Power Measurement Data Research Articles

Application Research of Power Measurement Data Exchange Based on Uninterruptible Power Supply

With the maturity of the smart meter embedded system integration mode, the power consumption information can be fed back to the management center by the intelligent terminal through the power Internet of Things. The smart terminal adopts the narrowband data communication mode. However, the narrowband channel interval is hindered by interference signals, resulting in serious data packet loss. Therefore, a stable and efficient acquisition scheme is needed to ensure its smooth interaction. Based on the application of smart meter data communication, we analyze the power consumption optimization and carrier transmission optimization of the power Internet of Things and propose a data analysis method based on self-learning and external characteristics, which is used to calculate the offline execution strategy of smart meters when they encounter factors such as power outage or intrusion. A production task-driving rule is designed. The external characteristics of deep learning data of the power Internet of Things are collected. The accuracy of data classification is judged according to the parallel Internet of Things of a convolutional neural network and a long-term and short-term memory deep neural network. When dealing with high-dimensional original data, the data network is graded to reduce the work of feature selection and the difficulty of training. Moreover, we improve the autonomous execution ability of an embedded terminal device in an offline state. We also strengthen the ability of abnormal data detection and judgment learning to meet the requirements of efficient and stable operation of the power grid. Simulation results verify that compared with the current mainstream schemes, the performance of the proposed scheme is improved by more than 17%. Besides, the fault tolerance rate is enhanced by 29%, and the power consumption after optimization is reduced by 26.3% and 42.8%. These effects highlight the role of data processing in embedded systems. It solves the problem of abnormal data and energy loss affecting the accuracy of error estimation of electric energy meters, opens up a new way for data acquisition and processing of large-scale smart meters, and further improves the service level of information management of power systems.

The data recovery strategy on machine learning against false data injection attacks in power cyber physical systems

During the transmission of power measurement data through communication networks from remote terminal unit (RTU) to the state estimator in Supervisory Control and Data Acquisition (SCADA), power cyber-physical systems (PCPSs) are more susceptible to cyber-attacks. To mitigate that threat, this paper is concerned with a new data recovery strategy on machine learning against false data injection attacks (FDIAs) in PCPSs. Firstly, in view of the limited resources (such as limited energy) of adversaries and system protections, a sparse target false data injection attack (FDIA) is constructed. Then, the FDIA detection problem is transformed into a tripartite separation problem, and the alternating direction method of multipliers on proximal exchange (ADMM-PE) is adopted to complete the intrusion detection of FDIAs. In addition, with the help of reliable mask information and real incomplete measurement data provided by the FDIA detection, a similar supervised generative adversarial imputation networks (GAIN) is proposed to complete the measurement data recovery after FDIAs. Specifically, the pseudo labels generated by data analysis methods such as k-means clustering and support vector machine (SVM) to improve the accuracy of measurement data recovery. Finally, the experimental results of PCPSs show the effectiveness and superiority of the proposed data recovery strategy against FDIAs.

Consortium Blockchain-based Measurement Data Trusted Sharing and Interaction in New Power System

The trusted sharing and interaction of power measurement data are crucial in developing new-type power systems. However, there are some problems, such as complex interaction environments and data privacy leaks in the flow process of electric measurement data. We first design a measurement data sharing and interaction information uplink storage scheme based on consortium blockchain certificate storage. Then, a credibility-based blockchain consensus mechanism is designed to reduce the burden of blockchain storage. The experiment shows that the proposed method performs better in sharing power measurement data.

Thermal design of a system for mobile hydrogen powersupply

Ever more stringent emission regulations for vehicles encourage increasing numbers of battery electric vehicles on the roads. A drawback of storing electric energy in a battery is the comparable low energy density, low driving range and the higher propensity to deplete the energy storage before reaching the destination, especially at low ambient temperatures. When the battery is depleted, stranded vehicles can either be towed or recharged with a mobile recharging station. Several technologies of mobile recharging stations already exist, however, most of them use fossil fuels to recharge battery electric vehicles. The proposed novel zero emission solution for mobile charging is a combined high voltage battery and hydrogen fuel cell charging station. Due to the thermal characteristics of the fuel cell and high voltage battery (which allow only comparable low coolant temperatures), the thermal design for this specific application (available heat exchanger area, zero vehicle speed, air flow direction) becomes challenging and is addressed in this work. Experimental methods were used to obtain reliable thermal and electric power measurement data of a 30 kW fuel cell system, which is used in the Mobile Hydrogen Powersupply. Subsequently, simulation methods were applied for the thermal design and optimisation of the coolant circuits and heat exchangers. It is shown that an battery electric vehicle charging power of 22 kW requires a heat exchanger area of 1 m2, of which 60 % is used by the fuel cell heat exchanger and the remainder by the battery heat exchanger to achieve steady state operation at the highest possible ambient temperature of 43,6 °C. Furthermore, the simulation showed that when the charging power of 22 kW is solely provided by the high voltage battery, the highest possible ambient temperature is 42 °C. When the charging power is decreased, operation up to the maximum ambient temperatures of 45 °C can be achieved. The results of maximum charging power and limiting ambient temperature give insights for further system improvements, which are: sizing of fuel cell or battery, trailer design and heat exchanger area, operation strategy of the system (power split between high voltage battery and fuel cell) as well as possible dynamic operation scenarios.

