Switched-capacitor converters (SCCs) are subset of switched-mode converters which can be designed to buck/boost the input voltage. However, synthesizing SCC has been a challenging task due to the large number of possible circuit realizations. This work proposes a synthesis method for constructing an efficient reconfigurable SCC that adheres to the Fibonacci canonical structure. The optimization is achieved by operating the converter with the minimum number of capacitors required to achieve a certain voltage conversion ratio (VCR). Indeed, decreasing the number of flying capacitors minimizes the equivalent output resistance of the converter which improves the converter performance, like the settling time and the power conversion efficiency (PCE). Moreover, by analyzing the differences between each VCR terminal, the number of switches required to create a unique VCR is also decreased. The performance of the proposed synthesis tool is verified using SPICE simulations for 4-stage reconfigurable SCC. The simulated converter efficiency ranges from 85% to 95% for a 50mA load tested at 1 MHz switching frequency, compared to 43.5% to 86% using the conventional method. The performance of the converter is compared with recent reconfigruable SCCs in the literature.
We are designing and implementing a solar inverter system that generates green power from solar energy and reduces air pollution and other environmental impacts. Our system uses a pure sine wave inverter that produces a sine wave virtually identical to the utility grid. The IoT-based MPPT solar charge controller ensures that the maximum amount of power is transferred from the solar panels to the battery bank and monitors the system in real-time. We also use a solar tracker with a single-axis rotation that orients the panels toward the sun in two directions. Our solar inverter system can handle a maximum load of 300 watts.
In this study, we present an analytical model for predicting the magnetic properties and optimization of thermomagnetic devices using mathematical models. The 3D analytical magnetic model is firstly validated by the dipole model and confirmed through experimentation, enabling to accurately estimate the magnetization of the used magnet. The stray field induced by the permanent magnet over the lateral surface of the rotor is computed. Then, the resultant force and torque are derived allowing to estimate the exact number of ferromagnetic active material required and their angular gap.
The energy sector is shifting towards decentralization and digitalization in view of climate change mitigation and decarbonization. In this context, the Web of Cells (WoC) approach is emerging. It aims at providing seamless coordination of flexibilities across network operators with the potential to incentivise flexibility usage more approriately, aligning it with system needs. Within this context, this paper makes use of a large-scale multi-energy system model to delve into the role of decentralized, small-scale flexibilities, with an emphasis on heat pump systems in residential buildings. Using a computationally tractable approximation of demand-side flexibilities, we explore the economic ramifications of integrating these flexibilities into the future energy system. A case study, envisioning decarbonization in Germany by 2050, sheds light on the implications for a decarbonized and decentralized energy sector. Key findings reveal that while these demand-side flexibilities can be economically advantageous for consumers, they also exert influence on system prices and the profitability of storage, as well as cross-sectoral units. The potential surge in market value for renewables and other technologies within a system following the WoC approach is highlighted. To foster collaborative exploration, the model's open-source code and the data set, vital for a 2050 scenario, are made available, supporting a deeper, collective assessment of the future's energy system.
Controlling the charging and discharging procedures of Lithium-Ion Batteries is of paramount importance as violating safety constraints, such as current deviations, can lead to significant damage to the battery or circuit or interruption in service. Thus, it is crucial to employ a robust controller capable of handling uncertainties and unexpected scenarios. PI controllers have become prevalent in recent years for managing battery dynamics, but they exhibit limited robustness in unpredictable situations. In this paper, we propose a Reinforcement Learning (RL) driven control method as a substitute for the PI controller. The agent is trained using a co-simulation approach with simultaneous employment of Python and Matlab, ensuring an accurate estimation of the environment and, consequently, enhanced performance. A prototype of the proposed controller is developed using dSPACE rapid control prototyper. The performance is compared with the benchmark controller (PI) across different fault scenarios, considering three criteria: overshoot, undershoot, and stabilization time. The comparative analysis reveals that, in most scenarios, the RL agent outperforms the PI controller, exhibiting a remarkable 50% reduction in both overshoot and undershoot compared to the benchmark controller. This research contributes to advancing battery control systems by introducing an RL-based controller that proves to be a more robust alternative, delivering improved performance in the face of uncertainties and fault scenarios.
Transitioning to renewable energy in the distribution grid (DG) is essential for combating climate change and ensuring energy security. However, this transition can introduce grid instability. To combat this, we need improved control capabilities for these energy resources; which requires accurate information on system state variables and distribution grid line parameters. This study presents a way to simultaneously estimate the system state variables, active and reactive power, and the line parameters of the distribution grid without the need for any information on voltage angles. This is achieved by formulating it as a maximum likelihood problem that we solve using the expectation maximization (EM) algorithm, which we adapt to this problem and provide details of a numerically robust implementation. The study uses the modified Distflow model which provides a way to consider line losses in the system and improves system accuracy. The proposed method is demonstrated on the IEEE 37-node test feeder. The proposed study is compared to state-of-the-art, where we achieve a 70% reduction in voltage error and more than 10, 000 times lower error for state variables.
