PI Passivity-Based Control and Performance Analysis of MMC Multiterminal HVDC Systems

Abstract

In this work, a decentralized PI passivity-based controller (PI-PBC) is applied to Modular Multilevel Converters (MMCs) to ensure global stability of a multi-terminal HVDC system. For the derivation of the controller, an appropriate model with constant steady-state solutions is obtained via a multifrequency orthogonal coordinates transformation. The control design is next completed using passivity arguments and performance guarantees are established by a small-signal analysis. The obtained results are validated by means of detailed timedomain simulations both on a single-terminal and a four-terminal benchmark.

Particle Swarm Optimization Tuning of Modular Multilevel Converters in a Time-Invariant Framework

“…Remark 2. It is worth mentioning that the equivalent MMC model with time-invariant solutions given in (2) has been validated against detailed models in [17] under common MMC controllers, and in [5] under the control structure investigated in this work.…”

“…A possible approach for a plug and play stability guaranteed control design with simple PI controllers for the MMC was implemented in [5], [6], based on the passivity theory [7]- [10], and therefore extending to the MMC case the works of [11], [12] originally applied to 2L-VSCs. Nonetheless, even if global asymptotic stability was guaranteed, the controller in [5], [6] had sub-optimal tuning, and therefore below par performance. Thus, we are interested here in improving the tuning and consequently the performance of the nonlinear control applied to the MMC in [5], [6].…”

“…Nonetheless, even if global asymptotic stability was guaranteed, the controller in [5], [6] had sub-optimal tuning, and therefore below par performance. Thus, we are interested here in improving the tuning and consequently the performance of the nonlinear control applied to the MMC in [5], [6].…”

“…Towards this end, in this paper we investigate the potential of applying the Particle Swarm Optimization (PSO) algorithm [13], originally proposed in [14], to tune the MMC control parameters of the nonlinear control structure presented in [5], [6]. The PSO algorithm has been chosen due to its capability of handling system nonlinearities, its simple implementation and relatively low computational cost, compared to other methods [15], as well as its success in the power system community [16].…”

“…In section II, the MMC average modelling conventions, as well as the equivalent model with time-invariant solutions proposed in [17] are briefly recalled. Section III gives a brief summary of the PSO algorithm, while section IV outlines the control structure of interest applied to the MMC in [5]. In addition, time-domain simulation results of an MMC in single-terminal configuration with the control parameters tuned with the PSO methodology are given in section V.…”