In modern power grids, an increasing number of renewable sources are integrated via inverters, affecting the inertia of the overall power system. Thus, a low-inertia system formed is potentially vulnerable to fast grid collapse and catastrophic failures in case of any disturbances. The study of low-inertia systems is an important aspect of the stability and control of the power grid. The stability studies require system model representation in the time or frequency domain. The traditional modeling method is the state-space approach which requires detailed system knowledge that may not be available always. The impedance-based approach is computationally efficient but fails to provide information about the internal states of the system. To overcome these drawbacks, we develop a transfer function-based approach. The purpose of this study is to present a computationally efficient modeling method for a converter system that incorporates internal knowledge of the system. It provides flexibility in understanding the stability of the overall system through Bode, Nyquist, and pole-zero plots. The proposed Transfer Function-based approach is applied to the generalized model of the converter consisting of two types of inverters Grid Forming (GFM) and Grid Following (GFL), and its stability assessment is carried out in MATLAB.

The control of an induction motor (IM) drive is a complex process and requires speed estimation, which is dependent on various machine parameters. The linear regression approach reduces this dependency by eliminating the need for flux computation and gain adjustment of the PI controllers. The performance of the linear regression model deteriorates when data is noisy, the best fit deviates from the desired value. The Gaussian Process (GP) model is a non-parametric model, that can incorporate these noisy measurements and model uncertainties. In this paper, the speed of the IM drive with vector control is estimated using Gaussian Process Regression (GPR). The fictitious quantity$X$= ū*×$I$is used to eliminate the calculation of flux in the stator or rotor and its characteristic of stable drive in all four quadrants. The GP is viewed as a surrogate model, the prediction distribution obtained gives a confidence interval, used for usefulness validation that also distinguishes GP model from other black-box models. Furthermore, the Bayesian approach of GPR involves lesser complex evaluations making this approach simple and agile.

In transmission and distribution substations, power transformers account for the majority of capital investment. As they are expensive, effective thermal performance monitoring is necessary for life extension. The hot-spot temperature value is among the most significant factors affecting a transformer's life expectancy. As HST evaluation require some Top-oil Temperature model parameters, accurate estimations of TOT are required to analyze the thermal performance and lifespan of transformers. Conventionally, the TOT parameters from the Resistance-Capacitance network are determined using the input-output data. While the regressor signals meet the Persistence of Excitation criterion in the Gradient Estimator a parametric estimate error approaches zero and the parameter converges to their true value. The Design of Experiment is often carried out in the test center to meet the PE requirement. As a consequence, actual operating transformer data is utilized for the identification and estimation of the parameters associated with the TOT model by using finite-time estimators. The detected PE issue, the impact of DoE, and the efficiency of FTEs with different filters for non-PE data obtained from the actual operating transformers' thermal model are demonstrated by experimental analysis using MATLAB.

Battery technology is an important component of an electric vehicle (EV). The modelling and state estimation of the battery are very relevant in the theoretical and practical operation. In addition to that, it helps to increase the battery life span, maximize its output, and reduces its cost. The state of charge (SOC) is a key parameter in the Battery Management System (BMS) because it helps to operate the battery in safe operating conditions. However, because of strong nonlinear characteristics of batteries, the internal states of the batteries cannot be measured directly. In the literature, various methods for SOC estimation have been developed. Though some of these methods are widely used in industries, some of these methods have their limitations like heavy computation cost, complex behaviour, etc. These methods fail to consider various parameters that affect the SOC of the battery. Thus, to address this issue the paper proposes a method which assists in the accurate estimation of SOC considering the parameters like temperature, aging, and charging-discharging rate of the battery. In this paper, a novel approach for the estimation of SOC of batteries with the modified state-space model using a Nonlinear Observer (NLO) is presented. The state equations derived from the second-order circuit model (Thevenin's model) is used to simulate a battery's complex dynamical behaviours. Further, the controller is designed using feedback linearization approach for optimal charging of the battery. The results obtained in MATLAB show a small error in estimated and actual SOC, along with this it highlights the effect of the temperature on SOC.

This paper presents a new approach to estimate the speed of vector-controlled three-phase induction motor drive using polynomial regression. The speed estimation via polynomial regression does not require any gain adjustment as well as PI controllers. The identification method is relying on the learning algorithm to enable an estimator fit between the reactive power (Q = Vsq.isd- Vsd.isq) and speed (ωr). The regressor quantity (Q) explicitly dependent on the voltage and current vectors and thus computation of flux is not required. The formulation of polynomial regression based speed estimation technique is simply realizable as the intensive computation of differential/integral is not required which makes the approach efficient and less time consuming. The Simulation results validates precise tracking of rotor speed for high speed, low speed and reversal from motoring to regenerative mode is achieved through this approach.

In the era of data-driven, the next state of the system can be predicted and controlled with the help of system data set even in the absence of system model knowledge. Dynamic Mode Decomposition (DMD) algorithm is one of the techniques for predicting system states by breaking data into its principal modes, derived from a compilation of training data. The principal modes are useful for finding the behavior of the system and for predicting its future states, even in a noisy environment. The paper focuses on predicting variation in the rotor angle during a severe fault on the power system with the help of DMD. The paper also proposes the enhancement in the rotor angle with the help of Model Predictive Control (MPC) technique. For prediction and enhancement of the rotor angle, the multi-machine and single-machine systems are considered in the paper and the results are obtained for various operating scenarios. The results show the effectiveness of the proposed technique for prediction and enhancement of the rotor angle in the case of multi-machine and single-machine system with the help of system data only.

The smart building having multiple subsystems are gaining momentum due to the growing trend of smart cities. These multiple subsystems interact with each other through a communication channel and coordinate through a building management system (BMS) for the effective operation of the smart building. The communication channels are prone to vulnerabilities (cyber attacks) which may lead to anomalies condition. However, with a proper prediction of future data beforehand various types of anomalies can be avoided. The task of predicting data requires extensive knowledge of the system model as well as the process. In view of this, the paper proposes a prediction technique known as the Dynamic Mode Decomposition (DMD) which can predict future temperature profile data with the help of available past data in an equation-free environment. The temperature data of the major component of BMS i.e. heating, ventilation, and air conditioning (HVAC) system is predicted using past temperature data with the help of DMD. After the prediction of the temperature profile, the concept of a process control chart is used for analyzing the HVAC system as normal or anomalies condition. The effectiveness of the proposed method for prediction of data using DMD where all system states may not be observable and the analysis of predicted data using the process control chart is verified using different test scenarios. Finally, from the result, it can be highlighted that DMD predicts the data effectively without the need for a system model, and the process control chart helps to identify the presence of anomalies.