Voltage Dynamics Research Articles

Stability enhancement of bi‐directional voltage source converters in modern power systems

AbstractIn future modern power systems, reliability and resilience could be an extreme challenge caused by the stability issues of the bidirectional power converters (BPCs). The non‐linear dynamics of DC link voltage (DCLV) of BPCs in interaction with the existing linear control schemes may decrease the stability margin and cause operating‐point‐dependent instability issues. Existing approaches may solve this issue by reducing the DCLV control loop bandwidth, which considerably degrades the system performance. To tackle this issue, first, the root cause of the instability challenge is analytically investigated, and then, a non‐linear stabilizer control scheme based on Lyapunov theorem is proposed. Considering the non‐linear dynamic of the BPCs and the interaction between dynamics of DC link voltage and AC currents in the proposed stabilizer, it guarantees the stability of the converter in both directions of power flow and the full range of loading conditions. The performance of the proposed scheme is verified through simulation of the system under various operating conditions, considering uncertainties, disturbances, and short‐circuit events, and comparing it with that of prevalent controllers.

Non-Abelian Inverse Anderson Transitions.

Inverse Anderson transitions, where the flat-band localization is destroyed by disorder, have been wildly investigated in quantum and classical systems in the presence of Abelian gauge fields. Here, we report the first investigation on inverse Anderson transitions in the system with non-Abelian gauge fields. It is found that pseudospin-dependent localized and delocalized eigenstates coexist in the disordered non-Abelian Aharonov-Bohm cage, making inverse Anderson transitions depend on the relative phase of two internal pseudospins. Such an exotic phenomenon induced by the interplay between non-Abelian gauge fields and disorder has no Abelian analogy. Furthermore, we theoretically design and experimentally fabricate non-Abelian Aharonov-Bohm topolectrical circuits to observe the non-Abelian inverse Anderson transition. Through the direct measurements of frequency-dependent impedance responses and voltage dynamics, the pseudospin-dependent non-Abelian inverse Anderson transitions are observed. Our results establish the connection between inverse Anderson transitions and non-Abelian gauge fields, and thus comprise a new insight on the fundamental aspects of localization in disordered non-Abelian flat-band systems.

High-speed optical mapping of heart and brain voltage activities in zebrafish larvae exposed to environmental contaminants

Environmental contaminants represent a poorly understood ecotoxicological and health risk. Here, we advanced a high-speed optical mapping (OM) technique to non-invasively track voltage dynamics in living zebrafish larvae’s heart and brain and investigate the effects of selected pesticides.OM allowed high resolution (∼17x) and fast acquisition (100 to 200 frames/s) of the voltage signal generated in the heart and brain after immersion of the zebrafish larvae in a voltage-sensitive dye. First, we used varying temperatures (20 °C to 25 °C) to test the adequacy of OM in capturing cardiac and brain voltage changes. Then, we tested the effects of glyphosate or a selected pesticide cocktail (2 to 120 h post-fertilization), accounting for their environmental thresholds and mimicking high-level exposure. Glyphosate (0.1 and 1000 μg/L) and the pesticide cocktail (0.1 and 10 μg/L) did not alter cardiac activity, except for a trend increase in heart rate variability at high glyphosate dose. Fourier transform (FT) analyses indicated that glyphosate reduced the abundance of low-amplitude voltage activities in the brain at the target low-frequency range of 0.2–15 Hz. The anatomical fragmentation of the brain into four regions, right and left diencephalon (RD and LD) and right and left optic tectum (ROT and LOT), confirmed the impact of glyphosate on the larvae brain and revealed a specific adaptation to the pesticide cocktail in the RD and ROT regions.In summary, OM captured heart and brain voltage changes in zebrafish larvae, with discrete patterns of brain depolarization in the presence of specific water contaminants. Here we discuss the relevance of these findings to ecotoxicology and exposome research.

Transient photocurrents in a subthreshold evidence accumulator accelerate perceptual decisions

Perceptual decisions are complete when a continuously updated score of sensory evidence reaches a threshold. In Drosophila, αβ core Kenyon cells (αβc KCs) of the mushroom bodies integrate odor-evoked synaptic inputs to spike threshold at rates that parallel the speed of olfactory choices. Here we perform a causal test of the idea that the biophysical process of synaptic integration underlies the psychophysical process of bounded evidence accumulation in this system. Injections of single brief, EPSP-like depolarizations into the dendrites of αβc KCs during odor discrimination, using closed-loop control of a targeted opsin, accelerate decision times at a marginal cost of accuracy. Model comparisons favor a mechanism of temporal integration over extrema detection and suggest that the optogenetically evoked quanta are added to a growing total of sensory evidence, effectively lowering the decision bound. The subthreshold voltage dynamics of αβc KCs thus form an accumulator memory for sequential samples of information.

