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The rapid development of electric vehicles (EVs) has created more possibilities for their flexible participation in electric power dispatching. Considering the clustering and fast mobility of EVs coinciding with real-time market requirements for responsive demands, a bidding strategy is proposed in this paper to assist EV aggregators with submitting reasonable quotations for the real-time market. Based on Bayesian updating, the strategy in this paper considers relevant factors such as aggregator cost and expected return. First, a quotation strategy in electricity spot market is proposed for EV aggregators. Second, the applicable scope of the strategy is given under different scenarios. Then, the objective function of learning is proposed, and the specific process of Bayesian updating is demonstrated. Finally, a case study of the aggregator quotation strategy based on Bayesian updating is simulated to verify the effectiveness and feasibility of the proposed strategy, which helps improve the bidding success rate of EVs and peak-to-valley balance of the grid. [ABSTRACT FROM AUTHOR]

In the ship's electrical power system, the largest percentage of a load powered by an induction machine (IM) can be represented through the fan load characteristics. Likewise, it is well known that, from the point of view of the current load, the "most problematic" way of starting the induction machine is the direct start. This paper deals with a novel analytical modeling of the IM direct start-up speed–time curve under fan load. The derived analytical expressions have been used for modeling the start of induction machines of different powers and voltage levels. Also, the obtained results have been compared with the corresponding results provided by using the realized MATLAB/Simulink model of the induction machine coupled with fan load. Moreover, the results obtained have been compared with the methods presented in the literature as regards the modeling of start-up characteristics. The experimental validation of the proposed analytical expressions for modeling the direct start of IM driving fan load has also been carried out on a 300 W machine in a laboratory environment. Both the simulations and experimental verification have demonstrated an exceptional accuracy of the derived analytical expressions. The paper also provides the implemented MATLAB code for determining the speed–time curve of IM during direct start under fan-type load. [ABSTRACT FROM AUTHOR]

The detection of defects on small cotter pins that are installed in electric power fittings is an essential part of the inspection task of overhead lines using Unmanned Aerial Vehicles (UAV). It is challenging to detect small defects from a large number of UAV images. In this paper, an efficient and high-performance defect detection model called DDNet is proposed to recognize defects from images of unmanned aerial vehicles. The attention mechanism was adopted in the improved detection model in order to enhance the representation learning of the image. Inspired by the human visual system, the RFB module is added to the FPN module, increasing the receptive field of the entire detection network, which is conducive to the detection of small objects. Then a dataset of cotter pins for model training and testing was introduced. The study demonstrates that the proposed DDNet increases the average precision from 82.0 to 90.1% and reduces the miss rate of defect detection from 14.5 to 7.4% in our dataset compared to the baseline RetinaNet model. We also compared existing frameworks for object detection and discussed other common ways to improve precision. The results showed that our optimized model in this paper improved detection performance, which subsequently proved the practicability and effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Industries using electrical power have a critical and pressing need for improved power quality (PQ). Several important problems have already been identified, such as voltage swell, sag, and harmonics. However, PQ fluctuation issues remain unresolved. A series of active power filters are used to shield delicate loads from PQ related problems. This paper present a low-complexity control technique for voltage compensation using a type of custom power device used with different inverter topologies called Dynamic Voltage Restorer (DVR). In this study, a three-phase Z-Source Inverter-based DVR is employed to reduce voltage disruption, including voltage fluctuation, sag, swell, and harmonics. Error-driven Proportional-Integral-Derivative (PID) controller provides higher PQ performance in terms of voltage augmentation, stabilization, and reduction in harmonic distortion in the distribution system. This strategy keeps the load voltage near or close to the nominal value and addresses many other voltage issues. The gain parameter of a PID controller is tuned using Harris Hawks Optimization (HHO) method. The proposed work is compared with traditional controllers as well as Genetic Algorithm-based PID controllers, and the distortion of total harmonics comparisons are also conducted to show the accuracy of the proposed Z-DVR. To demonstrate the effectiveness of the suggested methodology, it is implemented in MATLAB/Simulink R2020a tool and the results of the mitigated sag, swell fluctuation, and total harmonic distortion (THD) are studied. [ABSTRACT FROM AUTHOR]

