Real-time Voltage Research Articles

FPGA-based pulsed laser with high modulation depth from Mach-Zehnder electro-optic modulator and its application

Mach-Zehnder electro-optic modulator as the essential component of high-speed modulation in the optical fiber communication and sensing system, its stability control of operating bias-point plays a key role in determining the transmission signal quality. Based on the output characteristic principle of Mach-Zehnder electro-optic modulator, researches on its bias-point drifting for causing the reduction mechanism of modulation depth and extinction ratio are analyzed. Switching its internal state machine from FPGA is proposed to automatically adjust the bias voltage in real time realizing the operating bias-point control of Mach-Zehnder electro-optic modulator, which is studied through the theoretical investigation. A pulsed laser with the temperature sensing system of fiber Bragg grating ring-down cavity based on FPGA is established experimentally to demonstrate its effectiveness. The experimental results show the modulation depth and the extinction ratio of pulsed laser are improved by 40.45% and 32.85 dB, respectively. Output characteristic curve of the sensing system is optimized, which includes the phenomenon of initial curve rise of ring-down signals and the distortion of ring-down waveform are eliminated. The good linearity, repeatability, stability and accuracy of sensing system are investigated at the continuous variation temperature from −30℃ to 30℃.

Robust voltage-controlled transcutaneous energy transfer system for artificial anal sphincter.

The artificial anal sphincter (AAS) system has gained significant attention as a solution for treating fecal incontinence (FI). It relies on transcutaneous energy transfer (TET) as its primary energy source. However, changes in posture or biological tissue can cause misalignment of the coil, resulting in unstable power reception. Inadequate power affects charging efficiency, while excessive power leads to excessive heating at the receiver side. Consequently, achieving safe and constant voltage charging for the AAS becomes a complex challenge. To maintain a consistent charging voltage and overcome the issue of variations in load and coil coupling strength, this article proposes a wireless charging control system that utilizes an LCC-S-type resonant network and phase shift to adjust the transmitting voltage based on feedback charging voltage in real time. In particular, the PI controller and neural network are introduced to change the phase-shift angle swiftly. The dynamic performance is then evaluated under different misalignments and presented with comparative results. The results indicate that the multilayer perceptron control system outperforms the PI. Under the complex misalignment disturbance, the average error of receiver side load voltage is only 0.007 V, with an average settling time of 960 ms. Additionally, the average temperature at the receiver side is 40.4°C. The experiments demonstrate that the proposed system effectively addresses the misalignment issue in TET during the charging, ensuring constant voltage charging at the receiver side and thermal safety.

A proposed fault identification-based fuzzy approach for active distribution networks with photovoltaic systems

Recently, the issue of protection in active distribution networks has been greater than before due to mushrooming use of photovoltaic (PV) generation units. PV systems coupled to power electronics devices and their control strategies causes significant changes to fault current characteristics leading to maloperation of fault identification schemes. Therefore, this paper proposes a robust self-adaptive fault identification scheme that incorporates the presence of PV systems and considers the effect of uncertainty arising due to their high penetration levels. The real-time voltage and current measurements are captured locally by advanced one-end measuring facilities, considering the uncertain temperature and irradiation conditions, of solar generation units. First, the proposed scheme employs wavelet transform, statistical alienation concept, and fuzzy logic to identify the fault criteria. Then, it uses fuzzy concept-based the system features to obtain the optimal protection scheme setting. For simulation analysis, a 10 MW solar farm-connected to a real-world active distribution system is emulated in PSCAD-EMTDC simulator to create intensive study cases, and the proposed scheme is implemented in MATLAB software. Several fault parameters are examined in the scheme comprising different fault types, fault locations, and transition resistances. Also, the uncertainty arising from PV plants and different loading conditions are taken full consideration in the developed scheme. Simulation results have verified the validity of fault identification scheme; nonetheless, the computational burden is kept reasonable. The proposed scheme is investigated under white gaussian and impulsive noise. Furthermore, sensitivity analysis and comparative study are conducted in terms of accuracy, high decision speed, and sampling frequency variation. The proposed scheme succeeds to detect and classify internal faults for both on/off-grid operation modes and differentiate them from external ones in the case of on-grid mode. The proposed scheme has dominance performance including high speed fault detection within, a half cycle, and provides accuracy more than 98 %.

