System Topology Research Articles

Ship Power System Network Reconfiguration Based on Swarm Exchange Particle Swarm Optimization Algorithm

As one of the important components of a ship, the ship’s integrated power system is an important safeguard for ships. In order to improve the service life of the ship’s power grid, the power system should be able to realize rapid reconstruction to ensure continuous power supply of important loads when the ship is attacked or fails suddenly. Therefore, it is of vital importance to study the reconfiguration technology of the ship’s integrated power system to ensure that it can quickly and stably cope with all kinds of emergencies in order to guarantee the safe and reliable navigation of the ship. This paper takes the ship’s ring power system as the research object and sets up the maximum recovery load and the minimum number of switching operations. The load is divided uniformly and the generator efficiency is balanced for the reconstruction of comprehensive function. It also sets up the system capacity, topology, and branch current limitations of the constraints to establish a mathematical model. The load branch correlation matrix method is used for branch capacity calculation and generator efficiency equalization calculation, and the load backup power supply path matrix is added on the basis of the matrix to judge the connectivity of some loads before reconfiguration. In this paper, for the network reconfiguration of the ship circular power system, which is a discrete nonlinear problem with multiple objectives, multiple time periods, and multiple constraints, we choose to use the particle swarm algorithm, which is suitable for global optimization, with a simple structure and fewer parameters; improve the particle swarm algorithm using the swarm exchange strategy by setting up two main and auxiliary swarms for global and local search; and exchange some of the particles with the golden ratio in order to keep the diversity of the populations. The simulation results of the network reconfiguration of the ship power system show that the improved algorithm can solve the power system network reconfiguration problem more effectively and provide a feasible reconfiguration scheme in a shorter time compared with the chaotic genetic algorithm under the same fault case test, and it also proves that the use of the swarm exchange particle swarm algorithm greatly improves the performance of reconfiguring the power grid of the ship.

Dual‐state and self‐oscillating wireless charging system topology for direct‐drive battery

AbstractThis study proposes a novel topology with bi‐stable controlling for wireless charging system (WCS). The advantage of the proposal is that a desired charging current can be directly output from rectifier without requiring charging current manager, thereby greatly simplifying the structure of the receiver. The proposed topology not only improves the transmission efficiency of system, but also reduces the loss and heat dissipation in the receiver. The principle of the topology is outlined and the circuitry model and control strategy are established and analyzed in detail. The innovation of this method lies in treating the coupler as a current transformer, which enables more direct and simpler controlling of the charging current. The experiment is conducted to verify that when the peak charging current is increased from 5A to 10A, the system efficiency is increased up to 82.0% and the receiver efficiency up to 96.7%. Both computational simulation and experimental validation have confirmed the feasibility of the proposed method. Compared with conventional WCS topology with compensation, the proposed method improves the system efficiency by 3% and the receiver efficiency by 5%, which is more suitable for practical applications where compact and light‐weight receiver is required.

Reliability assessment method of wind power DC collection system based on MLFTA-SMC

Wind power DC collection system, as a crucial component of wind farms, plays a vital role in ensuring the safe and stable operation of the entire wind farm. This paper proposes a reliability assessment method for wind power DC collection systems based on MLFTA-SMC. Firstly, it analyzes the topology and key equipment of wind power DC collection systems. Secondly, based on the topology of different wind power DC collection systems, it constructs multi-level fault tree models to calculate the comprehensive importance of different events, thus providing data support for subsequent reliability assessment. Then, it utilizes the MLFTA-SMC method to assess and analyze the reliability of different wind power DC collection system topologies. Finally, taking a 100 MW wind farm in Northwest China as an example, the proposed reliability assessment method is verified through simulation. The results indicate that this method exhibits good effectiveness and superiority.

Safety Concepts for Future Electromechanical Brake Central Control Systems

<div>Electromechanical brakes (EMB) are currently coming into focus in the automotive industry. This trend was confirmed in 2022, when a first automotive supplier [<span>1</span>] announced the series production of EMB systems. One major driver is safety, especially if EMB systems are implemented with smart actuators that install redundant electronic control units (ECU) and distributed software [<span>1</span>]. Earlier, the authors have addressed safety mechanisms in EMB actuators [<span>2</span>]. In this article the authors extend their investigation to address safety mechanisms in future EMB central control systems (CCS). Impact of different brake system topologies (X-, H-, centralized) vis-à-vis potential safety mechanisms within communication buses and ECUs is analyzed.</div>

