PV power production is highly dependent on environmental and weather conditions, such as solar irradiance and ambient temperature. Because of the single control condition and any change in the external environment, the first step response of the converter duty cycle of the traditional MPPT incremental conductance algorithm is not accurate, resulting in misjudgment. To improve the efficiency and economy of PV systems, an improved incremental conductance algorithm of MPPT control strategy is proposed. From the traditional incremental conductance algorithm, this algorithm is simple in structure and can discriminate the instantaneous increment of current, voltage and power when the external environment changes, and so can improve tracking efficiency. MATLAB simulations are carried out under rapidly changing solar radiation level, and the results of the improved and conventional incremental conductance algorithm are compared. The results show that the proposed algorithm can effectively identify the misjudgment and avoid its occurrence. It not only optimizes the system, but also improves the efficiency, response speed and tracking efficiency of the PV system, thus ensuring the stable operation of the power grid.

No abstract

No abstract

Accurate classification of power quality disturbance is the premise and basis for improving and governing power quality. A method for power quality disturbance classification based on time-frequency domain multi-feature and decision tree is presented. Wavelet transform and S-transform are used to extract the feature quantity of each power quality disturbance signal, and a decision tree with classification rules is then constructed for classification and recognition based on the extracted feature quantity. The classification rules and decision tree classifier are established by combining the energy spectrum feature quantity extracted by wavelet transform and other seven time-frequency domain feature quantities extracted by S-transform. Simulation results show that the proposed method can effectively identify six types of common single disturbance signals and two mixed disturbance signals, with fast classification speed and adequate noise resistance. Its classification accuracy is also higher than those of support vector machine (SVM) and k-nearest neighbor (KNN) algorithms. Compared with the method that only uses S-transform, the proposed feature extraction method has more abundant features and higher classification accuracy for power quality disturbance.

No abstract