Upcrossing-rate dynamics for a minimal neuron model receiving spatially distributed synaptic drive

Few analytic results exist for the firing-rate dynamics of spatially extended neuron models driven by distributed synaptic input. Using an upcrossing approximation for the firing rate, the statistics of spatiotemporal voltage dynamics are derived and show that minimal dendritic models exhibit an unexpectedly sustained high-frequency response.

Distributed cooperative voltage mode control for DC-isolated microgrids powered by renewable energy sources

This article proposes a distributed model-based cooperative controller to improve voltage synchronization in DC-isolated microgrids that are powered by variable renewable energy sources. Based on the second-order voltage dynamics of dc–dc buck converters, we aim to develop an integral-based linear-quadratic regulator that synchronizes not only the output voltages but also their derivatives. It is necessary not only in reducing voltage ripples and their integrals but also in eliminating steady-state errors. This objective is achieved mainly by introducing a global performance index, which minimizes neighbor synchronization errors, apart from local synchronization errors, and thus transfers the interactions between the dc–dc buck converters from the physical layer to the communication layer. Furthermore, it is demonstrated that the proposed controller ensures global system stability according to the Lyapunov stability theory. Several simulations are made to show the effectiveness of the proposed technique in terms of voltage synchronization and stability, voltage ripple elimination, and voltage convergence speed when input voltage disturbances are present.

Maximum power point tracking using cross-correlation algorithm for PV system

This paper suggests an online, rapid, dynamic, and pragmatic cross-correlation (CC) technique to effectively realize maximum power point tracking (MPPT) of the photovoltaic (PV) array. The PV voltage and current are extracted, averaged, and the rate of change of the power and terminal voltage dynamics are integrally cross-correlated to regulate the duty ratio of the converter. According to this approach, the maximum power point (MPP) is defined in terms of the polarity of the CC coefficient (rcc). If the CC coefficient is positive, the operating point is at the left of the MPP, while a negative CC coefficient indicates that the operating point is located at the right of the MPP, otherwise, the MPP is attained at zero CC coefficient with lower steady-state fluctuations. In this regard, a tracking optimization can be realized with a confined drift either under gradual or step solar irradiance changes. The proposed CC technique quickly converges to the MPP without the identification of PV array parameters. Besides, the proposed CC tracking algorithm tracks MPP with wide-range small step-size iterations since the search area is restricted to the vicinity of the MPP rather than the entire PV curve. Moreover, the proposed technique is featured by robustness against system parameters and load variations with simple hardware implementation as compared to several existing techniques. The effectiveness of the proposed technique is thoroughly explored via time-domain, experimental, and hardware-in-loop investigations and meanwhile compared to the two most popular techniques: perturb and observe (P&O) and incremental conductance (INC) methods, for both gradual and step irradiance changes and also shadow effects. The performed case studies revealed superior behavior, rapid convergence with high tracking efficiency and low oscillations, and overriding capability of the influence of the system parameters and load variations.

Adaptive neuro-Kpis approach in the evaluation of the performance and parameters of a PV module

This paper presents an adaptive approach to performance analysis and estimation of unmeasurable and inaccessible parameters of a PV power plant based on the neural structures and mechanism proposed by Massachusetts Institute of Technology (MIT). This approach takes into account the single diode model, the empirical efficiency analysis model and an additional simple electronic circuit that allows to establish the voltage and current dynamics. The new scheme found here develops the observers using neural networks for the estimation of the unmeasurable and inaccessible parameters of the one-diode model. The MIT mechanism combined with the proposed ADALINE neural network has interesting features for the accurate determination of the k-pis parameters of the empirical model for PV plant performance analysis. The implementation of the proposed tool is done in the numerical environment Matlab/simulink and in real time via the Arduino target for the experimental part. The numerical and experimental results are presented in order to estimate the parameters and their performances. The results found here show that our approach stands for a robust tool to analyze defects. Another particularity of the new approach concerns the fault analysis based on the resistor behaviors in a three-dimensional frame. Hence, the proposed tools contribute to the fault analysis, the parameter estimation and the performance evaluation of the PV source whatever the operating conditions.

