Active Power Measurement Research Articles

Determination of Generator Efficiency on Performance Test of Prototype Turbine

As an essential parameter to be determined on performance test of prototype turbine, the output power of turbine is generally determined by measured active power and efficiency of generator. Determination method of generator efficiency on turbine performance test is discussed in this paper. A determination method is presented as generator efficiency could be determined by actual generator losses. Analysis results show that it is a better solution rather than reading from the generator efficiency curve. Furthermore, due to the location of active power measurement, excitation loss must be excluded from the generator losses in determining generator efficiency. And the copper losses of rotor and stator must be converted to the actual measured temperature on turbine performance test. Then correct generator efficiency and output power of turbine are determined.

Advanced metering infrastructure smart metering based on cloud architecture for low voltage distribution networks in application of smart grid monitoring

In most circumstances, accessing measurements from entire smart metres set in less-voltage grid area for distribution monitoring is not practicable because of less bandwidth as well as excessive delays in accessing Smart Meter monitoring. Estimating the condition of a distribution system can be done using voltage, active and reactive power measurements from smart metres subset. On one hand, increasing number of selected smart metres improves distribution state assessment accuracy, it degrades monitoring data time. It improves the electrical system's dependability and efficiency while also providing security to the system. Smart grid infrastructure is a modified form of smart grid. Smart grid infrastructure is a modified form of the smart grid. It has high-capacity converters, sensing and metering technologies, and automated control to improve efficiency and dependability. The goal of this article is to use smart metering technology to improve the observer of low-voltage distribution grids. The use of a distribution network-based state estimation technique employing smart metre readings for near-real-time monitoring of low-voltage distribution grids is the focus of this research. This paper propose smart metering based on cloud architecture for low voltage distribution networks with distribution network based state estimation algorithm in application of monitoring. The article will discuss the critical role of cloud solution in achieving interoperability, scalability and flexibility as well as enabling integration of various services for distribution system automation. As an example of coordinated operation of diverse distribution grid services by cloud, distributed state estimation technique as well as autonomous network reconfiguration will be demonstrated.

Optimal Placement of Measuring Devices for Distribution System State Estimation Using Dragonfly Algorithm

This paper presents the challenges of optimal measurement devices placement (MDP) in the distribution system by considering the improvement of accuracy and speed for state estimation (SE) in the presence of distributed generations (DGs). We assumed that active and reactive power measurements (both injection and flow) with voltage magnitude measurements were used to estimate the power system’s state. The paper employed phase measurement unit (PMU) and smart meters, which are the two commonly used measuring devices. For numerical evaluation of the system, the power system states are based on the angle and magnitude of voltages at every bus. The issues normally experienced in the optimal measurement devices placement in distribution networks were investigated using the binary dragonfly algorithm (BDA), in this study. As a way forward to proffer solutions to these issues, a fair compromise between accuracy, speed, and the number of measurements (NoMs) was reached, and the proposed solution was tested in two different scenarios applied in the IEEE 33-bus distribution test system. The results illustrate that by increasing the accuracy, NoMs and the cost are going to rise as well. On the other hand, by escalating the speed, NoMs decrease and the accuracy falls dramatically.

A Novel Denoising Auto-Encoder-Based Approach for Non-Intrusive Residential Load Monitoring

Mounting concerns pertaining to energy efficiency have led to the research of load monitoring. By Non-Intrusive Load Monitoring (NILM), detailed information regarding the electric energy consumed by each appliance per day or per hour can be formed. The accuracy of the previous residential load monitoring approach relies heavily on the data acquisition frequency of the energy meters. It brings high overall cost issues, and furthermore, the differentiating algorithm becomes much more complicated. Based on this, we proposed a novel non-Intrusive residential load disaggregation method that only depends on the regular data acquisition speed of active power measurements. Additionally, this approach brings some novelties to the traditionally used denoising Auto-Encoder (dAE), i.e., the reconfiguration of the overlapping parts of the sliding windows. The median filter is used for the data processing of the overlapping window. Two datasets, i.e., the Reference Energy Disaggregation Dataset (REDD) and TraceBase, are used for test and validation. By numerical testing of the real residential data, it proves that the proposed method is superior to the traditional Factorial Hidden Markov Model (FHMM)-based approach. Furthermore, the proposed method can be used for energy data, disaggregation disregarding the brand and model of each appliance.

