This paper presents the selection methods of typical substations based on grey correlation analysis method, and proposes classification principles of load characteristics. The actual example shows that using this method in process of statistical synthesis method modelling, the typical substations selected could be more representative, and the popularization and application of the modelling results are more accurate and effective.
In order to obtain accurate load forecasting of coal-fired unit, a new algorithm based on Support Vector Machine (SVM) method is presented. This algorithm establishes a model to reflect the complicated relation between the load of coal-fired unit and the furnace flame Images. The trained SVM model is applied to a 660MW coal-fired unit to forecast the load with two groups of test samples. The results are compared with that of BP neural network model. It is shown the SVM model is more accurate than the BP NN model. The SVM method can satisfy the demand of engineering applications with the advantages of high forecasting accuracy and more generalized performance.
In order to obtain more output power of photovoltaic (PV) array, which depends on solar irradiation and ambient temperature, maximum power point tracking (MPPT) techniques are employed. Among all the MPPT strategies, the Perturb and Observe (P&O) algorithm is more attractive due to the simple control structure. Nevertheless, steady-state oscillations always appear due to the perturbation. In this paper, a new MPPT method based on BP Neural Networks and P&O is proposed for searching maximum power point (MPP) fast and exactly, and its effectiveness is validated by experimental results using hardware platform based on microcomputer.
One of the basic conditions that self-organizing network can run properly is the correctness of the spreading of the nodes information in the network. This paper studied the according problem, designed a protocol for nodes in small self-organizing network finding each other and transmitting information.
Load model has a great impact on the digital simulation result. In this paper, the measurement-based method is applied to model the load. If all the measured data are used for modeling respectively, the workload would be increased greatly. But if only one model is generated with the multi-curve fitting parameter identification method, the accuracy of modeling would be reduced greatly. The clustering analysis theory supplies an effective way to solve the problem above. There are some methods for clustering introduced in this paper. But a suitable method needs be studied firstly. The case study is presented to compare these methods. According to simulation result, it is concluded that the Kmeans method is best, while the usually adopted central clustering is actually not suitable for the load time-variation research.
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