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Branislav Hredzak

Publications and source records attributed to Branislav Hredzak.

8 recordsLinked to original sources

Accurate Small-Signal Modeling of Digitally Controlled Buck Converters with ADC-PWM Synchronization

Digital control has become increasingly widespread in modern power electronic converters. When acquiring feedback signals such as the inductor current, synchronizing the analog-to-digital converter (ADC) with the digital pulse-width modulator (DPWM) is commonly employed to accurately track their steady-state average. However, the small-signal implications of such synchronization have not been investigated. This paper presents an exact small-signal model for digitally controlled buck converters operating in forced continuous-conduction mode (FCCM) under constant-frequency current-mode control, explicitly accounting for DPWM-ADC synchronization. Using a sampled-data framework, the proposed model captures all sideband effects introduced by the sampling process, yielding precise predictions of both analog and digital loop gains, even at frequencies beyond the switching and sampling frequencies. Both asymmetrical and symmetrical carrier modulations are considered. Furthermore, the digital loop gain is derived in closed form using the modified z-transform, enabling low-complexity compensator design and stability assessment. Within this framework, the analog loop gain can be directly obtained from the digital loop gain, thereby eliminating the need for computationally intensive infinite series evaluations. The validity of the proposed model is confirmed through both simulation and experimental results.

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Competent Discrete Time Modeling For analogue controlled PWM Converter Considering State-Feedback

Ever since R.D.Middlebrook proposed the state space averaging notion. The small signal model has been widely used as a design tool to tune control parameters. As Moore's law is continuing and the AI chip's high demand for power consumption and dynamic response, the control bandwidth needs to be boosted. However, the average model has two basic assumptions: the low-frequency assumption, the small ripple assumption. In high-bandwidth design, these two assumptions are violated. In order to solve this, various methods have been proposed. This paper gives a comprehensive overview of the existing small signal model for PWM converters from the following perspectives: 1. model fidelity, 2. analytical tractability. 3. complexity of the derivation process and result 4.generality.

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State-Space Averaging Revisited via Reconstruction Operators

This paper presents an operator-theoretic reconstruction of an equivalent continuous-time LTI model from an exact sampled-data (Poincaré-map) baseline of a piecewise-linear switching system. The rebuilding is explicitly expressed via matrix logarithms. By expanding the logarithm of a product of matrix exponentials using the Baker--Campbell--Hausdorff (BCH) formula, we show that the classical state-space averaging (SSA) model can be interpreted as the leading-order truncation of this exact reconstruction when the switching period is small and the ripple is small. The same view explains why SSA critically relies on low-frequency and small-ripple assumptions, and why the method becomes fragile for converters with more than two subintervals per cycle. Finally, we provide a complexity-reduced, SSA-flavoured implementation strategy for obtaining the required spectral quantities and a real-valued logarithm without explicitly calling eigen-decomposition or complex matrix logarithms, by exploiting $2\times 2$ invariants and a minimal real-lift construction.

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Dynamic Power Balancing Algorithm for Single-Phase Energy Storage Systems in LV Distribution Network with Unbalanced PV Systems Distribution

Unbalanced power, due to high penetration of single-phase PV rooftops into a four-wire multi-grounded LV distribution system, can result in significant rise in the neutral current and neutral voltage. This preprint proposes a distributed clustering algorithm for dynamic power balancing, using single-phase battery storage systems distributed in the LV distribution system, in order to reduce the neutral current and neutral voltage rise. The distributed clustering algorithm aggregates households connected to the same phase into clusters. Within each cluster, another distributed clustering algorithm is applied to calculate the total grid power exchanged but the corresponding phase. Then, the dynamic power balancing control is applied to balance the powers at the bus, based on battery storage systems' charge/discharge constraints, power minimization and willingness of the households to participate in the power balancing control.

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Remote-Strong-Grid-Point-Based Synchronization Strategy with Fault Ride-Through Capability for Distributed Energy Resources Connected to Weak Grids

This paper proposes a novel strategy for the current injection based control for distributed energy resources connected to weak grids through a voltage source converter experiencing faults. The current injection controller is no longer synchronized with the point of common coupling at which the measured voltage signal can be severely affected by faults in the weak grid, but with the strong grid point at which the voltage is rigid. It is shown that the phase difference between the voltage source converter and the strong grid voltages caused by the long power lines does not affect the power control. Furthermore, a time delay compensation method which tolerates communication time delay introduced by the transmission of the synchronization signal from the strong grid point is proposed. The performance of the proposed control strategy is verified with an RTDS Technologies real-time digital simulator using switching converter models.

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A Novel Dynamic Peer-to-Peer Clustering Algorithm and Its Application to Aggregate Energy Storage Systems

The proposed distributed dynamic clustering algorithm enables to group agents based on their pre-selected feature states. The clusters are determined by comparing the distance of the agents' current feature states with average estimates of the states in all clusters. The algorithm also provides average estimates of pre-selected auxiliary states that can be utilized for control purposes. Two example applications of the algorithm are introduced. In the first application, the algorithm is applied to a microgrid with distributed batteries that are controlled to achieve a common state of charge within a group. However, a random selection of the batteries' groups results in additional power losses during operation. The algorithm reduces the power losses by clustering the batteries based on the selected feature states: local loads and battery capacities, while the state of charges and output voltages are selected as auxiliary states for control purposes. In the second application, the algorithm is used to form a virtual energy storage from batteries distributed in a microgrid.

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Model Predictive Control for Distributed Microgrid Battery Energy Storage Systems

This paper proposes a new convex model predictive control strategy for dynamic optimal power flow between battery energy storage systems distributed in an AC microgrid. The proposed control strategy uses a new problem formulation, based on a linear d-q reference frame voltage-current model and linearised power flow approximations. This allows the optimal power flows to be solved as a convex optimisation problem, for which fast and robust solvers exist. The proposed method does not assume real and reactive power flows are decoupled, allowing line losses, voltage constraints and converter current constraints to be addressed. In addition, non-linear variations in the charge and discharge efficiencies of lithium ion batteries are analysed and included in the control strategy. Real-time digital simulations were carried out for an islanded microgrid based on the IEEE 13 bus prototypical feeder, with distributed battery energy storage systems and intermittent photovoltaic generation. It is shown that the proposed control strategy approaches the performance of a strategy based on non-convex optimisation, while reducing the required computation time by a factor of 1000, making it suitable for a real-time model predictive control implementation.

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