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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. 

Timely and accurate detection of events affecting the stability and reliability of power transmission systems is crucial for safe grid operation. This paper presents an efficient unsupervised machine-learning algorithm for event detection using a combination of discrete wavelet transform (DWT) and convolutional autoencoders (CAE) with synchrophasor phasor measurements. These measurements are collected from a hardware-in-the-loop testbed setup equipped with a digital real-time simulator. Using DWT, the detail coefficients of measurements are obtained. Next, the decomposed data is then fed into the CAE that captures the underlying structure of the transformed data. Anomalies are identified when significant errors are detected between input samples and their reconstructed outputs. We demonstrate our approach on the IEEE-14 bus system considering different events such as generator faults, line-to-line faults, line-to-ground faults, load shedding, and line outages simulated on a real-time digital simulator (RTDS). The proposed implementation achieves a classification accuracy of 97.7%, precision of 98.0%, recall of 99.5%, F1 Score of 98.7%, and proves to be efficient in both time and space requirements compared to baseline approaches. 

The push to automate and digitize the electric grid has led to widespread installation of Phasor Measurement Units (PMUs) for improved real-time wide-area system monitoring and control. Nevertheless, transforming large volumes of highresolution PMU measurements into actionable insights remains challenging. A central challenge is creating flexible and scalable online anomaly detection in PMU data streams. PMU data can hold multiple types of anomalies arising in the physical system or the cyber system (measurements and communication networks). Increasing the grid situational awareness for noisy measurement data and Bad Data (BD) anomalies has become more and more significant. Number of machine learning, data analytics and physics based algorithms have been developed for anomaly detection, but need to be validated with realistic synchophasor data. Access to field data is very challenging due to confidentiality and security reasons. This paper presents a method for generating realistic synchrophasor data for the given synthetic network as well as event and bad data detection and classification algorithms. The developed algorithms include Bayesian and change-point techniques to identify anomalies, a statistical approach for event localization and multi-step clustering approach for event classification. Developed algorithms have been validated with satisfactory results for multiple examples of power system events including faults and load/generator/capacitor variations/switching for an IEEE test system. Set of synchrophasor data will be available publicly for other researchers.