Incisive selection of the LCL filter parameters for a grid-connected inverter (GCI) is crucial to meet the grid interconnection standards with a reduced hardware footprint. Various design methods are available in the literature for selecting the LCL filter parameters. While the grid-side inductor of the LCL filter can utilize an iron core and follow the standard grid frequency inductor design, the inverter-side inductor design needs attention since it has significant switching frequency harmonics. This paper presents an extensive discussion on the design of the inverter-side inductor for GCIs. The inverter-side inductor (L i) is calculated based on the allowable inverter peak-peak ripple current to reduce the losses due to the ripple component. The value or size of L i depends on the inverter configuration, switching technique, and the application. The initial sections of the paper present a comprehensive analysis, comparing the value and hence the size of L i for different wiring configurations and applications. Closed-form expressions are developed for L i and are used in selecting the minimum value of L i. The suitability of an amorphous core for the inverter-side inductor is discussed. The amorphous-core inductor designs in literature can lead to a wide variation of inductance with current and have been analyzed to cause differential and common mode noise. To address this, a novel amorphous-core inductor design is proposed in the later sections of this work. The proposed approach ensures a minimal variation in the inductance over the operating current range. Experimental results are provided to support the various theoretical assertions.
A load-independent constant current (CC)-constant voltage (CV) output is an important requirement of inductive power transfer (IPT) systems for electric vehicle charging applications. Zero phase angle (ZPA) is also a desirable feature, to ensure a lower power rating requirement for the switching converter. CC and CV output along with ZPA can be achieved by using a suitable compensation topology. Equation manipulation techniques can be used for designing the compensation topology. But, it can be mathematically intensive especially for higher order topologies. To overcome this problem, resonant-tank based approaches are adopted in several works to achieve CC and CV conditions. However, equation-based approaches are depended upon either wholly or partly for realizing ZPA. This approach can be tedious and lacks physical insight. The proposed method extends resonant tank approach to achieve ZPA also, besides CC and CV. The need for a separate method to achieve ZPA is eliminated. Further, it simplifies the process in arriving at the constraints that ensure ZPA. As a sample validation, the proposed method is applied to a S-SP compensation topology. The CC-ZPA and CV-ZPA constraints for the S-SP topology are shown to be in line with the ones arrived at using an existing equation-based impedance approach. The simplicity of the proposed method can be observed from the sample validation.
Nowadays, simple and cost-effective solutions to extract flexibility from any possible energy asset are being heavily investigated, along with optimal strategies to offer flexibility in different markets. In this context, this work proposes an Electrical Flexibility Forecasting Engine (EFFE) conceived for district heating systems based on centralised heat pumps. The idea is implemented in the case study of Culemborg (ND), demo site of the H2020-ACCEPT project. Here, the engine is run in a typical winter day to forecast and asses both upwards and downwards flexibility, along with the minimum economically viable bids for a local market.
The power system is undergoing a large change towards renewable energy technologies. While using these energy sources, managing the generation, storage and distribution of energy can be optimized with information about future energy consumption. The forecasting of consumption load for individual residents plays a key role for load balancing but is a challenging task due to the volatile nature of individual consumption. Due to this reason, current literature has only been limited to forecasting individual load to a small window in the future. In this paper, we introduce a Sequence-To-Sequence Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) framework to generate a 24 hour load forecast. We show comparisons with other deep neural network models of 1) Model performance over varying forecast window sizes, 2) Average model performance over multiple houses and 3) Performance for forecasting the aggregated load of all houses. We also conduct analyses on the forecasts to show performance improvement for households with consistent load patterns and to detect model degradation. Our extensive experiments show that the Sequence-To-Sequence LSTM RNN can significantly increase the forecast window and performs best for all scenarios.
Exploring the fundamental principles of system modeling in electrical engineering, this study delves into the transformative power of the d-q transformation, highlighting its pivotal role in rendering time-varying systems into a coherent steady-state representation. Departing from conventional approaches, the study carefully navigates the complexities of single-phase transformer configurations, utilizing the Clarke and Park transformations to seamlessly transition between electrical coordinates and the d-q frame. Through extensive derivations, dynamic equations are formulated in both α-β and d-q coordinates, providing a detailed understanding of system dynamics under specific loads. In addition, the study extends the analysis to a generalized multi-winding transformer model that accommodates a wide range of transformer setups. With detailed mathematical derivations, insightful visual aids, and clear state-space representations, this work attempt to be a resource for researchers, engineers, and practitioners seeking to unravel the intricacies of electrical system modeling and analysis.
This paper details an experiment utilizing ESP8266 modules as servers to wirelessly control diverse electrical appliances in home automation. The experiment showcased the modules' capability to respond to commands via a web interface on both mobile and desktop platforms or even tablets. While most of the experiment ran smoothly, occasional freezing and connectivity disruptions were observed. The abstract encapsulates the experiment's successes, discusses encountered challenges, and outlines a forward-looking perspective, including the integration of a custom PCB for enhanced system stability.
