Voltage Distribution Research Articles

An Active Learning Local Control Method for Optimal Power Flow in Low Voltage Distribution Networks Considering Missing Data

An Active Learning Local Control Method for Optimal Power Flow in Low Voltage Distribution Networks Considering Missing Data

A Redundant Secondary Control Scheme to Resist FDI Attacks in AC Microgrids

Hierarchical control of AC microgrids, despite providing stable voltage, frequency, and optimal power distribution, is susceptible to false data injection (FDI) attacks on its secondary control. Electric power systems in countries such as the United States and Venezuela have suffered cyberattacks. This paper proposes a redundant secondary control method to resist FDI attacks. This method uses distributed generators (DGs) online as redundant units on standby, generating dynamic corrections to the attacked DGs, thus eliminating the attack impact. It effectively addresses the numerical tampering and addition brought by FDI to the frequency and voltage setting values of the secondary control, ensuring the microgrid can still output stable frequency and voltage. The principle of this control method is direct and practical; it doesn't require complex controllers and can respond quickly to FDI attacks. Then, focusing on a microgrid system containing six DGs, the setting values of the microgrid system are tampered by multiple values in different time periods, the simulation results confirm that the proposed method can effectively resist FDI attacks under various scenarios. Finally, FPGA-in-the-loop experiments further verify the effectiveness of the proposed method.

Swarm‐intelligence‐based coordinated control of electric heatings for voltage stabilization with zero communication burden

AbstractThe movement towards electrification is entailing deep changes in power systems. On the demand side, the adoption of electric heating (EH) has grown rapidly in recent years. To eliminate the voltage fluctuations caused by the random switching‐on/‐off behaviors of EHs in some application scenes with special‐power‐line in low voltage distribution networks (LVDNs), a swarm‐intelligence‐based coordinated control approach of EHs for voltage stabilization with zero communication burden while guaranteeing the users’ thermal comforts is researched. A novel bird‐perch‐on‐branch (BPB) ‐based swarm intelligence theory is proposed, which reveals the mapping relation between local observations and global states. On this theoretical basis, a multi‐agent reinforcement learning (MARL) framework is developed for the coordinated dispatch of EHs, where the deep‐Q‐network (DQN) algorithm is adopted to learn and generate the optimal control policy for each EH. The implementation of the proposed MADQN‐BPB approach is described based on the principle of centralized‐training and decentralized‐execution. Comparative control performances of MADQN‐BPB with a commercially available approach, and the global optimal solution are evaluated. Simulation results verify the capability of MADQN‐BPB in voltage stabilization with zero communication burden. Its control performance is close to that of the global optimal solution, and is scalable to the variations of environment.

A method for measuring capacitive current in distribution networks based on injection signal method

Abstract In view of the problems that exist in the current distribution network, such as the narrow range of measurement capacitance to the ground and the difficulty of selecting the appropriate signal frequency. Based on the phasor method, this paper proposes a method for measuring capacitance current in a distribution network based on injected signal and analyzes and calculates the error of the method. The measurement technique involves inputting two constant-amplitude signals—one high-frequency and one low-frequency—into a zero-sequence PT (Potential Transformer) configured with an open delta connection. Based on the equivalent circuit models for both high and low-frequency signals, the high-frequency signal’s increased frequency allows the magnitude to be disregarded, simplifying the calculations. Using a simplified equation for the low-frequency signal component, this method enables rapid calculation of the capacitor current in the distribution network. Based on MATLAB software, the simulation calculation model is established, and the simulation results show that the measurement method is simple to calculate and has high accuracy, which can be used to measure the large-capacity grounding capacitance current test, so as to improve the measurement accuracy of the capacitance current of medium and low voltage distribution network.

Optimal allocation of distributed energy resources to cater the stochastic E-vehicle loading and natural disruption in low voltage distribution grid

The everyday extreme uncertainties become the new normal for our world. Critical infrastructures like electrical power grid and transportation systems are in dire need of adaptability to dynamic changes. Moreover, stringent policies and strategies towards zero carbon emission require the heavy influx of renewable energy sources (RES) and adoption of electric transportation systems. In addition, the world has seen an increased frequency of extreme natural disasters. These events adversely impact the electrical grid, specifically the less hardened distribution grid. Hence, a resilient electrical network is the demand of the future to fulfill critical loads and charging of emergency electrical vehicles (EV). Therefore, this paper proposes a two-dimensional methodology in planning and operational phase for a resilient electric distribution grid. Initially stochastic modelling of EV load has been performed duly considering the geographical feature and commute pattern to form probability distribution functions. Thenceforth, the impact assessment of extreme natural events like earthquakes using damage state classification has been done to model the impact on distribution grid. The efficacy of the proposed methodology has been tested by simulating an urban Indian distribution grid with mapped EV on DigSILENT PowerFactory integrated with supervised learning tools on Python. Subsequently 24-h load profile before event and after event have been compared to analyze the impact.