Generator rotor angle oscillations can be caused by sudden changes in its mechanical power input or its electrical power output. When dampening or synchronizing torque is inadequate, rotor angle instability occurs, resulting in an increase in rotor angle, loss of synchronism, or oscillatory swings of the rotor angle with increasing amplitude. To this end, in this work, we offer a novel two-stage approach for predicting rotor angle stability following a large disturbance. The process begins with the creation of a database of dynamic simulation scenarios. This collection contains a wide set of stable and unstable rotor angle trajectories derived from different fault simulations. Then, the fuzzy c-means (FCM) clustering algorithm is utilized to put the sampled data of rotor angles with the highest degree of similarity in the same cluster. Angle sets generated by FCM are used to train the adaptive neuro-fuzzy inference system (ANFIS). Finally, the trained ANFIS is used to predict the future rotor angle stability situation of generators. In addition, a stability index is suggested in this article, which will assist ANFIS in more accurately predicting the stability condition. The efficiency of the proposed method is tested on the IEEE 39-bus system. The obtained results from simulations confirm that the proposed strategy can correctly predict the rotor angle instability or stability situation of generators in a few cycles after fault clearance. [ABSTRACT FROM AUTHOR]

To minimize total operating costs, emissions, and power losses, the optimum power generation and scheduling of renewable integrated hydro-thermal systems are one of the most significant objectives in short-term scheduling. The solution to these problems becomes more difficult with constraints and renewable uncertainties. The paper presents an improved cheetah optimizer (ICO) for solving the optimum wind-solar-hydro-thermal scheduling problem considering valve loading effects, ramp-rate limits, power loss, and prohibited operational zone constraints. The main objective is to optimize the total fuel cost and emissions for thermal power generators, where electric power can be fully harnessed from renewable generators. Different test systems are employed to evaluate the proposed ICO solution method's performance. The proposed ICO solution method is compared with other algorithms like grey wolf optimizer, and particle swarm optimizer, in terms of optimal fuel costs, emissions, convergence success rate, and computation time. The test systems are incorporated with wind farms, solar farm, hydropower generators, and thermal power generators scheduled for 24-h, 1-h subintervals. The simulation solutions of the renewable integrated system have been acquired by ICO, CO, GWO, and PSO. The total generation cost obtained by ICO is 0.0698%, and 0.1514% lower than the cost obtained by GWO, and PSO respectively. The total power loss was minimized by 1.8554% and 7.4002%. The total emissions can be reduced to 25% with increasing penetration of renewable energy sources. It is realized from the comparison that the proposed ICO method has the potential to provide better-quality solutions. [ABSTRACT FROM AUTHOR]

The electrical power system (EPS) has been heavily stressed due to high load demand. It operates close to the total capacity limits, resulting in voltage instability that can lead to voltage collapse. In this regard, incorporating flexible alternating current transmission system (FACTS) devices and renewable energy sources (RESs) to obtain the optimum values of the generator voltage, reactive compensation, and transformer tab in optimal reactive power dispatch (ORPD) is essential in increasing the reliability and safety of the system. ORPD involves discrete and continuous variables, which are nonlinear, noncontinuous, non-convex, and complex problems. The objective functions of ORPD are reduction in active power loss (Ploss), voltage deviation, and voltage profile enhancement. This paper presents a recent advancement of the ORPD problem, mathematical formulation of the objectives function, and a summary of various metaheuristic optimization methods (single and hybrid) used to solve the ORPD problems. The hybrid method combines two or more methods to improve the demerits of one method to obtain a quality solution to a problem. This review covered incorporating FACTS devices and RESs used in solving the ORPD problem to reduce the active Ploss and improve the voltage profile in the EPS. The benefits of FACTS devices and RESs are also discussed. Also, various metaheuristic algorithms (single, modified, and hybrid) employed to solve the ORPD problem were discussed. The future direction for researchers in this field was provided to give insight into the applicability and performance. Overall, this research explores different techniques used in solving ORPD problems from the optimization point of view to incorporating RESs and FACTS devices to obtain quality solutions. Some existing methods do not guarantee an optimum solution, but incorporating RESs and FACTS devices will help attain the best solution to the problem for better power system operation to improve system reliability and voltage profile. Based on the review journal, it can be concluded that hybrid techniques offer efficient quality solutions to the ORPD problem. [ABSTRACT FROM AUTHOR]