This work aims at optimizing the converter design of the double-T MMC DC-DC converter in terms of transmitted power per submodule and also in terms of transmitted power per silicon area, while, at the same time, providing the capability to block dc faults. Firstly, the converter operation is described and the optimal values of the inner ac and dc voltages that minimize device power rating are derived. Next, the submodule topology is analyzed and a thorough study on the converter capability for blocking fault currents is carried out, showing that the converter is able to isolate dc faults both at the input and at the output of the converter. Finally, the previous analytical study is verified by means of detailed PSCAD simulations.Copyright (c) 2020 IEEE. Personal use is permitted. For any other purposes, permission must be obtained from the IEEE by emailing [email protected] final version of record is available at http://dx.doi.org/10.1109/TPWRD.2020.2966751
P versus NP is considered as one of the most fundamental open problems in computer science. This consists in knowing the answer of the following question: Is P equal to NP? It was essentially mentioned in 1955 from a letter written by John Nash to the United States National Security Agency. However, a precise statement of the P versus NP problem was introduced independently by Stephen Cook and Leonid Levin. Since that date, all efforts to find a proof for this problem have failed. Another major complexity class is NP-complete. It is well-known that P is equal to NP under the assumption of the existence of a polynomial time algorithm for some NP-complete. We show that the Monotone Weighted Xor 2-satisfiability problem (MWX2SAT) is NP-complete and P at the same time. Certainly, we make a polynomial time reduction from every directed graph and positive integer k in the K-CLOSURE problem to an instance of MWX2SAT. In this way, we show that MWX2SAT is also an NP-complete problem. Moreover, we create and implement a polynomial time algorithm which decides the instances of MWX2SAT. Consequently, we prove that P = NP.
Due to the complex rotor design of reluctance synchronous machines, a finite element analysis is essential for the accurate calculation of machine relevant performance objectives. Reluctance synchronous machines tend to have a large torque ripple, if this objective is not considered during the machine design. This necessitates a large number of simulation steps, resulting in a high computational burden and a long simulation time per design evaluation. Therefore, an efficient optimization algorithm is required. This paper proposes a complete framework for single-objective machine design optimization using Gaussian process regression and Bayesian optimization. Focusing on reluctance synchronous machine design, different kernel functions (squared exponential, Matérn, rational quadratic) and hyperparameter configurations are assessed for regression accuracy of the optimization objectives mean torque, torque ripple, and power factor. The impact of noise in the input data on the regression results is also investigated. Bayesian optimization with the infill criterion Expected Improvement is finally used to perform machine design optimization for 18 design variables. Bayesian optimization outperforms the classical algorithms such as genetic or particle swarm algorithms. It results in a faster design optimization, even for such a high number of design variables.
The growing field of superconducting technology is witnessing remarkable advancements driven by the global energy transition. These advancements bear significance, offering promising avenues for optimizing the design and performance of superconducting systems modeled by homogenized technique and/or thin strip approximation, including electrical machines, fusion energy generation, fault current limiters, superconducting cables, and superconducting maglev trains. This article introduces a thin strip homogenizing approximation and a versatile constraint methodology tailored for analyzing series, parallel, or series-parallel connections within superconducting devices. Leveraging the J-A formulation, comprehensive modeling of coils, stacks, thin strips, racetracks, and double crossed loops (DCLs) is undertaken to validate the proposed methodologies. Through meticulous investigations, encompassing analyses of magnetic flux density, current density distribution, current profiles, and losses, the efficacy and applicability of the proposed techniques are demonstrated.
The formulation of the Complex Dissipating Energy Flow (CDEF), used for locating sources of forced oscillations, is expressed by the sum of terms that represent the contributions of power system components to the complex energy function of the system. These terms are calculated through integrals that are evaluated over the trajectory of the system. The integrands are the active and reactive power flows through system branches, and the integration variables are the voltage magnitude and the voltage angle. In this letter, we show that the terms whose integration variable is the voltage magnitude can be grouped separately from the terms whose integration variable is the voltage angle. Furthermore, the contribution to the complex energy function of the system of each of these grouped terms is zero. This allows for an alternative definition of CDEF using only the terms whose integration variable is voltage angle (discarding terms whose integration variable is voltage). It is shown that using the alternative definition for CDEF produces a higher success rate compared to the original CDEF. The tests are performed on the WECC 240 system model considering sources of FO located in the governor and the excitation system of conventional generators, as well as in renewable plants, alternatively.
Integration of grid-forming inverter-based resources (GFM-IBRs) is considered as a viable solution to address the challenges associated with the dynamics of power systems with high penetration levels of inverter-based resources. Yet, various aspects of dynamic behaviour of grid-forming inverters during disturbances have remained unknown. This paper for the first time introduces the concept of pole slipping in GFM-IBRs. The differences between stable and unstable pole slipping are highlighted. The determining factors for the occurrence of stable and unstable pole slipping in grid forming inverters and equipment-level implications of pole slipping are identified and studied. Afterwards, a solution is proposed to mitigate stable and unstable pole slipping in grid-forming inverters. The validity of the proposed concept of pole slipping in GFM-IBRs and the proposed solution is tested and verified through extensive timedomain simulations.