Real-time Voltage Research Articles

Intelligent Flink Framework Aided Real-Time Voltage Computing Systems in Autonomous and Controllable Environments

Motivated by the progress in artificial intelligence such as deep learning and IoT networks, this paper presents an intelligent flink framework for real-time voltage computing systems in autonomous and controllable environments. The proposed framework employs machine learning algorithms to predict voltage values and adjust them in real-time to ensure the optimal performance of the power grid. The system is designed to be autonomous and controllable, enabling it to adapt to changing conditions and optimize its operation without human intervention. The paper also presents experimental results that demonstrate the effectiveness of the proposed framework in improving the accuracy and efficiency of voltage computing systems. Simulation results are provided to verify that the proposed intelligent flink framework can work well for real-time voltage computing systems in autonomous and controllable environments, compared with the conventional DRL and cross-entropy methods, in terms of convergence rate and estimation result. Overall, the intelligent flink framework presented in this paper has the potential to significantly improve the performance and reliability of power grids, leading to more efficient and sustainable energy systems.

Design and Build Voltage and Current Monitoring Parameters Device of Rechargeable Batteries in Real-Time Using the INA219 GY-219 Sensor

Research on the manufacture of devices for monitoring battery voltage and current parameters has been carried out and developed using different types of sensors. This research have been released a device that can monitor battery voltage and current parameters in real time in the charge and discharge process using one sensor. The device is made using a microcontroller in the form of an Arduino Mega XPro 2560 R3, with an input in the form of an INA219 GY-219 sensor to monitor battery voltage and current. The output of device is in the form of automatic control of the charge and discharge circuit through the LCD and relay to obtain data from monitoring the battery voltage and current parameters. The batteries used as materials to be tested in this study are rechargeable Li-ion types. The device can monitor the voltage and current parameters of the battery as it should and can work automatically according to the control program created and designed by the manufacturer. Based on the test results of the INA219 GY-219 sensor in reading the voltage, the average sensor accuracy level is 99.96%, the sensor error average rate is 0.034%, and the sensor precision average is 99.97%. Meanwhile, in the reading current, the average sensor accuracy level is 98.39%, the sensor error average rate is 1.608%, and the sensor precision average is 98.98%.

Voltage Optimization Control and Risk Assessment Technology for High-proportion Distributed Photovoltaic Access to Distribution Network

The high proportion of distributed photovoltaic connected to the distribution network will lead to an increase in terminal voltage and network loss, which will not only generate many adverse effects on the safe and reliable operation of the distribution network but also easily lead to power supply disconnection and power supply interruption accidents caused by voltage out of limit. At present, the voltage control of the distribution network does not fully consider the regulation characteristics of the photovoltaic inverter itself and ignores the impact of different control methods on the distribution network security risk management and control. Therefore, this paper establishes a voltage closed-loop control model for high-proportion distributed photovoltaic access to the distribution network and proposes a real-time active voltage optimization control strategy with the goal of minimizing the deviation of the actual operation state of the distribution network from the scheduling plan. Finally, a typical industrial park is taken as a case for calculation and analysis. The results show that using real-time data to formulate a real-time voltage optimization control strategy can not only ensure the economic operation of each voltage regulating unit in the distribution network but also reduce operational risks. The control method proposed in this paper provides a new idea for improving the voltage control level of distributed photovoltaic access distribution network and fully guarantees the operation reliability of the entire distribution network.