Innovative Strategies for Combining Solar and Wind Energy with Green Hydrogen Systems

The integration of wind and solar energy with green hydrogen technologies represents an innovative approach toward achieving sustainable energy solutions. This review examines state-of-the-art strategies for synthesizing renewable energy sources, aimed at improving the efficiency of hydrogen (H2) generation, storage, and utilization. The complementary characteristics of solar and wind energy, where solar power typically peaks during daylight hours while wind energy becomes more accessible at night or during overcast conditions, facilitate more reliable and stable hydrogen production. Quantitatively, hybrid systems can realize a reduction in the levelized cost of hydrogen (LCOH) ranging from EUR 3.5 to EUR 8.9 per kilogram, thereby maximizing the use of renewable resources but also minimizing the overall H2 production and infrastructure costs. Furthermore, advancements such as enhanced electrolysis technologies, with overall efficiencies rising from 6% in 2008 to over 20% in the near future, illustrate significant progress in this domain. The review also addresses operational challenges, including intermittency and scalability, and introduces system topologies that enhance both efficiency and performance. However, it is essential to consider these challenges carefully, because they can significantly impact the overall effectiveness of hydrogen production systems. By providing a comprehensive assessment of these hybrid systems (which are gaining traction), this study highlights their potential to address the increasing global energy demands. However, it also aims to support the transition toward a carbon-neutral future. This potential is significant, because it aligns with both environmental goals and energy requirements. Although challenges remain, the promise of these systems is evident.

Enhancing distance protection in transmission grids with high penetration of renewable energy sources through cooperative protection

AbstractThis article introduces innovative protection strategies, including cooperative protection, for power transmission grids amidst a significant shift towards renewable energy sources (RES) such as wind and solar power, as well as inverter‐based resources (IBRs). The method employs a global consensus algorithm to achieve cooperative protection efficiently. This scheme leverages consensus protocols to dynamically oversee distance relay decisions, ensuring efficient fault detection and localization. The decentralized nature of the proposed method enhances robustness and security, while its high‐speed operation is ensured through non‐iterative global consensus algorithms, which provide rapid fault detection and localization crucial for real‐time protection. By incorporating virtual leaders and leveraging existing communication infrastructure, the method achieves superior selectivity in identifying faulty lines, enhancing the reliability and stability of power transmission grids with high‐RES penetration. Notably, the method does not require learning and training processes, making it adaptable to varying power system topologies without the need for extensive retraining or adaptation periods. The proposed methodology enables simultaneous participation in multiple protection zones by establishing interaction rules between agents. Virtual leaders simplify the selection of protection areas, enhancing scalability and fault localization. Simulation results conducted on the IEEE 39‐bus test system validate the effectiveness of the proposed method.

Corner and edge states in topological Sierpinski Carpet systems

Fractal lattices, with their self-similar and intricate structures, offer potential platforms for engineering physical properties on the nanoscale and also for realizing and manipulating high order topological insulator states in novel ways. Here we present a theoretical study on localized corner and edge states, emerging from topological phases in Sierpinski Carpet within a $\pi$-flux regime. A topological phase diagram is presented correlating the quadrupole moment with different hopping parameters. Particular localized states are identified following spatial signatures in distinct fractal generations. The specific geometry and scaling properties of the fractal systems can guide the supported topological states types and their associated functionalities. A conductive device is proposed by coupling identical Sierpinski Carpet units providing transport response through projected edge states which carry on the details of the system's topology. Our findings suggest that fractal lattices may also work as alternative routes to tune energy channels in different devices.

Optimization of offshore wind farm cluster transmission system topology based on Stackelberg game

Offshore wind energy is pivotal in the global energy transition, with a global installed capacity reaching 64.3 GW by 2022 and an expected annual increase of 60.2 GW over the next decade. This study aims to optimize the topology of transmission systems (TS) for offshore wind farm (OWF) clusters using Stackelberg game theory. The OWF investor (OWFI) acts as the leader, optimizing investment returns while considering wake effects, and the offshore TS operator (OTSO) follows by adjusting transmission strategies to reduce costs. The analysis includes the wake effects within OWF clusters and their impact on power generation efficiency. Simulation results demonstrate that the proposed model can balance stakeholder interests and enhance the economic viability of OWF clusters, showing a potential increase in net present value (NPV) by up to 30 %. This study validates the practical application of the Stackelberg game model in optimizing OWF cluster TS topology, contributing to more efficient and cost-effective renewable energy integration.