Joint Topology and State Estimation Using TTU Monitoring Data Considering Three-Phase Imbalance

A grand challenge for operators to develop optimal controls at the distribution level lies in how to obtain the real-time network topology and operation state in lack of specialized high precision monitoring devices, especially with high penetration of distributed renewable energy. Driven by this motivation, this paper proposes a novel topology and operation state joint estimation framework based on the monitoring data from transformer terminal units (TTUs). In the framework, the bottom layer generates an equivalent network model and nodal voltage and current measurements with synchronized phase angles from the distribution network and the voltage and power measurement data from TTU. The middle layer formulates, linearizes and solves an optimization problem to fit the model with the measurements. A global piecewise linearization method is proposed to convert the original mixed integer quadratically constrained program (MIQCP) problem into a mixed integer linear program (MILP) problem. In the top layer, the line switch states and feeder power flows are estimated with the solution of the optimization problem. Case studies are conducted in test systems to verify the accuracy, efficiency and robustness of the proposed framework and show that the proposed framework yields obvious advantages over the existing approaches on the estimation accuracy when using TTU measurements.

Input Power Measurement System For Driving Motor In Testing Low-Speed Generator

Rapid technological advances are affecting the greater use of electrical energy. One of the devices that can generate electrical energy is a generator. Testing the characteristics of the generator required a drive motor to rotate the generator shaft. This research aims to create a three-phase input power measurement system for driving a motor. The method of measuring input power is by measuring the current and voltage of each phase. The power is obtained from the multiplication between current and voltage. The system consists of current sensors, voltage sensors, a signal conditioning circuit, and an Arduino Mega microcontroller for data processing. The system is equipped with a graphical user interface, data storage, and application. The generator input power measurement system has been created and tested. The measurement system has successfully measured the input power of the generator's driving motor, which in real-time is displayed on the trend graph via the graphical user interface on the laptop. The input power measurement data on the three-phase generator and the time data have been successfully stored inside the micro-SD. The average error of the voltage reading is 2% compared to the measurement of the reference voltmeter. The current reading error was 2% compared to the reference meter ampere measurement.

Non-Aqueous Zn-Ion Hybrid Supercapacitors: Acetonitrile vs Propylene Carbonate Based Electrolyte

The development of high efficiency energy storage systems is increasingly important as these systems enable utilize energy from renewable sources and reduce greenhouse gas evolution caused by fuel combustion technologies at the same time. Electrochemical characteristics of Zn-ion hybrid supercapacitor (ZIHS) cells based on 1 M acetonitrile and propylene carbonate electrolytes in zinc tetrafluoroborate (Zn(BF4)2), zinc di[bis(trifluoromethylsulfonyl)imide] (Zn(TFSI)2) and zinc trifluoromethanesulfonate (Zn(OTf)2) have been studied using cyclic voltammetry, constant current charge/discharge and electrochemical impedance methods. The Ragone plots have been calculated from constant power measurement data. Very high energy and power densities (80 Wh kg−1 and 21.2 kW kg−1) have been calculated for 1 M Zn(BF4)2/AN based Zn-ion hybrid supercapacitor. Some assembled ZIHSs had shown excellent cycling and energy stability over 20000 cycles.

STUDY OF WAVE HEIGHT POTENTIAL FOR DETERMINING THE LOCATION OF WECS IN THE WATERS OF KETAPANG REGENCY BY USING CEDAS SOFTWARE

Ketapang Regency's increasing demand for electrical energy could lead to an energy crisis. According to these issues, the Sungai Kinjil and Kinjil Pesisir coastal communities in the Ketapang Regency have wave potential that can be used as energy for marine wave power plants. As a result, The expertise required to evaluate ocean wave resources and determine the output power of electrical energy produced by ocean waves. CEDAS and ArcGIS software is used for wave modeling, with CEDAS input data in the form of wave forecasting from BMKG wind data, bathymetry, and coastline data obtained from the official Indonesia Geospatial website, and primary data in the form of wave electric power measurement data obtained from Wave energy conversion system prototypes. CEDAS processing results show the highest wave height of 0,998 meters at a depth of 1,3 meters. The potential for electrical energy at the research site is 1,908–4,512 kw/m2.

