CubeSats have emerged as an enabling technology for a new generation of space missions, offering relatively low cost and rapid development opportunities for scientific, educational, and technology demonstration. The reliable operation of a CubeSat depends critically on its electrical power system (EPS), which serves as the primary energy backbone for all onboard subsystems and payloads. Several studies have indicated that EPS as a subsystem is most susceptible to failure in the satellite missions, as it is under tight power, volume, and reliability constraints exposed to harsh and variable orbital conditions. In this context, this paper proposes the design, hardware implementation and experimental validation of an autonomous EPS architecture to enhance the operational lifetime of CubeSats. The proposed EPS autonomously manages energy to deal with solar irradiance variations in the low earth orbit (LEO). It combines maximum power point tracking (MPPT), battery management system (BMS), regulated power distribution and sensor based telemetry based on a microcontroller unit (MCU) controlled system. A real time decision making algorithm autonomously monitors the photovoltaic (PV) array voltage. The supervisory algorithm implements a three mode graduated control strategy, i.e. normal, moderate, and power-down. It is governed by two discrete PV voltage thresholds, enabling more precise and graduated load management compared to binary single threshold schemes reported in prior work. When nominal irradiance level is regained, the system returns autonomously to desired operational mode with no interference needed from the ground station. The modular design of the system allows to upgrade its components easily without the need to redevelop entire architecture. A detailed power budget analysis yields a total system load of 1,460mW, divided between telemetry (22mW), payload (1,400mW) and communication subsystems (38mW). The deployed EPS uses a Li-ion 3-cell battery pack (11.1V, each cell 1,800mAh) and PV panels of (12V, 2,100mW). Experimental tests validate the acquisition of data from various sensors and importantly accurate mode transition from normal to power down mode and vice-versa under fluctuating irradiance conditions. Furthermore, dynamic experiments involving controlled variation of PV voltage are also conducted to evaluate both degradation and recovery behavior of the system. The results demonstrate stable, repeatable, and threshold consistent mode transitions under varying input power scenarios. The results collectively demonstrate that autonomous mode transition and load management can be achieved using low cost commercial off the shelf components (COTS), making the proposed EPS a practical, reproducible, and scalable testbed for academic and small mission CubeSat platforms.

Brushless Direct Current (BLDC) motors are increasingly utilized in dynamic industrial and automotive applications due to their high efficiency and compact design. However, their performance is often compromised by excessive torque ripples, which lead to undesirable effects such as mechanical vibrations, acoustic noise and reduced reliability. The primary objective is to minimize torque ripples along with achieving speed regulation and high efficiency. Traditional control strategies, such as Direct Torque Control (DTC), Field-Oriented Control (FOC), various intelligent methods etc. have shown limited success in fully mitigating this issue. This study proposes a novel hybrid control approach that combines a conventional Proportional-Integral (PI) controller with a Deep Reinforcement Learning (DRL) agent for effective torque ripple reduction in BLDC motors. More specifically, the PI controller is used in the outer loop to achieve speed regulation and generates a torque reference signal, while the DRL agent adaptively adjusts the duty cycle of the three-phase inverter based on real time BLDC motor's parameters. The DRL agent processes key system observations and adaptively tunes the duty cycle through a correction signal, which is combined with the normalized PI output to generate the final control signal. The proposed controller is evaluated under varying load torque conditions at reference speeds of 1000 rpm and 1250 rpm. Quantitative performance metrics including magnitude of torque ripples and reduction in percentage of magnitude of torque ripples, are analyzed and compared with other conventional controllers. Comparative analysis with conventional PI, PI-PSO, DTC, FOC, and other intelligent controllers demonstrates the superiority of the hybrid method. Results show a torque ripple reduction of up to 44.9 % as compared to DTC, 37.7 % as compared to FOC, and a 47.9 % reduction relative to the PI-PSO approaches of both cases. This reduction in torque ripples gave an increase in efficiency up to 24.45 %, while keeping the values of rise time and overshoot percentage within limits. These findings validate the effectiveness and adaptability of the proposed hybrid technique in enhancing the performance and reliability of BLDC motor drives. Overall, the quantitative results obtained confirm that the proposed novel hybrid PI-DRL strategy achieves significant torque ripples reduction without compromising dynamical performance, highlighting its practical applicability for industrial BLDC motor drives.