This paper presents the systematic design methodology of a 3.3 kW, level 2 battery charger with improved grid power factor for EV applications. The charging of the battery bank from the utility grid through bridgeless interleaved boost (BIB) converter and the proposed three-level modified series–parallel resonant converter is explained in detail. The proposed topology offers low voltage stress equal to half of the DC link voltage across the switches as three-level is implemented in the resonant stage. The transformer parasitics are taken into consideration and the design of resonant stage for frequency modulation is elaborated. One of the major concerns of the charging system is the grid power factor which is largely affected by power electronic switching operation. The detailed controller design procedure for power factor correction of BIB and DC link voltage regulation is elaborated in this paper. Simulation results obtained using PSIM software prove that the power factor is close to unity with less supply current total harmonic distortion as per IEC 61000-3-2 standard. The proposed resonant converter switches achieve zero voltage switching for above resonant frequency operation. [ABSTRACT FROM AUTHOR]

Large solar power plants are installed in many countries, and they are connected to an electrical grid thereby providing an alternative to conventional energy sources. Photovoltaic cells are using solar energy directly and convert it into electrical power. To ensure there liable and safe operation of PV arrays, it is very essential to diagnose faults in PV arrays. Traditional fault detection devices are used for detecting line-to-line fault and open-circuit faults, if the magnitude of fault current defined as per international standards exceeds the threshold value. Due to several aspects like low irradiance condition, minimal fault mismatch, and active maximum power point trackers which minimizes the magnitude of fault current thereby resulting in fault identification becoming much more difficult. In case a fault in a PV array is not identified and the fault persists for a very long duration, it results in reliability issues and also it may lead to severe fire hazards. Hence, it is essential to identify the fault in a timely manner, and also localization is very much essential for sustainable power generation. This paper proposes new fault identification scheme for solar PV arrays connected in bridge and honeycomb configuration, and this method is capable to detect line-to-line fault and open-circuit faults with a minimum number of sensor requirement and also irrespective of the detection challenges. For real time, validation testing is carried out in a PV array, and the power gain due to the identification of line-to-line fault and line-to-ground fault is also investigated. [ABSTRACT FROM AUTHOR]

An equivalent model for synchronous generators (SGs) in a wide-area power system is indispensable for lessening the computation burden of dynamic analysis. In this paper, we propose a discrete-time models (DTM) for SGs. DTM of SGs expedites the interconnection of equivalent models for describing SGs, and also improving the veracity of SGs in dynamic simulations. Firstly, the paper investigates the operational forms of SGs. Then, the paper expands the intrinsic relations among DTM parameters of SGs in the DTM with and without considering the bus angle. Besides, numerical contrast experiments are conducted based on the China Electric Power Research Institute-36 (CEPRI-36) system, and the veracity of DTM for SGs is verified by simulation results. Finally, the reason why the bus voltage phase angle variations are ignored of the DTM for SGs (or an equivalent aggregated generator) is shown in this paper. [ABSTRACT FROM AUTHOR]

The main purpose of this paper is to develop an efficient machine learning model to estimate the electric power load. The developed machine learning model can be used by electric power utilities for proper operation and maintenance of grid and also to trade electricity effectively in energy market. This paper proposes a machine learning model using gated recurrent unit (GRU) and random forest (RF). GRU has been employed to predict the electric power load, whereas RF has been used to reduce the input dimensions of the model. GRU has been estimating the load with good accuracy. RF reduces the input dimensions of the GRU that leads lightweight GRU model. The main benefits of the lightweight GRU models are less computation time and memory space. However, lightweight GRU models will loss small amount of accuracy comparing to the original GRU model. GRU along with RF has been used for the first for short load forecasting. All the machine learning model's performance has been observed in stochastic environment. Impact of weekends on load forecasting also observed by considering the last 3-week load data. [ABSTRACT FROM AUTHOR]

