arXiv subjects

Marko Hinkkanen

Publications and source records attributed to Marko Hinkkanen.

4 recordsLinked to original sources

arXiv2026

Gradient Networks for Universal Magnetic Modeling of Synchronous Machines

This paper presents a physics-constrained neural network framework for magnetic modeling of saturable synchronous machines, including spatial harmonics. By embedding gradient networks into the machine equations to model conservative electromagnetic behavior, the framework satisfies reciprocity and energy conservation by construction, while universally approximating any physically feasible magnetic characteristic. Unlike lookup tables and black-box neural networks, it guarantees monotonicity, invertibility, and smooth outputs, and remains highly data efficient. The method is validated using measured and finite-element method (FEM) data from a 5.6-kW permanent-magnet (PM) synchronous reluctance machine, and is demonstrated in real-time closed-loop control on an embedded platform. The results confirm accurate, physically consistent, and computationally efficient performance.

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arXiv2026

Disturbance-Observer-Based Grid-Forming Control for Unbalanced Grids

This article proposes a grid-forming control method for operation under unbalanced grid-voltage conditions. The method regulates the positive-sequence active power delivered to the grid and actively suppresses the negative-sequence converter voltage, controlling the voltage magnitude to be constant also during unbalanced faults when within the physical limits of the converter. Only the converter current is measured on the AC side, and a disturbance observer is used for synchronization as well as providing integral and resonant action. Estimates for the positive- and negative-sequence grid voltage are obtained from the disturbance observer. A current-limitation scheme for both balanced and unbalanced faults is integrated. Comprehensive stability analysis and tuning guidelines are provided. Experimental results using a 12.5-kVA converter demonstrate that the proposed method can operate during severe balanced and unbalanced faults.

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arXiv2026

Open-Source Python Tool for Grid Converter Output Admittance Identification

Frequency-domain analysis based on converter output admittance is a key tool for studying converter-driven stability in power grids. This paper presents a Python-based identification tool built on a completely open-source simulator, eliminating the need for commercial licenses such as MATLAB or PSCAD and improving configurability through an MIT-licensed stack. The identification method uses steady-state signal injection with a sinusoidal sweep, deriving frequency-domain admittance from time-domain simulations. Analytical output-admittance models are developed for both grid-forming (disturbance-observer-based) and grid-following (phase-locked-loop-based) control to verify the numerical results. The tool's results are compared against a commercial PSCAD-based alternative, demonstrating accurate admittance identification across control methods. Code and examples are available online to support reproducibility.

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arXiv2026

Decoupled Online Feedforward Generation of Optimal References for Saturated Synchronous Machine Drives

This paper presents a modular method for generating reference signals online for saturable synchronous machine drives. The method dynamically generates optimal references without precomputed lookup tables, following the maximum-torque-per-ampere (MTPA) trajectory while respecting maximum-torque-per-volt (MTPV), current, and voltage limits. The proposed tracking laws are formulated to yield exact, decoupled first-order error dynamics, ensuring predictable tracking responses and simplifying system tuning. The algorithm requires only the forward flux map, thereby eliminating the need for current-map inversion. By operating in a feedforward manner, the method ensures noise-free reference signals and structural separation from the feedback control. Both simulation and experimental results are presented, demonstrating that the proposed method achieves dynamic and steady-state performance on par with conventional lookup-table-based approaches, while avoiding the need for precomputed reference tables.

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