Modelling of Large-scale Grid-connected PV System with Reactive Power Control Methods

This paper describes detailed scrutiny of the main components contained in the system. There is a DC-DC converter that extracts the maximum amount from the photovoltaic generator. The interface inverter was developed to transfer energy from the PV module into the grid with constant common dc voltage. A 90MW PV system with a 3-phased framework linked is planned and reviewed. This system is a high-fidelity exemplary with power control in the inverters, DC-DC converters, DC-AC inverters, and three-phase service grids used as components designed and formed mathematically as a reproduction model. The model contains various components such as the boost converter, the PV control modes, the inverter, and the framework utility. The essence of the reactive power generation in the PV inverter is represented. The components are modelled physically to generate high-fidelity simulation. Active and reactive power measurements are accurately taken and analysed. The study of the PQ controller is presented in the framework connection control of the system. The simulation results obtained in the modelling process show an effective power-controlled model.

Distributed fixed-time secondary control of an islanded microgrid via distributed event-triggered mechanism

The fixed-time secondary control problem for an islanded microgrid (MG) through an event-triggered communication mechanism is investigated in this paper. A distributed event-triggered fixed-time control strategy is proposed to achieve frequency and voltage secondary restoration. And a proportional active power-sharing is also achieved through an undirected topology. The given control protocol using frequency, voltage magnitude and active power measurement values of neighboring distributed generations only when the predefined event-triggered condition is satisfied. In comparison with the periodic communication way, the presented method exhibits excellent performance in alleviating communication burden and promoting control efficiency. Moreover, compared with conventional asymptotic control algorithms, the objectives of islanded MG secondary control are obtained within a fixed settling time by using the presented fixed-time control approach. The estimation of settling time is unrelated to any initial states. Several simulation experiments are conducted to demonstrate the effectiveness of the proposed control scheme.

Semi-Contactless Power Measurement Method for Single-Phase Enclosed Two-Wire Residential Entrance Lines

This paper presents a semi-contactless power measurement method aimed at offering power measurements of residential homes, which are power supplied by the single-phase two-wire entrance lines, for studying non-intrusive load monitoring (NILM) technique. The active power is calculated using the waveforms of the line current and voltage. To measure the line current, a magnetic sensor array consisting of seven tunnel magnetoresistance (TMR) sensors are utilized to detect the magnetic fields around the entrance lines. The single-phase time-domain current is then reversely calculated on real time by matrix multiplication using the time-domain output signals of the TMR sensor array once the entrance line positions are identified through solving the formulated nonlinear least square (NLLS) problem. In terms of the line voltage, a capacitively coupling voltage sensor is proposed to achieve almost in-phase tracking with the line voltage waveform. The voltage ratio of the voltage senor is calibrated using the measured RMS value of line voltage from the outlet sockets or power strips in-situ by a multimeter (thus called semi-contactless) due to the main disadvantage for capacitively coupling voltage sensing. That is, accuracy in voltage amplitude measurement is low because of the great difficulty in accurately determining the coupling capacitances. Numerical integration method for calculating the active power based on quasi-synchronous algorithm is proposed to eliminate the errors caused by the non-integer period sampling. Prototype instrument is designed and fabricated. Frequency response characterization shows that the TMR sensor (voltage sensor) has an almost constant gain factor (voltage ratio) and an approximate zero-phase shift (< 1°) over a frequency range of 50 Hz - 3.05 kHz. Laboratory tests for both fundamental current phasor and active power measurement along with their measurement uncertainty evaluations are first conducted to verify the feasibility of the proposed method. Field tests in a residential home are subsequently performed to further demonstrate the effectiveness of the proposed method. A power measurement error of less than 5% can be obtained, which is sufficient for home energy tracking.