An adaptive threshold method for multi-faults diagnosis of lithium-ion batteries based on electro-thermal model

Lithium-ion batteries have emerged as the primary power source for electrochemical energy storage, making battery safety a crucial concern. This study focuses on the detection of thermal faults in lithium-ion batteries, specifically heat generation faults, thermal parameter faults, and temperature sensor faults. To address the issue of low accuracy in temperature indirect estimation models, a framework that combines electrical and thermal models is proposed. This framework employs particle swarm optimization to identify optimal thermal model parameters using real-time open circuit voltage and terminal voltage data. Additionally, an unscented Kalman filter estimator is utilized to predict core temperature based on surface temperature measurements in real time. The results demonstrate that the proposed framework achieves rapid convergence of core and surface temperature biases within a range of 1 ℃ and 0.1 ℃, respectively. Furthermore, an adaptive threshold method is developed to diagnose faults by analyzing the change rates of the estimated temperatures. Experimental results reveal that the proposed methods exhibit quick response times for four different fault conditions, indicating their effectiveness and accuracy in multi-fault diagnosis.

Real Time Voltage Stability Estimation under Contingencies by Generalized Curve Fit Method using PMU

An accurate voltage stability monitoring scheme is necessary to take possible remedial action. Recent developments in Phasor Measurement Units (PMUs) technology provide the foundation for online voltage stability monitoring of a power system network. In this paper, the generalized curve fit method using PMUs in the system has been used to access voltage stability of the system and a sensitivity based approach has been used to identify critical buses of the system. Voltage stability margin has been obtained and compared using generalized curve fit method and continuation power flow method using PMUs in the system. Variations in voltage stability margin caused by changing load patterns have also been considered while estimating voltage stability under contingencies. Nose curves obtained using generalized curve fit and continuation power flow methods are also compared. Case studies have been performed on a standard IEEE 14-bus system and practical 246-bus Northern Regional Power Grid (NRPG) system representing power network of nine states of India, with the help of Real Time Digital Simulation (RTDS) and Power System Analysis Toolbox (PSAT) software.

Direct Estimation of Equivalent Bioelectric Sources Based on Huygens' Principle.

An estimation of the electric sources in the heart was conducted using a novel method, based on Huygens' Principle, aiming at a direct estimation of equivalent bioelectric sources over the heart's surface in real time. The main scope of this work was to establish a new, fast approach to the solution of the inverse electrocardiography problem. The study was based on recorded electrocardiograms (ECGs). Based on Huygens' Principle, measurements obtained from the surfaceof a patient's thorax were interpolated over the surface of the employed volume conductor model and considered as secondary Huygens' sources. These sources, being non-zero only over the surface under study, were employed to determine the weighting factors of the eigenfunctions' expansion, describing the generated voltage distribution over the whole conductor volume. With the availability of the potential distribution stemming from measurements, the electromagnetics reciprocity theorem is applied once again to yield the equivalent sources over the pericardium. The methodology is self-validated, since the surface potentials calculated from these equivalent sources are in very good agreement with ECG measurements. The ultimate aim of this effort is to create a tool providing the equivalent epicardial voltage or current sources in real time, i.e., during the ECG measurements with multiple electrodes.

Data-Driven Linear Koopman Embedding for Networked Systems: Model-Predictive Grid Control

This article presents a data-learned linear Koopman embedding of nonlinear networked dynamics and uses it to enable real-time model predictive emergency voltage control in a power grid. The approach involves a novel data-driven “basis-dictionary free” lifting of the system dynamics into a higher dimensional linear space over which a model predictive control is exercised, making it both scalable and rapid for practical real-time implementation. A <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Koopman-inspired deep neural network</i> (KDNN) encoder–decoder architecture for the linear embedding of the underlying networked dynamics under distributive control is presented, in which the end-to-end components of the KDNN, comprising of a triple of transforms is learned from the system trajectory data in one go: a neural network (NN)-based lifting to a higher dimension, a linear dynamics within that higher dimension, and an NN-based projection to the original space. This data-learned approach relieves the burden of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ad hoc</i> selection of the nonlinear basis functions (e.g., radial or polynomial) used in conventional approaches for lifting to a higher dimensional linear space. We validate the efficacy and robustness of the approach via application to the standard IEEE 39-bus system.

