Search Results (120)

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder, and its early detection can be critical for a prompt intervention that can potentially slow down the disease progression and improve the patient’s quality of life. However, a diagnosis based solely on clinical symptoms can be challenging, especially in the early stages, while the detection of specific biomarkers such as amyloid-β peptide (Aβ) and tau proteins can provide objective evidence for diagnosis. In this work, we explored the effects of Aβ peptide on cell membrane properties thanks to fluorescence lifetime imaging (FLIM) combined with the phasor analysis (FLIM-ϕ). The results showed that the membrane viscosity is altered by the presence of Aβ peptide and that cells experience this effect even at nanomolar concentrations of peptide. This considerable sensitivity opens up the possibility of envisioning a cell-based biosensor able to detect very low concentrations of Aβ in a biological fluid, thus enabling timely diagnosis and intervention. Full article

The evolution of phasor measurement units (PMUs) marked a significant development in the field of electrical engineering. The integration of PMU-based linear state estimators or the augmentation of SCADA measurements with PMU measurements has emerged as a promising solution to address the limitations of conventional state estimation. The utilization of PMU measurements has been demonstrated to effectively address measurement errors, leading to the precise determination of the system’s state within a single iteration. This article provides a comprehensive discussion of both linear and nonlinear state estimation computational processes. A comparative analysis is conducted to assess their performance. PMU-based state estimator models are proposed based on different PMU locations and are further examined under abnormal conditions. The results are compared with the simulated results from a Simulink PSCAD model of the IEEE 14-bus system to ensure validation. The test results demonstrated that the proposed models outperformed the conventional model in terms of robustness and accuracy. The estimated and simulated states matched during both normal and abnormal conditions, demonstrating the model’s capability to track sudden changes in voltage profiles. This article promotes further investigations to develop new control and protection functions using this model. Full article

The three-phase induction motor (IM) plays an essential role in industry; therefore, ensuring these motors operate under faultless and reliable conditions. Previous studies have reported that 28% to 38% of IM failures are related to the stator windings. For this reason, numerous efforts have been made to develop low-cost procedures for early fault detection in IMs. However, their primary drawback is that they involve complex mathematical operations for analyzing acquired information in the frequency or time-frequency domains, making online implementation challenging. Consequently, there remains a need for a reliable, online method to detect faulty conditions in IMs. In this work, a straightforward methodology based on the phasor-representation analysis and a fuzzy logic classifier is proposed for the online detection and classification of short-circuit faults in a three-phase IM. The signal-processing technique in this approach involves basic arithmetic operations, making it feasible for implementation on the Texas Instruments 32-bit, floating-point digital signal processor TMS320F28335. The obtained results demonstrate the efficacy and utility of the proposed methodology in detecting and classifying incipient or simultaneous short-circuit faults. It significantly reduces processing time while achieving high precision and accuracy compared to previous approaches reported in recent literature. Full article

This paper presents a novel methodology to estimate the harmonic transfer impedances in electric power systems with multiple harmonic sources (HSs). The purpose is to determine the responsibility of each HS for the total harmonic distortion at a specific bus within the system, addressing a critical issue in the power quality field. To achieve this objective, it is necessary to estimate not only the individual HS, but also the transfer impedances between each source and the bus under analysis (BUA). Most methods for solving this problem are based on proper network modeling or restrict variations in harmonic sources to a single source at a time. The proposed methodology has overcome this limitation. For this, synchronized current and voltage phasors are measured at the BUA. Once the measurements are gathered, the Independent Component Analysis (ICA) method is applied to estimate the Norton equivalent. The harmonic transfer impedance (HTI) is then determined using the information provided by the ICA. To enhance the accuracy of HTI estimation, three procedures are employed for data mining the parameters provided by ICA over time to generate a well-conditioned system. Once the HTI is satisfactorily determined, the individual harmonic contributions (IHCs), i.e., the harmonic responsibility, can be estimated accurately. The effectiveness and performance of the method are demonstrated based on computational simulations using distribution and transmission systems. Additionally, the methodology is validated with real data collected from a Brazilian transmission system monitored by synchronized power quality measurement units. Simulated results show that the Total Vector Error (TVE) is less than 0.4%, and for the field data test, the TVE is less than 2%. Full article