Detailed Primary and Secondary Distribution System Model Enhancement Using AMI Data

Reliable and accurate distribution system modeling, including the secondary network, is essential in examining distribution system performance with high penetration of distributed energy resources (DERs). This paper presents a highly automated, novel method to enhance the accuracy of utility distribution feeder models to capture their performance by matching simulation results with corresponding field measurements. The method is demonstrated using an actual feeder from an electrical utility with high penetration of DERs. The method proposed uses advanced metering infrastructure (AMI) voltage and derived active power measurements at the customer level, and data acquisition systems (DAS) measurements at the feeder-head, in conjunction with an AC optimal power flow (ACOPF) to estimate customer active and reactive power consumption over a time horizon, while accounting for unmetered loads. The ACOPF uses the measured voltage magnitudes, derived active power measurements, and the feeder head measurements to obtain a complete active power and reactive power capture of the feeder loads. Additionally, the method proposed estimates both voltage magnitude and angle for each phase at the unbalanced distribution substation. The accuracy of the method developed is verified in two stages: by comparing the time-series power flow results obtained from the enhancement algorithm with OpenDSS results and with the field measurements available. The proposed approach seamlessly manages the data available from the optimization procedure through the final model verification automatically.

Evaluation of active power in dielectric barrier discharge with pulse power supply

The paper describes a practical method for real-time measurement of active power in a dielectric barrier discharge by calculating a Lissajous figures based on the Manley formula. Such a method is very useful for controlling the output ozone concentration in ozone generators taking into account the active power in a DBD reactor. A dielectric barrier discharge (DBD) in atmospheric pressure air was excited by a high-voltage pulse power supply. The active power of the reactor was estimated from the area of the Lissajous figure formed by the V-Q traces, which is directly proportional to the energy consumed per cycle at voltages of 8 and 12 kV, respectively.

Analysis of Errors in Active Power and Energy Measurements Under Random Harmonic Distortion Conditions

Abstract As harmonic distortion of voltage and current is reality in the power system, the need for accurate measurement of electrical power and energy goes beyond the instruments’ specifications and calibration procedures regarding pure sine wave signals. Several international standards and recommendations provide test signals for examination of electricity meters under non-sinusoidal conditions, however, not all of the test signal parameters’ possible states are faithfully represented in those documents. Because the high order harmonics may possess random amplitudes and phase shifts in relation to components at fundamental frequency, it is important that the meter’s performance is verified with random waveforms as well. The non-linear dependence between the measured power/energy and the phase shifts, both between fundamental and harmonic components, provides additional complexity of such an analysis. Simple test signals, which are in accordance with the standards’ demands and propositions, are used for determination of the measurement error in case of different harmonic distortion parameter change. In order for a general error function for any measurement device to be determined, mathematical modelling, regarding the results from multiple tests, is performed. The mathematical model presents a strong dependence between a single component’s phase shifts and a meter’s error and it provides a systematization of all signal parameters’ influence on the measurement accuracy.

Research on algorithm of active power measurement on synchronous coordinate system

In the electrical system, determining the value of current, voltage are a very important role. They may serve the purpose of measuring or protecting electrical equipment. This paper researches active power measurement based on transforming voltage and current vectors from static frames into a synchronous rotating frame of reference. The sinusoidal values are replaced by direct current (DC) on the reference system, so the determination of the active power becomes simpler than the static reference system. The paper performed simulations and experimented on a simple physical model.

ANALYSIS OF ELECTRICAL LOAD SCHEDULES OF 10/0.4 KV TRANSFORMER SUBSTATIONS OF RESIDENTIAL AREAS FOR THE IDENTIFICATION OF STATIONARITY PLOTS

It is proposed on the electrical load graphs of transformer substations 10/0.4 kV in residential areas to allocate the stationarity areas for further modelling of load schedules and the implementation of controlling influences on the modes of consumers-regulators in order to align the overall graph of the electrical load. The relevance and complexity of the problem under consideration is caused by the fact that the load variation of transformer substations 10/0.4 kV in residential areas occurs randomly. This is due to the significant number, nomenclature and diversity of types of connected consumers, as well as the lack of deterministic connections between consumers of electricity, in addition, the random load function in the daily interval is non-stationary. In this regard, there was a need to develop the stages of selecting the areas of stationarity on the electrical load graphs of transformer substations 10/0.4 kV of residential areas. A measurement of the load graphs of 10/0,4 kV transformer substations is carried out, according to the results of which the distribution law of active and reactive power measurements is investigated. After confirming the hypothesis of normal distribution law, parametric tests are performed. Fisher's F-criterion is used to confirm the hypothesis of a constant variance, and Student's t-criterion is used to confirm the hypothesis of a constant mathematical expectation. The next stage, based on constancy of the variance and mathematical expectation, is the determination of autocorrelation coefficients of the studied random function and plotting of the autocorrelation function. To approximate the function the autocorrelation coefficients are determined by the least squares method and the autocorrelation function attenuation analysis is performed. The implementation of the defined stages allows to identify the areas of stationarity on the load graphs of transformer substations 10/0.4 kV. For a reliable description of the process of changing the load of transformer substations 10/0.4 kV the use of probabilistic-statistical method of modelling is justified that takes into account the stochastic nature of the load changes on the selected areas of stationarity.