Dynamic Determination Method of Line Drop Compensator Parameters for Voltage Regulators Based on Mixture of Experts Using Real‐Time Information

In this paper, a method for determining line drop compensator (LDC) parameters for step voltage regulators and on‐load tap changers is proposed to avoid voltage violations in distribution systems with voltage fluctuations due to photovoltaic (PV) generation and electric vehicle (EV) charging. Distribution system operators need to effectively solve voltage problems caused by the widespread installation of distributed energy resources with the existing voltage regulators to construct an efficient and rational infrastructure. Our focus was on improving the method for determining the LDC parameters for the existing voltage regulators based on LDC control. A mechanism to dynamically change the LDC parameters depending on the situations in the distribution system was developed by using a mixture of experts, one of the machine learning techniques, and real‐time voltage and power flow information from information technology switches. The power flow calculations were performed using a distribution system model constructed based on the topology, loads, and generation characteristics of an actual distribution system in the Hokuriku region. The effectiveness of the proposed method was evaluated in terms of its improved effect on PV and EV hosting capacity, as well as the impact on the frequency of tap operations of voltage regulators. © 2024 The Author(s). IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Application of Binary Decision Diagrams in Time-Dependent Reliability Analysis

Binary Decision Diagrams (BDDs) are often used in specific types of reliability analysis known as topological (or structure) analysis and time-independent analysis. The previous focuses only on the analysis of system topology, which is defined by structure function. The latter takes into account the structure function together with the time-independent reliabilities of components that the system is composed of. However, the most interesting type of reliability analysis is time-dependent analysis in which reliabilities of the components are time-dependent functions. In this paper, we first present the development of a mathematical model of a non-repairable system composed of independent non-repairable components and explain the properties of this model from the point of view of time-dependent, time-independent, and topological reliability analysis. In the second part of the paper, we present and experimentally compare two methods for time-dependent reliability analysis of the considered mathematical model. The first method is based on the direct application of BDDs and we label it as a basic approach. The second, symbolic approach, combines BDDs with expression trees. The experimental comparison implemented using opensource C++ libraries TeDDy and GiNaC shows that the first method based on the basic approach is much faster than the second method using expression trees.

Integrated LCC-LCC Topology for WPT System With CC Output Regarding Air Gap and Load Variations

Integrated LCC-LCC Topology for WPT System With CC Output Regarding Air Gap and Load Variations

Reliability evaluation of direct current gathering system in onshore wind farm based on reliability block diagram-sequential monte carlo

The full DC wind power generation system has effectively overcome the harmonic resonance, reactive power transmission, and other problems of the traditional AC wind power system, which has broad prospects for development. As a key component of the mentioned system, the reliability of the collection system is critical to the safe and stable operation of the entire onshore wind farm. Firstly, this paper investigates the key equipment and topology of the onshore wind farm DC collection system. Secondly, considering both the internal components and external environment of the wind farm, a component outage probability model based on weather factors is constructed to provide accurate data for the reliability evaluation of the DC collection system of the wind farm. The Reliability Block Diagram is used to analyze the internal logical connection of different topologies of onshore wind farm DC collection systems in detail. Then, a reliability evaluation method of an onshore full DC wind farm collection system based on Reliability Block Diagram-Sequential Monte Carlo is proposed. Finally, a 50 MW onshore wind farm is studied as a sample to compare and analyze the assessment results of the reliability of different collection system topologies. The results show that the reliability of the DC collection system of onshore wind farms has significant advantages.

An efficient hybrid LC-S compensation topology for wireless power transfer system

An efficient hybrid LC-S compensation topology for wireless power transfer system

