A Real-Time and Closed-Loop Control Algorithm for Cascaded Multilevel Inverter Based on Artificial Neural Network

In order to control the cascaded H-bridges (CHB) converter with staircase modulation strategy in a real-time manner, a real-time and closed-loop control algorithm based on artificial neural network (ANN) for three-phase CHB converter is proposed in this paper. It costs little computation time and memory. It has two steps. In the first step, hierarchical particle swarm optimizer with time-varying acceleration coefficient (HPSO-TVAC) algorithm is employed to minimize the total harmonic distortion (THD) and generate the optimal switching angles offline. In the second step, part of optimal switching angles are used to train an ANN and the well-designed ANN can generate optimal switching angles in a real-time manner. Compared with previous real-time algorithm, the proposed algorithm is suitable for a wider range of modulation index and results in a smaller THD and a lower calculation time. Furthermore, the well-designed ANN is embedded into a closed-loop control algorithm for CHB converter with variable direct voltage (DC) sources. Simulation results demonstrate that the proposed closed-loop control algorithm is able to quickly stabilize load voltage and minimize the line current’s THD (<5%) when subjecting the DC sources disturbance or load disturbance. In real design stage, a switching angle pulse generation scheme is proposed and experiment results verify its correctness

Construction of adaptive particle swarm optimizers and optimization of parameters in switched dynamical systems

研究成果の概要 (和文) : 様々な問題に柔軟に適応できる粒子群最適化法(PSO)について考察し、3つの新しいPSOを提案した。(1)確定的な差分方程式に支配されるPSO。これは安定性解析や再現性のある動作制御に有利である。(2)粒子の動作を鈍感で接近粒子は衝突するPSO。これは複数解探索に有利である。(3)粒子間の結合にスイッチを導入したPSO。スイッチ頻度にを調節することにより、粒子間の疑似距離の制御や柔軟な粒子間の情報交換が可能である。また、パワーエレクトロニクスへの応用を検討し、スイッチング電源の制御信号最適化や、光電変換系の電源の最大電力点探索への応用の基礎となる成果を得た。研究成果の概要 (英文) : We have studied particle swarm optimizers(PSOs) with flexible adaptation function to various problems and have proposed three novel PSOs. (1) The PSO governed by a deterministic difference equation. It has advantages in analysis of stability and control of reproducible dynamics. (2) The PSO with insensitive particle movement and inter-near-particle collision. It is suitable for multi-solution problems. (3) The PSO with switched inter-particle connection. Adjusting the switching frequency, we can control inter-particle pseudo-distance and can realize flexible inter-particle communication. We have also studied applications to power electronics and have obtained basic results for applications to control signals optimization in switching power converters and to maximum power point search in photovoltaic systems

Hardware Approach To Mitigate The Effects Of Module Mismatch In A Grid-Connected Photovoltaic System: A Review

This study reviews the hardware approach to mitigate the effects of module mismatch in a grid-connected photovoltaic (PV) system. Unlike software solutions, i.e. the maximum power tracking algorithm, hardware techniques are well suited to enhance energy yield because of their inherent ability to extract energy from the mismatched module. Despite the extra cost of the additional circuitry, hardware techniques have recently gained popularity because of their long-term financial benefits. Notwithstanding the growing interest in this topic, review papers that provide updates on the technological developments of the three main hardware solutions, namely micro inverter,DC power optimizer, and energy recovery circuits, are lacking. This is in contrast to software solutions, which have had a considerable number of reputable reviews. Thus, a comprehensive review paper is appropriate at this juncture to provide up-to-date information on the latest topologies, highlight their merits/drawbacks, and evaluate their comparative performance

Modeling and Optimization Algorithm for SiC-based Three-phase Motor Drive System

More electric aircraft (MEA) and electrified aircraft propulsion (EAP) becomes the important topics in the area of transportation electrifications, expecting remarkable environmental and economic benefits. However, they bring the urgent challenges for the power electronics design since the new power architecture in the electrified aircraft requires many benchmark designs and comparisons. Also, a large number of power electronics converter designs with different specifications and system-level configurations need to be conducted in MEA and EAP, which demands huge design efforts and costs. Moreover, the long debugging and testing process increases the time to market because of gaps between the paper design and implementation. To address these issues, this dissertation covers the modeling and optimization algorithms for SiC-based three-phase motor drive systems in aviation applications. The improved models can help reduce the gaps between the paper design and implementation, and the implemented optimization algorithms can reduce the required execution time of the design program. The models related to magnetic core based inductors, geometry layouts, switching behaviors, device loss, and cooling design have been explored and improved, and several modeling techniques like analytical, numerical, and curve-fitting methods are applied. With the developed models, more physics characteristics of power electronics components are incorporated, and the design accuracy can be improved. To improve the design efficiency and to reduce the design time, optimization schemes for the filter design, device selection combined with cooling design, and system-level optimization are studied and implemented. For filter design, two optimization schemes including Ap based weight prediction and particle swarm optimization are adopted to reduce searching efforts. For device selection and related cooling design, a design iteration considering practical layouts and switching speed is proposed. For system-level optimization, the design algorithm enables the evaluation of different topologies, modulation schemes, switching frequencies, filter configurations, cooling methods, and paralleled converter structure. To reduce the execution time of system-level optimization, a switching function based simulation and waveform synthesis method are adopted. Furthermore, combined with the concept of design automation, software integrated with the developed models, optimization algorithms, and simulations is developed to enable visualization of the design configurations, database management, and design results

