Neural network based adaptive event trigger control for a class of electromagnetic suspension systems

The islanded and network-connected modes are expected to be modeled into a unified form as well as in a distributed fashion for multi-energy system In this way, the adaptability and flexibility of multi-energy system can be enhanced To this aim, this paper establishes a double-mode energy management model for the multi-energy system It is formed by many energy bodies With such a model, each participant is able to adaptively respond to the change of mode switching Furthermore, a novel distributed dynamic event-triggered Newton-Raphson algorithm is proposed to solve the double-mode energy management problem in a fully distributed fashion In this method, the idea of Newton descent along with the dynamic event-triggered communication strategy are introduced and embedded in the execution of the proposed algorithm With this effort, each participant can adequately utilize the second-order information to speed up convergence The optimality is not affected Meanwhile, the proposed algorithm can be implemented with asynchronous communication and without needing special initialization conditions It exhibits better flexibility and adaptability especially when the system modes are changed In addition, the continuous-time algorithm is executed with discrete-time communication driven by the proposed dynamic event-triggered mechanismIt results in reduced communication interaction and avoids needing continuous-time information transmission It is also proved that each participant can asymptotically converge to the global optimal point Finally, simulation results show the effectiveness of the proposed model and illustrate the faster convergence feature of the proposed algorithm

Double-Mode Energy Management for Multi-Energy System via Distributed Dynamic Event-Triggered Newton-Raphson Algorithm

In practical microgrids, the inhomogeneous initial values are widely appeared due to soft-starting operation. If traditional model order reduction approaches are applied, the input-output maps error between the original system and reduced-order system is large. To address this problem, this paper proposes a reduced-order aggregate model based on balanced truncation approach to provide the preprocessing approach for the real-time simulation of large-scale converters with inhomogeneous initial conditions in DC microgrid. Firstly, the standard linear time-invariant model with inhomogeneous initial conditions is established through non-leader multiagents concept. To end this, it is convenient for scholars to build complex system modeling with switched topology. Furthermore, the full system is divided into two components, i.e., the unforced component with nontrivial initial conditions and forced component with null initial conditions. Moreover, this paper presents an aggregated approach that involves independent reducing component responses and combining reducing component responses. Based on this, the input-output maps error is reduced. Then, the approximated error estimate of the reduced-order aggregate model regarding large-scale converters in DC microgrid is first provided, which provides prior knowledge and theoretical basis for DC microgrid designers. Finally, the simulation results illustrate the accuracy of the proposed approach.

Reduced-Order Aggregate Model for Large-Scale Converters With Inhomogeneous Initial Conditions in DC Microgrids

Energy costs are the key factors regarding the selection of appropriate renewable energy (RWG) alternatives. All costs of a power plant, such as investment, operation, maintenance, and repair are considered in the scope of levelized costs. Therefore, for the effective determination of the selling price of the energy, levelized cost has a guiding role. Because the levelized costs of RWG alternatives develop the sustainable production and energy consumption for the long term, the leading indicators of these costs should be analyzed significantly. Accordingly, in this study, it is aimed to investigate the levelized cost of RWG alternatives by using bipolar q-rung orthopair fuzzy (q-ROF) hybrid decision-making approach. The novelty of this study is to recommend an integrated decision-making model based on bipolar and q-ROFSs with golden cut. At the first stage, bipolar q-ROF multi stepwise weight assessment ratio analysis (M-SWARA) is employed for weighting the selected criteria of levelized costs of RWG alternatives. At the following stage, bipolar q-ROF technique for order preference by similarity to ideal solution (TOPSIS) is considered to rank the alternatives in terms of the levelized cost performance. On the other side, vise kriterijumska optimizacija i kompromisno resenje (VIKOR) model is also considered to rank the alternatives. In addition to this issue, the sensitivity analysis is also performed with four cases comparatively. Hence, consistency, reliability and coherency of the proposed model can be measured. It is identified that capacity loss has the greatest importance regarding the levelized cost of RWG projects. Solar is found as the best clean energy type with respect to the levelized cost management performance. In this context, it would be appropriate for investors to design projects close to the center. This will contribute to increasing the efficiency and productivity of these projects. 