Design of solar power plant and real-time voltage quality monitoring system using LabVIEW

This research presents the design of a solar power generation system in combination with energy storage unit. The objective of this research is to apply renewable energy production technology to reduce the use of external energy. this research was to design a solar power generation system for beef cattle breeding group, Na Yom sub-district, Mueang district, Phetchabun province. The solar power generation system is designed to be installed on the roof. In addition, this research study and development of innovative prototypes for real-time solar voltage quality monitoring system using the LabVIEW program. The study results showed that this solar power generation system in combination with the energy storage unit can supply the load covering the energy consumption of the beef cowshed, which can reduce the use of external electricity by 23.10% and the solar voltage quality monitoring system can display the results for the monitoring of the solar power generation system in conjunction with the real-time voltage quality. In addition to the objectives of this solar power generation system can be developed to further the innovation of renewable energy generation. If a group of farmers has built a widespread power generation system, including electricity generation from other types of renewable energy, it can be developed into a micro grid system for energy security

Real-Time Topology Estimation for Active Distribution System Using Graph-Bank Tracking Bayesian Networks

Real-time topology estimation in distribution grid with high penetration of distributed energy resources remains a challenging task due to the insufficient high-precision measurements and frequent topology variations. This article proposes a real-time distribution system topology estimation approach building on the graph theory and Bayesian networks with sparse measurements. The graph theory develops the topology graph bank to effectively leverage the prior knowledge of topology models, including the topology structure and the switching relationship between different topologies. This allows the development of the Bayesian networks for topology tracking using real-time voltage and power injection measurements. A novel discrete method considering the similarity of data correlation information is proposed for the optimal placement of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">μ</i> PMUs to ensure the performance of topology estimation. Numerical results on the IEEE 33-node and 123-node systems show that the BN-based topology estimation model has better performance against incomplete information, i.e., missing data, than other alternatives.

Research on portable constant pulse current source device

A set of portable constant pulse current sources with STM32 as the control core is developed, which mainly involves the SCM control module, operational amplifier circuit module, pulse output module, power module, key module, and display module. Based on the traditional constant-current source circuit design, the real-time display control system is constructed by software programming optimization, which has the characteristics of portable and strong stability. By connecting the field effect tube and operational amplifier in series to form a feedback loop, the current output accuracy is improved and the stability of the current output is ensured. The experimental results show that after the op-amp circuit is introduced into the series feedback system, the front and rear order circuit can be more stable, and ensure the reference voltage is stable at the same time, through the duty cycle of PWM wave to adjust the current output, so that the constant current source output 0~2mA adjustable low-frequency stable current. At the same time, in terms of human-computer interaction, in addition to the use of buttons to adjust the current at both ends of the load, a small OLED display screen is used to display the current, voltage, and pulse frequency in real time. The experiment shows that the instrument has high stability and load adaptability, and has high practical value.

Realtime And Centralized Solar Panel Online Monitoring System Design Using Thingspeak

Indonesia is a country with a tropical climate and has a high intensity of solar irradiation. Seeing this situation, Indonesia is the right region to implement the installation of new and renewable energy such as PLTS. In the use of solar panels, the magnitude of the output power is determined by several environmental conditions, such as the intensity of sunlight, temperature, and the direction in which sunlight comes. To prevent damage and deterioration in the performance of solar panels, research is needed for a more accurate and efficient use of renewable energy. The study was a solar panel system with a Wemos D1 microcontroller that monitored online using Thing speak with the help of INA 219 and LDR sensors. Online monitoring system by recording data on current, voltage, and light intensity in real time and centralized. The system will store and record measurement data every 15 seconds in the form of JSON, XML, CSV file extensions. Error on the voltage sensor of 1.7% for and 3.2% for the current sensor. average panel voltage value of 12.77 V, average panel current value of 0.43 A and 2410 lux for light intensity at interval testing from 07:00 a.m.to 16:00p.m.

Research and Design of Microgrid Simulation System Based on Single Chip Microcomputer

With the continuous development of modern power electronics and chip technology, numerical control inverter power supply is more and more widely used in industrial control and civil fields. High performance numerical control inverter has become one of the research focuses in the power supply field. In this paper, a micro grid simulation system based on single-chip microcomputer is designed. The effective value of the load line current of the inverter part is within 2A, the effective value of the line voltage changes within 24V ± 0.07V, the frequency fluctuation is 50Hz ± 0.2Hz, the total harmonic distortion (THD) of the AC bus voltage is not more than 1.6%, and the power output efficiency reaches 91.6%, achieving good performance. In addition, the system can manually set the power supply output voltage frequency by pressing the key, and display the output voltage and current in real time. At the same time, the system has over-current protection function, which greatly improves the safety and stability when the current is too large.

