Local Voltage Research Articles

A Wireless Hysteretic Controlled Wireless Power Transfer System With Enhanced Efficiency and Dynamic Response for Bioimplants

This article presents a 6.78-MHz wireless power transfer (WPT) system with system-level wireless hysteretic control technique and circuit-level designs for both the receiver (RX) and transmitter (TX) chips. The proposed system achieves RX local voltage regulation and TX global power regulation without using any off-chip components (e.g., MCU, DAC, or various controllers and decoders) nor TX current sensing coils, which were required in previous works. A higher light-load efficiency and instant load-transient response are also achieved with the proposed wireless hysteretic control because of its operation principle. To further improve the efficiency, dynamic switch timing calibrations in the active rectifier in the RX are also designed, with resolved dual steady-state operation issues during the transitions between on-/off-delay compensations. Both the RX and TX chips have been fabricated in 180-nm standard CMOS. Measurement results show a 68.9% peak end-to-end (E2E) efficiency, with an up-to-20% enhancement at light-load conditions over a previous non-linearly controlled WPT system design. When compared to the other state-of-the-art designs, this work achieves an overall higher E2E efficiency, unnoticeable under-/over-shoots with instant recovery in load transients, and a lower system complexity with a higher level of integration without using any extra components other than the LCs to close the wireless loop.

Circuit-level design principles for transmission-mode microwave impedance microscopy

A recently developed technique of transmission-mode microwave impedance microscopy (T-MIM) has greatly extended the capabilities of standard reflection-mode MIM to novel applications, such as the in operando study of nanoscale electro-acoustic devices. As is common for new techniques, systematic design principles for boosting sensitivity and balancing bandwidth are lacking. Here, we show numerically and analytically that the T-MIM signal is proportional to the reflection-mode voltage enhancement factor η of the circuit, as long as the output impedance of the local voltage source is properly treated. We show that this proportionality holds in the currently achievable “weak sampling” regime and beyond, for which we demonstrate a realistic path with commercially available superconducting components and critically coupled impedance matching networks. We demonstrate that for these next-generation designs, the sensitivity is generally maximized at a slightly different frequency from the unloaded S11 resonance, which can be explained by the maximum power transfer theorem.

Evaluation of residential demand response trials with smart heat pumps and batteries and their effect at the substation feeder

Residential demand response using low carbon technologies can potentially offer energy flexibility to the electricity network along with integration of renewable generation. This paper seeks to empirically evaluate the effectiveness of residential demand response trials on a low voltage feeder of a secondary substation in Barnsley (UK). The study used a sample of 14 well-insulated dwellings equipped with home batteries, heat pumps and solar photovoltaic systems coordinated using automated control. Statistical analysis was undertaken using time-series monitoring data obtained at the individual dwelling level, dwelling sample and at the feeder level of the local low voltage network. Resident experience of the trials was assessed through qualitative data obtained from household telephone surveys. Over three weeks of trials, daily demand response interventions of 2 h duration were applied to the sample of 14 dwellings. For evening peak times, the mean reduction in grid electricity import was found to be 1.3 kWh (67%) per dwelling for turn-down interventions which aimed to minimise import. For turn-up interventions between 1 and 3 pm, the mean increase in grid electricity import was found to be 5.8 kWh (645%) per dwelling. The effect of interventions was measured at the low voltage network level for two single-phase feeders, where penetration of trial homes was approximately one-third. A reduction in mean real power up to 21% was observed for turn-down interventions as well as an increase in real mean power up to 307% for turn-up interventions. In general, the trials had little effect on residents in terms of thermal comfort, hot water availability, noise disturbance or disruption to routines, and where such effects were noticed, they were broadly acceptable. The widespread implementation of residential demand response schemes will require increased roll-out of time-of-use tariffs, enhanced resident support and extensive monitoring of low voltage feeders in electricity substations.