An adaptive data-driven approach for two-timescale dynamics prediction and remaining useful life estimation of Li-ion batteries

During the multi-cycle operation of a Li-ion battery, its process dynamics evolve in two distinct timescales: slow degradation dynamics over multiple cycles and fast cycling dynamics during each cycle. The slow inter-cyclic dynamics of capacity degradation describes remaining useful life (RUL), and the fast intra-cyclic dynamics of state of charge (SoC) and voltage provides an insight into available power, temperature change, and charge and discharge times. Hence, predicting both intra and inter-cyclic dynamics aids in understanding battery degradation and assessing its performance. To this end, we develop a data-driven approach to model both fast and slow degradation dynamics using operable adaptive sparse identification of systems (OASIS). Specifically, the developed method determines two battery models: inter-OASIS and intra-OASIS. The inter-OASIS model predicts capacity degradation and estimates RUL, and utilizing this prediction, the intra-OASIS model accurately predicts SoC and voltage dynamics. The developed method is demonstrated on a LiFePO4/graphite battery system.

Expanding the family of genetically encoded voltage indicators with a candidate Heliorhodopsin exhibiting near-infrared fluorescence

Genetically encoded voltage indicators, particularly those based on microbial rhodopsins, are gaining traction in neuroscience as fluorescent sensors for imaging voltage dynamics with high-spatiotemporal precision. Here we establish a novel genetically encoded voltage indicator candidate based on the recently discovered subfamily of the microbial rhodopsin clade, termed heliorhodopsins. We discovered that upon excitation at 530 to 560nm, wildtype heliorhodopsin exhibits near-infrared fluorescence, which is sensitive to membrane voltage. We characterized the fluorescence brightness, photostability, voltage sensitivity, and kinetics of wildtype heliorhodopsin in HEK293T cells and further examined the impact of mutating key residues near the retinal chromophore. The S237A mutation significantly improved the fluorescence response of heliorhodopsin by 76% providing a highly promising starting point for further protein evolution.

Research on dynamic temporal and spatial distribution characteristics of electrical power system voltage based on random forest algorithm

AbstractThe theme of this study is to find a method that can more accurately classify the spatiotemporal distribution characteristics of power system voltage dynamics. As more and more new energy power generation devices are added to the power network, the accuracy of traditional methods for classifying the spatiotemporal distribution characteristics of power system voltage dynamics has gradually decreased. It is significant to reduce the power loss caused by error in identifying voltage dynamic spatiotemporal distribution characteristics. Hence it is necessary to design methods that can more accurately classify power system voltage dynamic spatiotemporal distribution characteristics data. This study proposes a method for studying the spatiotemporal distribution characteristics of power system voltage dynamics based on an improved random forest algorithm. The information entropy calculation method of decision tree in random forest algorithm is improved. According to the experimental results, the classification accuracy of the method for voltage dynamic spatiotemporal distribution characteristics is 99.55%. It can effectively achieve the dynamic spatiotemporal distribution of power system voltage.

Exploring the effect of voltage amplitude and phase difference on resilient ability for power network with third-order model

Power networks and their ability play crucial role on our modern society. An effective approach to characterize the stability of power network against various disturbances is the resilience, which was presented in recent years. This paper investigates the resilience of power network with third-order model which takes into account voltage dynamics. Firstly, we analyze the local stability of the third order model including the transient dynamics of voltage magnitudes to gain insights into the critical coupling strength of power network. Subsequently, we study the resilience of power network from the view point of synchronous dynamics and voltage stability. Specifically, we focus on two factors: phase angle difference and voltage amplitude. Then, we pin down certain disturbance scenarios in networks with small size simulated according to the third-order model and discuss the transmitted power flow under different conditions. Simulation results show that power network is robust under fixed voltage amplitude. On the contrary, the power network is vulnerable under fixed phase differences. The analysis of the model can further help to understand the dynamics resilience of network in reality, and it is also of practical significance to predict cascading failure and design effective measures to control larger-scale outages.

Voltage evolution and electrochemical behaviour of Soil microbial fuel cells operated in different quality soils

The desire for a net-zero carbon future is a key driver for innovation in renewable energy. Amongst several emerging solutions, soil microbial fuel cells (SMFCs) pose an interesting addition as a low-cost, carbon–neutral technology. A full understanding on the electro-generative processes in SMFCs has, however, yet to be achieved, hindering the technology’s translation into practical applications. In this study, an in-depth investigation into the evolution of the output voltage generated by membrane-less, flat-plate SMFCs that accounts for the contribution of both the anode and cathode potential is provided for the first time, along with a study of the influence that organic matter content and porosity in soil has on voltage dynamics. Four stages in voltage evolution over time were observed, which depended on soil properties. The content of organic matter had the greatest effect, leading to an output voltage nearly-three times higher, when it increased from 10 % to 50 %. In this case, the anode potential reached a value of −450 mV, which prompted an exponential increase in the cathode potential and led to a power density of 68 mWm−2. The experimental findings were used to develop a novel computational model that, by predicting the electrochemical behaviour of the SMFC in different soils, becomes a powerful guide for operating strategies that can markedly enhance electricity generation. Consequently, this study sets the foundation for effective system optimisation and real applications.