تصميم متحكم المنطق الضبابي لتتبع أقصى نقطة قدرة لنظام الطاقة الكهروضوئية

This research paper presents the method of fuzzy logic system to optimize the energy extraction in a photovoltaic (PV) power system. The maximum power of a (PV) module varies due to changing temperature, solar radiation, and load. To maximize efficiency, PV systems use a maximum power point tracker (MPPT) to constantly extract the highest power that can be produced by a solar panel and then deliver it to the load. The general structure of an (MPPT) system contains a (DC-DC) converter (an electronic device that converts a source of direct current DC from one voltage level to another) and a controller. The (MPPT) finds and maintains operations at the maximum power point using a tracking algorithm during variations in weather conditions. Because of the nonlinear behavior of (PV) module current-voltage characteristics and the nonlinearity of (DC-DC) converters due to switching, conventional controllers are unable to provide the best response, especially when dealing with wide parameter variations and line transients. The goal of this work is to design andimplement a maximum power point tracker that uses a fuzzy logic control algorithm. Fuzzy logic naturally provides a superior controller for this type of nonlinear application. This method also benefits from the artificial intelligence approach for overcoming the complexity in modeling nonlinear systems. In order to succeed in this work, an (MPPT) system consisting of a (PV) module, a (DC-DC) converter, and a fuzzy logic controller is designed and simulated in Simulink. Analyses of buck- boost converter characteristics are carried out to find the most suitable topology for the PV system used. An integrated model of the PV module with the identified converter is simulated in MATLAB to derive the expert knowledge needed to formulate and tune the fuzzy logic controller. The simulation results show that, the fuzzy logic controller is able to obtain the desired outcomes which are actual power generation of solar panels at different irradiance and temperature. Keywords: Fuzzy logic, PV, MPPT, DC-DC converter, MATLAB programming

Opportunities to Enhance sCO2 Power Cycle Turbomachinery with Bearingless Motor/Generators

Thermal power cycles using sCO2 as a working fluid place extreme demands on their turbomachinery components and their electric motors/generators. In this paper, new system topologies for sCO2 turbomachinery are proposed which take advantage of “bearingless” electric machine technology to improve performance. Bearingless motors/generators are a new type of electric machine which integrate the functionality of active magnetic bearings into the existing hardware of an electric motor/generator. The existing electromagnetic surfaces and materials are reused to enable controllable production of radial forces on the machine shaft. This is envisioned to improve hermetic direct-drive turbomachinery systems by either augmenting existing bearings (i.e., bearing assist) or replacing existing bearings (i.e., bearing removal). The state-of-the-art technologies for several bearing types (gas foil bearings, externally pressurized porous (EPP) gas bearings, and active magnetic bearings) and electric machines are reviewed to motivate the introduction of bearingless technology. Two system designs using bearingless machines are proposed and compared against existing commercial solutions in terms of maximum shaft weight, number of passthroughs into the hermetic environment, cost, and complexity. A case-study bearingless motor/generator is assessed via simulations and a hardware prototype to investigate practical considerations for using bearingless technology in sCO2 turbomachinery. The proposed bearingless solutions have potential to enable a new generation of sCO2 turbomachinery with improved reliability, reduced complexity, and lower cost. This paper shows that by transforming the motor/generator already present in turbomachinery into a bearingless motor/generator, the technical challenges involved with sCO2 can be overcome without adding significant cost.

Dispatch of Decentralized Energy Systems using Artificial Neural Networks: A Comparative Analysis with Emphasis on Training Methods

Due to the availability of flexibility, Decentralized Energy Systems (DES) play a central role in integrating renewable energies. To efficiently utilize renewable energy, dispatchable components must be operated to bridge the time gap between inflexible supply and energy demand. Due to the large number of energy converters, energy storage systems, and flexible consumers, there are many ways to achieve this. Conventional rule-based dispatch strategies often reach their limits here, and optimized dispatch strategies (e.g., model predictive control or the optimal dispatch) are usually based on very good forecasts. Reinforcement learning, particularly the application of Artificial Neural Networks (ANN), offers the possibility to learn complex decision-making processes. Since long training times are required for this, an efficient training framework is needed. The present paper proposes different training methods to learn an ANN-based dispatch strategy. In Method I, the ANN attempts to learn the solution to the corresponding optimal dispatch problem. In method II, the energy system model is simulated during the training to compute the observation state and operating costs resulting from the ANN-based dispatch. Method III uses the fast executable Method I solution as a warm-up solution for the computationally expensive training with Method II. In the present paper, a model-based analysis compares the different ANN-based dispatch strategies with rule-based dispatch strategies, model predictive dispatch (MPC), and optimal dispatch regarding their computational efficiency and the resulting operating costs. The dispatch strategies are compared based on three case studies with different system topologies for which training and test data are applied.Training method I proved to be non-competitive. However, training methods II and III significantly outperformed rule-based dispatch strategies across all case studies for both training and test data sets. Notably, methods II and III also surpassed MPC-based dispatch strategies under high and medium uncertainty forecasts for the training data in the first two case studies. In contrast, MPC-based dispatch was superior in the third case study, likely due to the higher system’s complexity and in the test data set due to the methodological advantage of being optimized for each specific data set. The effectiveness of training method III depends on the performance of the warm-up training with method I: warm-up is beneficial only if this results in an already promising dispatch (as seen in case study two). Otherwise, training method II proves more effective, as observed in case studies one and three.