It has been admitted globally that climate change is real, driven by fossil fuels, leading to alternate sustainable energy systems. As most of the renewable energy sources (RES) and modern loads operate on direct current (DC), the interconnection of these can be more efficiently achieved via a DC bus, leading to direct current distributed energy systems (DC DES). Development of system level models is crucial for analysis of DC DES before deployment. The integration of RES and various loads in DC DES has been accomplished via deploying off the shelf power electronic converters (PECs). Behavioural modelling technique has been successful to capture the dynamics of these off the shelf PEC in stand-alone mode. However, in modern DC DES, the dynamic interactions at the input–output of PEC lead to terminated transfer functions which result in behavioural model that is unable to model the true behaviour of the system. The current work proposes a methodology to address this issue and acquire unterminated transfer functions for system level analysis. The proposed method is based on the modular concept, i.e. it can be applied to a single PEC and also extended to a system involving multiple PEC. For validation, it is first applied to a cascaded converter system and then extended to a DC DES. The results demonstrate that using the proposed technique, the unterminated transfer functions can be obtained for PEC in any complex system, thus leading to construction of its behavioural model that accurately represents the true behaviour of the system.

Brushless DC (BLDC) motors because of their efficiency, small size, and quick dynamical behavior have achieved dramatic importance in the. But due to several critical issues of torque ripples, back-EMF irregularities, and stator phase current distortions, its work has been spoiled, and it becomes exacerbated when the load becomes variable. This study is focused on examining the performance of a three-phase BLDC motor in a detailed open-loop analysis setup, serving as a reference baseline before adoption of advanced control strategies, ensuring its operation at higher efficiency. The research investigated system is powered through a three-phase inverter, which is linked to a DC source of 240 V and the nature of the system output, such as electromagnetic torque, mechanical speed, back-electromotive force, and stator phase currents, is carefully studied. The simulation outcomes of the different parameters obtained depict the existence of the effect of torque ripple, the commutating abnormalities, and its nonlinearity, even in the steady-state working. These findings demonstrate the imperfection of the open-loop analysis and the necessity of adopting both traditional and newer approaches including Deep Reinforcement Learning in order to reduce the torque ripples and increase the overall performance of brushless DC motor.

CubeSats have revolutionized space research by providing low cost platforms with small form factors fit for a variety of space missions. However, the Electrical Power System (EPS) remains a crucial and challenging area with evolving demands of reliability, modularity, and efficiency. This paper presents a scalable and fault-tolerant EPS for 2U cubesats and above, featuring Maximum Power Point Tracking (MPPT), a dual layered Battery Management System (BMS), and an MCU-based supervisory unit. Designed with modularity in mind, this system enables component upgrade without major design overhaul, additionally the proposed system has heightened fault tolerance through incorporation of an automatic backup power sub-system in the event of failure. A detailed power budget analysis revealed a total system load of 1.458W, distributed across telemetry (20mW), payload (1.4W), and communication subsystems (38mW). The EPS is powered by a primary three-cell lithium-ion battery pack (11.1V, 1800mAh per cell, 19.98Wh total) and a solar array of five 12V, 2.1W panels. Under optimal conditions, three of these panels can fully charge the battery in approximately 3.43 hours with a solar irradiance of 1370 W/m2. The system also incorporates an MCU-based supervisory module (LGT8F32P), enabling real-time power monitoring, fault detection, and automated power source switching. Simulation results validated the system’s performance in terms of power regulation, fault tolerance, and emergency power switching, with a working breadboard prototype demonstrating its viability. The proposed EPS creates a new benchmark in CubeSat power architectures by optimizing mission reliability, flexibility, and energy efficiency.