Safe multi-agent deep reinforcement learning for real-time decentralized control of inverter based renewable energy resources considering communication delay

The increasing penetration of distributed renewable energy resources brings a great challenge for real-time voltage security of distribution grids. The paper proposes a safe multi-agent deep reinforcement learning (MADRL) algorithm for real-time control of inverter-based Volt-Var control (VVC) in distribution grids considering communication delay to minimize the network power loss, while maintaining the nodal voltages in a safe range. The multi-agent VVC is modeled as a constrained Markov game, which is solved by the MADRL algorithm. In the training stage, the safety projection is added to the combined policy to analytically solve an action correction formulation to promote more efficient and safe exploration. In the real-time decision-making stage, a state synchronization block is designed to impute the data under the latest timestamp as the input of the agents deployed in a distributed manner, to avoid instability caused by communication delay. The simulation results show that the proposed algorithm performs well in safe exploration, and also achieves better performance under communication delay.

Joint Control Strategy of Energy Storage System and Cutting Machine for Transient Stability of Direct Current Locking Rear Delivery Terminal System

With the gradual operation of large-capacity HVDC transmission, HVDC), the characteristics of the “strong and weak communication” of the power grid are increasingly obvious. The power impact of the DC line after locking has a great impact on the power angle stability of the system and seriously threatens the transient stability of the delivery end system. By establishing the equivalent model of the AC/DC system with the energy storage power station and analyzing the transient process after DC locking, we propose a control strategy for the transient stability of the energy storage system and the delivery system after DC locking. This strategy controls the charge and discharge of the energy storage system by collecting real-time power angle and voltage data of the grid, uses the equal area rule, and initiates the cutting machine after the energy storage system is withdrawn. To verify the validity and correctness of the proposed method, a simulation analysis was performed in a modified cross-flow CEPRI-36 node AC–DC system.

Real-Time Voltage Drop Compensation Method With Cable Impedance Detection Capability for Remote Power Supply Systems

When a power supply runs for a remote load through long cables, a voltage drop will occur due to the cable impedance. To maintain sufficient load voltage, the compensation of the cable voltage drop is necessary. In this letter, a real-time voltage drop compensation method with cable impedance detection capability is proposed. This method does not require additional auxiliary components, and the detection of cable impedance can be realized by simply adjusting the position of the filter capacitor. Further, this method can implement cable voltage drop compensation by controlling the cable current, and then realize real-time accurate control of the remote load voltage by setting the correction period. Compared with traditional methods, this method operates at the power supply side, does not affect the system efficiency and power density, and is not limited by cable parameters and environmental factors, which is more attractive in engineering applications.

Voltage Stability Margin Estimation Using Machine Learning Tools

Real-time voltage stability assessment, via conventional methods, is a difficult task due to the required large amount of information, high execution times and computational cost. Based on these limitations, this technical work proposes a method for the estimation of the voltage stability margin through the application of artificial intelligence algorithms. For this purpose, several operation scenarios are first generated via Monte Carlo simulations, considering the load variability and the n-1 security criterion. Afterwards, the voltage stability margin of PV curves is determined for each scenario to obtain a database. This information allows structuring a data matrix for training an artificial neural network and a support vector machine, in its regression version, to predict the voltage stability margin, capable of being used in real time. The performance of the prediction tools is evaluated through the mean square error and the coefficient of determination. The proposed methodology is applied to the IEEE 14 bus test system, showing so promising results.

Global Asymptotic Stability of Submodule Capacitor Voltage Self-converging Modular Multilevel Converters Based on Characteristic Root Method

This study presents global asymptotic stability verification of submodule capacitor voltage self-converging modular multilevel converters (SC-MMCs) according to the characteristic root method, which constructs a submodule switching state matrix for voltage balancing control. The mathematical model of MMC is established in form of the state-space equation. It is proved that SC-MMCs have the characteristic of global asymptotic stability at a certain time from the perspective of switching state matrix characteristic roots, according to the theories of Lyapunov stability and continuous-time piecewise linear system stability. Then, the submodule capacitor voltage self-convergence over a period of time is deduced based on the capacitor voltage deviation vector. Under the constraints of linearly independent switching state matrix row vectors, submodule capacitor voltage self-convergence without real-time voltage calculation and feedback can be achieved. The global asymptotic stability of SC-MMCs and the efficacy of submodule capacitor voltage self-convergence based on switching state matrix are testified by simulations.