The latest changes on the distribution network due to the presence of distributed energy resources (DERs) and electric vehicles make it necessary to monitor the grid using a real-time high-precision system. The present work centers on the development of an open-source software platform that allows for the joint management of, at least, power quality monitors (PQMs), phasor measurement units (PMUs), and smart meters (SMs), which are three of the most widespread devices on distribution networks. This framework could work remotely while allowing access to the measurements in a comfortable way for grid analysis, prediction, or control tasks. The platform must meet the requirements of synchronism and scalability needed when working with electrical monitoring devices while considering the large volumes of data that these devices generate. The framework has been experimentally validated in laboratory and field tests in two photovoltaic plants. Moreover, real-time Artificial Intelligence capabilities have been validated by implementing three Machine Learning classifiers (Neural Network, Decision Tree, and Random Forest) to distinguish between three different loads in real time. Full article

As renewable energy integration scales up, power systems increasingly depend on sources interfaced through power electronic converters, which lack rotating mass and substantially diminish system inertia. This reduction in inertia, coupled with the complex and diverse control strategies governing power electronics, presents significant challenges in accurately assessing the equivalent inertia levels within modern power systems. This paper introduces an online method for estimating the inertia time constant of power nodes, grounded in the node power flow equation, to address these challenges. The approach begins by deriving the rotor motion equation for synchronous generators and defining the inertia time constant of power nodes through an analysis of the power flow equations. Real-time frequency and voltage phasor data are collected from system nodes using phasor measurement units. The frequency state of the power equipment is then characterized using a divider formula, and the equivalent reactance between the power equipment and the node is further derived through the node power flow equation. This enables the real-time estimation of the equivalent inertia time constant for power nodes within the system. The effectiveness of the proposed method is demonstrated through simulations on the WSCC9 system, confirming its applicability for real-time system analysis. Full article

The requirements for accurate models of renewable energy power plants are urgent for power system operation analysis. Most existing model research in this area is for wind turbine and photovoltaic (PV) power generation units; a rare renewable power plant model validation mainly adopts the single-machine infinite-bus system. The single equivalent machine method is always used, and the interactions between the power plant and the grid are ignored. The voltage at the interface bus is treated as constant, although this is not consistent with its actual characteristics. The phase shifter method of hybrid dynamic simulation has been applied in the model validation of wind farms. However, this method is heavily dependent on phasor measurement units (PMU) data, resulting in a limited application scope, and it is difficult to realize the model error location step by step. In this paper, the fast-responding generator method is used for renewable power plant model validation. The complete scheme comprising model validation, error localization, parameter sensitivity analysis, and parameter correction is proposed. Model validation is conducted based on measured records from a large-scale PV power plant in northwest China. The comparison of simulated and measured data verifies the feasibility and accuracy of the proposed scheme. Compared to the conventional model validation method, the maximum deviation of the active power simulation values obtained by the method proposed in this paper is only 38.8% of that of the conventional method, and the overall simulation curve fits the actual measured values significantly better. Full article

To enhance the performance of resonant DC–DC converters, particularly under low-load conditions, a semi-series-resonant dual-active-bridge (Semi-SRDAB) converter with a hybrid optimization strategy is proposed. This strategy aims to reduce conduction-related losses and is designed for applications requiring a wide voltage range. The proposed Semi-SRDAB converter comprises a full-bridge inverter on the primary side and a hybrid-output bridge rectifier on the secondary side. It adopts phase-shift modulation combined with frequency modulation for power control. The hybrid optimization strategy for the Semi-SRDAB converter is investigated, beginning with the deduction of resonant current minimization using phasor analysis. Based on these analysis results, zero reactive power operation and soft-switching operation are achieved for both buck and boost modes. Successful validation has been demonstrated through experimental testing on a 300 W laboratory prototype. Enhanced conversion performance is confirmed by comparing the results with those from previous works. Full article