Efficient coordination of directional overcurrent relays (DOCRs) is vital for maintaining the stability and reliability of electrical power systems (EPSs). The task of optimizing DOCR coordination in complex power networks is modeled as an optimization problem. This study aims to enhance the performance of protection systems by minimizing the cumulative operating time of DOCRs. This is achieved by effectively synchronizing primary and backup relays while ensuring that coordination time intervals (CTIs) remain within predefined limits (0.2 to 0.5 s). A novel optimization strategy, the fractional-order derivative war optimizer (FODWO), is proposed to address this challenge. This innovative approach integrates the principles of fractional calculus (FC) into the conventional war optimization (WO) algorithm, significantly improving its optimization properties. The incorporation of fractional-order derivatives (FODs) enhances the algorithm’s ability to navigate complex optimization landscapes, avoiding local minima and achieving globally optimal solutions more efficiently. This leads to the reduced cumulative operating time of DOCRs and improved reliability of the protection system. The FODWO method was rigorously tested on standard EPSs, including IEEE three, eight, and fifteen bus systems, as well as on eleven benchmark optimization functions, encompassing unimodal and multimodal problems. The comparative analysis demonstrates that incorporating fractional-order derivatives (FODs) into the WO enhances its efficiency, enabling it to achieve globally optimal solutions and reduce the cumulative operating time of DOCRs by 3%, 6%, and 3% in the case of a three, eight, and fifteen bus system, respectively, compared to the traditional WO algorithm. To validate the effectiveness of FODWO, comprehensive statistical analyses were conducted, including box plots, quantile–quantile (QQ) plots, the empirical cumulative distribution function (ECDF), and minimal fitness evolution across simulations. These analyses confirm the robustness, reliability, and consistency of the FODWO approach. Comparative evaluations reveal that FODWO outperforms other state-of-the-art nature-inspired algorithms and traditional optimization methods, making it a highly effective tool for DOCR coordination in EPSs.

Power electronic converters are integral to the operation of modern smart grids, distributed energy systems, electric vehicles, and other advanced electrical system. These converters enable efficient power conversion, voltage regulation, and power flow control, thereby supporting system reliability, scalability, and efficient power consumption. Among various converter topologies, the DC-DC boost converter holds particular significance due to its ability to step up low input voltages to higher levels, making it essential for photovoltaic systems, battery operated devices, and distributed energy systems. Therefore, accurate modeling of these converters is essential for proper operation of aforementioned applications. However, conventional analytical methods, often fail to capture the nonlinear, time-varying behavior of converters under dynamic operating conditions. Additionally, these methods can’t be applied to black-box type commercial off the shelf converters. To overcome these limitations, this research explores a data-driven approach for modeling the DC-DC boost converter using supervised machine learning (ML) techniques. Four ML models, i.e. random forest, extreme gradient boosting (XG Boost), gradient boosting, and nonlinear auto regressive network with exogenous inputs (NARX-ANN) are employed. First, a simulation-based model of a DC-DC boost converter is developed using MATLAB/Simulink; A comprehensive dataset comprising three input parameters, i.e. duty cycle, input voltage, and load current and two output parameters, i.e. output voltage and inductor current is developed. Next, the same modeling approach is applied to data collected from a commercial off the shelf DC-DC boost converter. This ensures validation of the modeling framework in both virtual and physical environments. The ML models are evaluated based on performance metrics including mean squared error (MSE), mean absolute error (MAE), and R2 score. In both software and hardware based modeling, the random forest model demonstrated the highest predictive accuracy. The results confirm the effectiveness of ML-based modeling approaches in capturing converter dynamics, offering a scalable, flexible, and hardware-verifiable alternative to the conventional methods.

Brushless DC (BLDC) motors are widely used in industrial applications due to their high efficiency and performance. However, accurately predicting key parameters such as torque and speed remains a challenge because of the motor’s inherently nonlinear dynamics. This study presents a data-driven modeling approach using a Nonlinear Autoregressive Neural Network with Exogenous Inputs (NARX-NN) to predict the torque and speed of a BLDC motor. Input-Output data were obtained from a Simulink-based BLDC motor model under varying input voltages and load conditions. The proposed NARX-NN architecture was trained on this data, effectively learning the nonlinear Multi-Input Multi-Output (MIMO) system dynamics. The model achieved high prediction accuracy, with a Mean Squared Error (MSE) of 3.4162e-04 (training), 3.0296e-04 (validation) and 8.4225e-04 (testing) while R-values of 1 in each in case of speed. While the model also achieved high prediction accuracy, with a Mean Squared Error (MSE) of 0.0062 (training and validation), and 0.0065 (testing) while R-values of 0.9997 (training and validation) and 0.9998 (testing) in case of torque. These statistical results are compared with the work already carried out for prediction of speed of BLDC motor, dominating the superiority of the proposed approach. Hence, it confirms the model’s robustness in capturing complex motor behavior. The proposed approach offers a reliable predictive tool suitable for integration into real-time control systems, enabling enhanced motor efficiency, early fault detection, and torque ripple mitigation.