Periodic and chaotic dynamics in a map‐based neuron model

Map‐based neuron models are an important tool in modeling neural dynamics and sometimes can be considered as an alternative to usually computationally costlier models based on continuous or hybrid dynamical systems. However, due to their discrete nature, rigorous mathematical analysis might be challenging. We study a discrete model of neuronal dynamics introduced by Chialvo in 1995. In particular, we show that its reduced one‐dimensional version can be treated as an independent simple model of neural activity where the input and the fixed value of the recovery variable are parameters. This one‐dimensional model still displays very rich and varied dynamics. Using the fact that the map whose iterates define voltage dynamics is S‐unimodal, we describe in detail both the periodic behavior and the occurrence of different notions of chaos, indicating corresponding regions in parameter space. Our study is also complemented by a bifurcation analysis of the mentioned dynamical model.

Performance Improvement of Weak Grid-Connected Wind Energy System Using FLSRF-Controlled DSTATCOM

Wind energy (WE) is emerging as a green and clean energy source. The power quality (PQ) significantly deteriorates when WE sources are connected to the weak ac grid. This is due to the variable wind speed, the impedance of weak grids, and nonlinear loads. This article proposes a methodology to improve the PQ in the weak grid with WE penetration using additional distribution static compensator (DSTATCOM). Fuzzy logic (FL) controller is implemented in the conventional synchronous reference frame (SRF) scheme to minimize the oscillations in the voltage signals. Also, a feedforward block of the WE source is incorporated in the FLSRF control scheme for adapting changes associated with the wind speed. The proposed control scheme accommodates the changes related to the grid’s strength, wind speed, load currents, and dc-link voltage dynamics to estimate the switching signals for the DSTATCOM. The MATLAB simulation and experimental studies established the effectiveness of the proposed method for enhancing the PQ performance of weak grid-connected WE sources in the presence of nonlinear loads.

Modeling and Stability Analysis Based on Internal Voltage Dynamics in Synchronverter

With the large-scale centralized penetration of renewable energy represented by wind turbine and photovoltaic power generation, the equivalent inertia and synchronous torque of traditional power grids have decreased, which is worsening the frequency dynamics and threatening the stability and reliability of power grids. Virtual synchronous generators (VSGs) are a type of grid-friendly inverter in microgrids (MGs) that mimic rotational synchronous generators (SGs) to maintain system stability with the increasing penetration of power electronic converters. In this paper, the stability analysis method of one type of VSG, synchronverter, is investigated based on internal voltage dynamics. The torque components affecting the stability mechanism of synchronizing, inertia, and damping torque is introduced, which offers a physical insight into transient stability and dynamic performance. Insufficiency in either damping or synchronizing torque would increase the unstable possibility. The characterization method of stability mechanisms can be illustrated by the phasor diagram of synchronverter internal voltage. Some cases under different disturbances which change the pattern of synchronizing and damping torque and in turn influence the internal voltage dynamics of synchronverters, are also discussed. In addition, an auxiliary correction control loop is proposed and added in the synchronverter control loop to enhance the robustness of the synchronverter against disturbances. The tunable coefficient of the correction loop is analyzed based on the internal voltage method. Simulation results verify the validity of the internal voltage stability method in synchronverters.

Observation of flat-band localization and topological edge states induced by effective strong interactions in electrical circuit networks

Flat-band topologies and localizations in noninteracting systems are extensively studied in different quantum and classical-wave systems. Recently, the exploration on the novel physics of flat-band localizations and topologies in interacting systems has aroused great interest. In particular, it is theoretically shown that the strong interaction could drive the formation of nontrivial topological flat bands; even dispersive trivial bands dominate the single-particle counterparts. However, the experimental observation of those interesting phenomena is still lacking. Here, we experimentally simulate the interaction-induced flat-band localizations and topological edge states in electrical circuit networks. We directly map the eigenstates of two correlated bosons in one-dimensional Aharonov-Bohm cages to modes of two-dimensional circuit lattices. In this case, by tuning the effective interaction strength through circuits' groundings, the two-boson flat bands and topological edge states are detected by measuring frequency-dependent impedance responses and voltage dynamics in the time domain. Our finding suggests a flexible platform to simulate the interaction-induced flat-band topology, and may possess potential applications in designing novel electronic devices.

