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The current task explores automatic generation control knowledge under old-style circumstances for a triple-arena scheme. Sources in area-1 are thermal-solar thermal (ST); thermalgeothermal power plant (GPP) in area-2 and thermal-hydro in area-3. An original endeavour has been set out to execute a new performance index named hybrid peak area integral squared error (HPA-ISE) and two-stage controller with amalgamation of proportional-integral and fractional order proportional-derivative, hence named as PI(FOPD). The performance of PI(FOPD) has been compared with varied controllers like proportional-integral (PI), proportional-integralderivative (PID). Various investigation express excellency of PI(FOPD) controller over other controller from outlook regarding lessened level of peak anomalies and time duration for settling. Thus, PI(FOPD) controller’s excellent performance is stated when comparison is undergone for a three-area basic thermal system. The above said controller’s gains and related parameters are developed by the aid of Artificial Rabbit Optimization (ARO). Also, studies with HPA-ISE enhances system dynamics over ISE. Moreover, a study on various area capacity ratios (ACR) suggests that high ACR shows better dynamics. The basic thermal system is united with renewable sources ST in area-1 also GPP in area-2. Also, hydro unit is installed in area-3. The performance of this new combination of system is compared with the basic thermal system using PI(FOPD) controller. It is detected that dynamic presentation of new system is improved. Action in existence of redox flow battery is also examined which provides with noteworthy outcome. PI(FOPD) parameters values at nominal condition are appropriate for higher value of disturbance without need for optimization.

This article validates the application of RT-Lab for the AGC studies of three-area systems. All the areas are employed with thermal-DSTS systems. A new controller named cascade FOPDN-FOPPIDN is employed. Its parameters are optimized using a CSA, subjecting to a new PI named HPA-ISE. The responses of the FOPDN-FOPIDN controller are related and are superior over PIDN and TIDN controllers. Moreover, the dominance of HPA-ISE is verified with ISE, and it performs better in terms of system dynamics. Further, the system performance reliability is analyzed with the AC-HVDC and is better than the AC system. Besides, sensitivity analysis recommends that the proposed FOPDN-FOPIDN at diverse conditions is robust and more reliability.

This paper presents the formulation and numerical simulation for linear quadratic optimal control problem (LQOCP) of free terminal state and fixed terminal time fractional order discrete time singular system (FODSS). System dynamics is expressed in terms of Riemann-Liouville fractional derivative (RLFD), and performance index (PI) in terms of state and costate. Because of its complexity, finding analytical and numerical solutions to singular system (SS) is difficult. As a result, we use coordinate transformation to convert FODSS to its corresponding fractional order discrete time nonsingular system (FODNSS). After that, we obtain the necessary conditions by employing a Hamiltonian approach. The relevant conditions are solved using the general solution approach. For the analysis of formulation and solution algorithm, a numerical example is illustrated. Results are obtained for various �� values. According to state of the art, this is the first time that a formulation and numerical simulation of free terminal state and fixed terminal time optimal control problem (OCP) of FODSS is presented.

Abstract Distribution networks frequently experience cross‐country failures (CCFs).This study presents research that demonstrates how Cross‐Correlation may be used to extract distinctive features from faulty current signals, hence providing a threshold‐based technique for fault identification. The most challenging aspect of dealing with HIF syndrome‐related CCFs is recognizing and categorizing them. In this research, the complex, aperiodic, asymmetric, and nonlinear features of the signals generated by CCF with HIF syndrome were recovered using the Cross Correlation method. The low frequencies are filtered out using a high pass filter, leaving only the positive peak and two negative peaks in the vicinity as unique features of the correlogram. In this work, the proposed approach is put to the test on the modified imbalanced IEEE 240 Bus system. The suggested method is assessed through the lens of several different case studies, such as the switching of a capacitor bank, a reactor string, a load, a feeder, the effects of a power swing, nonlinear loads, lightly laden conditions, and measurement noise. The normal and faulty signals can be distinguished by their normalized QRS levels. By selecting the most appropriate sensors using the Jellyfish optimization method, we were also able to ascertain which bus housed CCHIF.

Abstract Here, stochastic‐gradient‐based adaptive control algorithms have been discussed and employed for power quality enhancement in a Photovoltaics (PV) integrated distribution system. Least mean square (LMS), least mean fourth (LMF), sign‐error LMS, and ε‐normalised LMS (ε‐NLMS) have been implemented as control algorithms for the estimation of fundamental load current. The performances of these adaptive algorithms are compared under steady‐state and dynamic conditions under the non‐linear load conditions in a closed‐loop three‐phase system. The main aim of implementing these algorithms is reactive power compensation, power quality enhancement, and load balancing in a single‐stage three‐phase grid‐tied PV system. The hysteresis current control (HCC) technique is used to generate switching pulses for the three‐phase Distribution Static Power Compensator (DSTATCOM). An MPPT is also employed to ensure maximum power delivery from the solar PV array. PV integrated three‐phase single‐stage distribution system with adaptive control algorithms is implemented in MATLAB/Simulink environment as well as in experimental environment to achieve the goals per standard IEEE‐519.