A study on the energy saving of lighting through the development of integrated control of multifunctional shading system

Recently, interest in integrated shading and lighting control, which are building components, is increasing to reduce the energy used in buildings. However, the multi-functional shading system, which has recently been distributed, has a natural lighting function through ceiling reflection by introducing sunlight into the room, but it is difficult to control interworking with surrounding electric lighting and integrated control is impossible due to solar reflection or acquisition. In order to solve this problem, we intend to design and implement an integrated control system for multifunctional shading devices that affect the indoor light environment and thermal environment and are effective in reducing lighting energy. This study can reduce energy in buildings by controlling the temperature through an internally integrated control system for the inflow of sunlight into the room. In addition, integrated monitoring control is possible through real-time power and energy measurement and on-site sensor data management. As a result of this study, 34.5% of lighting energy could be saved through the study of the automated system of integrated control of multifunctional shading devices.

A New AC False Data Injection Attack Method Without Network Information

Cyberattack emerges as a critical concern in Internet-of-Things integrated power systems. Cybersecurity capabilities of power systems rely on a deep understanding of potential cyberattacks for identifying and protecting against cyber vulnerabilities. The common assumption of system network information in most existing works is suspicious, which fails to hold in practical fields. This paper proposes a novel data-driven false data injection attack method, where only easily accessible measurement data are required, i.e., power injections at buses, flows of lines and the tie line information (connection relationship and power measurement data). In the proposed method, generative adversarial network (GAN) is adopted to extract the physical model using historical measurement data, and a self-attention mechanism is integrated to further capture the power flow laws in the data. After offline training, the effective false data can be constructed in a timely fashion without system network information. The effectiveness of the proposed attack method is validated using the IEEE14 and IEEE118 systems, in which the constructed false data injection attack can evade the system residual detection with an average success rate over 90% under different levels of measuring errors.

Methodology for Security Analysis of Grid- Connected Electric Vehicle Charging Station With Wind Generating Resources

The project Carbon-Free Island Jeju by 2030 promoted by the Republic of Korea aims to expand the renewable energy sources centered on wind power in Jeju Island and supply electric vehicles for eco-friendly mobility. However, the increased penetration rate of electric vehicles and expansion of variable renewable energy sources can accelerate the power demand and uncertainty in the power generation output. In this paper, power system analysis is performed through electric vehicle charging demand and wind power outputs prediction, and an electric vehicle charging decentralization algorithm is proposed to mitigate system congestion. In order to predict electric vehicle charging demand, the measurement data were analyzed, and random sampling was performed by applying the weight of charging frequency for each season and time. In addition, wind power outputs prediction was performed using the ARIMAX model. Input variables are wind power measurement data and additional explanatory variables (wind speed). Wind power outputs prediction error (absolute average error) is about 9.6%, which means that the prediction accuracy of the proposed algorithm is high. A practical power system analysis was performed for the scenario in which electric vehicle charging is expected to be higher than the wind power generation due to the concentration of electric vehicle charging. The proposed algorithm can be used to analyze power system problems that may occur due to the concentration of electric vehicle charging demand in the future, and to prepare a method for decentralizing electric vehicle charging demand to establish a stable power system operation plan.

2Deep: Enhancing Side-Channel Attacks on Lattice-Based Key-Exchange via 2-D Deep Learning

Advancements in quantum computing present a security threat to classical cryptography algorithms. Lattice-based key exchange protocols show strong promise due to their resistance to theoretical quantum-cryptanalysis and low implementation overhead. By contrast, their physical implementations have shown vulnerability against side-channel attacks (SCAs) even with a single power measurement. The state-of-the-art SCAs are, however, limited to simple, sequentialized executions of post-quantum key-exchange (PQKE) protocols, leaving the vulnerability of complex, parallelized architectures unknown. This article proposes 2Deep-a deep-learning (DL)-based SCA-targeting parallelized implementations of PQKE protocols, namely, Frodo and NewHope with data augmentation techniques. Specifically, we explore approaches that convert 1-D time-series power measurement data into 2-D images to formulate SCA an image recognition task. The results show our attack's superiority over conventional techniques including horizontal differential power analysis (DPA), template attacks (TAs), and straightforward DL approaches. We demonstrate improvements up to 1.5× to recover a 100% success rate compared to DL with 1-D input data while using fewer data. We furthermore show that machine learning improves the results up to 1.25× compared to TAs. Furthermore, we perform cross-device attacks that obtain profiles from a single device, which has never been explored. Our 2-D approach is especially favored in this setting, improving the success rate of attacking Frodo from 20% to 99% compared to the 1-D approach. Our work thus urges countermeasures even on parallel architectures and single-trace attacks.