Coordinated voltage regulation of high renewable-penetrated distribution networks: An evolutionary curriculum-based deep reinforcement learning approach

With the increasing penetration of renewable energy in active distribution networks (ADNs), voltage regulation problem is becoming more and more challenging. In this article, we focus on providing a scalable data-driven approach to ensure the voltage security of ADNs with high penetration of PVs. To this end, we first formulate an optimization problem for real-time voltage control considering source-load-storage collaboration while minimizing the total active power curtailment of PVs. Then, we reformulate the above problem as a Markov game and propose a novel voltage regulation algorithm based on evolutionary curriculum-based multi-agent deep reinforcement learning (EC-MADRL) to solve it. The key idea of the proposed algorithm is to adopt a multi-stage parallel training framework based on attention multi-agent deep deterministic policy gradient algorithm (Attention-MADDPG) and use an evolutionary mechanism to select voltage regulation models with high fitnesses in the previous stage, which means that good agents with best adaptability could be utilized for learning in the environment with a larger number of PVs. Simulation results show the effectiveness and scalability of the proposed algorithm.

Coordinated volt/VAR control for photovoltaic inverters: A soft actor-critic enhanced droop control approach

The reactive power capacity of photovoltaic inverters can be utilised to minimise power loss and mitigate rapid voltage fluctuations in an active distribution network. This paper proposes a coordinated volt/VAR control framework that simultaneously optimises the base reactive power output of photovoltaic inverters and the voltage intercept of each droop control function in the central hierarchy based on whole-system information to minimise the power loss. The photovoltaic inverters use the optimised droop function to eliminate the voltage violation based on the real-time local information in the local hierarchy. Subsequently, the optimisation problem is formed as a Markov decision process and solved using an improved deep reinforcement learning algorithm called a soft actor-critic to achieve fast control with full consideration of uncertainties. Thus, the proposed method can simultaneously guarantee the cooperation and rapidity of the control performance to minimise power loss and quickly respond to real-time voltage fluctuations. Case studies were implemented using IEEE 33-bus and 123-bus distribution networks. The comparison results demonstrate that the proposed method can reduce the power loss and voltage violation rate by up to 12.3% and 2.71%, respectively, compared with other benchmark methods, which demonstrates the superiority of the proposed method in terms of power loss minimisation and voltage fluctuation mitigation.

Data-Driven Thermal Anomaly Detection in Large Battery Packs

The early detection and tracing of anomalous operations in battery packs are critical to improving performance and ensuring safety. This paper presents a data-driven approach for online anomaly detection in battery packs that uses real-time voltage and temperature data from multiple Li-ion battery cells. Mean-based residuals are generated for cell groups and evaluated using Principal Component Analysis. The evaluated residuals are then thresholded using a cumulative sum control chart to detect anomalies. The mild external short circuits associated with cell balancing are detected in the voltage signals and necessitate voltage retraining after balancing. Temperature residuals prove to be critical, enabling anomaly detection of module balancing events within 14 min that are unobservable from the voltage residuals. Statistical testing of the proposed approach is performed on the experimental data from a battery electric locomotive injected with model-based anomalies. The proposed anomaly detection approach has a low false-positive rate and accurately detects and traces the synthetic voltage and temperature anomalies. The performance of the proposed approach compared with direct thresholding of mean-based residuals shows a 56% faster detection time, 42% fewer false negatives, and 60% fewer missed anomalies while maintaining a comparable false-positive rate.