Due to its vulnerability to a variety of cyber attacks, research on cyber security for power systems has become especially crucial. In order to maintain the safe and stable operation of power systems, it is worthwhile to gain insight into the complex characteristics and behaviors of cyber attacks from the attacker’s perspective. The consensus-based distributed state estimation problem is investigated for power systems subject to collaborative attacks. In order to describe such attack behaviors, the denial of service (DoS) attack model for hybrid remote terminal unit (RTU) and phasor measurement unit (PMU) measurements, and the false data injection (FDI) attack model for neighboring estimation information, are constructed. By integrating these two types of attack models, a different consensus-based distributed estimator is designed to accurately estimate the state of the power system under collaborative attacks. Then, through Lyapunov stability analysis theory, a sufficient condition is provided to ensure that the proposed distributed estimator is stable, and a suitable consensus gain matrix is devised. Finally, to confirm the viability and efficacy of the suggested algorithm, a simulation experiment on an IEEE benchmark 14-bus power system is carried out. Full article

This paper proposes a correction technique for bad data and high-precision analysis based on micro-phasor measurement unit (μPMU) data for a stable and reliable smart substation. First, a high-precision wide-area monitoring system (WAMS) with 35 μPMUs installed at Korea’s Yeonggwang substation, which is connected to renewable energy sources (RESs), is introduced. Time-synchronized μPMU data are collected through the phasor data concentrator (PDC). A pre-processing program is implemented and utilized to integrate the raw data of each μPMU into a single comma-separated values (CSV) snapshot file based on the Timetag. After presenting the technique for identification and correction of event, duplicate, and spike bad data of μPMU, causal relationships are confirmed through the voltage and current fluctuations for a total of five states, such as T/L fault, tap-up, tap-down, generation, and generation shutdown. Additionally, the difference in active power between the T/L and the secondary side of the M.Tr is compared, and the fault ride through (FRT) regulations, when the fault in wind power generation (WP), etc., occurred, is analyzed. Finally, a statistical analysis, such as boxplot and kernel density, based on the instantaneous voltage fluctuation rate (IVFR) is conducted. As a result of the simulation evaluation, the proposed correction technique and precise analysis can accurately identify various phenomena in substations and reliably estimate causal relationships. Full article

Phasor-domain (RMS) simulations have become increasingly vital in modern power system analysis, particularly as the complexity and scale of these systems have expanded with the integration of renewable energy sources. ePHASORSIM, an advanced phasor-based simulation tool developed by OPAL-RT, plays a crucial role in this context by enabling real-time phasor-domain simulation and hardware-in-the-loop testing. To keep pace with these evolving needs, continuous efforts are being made to further improve the accuracy, efficiency, and reliability of ePHASORSIM-based simulations. These efforts include automating model conversion processes for enhanced integration with ePHASORSIM, extending ePHASORSIM’s simulation range with custom models, developing hybrid co-simulation techniques involving ePHASORSIM and an EMT simulator, enhancing simulation scalability, and refining HIL testing to achieve more precise validation of control and protection systems. This paper provides a comprehensive review of these recent advances. Additionally, the paper discusses the conversion of models from PowerFactory—a widely used and comprehensive modeling environment—to ePHASORSIM through both automated tools and manual methods using Excel workbooks, which has been discussed little in the literature. Furthermore, as ePHASORSIM is a relatively new tool with limited cross-validation studies, the paper aims to contribute to this area by presenting a comparative validation against DIgSILENT PowerFactory, with a specific emphasis on its application in electric vehicle charging management systems. Full article