Suppression of seizure in childhood absence epilepsy using robust control of deep brain stimulation: a simulation study

Deep brain stimulation (DBS) is a promising technique to relieve the symptoms in patients with intractable seizures. Although the DBS therapy for seizure suppression dates back more than 40 years, determining stimulation parameters is a significant challenge to the success of this technique. One solution to this challenge with application in a real DBS system is to design a closed-loop control system to regulate the stimulation intensity using computational models of epilepsy automatically. The main goal of the current study is to develop a robust control technique based on adaptive fuzzy terminal sliding mode control (AFTSMC) for eliminating the oscillatory spiking behavior in childhood absence epilepsy (CAE) dynamical model consisting of cortical, thalamic relay, and reticular nuclei neurons. To this end, the membrane voltage dynamics of the three coupled neurons are considered as a three-input three-output nonlinear state delay system. A fuzzy logic system is developed to estimate the unknown nonlinear dynamics of the current and delayed states of the model embedded in the control input. Chattering-free control input (continuous DBS pulses) without any singularity problem is the superiority of the proposed control method. To guarantee the bounded stability of the closed-loop system in a finite time, the upper bounds of the external disturbance and minimum estimation errors are updated online with adaptive laws without any offline tuning phase. Simulation results are provided to show the robustness of AFTSMC in the presence of uncertainty and external disturbances.

Diversity of dynamic voltage patterns in neuronal dendrites revealed by nanopipette electrophysiology.

Dendrites and dendritic spines are the essential cellular compartments in neuronal communication, conveying information through transient voltage signals. Our understanding of these compartmentalized voltage dynamics in fine, distal neuronal dendrites remains poor due to the difficulties inherent to accessing and stably recording from such small, nanoscale cellular compartments for a sustained time. To overcome these challenges, we use nanopipettes that permit long and stable recordings directly from fine neuronal dendrites. We reveal a diversity of voltage dynamics present locally in dendrites, such as spontaneous voltage transients, bursting events and oscillating periods of silence and firing activity, all of which we characterized using segmentation analysis. Remarkably, we find that neuronal dendrites can display spontaneous hyperpolarisation events, and sustain transient hyperpolarised states. The voltage patterns were activity-dependent, with a stronger dependency on synaptic activity than on action potentials. Long-time recordings of fine dendritic protrusions show complex voltage dynamics that may represent a previously unexplored contribution to dendritic computations.

Transient Dynamics and Propagation of Voltage and Frequency Fluctuations in Transmission Grids

The energy transition towards increased electric power production from renewable energy (RE) resources creates new challenges to ensure the stability of power grids. In conventional power grids voltage fluctuations can be controlled locally. Here, it is explored whether this may be changed by the energy transition. It is well established that the increase of RE resources in power grids increases the amplitude of frequency deviations and the velocity with which these deviations spread throughout the power grid. However, its effect on voltage dynamics and propagation has not been systematically studied. Here, a systematic study is carried out of the transients of voltage amplitude, phase and frequency deviations due to local contingencies in dependence on system inertia, heterogeneity and topology. The 3rd order dynamic power grid model is studied numerically and analytically and compared with real grid simulations for the Nigerian (330 kV) power grid and other grid models, using DigSILENT PowerFactory software. A quantitative analysis of the parametric dependence of the velocity with which a disturbance propagates throughout the grid and of the period of oscillations of the frequency and voltage transients is provided. Beating patterns are found in the transients and are identified as footprints of the location of the fault bus, as caused by multiple reflections of propagating disturbances from the grid boundary. These may result in interarea oscillations. It is confirmed that voltage deviations remain local for realistic ranges of parameters, but that it can propagate by literally surfing on the frequency deviation wave. However, it is found that this no longer holds true when the electrical power in the grid approaches its critical value beyond which no stationary solution exists. Furthermore, time dependent second moments of the geodesic distance weighted with frequency deviations <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S_{\delta \omega }(t)$ </tex-math></inline-formula> and voltage deviations <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S_{\delta V}(t)$ </tex-math></inline-formula> , respectively are evaluated, confirming a ballistic disturbance propagation in homogeneous model grids. However, in real grid simulations, a linear time dependence of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S(t)$ </tex-math></inline-formula> is observed, indicating a diffusive propagation due to multiple scattering from the inhomogeneities in these power grids.