Search Results (9)

Modern power systems have become increasingly complex, making the detailed modeling and analysis of large-scale networks computationally demanding and often impractical. Therefore, network reduction techniques are essential for representing a smaller area of interest while preserving the electrical behavior of the complete system. For electromagnetic transient (EMT) studies, such as load rejection analysis, reduced networks are commonly derived using classical methods like Kron reduction under maximum power transfer conditions. However, this approach can lead to discrepancies in load flow and short-circuit levels between the reduced and complete systems. In addition, Kron reduction may introduce negative resistances in the reduced-order model, compromising system stability by producing non-passive equivalents and potentially causing unrealistic or numerically unstable EMT simulations. To address these limitations, this paper proposes an optimization-based approach, termed PSO-OPF-Kron, which integrates Optimal Power Flow (OPF) with the Particle Swarm Optimization (PSO) algorithm to refine the equivalent network parameters. The method optimally determines power injections, bus voltages, transformer tap settings, and impedances to align the reduced model with the full system’s operating point and short-circuit levels. Validation on the IEEE 39-bus system demonstrates that the proposed method significantly improves accuracy and numerical stability, ensuring reliable EMT simulations for load rejection studies. Full article

The current manuscript addresses the problem of parameter estimation for kinetic models of chemical reaction networks from observed time series partial experimental data of species concentrations. It is demonstrated how the Kron reduction method of kinetic models, in conjunction with the (weighted) least squares optimization technique, can be used as a tool to solve the above-mentioned ill-posed parameter estimation problem. First, a new trajectory-independent measure is introduced to quantify the dynamical difference between the original mathematical model and the corresponding Kron-reduced model. This measure is then crucially used to estimate the parameters contained in the kinetic model so that the corresponding values of the species’ concentrations predicted by the model fit the available experimental data. The new parameter estimation method is tested on two real-life examples of chemical reaction networks: nicotinic acetylcholine receptors and Trypanosoma brucei trypanothione synthetase. Both weighted and unweighted least squares techniques, combined with Kron reduction, are used to find the best-fitting parameter values. The method of leave-one-out cross-validation is utilized to determine the preferred technique. For nicotinic receptors, the training errors due to the application of unweighted and weighted least squares are 3.22 and 3.61 respectively, while for Trypanosoma synthetase, the application of unweighted and weighted least squares result in training errors of 0.82 and 0.70 respectively. Furthermore, the problem of identifiability of dynamical systems, i.e., the possibility of uniquely determining the parameters from certain types of output, has also been addressed. Full article

Greater numbers of power electronics (PEs) converters are being connected to energy systems due to the development of renewable energy sources, high-voltage transmission, and PE-interfaced loads. Given that power electronics-based devices and synchronous machines have very different dynamic behaviours, some modelling approximations, which may commonly be applied to run transient simulations of transmission systems, may not be optimal for future grids. Indeed, the systematic utilisation of the phasor approximation for power lines, implemented in most transient simulation programs, is increasingly not appropriate anymore. In order to avoid the requirement for full electromagnetic transient simulations, which can be resource-demanding and time-consuming, this paper proposes a combination of an event-based state residualisation approximation and the Kron reduction technique. The proposed technique has the advantage of allowing accurate transient simulations based on the optimal reduction of the number of state variables, depending on the observed variables, the considered events, and the tolerated approximation error, along with simplifying power systems equations for accelerated simulations. Full article

Kron reduction is a general tool of network simplification for flow calculation. With a growing number of flexible loads appearing in distribution networks, traditional Kron reduction cannot be widely used in control and scheduling due to the elimination of controllable and variable load buses. Therefore, this paper proposes an improved Kron reduction based on node ordering optimization whose principles guarantee that all the boundary nodes are retained eventually after eliminating the first row and the first column in every step according to the order, thereby making it possible to take full advantage of their potential to meet different requirements in power system calculation and dispatching. The proposed method is verified via simulation models of IEEE 5-bus and 30-bus systems through illustrating the dynamic consistency of the output active power of the generator nodes and the power flow data of preserved nodes before and after reduction. Full article