Bipolar q-ROF Hybrid Decision Making Model With Golden Cut for Analyzing the Levelized Cost of Renewable Energy Alternatives

In DC microgrids, constant power loads (CPLs) reduce the effective damping of the DC-DC converter and may induce destabilizing effects into the DC-DC converter. To overcome such problems regarding CPL and ensure large-signal stability of DC-DC converters in DC microgrids, some feedforward terms are added to $V$ - $I$ droop-based dual-loop controller for a DC-DC converter based on the large-signal model. It is proven that the feedforward terms can not only improve the transient response but also guarantee the exponential stability of the closed-loop system in the whole operating range in regards to a large-signal manner, which is verified by using a singular perturbation model. Moreover, a disturbance observer is designed to estimate the output current, thereby enabling the removal of the current measurement sensor. The proposed technique can be easily plugged into a pre-defined $V$ - $I$ droop-based dual-loop controller without an additional sensor being required. Ultimately, both simulation and experimental tests verify the effectiveness of the proposed method.

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Large-Signal Stability Improvement of DC-DC Converters in DC Microgrid

This article proposes a reduced-order small-signal closed-loop transfer function model based on Jordan continued-fraction expansion to assess the dynamic characteristics of the droop-controlled inverter and provide the preprocessing method for the real-time simulation of power systems. Firstly, dynamic phasors, time delay and zero-order hold are embedded into the small-signal model at the same time, then the closed-loop transfer function of the droop-controlled inverter is built. Compared with the existing closed-loop transfer function approaches, the accuracy of the built transfer function model is dramatically enhanced. Meanwhile, the inner cascaded voltage/current controller parameters are also designed. In order to directly obtain and preserve the maximum overshoot and settling time, which are main features to evaluate the system input-output dynamic response characteristics, the reduced second order closed-loop transfer function is proposed through the continued-fraction expansion regarding arbitrary points on the real frequency axis. Therein, this second order closed-loop transfer function with dynamic response of the original inverter is reduced to the lowest order. Furthermore, combined with the impedance-based approach, the proposed stability assessment approach is utilized to analyze the stability of the microgrid with multiple converters. Finally, simulations and experimental results demonstrate the convenience and accuracy of the proposed approach.

Reduced-Order Transfer Function Model of the Droop-Controlled Inverter via Jordan Continued-Fraction Expansion

Weak grids are gaining considerable attention since power generation resources are remote from constant power loads (CPLs), which results in low-frequency/harmonic oscillation. Meanwhile, due to the play, and plug demand of modern power system, the line inductance of weak grids often changes, which is also regarded as the variation regarding short circuit ratio (SCR). Based on this, the conventional impedance-based stability operation point assessment approaches should be expanded into stability domain assessment approach considering the line inductance variation. Therefore, the stability-oriented line inductance stability domain assessment approach for weak grids with CPLs is proposed in this paper. Firstly, the source impedance matrix of weak grid, and load admittance matrix of CPLs are separately built. Secondly, an improved stability forbidden domain criterion is proposed through related mapping transformation process, which has lower conservatism than two previous improved stability criteria. Thirdly, the improved stability forbidden domain criterion is switched into the condition that the intermediate matrices are Hurwitz. Meanwhile, the line inductance stability domain is directly obtained through these intermediate matrices, and guardian map theory. Finally, the simulation, and experiment results illustrate that the proposed approach has less conservatism, and high efficiency.

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Wang Rui

Papers

Reduced-Order Transfer Function Model of the Droop-Controlled Inverter via Jordan Continued-Fraction Expansion

Line Inductance Stability Operation Domain Assessment for Weak Grids With Multiple Constant Power Loads