Distributed Optimal Voltage Control Considering Latency and Asynchronous Communication for Three Phase Unbalanced Distribution Systems

Integration of distributed energy resources (DERs) helps realize sustainability and reduce carbon footprint, but may cause voltage control issues in the distribution power system. The impact is mainly driven by the intermittency and variability of DERs typically connected at the edge. Traditional centralized optimal voltage control lacks scalability, and local voltage controls are not optimal. Distributed algorithms are scalable, robust to failures, and possibly require less communication resources compared to centralized. This paper presents a new online distributed voltage control algorithm with primal-dual updates and auxiliary variables for three-phase unbalanced distribution systems. The phase-specific active and reactive power of participating voltage control agents is regulated considering the power converter operational limits. The algorithm is validated to regulate voltage for both static and dynamic loading conditions for three-phase unbalanced IEEE 13 and 123 bus systems modeled in the OpenDSS. The robustness of the algorithm is validated for delayed asynchronous communication, modeling errors, and measurement noises. Simulation results and convergence analysis show the superior performance of the proposed algorithm.

Band structure engineering and transport properties of graphene/BN van der Waals heterostructures

It is an important topic in spintronics to find regulable half-metallic materials. In this work, by using density functional theory and nonequilibrium Green’s functional method, we find that the van der Waals heterostructures formed by zigzag-edged boron nitride nanoribbons and zigzag-edged graphene nanoribbons (ZGNRs) are spin splitting semiconductors. The energy band gap of the heterostructures can be regulated by applying a local voltage and the semiconductor–metal–semiconductor phase transition can be realized. Two spin-dependent transport devices are constructed based on the heterostructures. One device can generate a fully spin-polarized current within a small bias range, and the other device can generate high spin polarization within a large bias range. Moreover, based on zigzag-edged graphene/boron nitride nanoribbon (ZGBNNR) heterostructures, a dual-probe device with double vertical gates in the electrode regions is designed, which can form a metal–semiconductor–metal tunneling device. These findings indicate that ZGBNNRs can be used in excellent spintronic devices.

Voltage Control Market Integration: Technical and Regulatory Challenges for the Greek Electricity Market

Stochastic power generation is the new reality in power system management. Voltage Control mechanisms based on physical assets of the power system are deemed inadequate and are not guaranteed to lead the energy transformation in a way that ensures system security as well as cost-effective operation. Many countries that recently attained deregulated Balancing Market environments are in need of regulatory provisions and rigorous extension of electricity market mechanisms. On 1 November 2020, the Greek Electricity Market commenced operations conforming to the European Target Model. Apart from the innate difficulties a transformation such as this contains, more challenges occur as Greece is bound by European law to design market-based incentive mechanisms to remunerate Ancillary Services provided to the power system. This paper aims to examine some of the technical and regulatory aspects linked with—future—Transmission System Operator (TSO) and Distribution System Operator (DSO) cooperation in overcoming local transmission system problems concerning Voltage regulation. The interaction between localized Voltage Control Market (VCM) and the Balancing Market, the incorporation and competition of Distributed Energy Resources (DER) and Transmission Energy Resources (TER) within the VCM along with the TSO - DSO procedures and products standardization are the focus points of the present research paper.

Protection scheme for multi-terminal HVDC system with superconducting cables based on artificial intelligence algorithms

This paper presents the development of a novel data-driven fault detection and classification scheme for DC faults in multi-terminal HVDC transmission system which incorporates superconducting cables and modular multi-level converters. As the deployment of superconducting cables for bulk power transmission from remote renewable generation is progressively increasing in the future energy grids, many fault-related challenges have been raised (i.e., fault detection, protection sensitivity/stability). In this context, the applications of Artificial Intelligence techniques have started to be considered as a powerful tool for the development of robust fault management solutions. The proposed artificial intelligence-based method utilizes local current and voltage measurements to detect and classify all types of faults on the DC cables and DC buses, without the requirement of measurements exchange among different DC substations. The performance of the proposed scheme has been assessed through detailed transient simulation analysis and the results confirmed its effectiveness against a wide range of fault conditions (i.e., various fault types, fault locations and fault resistances). Furthermore, the feasibility of the developed scheme for real-time implementation has been validated using real-time software in the loop testing. The results revealed that the proposed algorithm can correctly, and within a very short period of time (i.e. less than 2 ms) detect and classify the faults within the protected zone and concurrently remain stable during external faults. Additionally, the generalization capability of the algorithm has been verified against influencing factors such as the addition of noise, highlighting the robustness of the presented scheme.