In this paper, we introduce a novel approach to enhance the accuracy and convergence behavior of Self-Organizing Maps (SOM) by incorporating a reweighted zero-attracting term into the loss function. We evaluated two SOM versions: conventional SOM and robust adaptive SOM (RASOM). The enhanced versions, reweighted zero-attracting SOM (RZA-SOM) and reweighted zero-attracting RASOM (RZA-RASOM), include an l1 norm in the error function to add a zero-attractor term, which improves weight coefficient adjustments while preserving topology. The models were assessed for convergence speed and misadjustment under sparsity assumptions of the true coefficient matrix, and their robustness was tested under conditions of increased non-zero taps. Using six different datasets, we compared the performance of RZA-SOM and RZA-RASOM against conventional SOM and RA-SOM in terms of accuracy, quantization error, and topology preservation. Experimental results consistently demonstrated that RZA-SOM and RZA-RASOM surpassed the performance of conventional SOM and RA-SOM.

In this paper, simulation, and performance comparison of a switched reluctance motor (SRM) for a classical asymmetric bridge converter and an improved reduced switch converter topology are provided. In an improved converter, switches are shared by adjacent phases, and one diode is connected in series with each phase winding to avoid reversal of current. Double-phase magnetization is adopted for the analysis of both converter topologies. The performance of both converter topologies are demonstrated using a proportional plus integral (PI) based hysteresis current controller and dynamic load on a 7.5 kW, 8/6 pole, 4- phase SRM. Improved reduced switch converter topology uses half the number of switches as compared to the classical asymmetric bridge converter. According to the simulation results, an improved converter offers superior starting and average torque with less speed settling time. An improved converter reduces the most serious problem of torque ripple in SRM by 50% compared to the classical topology. The reduced switches lower the winding losses and improve the efficiency of the drive system.

The multilevel inverter (MLI) is a power electronic circuit applied for power and voltage applications. It has the advantages of minimum total harmonic distortion (THD), less voltage stress on the switching devices, fewer switching losses, and a smaller size of passive filters. These high-level inverters are applied in applications like AC drives, FACTS, and in the field of renewable energy. There are various inverters for such applications. A PUC multilevel inverter contains fewer components, fewer switching losses, and easy-to-balance voltage on capacitor sides. In this paper, the performance of the transformer less seven-level inverter and the five-level inverter is analyzed. The sinusoidal pulse width modulation (PWM) has been used with this PUC-based structure. The new control strategy is designed to reduce harmonic contents and low filter ratings. A relative analysis is carried out to focus on the superiority of the recently developed packed U-cell topology. The performance of packed U-cell, seven-level, and five-level inverters has been compared. These types of inverters consist of two power switches and a single capacitor to make this inverter effective. The PUC 7 inverter contains a lot of sensors and other control components to balance the voltage on the capacitor side by one-third of the input voltage, so the structure becomes complex. While PUC 5 is sensor less technology, there is no voltage balancing issue and no complexity found in this inverter. In PUC5, the only level is low, but it is very easy to interface with grids and other devices. So it is the most preferable as compared to PUC7. The performance analyses of these inverters have been verified through simulation.

The whole world is now widely using green energy compared to fossil because of the depletion of fossil fuels, the rising temperature of the earth, and changing weather conditions, all these things are becoming a big threat to the life of the earth. This study proposed a stand-alone hybrid renewable energy system using different types of batteries. This model includes photovoltaic arrays, wind turbines, diesel generators, converters, and batteries. Lead-acid and lithium-ion batteries have been compared for the selection of optimal battery based on hybrid renewable energy system and sustainable development requirements. The purpose of this study is to find the optimal configuration, and techno-economic characteristics, using the hybrid optimization of multiple energy resources technique. The results of Lithium-ion and Lead Acid have been compared and it is found that the best configuration is photovoltaic arrays/wind turbines/ diesel generators /Battery/converter with lithium-ion Batteries. The net present cost and cost of energy are found to be 1.64M and 0.144$ respectively, for the selected study location. The carbon dioxide emission for configuration with LI batteries is 107314 kg/year as against the LA batteries which have 351288 kg/year. The results show LI batteries are technically as well as economically better than the LA batteries.