Power Flow Control In Hybrid Ac/Dc Microgrids

Microgrid structures allow for more efficient utilization of renewable resources as well as autonomous operation. Ideally, a centralized controller would be available to allow for an optimizer to take all components into account so that they may collaboratively work towards a shared goal. To this end, a centralized optimization method was developed called the squared slack interior point method. The novelty of this method is that it incorporates the fraction to bound rule to alleviate the known ill-conditioning introduced by utilizing squared slack variables to handle inequality constraints. In addition, this method also allows for inequality constraint violations to be quantified in the same manner that equality constraints are quantified. The proposed method is found to quickly and accurately calculate the optimal power flow and reject solutions that violate the inequality constraints beyond some specified tolerance. Where centralized information is not available, a decentralized method is required. In this method, constrained game theoretical optimization is utilized. However, due to unknown information about remote loads, inconsistent solution among players result in overloaded generators. To alleviate this issue, two perturbation methods are introduced. The first is overload feedback and the second is the perturb and observe squeeze method. In both methods, the goal is to adjust voltage angles and magnitudes to locally manage generator output. Both methods are found to rapidly drive overloaded sources back within their desired tolerances. Moreover, the game theoretical approach is found to have poor performance in the absence of shared load information among players. It is determined that the localized optimizers should be removed to reduce cost and that the operating condition should be perturb starting from the most recently available power flow calculation or starting from the nominal value. Also, to manage voltage stability in the absence of communication, a Hamiltonian approach is implemented for the voltage source rectifier. This approach resulted in a highly stable voltage and a fast response to large step changes. The method was able to maintain the reference dc output at unity power factor while not requiring any information about loading or interconnection

Multi-objective power quality optimization of smart grid based on improved differential evolution

In the modern generation, Electric Power has become one of the fundamental needs for humans to survive. This is due to the dependence of continuous availability of power. However, for electric power to be available to the society, it has to pass through a number of complex stages. Through each stage power quality problems are experienced on the grid. Under-voltages and over-voltages are the most common electric problems experienced on the grid, causing industries and business firms losses of Billions of dollars each year. Researchers from different regions are attracted by an idea that will overcome all the electrical issues experienced in the traditional grid using Artificial Intelligence (AI). The idea is said to provide electric power that is sustainable, economical, reliable and efficient to the society based on Evolutionary Algorithms (EAs). The idea is Smart Grid. The research focused on Power Quality Optimization in Smart Grid based on improved Differential Evolution (DE), with the objective functions to minimize voltage swells, counterbalance voltage sags and eliminate voltage surges or spikes, while maximizing the power quality. During Differential Evolution improvement research, elimination of stagnation, better and fast convergence speed were achieved based on modification of DE’s mutation schemes and parameter control selection. DE/Modi/2 and DE/Modi/3 modified mutation schemes proved to be the excellent improvement for DE algorithm by achieving excellent optimization results with regards to convergence speed and elimination of stagnation during simulations. The improved DE was used to optimize Power Quality in smart grid in combination with the reconfigured and modified Dynamic Voltage Restorer (DVR). Excellent convergence results of voltage swells and voltage sags minimization were achieved based on application of multi-objective parallel operation strategy during simulations. MATLAB was used to model the proposed solution and experimental simulations.Electrical and Mining EngineeringM. Tech. (Electrical Engineering

Advanced Modeling, Control, and Optimization Methods in Power Hybrid Systems - 2021

The climate changes that are becoming visible today are a challenge for the global research community. In this context, renewable energy sources, fuel cell systems and other energy generating sources must be optimally combined and connected to the grid system using advanced energy transaction methods. As this reprint presents the latest solutions in the implementation of fuel cell and renewable energy in mobile and stationary applications such as hybrid and microgrid power systems based on the Energy Internet, blockchain technology and smart contracts, we hope that they will be of interest to readers working in the related fields mentioned above