Research on False Data Injection Attack Detection of S mart Grid Based on Machine Learning

The safe and reliable operation of power systems is an important guarantee for the healthy development of the national economy. Industry and people’s lives are inseparable from electricity, so the safety and reliability of electricity supply is very important. The sudden interruption of power supply will not only bring serious economic losses, but also seriously affect people’s normal lives and even endanger social stability. False data injection attack (FDIAs) are a new type of power system network attack method. FDIAs are a new type of power system network attack method. It can successfully bypass the bad data detection mechanism, offset the power measurement data, and mislead the control center under extremely subtle conditions. Therefore, it poses a very serious threat to the stable operation of the power system. Therefore, this article first analyzes the principle of false data injection attacks, in order to provide a theoretical basis for subsequent attack detection. Then this paper constructs a detection method based on extreme learning from the perspective of optimizing learning efficiency. Based on the IEEE14-bus standard test system, the method is verified through simulation, which shows the feasibility of this method, which provides a direction for building a safe and stable smart grid.

Perbandingan Perolehan Daya Solar Panel Monocrystalline Terhadap Solar Panel Polycrystalline

The availability of two types of solar panels that are common in the market namely monocrystalline and polycrystalline types cause confusion in the selection so that many solar panel users are questioning the differences of these two types of solar panel. This study produced a data logger system using Arduino Uno R3 to control voltage, current and temperature sensors for logging data that stores power measurement data from monocrystalline and polycrystalline solar panel in a micro SD. After it we can manage data to compare power produced between two types the solar panel. From the results of testing this data logger system it can be seen that monocrystalline solar panel are 9.18% better on power produced than polycrystalline when the maximum power conversion is generated.

Unsupervised algorithm for disaggregating low-sampling-rate electricity consumption of households

Non-intrusive load monitoring (NILM) has been extensively researched over the last decade. The objective of NILM is to identify the power consumption of individual appliances and to detect when particular devices are on or off from measuring the power consumption of an entire house. This information allows households to receive customized advice on how to better manage their electrical consumption. In this paper, we present an alternative NILM method that breaks down the aggregated power signal into categories of appliances. The ultimate goal is to use this approach for demand-side management to estimate potential flexibility within the electricity consumption of households. Our method is implemented as an algorithm combining NILM and load profile simulation. This algorithm, based on a Markov model, allocates an activity chain to each inhabitant of the household, deduces from the whole-house power measurement and statistical data the appliance usage, generate the power profile accordingly and finally returns the share of energy consumed by each appliance category over time. To analyze its performance, the algorithm was benchmarked against several state-of-the-art NILM algorithms and tested on three public datasets. The proposed algorithm is unsupervised; hence it does not require any labeled data, which are expensive to acquire. Although better performance is shown for the supervised algorithms, our proposed unsupervised algorithm achieves a similar range of uncertainty while saving on the cost of acquiring labeled data. Additionally, our method requires lower computational power compared to most of the tested NILM algorithms. It was designed for low-sampling-rate power measurement (every 15 min), which corresponds to the frequency range of most common smart meters.

Analysis of the BEAVRS PWR benchmark using SCALE and PARCS

Abstract This paper presents an analysis of the Benchmark for Evaluation And Validation of Reactor Simulations (BEAVRS) performed using SCALE 6.1.2 and PARCS 3.2 computer codes. The benchmark specification contains a detailed design, operational data and measurements for a real 4-loop Westinghouse pressurized water reactor (PWR). The lattice physics simulations were prepared using TRITON depletion sequence and NEWT neutron transport solver (SCALE package). The 238-neutron group library based on evaluated nuclear data file – ENDF/B-VII nuclear data libraries was applied. A set of branch and burnup calculations was prepared, and group constants in the form of PMAXS files were generated with GenPMAXS. The full-core models were prepared using the PARCS nodal-diffusion core simulator. The PMAXS libraries were used with PARCS to investigate the core operation. The hot zero power measurement data, including control rod worths and critical boron concentrations, were compared using simulations, and satisfactory results were achieved. The first fuel cycle was simulated, and acceptable agreement with boron letdown curve and measurements were obtained. Finally, conclusions and recommendations for future research were presented.