This study investigates the nonlinear dynamics of Brushless DC (BLDC) motors using the MATLAB/Simulink platform, emphasizing system identification through the Least Squares (LS) method and Nonlinear Autoregressive Network With Exogenous Inputs (NARX) models with variable regressors. Accurate data-driven models derived from these techniques are essential for designing efficient feedback control systems, enabling precise motor dynamics representation and facilitating early fault detection by identifying deviations from normal operation. A detailed simulation of the BLDC motor under no-load conditions is performed to analyze the speed response and ripple effects in torque and speed, underscoring the need for effective modeling and control. Comprehensive datasets are generated to develop LS and NARX models across varying operational conditions. A variable step input voltage signal with both ascending and descending steps is employed for training, while a distinct validation signal of similar trends but different magnitudes is used for performance evaluation. All techniques are benchmarked using identical training and validation signals. Among the models, the NARX model with customized regressors demonstrated superior performance, achieving 99.1% training accuracy and 98.01% validation accuracy in predicting motor dynamics. All the models are further tested under real-time signal conditions like ramp-up acceleration, deceleration, & turning and noisy signal conditions to evaluate the robustness and accuracy of these models in real-world conditions. The findings highlight the NARX model’s potential to enhance control strategies and improve BLDC motor stability, with statistical analysis confirming the robustness and effectiveness of the proposed approach.

Modeling the complex nonlinear dynamics of Brushless DC motors has been a prominent research focus over the past two decades, driven by their superior advantages and widespread industrial applications. Despite extensive efforts, achieving high-efficiency prediction of speed and torque responses remains a challenge. This study proposes a hybrid machine learning-based approach using the Nonlinear Autoregressive Neural Network with Exogenous Inputs. The method combines artificial neural networks and system identification techniques to enhance predictive accuracy in nonlinear dynamic systems. For both speed and torque modeling, optimal time delays and neural network layer sizes are selected to accurately capture the ripple effects under a multi-step input signal applied to a three-phase inverter. The proposed models yield Mean Square Error values as low as [Formula: see text] for speed and [Formula: see text] for torque. Regression coefficients of 1.000 for speed and 0.998 for torque are achieved consistently across training, validation, testing, and additional testing phases, following a data split of 70% for training and 15% each for validation and testing. To further evaluate generalization, the approach is tested using a distinct multi-step input voltage signal, with the results confirming the robustness and superiority of the proposed method in both speed and torque prediction. Comparative analysis with existing literature demonstrates the dominance of the proposed models. These high-fidelity models can serve as a foundation for designing advanced controllers aimed at efficient speed regulation and torque ripple mitigation in Brushless DC motors.

Microgrids (MGs) have become popular owing to their ability to efficiently integrate renewable energy sources, energy storage systems, and the main utility grid. This combination results in enhanced flexibility while reducing the overall system losses. However, almost all conventional control approaches face serious difficulties in dealing with fast-changing operating conditions and the nonlinear nature of the power electronic converters widely used within MGs. As such, new challenges arise for advanced, adaptive, and robust control methodologies. Voltage regulation in PE converters remains one of the most challenging issues since it directly impacts the stability and general performance of any PE-based system used within MG applications. Therefore, this paper focuses on the design of a Neural Network Predictive Controller (NNPC) for regulating the output voltage of a boost converter. The idea behind the proposed NNPC relies on obtaining datasets from the process to be controlled and using neural networks to learn system dynamics and approximate nonlinear behavior without explicit mathematical modeling. The NNPC is implemented under a model predictive control framework based on neural networks. It has been shown through simulation that the presented NNPC enhances the performance of a classic $\mathbf{P I}$ controller in terms of improved voltage regulation, faster transient response, and increased robustness for a wide range of load conditions.

In electrical power systems, ensuring a reliable, precise, and efficient relay strategy is crucial for safe and trustworthy operation, especially in multi-loop distribution systems. Overcurrent relays (OCRs) have emerged as effective solutions for these challenges. This study focuses on optimizing the coordination of OCRs to minimize the overall operational time of main relays, thereby reducing power outages. The optimization problem is addressed by adjusting the time multiplier setting (TMS) using the War Strategy Optimization (WSO) algorithm, which efficiently solves this constrained problem. This algorithm mimics ancient warfare strategies of attack and defense to solve complex optimization problems efficiently. The results show that WSO provides superior performance in minimizing total operating time and achieving global optimum solutions with reduced computational effort, outperforming traditional optimization methods (i.e., SM, HPSO, GA, RTO, and JAYA). The proposed algorithm shows a net time gains of 7.77 s, 2.57 s, and 0.8484 s when compared to GA, RTO, and JAYA respectively. This robust protection coordination ensures better reliability and efficiency in multi-loop power systems.