Experimental investigation of fuel starvation on industrial-sized solid oxide fuel cells using segmented cathodes and area specific resistances

Fuel starvation must be detected timely to avoid anode oxidation, especially for industrial-sized Solid oxide fuel cells (SOFCs). In this work, a practical segmented cell measurement method is developed to detect fuel starvation occurring in local regions. For this developed method, the cathode of SOFCs is segmented, and only the voltage signal is measured in the segments. The local current-voltage (I–V) curve per segment is determined using the local voltage and the global I–V curve. Then, area specific resistance (ASR) is calculated and compared among segments to validate the data reliability. Varieties of current density loading rates and fuel flow rates are correspondingly investigated. Furthermore, fuel starvation is estimated using the rapid voltage drop in I–V curves and the rapid ASR increment in I-ASR curves. The results show that the intrinsic ASR increment is a more sensitive and robust criterion to identify the occurrence of fuel starvation than the voltage drop. This work will considerably benefit the progress of fuel starvation avoidance by setting a constant ASR threshold.

Ensemble models for circuit topology estimation, fault detection and classification in distribution systems

This paper presents a methodology for simultaneous fault detection, classification, and topology estimation for adaptive protection of distribution systems. The methodology estimates the probability of the occurrence of each one of these events by using a hybrid structure that combines three sub-systems, a convolutional neural network for topology estimation, a fault detection based on predictive residual analysis, and a standard support vector machine with probabilistic output for fault classification. The input to all these sub-systems is the local voltage and current measurements. A convolutional neural network uses these local measurements in the form of sequential data to extract features and estimate the topology conditions. The fault detector is constructed with a Bayesian stage (a multitask Gaussian process) that computes a predictive distribution (assumed to be Gaussian) of the residuals using the input. Since the distribution is known, these residuals can be transformed into a Standard distribution, whose values are then introduced into a one-class support vector machine. The structure allows using a one-class support vector machine without parameter cross-validation, so the fault detector is fully unsupervised. Finally, a support vector machine uses the input to perform the classification of the fault types. All three sub-systems can work in a parallel setup for both performance and computation efficiency. We test all three sub-systems included in the structure on a modified IEEE123 bus system, and we compare and evaluate the results with standard approaches.

Finite-time correlations boost large voltage angle fluctuations in electric power grids

Decarbonization in the energy sector has been accompanied by an increased penetration of new renewable energy sources in electric power systems. Such sources differ from traditional productions in that, first, they induce larger, undispatchable fluctuations in power generation and second, they lack inertia. Recent measurements have indeed reported long, non-Gaussian tails in the distribution of local voltage frequency data. Large frequency deviations may induce grid instabilities, leading in worst-case scenarios to cascading failures and large-scale blackouts. In this article, we investigate how correlated noise disturbances, characterized by the cumulants of their distribution, propagate through meshed, high-voltage power grids. For a single source of fluctuations, we show that long noise correlation times boost non-Gaussian voltage angle fluctuations so that they propagate similarly to Gaussian fluctuations over the entire network. However, they vanish faster, over short distances if the noise fluctuates rapidly. We furthermore demonstrate that a Berry–Esseen theorem leads to the vanishing of non-Gaussianities as the number of uncorrelated noise sources increases. Our predictions are corroborated by numerical simulations on realistic models of power grids.

Data-Mining Techniques Based Relaying Support for Symmetric-Monopolar-Multi-Terminal VSC-HVDC System