With the growing concern over climate change and energy security, the Government of India expedited enhancing the share of renewable energy (RE) derived from solar, wind and biomass sources within the energy blend. In this paper, a techno-economic and environmental analysis of a microgrid-integrated electric vehicle charging stations fueled by renewable energy is proposed for a typical area in the State of Karnataka, South India. The power transaction with the grid and the sell-back price to the national grid were investigated. Carbon emissions were also assessed, and 128,406 CO₂ kg/Yr can be saved in the grid-connected mode. Also, in this work, different scenarios such as injecting active power, reactive power, and active and reactive power, and injecting active and absorbing reactive power to the grid are comprehensively assessed. Out of four types, type 3 (inject real and reactive power) provides significant reduction in power losses by up to 80.99%. The synchrophasor-technology-based monitoring method is adopted in order to enhance the microgrid system’s overall performance. The execution times for different cases with distributed generators (DGs) and electric vehicle charging stations (EVCSs) for conventional systems and micro-phasor measurement units (µPMU) were observed to be 19.07 s and 5.64 s, respectively, which is well accepted in the case of online monitoring. Full article

Gait phase recognition systems based on surface electromyographic signals (EMGs) are crucial for developing advanced myoelectric control schemes that enhance the interaction between humans and lower limb assistive devices. However, machine learning models used in this context, such as Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM), typically experience performance degradation when modeling the gait cycle with more than just stance and swing phases. This study introduces a generalized phasor-based feature extraction approach (PHASOR) that captures spatial myoelectric features to improve the performance of LDA and SVM in gait phase recognition. A publicly available dataset of 40 subjects was used to evaluate PHASOR against state-of-the-art feature sets in a five-phase gait recognition problem. Additionally, fully data-driven deep learning architectures, such as Rocket and Mini-Rocket, were included for comparison. The separability index (SI) and mean semi-principal axis (MSA) analyses showed mean SI and MSA metrics of 7.7 and 0.5, respectively, indicating the proposed approach’s ability to effectively decode gait phases through EMG activity. The SVM classifier demonstrated the highest accuracy of 82% using a five-fold leave-one-trial-out testing approach, outperforming Rocket and Mini-Rocket. This study confirms that in gait phase recognition based on EMG signals, novel and efficient muscle synergy information feature extraction schemes, such as PHASOR, can compete with deep learning approaches that require greater processing time for feature extraction and classification. Full article

The increase in renewable energy sources (RESs) in power systems is causing significant changes in their dynamic behavior. To ensure the safe operation of these systems, it is necessary to develop new methods for preserving transient stability that follow the new system dynamics. Fast-response devices such as flexible AC transmission systems (FACTSs) can improve the dynamic response of power systems. One of the most frequently used FACTS devices is the Static Var Compensator (SVC), which can improve a system’s transient stability with a proper control strategy. This paper presents a reactive power control strategy for an SVC using synchronized voltage phasor measurements and particle swarm optimization (PSO) to improve the transient stability of a multimachine power system. The PSO algorithm is based on the sensitivity analysis of bus voltage amplitudes and angles to the reactive power of the SVC. It determines the SVC reactive power required for damping active power oscillations of synchronous generators in fault conditions. The sensitivity coefficients can be determined in advance for the characteristic switching conditions of the influential part of the transmission network, and with the application of the PSO algorithm, enable quick and efficient finding of a satisfactory solution. This relatively simple and fast algorithm can be applied in real time. The proposed control strategy is tested on the IEEE 14-bus system using DIgSILENT PowerFactory. The simulation results show that an SVC with the proposed control strategy effectively minimizes the rotor angle oscillations of generators after large disturbances. Full article

An induction motor is an electric machine widely used in various industrial and commercial applications due to its efficiency and simple design. In this regard, a methodology based on the electric phasor analysis of line currents and the variations in the phase angles among these line currents is proposed. The values in degrees of the angles between every pair of line currents were introduced to a fuzzy logic algorithm based on the Mamdani model, developed using the Matlab toolbox for detection and isolation of the inter-turn short-circuit faults on the windings of an induction motor. To carry out the analysis, the induction motor was modified in its stator windings to artificially induce short-circuit faults of different magnitudes. The current signals are acquired in real time using a digital platform developed in the Delphi 7 high-level language communicating with a float point unit Digital Signal Processor (DSP) TMS320F28335 by Texas Instruments. The proposed method not only detects the short circuit faults but also isolates the faulty winding. Full article