Condenser Fault Diagnosis Research Articles

Analysis on Mechanism and Characteristic of Vibration Fault on New Large Capacity Synchronous Condenser

Vibration faults are the most common faults in large capacity synchronous condenser. The article proposes 11 common vibration faults of condenser, including imbalance, misalignment, looseness, resonance, oil film oscillation, steam flow excitation, dynamic static friction, rotational stall, gear fault, rolling bearing fault, and other vibration faults. The article also proposes the mechanism, characteristics and case analysis of each type of fault separately. The research results on the mechanisms and characteristics of these faults are the foundation of condenser fault diagnosis, and are crucial for the development of intelligent decision-making systems and platforms. Which can provide the strongest technical support for improving power transmission and safety and stability of the power grid.

Fault Diagnosis of Power Plant Condenser With the Optimized Deep Forest Algorithm

As an important component of power plant operation, condenser fault diagnosis plays a vital role in the safe and stable unit performance. However, the precision of most existing diagnostic methods is not high enough for condenser fault diagnosis. It is considerably difficult to diagnose a condenser fault even under various complicated conditions. In this study, a novel classification hybrid model (PCA-DF) combining the Principal Component Analysis (PCA) method with the Deep Forest (DF) model is proposed based on the motivation of improving the diagnosis accuracy of condenser fault. The algorithm of this hybrid model takes the dimension reduction result of the PCA method as the input to the DF model. The multigrained scanning structure and the dimension reduction method are considered to create a good effect. The experimental results verify the feasibility and effectiveness of this method on the historical fault sample data of the condenser. The focus on the work presented of this paper is to optimize the DF model based on PCA and study the fault diagnosis effect of the hybrid model. Results show that (1) the prediction accuracy for the condenser fault diagnosis can be improved by increasing the sample size with the PCA-DF method. (2) The accuracy of the results obtained by proposing the improved hybrid models is 1%-8% higher than the accuracy of the results obtained by directly introducing the DF model. The modified hybrid models still have advantages over the DF model for a small sample. (3) With an increase in the proportion of training sets, the accuracy of the modified hybrid models is improved correspondingly from 88.18% to 99.23%. (4) Compared with the backpropagation neural network, convolutional neural network, relevance vector machine and kernel Fisher discriminant analysis models, the PCA-DF model has higher accuracy. In this study, the proposed models can eliminate the influence of autocorrelation between data, and condenser fault diagnosis based on modified models has the fastest convergence speed and best accuracy. Furthermore, the proposed novel models can be extended to more complex fault diagnosis in other fields.

Thermal Power Plant Condenser Fault Diagnosis Using Coordinated Condition Monitoring Approach

Thermal Power Plant Condenser Fault Diagnosis Using Coordinated Condition Monitoring Approach

The Application of Improved Fuzzy Analytic Hierarchy Process (FAHP) in the Condenser Fault Diagnosis in Power Plant

Since the influential factors of vacuum reduction of condenser are fuzzy and uncertain, the improved FAHP is used. By comparing every two of the influential factors, the fuzzy matrix is constructed. The weight index of influential factors of vacuum reduction of condenser in a certain power plant is calculated out quantitatively. The most important influential factor is found out. And there is a verification which exactly identified the fault of the condenser by using a real instance.

A Comparative Research on Condenser Fault Diagnosis Based on Three Different Algorithms

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.

The Application of Improved Fuzzy Analytic Hierarchy Process (FAHP) in the Condenser Fault Diagnosis

The vacuum of condenser is an important parameter in the operation of steam turbine, which has a great influence on the safe operation and regulation performance of the turbine. Since the influential factors of vacuum reduction of condenser are fuzzy and uncertain, the improved FAHP is used. By comparing every two of the influential factors, the fuzzy matrix is constructed. The weight index of influential factors of vacuum reduction of condenser in a certain power plant is calculated out quantitatively. The most important influential factor is found out. And there is a verification which exactly identifies the fault of the condenser by using a real instance.

Fault Diagnosis of Condenser in Ship Steam Power System Based on Unsupervised Learning Neural Network

A diagnostic method is proposed for the faults of the condenser in the ship steam power system by using the unsupervised learning neural network. First, we analyzed the reasons leading to the condenser faults according to the operating features of the condenser in the steam power system. Combined with the expert knowledge, we summed up the training sample model for the fault diagnosis of the condenser. Then we adopted two types of unsupervised learning neural network to diagnose the fault of the condenser. The diagnostic method was proved to be rapid and accurate by test. Finally, we analyzed and compared the performance and the optimizing approach of the unsupervised learning neural network for fault diagnosis. The diagnostic method is of guiding significance for the safe operation of the ship steam power system.

Research of Improved Credibility Theory for the Fault Diagnosis of Condenser

Research on fault diagnosis of condenser has a great significance to safe and efficient operation of power plant. In view of shortcomings of existing diagnosis methods, this paper proposed a fault diagnosis method of condenser based on improved credibility theory，and applied the merits of credibility reasoning to the fault diagnosis of condenser. By proposing a more rational expression way of symptoms and some mathematical equations which can convert field data into symptoms’ credibility, this paper provided a solid basis for acquiring symptoms’ information accurately. Meanwhile, the setting method of rule confidence of credibility rule was improved, which can make the process of credibility reasoning closer to human experts thinking. Experiment results show that this model meets the demand of condenser fault diagnosis well and has important theoretical significance and practical value.

Condenser Fault Diagnosis Based on FNN and Data Fusion

In order to improve the fault diagnosis result of the condenser, one new approach based on the fuzzy neural network and data fusion is proposed in this paper. Firstly, the data from the various sensors can be processed through the specific membership functions. With the fault symptoms and fault types of condenser, the fuzzy neural network is constructed for the primary fault diagnosis. Some likelihood of the neural network outputs is too close to make the correct decision of fault diagnosis. The problem can be solved by the data fusion technology. This method was successfully adopted in the application of condenser fault diagnosis. Compared with the general method of FNN, this approach can enhance the accuracy in the domain of fault diagnosis, especially for reducing the uncertainty in the fault diagnosis.

A Hybrid KPCA-PNN Approach to Condenser Fault Diagnosis with a Particle Swarm Optimizer

Diagnosis of condenser abnormality is very important for turbine generator reliability. This paper presents a novel approach for condenser fault diagnosis based on kernel principle component analysis (KPCA) and probabilistic neural network (PNN). KPCA is applied to PNN for feature extraction. It firstly maps data from the original input space into high dimensional feature space via nonlinear kernel function and then extract optimal feature vector as the inputs of PNN to solve condenser fault classification problems. A global optimizer, particle swarm optimizer (PSO), is employed to optimize the parameters of PNN to improve fault classification accuracy. The experimental results show that the proposed approach has a better ability in terms of diagnosis accuracy and computational efficiency compared with a number of popular fault diagnosis techniques.