Influence of the weight coefficients of measurements on the consistency of the assessment and calculation results of the power supply system steady-state operation conditions

The aim of this work is to develop an improved procedure for assessing the state of power supply systems based on adjusting the weight coefficients of measurements. To this end, non-linear optimisation methods were used. The control equations and the solution of the simultaneous linear equations were performed using the Crout method. The results of the calculation of the electrical power steady-state mode were considered as a reference. The lower the difference between the evaluation and steady-state calculation results, the higher the accuracy of the overall state assessment procedure. The problem of correcting the weight factors is set and solved as a nonlinear optimisation problem, where the optimisation parameters are taken as the dispersion of the measurements. The objective function was formulated as follows: to minimise the measurement evaluation dispersions that are part of a single control equation by maximising the active power measurements dispersion in the swing bus of the power supply system. In this study, limitations in the form of equation and inequality are monitored. The problem of optimising the dispersions is solved after the first iteration of the state assessment; starting with the second iteration, the state assessment is performed with new measurement weight factors. The calculations were performed on a 6-node test circuit. The control equations are drawn from the current measurements. The measurements data on the selected control equation of the test circuit are used to calculate the target function. The accuracy of the dispersions redistribution and their extreme values are controlled by the limitations. The results showed that, when adjusting the dispersion of measurements, the power assessments at all nodes are closer to the steady-state mode calculation results.

On-Line Optimization of Energy Consumption in Electromagnetic Mill Installation

Milling is one of the most energy consuming stages of the value production chain in many industries. To minimize the specific energy required, new and more efficient devices and circuits are designed and dedicated optimizing control strategies are applied. This research presents the results of innovative electromagnetic mill energy consumption reduction with dedicated supervisory on-line optimizing control algorithm. The paper describes an algorithm that uses the active power measurement and searches for the minimum on the active constraints of the optimization problem. The constraints follow from the product quality, mill supply voltage and magnetic induction requirements. Algorithm performance was tested in simulations, but the main validation was performed on a semi-industrial dry grinding and classification circuit equipped with an electromagnetic mill. The results of the experiments presented in this paper show that the application of the on-line optimization algorithm allows for even a 40% reduction in the electromagnetic mill energy consumption when compared to the nominal operating point.

A non-intrusive load monitoring approach for very short-term power predictions in commercial buildings

In this study, a new algorithm is developed to extract device profiles in a fully unsupervised manner from three-phases reactive and active aggregate power measurements. The extracted device profiles are then applied to disaggregate the aggregate power measurements by means of particle swarm optimization. Then, a new approach to very short-term power predictions is presented, which makes use of the disaggregation data. For this purpose, a state change forecast is carried out for each device by an artificial neural network and subsequently converted into a power prediction by reconstructing the power profile with respect to the state changes and device profiles. The forecast horizon is 15 min. In order to demonstrate the developed approaches, three-phase reactive and active aggregate power measurements of a multi-tenant commercial building are employed as a case study. The granularity of the data used is 1 s. In total, 52 device profiles are extracted from the aggregate power data. The disaggregation exhibited a highly accurate reconstruction of the measured power with an energy percentage error of approximately 1 %. The indirect power prediction method developed is then applied to the measured power data and outperforms the two persistence forecasts, as well as an artificial neural network designed for 24h-ahead power predictions working in the power domain.

Data-Driven Load Modeling to Analyze the Frequency of System Including Demand Response: A Colombian Study Case

This paper analyzes the potential impact of implementing demand response strategies in a power system. This work aims to present a methodology to evaluate three demand response models to reduce frequency variations in the system. The method starts with the modeling of the system load and the demand response strategies. The power loads are modeled through active power and reactive power measurements in the system's different buses. A data-driven methodology is proposed to obtain three profiles that simulate residential, commercial, and industrial users' behavior. Mathematical modeling is proposed for demand response strategies. Time of Use tariff, Solar PV Distributed Generation, and Load Curtailment are the strategies used for residential, commercial, and industrial users, respectively. A brand-new combination of scenarios is developed in this paper with different penetration levels of the demand response strategy. Besides, a novel analysis of the frequency profile is performed for the proposed scenarios. A modified IEEE-39 power system is proposed, adjusting generation and demand using the Colombian demand profile and the generating units' energy mix. The results indicate that the implementation of demand response strategies improves the system's frequency profile. The frequency drop was reduced by 11.4 %, and power generator units released up to 2.1 GWh through the day with the implementation of the DR strategies.

