This paper proposes a hybrid approach combining Wigner-Ville distribution (WVD) with convolutional neural network (CNN) for power quality disturbance (PQD) classification. Firstly, a WVD technique is developed to transfer a 1D voltage disturbance signal into a 2D image file, followed by a CNN model developed for the image classification. Then, the feature maps are extracted automatically from the image file and different patterns are extracted from variables on CNN. A set of synthetic signals, as well as real-world measurement data, are used to test the proposed method. The high classification accuracy of test results is achieved to confirm the effectiveness of the proposed method. Furthermore, the model is simplified and optimized by visualizing the output of convolutional layers. On this basis, one visualizing technique called the class activation map (CAM) is used to identify the location and shape of “hotspots (PQDs)”. The effect of incorrect classification of the model is analyzed with the CAM. Therefore, the proposed method is proved to have the capability of providing necessary and accurate information for PQDs, which will then be used to determine the subsequent PQ remedy actions accordingly.

A series of integrated equalizers based on joint buck-boost (BB) and switched-capacitor (SC) converters are proposed for balancing the voltages of series-connected battery packs. All these equalizers realize the any-cells-to-any-cells (AC2AC) equalization mode without increasing any MOSFETs and drivers. Corresponding operational principles are analyzed and the expressions of balancing currents are verified by experimental waveforms. According to the comparative balancing experiments for four and six series-connected Li-ion cells, one proposed CBB-PCSC equalizer, which achieves the dual AC2AC balancing modes through the integration of both coupled buck-boost (CBB) and parallel-connected switched-capacitor (PCSC) converters, leads to the highest balancing speed and efficiency. Moreover, compared with several conventional equalizers, this CBB-PCSC topology also has the compact size and low cost, making it become a well-performing integrated topology for automotive battery voltages equalization.

This paper presents an accelerated particle swarm optimization (PSO)-based maximum power point tracking (MPPT) algorithm to track global maximum power point (MPP) of photovoltaic (PV) generation under partial shading conditions. Conventional PSO-based MPPT algorithms have common weaknesses of a long convergence time to reach the global MPP and oscillations during the searching. The proposed algorithm includes a standard PSO and a perturb-and-observe algorithm as the accelerator. It has been experimentally tested and compared with conventional MPPT algorithms. Experimental results show that the proposed MPPT method is effective in terms of high reliability, fast dynamic response, and high accuracy in tracking the global MPP.

Integrating renewable energy into power grids is seen in increase in recent years since these energy sources are sustainable and clean. However, the integration brings about considerable technical challenges associated with fluctuations and uncertainties of the energy availability whilst maintaining the stability of smart grids. The prediction of renewable energy generation is key to achieve optimal power dispatch in renewable-intensive smart grids. However, uncertain interruption and prediction errors will make an optimal decision more challenging. Model predictive control (MPC) is an effective way to overcome the discrepancies between the prediction and the real-world system through a closed-loop correction over iteration process. This study develops an improved MPC scheme used with a hybrid energy storage system for optimal power dispatch in a smart grid. This hybrid renewable energy system consists of a wind farm, a hydrogen/oxygen storage system and several fuel cells (FCs). In this study, particle swarm optimization (PSO) with a back propagation (BP) artificial neural network is developed to predict the wind energy availability by using measured data. Then, a genetic algorithm (GA) is combined with a state space model (SSM) to achieve the MPC control. A dataset of 24-hour ahead predictive generation is calibrated from the measured data and is defined for optimal power flow between the grid, the wind farm and the storage subsystem so as to balance the supply and load. The optimization target is to achieve a minimal energy exchange between the power grid and the hybrid renewable energy storage system. Based on actual measured data, the test results have shown that the proposed methodology can maximize the local usage of wind power whilst minimizing the power exchange with the grid. An optimal power dispatch strategy is proved to be effective to meet the demand and efficiency with dynamic control of the FCs. The usage of the intermittent wind power is increased from 45% to 90% in the four test studies. Therefore, this work can minimize the impact of fluctuating renewable energy on the power grid and enhance uptakes of FC-based energy systems. This is particularly economic and relevant to the remote and under-developed regions where their power networks are weak and vulnerable.

An active balancing method based on two flyback converters is proposed for series-connected battery pack. Balanced energy can be transferred between the whole battery and any single cell. The proposed topology reduces the number of energy storage components, the volume and the cost of the balancing system. And it has the characteristics of fast balancing speed and high balancing efficiency. Based on the topology, a dual-objective hybrid control strategy is proposed, which can reduce the highest voltage and boost the lowest voltage in the charging or discharging process of the cells in one sampling period, so as to improve the balancing speed. Simulation and experimental results show that the proposed method has a good balancing effect and can significantly improve the consistency of series battery pack. This work is potentially significant in terms of improved reliability of battery packs and savings of costs and lives in safety-critical applications.

The influence of static eccentricity (SE) and dynamic eccentricity (DE) on the electromagnetic forces in internal- and external-rotor permanent magnet synchronous motors (PMSMs) is investigated in this article. First, the analytical model of the electromagnetic force is derived. By introducing the eccentricity coefficient, the influence of eccentricity on the electromagnetic force is then analyzed. The results show that SE changes the spatial component of the electromagnetic force in internal-rotor motors, while the frequency remains unchanged. While for the external-rotor motors, both the spatial order and frequency of electromagnetic force are affected by SE. Contrary to SE, DE introduces additional spatial components and sideband frequency harmonics to the electromagnetic force of internal-rotor motors. For external-rotor motors, only the spatial order is changed. In addition, axial-flux PMSMs suffer from the extra unbalanced bending moment (UBM), which is a unique feature that differs from traditional radial-flux PMSMs. The expression of UBM caused by eccentricity is further derived. Finally, the theoretical analysis is validated by the simulation and test results, respectively. This study will benefit the researchers and designers in terms of source identification and reduction of acoustic noise in PMSMs.