Design of microenvironment intelligent monitoring system of network cabinet

To improve the ability of network cabinet to deal with early warning and failure, a microenvironment intelligent monitoring system of network cabinet was designed. The system installs embedded terminal in the cabinet, collects power and non-power information and transmits to Web server via Ethernet. When collecting information was abnormal or cabinet fault occurs, the terminal can alarm intelligently and send SMS tips to remind abnormal situation. The experimental results show that the system can quickly and accurately locate abnormal and failure, and its remote control function is stable and reliable

Fault diagnosis of transformer based on fuzzy clustering and the optimized wavelet neural network

In order to solve the disadvantages of the traditional wavelet neural network (WNN) algorithm applied in transformer fault diagnosis, such as uneven sample distribution of training samples and high diagnostic error rate and long training time, an improved fault diagnosis method is proposed based on fuzzy clustering and the flower pollination algorithm. Firstly, fuzzy clustering is applied to deal with transformer fault sample data so as to remove the bad data; secondly, the flower pollination algorithm is applied to obtain the optimal parameters of the WNN. The example analysis results show that WNN based on the flower pollination algorithm (FPA-WNN) has better convergence, lower diagnosis error rate and shorter training time compared with WNN based on the particle swarm algorithm (PWA-WNN) and it is more suitable for transformer fault diagnosis

A new fault diagnosis method of rolling bearing of shearer

In view of unstable problem existed in fault diagnosis result for rolling bearing of shearer based on K-means clustering algorithm, a new fault diagnosis method of rolling bearing of shearer based on TDKM-RBF neural network was proposed. The method adopts Tree Distribution algorithm to determine initial clustering center of the K-means clustering algorithm, so as to eliminate volatility of K-means clustering results. The method uses K-means algorithm to determine the parameters of the RBF neural network, then the trained neural network was used for fault diagnosis. The simulation results show that the method has quick clustering process,higher steability, and obviously improves accuracy of fault diagnosis for rolling bearing of shearer

A Method for Identification of Transformer Inrush Current Based on Box Dimension

Magnetizing inrush current can lead to the maloperation of transformer differential protection. To overcome such an issue, a method is proposed to distinguish inrush current from inner fault current based on box dimension. According to the fundamental difference in waveform between the two, the algorithm can extract the three-phase current and calculate its box dimensions. If the box dimension value is smaller than the setting value, it is the inrush current; otherwise, it is inner fault current. Using PSACD and MATLAB, the simulation has been performed to prove the efficiency reliability of the presented algorithm in distinguishing inrush current and fault current

Fault diagnosis of mine hoist based on optimizing fuzzy Petri networks

In order to improve accuracy of fault diagnosis of mine hoist, a new fault diagnosis method of mine hoist based on optimizing fuzzy Petri networks was proposed. Firstly, momentum BP network was used to optimize weights, thresholds and reliability parameters of the fuzzy Petri networks, and then the optimal fuzzy Petri networks model was used for fault diagnosis of mine hoist. The test results show that the method has fast convergence rate of parameter and high accuracy of fault diagnosis of mine hoist, which has a certain practical value

Research of reliability evaluation method of distributed generation supply power for coal mine

In view of problem of low computation efficiency and poor generality of existing evaluation method of coal mine power grid, an improved BFS-the minimal path evaluation method was proposed. The method uses the breadth-first search method to quickly solve division optimization model of distribution network island, and then adopts the minimal path evaluation method to evaluate reliability of power supply according to the operation scope of island. The experimental results show that the method improves evaluation speed of weak links of power grid, and evaluation results are safe, reliable, accurate and effective

Research and design of low-power grid-connected PV power generation system based on automatic solar tracking

A low-power grid-connected photovoltaic (PV) power generation system based on automatic solar tracking is designed in this paper. In order to increase the level of accuracy of automatic solar tracking, the part of automatic solar tracking adopts the method of hybrid tracking and uses pin-cushion two-dimensional position sensitive detector plus four silicon PV cells as the photosensitive element. An adaptive topological structure of the main circuit based on a cascaded Boost converter circuit and the H6-type inverter topology are designed for the system to obtain a better working condition. Then an MPPT algorithm combined modified constant voltage algorithm with modified variable step-size increment conductance algorithm is proposed in order to improve the efficiency of PV power generation. The experimental results showed the effectiveness of the control system. Finally, the hardware and software of the system are also designed. Moreover, a graphic interface made with the LabVIEW software allows the user to monitor and save the data in a file

Prediction Method of Underwater Acoustic Transmission Loss Based on Deep Belief Net Neural Network

The prediction of underwater acoustic transmission loss in the sea plays a key role in generating situational awareness in complex naval battles and assisting underwater operations. However, the traditional classical underwater acoustic transmission loss models do not consider the regional hydrological elements, and the performance of underwater acoustic transmission loss prediction under complex environmental conditions in a wide range of sea areas is limited. In order to solve this problem, we propose a deep learning-based underwater acoustic transmission loss prediction method. First, we studied the application domains of typical underwater acoustic transmission loss models (ray model, normal model, fast field program model, parabolic equation model), analyzed the constraint rules of its characteristic parameters, and constructed a dataset according to the rules. Then, according to the characteristics of the dataset, we built a DBN (deep belief net) neural network model and used DBN to train and learn the dataset. Through the DBN method, the adaptation and calculation of the underwater acoustic transmission loss model under different regional hydrological elements were carried out in a simulation environment. Finally, the new method was verified with the measured transmission loss data of acoustic sea trials in a certain sea area. The results show that the RMSE error between the underwater acoustic transmission loss calculated by the new method and the measured data was less than 6.5 dB, the accuracy was higher than that of the traditional method, and the prediction speed was faster, the result was more accurate, and had a wide range of adaptability in complex seas

Fault recovery of distribution network containing distributed generation based on heuristic search algorithm and multi-population genetic algorithm

In order to solve low optimization efficiency and ‘premature convergence’ of the traditional genetic algorithm in fault recovery of the distribution network with DG, a new recovery scheme based on the heuristic algorithm and multi-population genetic algorithm is proposed. During the initial process, the heuristic algorithm is used to obtain certain individuals who are close to the local optimized solutions, so that it can search in the better individuals. And then, the multi-population genetic algorithm is introduced to achieve co-evolution among various groups, which takes into account the balance between the global and local searching abilities. Due to existing large number of infeasible solutions that do not satisfy the radial requirements in the searching process, topology simplification is performed to improve the searching speed and computational efficiency. Before the failure recovery, this paper determines whether to put into load shedding by using the repeated power flow method. Finally, the feasibility and validity of the optimization algorithm are demonstrated by case studies in the paper

Location and classification of power quality disturbance based on wavelet packet and PN

A new method of location and classification of power quality disturbance based on wavelet packet and PNN was proposed according to essential characteristics of transient power quality disturbance. The disturbance signals were sampled and decomposed by using wavelet packet to extract wavelet packet reconstructed coefficient and to locate signal saltation point, then the energy of each band was calculated and normalized, energy feature vectors were constructed as input sample of PNN for network training and testing, and finally classification of different disturbance signal was achieved. Matlab simulation results show that the method can quickly and accurately locate and classify disturbance signal