With increasing concerns about climate change, there is a transition from high-carbon-emitting fuels to green energy resources in various applications including household, commercial, transportation, and electric grid applications. Even though renewable energy resources are receiving traction for being carbon-neutral, their availability is intermittent. To address this issue to achieve extensive application, the integration of energy storage systems in conjunction with these resources is becoming a recommended practice. Additionally, in the transportation sector, the increased demand for EVs requires the development of energy storage systems that can deliver energy for rigorous driving cycles, with lithium-ion-based batteries emerging as the superior choice for energy storage due to their high power and energy densities, length of their life cycle, low self-discharge rates, and reasonable cost. As a result, battery energy storage systems (BESSs) are becoming a primary energy storage system. The high-performance demand on these BESS can have severe negative effects on their internal operations such as heating and catching on fire when operating in overcharge or undercharge states. Reduced efficiency and poor charge storage result in the battery operating at higher temperatures. To mitigate early battery degradation, battery management systems (BMSs) have been devised to enhance battery life and ensure normal operation under safe operating conditions. Some BMSs are capable of determining precise state estimations to ensure safe battery operation and reduce hazards. Precise estimation of battery health is computed by evaluating several metrics and is a central factor in effective battery management systems. In this scenario, the accurate estimation of the health indicators (HIs) of the battery becomes even more important within the framework of a BMS. This paper provides a comprehensive review and discussion of battery management systems and different health indicators for BESSs, with suitable classification based on key characteristics.

With the advent of Phasor Measurement Units (PMUs), high resolution synchronized phasor measurements enables real time system monitoring and control. PMUs transmit data to local controllers in substations and Phasor Data Concentrators for system wide monitoring and control application in the control center. They provide real-time phasor data for critical power system applications such as remedial action schemes, oscillation detection and state estimation. The quality of phasor data from PMUs is critical for smart grid applications. Several methods are developed to detect anomalies in time series data, tailored for PMU data analysis. Nevertheless, applying stand alone methods (with fixed parameters) takes great tuning effort, and does not always achieve high accuracy. In this paper, we adopt an unsupervised ensemble learning approach to develop fast, scalable bad data/event detection for PMU data. The ensemble method invokes a set of base detectors to generate anomaly scores of the PMU data, and makes decisions by aggregating the scores from each detector. We develop two algorithms: 1) a learning algorithm that trains the ensemble model, and 2) an online algorithm that infers the anomaly scores with the ensemble model over PMU data streams. The proposed method provides flag for data anomalies and triggers further analysis to differentiate between events and bad data. Using both simulated and real-world PMU data, we show that our ensemble model can be efficiently trained, can achieve high accuracy in detecting diversified errors/events, and outperforms its counterparts that use single standalone detection method.

Accurate voltage and current phasor measurements are required to be provided by phasor measurement units placed at various buses in the system. This paper presents an effective method for estimation of alternating current signal fundamental phasor under dynamic conditions. The model-order estimation of the current signal has been carried out to separate the signal space and the noise space in the signal correlation matrix. The Vandermonde matrix of the total least square estimation of signal parameters via rotational invariance techniques method is extended to second-order Taylor's series approximation to estimate the dynamic phasor of the current signal. The suggested technique for the phasor estimation has been tested on simultaneous multimode amplitude and phase oscillations with decaying dc offset and noninteger harmonics, in the presence of noise and also for a fault scenario in New England 39-bus system. This paper also provides a few suggestions for a few possible amendments in the IEEE synchrophasor standard.

In this paper, estimation of dynamic phasors, under power-system oscillations, has been proposed using subspace-based techniques. A high sampling rate and few modifications in the subspace-based techniques are suggested to estimate the voltage phasor with a fundamental frequency component without using antialiasing filter to the input signal. The total least squares estimation of signal parameters via rotational invariance techniques and the propagator method are the two subspace-based techniques used for estimating the frequency, as well as the amplitude and the phase, respectively. A compensation for the amplitude and the phase has been proposed to address the dynamic nature of the power-system oscillations. The suggested techniques for the phasor estimation have been tested on simultaneous multiple amplitude and phase oscillations in the presence of noise and also for a fault scenario in New England 39-bus system. The hardware requirement of the proposed phasor estimation technique has also been discussed for its practical viability.

During the life span of an instrument transformers (IT), non-catastrophic and typically undetected problems may degrades the overall performance of the instrumentation system. Synchrophasor provides high resolution synchronized data compared to the Supervisory Control and Data Acquisition (SCADA) systems. In the proposed non-intrusive method, time-series Phasor Measurement Units (PMUs) data are processed using three step approach for real time condition monitoring of IT at the control center. Bad data are filtered or flagged using MLE based ensemble method before applying density-based spatial clustering of applications with noise (DBSCAN) to detect clusters, changing over time within each time window. In the third step, a decision logic diagram is developed using additional data and system information to categorize the observed anomalies into different types of malfunction, failure, event or degradation. The proposed method has been validated using IEEE test system modeled in the Real Time Digital Simulator (RTDS) interfaced with industrial hardware PMU device. Detecting equipment failures at an early stage will result in improved system reliability and reduced maintenance cost. Several test cases demonstrate superiority of the proposed method for detecting incipient failures in the IT, and provides early warning mechanism for preventing full blown faults resulting in major impact on the power grid.

Large Language Models (LLMs) like ChatGPT have become the most popular regenerative AI applications, used for obtaining responses for queries in different domains. The responses of ChatGPT are already becoming mainstream and are challenging conventional methods of learning. This article focuses on the application of ChatGPT for academic instructional purposes in the field of computer engineering and related majors. The capability of ChatGPT for instructional purposes is evaluated based on the responses to different questions about these engineering streams. This article explores different opportunities (with use cases), that ChatGPT can provide in augmenting the learning experience. It also provides scenarios of limitations and modifying the evaluation process to prevent the use of ChatGPT, which may lead to an inaccurate dissemination of accepted facts. In this paper, common classroom problems and their respective responses from ChatGPT in the domains of Computer Science, Cyber Security, Data Science, and Electrical Engineering are analyzed to determine the categories of queries for which ChatGPT offers reliable responses and those for which it may be factually incorrect. A student survey is performed to demonstrate that students must be made aware that ChatGPT may not be suitable for certain types of queries and means of upgrading the evaluation process.