Hybrid Weighted Least Square Multi-Verse Optimizer (WLS–MVO) Framework for Real-Time Estimation of Harmonics in Non-Linear Loads

The electric power quality has become a serious concern for electric utilities and end users owing to its undesirable effects on system capabilities and performance. Harmonic levels on power systems have been pronounced to a greater extent with the continuous growth in the application of solid-state and reactive power compensatory devices. Harmonics are the key constituents that are mainly responsible for power quality deterioration. Power system harmonics need to be correctly estimated and filtered to increase power quality. This research work focuses on accurate estimation of power system harmonics with the proposed hybrid weighted least-square multi-verse optimizer (WLS–MVO) based framework. Multi-verse optimizer replicates the phenomenon of the formation of new universes as described by multi-verse theory to solve complex real-world optimization problems. The proposed WLS–MVO framework is tested and validated by estimating the harmonics present in multiple test signals with different noise levels. Amplitudes and phases of harmonics present in the polluted signal were estimated, and the framework computational time was compared with the previously developed technique’s results which are reported in the literature. There was 80% reduction in computational time and 82% improvement in terms of accuracy in estimating harmonics using WLS–MVO as compared to previously developed techniques. The performance of the developed framework is further validated by estimating the harmonics present in the real-time voltage and current waveforms obtained from axial flux permanent magnet generator (AFPMSG), uninterruptible power supply (UPS), and light-emitting diode (LED). The purposed technique technique outperforms the already-developed techniques, in terms of accuracy and computational time.

Real-time long-term voltage stability assessment based on eGBDT for large-scale power system with high renewables penetration

• A data-driven voltage stability assessment framework is established, which can be performed utilizing observed PMU measurements and is completely independent from system modeling. • Based on the complete datasets, considering the random and uncertainty of renewable energy in multiple scenarios, we developed a refined voltage state classification ( secure, warning and emergency ) for high renewables penetration power systems. • Dramatic speedup by virtue of GOSS and HEEFB techniques enables the traditional GBDT-based learning algorithms to be effective for handling massive high-dimensional data, realizing the real-time assessment of voltage stability. Real-time voltage stability assessment is a critical issue for ensuring the safety of large-scale renewable energy connected power system. In this paper, a data-driven approach based on the enhanced Gradient Boost Decision Tree (eGBDT) for long-term voltage stability assessment from massive high-dimensional PMU measurements was proposed. This approach effectively improves the ability of the traditional GBDT algorithm to handle high-dimensional observed data by utilizing gradient-based one-side sampling (GOSS) and histogram-enhanced exclusive feature bundling (HEEFB) techniques, realizing spatial dimension reduction and feature compression. The proposed method exploits both the accuracy of the GBDT algorithm and the fast computational capacity of GOSS and HEEFB techniques, which effectively reduces the calculation time. Case studies on the Nordic32 system and IEEE 118-bus test system demonstrated the accuracy and the speed of the proposed approach. Extensive studies on the robustness of dealing with missing data illustrate that the proposed method is practically suitable for real-time application.

Real-Time Coordinated Control of a Grid-VSC and ESSs in a DC Distribution System for Total Power Loss Reduction Considering Variable Droop Using Voltage Sensitivities

Recent advances in direct current (DC) distribution systems that involve renewable energy sources (RESs) and energy storage systems (ESSs) have shown that DC voltage control improves operational stability. This paper proposes an efficient real-time DC voltage control strategy that reduces total power losses while maintaining the DC voltages within acceptable ranges via coordination of the grid-connected voltage source converter (GVSC) and the ESSs. A new strategy determines whether to invoke cooperative GVSC/ESS control or ESS voltage control alone. The GVSC and ESSs exhibit proportional-integral (PI) and variable droop controllers, respectively. In particular, the variable droop controllers are developed with consideration of the voltage sensitivities. The ESS outputs are then compensated by reference to the droop coefficients and the available ESS output powers. Case studies confirm that our voltage control strategy permits higher minimum DC voltages (and thus smaller total power losses) when consecutive events occur, compared with conventional strategies using PSCAD/EMTDC.

