Controlling Grid-Connected Inverters under Time-Varying Voltage Constraints

Inverter-based resources (IBRs) are becoming increasingly prevalent in power systems. Due to the inherently low inertia of inverters, there is a heightened risk of disruptive voltage oscillations. A particular challenge in the operation of grid connected IBRs is the variations in the grid side voltage. The changes in the grid side voltage introduces nonlinear and time-varying constriants on the inverter voltages themselves. For an operator, it would be useful to know the set of active and reactive powers that can be tracked under these time-varying conditons. This paper introduces an optimization model designed to assess the achievability of power setpoints within the framework of constrained static state-feedback power control. Additionally, we present a Monte Carlo simulation-based method to optimize the set of achievable power setpoints. The efficacy of the proposed approach is validated through simulation results

Nonlinear Stability Analysis of the Classical Nested PI Control of Voltage Sourced Inverters

This note provides the first nonlinear analysis of the industry standard "partial decoupling plus nested PI loops" control of voltage sourced inverters. In spite of its enormous popularity, to date only linearization-based tools are available to carry out the analysis, which are unable to deal with large-signal stability and fail to provide estimates of the domain of attraction of the desired equilibrium. Instrumental to establish our result is the representation of the closed-loop dynamics in a suitable Lure-like representation, that is, a forward system in closed-loop with a static nonlinearity. The stability analysis is then done by generating an adequate Popov multiplier. Comparison with respect to linearization is discussed together with numerical results demonstrating non-conservativeness of the proposed conditions

Control of a Buck DC/DC Converter Using Approximate Dynamic Programming and Artificial Neural Networks

This paper proposes a novel artificial neural network (ANN) based control method for a dc/dc buck converter. The ANN is trained to implement optimal control based on approximate dynamic programming (ADP). Special characteristics of the proposed ANN control include: 1) The inputs to the ANN contain error signals and integrals of the error signals, enabling the ANN to have PI control ability; 2) The ANN receives voltage feedback signals from the dc/dc converter, making the combined system equivalent to a recurrent neural network; 3) The ANN is trained to minimize a cost function over a long time horizon, making the ANN have a stronger predictive control ability than a conventional predictive controller; 4) The ANN is trained offline, preventing the instability of the network caused by weight adjustments of an on-line training algorithm. The ANN performance is evaluated through simulation and hardware experiments and compared with conventional control methods, which shows that the ANN controller has a strong ability to track rapidly changing reference commands, maintain stable output voltage for a variable load, and manage maximum duty-ratio and current constraints properly

SiC-based improved neutral legs with reduced capacitors for three-phase four-wire EV chargers

An electric vehicle (EV) charger can operate in an autonomous mode to create its own grid by utilizing the EV batteries during grid blackouts. This requires three-phase four-wire inverters as the grid-side ac/dc port of the EV charger to supply unbalanced loads. Although silicon carbide (SiC) MOSFETs can be adopted to increase the power density of these inverters, the second order ripples exhibited on the dc bus caused by unbalanced loads need to be mitigated by a large dc capacitance—increasing the size of inverters. In this paper, an improved neutral leg for three-phase four-wire inverters is presented, which not only provides the neutral current for unbalanced loads like a conventional neutral leg, but also reduces the second order ripples on the dc bus without the need for additional hardware components. Furthermore, it can reduce by 50% the dc capacitance compared to its conventional counterpart. A control strategy featuring power decoupling capability is included for the improved leg. It was built with SiC MOSFETs and experimentally assessed with a three-phase inverter, with results verifying its effectiveness. For completeness, the performance of the improved neutral leg is also evaluated through simulations in PLECS and compared to a conventional neutral leg

A Virtual Space Vectors based Model Predictive Control for Three-Level Converters

Three-phase three-level (3-L) voltage source converters (VSC), e.g., neutral-point clamped (NPC) converters, T-type converters, etc., have been deemed to be suitable for a wide range of medium- to high-power applications in microgrids (MGs) and bulk power systems. Compared to their two-level (2-L) counterparts, adopting 3-L VSCs in the MG applications not only reduces the voltage stress across the power semiconductor devices, which allows achieving higher voltage levels, but also improves the quality of the converter output waveforms, which further leads to considerably smaller output ac passive filters. Various control strategies have been proposed and implemented for 3-L VSCs. Among all the existing control methods, finite-control-set model predictive control (FCS-MPC) has been extensively investigated and applied due to its simple and intuitive design, fast-dynamic response and robustness against parameter uncertainties. However, to implement an FCS-MPC on a 3-L VSC, a multi-objective cost function, which consists of a term dedicated specifically to control the dc-link capacitor voltages such that the neutral-point voltage (NP-V) oscillations are minimized, must be designed. Nevertheless, selecting proper weighting factors for the multiple control objectives is difficult and time consuming. Additionally, adding the dc-link capacitor voltages balancing term to the cost function distributes the controller effort among different control targets, which severely impacts the primary goal of the FCS-MPC. Furthermore, to control the dc-link capacitor voltages, additional sensing circuitries are usually necessary to measure the dc-link capacitor voltages and currents, which consequently increases the system cost, volume and wiring complexity as well as reduces overall reliability. To address all the aforementioned challenges, in this dissertation research, a novel FCS-MPC method using virtual space vectors (VSVs), which do not affect the dc-link capacitor voltages of the 3-L VSCs, was proposed, implemented and validated. The proposed FCS-MPC strategy has the capability to achieve inherent balanced dc-link capacitor voltages. Additionally, the demonstrated control technique not only simplifies the controller design by allowing the use of a simplified cost function, but also improves the quality of the 3-L VSC output waveforms. Furthermore, the execution time of the proposed control algorithm was significantly reduced compared to that of the existing one. Lastly, the proposed FCS-MPC using the VSVs reduces the hardware cost and complexity as the additional dc-link capacitor voltages and current sensors are not required, which further enhances the overall system reliability