A 2D Simulation‐Assisted Investigation of a Low Breakdown Voltage Module

Upscaling perovskites modules from laboratory to market specifications requires modules to resist heat, humidity, and partial shading. When a device is partially covered or damaged, the unbalanced photocurrent generated can create an irreversible damage. Mitigation via bypass diodes can be expensive. This might not be necessary for perovskite‐based modules. After an initial validation of the finite difference model for a simulation‐assisted investigation, a module is systematically damaged and characterized to reproduce an unbalanced photocurrent generation. The device shows a breakdown voltage (< 1 V), which is unexpectedly much lower than for a standalone perovskite cell (≈7 V). The effect is reproducible over time, and it demonstrates that in a reverse bias scenario, electricity can tunnel through the monolithic interconnection (P1P2P3) and operate similar to a low‐voltage bypass diode. Eventually, the current and voltage distribution via electroluminescence is mapped to validate the 2D simulations and reproduce the device behavior at different loads.

Event triggering fixed time secondary control of DC microgrid considering FDI attacks

SummaryAiming at the problem that distributed secondary control is vulnerable to FDI attacks in DC microgrid, which affects the stability of the system, and the problems of communication redundancy and energy loss in traditional distributed secondary control, an event triggered fixed time distributed secondary control scheme based on high‐order differentiator is proposed. The high‐order differentiator is used to estimate the FDI attack signal of the system. Then, using the estimated attack signal, a fixed time distributed secondary controller based on event triggering is designed to eliminate the impact of the attack signal, and realize the adjustment of bus voltage and accurate power distribution. The strict stability and the absence of Zeno behavior are proved. The simulation results show that the proposed control strategy can significantly reduce the impact of attacks on the system, and the event triggered fixed time control reduces the energy consumption of the system and the update numbers of the controller, making the system have better dynamic performance.

Analysis of Improvements to Bank Capacitor Requirements in 20kv Voltage Distribution Network Panels for Power Plant 2 at PT. Toyota Motor Manufacturing Indonesia

This research aims to describe and analyze the effectiveness of using capacitor bank capacity in PT's power plant 2 distribution system. TMMIN. The focus is to analyze the initial design of capacitor bank requirements for each panel in the substation production line, including Painting, Assembly and Welding, to increase capacitor bank requirements according to electrical theory standards. The Power Factor calculation method is used to measure the required capacitor bank capacity. The power factor is influenced by the type of electrical load which is resistive, inductive or capacitive, with a value range between 0 and 1. A power factor close to 1 indicates high active power and better electrical power quality, while a power factor close to 0 indicates high reactive power. reducing power quality and increasing electrical energy use. The current power factor value is obtained from the actual voltage and active power conditions, and the required capacitor bank value is calculated with a target power factor of 0.9 or 0.95. According to calculations, the panels that can be repaired are panels 3, 4, 5, 6 Painting, panel 1 Assembly, and panel 1 Welding. The results show that 2 MDB panels reach ideal conditions with 6 steps of 2x50kVAR capacitor bank, while the other 6 MDB panels require an additional 2 steps of 2x50kVAR capacitor bank to achieve ideal conditions.

Novel Reactive Power Control Strategy for Mitigating Voltage Rise in the Malaysian Low Voltage Distribution Network with High PV Penetration

Novel Reactive Power Control Strategy for Mitigating Voltage Rise in the Malaysian Low Voltage Distribution Network with High PV Penetration

A novel application of artificial intelligence technology for outdoor high-voltage composite insulator

The distribution of the electric field plays a crucial role in evaluating the effectiveness of composite insulators. This research presents an innovative approach for optimizing the design of corona rings, aiming to decrease the electric field intensity in critical areas to satisfactory levels. The investigation examined the correlation among corona ring design variables and the intensity of the electric field close to the energized-end fitting in a 220 kV composite insulator equipped with a corona ring. Using design of experiment methods, the impact of three corona ring design parameters – corona ring diameter (R), ring tube diameter (Dr), and corona ring height (H) – was assessed. A novel nonlinear mathematical objective function was formulated, connecting the electric field magnitude to the structural parameters of the corona ring. Subsequently, the Bat algorithm, a bio-inspired optimization technique, was employed to enhance the initial corona ring design and mitigate the electric field intensity. Finally, the Finite Element Method (FEM) was utilized to simulate and evaluate the voltage distribution and electric field stress. The optimization process resulted in a substantial decrease in the highest electric field magnitude, by 58.6 % compared to the insulator with the threshold value, and 75 % when devoid of a corona ring. Additionally, the research highlighted the significant influence of ring tube thickness on the distribution of the electric field. The combination of experimental design and the Bat algorithm proved to be a powerful tool for optimizing the design of corona rings on transmission line composite insulators, providing precise solutions for optimizing corona discharge problems and enhancing the reliability of power transmission systems.