A State-of-the-Art Review of Smart Energy Systems and Their Management in a Smart Grid Environment

A smart grid (SG), considered as a future electricity grid, utilizes bidirectional electricity and information flow to establish automated and widely distributed power generation. The SG provides a delivery network that has distributed energy sources, real-time asset monitoring, increased power quality, increased stability and reliability, and two-way information sharing. Furthermore, SG provides many advantages, such as demand response, distribution automation, optimized use of electricity, economical energy, real-time grid status monitoring, voltage regulation or VAR control, and electricity storage. In this survey, we explore the literature on smart Grid enabling technologies until 2022. We dig out four major systems: (1) the smart grid’s prominent features and challenges; (2) the smart grid standard system and legislations; (3) smart grid energy subsystem; and (4) the smart grid management system and protection system for new researchers for their future projects. The research challenges and future recommendations are also presented in the conclusion section to explore the new paradigm.

On-line Monitoring of Instantaneous Over-voltage Intelligent Monitoring of EMUs

Since high-speed EMUs have high requirements for the reliability of the traction power supply system and the safety of train operation, the online monitoring and analysis of transient over-voltage are of great significance. By analyzing and comparing the characteristics of the existing power grid over-voltage online monitoring devices at home and abroad, and due to the difficulty in detecting the power grid voltage on the high-speed train, a new type of over-voltage online monitoring using the broadband transmission characteristics of the equipment is proposed for the high-speed train. The principle of the scheme is to reconstruct the real-time voltage waveform of the primary side of the transformer by convolving the secondary side voltage waveform of the vehicle-mounted voltage transformer through the fast Fourier transform with the transformer-specific transmission parameter model and to correlate the relevant data, such as power, voltage fluctuation, harmonic distortion, etc. Simultaneously, simulation and experiments are carried out to verify the validity and stability of the scheme algorithm. The experimental results show that the system has good response characteristics, high measurement accuracy, and stable and reliable working conditions. The system achieves the expected design goals, and it has been successfully applied to the online monitoring equipment of EMU power quality. While monitoring the over-voltage of the power grid, this system provides data support for the research on the aging and life of the onboard high-voltage electrical equipment.

A Genetic Algorithm Approach to Optimal Sizing and Placement of Distributed Generation on Nigerian Radial Feeders

Mitigating power loss and voltage profile problems on radial distribution networks has been a major challenge to distribution system operators. While deployment of distributed generation, as compensators, has made a suitable solution option, optimum placement and sizing of the compensators has been a concern and it has thus been receiving great attention. Meta-heuristic algorithms have been found efficacious in this respect, yet the use of the algorithms in addressing problems of radial feeders is still comparatively low in Nigeria where analytical and numerical programming methods are common. Hence; the use of genetic algorithm to site and size distributed generator for real-time power loss reduction and voltage profile improvement on the Nigerian secondary distribution networks is presented. Backward-forward sweep load flow analysis, together with loss sensitivity factor, is deployed to identify the buses suitable for the installation of the distributed generation, while the algorithm is employed in estimating the optimum size. This approach is tested on the standard IEEE 15-bus system and validated using a Nigerian 11 kV feeder. The result obtained on the IEEE test system shows 183 kW loss using the compensator, as compared to 436 kW loss without the compensator; while on the Nigerian network the loss with the compensator was 4.99 kW, in comparison with no-compensation loss of 10.47kW. By the approach of this study, real power loss on the Nigerian feeder decreased by 52.3% together with energy cost reduction from N658,789.12 to N314,227.38. Likewise the minimum bus voltage magnitude and the voltage stability index of the network are improved to acceptable limits. This approach is therefore recommended as capable of strengthening the performance of the Nigerian radial distribution system.