Considering the advantage of the ability of data-mining techniques (DMTs) to detect and classify patterns, this paper explores their applicability for the protection of voltage source converter-based high voltage direct current (VSC-HVDC) transmission systems. In spite of the location of fault occurring points such as external/internal, rectifier-substation/inverter-substation, and positive/negative pole of the DC line, the stated approach is capable of accurate fault detection, classification, and location. Initially, the local voltage and current measurements at one end of the HVDC system are used in this work to extract the feature vector. Once the feature vector is retrieved, the DMTs are trained and tested to identify the fault types (internal DC faults, external AC faults, and external DC faults) and fault location in the particular feeder. In the data-mining framework, several state-of-the-art machine learning (ML) models along with one advanced deep learning (DL) model are used for training and testing. The proposed VSC-HVDC relaying system is comprehensively tested on a symmetric-monopolar-multi-terminal VSC-HVDC system and presents heartening results in diverse operating conditions. The results show that the studied deep belief network (DBN) based DL model performs better compared with other ML models in both fault classification and location. The accuracy of fault classification of the DBN is found to be 98.9% in the noiseless condition and 91.8% in the 20 dB noisy condition. Similarly, the DBN-based DMT is found to be effective in fault locations in the HVDC system with a smaller percentage of errors as MSE: 2.116, RMSE: 1.4531, and MAPE: 2.7047. This approach can be used as an effective low-cost relaying support tool for the VSC-HVDC system, as it does not necessitate a communication channel.

The use of numerical method in an electric grid coupled to a wind farm to compare different kinds of flexible AC transmission systems

The PSAT software was used in this study to analyse and compare the performance of hybrid compensators such as SSSC-STATCOM (controllers), TCSC-SVC (compensators), and UPFC in an electric grid coupled to a wind farm. The flexible AC transmission system (FACTS) technology is used to provide a continual power flow and to provide new ways to control the electric system network. In the FACTS devices, the UPFC (Unified Power Flow Controller) is one of the most adaptable, flexible, and complicated power electric devices. The active and reactive power flows and the local voltage on the bus can be regulated by UPFC; it can also resolve the problem of harmonics. The TCSC (Thyristor-Controlled Series Capacitor) consists as a series-compensating capacitor shunted by a thyristor-controlled reactor. The SVC (Static Var Compensator) is the first shunt generation FACTS controller. We can observe that the UPFC controller has an effective power flow control, shorter setting time and a shorter overshoot. The UPFC obtained a well-known reputation for high controllability in power systems. The multilevel Unified Power Flow Controller can be operated in Static Synchronous Compensator (STATCOM), in Static Synchronous Series Compensator SSSC and exactly in the UPFC compensator. The results of this research compare the hybrid controllers and investigate the effects of TCSC-SVC, SSSC-STATCOM, and UPFC on voltage, phase angle stability, and the active and reactive power in the tested system. The purpose of this comparison is to improve dynamic voltage regulation, especially when the utilisation of nonlinear loads and the presence of fault and breaker rise. These hybrid controllers have been shown to outperform series or shunt compensators; however, when compared to hybrid compensators SSSC-STATCOM, TCSC-SVC, and UPFC, the results employing the numerical method in the UPFC are more significant. The UPFC is the best hybrid controller in this study, and the compensators SSSC-STATCOM outperform the controllers TCSC-SVC.

Electrophysiological and histological characterization of atrial scarring in a model of isolated atrial myocardial infarction.

Background: Characterization of atrial myocardial infarction is hampered by the frequent concurrence of ventricular infarction. Theoretically, atrial infarct scarring could be recognized by multifrequency tissue impedance, like in ventricular infarction, but this remains to be proven. Objective: This study aimed at developing a model of atrial infarction to assess the potential of multifrequency impedance to recognize areas of atrial infarct scar. Methods: Seven anesthetized pigs were submitted to transcatheter occlusion of atrial coronary branches arising from the left coronary circumflex artery. Six weeks later the animals were anesthetized and underwent atrial voltage mapping and multifrequency impedance recordings. The hearts were thereafter extracted for anatomopathological study. Two additional pigs not submitted to atrial branch occlusion were used as controls. Results: Selective occlusion of the atrial branches induced areas of healed infarction in the left atrium in 6 of the 7 cases. Endocardial mapping of the left atrium showed reduced multi-frequency impedance (Phase angle at 307kHz: from -17.1° ± 5.0° to -8.9° ± 2.6°, p < .01) and low-voltage of bipolar electrograms (.2 ± 0.1mV vs. 1.9 ± 1.5mV vs., p < .01) in areas affected by the infarction. Data variability of the impedance phase angle was lower than that of bipolar voltage (coefficient of variability of phase angle at307kHz vs. bipolar voltage: .30 vs. .77). Histological analysis excluded the presence of ventricular infarction. Conclusion: Selective occlusion of atrial coronary branches permits to set up a model of selective atrial infarction. Atrial multifrequency impedance mapping allowed recognition of atrial infarct scarring with lesser data variability than local bipolar voltage mapping. Our model may have potential applicability on the study of atrial arrhythmia mechanisms.

