arXiv subjects

Maryam Saeedifard

Publications and source records attributed to Maryam Saeedifard.

3 recordsLinked to original sources

arXiv2026

Machine Learning-Assisted Stability Boundary Determination of Multiport Autonomous Reconfigurable Solar Power Plants

The multiport autonomous reconfigurable solar power plant (MARS) is a promising solution to integrate renewable energy resources and energy storage systems into the ac power grid and HVdc links. In the MARS system, various input power sources are connected to the individual submodules (SMs) through dc-dc converters. However, the presence of external power sources can result in unbalanced capacitor voltages of SMs, thereby violating stability constraints under multiple/diverse operating conditions. This paper aims to address the research gap by accurately determining the stability boundary for the MARS system. A novel machine learning (ML)-assisted energy balancing control (EBC) criterion is proposed. In conjunction with a refined EBC, this approach ensures balanced capacitor voltages across various types of SMs, significantly enhancing the overall system efficiency. The proposed EBC criterion effectively controls EBC activation and deactivation, achieving remarkable accuracy. Both PSCAD/EMTDC simulations and control hardware-in-the-loop (cHIL) tests are conducted to validate the feasibility and efficiency of the proposed method. By combining the EBC and ML-assisted EBC criteria, efficient energy management becomes achievable for systems featuring multiple input power sources, such as MARS. This approach enables the system to fully exploit its potential across an expanded operational range while upholding high efficiency standards.

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arXiv2024

Black Start Operation of Grid-Forming Converters Based on Generalized Three-phase Droop Control Under Unbalanced Conditions

This paper focuses on the challenging task of bottom-up restoration in a complete blackout system using Grid-forming (GFM) converters. Challenges arise due to the limited current capability of power converters, resulting in distinct dynamic responses and fault current characteristics compared to synchronous generators. Additionally, GFM control needs to address the presence of unbalanced conditions commonly found in distribution systems. To address these challenges, this paper explores the black start capability of GFM converters with a generalized three-phase GFM droop control. This approach integrates GFM controls individually for each phase, incorporating phase-balancing feedback and enabling current limiting for each phase during unbalanced faults or overloading. The introduction of a phase-balancing gain provides flexibility to trade-off between voltage and power imbalances. The study further investigates bottom-up black start operations using GFM converters, incorporating advanced load relays into breakers for gradual load energization without central coordination. The effectiveness of bottom-up black start operations with GFM converters, utilizing the generalized three-phase GFM droop, is evaluated through electromagnetic transient (EMT) simulations in MATLAB/Simulink. The results confirm the performance and effectiveness of this approach in achieving successful black start operations under unbalanced conditions.

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arXiv2020

A Hybrid DC Fault Primary Protection Algorithm for Multi-Terminal HVdc Systems

Protection against dc faults is one of the main technical hurdles faced when operating converter-based HVdc systems. Protection becomes even more challenging for multi-terminal dc (MTdc) systems with more than two terminals/converter stations. In this paper, a hybrid primary fault detection algorithm for MTdc systems is proposed to detect a broad range of failures. Sensor measurements, i.e., line currents and dc reactor voltages measured at local terminals, are first processed by a top-level context clustering algorithm. For each cluster, the best fault detector is selected among a detector pool according to a rule resulting from a learning algorithm. The detector pool consists of several existing detection algorithms, each performing differently across fault scenarios. The proposed hybrid primary detection algorithm: i) detects all major fault types including pole-to-pole (P2P), pole-to-ground (P2G), and external dc fault; ii) provides a wide detection region covering faults with various fault locations and impedances; iii) is more robust to noisy sensor measurements compared to the existing methods. Performance and effectiveness of the proposed algorithm are evaluated and verified based on time-domain simulations in the PSCAD/EMTDC software environment. The results confirm satisfactory operation, accuracy, and detection speed of the proposed algorithm under various fault scenarios.

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