Smart lighting is considered an emerging trend in the global lighting industry. In terms of scale, expense, and performance, smart LED lighting applications are more advantageous. However, the input current of the power supply used for driving LEDs is more polluted due to the presence of switching devices, thereby lowering the power factor (PF). The bridge circuit of conventional DC–DC converters that use a diode bridge rectifier (DBR) as an interface between the main supply and the LED load have substantial losses and hence offers low efficiency. It also presents adverse power quality problems in the supply current resulting in poor PF. In this paper, artificial bee colony optimization algorithm-based closed loop digital proportional integral controller design and implementation of modified Type-III Bridgeless Cuk (BLCUK) LED driver are presented to obtain unity power factor and higher efficiency for smart lighting applications. MATLAB software is used to conduct the computer simulation analysis of the BLCUK converter. The experimental prototype is developed and tested using C2000 32-bit Microcontroller TMS320F28027. The digital controller (2P2Z-two poles two zeros controller) is implemented for the proposed control scheme, and the obtained experimental results are in consonance with the simulation outcomes. [ABSTRACT FROM AUTHOR]

This paper proposes a novel capacitive power transfer (CPT) system with switchable compensation for achieving constant current (CC) or constant voltage (CV) output against load and coupling capacitance variations. Meanwhile, a parameter design method is proposed to ensure a low input impedance angle at constant operation modes. Mathematical models of voltage and current gains are established, which are used to analyze the CC and CV operation modes of the proposed CPT system. Two relays are adopted in receiver to reconfigure the compensation network to switch the CC mode and CV mode. The communication between the transmitter and receiver is not needed, and complex control is not required either for changing two operation modes. An experimental setup with 2A output current in CC mode and 96 V output voltage in CV mode is constructed. Experimental results show that the maximum variation of the output current is 4.0% in CC mode, and the maximum variation of the output voltage is 5.0% in CV mode, when the load resistance and coupling capacitance vary from 24 to 72 ohms, and 500 to 1000 pF, respectively. Experimental results verify the CC/CV output and the effectiveness of the proposed parameter design method. [ABSTRACT FROM AUTHOR]

In the realm of economics, agriculture holds supreme importance. The Internet of Things (IoT) is now pivotal in agriculture, aiding farmers in monitoring crop yield. Smart meters and control methods streamline agricultural operations, managing intelligent equipment, bidirectional communication, and user interaction. Data from sensors capturing soil and environmental parameters like moisture, humidity and temperature are integrated into Neural Networks for predictive analysis. Water scarcity, irrigation, and electrical power utilization creates impact on global crop growth and quality. This paper introduces an IoT-enabled product for coconut farming, enabling real-time monitoring and control of irrigation, energy usage, and power quality. The Smart Agriculture Irrigation Management System (AIMS) monitors valves, pumps, water levels, soil, and environmental conditions autonomously. Users can implement automated or manual decision-making processes. Additionally, a Smart Agriculture Energy Management System with integrated Smart Agriculture Energy Meter monitors power consumption, Power Quality, anomalies, and disturbances, notifying farmers via cloud services with predicted values. Implemented in a coconut farm in Sirumalai, Tamil Nadu, India, the system aims to reduce manual stress, enhancing productivity, yield, and water saving by over 30%. Predicted energy consumption patterns and tariffs help farmers avoid excessive costs, resulting in around 40% energy savings, facilitated by the superior performance of RNN-LSTM model over traditional methods. [ABSTRACT FROM AUTHOR]

The integration of large amounts of small-scale distributed energy resources such as solar photovoltaics complicated the stability and quality issues in the existing electrical power system. For an electric grid to be sustainable, grid-integrated solar photovoltaic power-generation conversion systems (SPCS) must provide ancillary services including improving power quality, capturing actual power, and maintaining high conversion efficiency. This paper proposes a combine affine projection sign (CAPS) algorithm for performing power quality (PQ) ancillary services in a grid-interactive SPCS. The principle objective of the proposed method is to provide steady and reliable operation while mitigating the impact of power quality issues. The proposed CAPS algorithm efficiently extracts the active and non-active harmonic free fundamental weight components of the contaminated grid/load currents. These weights enhance reference signal estimation accuracy and provide switching signals for the grid-solar PV integrating voltage source inverter. Furthermore, the characteristics of the solar PV strings exhibit many peaks when exposed to non-uniform irradiation making it difficult to track the global peak power. Therefore, a metaheuristic JAYA-based maximum power point tracking algorithm is proposed to overcome this problem. This algorithm offers an extremely fast dynamic response with high accuracy and minimal noise in the steady state in contrast to conventional methods, especially under partial shading conditions. The experimental validation is carried out, and results support the effectiveness of the proposed algorithms for enhancing PQ functionality under varying scenarios. Additionally, implementing the proposed CAPS controller significantly reduces the harmonic contamination of the grid current, from 23.5%\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\%$$\end{document} to 3.07%\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\%$$\end{document}, thus complying with the IEEE 519-2014 and IEEE 1547-2018 standards for total harmonics distortion in an adequate manner. [ABSTRACT FROM AUTHOR]