Model predictive control based virtual synchronous generator for parallel-connected three-phase split-source converters in islanded AC Microgrids

Due to high penetration of renewable generation in power systems, and the need to provide the interface between distributed energy resources, the split-source inverter (SSI) provides both the boosting and the conversion capabilities in one single-stage. Also the need for converter-based artificial inertia has become more important. In this paper a model-predictive control (MPC) based on virtual synchronous generator (VSG) algorithm for a parallel-connected three-phase SSI is proposed for conceiving regulation of local voltage and realizing power-sharing of an islanded AC microgrid (MG). A virtual synchronous generator (VSG) is deployed to ensure active-power-sharing and provide inertia-emulation and hence reducing the rate of change of frequency (RoCoF) that results from sudden load change. To accomplish a simple control construction, quick dynamic performance, high stability, and enhanced current limitation, a finite-set MPC (FS-MPC) is used. The analysis and modeling of the proposed technique are presented in detail. A simulation model is used to investigate the proposed system performance.

Master-slave strategy based in artificial intelligence for the fault section estimation in active distribution networks and microgrids

Fault location plays an essential role in the integration of self-healing functionalities in active distribution networks and microgrids. However, the fault location methods formulation presents great challenges for these types of networks because the operating changes that occur them, such as changes in topology, DER connection/disconnection and microgrids operating modes. Several fault location solutions have been proposed; nevertheless, these are strongly dependent on robust communication systems. This paper presents an artificial intelligence-based master–slave strategy for the estimation of the fault section in active distribution networks and microgrids using dispersed measurements. The strategy is composed by two stages. The master stage uses a genetic algorithm that determines the location and number of devices which maximize the faulted location e performance. The slave stage uses artificial neural networks to predict the fault section by using local voltage and current measurements trough an intelligent electronic device (IED). This approach is useful because it neglects the need of a robust communication systems and synchronization process between measurements. Here, each IED estimates the faulted section and then sends it through the single communication system to the distribution system operator control center. The presented method is validated on the modified IEEE 34-nodes test feeder where the accuracy of the strategy was 95%. The results obtained and its easy implementation indicate potential for real-life applications.

Constraints on OPF Surrogates for Learning Stable Local Volt/Var Controllers

We consider the problem of learning local Volt/Var controllers in distribution grids (DGs). Our approach starts from learning separable surrogates that take both local voltages and reactive powers as arguments and predict the reactive power setpoints that approximate optimal power flow (OPF) solutions. We propose an incremental control algorithm and identify two different sets of slope conditions on the local surrogates such that the network is collectively steered toward desired configurations asymptotically. Our results reveal the trade-offs between each set of conditions, with coupled voltage-power slope constraints allowing an arbitrary shape of surrogate functions but risking limitations on exploiting generation capabilities, and reactive power slope constraints taking full advantage of generation capabilities but constraining the shape of surrogate functions. AC power flow simulations on the IEEE 37-bus feeder illustrate their guaranteed stability properties and respective advantages in two DG scenarios.

Coordinated Charging of Spatially Distributed Electric Vehicles for Mitigating Voltage Rise and Voltage Unbalance in Modern Distribution Networks