This work proposes an application of Fractional Order Particle Swarm Optimization (FO-PSO), a meta-heuristic method for parameters estimation of photo-voltaic (PV) module as a non-linear, transcendental, multi-modal and implicit optimization problem. The uses single diode model (SDM), double diode model (DDM) and three-diode model (TDM) of PV modules with the constraint that only data-sheet information may be utilized. A fitness function based on the error amongst the computed values of current and voltage and the ones given in characteristic I-V curves of data-sheet, is minimized using the FO-PSO to get the required parameters. The comparative study between the estimated and data-sheet values provided by PV module manufacturers will determine the effectiveness of this research. The effectiveness of the FO-PSO is demonstrated by comparing the fitness values with that of other techniques. The FO-PSO technique makes a novel contribution to the PV power systems industry by making it possible to obtain a nearly realistic model of any commercial PV module. The effectiveness of the FO-PSO is determined by comparing the results for all the three models with the state of the art optimization techniques. The Root Mean Square error values calculated for the TDM is less than 10e-16, producing very consistent FO-PSO results. Therefore, FO-PSO is anticipated to be a competitive method for obtaining PV module specifications.

Introduction/Importance of Study:The optimization of the power system is a complicated problem that is extremely non-convex, nonlinear,and important for reducing the cost of production.Novelty Statement:Despite the fact that several metaheuristic algorithms are proposed for solving power system optimization problems, the strength of hybridized global search-based techniques has not commonly been applied to power system optimization. Material and Method:Deterministic power system optimization strategies are unable to yield global optimal outcomes becauseofthe entrapment in local optimum zones.Stochastic approaches like those in whichAnt-Lion Optimizer is used and hybridization algorithms with local search methods SQP, IPA,and active set give better results.Result and Discussion:Hybridized global search-based techniques havebeen successfully applied to power system optimization with economic load dispatch in particular. Results from findings hybridized-ALO outperforms modern optimization methods.Concluding Remarks:Results from findings show 3 and 13 generator systemsthat hybridized-ALO outperforms modern optimization methods

The aim of this paper is to numerically analyze the hydrothermal behavior of different cross-sectional geometries of microchannel heat sinks (MCHSs) and conduct a comparative analysis of traditional and non-traditional designs using ANSYS Fluent. It is expected that the proposed design discussed in this paper will improve the performance of MCHSs by maximizing the cooling capability and minimizing the thermal resistance and entropy generation rate, thus leading to better energy efficiency. The channel designs include a rectangular microchannel (RMC), a circular microchannel (CMC), an elliptical microchannel (EMC), a trapezoidal microchannel (TMC), a hexagonal microchannel (HMC), and a new microchannel (NMC) which has a plus-like shape. The discussed geometry of the NMC is designed in such a way that it maximizes the cross-sectional area and the wetted perimeter of the channel, keeping the hydraulic diameter constant (D-h = 412 mu m). The performance of various channels is compared on the basis of pressure drop, wall temperature, thermal enhancement factor, thermal resistance, thermal transport efficiency, and entropy generation rates. It has been observed that the NMC is capable of cooling effectively and it can achieve a minimum wall temperature of 305 K, thus offering the lowest thermal resistance (R-th), irreversible heat loss, and entropy generation rate. Moreover, the NMC has achieved the highest value of the thermal enhancement factor, i.e., 1.13, at Re = 1,000. Similarly, it has the highest thermal transport efficiency of almost 97 % at Re = 1,000, followed by the TMC and the RMC. Overall, the NMC has achieved the best performance in all aspects, followed by the RMC and TMC. The performance of the EMC, the CMC, and the HMC was found to be the worst in this study.