Low and medium voltage distribution network planning with distributed energy resources: a survey

Abstract The penetration of distributed energy resources (DERs) such as photovoltaic systems, energy storage systems, and electric vehicles is increasing in the distribution system. The distinct characteristics of these resources, e.g., volatility and intermittency, introduce complexity in operation and planning of the distribution system. This paper first summarized the physical characteristics and morphological evaluation of the current and future distribution networks. Then, the impact of these changes on system operation and planning is outlined. Next, the tools, methods, and techniques for energy forecasting, optimal planning, and distribution system state estimation are reviewed and discussed, along with the challenges. As the main contributions, this research systematically organized the published works and assessed the relevant milestones regarding distribution system planning with DERs and emerging technologies. Finally, the key research directions in this domain are outlined. Graphical abstract

Intelligent System of Constructing Vector Diagrams of Electrical Circuits

Phasor diagrams are a powerful tool for visualizing and understanding the distribution of current, voltage, and power in electrical systems. During Russia's war against Ukraine, our energy industry has become very vulnerable to enemy attacks, and therefore needs a quick and effective recovery. Energy specialists lack software tools for working with the power system, and in the period of development of artificial intelligence, creating such tools is not so difficult. For example, familiar specialists often have to build vector diagrams - tools for visualizing and understanding the distribution of current, voltage and power in electrical systems. Using a combination of frameworks for working with artificial intelligence and creating a graphic shell, you can achieve the desired result in a few months, and at the same time do a useful thing for our victory. The YOLO model (based on the PyTorch framework) and the QT framework are considered among the proposed tools for creating an intelligent vector diagram construction system. The role of artificial intelligence is to recognize electrical elements in circuits and their connections to each other. Creating a user interface is an equally important thing, which is already implemented in many places with the help of QT. As of today, there is no specialized software tool for solving the problems of manual construction of vector diagrams, but the proposed approaches are already used for its creation. The system uses the Yolov5 model to recognize electrical elements on the circuit. The model is trained on more than 150 images and is able to recognize hand-drawn diagrams. Recognition is run as a separate process from the main program written in C++. This part of the system processes the input data from Yolo, saves it in a convenient format, creates a user interface, and displays the result as a vector diagram.

Voltage mapping in subcellular nanodomains using electro-diffusion modeling.

Voltage distribution in sub-cellular micro-domains such as neuronal synapses, small protrusions, or dendritic spines regulates the opening and closing of ionic channels, energy production, and thus, cellular homeostasis and excitability. Yet how voltage changes at such a small scale in vivo remains challenging due to the experimental diffraction limit, large signal fluctuations, and the still limited resolution of fast voltage indicators. Here, we study the voltage distribution in nano-compartments using a computational approach based on the Poisson-Nernst-Planck equations for the electro-diffusion motion of ions, where inward and outward fluxes are generated between channels. We report a current-voltage (I-V) logarithmic relationship generalizing Nernst law that reveals how the local membrane curvature modulates the voltage. We further find that an influx current penetrating a cellular electrolyte can lead to perturbations from tens to hundreds of nanometers deep, depending on the local channel organization. Finally, we show that the neck resistance of dendritic spines can be completely shunted by the transporters located on the head boundary, facilitating ionic flow. To conclude, we propose that voltage is regulated at a subcellular level by channel organization, membrane curvature, and narrow passages.