In order to improve energy efficiency, reduce dependence on fossil fuels, and enhance the sustainable development capability of the power system, this paper proposes a virtual grid and thermal power joint peak shaving optimization model based on the power Internet of Things. Establish an objective function to optimize the charging and discharging loss cost of energy storage equipment, the interruption compensation cost of interruptible loads, and the operating cost of thermal power units, in order to improve the optimization scheduling effect of the power system. Set power balance constraints, interruptible load constraints, energy storage constraints, and power constraints for thermal power units to maximize their operational efficiency, and construct a virtual grid and thermal power joint peak shaving optimization model. Using ant colony algorithm to solve the model and obtain the optimal peak shaving value that meets the development needs of the power system. The simulation results show that the mean r2 of our method is 0.97, and the average RMSE is 0.17. It has been proven that the model has good joint shaving optimization ability and high optimization accuracy. It can ensure the output of thermal power units and lower the peak shaving pressure of the power system, thereby promoting the sustainable development and stable operation of the power system. [ABSTRACT FROM AUTHOR]

The power grid is intended to deliver electric power from large, remote power generation units to end-consumers in residential, commercial, and industrial sectors. The traditional grid design is drastically altering as a result of recent trends such as increased allocation of renewables, distributed energy resources (DERs) with uncertainty in power, and environmental changes that are pushing weather scenarios toward extremes. Reliability and resiliency are quickly evolving as key issues in the power system in order to react quickly and securely to those significant changes. Extreme weather conditions may result in numerous contingency outages, cascade failures, zonal attacks, and widespread blackouts. This paper proposes a framework for effective quantification analysis of zonal attack and formation of two-stage constrained spectral clustering. This research focuses on a two-stage constrained spectral clustering method for managing critical infrastructure in an integrated DER power distribution network. The primary objective is to provide a virtual clustering framework based on the real power flowing of lines in the power distribution network that can deliver a reliable and consistent cluster. The proposed control algorithm is tested on IEEE 123 bus distribution system. Based on the position of sectionalizing switches, the test system is divided into seven zones. Zone-7 and zone-5 attacks are visualized in the MATLAB/SIMULINK (2023a) environment and are found to perform better than the alternatives and provide an effective power balance. The proposed model is also developed in OPAL-RT OP-4512 HIL real-time simulation platform; the zone-4 attack is created, and the clustering results are verified using a real real-time digital simulator. In addition to offline simulations, the performance of the proposed methodology has been verified in real time by running hardware-in-the-loop simulations on the OPAL-RT real-time simulator. Finally, the clustering quality is evaluated using metrics like Ncut, discontinuities, maximum expansion, and ratio of cluster sizes. [ABSTRACT FROM AUTHOR]

Distribution networks transmit the electrical power generated by generation systems to consumers. The distribution network poses a significant challenge in a power system, as it incurs the highest losses due to its low voltage and high current characteristics. Therefore, addressing the issue of reducing distribution network losses is crucial. Over the past 46 years, distribution network reconfiguration (DNR) has been extensively studied as a combinatorial optimization problem. DNR is one of the most intensively investigated topics, accompanied by new challenges. This study focuses on the optimal reconfiguration of the distribution network and the allocation of shunt capacitors to reduce losses and enhance voltage profiles. The cultural algorithm (CA) and the cuckoo search algorithm (CSA) were compared for solving network reconfiguration and capacitor allocation. The cultural algorithm (CA) and the cuckoo search algorithm (CSA) were compared for solving network reconfiguration and capacitor allocation by using direct load flow to minimize power loss and voltage deviation. By comparing the performance of these two algorithms and examining their advantages and disadvantages, the appropriate algorithm can be selected for future studies. The performance of the simulations was verified using MATLAB software on IEEE 33-bus and IEEE 69-bus systems. [ABSTRACT FROM AUTHOR]