With the increase in energy demand, renewable energy has become a need of almost every country. Solar Energy is an important constituent of it and contributes a large portion in it. Forecasting the output power of a Photovoltaic (PV) system has always been a challenging problem in the power sector from the last few decades. The output power of a PV system depends upon several environmental factors such as irradiance (G), temperature (T), humidity (H), wind speed (W), provided the tilt angle is kept constant, among which the vital role is played by irradiance. Researchers have utilized several techniques to accurately predict the output power of PV module but every method has various pros and cons. In this paper, an experimental measurement dataset of 28296 samples with all the environmental parameters mentioned above are taken as the inputs and power as its output, of a Poly-Silicon (Poly-Si) PV module, is trained through Artificial Neural Network (ANN), to predict the output power accurately. The proposed ANN contains a layer size of 15 and training algorithm used is Levenberg-Marquardt. A detailed analysis and preprocessing of the data is carried out through Pearson's correlation method prior to training. The hyperparameters of Neural Network tuning are selected through heuristic method. The data division is done randomly with 70% dataset used for training, 15% dataset used for each validation and testing. The statistical results show that ANN accurately predicted the power output of PV module. The regression analysis values acquired are 98% and the MSE of all the three phases is 0.0604.

System identification of a Two-Wheeled Robot (TWR) through nonlinear dynamics is carried out in this paper using a data-driven approach. An Artificial Neural Network (ANN) is used as a kinematic estimator for predicting the TWR’s degree of movement in the directions of x and y and the angle of rotation Ψ along the z-axis by giving a set of input vectors in terms of linear velocity ‘V’ (i.e., generated through the angular velocity ‘ω’ of a DC motor). The DC motor rotates the TWR’s wheels that have a wheel radius of ‘r’. Training datasets are achieved via simulating nonlinear kinematics of the TWR in a MATLAB Simulink environment by varying the linear scale sets of ‘V’ and ‘(r ± ∆r)’. Perturbation of the TWR’s wheel radius at ∆r = 10% is introduced to cater to the robustness of the TWR wheel kinematics. A trained ANN accurately modeled the kinematics of the TWR. The performance indicators are regression analysis and mean square value, whose achieved values met the targeted values of 1 and 0.01, respectively.

A system identification of a two-wheeled robot (TWR) using a data-driven approach from its fundamental nonlinear kinematics is investigated. The fundamental model of the TWR is implemented in a Simulink environment and tested at various input/output operating conditions. The testing outcome of TWR's fundamental dynamics generated 12 datasets. These datasets are used for system identification using simple autoregressive exogenous (ARX) and non-linear auto-regressive exogenous (NLARX) models. Initially the ARX structure is heuristically selected and estimated through a single operating condition. We conclude that the single ARX model does not satisfy TWR dynamics for all datasets in term of fitness. However, NLARX fitted the 12 estimated datasets and 2 validation datasets using sigmoid nonlinearity. The obtained results are compared with TWR's fundamental dynamics and predicted outputs of the NLARX showed more than 98% accuracy at various operating conditions.

To control the power flow among various energy sources and loads of a power system of modern more electric aircrafts, power electronics converters are employed. The integration of multiple sources into distribution system and their interconnection with variety of loads through power electronic converters results in a complex dynamic system. Modeling of these systems prior to implementation becomes necessary to analyze and predict system’s behavior. The classical modeling approaches require detail knowledge about the topology and parameters of the active and passive components of the power electronics converters. While in modern system, most of the power electronics converters are ready to use power electronics modules. These modules come from different manufacturers, lacking the necessary information to build the conventional switch or average models. The chapter would cover dynamic behavioral modeling technique for power electronics systems to be employed in more electric aircrafts, which do not require any prior information about the internal details of the system.

The remarkable progress of power electronic converters (PEC) technology has led to their increased penetration in distributed energy systems (DES). Particularly, the direct current (dc) nanogrid-based DES embody a variety of sources and loads, connected through a central dc bus. Therefore, PECs are required to be employed as an interface. It would facilitate incorporation of the renewable energy sources and battery storage system into dc nanogrids. However, it is more challenging as the integration of multiple modules may cause instabilities in the overall system dynamics. Future dc nanogrids are envisioned to deploy ready-to-use commercial PEC, for which designers have no insight into their dynamic behavior. Furthermore, the high variability of the operating conditions constitute a new paradigm in dc nanogrids. It exacerbates the dynamic analysis using traditional techniques. Therefore, the current work proposes behavioral modeling to perform system level analysis of a dc nanogrid-based DES. It relies only on the data acquired via measurements performed at the input–output terminals only. To verify the accuracy of the model, large signal disturbances are applied. The matching of results for the switch model and its behavioral model verifies the effectiveness of the proposed model.