Development of a new tuning method for an radio frequency quadrupole accelerator

Tuning is a critical step in the low-power test of the radio frequency quadrupole (RFQ) accelerator. A 163 MHz RFQ for boron neutron capture therapy (BNCT) at Lanzhou University has been fully assembled. Its designed output energy is 2.6 MeV, and the designed output current in continuous wave mode is 30 mA. Before the feeding of high power, a low-power tuning was performed. First, based on the analysis of the tuning algorithm, a new tuning code capable of tuning the RFQ longitudinal field distribution to any desired target field distribution has been developed using Matlab. The tuning code mainly consisted of two parts: the data processing module and the tuning module, including sub-functions such as smoothing processing function and Fourier expansion function. Among them, the smoothing function based on the Gaussian distribution can effectively eliminate random errors in the measured voltage distribution, thereby optimizing the tuning procedure. Then, a new bead-pull system which can directly track the cavity phase shift was built. The bead-pull system was mainly composed of a vector network analyzer, a stepper motor, etc. Finally, the field unflatness of the RFQ was controlled below 1% after 4 times of tuning. The experiment also measured the radio-frequency performance of the RFQ cavity after installing the fixed copper tuners. The results showed that the RFQ operation frequency was 162.937 MHz, the unflatness of the quadrupole voltage was ±0.74%, and the asymmetry of the dipole voltage was ±1.19%, all of which met the requirements of beam dynamics.

Left atrial remodeling and voltage-guided ablation outcome in persistent atrial fibrillation patients according to CHA2DS2-VASc score

BackgroundCHA2DS2-VASc score-related differences have been reported in atrial fibrotic remodeling and prognosis of atrial fibrillation (AF) patients after ablation. There are currently no data on the efficacy of low voltage zone (LVZ)-guided ablation in persistent AF patients according to CHA2DS2-VASc score. We assessed in a cohort of persistent AF patients the extent of LVZ, the regional distribution of LA voltage and the outcome of LA voltage-guided substrate ablation in addition to PVI according to CHA2DS2-VASc score.Methods138 consecutive persistent AF patients undergoing a first voltage-guided catheter ablation were enrolled. 58 patients with CHAD2DS2-VASc score ≥ 3 and 80 patients with CHAD2DS2-VASc score ≤ 2 were included. LA voltage maps were obtained using 3D-electroanatomical mapping system in sinus rhythm. LVZ was defined as < 0.5 mV.ResultsIn the high CHAD2DS2-VASc score group, LA voltage was lower (1.5 [1.1–2.5] vs. 2.3 [1.5–2.8] mV, p = 0.02) and LVZs were more frequently identified (40% vs. 18%), p < 0.01). Female with CHA2DS2-VASc score ≥ 3 (p = 0.031), LA indexed volume (p = 0.009) and P-wave duration ≥ 150 ms (p = 0.001) were predictors of LVZ. After a 36-month follow-up, atrial arrhythmia-free survival was similar between the two groups (logrank test, P = 0.676).ConclusionsAF patients with CHAD2DS2-VASc score ≥ 3 display more LA substrate remodeling with lower voltage and more LVZs compared with those with CHAD2DS2-VASc score ≤ 2. Despite this atrial remodeling, they had similar and favorable 36 months results after one single procedure. Unlike male with CHAD2DS2-VASc score ≥ 3, female with CHAD2DS2-VASc score ≥ 3 was predictor of LVZ occurrence.

Dynamical sensitivity on thermal fluctuations of torsional homogeneous and nonhomogeneous microsystems under the influence of Casimir and electrostatic torques: Nonlinear actuation towards chaotic motion

We have studied how the mechanical Casimir torque between the components of a torsional double beam microdevice under the influence of thermal fluctuations could play significant role on the dynamics of the device, and under what conditions can even lead to chaotic behavior. The influence of thermal fluctuations on the dynamical behavior of torsional microsystems, whether homogeneous or nonhomogeneous, has been revealed through bifurcation and phase diagrams, and Poincare maps. For nonhomogeneous microsystems, the device with low conductivity reveals significant sensitivity to thermal fluctuations. Moreover, it is shown the significant dependence of the optimal voltage distribution, which leads to a significant increase in the stable operation range of the device, on the ambient temperature conditions and the optical properties of the constituent materials. For nonconservative torsional microsystem, the use of the Melnikov method and Poincaré plots have shown how thermal effects increase the probability of chaotic behavior. This is because different temperature enhances the effective Casimir torque and the possibility of chaotic motion. Furthermore, it is illustrated how the use of the optimal voltage distribution, by taking into consideration thermal fluctuations and optical properties, can keep the device away from chaotic behavior. This is because by dividing the electrostatic torques the influence of the effective Casimir torque is reduced. Finally, it is demonstrated that by transforming a homogeneous system into a heterogeneous one, the response of the system to thermal fluctuations is reduced making it possible to avoid to increase the possibility for chaotic behavior by changing the temperature.

