Narrow Results

Many of the new high-speed high-integration density Integrated Circuits (ICs) and future generation of microprocessor powering requirements can be successfully achieved with voltage-mode hysteretic control applied to interleaved multiphase Point-of-Load (POL) DC–DC converters or Voltage Regulator Modules (VRMs). This is because of the several advantages that can be achieved by combining the advantages of hysteretic control and interleaving. However, there are several challenges in combining the two techniques, the most prominent being the current sharing and equalization between the interleaved phases. In this communication, we present a solution based on a real-time DSP controller. Challenges of the implementation will be discussed and experimental results obtained from a prototype will be presented.

Discrete global-sliding mode (SM) control of switching DC-DC converters is presented in this paper. The existence and stability conditions of quasi global-sliding mode are proposed. The design method for the discrete global SM controller is studied and applied to Buck converter with global-sliding mode control. Computer simulation and experimental results verify the validity of the theoretical analysis.

Abnormal detection of wind turbine converter (WT) is one of the key technologies to ensure long-term stable operation and safe power generation of WT. The number of normal samples in the SCADA data of WT converter operation is much larger than the number of abnormal samples. In order to solve the problem of low abnormal data and low recognition rate of WTs, we propose a sample enhancement method for WT abnormality detection based on an improved conditional Wasserstein generative adversarial network. Since the anomaly samples of WT converters are few and difficult to obtain, the CWGANGP oversampling method is constructed to increase the anomaly samples in the WT converter dataset. The method adds additional category labels to the inputs of the generative and discriminative models of the generative adversarial network, constrains the generative model to generate few types of anomalous samples, and enhances the generative model’s ability to generate few types of anomalous samples, enabling data generation in a prescribed direction. The smooth continuous Wasserstein distance is used instead of JS divergence as a distance metric to measure the probability distribution of real and generated data in the conditional generative response network and reduce pattern collapse. The gradient constraint is added to the CWGANGP model to enhance the convergence of the WGAN model, so that the generative model can synthesize minority class anomalous samples more effectively and accurately under the condition of unbalanced sample data categories. The quality of anomalous sample generation is also improved. Finally, the anomaly detection is made on the actual operating variator dataset for the unbalanced dataset and the dataset after reaching Nash equilibrium. The experimental results show that the method used in this paper has lower MAR and FAR in WT converter anomaly detection compared with other oversampling data balance optimization methods such as SMOTE, RandomOverSampler, GAN, etc. The method can be well implemented for anomaly detection of large wind turbines and can be better applied in WT intelligent systems.

This paper describes a power supply of pulse capacitors. The charging system is designed for charging four 16mF capacitors up to 5kV in approximately 60s. These capacitors then charge the load up to 20kV. This power supply is based on a series resonant inverter followed by a step-up transformer. Simulation results show that the load capacitor voltage increases linearly.