This article investigates the impact of a high-voltage direct-current (HVDC) link and a wind turbine system (WTS) on the dynamics of a three-area thermal automatic generation control (AGC) system. A novel controller, the cascade of proportional-integral (PI) and tiltintegralderivative (TID) with filter coefficient (N) (PI-TIDN) controller is projected. The WTS units are subjected to various wind velocity scenarios, including fixed and random wind velocities. The controller parameters are concurrently enhanced using the hybrid crowsearch algorithm (HCSA). The system dynamics corresponding to the PI-TIDN controller are superior to those of PIDN and TIDN controllers. Additionally, studies with different wind velocities demonstrate that responses with fixed wind velocities are better than those with random wind velocities. Moreover, integrating WTS units with the thermal system improves dynamics compared to the thermal system alone. It is also apparent that the parallel AC-HVDC system enhances dynamics. Furthermore, sensitivity analysis exposes that the PI-TIDN controller values at nominal settings are vigorous and do not require retuning.

Electric vehicle charging stations (EVCS) that are based on DC microgrids are presented in this research. The system comprises a solar photovoltaic system (SPVS), storage battery (SB), electric vehicle (EV) and grid. The adaptive interaction artificial neural network (AI-ANN)-based vehicle to grid (V2G) and grid to vehicle (G2V) power management controller (PMC) is suggested for DC microgrid based EVCS. This EVCS is suitable for the residential building and offices where EV may be parked. This EVCS provides the facility to manage the power of the building in addition to charge the EVIt has two different modes of operation. The first mode uses the EV as a power source. In the second mode, the EV functions as a load. This controller is developed to acquire electrical power from the solar photovoltaic system (SPVS), storage battery, EV and grid respectively. If the solar photovoltaic system (SPVS) and storage battery power are insufficient to meet the demand, power is extracted from electric vehicle (V2G). If the solar photovoltaic system (SPVS), storage battery and EV are not sufficient to meet up demand, then deficit power is obtained from the grid (G2V). ANN based power management controller (PMC) Also provides a consistent DC bus voltage and reduces overshoot from 9.6 % to 0 %., settling time from 1.18 sec. to 0.52 sec. and rise time from 0.27 sec. to 0.25 sec. of DC bus voltage compared to conventional controller. The suggested power management controller tested for two different modes i.e., V2G and G2V using MATLAB Simulink software.

Effective microgrid control for system recovery and restoring normal operation necessitates fast event detection and implementation of remedial action (if need arises). However, fast and reliable event detection in microgrids is challenging because of low observability and inconsistencies in measurements. A novel technique is proposed in the present work for the real-time event detection and to identify the various emerging abnormalities in the microgrid. The continuous energy signature using TKEO (Teager-Kaiser Energy Operator) of the continuous varying voltage and frequency signal are extracted through μPMU. REII (Robust Event Identification Index) is constructed from these energy signatures and based on its abrupt post-event deviation from the nominal values an event is flagged in the proposed method. The proposed method is data–driven and only depends on the real-time inputs through μPMUs thus it automatically adapts the uncertainties associated with the intermittent sources of energy in the microgrid under different operating conditions. The traditional event detection techniques fail in identification of abnormalities for a microgrid connected to the transmission systems and equipped with multiple DERs such as PVDG, WG etc. To address this challenge, an integrated microgrid with multiple DERs viz. PVDG, WG and a SG (Synchronous Generator) is first developed in this work. The complexity of simultaneous operation of a static generator i.e. PVDG along with a rotor-based generator such as WG and SG is handled by the modeling the dynamic controllers of PVDG and WG for their frequency and voltage control. The simulation results depict the efficiency, accuracy and robustness of the proposed technique in terms of estimation time, event accuracy and applicability in all types of events. Moreover, the presented methodology is also compared with the four AI/ML based methods to highlight the superiority of the method.

This article introduces the design procedure of a 1-phase full-bridge inverter for grid-connected applications. The main idea of modelling a full-bridge inverter is that it plays a vital role in solar energy systems. The dynamic equations are formed with the state-space averaging (SSA) technique. A Small-signal averaged model of a full-bridge inverter is constructed by assuming grid current and capacitor voltage as the two states. The voltage and current controller scheme are based on the frequency domain approach. The voltage controller is designed to maintain fixed voltage, and the current controller is used for controlling grid current. The LCL filter is connected to the inverter to provide better harmonic elimination and filter size minimization. This filter is formed by parallel-series combination of inductance and capacitance to improve the overall system performance. Here, a passive damping method is incorporated to make a balanced steady system. Finally, a proportional integral and proportional resonant (PI-PR) controller is introduced to provide better stability and tracking. The detailed analysis of PR controller is explored and demonstrated by MATLAB simulation.