An investigation on low operating voltage induced self-rectifying multilevel resistive switching in AgNbO3

This study presents a resistive random-access memory (RRAM) based on silver niobate (AgNbO3) perovskite films prepared via sputtering for the first time. The device exhibits a self-rectifying behavior at low operating and switching voltages, with a set voltage of about −0.4 V. Bipolar resistive switching was realized at a low operating voltage and with small set and reset voltage distributions. Compared to previously reported resistive switching perovskite materials, the device displays a superior resistive switching capability while maintaining a uniform memory window of up to 102, excellent endurance of over 104 cycles, and a long retention period of over 106 s. Moreover, the RRAM device exhibits four levels of resistive switching when the compliance current is varied, with each level of resistance state demonstrating uniformity and discrete read windows. The carrier-transport mechanism of the device was elucidated by considering various conduction mechanisms, including Schottky emission and space-charge-limited conduction. The resistive switching behavior was found to follow the conductive filamentary model, which is governed by the migration of oxygen vacancies when an electric field is applied. These outstanding properties of AgNbO3 make it a promising perovskite material option for future nonvolatile multilevel RRAM device applications.

Gate driver, snubber and circuit design considerations for fast‐switching series‐connected SiC MOSFETs

AbstractSeries connection of Silicon Carbide (SiC) Metal‐Oxide‐Semiconductor Field‐Effect Transistors (MOSFETs) is an interesting solution to design switches for voltages that are not yet commercially available or limited for single‐die devices. However, inherent static and dynamic voltage balancing must be achieved. Voltage imbalance is caused by the device parameters spread, whose impact is pronounced in low‐inductive circuit layouts. This study investigates the optimal design and tuning limits of resistor‐capacitor (RC)‐snubber circuits and non‐adaptive, standard, voltage‐source gate drivers for achieving the best balancing in transient and steady‐state voltage distributions among series‐connected discrete SiC MOSFETs operating at speeds up to . It has been shown that a larger parameter mismatch will lead to uneven switching energy losses and larger voltage imbalances. It was also experimentally shown that increasing the gate resistor to slow down the devices will not always improve balancing when their parameter spread is large. Thus, tuning recommendations for the RC‐snubber circuit and gate driver were developed based on these findings.

A robust and optimal voltage control strategy for low-voltage grids utilizing group coordination of photovoltaic and energy storage systems via consensus algorithm

This study presents a novel voltage control strategy for low voltage (LV) distribution grids, addressing the lack of coordination between photovoltaic (PV) reactive control and energy storage system (ESS) active control. The proposed strategy concentrates on group coordination of PV and ESS to improve LV grid performance. Initially, it suggests employing PV reactive power for voltage regulation before utilizing ESS active power, relying on the voltage cost sensitivity factor (VCSF) of different regulating devices, to reach efficient PV resource utilization. Devices are categorized by their VCSF, prioritizing groups with larger VCSF for voltage regulation, enhancing overall coordination. In the PV reactive voltage regulation stage, the reactive power utilization rate is the consensus variable, while in the ESS active control stage, the change in state of charge (SOC) is the consensus variable, collectively working towards desired voltage regulation and SOC change. Coordinated control is utilized to converge key node voltages to set values and obtain effective LV grid voltage regulation. An IEEE 14-node LV grid simulation incorporating PV and ESS validates the proposed strategy, demonstrating its effectiveness and correctness in improving voltage control performance. The strategy achieves 70.56% of the capacity and 81.45% of the setup cost compared to similar research, with associated costs limited to 15.80% and 35.0% of accumulated costs.

High-Performance 2D Ambipolar MoTe2 Lateral Memristors by Mild Oxidation.

2D transition metal dichalcogenides (TMDCs) have been intensively explored in memristors for brain-inspired computing. Oxidation, which is usually unavoidable and harmful in 2D TMDCs, could also be used to enhance their memristive performances. However, it is still unclear how oxidation affects the resistive switching behaviors of 2D ambipolar TMDCs. In this work, a mild oxidation strategy is developed to greatly enhance the resistive switching ratio of ambipolar 2H-MoTe2 lateral memristors by more than 10 times. Such an enhancement results from the amplified doping due to O2 and H2O adsorption and the optimization of effective gate voltage distribution by mild oxidation. Moreover, the ambipolarity of 2H-MoTe2 also enables a change of resistive switching direction, which is uncommon in 2D memristors. Consequently, as an artificial synapse, the MoTe2 device exhibits a large dynamic range (≈200) and a good linearity (1.01) in long-term potentiation and depression, as well as a high-accuracy handwritten digit recognition (>96%). This work not only provides a feasible and effective way to enhance the memristive performance of 2D ambipolar materials, but also deepens the understanding of hidden mechanisms for RS behaviors in oxidized 2D materials.