Analysis of a Novel Four-Level DC/DC Boost Converter

In this paper, novel two-quadrant buck/boost and one-quadrant boost four-level DC/DC converters are introduced. The primary application for these converters is that of interfacing a low voltage DC source, such as a fuel cell or battery, to a high-voltage four-level inverter. One important feature of the four-level DC/DC converters proposed herein is the ability to perform the power conversion and balance the inverter capacitor voltages simultaneously. With the capacitor voltage balancing, it is possible to obtain the full voltage from the inverter. For the boost converter, the steady-state and nonlinear average-value (NLAM) models are developed. The NLAM is verified against a detailed simulation of a four-level converter/inverter drive system

Analysis of a Novel Four-Level DC/DC Boost Converter

In this paper, novel two-quadrant buck/boost and one-quadrant boost four-level DC/DC power converters are introduced. The primary application for these converters is that of interfacing a low-voltage DC source, such as a fuel cell or battery, to a high-voltage four-level inverter. One important feature of the four-level DC/DC power converters proposed is the ability to perform the power conversion and balance the inverter capacitor voltages simultaneously. With the capacitor voltage balancing, it is possible to obtain the full voltage from the inverter. For the boost converter, the steady-state and nonlinear average-value (NLAM) models are developed. The NLAM is verified against a detailed simulation of a four-level converter/inverter drive system. The proposed converter is experimentally verified using an 18 kW converter/inverter syste

Seven-Level Shunt Active Power Filter for High-Power Drive Systems

In high-power adjustable-speed motor drives, such as those used in electric ship propulsion systems, active filters provide a viable solution to mitigating harmonic related issues caused by diode or thyristor rectifier front-ends. To handle the large compensation currents and provide better thermal management, two or more paralleled semiconductor switching devices can be used. In this paper, a novel topology is proposed where two active filter inverters are connected with tapped reactors to share the compensation currents. The proposed active filter topology can also produce seven voltage levels, which significantly reduces the switching current ripple and the size of passive components. Based on the joint redundant state selection strategy, a current balancing algorithm is proposed to keep the reactor magnetizing current to a minimum. It is shown through simulation that the proposed active filter can achieve high overall system performance. The system is also implemented on a real-time digital simulator to further verify its effectiveness

Cancellation Predictive Control for Three-Phase PWM Rectifiers under Harmonic and Unbalanced Input Conditions

This paper presents an intuitive and simple-to-implement control scheme to improve the performance of three-phase boost-type PWM rectifiers under harmonic and unbalanced input conditions. Unlike most other control strategies, the proposed method does not need to extract either the harmonic or the negative-sequence components in the supply voltages and currents. A near-synchronous reference frame is used to determine the positive-sequence fundamental-frequency component in the input voltages. Utilizing only the extracted component, the DC-link voltage control and power factor control are implemented independently to determine the phase angle and magnitude of the PWM reference voltage. The commanded rectifier voltage adjustments are superimposed upon the grid voltages in such a way that the distortions (both harmonic and negative sequence components) are effectively cancelled. By employing a near-synchronous reference frame, no line-synchronization algorithm or hardware PLL is needed, so very little computational effort is required for its implementation. Simulation results show that the proposed method performs very well under extreme harmonic and unbalanced conditions such as when one or even two phases of the grid voltages are zero. In order to further verify its effectiveness, a laboratory hardware platform has been develope

Hardware Implementation of an AIS-Based Optimal Excitation Controller for an Electric Ship

The operation of high energy loads on Navy\u27s future electric ships, such as directed energy weapons, will cause disturbances in the main bus voltage and impact the operation of the rest of the power system when the pulsed loads are directly powered from the main dc bus. This paper describes an online design and laboratory hardware implementation of an optimal excitation controller using an artificial immune system (AIS) based algorithm. The AIS algorithm, a clonal selection algorithm (CSA), is used to minimize the effects of pulsed loads by improved excitation control and thus, reduce the requirement on energy storage device capacity. The CSA is implemented on the MSK2812 DSP hardware platform. A comparison of CSA and the particle swarm optimization (PSO) algorithm is presented. Hardware measurement results show that the CSA optimized excitation controller provides effective control of a generator\u27s terminal voltage during pulsed loads, restoring and stabilizing it quickly