Simultaneous optimization of capacity and topology of seismic isolation systems in multi-story buildings using a fuzzy reinforced differential evolution method

Inter-story isolation systems, as an alternative earthquake protection system, reduce in-building movement compared to base isolation systems. In this context, the current study focuses on simultaneously optimizing the topology and capacity of base and inter-story isolation systems for a multi-story building exposed to multiple earthquake scenarios. In addressing this challenge, an optimization model is developed that simultaneously considers both the topology (vertical arrangement) and capacity (required stiffness) of the seismic isolators as the decision variables of the model. To attain more practical and feasible solutions, the side constraints of the problem involve the inter-story drift and the total cost of seismic isolation systems. A gradient-free and self-adaptive search method, Fuzzy Differential Evolution incorporated Virtual Mutant (FDEVM), is employed to solve the optimization problem. The FDEVM approach applies a fuzzy mechanism to adopt its search behavior with governing condition(s) of the current problem. The selected method's performance is implicitly compared with its standard version. The obtained results indicate that optimally placing inter-story isolators with an optimal configuration and capacity not only improves the seismic performance of the systems but also its more cost-efficient approach compared with conventional based isolation systems. Also, the comparative outcomes indicate that the FDEVM method exhibits a high search capability for this class of problems.

Early Design Stage Evaluation of All Electric Aircraft Power Systems Focusing on Long-Term Behavior

In the aircraft industry, there is a shift towards more and all-electric power systems resulting in great research efforts on single components like batteries. At the same time there is an increasing need to investigate and evaluate the long-term behavior of the whole electric power system to ensure safe and sustainable aircraft operation. Focusing on this challenge, the objective of this article is to propose a framework for electric power system assessment in the early design stages. In particular, the focus is on identifying and handling uncertainties regarding failure behavior and degradation, both on the component and system level. The evaluation of different power system topologies is based on the integration of Model-Based Systems Engineering and robust design methods. In this context, another central aspect is the definition of system and component requirements derived from the flight mission profile. SysML diagrams are used to define use cases and possible system topologies. Sensitivity of degradation effects are evaluated using robust design methods. The application of the framework and these methods is illustrated using a short-range aircraft with an all-electric power system. The results highlight the applicability of the framework to cope with the uncertainties that occur in the early design stages and point out fields of further research.

Nash–Cournot Equilibrium and Its Impact on Network Transmission Congestion

This paper evaluates the impact of congestion on transmission lines when the operation cost is minimized using economic dispatch (ED), comparing the results obtained with the Nash–Cournot Equilibrium (NCE). A methodology is developed for the optimal power flow solution through the NCE, considering the network topology (upper and lower generation limits, upper and lower limits of the transmission lines, and power balance) for a nine-node system without and considering two bilateral power transactions. The results show that the operation cost is higher when the NCE is implemented than ED. However, the problem of congestion in the transmission lines is reduced due to the equilibrium obtained in the power dispatch against minimizing the operation cost in the dispatch; the transmission lines with the most significant participation tend to become congested when additional bilateral transactions occur. Finally, the above is verified by obtaining the mean and median of the transmission line percentages used in the two simulations.

Comprehensive case study on the technical feasibility of Green hydrogen production from photovoltaic and battery energy storage systems

AbstractThe growing demand for alternative energy sources to alleviate environmental impacts highlights the need to move from fossil fuels to renewable energy. This study demonstrated the technical feasibility of using a solar photovoltaic (PV) system for the production of green hydrogen. This research examined electrical and power data from a PV plant in Irecê, Bahia, using open data sources to provide insights into the production of green hydrogen from renewable sources. The system mainly depends on the use of a renewable source, PV solar energy, integrated with batteries, electrolyzers, and hydrogen tanks. Electrolyzer, battery, and hydrogen tank sizing analysis for optimal hydrogen production was effectively conducted using HOMER Energy software. The predicted system topology prioritizes a local DC network, optimizing efficiency for electrolyzers that have inherently low efficiency. The electrolyzer simulation involves initial Python‐based sizing and comprehensive sizing with HOMER Energy software, ensuring accuracy within a 10% discrepancy limit. This highlights the importance of analytical calculations and optimization software for sizing more complex systems.