The integration of renewable energy systems into the existing power grid has become a global necessity. While there are numerous advantages to this integration, it also presents challenges such as oscillations in system frequency and tie-line power. These issues can lead to instability and undesirable situations within the power system. To ensure a consistent and reliable supply of high-quality electrical power to consumers, a control system is essential. In recent years, cascaded controllers, known for their increased flexibility and degree of freedom compared to non-cascaded controllers, have gained popularity in the literature. In this study, we aim to examine the performance of cascaded fractional and integer order TI-TD and PI-PD controllers in comparison with non-cascaded PID and TID controllers for a challenging stability and robustness assessment. Our focus is on a two-area PV-reheat thermal power system. By conducting comprehensive tests, we aim to gain insights into the effectiveness of these controller types within this context. To ensure optimal controller responses, the designed controllers are optimized using the Mayfly algorithm, with a focus on minimizing the integral of the time-weighted absolute error performance index. The performance tests conducted in this study cover various aspects, including time domain analysis, robustness, random load changes, the impact of progressive nonlinearities, and cyber-attack (C-A) scenarios. The power system under examination incorporates several nonlinear components, namely governor dead band, generation rate constraints, boiler dynamics, and time delay. Additionally, two C-A models, namely resonance attack (ResA) and random attack, are applied to the controller systems. Unlike conventional robustness and C-A tests found in existing literature, this study goes further by measuring the response of the proposed controller to high parameter changes (uncertainty) and conducting a quantitative analysis of its resilience against C-A scenarios. The aim is to assess the controller's ability to withstand significant variations in system parameters and its effectiveness in dealing with cyber-attacks. The simulation results demonstrate the superior performance of the optimized and cascaded controllers, particularly the TI-TD controller. These controllers exhibit improved performance compared to other controllers reported in the literature. The findings validate the effectiveness of the proposed controllers and highlight their advantages in terms of system stability, response time, and overall control quality. [ABSTRACT FROM AUTHOR]

Advances in the areas of electrical and power electronic technologies play a key role in more electric aircraft (MEA), especially in power converters. Therefore, most electrical power loads on MEA are tightly controlled power converters that behave as constant power loads (CPLs). These CPLs look like a small-signal negative impedance that can significantly degrade the overall system stability. The destabilizing effect may result in poor performance until the DC bus voltage of the system does not adhere to the MIL-STD-704F standard. Thus, a stability study is very important to avoid unstable operations. A proposed MEA model which can be derived using the DQ approach is comprehensive in the introduced stability analysis methods in this article. These methods, i.e., a small-signal stability analysis using the eigenvalue theorem, a modal analysis technique called participation factor analysis, and a large-signal stability analysis via phase-plane analysis, are performed to investigate the stability margin. Moreover, the impact of key parameter variations on MEA stability is also taken into account to deliver the ways of parameter selection at the early design stages for an engineer. The MATLAB topology model and processor-in-loop (PIL) simulations validated the analytical results. The results indicate that a good agreement between theoretical, simulation, and PIL results can be achieved. [ABSTRACT FROM AUTHOR]

The microgrid technology must be conforming in electrical power system to cater the expanding development in power demand in the present scenario, and it is extremely desirable to maintain power quality of those networks in competitive environment. In such a situation, the identification of transient disturbances and classification are always exigent because of the power quality issues. This work presents a novel decision tree (DT) algorithm based on machine learning for the identification and classification of different possible transient conditions in microgrid. The transient study comprises of fault at distribution line, i.e., symmetrical and unsymmetrical, islanding from grid due to three-phase short-circuit fault, intentional islanding, etc. The proposed method starts with tracking the transient signal of phase voltage and current at different locations, and each signal is to be processed through discrete wavelet transform (DWT). The algorithm is followed by the feature selection from the performance indices of DWT coefficient for DT classification technique. The findings of the extensive case studies show that the suggested algorithm is used to detect and discriminate each transient event with 100% accuracy. The performance of the proposed method has been validated using the confusion matrix. [ABSTRACT FROM AUTHOR]