics of high-performance, low power dissipation and low-cost exactly. The experiments result reveals that the proposed methods are efficient and practicable compared to traditional methods. And the suggested system is satisfied with accuracy requirements and real-time performance.

The weight and volume of power supply devices can be further reduced, and noise can be decreased by using a three-phase converter in combination with a high-frequency AC link. A new control method of pulse density modulation (PDM) is used in the converters. The operation principle of the circuit is explained in detail. The expressions of the phase voltage and current and harmonics analysis are described separately. The simulation results prove the analysis to be true.