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Large gas turbine control system fail during operation inevitably. Due to the various failure causes and failure modes, the troubleshooting process is relatively complicated. Combining the current situation of gas turbine control system fault diagnosis, the fault examples of the gas turbine control system are sorted out and summarized into five types of faults. Using distributed database, micro-service framework Spring Cloud, front-end framework Vue.js, following the principle of separation of front-end and back-end , carry out the design and development of the fault diagnosis system for GT control system based on B/S architecture, so that power plant staff can use the system to analyze and diagnose faults, and improve the accuracy and efficiency of fault diagnosis of the GT control system.

In view of artificial neural network, there are some deficiencies in condenser fault diagnosis. The BP neural network used for condenser fault diagnosis is highly nonlinear pattern recognition and high precision of fault diagnosis. The PSO-BP neural network can effectively solve the problem of BP neural network that training time is long and training process is easy to fall into the local minimum. The training results of PSO-BP network in convergence speed and convergence effect are significantly improved. Under the condition of small samples, the calculation results of SVM method are better than the calculation result of the other two methods. Although the recognition ratio of improved PSO-BP(2) and SVM is the same, training time of improved PSO-BP(2) is longer than training time of SVM. The generalization ability of SVM is stronger, and the efficiency of SVM is higher than the neural network. With MATLAB programming, three different algorithms, which are BP neural network, PSO-BP neural network and SVM, are studied and compared for the performance of condenser fault diagnosis. In the models of this study, the research results show that condenser fault diagnosis based on SVM has the fastest convergence speed and the best accuracy.

To improve the electric vehicle charging infrastructure and accelerate the development of electric vehicles, the optimization of electric vehicle charging stations’ location and size are studied in this paper. The annual comprehensive cost of society, including user cost and charging station cost, is regarded as the objective function. Weight coefficients are added to the function for increasing the proportion of user cost. An optimized mathematical model for electric vehicle charging stations based on users’ benefit is established. The improved Quantum-behaved Particle Swarm Optimization (QPSO) algorithm is adopted to solve this mathematical mode, and result which contain the optimal location and size of electric vehicle charging stations is obtained. Finally, an actual area is taken as the case study to optimize the location and size of electric vehicle charging stations by solving the mathematical model proposed in this paper with the improved QPSO algorithm. Rationality and validity of the model are well improved by the scientific reasonable result.

Through the research of EPA Industrial Ethernet technology, MODBUS fieldbus technology, ARM embedded system and μC/OS-II real-time operating system, this paper discusses how to design and develop communication gateway of EPA and MODBUS. The communication gateway can realize bidirectional data transceiving on EPA protocol and MODBUS protocol. The communication gateway can provide a stable, secure, real-time and flexible solution for process control of the power plant.

Due to the complexity of the power plant production environment, it brings some difficulties to troubleshooting of turbine generator. Although the approach based on neural network has been widely used in fault diagnosis of equipment, the result of fault diagnosis, which is given by the single neural network, is often not ready to determine the fault type for turbine generator. In response to this situation, a fault diagnosis method based on multi-neural network is proposed on this paper. It means that the different neural network is to be used respectively for fault diagnosis of turbine vibration firstly. Then the results of these initial diagnoses are to be integrated with information fusion technology. Through this strategy, the reliable result of fault diagnosis is obtained and the disadvantage of inaccurate diagnosis based on a single neural network is overcome.

By monitoring a 600MW steam turbine generator unit, its structure is analyzed to acquire the parameters which are used for keeping the safety of steam turbine generator unit and their value ranges. We constructed the functional diagram of condition monitoring and fault diagnosing system for steam turbine generator unit, which functions are described in detail as follows. By applying various kinds of time domain and frequency domain methods to analyze the acquired vibration data, many figures information are obtained. Through the information, the status of the steam turbine generator unit can be better mastered. At last, with the data acquisition system and software LabVIEW of National Instrument Company (NI), some functions of this system, such as data acquisition, data analysis, status monitoring and so on, are verified on rotor test equipment. The verification proves the feasibility and effectiveness of the condition monitoring and fault diagnosing system for turbine steam unit.

The monitoring of operation states and fault diagnosis system of turbines in power plant are significant to guarantee the units long-term safety and economic operation. The fault diagnosis of turbines is influenced by various factors, the information provided by every sensor need to be used in comprehensive to enhance the accuracy and reliability of fault diagnosis. The paper has presented a fault diagnosis system based on D-S evidence theory and neural network. Firstly, the inputs of neural network were fuzzed through fuzzy membership function, using BP neural network to train and simulate them, but in some cases, the diagnostic results of neural network were unable to determine the fault type accurately, therefore the information fusion was need. The D-S evidence was applied. This method has been proved by the fault diagnosis of turbine equipment in power plant, which can determine the fault type accurately. Particularly, in the case when the fault type can't be determined by using neural network. Therefore, the method was reliable and effective, for fault diagnosis in power plant equipment, it was significant.

Through the research of EPA industrial Ethernet technology, ARM embedded system and µC/OS-II real time operating system, this paper discusses how to design and develop remote I/O data acquisition equipment based on EPA industrial Ethernet. The equipment uses EPA technology in thermal power plant on-site data acquisition, and provides a stable, secure, real-time and flexible solution for process control of the power plant.

For the reason that the application of the neural network in the condenser fault diagnosis has some restrictions, the result of the fault diagnosis based on neural network is unsatisfied. On the basis of the fault diagnosis of condenser based on neural network, the thought of data fusion is introduced in this paper and a method of condenser integrated fault diagnosis based on multiple neural networks and D-S evidence theory is proposed. According to BP neural network and CPN network, the respective diagnosis results regarded as the primary evidences of D-S theory evidence in decision layer are obtained first, and then these results are fused by using of the evidence theory to obtain the final diagnosis result. The result of simulation shows that: Comparing with the result from the single network, this method has a smaller error and higher diagnosis reliability.

A new method of fault diagnosis for turbines using preserving nonlinear and quasi-Newton algorithm for the learning process of BP neural network was introduced in the paper, Which take advantages of preserving nonlinear and quasi-Newton algorithm with fast speed, significant superiority for high-dimensional problems and more accurate mathematical models comparing with traditional BP neural network. Though simulation studies on typical fault diagnosis examples of turbines in power plant, the results have shown that the performances of BP neural network based on preserving nonlinear and quasi-Newton algorithm is superior to the traditional BP algorithm, improving the ability of online diagnosis for turbines and having broad application prospects and value.

An approach of image segmentation was used in the application of particle holography analysis for the diameter measurement. The whole process of image segmentation based on boundary detection was delivered. For improving the accuracy, the segmented particle image has been obtained by the bi-threshold method which decreases influence of background noise and inhomogeneous gray distribution of holography. The experiment results showed the most relative error is less than 6%. The study has some meanings on the data processing in particle field holography.

The high quality of the information source can improve the accuracy of target recognition when data is fused via multi-information sources. In this paper, it proposes a new method to determine the reliability coefficient of the information source by information criterion. With calculating the reliabilities of information sources, the more reliable source would be used in the combination process to improve the recognition rate. The experiment results prove that the better results will be obtained with the reliability coefficient to choose the source in information fusion.

In the application of particle holography analysis, the diameter measurement is basic and important. A novel approach of segmentation is proposed. For improving the accuracy of measurement, the method of bi-threshold is used firstly and then the edge linking method is adopted to get the more accuracy segmented particle image. The experiment tests showed the less relative error is in the diameter measurement with the algorithm, compared with the bi-threshold algorithm. The study has further meanings on the processing of particles field holography.