Real-Time Non-Intrusive Electrical Load Classification Over IoT Using Machine Learning

In this era of technological advancement, the flow of an enormous amount of information has become such an inevitable phenomenon that makes a path for the takeover of the internet of things (IoT) based smart grid from the currently available grid system. In a smart grid, demand-side management plays a crucial role in reducing the generation capacity by shifting the user energy consumption from peak period to off-peak period, which requires detailed knowledge of the user consumption at the individual appliance level. Non-intrusive load monitoring (NILM) provides an exceptionally low-cost solution for determining individual appliance levels using a single-point measurement. This paper proposed an IoT-based real-time non-intrusive load classification (RT-NILC) system considering the variability of supply voltage using low-frequency data. Due to the unavailability of smart meters at the household level in Bangladesh, a data-acquisition system (DAS) is developed. The DAS is capable of measuring and storing rms voltage, rms current, active power, and power factor data at a sampling rate of 1 Hz. These data are processed to train different multilabel classification models. The best-performed classification model has been selected and utilized for the implementation of RT-NILC over IoT. The Firebase real-time online database is considered for data storage to flow the data in two-way between end-user and service provider (energy distributor). The GPRS module is used for wireless data transmission as a Wi-Fi network may not be available everywhere. Windows and web applications are developed for data visualization. The proposed system has been validated in real-time, using rms voltage, rms current, and active power measurements at a real house. Even under supply voltage variability, the performance evaluation of the RT-NILC system has shown an average classification accuracy of more than 94%. Good classification accuracy and the overall operation of the IoT-based information exchange systems ensure the proposed system’s applicability for efficient energy management.

Understanding the actual situation of energy supply and demand in large-scale waste treatment and recycling facilities and evaluating the effect of CO2 reduction

In this paper, we examined the integration of power supply and demand and evaluated the CO2 reduction effect for a large-scale recycling center (hereinafter referred to as RC). Actual power measurement of each processing facility for the purpose of reducing carbon dioxide After evaluation and operation shift, it was shown that the CO2 reduction amount was 2590kg-CO2 / W in the week when surplus electricity was generated. In addition, the CO2 emission amount of RC was calculated by the annual coefficient method and the emission coefficient by time. It was shown that the error rate in November was 0.5% and the error rate in May was 18%, which differed depending on the season. Also, the CO2 reduction effect by utilizing RPF and trans heat container utilizing RC resources was shown. Evaluation was performed.

A comparison of photovoltaic models for estimating power generation: a case study of Brazilian data

This paper presents photovoltaic (PV) generation models used to predict the power output injected into the grid, taking into account the relevant environmental variables, such as irradiance and ambient air temperature. The purpose is to identify the models that have the necessary degree of accuracy and simplicity to be used in studies of technical impacts of connecting PV generation plants to power systems. The paper focuses on semi-empirical models that adopt analytical expressions based on empirical equations and information provided by manufacturers. Using time series of ground measured irradiance and local ambient air temperature, five models are used to generate power output time series in minutely and hourly resolution, which are then compared with actual power measurements using statistical metrics.

Monitoring of low voltage grids with multilayer principal component analysis

This article presents a monitoring strategy based on multilayer principal component analysis (PCA) to detect and diagnose power system disturbances in large amounts of data collected by intelligent electronic devices in low voltage smart grids. The PCA models are built on multiple sliding windows, sized (in terms of length and sampling time) according to the type of phenomena to detect. Abnormalities are detected with use of two complementary statistical indexes, then diagnosed by computing the individual contributions of each monitored variable to the constraint violation of those statistics. As a result, its implementation enables an automatic analysis of multiple phenomena of interest in parallel over time using distinct electrical quantities. Furthermore, the method is demonstrated within the RESOLVD project with data from the OpenLV project containing measurements of active and reactive power gathered at different low voltage distribution substations.