A continuous-time voltage control method based on hierarchical coordination for high PV-penetrated distribution networks

The photovoltaic (PV) integration brings both fast photovoltaic generation (PVG) variations and a large number of PV inverters to the distribution networks (DNs). The management of the PVG variation and the PV inverter is an urgent problem. The discrete-time model can’t describe PVG variations during the schedule interval and the regulation potential of the PV inverter is limited by a fixed dispatching value. This paper proposes a continuous-time voltage control method based on hierarchical coordination for high PV-penetrated DNs. In the central schedule, based on the Bernstein polynomial method, continuous-time models of PVGs are established and reference values of local inverter control curves are dispatched as continuous-time functions in a sub-hourly timescale. In the local control, reactive power and active power curtailment of PV inverters are set as the reference values, i.e., the corresponding values of the continuous-time functions, every 30 s to optimize the central dispatching objective under the voltage constrains, and deviate from the reference values according to both the local control curve and voltage measurement only when the real-time bus voltage violation occurs. In addition, the cooperation between inverter and mechanical equipment in the continuous-time framework is also considered, and the Bernstein polynomial method is developed to the continuous-time modeling of voltage control in DNs. The simulation shows that the proposed method has lower maximum voltage magnitude and lower energy loss compared with discrete-time central schedule method.

Coordinated control of multiple converters in model-free AC/DC distribution networks based on reinforcement learning

Taking into account the challenges of obtaining accurate physical parameters and uncertainties arising from the integration of a large number of sources and loads, this paper proposes a real-time voltage control method for AC/DC distribution networks. The method utilizes model-free generation and coordinated control of multiple converters, and employs a combination of agent modeling and multi-agent soft actor critic (MASAC) techniques for modeling and solving the problem. Firstly, a complex nonlinear mapping relationship between bus power and voltage is established by training an power-voltage model, to address the issue of obtaining physical parameters in AC/DC distribution networks. Next, a Markov decision process is established for the voltage control problem, with multiple intelligent agents distributed to control the active and reactive power at each converter, in response to the uncertainties of photovoltaic (PV) and load variations. Using the MASAC method, a centralized training strategy and decentralized execution policy are implemented to achieve distributed control of the converters, with each converter making optimal decisions based on its local observation state. Finally, the proposed method is verified by numerical simulations, demonstrating its sound effectiveness and generalization ability.

Geographic information data processing and physical simulation for power transmission lines

To address the issue of traditional transmission line characteristic parameter testing methods being susceptible to external environmental interference. Proposed a method for processing geographic information data of transmission lines and physical simulation. Firstly, the parameters of the transmission line are tested, a model is established at both ends of the transmission line, and variable frequency signals are collected using 3D GIS technology to develop a lead control box for transmission line parameter testing. Then, the transmission line parameters are calculated online, and real-time voltage and current measurements are conducted at both ends of the transmission line. A zero sequence current simulation diagram of the transmission line is constructed, and circuit parameters are calculated based on the simulation results, Finally, measure the zero sequence parameters of the transmission line using electricity and obtain the zero sequence impedance value of the transmission line using the least squares method. The six zero sequence parameter values were measured in a disturbance environment. The results show that when the power line reaches a certain length, the error of the traditional method has reached 50%, and the testing error of the method studied by the author is less than 3%. The measurement error value of the transmission line characteristic parameter test method based on 3D GIS technology is less than that of the traditional method.

Enhancing electric vehicle penetration and grid operation performance in old residential communities through hybrid AC/DC microgrid reconstruction

The widespread popularity of electric vehicles (EVs) imposes great challenges for distribution grids, such as feeder congestion, voltage deviation, and so forth. Although building new electrical facilities is feasible to release more capacity for EV integration, this is not a wise scheme to be applied in old residential communities owing to huge costs and limited spaces. In light of this, the paper proposes an easy-to-implement and cost-effective hybrid AC/DC microgrid reconstruction scheme for low-voltage distribution grids of old residential communities. The advanced power electronic device called smart interlinking unit (SIU) is incorporated in prior power distribution rooms of residential communities, featuring integrated structure, dedicated interfaces, and flexible control capability. The SIU-based architecture reuses existing redundant transformers and power lines to form AC/DC subgrids. An outstanding transfer capacity improvement and power loss reduction via the DC renovation are quantified. Considering the varied efficiency characteristics of multiple power converters and transformers, a hierarchical coordinated operation strategy is proposed for this architecture to realize the overall energy-saving operation. The optimal operation point tracking and AC/DC voltage regulation are included in the strategy design. Regarding the target scenario in the case study, the adopted reconstruction scheme would achieve a significant 297% of the maximum EV penetration. The real-time voltage regulation for AC and DC feeders is also realized to avoid possible voltage violations. Additionally, the proposed architecture and operation strategy could obtain about 10% of load ratio improvement and 1.5% of average efficiency promotion compared to the traditional AC architecture.