This paper addresses the problem of the optimal stabilization of DC microgrids using a hierarchical control design. A recursive optimal power flow formulation is proposed in the tertiary stage that ensures the global optimum finding due to the convexity of the proposed quadratic optimization model in determining the equilibrium operative point of the DC microgrid as a function of the demand and generation inputs. An exact feedback controller with integral action is applied in the primary and secondary controller layers, which ensures asymptotic stability in the sense of Lyapunov for the voltage variables. The dynamical model of the network is obtained in a set of reduced nodes that only includes constant power terminals interfaced through power electronic converters. This reduced model is obtained by applying Kron’s reduction to the linear loads and step nodes in the DC grid. Numerical simulations in a DC microgrid with radial structure demonstrate the effectiveness and robustness of the proposed hierarchical controller in maintaining the stability of all the voltage profiles in the DC microgrid, independent of the load and generation variations. Full article

The stabilization problem of multi-terminal high-voltage direct current (MT-HVDC) systems feeding constant power loads is addressed in this paper using an inverse optimal control (IOC). A hierarchical control structure using a convex optimization model in the secondary control stage and the IOC in the primary control stage is proposed to determine the set of references that allows the stabilization of the network under load variations. The main advantage of the IOC is that this control method ensures the closed-loop stability of the whole MT-HVDC system using a control Lyapunov function to determine the optimal control law. Numerical results in a reduced version of the CIGRE MT-HVDC system show the effectiveness of the IOC to stabilize the system under large disturbance scenarios, such as short-circuit events and topology changes. All the simulations are carried out in the MATLAB/Simulink environment. Full article

The Newton–Raphson (NR) method is still frequently applied for computing load flow (LF) due to its precision and quadratic convergence properties. To compute LF in a low voltage distribution system (LVDS) with unbalanced topologies, each branch model in the LVDS can be simplified by defining the neutral and ground voltages as zero and then using Kron’s reduction to transform into a 3 × 3 branch matrix, but this decreases accuracy. Therefore, this paper proposes a modified branch model that is also reduced into a 3 × 3 matrix but is derived from the impedances of the phase-A, -B, -C, neutral, and ground conductors together with the grounding resistances, thereby increasing the accuracy. Moreover, this paper proposes improved LF equations for unbalanced LVDS with both PQ and PV nodes. The improved LF equations are based on the polar-form power injection approach. The simulation results show the effectiveness of the modified branch model and the improved LF equations. Full article

We propose a new approach to the model reduction of biochemical reaction networks governed by various types of enzyme kinetics rate laws with non-autocatalytic reactions, each of which can be reversible or irreversible. This method extends the approach for model reduction previously proposed by Rao et al. which proceeds by the step-wise reduction in the number of complexes by Kron reduction of the weighted Laplacian corresponding to the complex graph of the network. The main idea in the current manuscript is based on rewriting the mathematical model of a reaction network as a model of a network consisting of linkage classes that contain more than one reaction. It is done by joining certain distinct linkage classes into a single linkage class by using the conservation laws of the network. We show that this adjustment improves the extent of applicability of the method proposed by Rao et al. We automate the entire reduction procedure using Matlab. We test our automated model reduction to two real-life reaction networks, namely, a model of neural stem cell regulation and a model of hedgehog signaling pathway. We apply our reduction approach to meaningfully reduce the number of complexes in the complex graph corresponding to these networks. When the number of species’ concentrations in the model of neural stem cell regulation is reduced by 33.33%, the difference between the dynamics of the original model and the reduced model, quantified by an error integral, is only 4.85%. Likewise, when the number of species’ concentrations is reduced by 33.33% in the model of hedgehog signaling pathway, the difference between the dynamics of the original model and the reduced model is only 6.59%. Full article

This paper proposes an active-current control strategy for large-scale wind turbines (WTs) to improve the transient stability of power systems based on a perturbation estimation (PE) approach. The main idea of this control strategy is to mitigate the generator imbalance of mechanical and electrical powers by controlling the active-current of WTs. The effective mutual couplings of synchronous generators and WTs are identified using a Kron-reduction technique first. Then, the control object of each WT is assigned based on the identified mutual couplings. Finally, an individual controller is developed for each WT using a PE approach. In the control algorithm, a perturbation state (PS) is introduced for each WT to represent the comprehensive effect of the nonlinearities and parameter variations of the power system, and then it is estimated by a designed perturbation observer. The estimated PS is employed to compensate the actual perturbation, and to finally achieve the adaptive control design without requiring an accurate system model. The effectiveness of the proposed control approach on improving the system transient stability is validated in the modified IEEE 39-bus system. Full article