This paper proposes a decentralised control strategy using droop-based control to vary the charging rates of electric vehicles (EVs) in real-time in response to nodal voltages, voltage unbalance and the state-of-charge (SOC). This strategy developed aims to mitigate excessive voltage rise caused by rooftop solar photovoltaic (PV) systems and voltage unbalance caused by unbalanced loads in a low-voltage (LV) distribution network. This will also smooth the voltage profile through peak reduction and valley filling by EV charging during times of low load, high generation and performing vehicle-to-grid operations during times of high load, low generation. Reference voltages are calculated and used to coordinate the control of the EV chargers by assigning each a target voltage based on their location and local conditions without the use for any common communication channel or a centralised controller. The local voltage, phase unbalance and current EV SOC are used to determine a suitable charging rate for the vehicle. A modified IEEE 13 node test feeder inclusive of an LV network based on a semi-rural Australian town was used to assess the impacts of PV and EV charging in the network and validate the proposed strategy as a means to reduce voltage rise and voltage unbalance in the network. Simulations were performed using OpenDSS and MATLAB in four scenarios, with and without charging control, to verify the effectiveness of the proposed strategy.

Noise-Immune Machine Learning and Autonomous Grid Control

Most recently, stochastic control methods such as deep reinforcement learning (DRL) have proven to be efficient and quick converging methods in providing localized grid voltage control. Because of the random dynamical characteristics of grid reactive loads and bus voltages, such stochastic control methods are particularly useful in accurately predicting future voltage levels and in minimizing associated cost functions. Although DRL is capable of quickly inferring future voltage levels given specific voltage control actions, it is prone to high variance when the learning rate or discount factors are set for rapid convergence in the presence of bus noise. Evolutionary learning is also capable of minimizing cost function and can be leveraged for localized grid control, but it does not infer future voltage levels given specific control inputs and instead simply selects those control actions that result in the best voltage control. For this reason, evolutionary learning is better suited than DRL for voltage control in noisy grid environments. To illustrate this, using a cyber adversary to inject random noise, we compare the use of evolutionary learning and DRL in autonomous voltage control (AVC) under noisy control conditions and show that it is possible to achieve a high mean voltage control using a genetic algorithm (GA). We show that the GA additionally can provide superior AVC to DRL with comparable computational efficiency. We illustrate that the superior noise immunity properties of evolutionary learning make it a good choice for implementing AVC in noisy environments or in the presence of random cyber-attacks.

The Design of An LDO Regulator

In today’s modern systems on chip (SOCs), a crucial power management circuit is the low-dropout (LDO) regulator. Of course, the need for supply voltage regulation, goes back many years in the past since the circuits have been designed. Today, LDO based voltage regulators are frequently used in a number of mixed-signal systems to produce local supply voltages that feed different building blocks. LDOs try to isolate the noise of the circuit and noise from the global supply and try to reduce their effect on device performance. For the state of desired achievement, each LDO’s architecture is circuited to the specific cell it feeds. An LDO designed to feed a flash analog-to-digital converter, for instance, differs greatly from one designed to input a VCO. In this paper, we direct an LDO for a VCO of 5-GHz LC and point the particulars of 1.2 V as input voltage, produces 1V as output voltage, 5 mA of maximum output current, power supply rejection greater than 40 dB up to 10 MHz and noise voltage present at output that is less than 25nV/√Hz at 1 MHz.

Harmonic Signature-Based One-Class Classifier for Islanding Detection in Microgrids

This article presents a new passive islanding detection technique in MGs that uses locally measured voltage signals at the PoC of DERs. The proposed method distinguishes islanding events from normal/non-islanding conditions by utilizing superimposed harmonic spectra extracted through a full-cycle discrete Fourier transform. Our solution utilizes a machine-learning-based one-class classifier to define and adjust thresholds for full harmonic spectra. Unlike other methods, our approach does not require data synchronization or communication infrastructure, nor does it suffer from common errors that often arise in current transformers. Moreover, our design is compatible with distributed and decentralized control strategies, as it relies solely on local voltage measurements at the PoC. Another advantage of this method is its low sampling frequency requirement, in the range of 1 kHz, making it cost-effective and implementable in most existing systems. In a comprehensive evaluation of a typical MG test system that included synchronous and inverter-based DERs, the proposed scheme demonstrated exceptional performance. Specifically, the scheme was able to detect 99.06% of different islanding events within the training range, with a detection time of just 10 to 21 ms. Additionally, the scheme remained 100% stable during various normal conditions, short-circuit faults, load changes, voltage changes, capacitor switching, and frequency changes.