Dynamic modeling and disturbance rejection compensation for hysteresis nonlinearity of high voltage piezoelectric stack actuators

High voltage piezoelectric stack actuators (HVPSA) are widely used in the field of active vibration control of engineering structures due to their strong load capacity, fast response rate, and high mechanical output efficiency. However, their inherent hysteresis will have a direct impact on the stability and control efficiency of the piezoelectric active control system. To compensate the hysteresis nonlinearity of HVPSA, a high-precision dynamic hybrid method based on linear active disturbance rejection control (LADRC) is proposed. Starting from the hysteresis analysis of piezoelectric actuators, an exponential function butterfly-shape hysteresis operator is constructed and combined with the asymmetric Bouc–Wen model to present a novel hybrid static hysteresis model. In order to implement rate-dependent hysteresis modeling, the Hammerstein rate-dependent hysteresis model of HVPSA is further established, and its inverse model is built for feedforward compensation. Subsequently, the LADRC is used to adjust the driving voltage in real time to form the hysteresis closed-loop compensator of HVPSA. The experimental results show that the Hammerstein rate-dependent hysteresis model established has satisfactory modeling accuracy in the working voltage range and frequency band under consideration. Furthermore, compared with the traditional inverse model feedforward compensation strategy, the proposed hybrid compensation method based on LADRC improves the compensation efficiency by more than 11% and reduces the hysteresis nonlinearity of HVPSA to less than 3%, with strong anti-disturbance ability.

Great new generation carbon microsphere-based composites: Facile synthesis, properties and their application in piezo-electric energy harvesting

In this work, new generation carbon microspheres (CMS) based rubber composites have been obtained for flexible electronics applications such as stretchable piezo-electric energy harvesting devices. The rubber matrix used was room-temperature-vulcanized silicone rubber (RTV-SR). Herein, the CMS was exploited as a source of reinforcement and conductive material for improving mechanical and electrical properties. For example, the compressive modulus was 2.1 MPa (virgin) and increased to as high as 3.3 MPa (5 phr CMS). Similarly, the Shore A hardness was 26 (virgin) and increased to 40 (5 phr CMS). The electrical properties were also influenced significantly with electro-chemical resistance of 22.05 kΩ (virgin) and decreased to 15.91 kΩ (5 phr CMS). CMS's XPS spectrum revealed a high carbon-to-oxygen atomic ratio (74.47 and 25.53 %). The carbon and oxygen-containing functional groups of CMS acted as a catalyst to improve interfacial interaction of the composites thereby leading to higher mechanical properties. These improved properties were explored in the real-time device and output voltage generation with high durability was tested. From measurements, it was found that the output voltage from the piezo-electric energy harvesting device was around 1.25 V and high durability of 0.5 million cycles was reported.

Calcium Titanate Orthorhombic Perovskite-Nickel Oxide Solar-Blind UVC Photodetectors with Unprecedented Long-Term Stability Exceeding 500 Days and Their Applications to Real-Time Flame Detection

In recent years, the rapid increase in fire frequency has led to demands for efficient fire monitoring systems. To realize sensitive and large-area fire monitoring, flame sensing based on an ultraviolet C (UVC) photodetector can be considered as a promising approach. However, the challenges such as the high-cost process, limited selection of photoactive materials with an appropriate band gap, inefficient power consumption, stability issues, and environmental noise interference restrict the development of UVC photodetectors (PDs) for practical flame safeguard applications. Here, we demonstrate flame detection with a UVC PD based on an ultrawide band gap calcium titanate (CTO) and nickel oxide (NiO) heterostructure. The proposed PD achieves a high solar-blinded rejection ratio (at UVC light/UVA light) of 2.9 × 104, on/off switching current ratio of 120 A/A under UVC light and dark state, robust and stable operation longer than 1 year (502 days), and specific detectivity as high as 4.44 × 1011 Jones under zero-voltage bias operation. During the flame combustion, the CTO/NiO UVC PD exhibits a systematic real-time output voltage change with flame intensity variation, which opens up new possibilities for rapid and accurate fire detection.