An Open-Circuit Faults Diagnosis Method for MMC Based on Extreme Gradient Boosting

The diagnosis of open circuit faults is required for the reliability of modular multilevel converters (MMC) and the complex operating environment of MMC may cause missing data and external noise in signal detections, where some of the present open circuit fault detection methods can be disabled. This paper proposed a machine learning (ML) diagnosis strategy for open circuit faults in MMC based on extreme gradient boosting (XG-Boost). In this method, after data processing, the data segments composed of real-time capacitance voltage and current data are input into the trained XG-Boost multi-classification model for fault diagnosis without manually setting the empirical threshold, as is required in traditional change-of-rate-of-voltage-based diagnosis methods. This method has excellent robustness against external noise and missing data while better accuracy, higher speed and lower real-time calculation cost than existing ML-based methods are achieved. The effectiveness of the proposed method is verified by experiment results. It can diagnose the fault within 20ms with accuracy as high as 99.6&#x0025;, even under the interference of Gaussian (GS) noise and the capacitor voltage data missing.

Artificial emotional deep Q learning for real-time smart voltage control of cyber-physical social power systems

Artificial emotional deep Q learning for real-time smart voltage control of cyber-physical social power systems

Model predictive real-time architecture for secondary voltage control of microgrids

The integration of new elements and technologies to the power grid, driven by the expansion of electric microgrids, brings various challenges requiring a suitable control architecture for systems to operate ensuring economic and reliable access to electricity. This paper presents the development of a time-varying constraint real-time model predictive secondary voltage control (MPVC) strategy for microgrids built up in a multi-class Python environment with InfluxDB and SQLExpress databases storing the controller and microgrid data, and its experimental implementation with Modbus communication to physical devices at the Universidad Pontificia Bolivariana (UPB) campus microgrid. The microgrid characteristics are described and the experimental setup is presented. The mathematical model of the resource used as case study is obtained through experimental identification, and it is integrated to the MPVC scheme. Polytopic invariant sets are used as terminal sets in the MPVC scheme to ensure stability of the solution, and time-varying constraints of reactive power availability are considered. Validation studies of the proposed control system in computational simulation are presented prior to real application in the UPB campus microgrid. Controller performance results are described for three different implementation scenarios of low, medium and high variability of external disturbances, presenting adequate voltage regulation around the reference value and considerable dependence on the meteorological forecast quality. The model predictive voltage controller is finally benchmarked against a commercial microgrid controller to evaluate its performance, standing out as a more comprehensive and tailor-made solution.

Probabilistic Optimal Active and Reactive Power Dispatch including Load and Wind Uncertainties considering Correlation

The increased integration of renewable energies (REs) raised the uncertainties of power systems and has changed the approach to dealing with power system challenges. Hence, the uncertain nature of all the power system variables needs to be considered while dealing with the optimal planning and operation of modern power systems. This paper presents a probabilistic optimal active and reactive power dispatch (POARPD) based on the point estimate method (PEM), considering the uncertainties associated with load variation and wind power generation. In the POARPD, the deterministic optimal active and reactive power dispatch (OARPD) is performed in two stages, which gives a deterministic two-stage OARPD (TSOARPD). The objectives of TSOARPD are the operating cost (OC) minimization in stage 1 and voltage stability (VS) maximization in stage 2, whereas the VS is improved by maximizing the system’s reactive power reserve (RPR). In this paper, instead of using multiobjective optimal power flow, this TSOARPD is used to give more importance to VS when the system is substantially loaded. The POARPD problem is solved using PEM for modified IEEE-9 bus and standard IEEE-30 bus test systems by considering the correlation between the loads. The results are compared with Monte Carlo simulation (MCS). While solving POARPD, the voltage-dependent load model is used to account for the real-time voltage dependency of power system loads. This paper discusses the detailed procedure of solving POARPD by considering correlation and the increased nonlinearities by giving more importance to VS when the system is heavily loaded.