Novel stochastic methods to predict short-term solar radiation and photovoltaic power

Solar forecasting has evolved towards becoming a key component of economical realization of high penetration levels of photovoltaic (PV) systems. This paper presents two novel stochastic forecasting models for solar PV by utilizing historical measurement data to outline a short-term high-resolution probabilistic behavior of solar. First, an uncertain basis functions method is used to forecast both solar radiation and PV power. Three possible distributions are considered for the uncertain basis functions - Gaussian, Laplace, and Uniform distributions. Second, stochastic state-space models are applied to characterize the behaviors of solar radiation and PV power output. A filter-based expectation-maximization and Kalman filtering mechanism is employed to recursively estimate the system parameters and state variables. This enables the system to accurately forecast small as well as large fluctuations of the solar signals. The introduced forecasting models are suitable for real-time tertiary dispatch controllers and optimal power controllers. The PV forecasting models are tested using solar radiation and PV power measurement data collected from a 13.5 kW PV panel installed on the rooftop of our laboratory. The results are compared with standard time series forecasting mechanisms and show a substantial improvement in the forecasting accuracy of the total energy produced.

A Solar Time Based Analog Ensemble Method for Regional Solar Power Forecasting

This paper presents a new analog ensemble method for day-ahead regional photovoltaic (PV) power forecasting with hourly resolution. By utilizing open weather forecast and power measurement data, this prediction method is processed within a set of historical data with similar meteorological data (temperature and irradiance), and astronomical date (solar time and earth declination angle). Furthermore, clustering and blending strategies are applied to improve its accuracy in regional PV forecasting. The robustness of the proposed method is demonstrated with three different numerical weather prediction models, the North American mesoscale forecast system, the global forecast system, and the short-range ensemble forecast, for both region level and single site level PV forecasts. Using real measured data, the new forecasting approach is applied to the load zone in Southeastern Massachusetts as a case study. The normalized root mean square error has been reduced by 13.80% to 61.21% when compared with three tested baselines.

Evaluation of Advanced Data Centre Power Management Strategies

In recent work, we proposed a new specification language for power management strategies as an extension to our AnyLogic-based simulation framework for the trade-off analysis of power and performance in data centres. In this paper, we study the quality of such advanced power management strategies based on both power and performance measurement data collected during system operation. These strategies take a wide variety of state variables into account. In order to ensure the quality of new strategies, they are studied for stability, efficiency, adaptability and robustness; these qualities will be formally defined. This paper presents an evaluation approach for these qualities for several power management strategies inspired by strategies presented in the literature (and extensions thereof). We show that the choice of power management strategy depends both on which qualities are given the highest priority and on the used state information. The new power management strategies show significant reductions in energy consumption in our case of up to 54% energy (compared to an “always on” strategy) for a typical data centre workload for a small 30-server cluster.

Photovoltaic systems coupled with batteries that are optimally sized for household self-consumption: Assessment of peak shaving potential

As the share of renewable energy sources in the energy mix is increasing, new challenges arise regarding the grid integration. This research focuses on a solution for one of these challenges, namely the employment of batteries to address the mismatch in electrical power between electricity supply from photovoltaic systems and household electricity demand. Herein, the optimal sizing of batteries for household self-consumption is combined with peak shaving at district level, whereas previous studies only looked at these questions in isolation. Our analysis makes use of a unique set of power measurement data from 79 households in the Dutch city of Amersfoort, in 295 evenly distributed days, with a resolution of 10 s. By using simulation of batteries and Net Present Value analysis, the average optimal storage size for self-consumption in the case of net metering abolishment for households with photovoltaic systems was determined to be 3.4 kWh. Large differences were observed between different households; photovoltaic system size, total net metered consumption and specific characteristics of load profiles resulted into optimal storage sizes in the range of 0.5–9 kWh. The impact of these optimally sized batteries on neighborhood peak demand was assessed and found to be limited, corresponding to a decrease of 5.7%. The peak shaving potential was further assessed under different control strategies of the batteries. Results show that the impact could be amplified to a decrease of 22% or 51% when the batteries are controlled by using heuristics or by assuming perfect foresight together with a power minimization algorithm, respectively. The findings of this paper emphasize the importance of collaboration between households and other stakeholders, such as distributed system operators and retailers in transitioning to a sustainable power system.