Implementation of a PSO Based Online Design of an Optimal Excitation Controller

The Navypsilas future electric ships will contain a number of pulsed power loads for high-energy applications such as radar, railguns, and advanced weapons. This pulse energy demand has to be provided by the ship energy sources, while not impacting the operation of the rest of the system. It is clear from studies carried out earlier that disturbances are created at the generator ac bus. This paper describes an online design and laboratory hardware implementation of an optimal excitation controller using particle swarm optimization (PSO) to minimize the effects of pulsed loads. The PSO algorithm has been implemented on a digital signal processor. Laboratory results show that the PSO designed excitation controller provides an effective control of a generatorpsilas terminal voltage during pulsed loads, restoring and stabilizing it quickly

A Novel Impedance Measurement Technique for Power Electronic Systems

When designing and building power systems that contain power electronic switching sources and loads, system integrators must consider the frequency-dependent impedance characteristics at an interface to ensure system stability. Stability criteria have been developed in terms of source and load impedance for both dc and ac systems and it is often necessary to measure system impedance through experiments. Traditional injection-based impedance measurement techniques require multiple online tests which lead to many disadvantages. The impedance identification method proposed in this paper greatly reduces online test time by modeling the system with recurrent neural networks. The recurrent networks are trained with measured signals from the system with only one injection. The measurement and identification processes for dc and three-phase ac interfaces are developed. Simulation tests demonstrate the effectiveness of this new technique

Identification of Induction Machines Stator Currents with Generalized Neurons

A new approach to identify the nonlinear model of an induction machine using two generalized neurons (GNs) is presented in this paper. Compared to the multilayer perceptron feedforward neural network, a GN has simpler structure and lesser requirement in terms of memory storage which is makes it attractive for hardware implementation. This method shows that with less number of weights, GN is able to learn the dynamics of an induction machine. The proposed model is made by two coupled networks. A modified particle swarm optimization algorithm is designed to solve this distinctive GN training problem. A pseudo-random binary sequence signal injected to the induction machine operating at its rated value was chosen as the test input signal. For validation, the trained GN model is applied on the different operating conditions of the system

Intelligent Scheduling of Hybrid and Electric Vehicle Storage Capacity in a Parking Lot for Profit Maximization in Grid Power Transactions

This paper proposes an intelligent method for scheduling usage of available energy storage capacity from plugin hybrid electric vehicles (PHEV) and electric vehicles (EV). The batteries on these vehicles can either provide power to the grid when parked, known as vehicle- to-grid (V2G) concept or take power from the grid to charge the batteries on the vehicles. A scalable parking lot model is developed with different parameters assigned to fleets of vehicles. The size of the parking lot is assumed to be large enough to accommodate the number of vehicles performing grid transactions. In order to figure out the appropriate charge and discharge times throughout the day, binary particle swarm optimization is applied. Price curves from the California ISO database are used in this study to have realistic price fluctuations. Finding optimal solutions that maximize profits to vehicle owners while satisfying system and vehicle owners\u27 constraints is the objective of this study. Different fleets of vehicles are used to approximate varying customer base and demonstrate the scalability of parking lots for V2G. The results are compared for consistency and scalability. Discussions on how this technique can be applied to other grid issues such as peaking power are included at the end

Optimal SVM Switching for a Multilevel Multi-Phase Machine using Modified Discrete PSO

This paper searches for the best possible switching sequence in a multilevel multi-phase inverter that gives the lowest amount of voltage harmonics. A modified discrete particle swarm (MDPSO) algorithm is used in an attempt to find the optimal space vector modulation switching sequence that results in the lowest voltage THD. As with typical PSO cognitive and social parameters are used to guide the search, but an additional mutation term is added to broaden the amount of area searched. The search space is the feasible solutions for the predetermined vectors at a given modulation index. Comparison of the MDPSO algorithm to an integer particle swarm optimization (IPSO) is presented for all three modulation indices tested. The resulting switching sequences found show that the MDPSO algorithm is capable of finding a minimal THD solution for all modulations indices tested. The MDPSO algorithm performed better overall than the IPSO in terms of converging to the best solution with significantly lower iterations