Abstract Avoiding financial losses requires preventing catastrophic oil‐filled power transformer breakdowns. Continuous online transformer monitoring is needed. The authors use paper insulation to evaluate transformer health for continuous online transformer monitoring. The study suggests a new artificial intelligence method for estimating paper insulation residual life in oil‐immersed power transformers. The four artificial intelligence models use backpropagation‐based neural networks to predict paper insulation lifespan. Four primary transformer insulating paper failure indices—degree of polymerisation, 2‐furfuraldehyde, carbon monoxide, and carbon dioxide—form the basis of these models. Each model, including the backpropagation‐based neural networks, estimates paper insulation life using one failure index, along with moisture and temperature data. Optimisation techniques enhance hidden layer neurons and epoch count for improved performance. Results are validated against literature‐based life models, establishing a precise input–output correlation. This method accurately predicts the remaining useable life of power transformer paper insulation, enabling utilities to take proactive measures for safe and efficient transformer operation.

Object-aware systems such as Deep Shape Prior SLAM (DSP-SLAM) provide a feasible technique for creating environment sparse maps while representing scene objects as complete 3D models. Such systems provide a compelling solution for improving the intelligence of care robots and enriching the user experience in augmented reality (AR) applications. However, owing to the abrupt and unpredictable movements exhibited by users during AR engagements and the need for real-time robot responses to changes, it is imperative that sensor data is processed robustly and speedily. DSP-SLAM suffers from a low-performance speed of 10–15 fps, although it is based on ORB-SLAM2 which can run at 30 fps. This is mainly because its instance segmentation approach has an average latency of 53ms(18.86fps). To improve tracking robustness, keyframes must be processed at a fast rate. We use a state-of-the-art one-stage deep learning detector, which significantly reduces the wait time for detection-based data association during keyframe creation, and finally present Robust Deep Shape Prior SLAM (RDSP-SLAM). The results show that segmentation was performed at 20ms (50fps), while the object 3D reconstruction quality was the same as that of DSP-SLAM. RDSP-SLAM accepts RGB sequential images at 30fps and tracks them at a mean latency of 38fps.

Hydrothermal scheduling is a significant concern in the field of power system economics that seeks to reduce the overall cost of generation by optimizing the hourly output of generators. However, this scheduling process suffers from a non-linear and complex problem due to set of uncertainty constraints from hydro and thermal units. Hence $\vartheta $ -constraint method has been proposed in which ramp constraints are considered that supply a feasible power region of the thermal units and minimize the uncertainty constraints. In addition, the existing solution techniques of hydro and thermal unit, commitment problems have not considered thermal constraints and their losses. This results in the frequent swapping of hydro units which decreases the energy conversion efficiency by a percentage at each repetition and increases the cost of the system. Therefore, a novel Chaotic geometric programming and chaotic approximation approach has been proposed that balance water discharge level without frequency swapping based on Armijo’s rule and Hazen Williams’s rule. Further, dynamic indexing is performed to increase the energy conversion efficiency. These balanced values are given to multiple-wave neural networks, to solve the hydro and thermal unit commitment problems. Furthermore, the cost of the system is minimized using Enhanced Ebola Optimization Search (EEOS) Algorithm in which the Srate parameter has been modified, thereby normalizing the hydrothermal scheduling problem for matured convergence. The proposed mathematical model has been implemented in the MATLAB R2022b on a 2.10 GHz, AMD Ryzen 5 3500U with Raden Vega Mobile Gfx processor, 8 GB RAM, 64 bit personal computer, and the results obtained show better performance than the previous approaches in terms of mean, median and standard deviation.

In this paper, a fractional order active (P) and reactive (Q) power controller of doubly fed induction generator (DFIG) is developed and it performance is demonstrated using hardware in loop (HIL) testing with Opal RT OP4510 real-time platform. The DFIG in this paper is modelled using real-time State Space Nodal (SSN) based solver. The Rotor Side Converter (RSC) and doubly fed induction machine are implemented using Opal-RT’s ARTEMiS – nodal admittance-based SSN solver which uses smaller time steps than the conventional algorithms to facilitate real-time hardware-in-loop performance analysis of the system. The MATLAB-based FOMCON platform is utilized for designing the proposed fractional order controller. The proposed controller is developed and tested for various dynamic conditions. The performance of conventional proportional integral control and proposed fractional order control using Opal-RT’s OP4510 real-time platform is compared. The experimental results demonstrate improved efficiency with the use of proposed fractional order controller.

Voltage stability assessment (VSA) is invariably an essential step in the satisfactory operation of power system. For this, several Voltage Stability Indices (VSIs) based on 1) Existence of solution for voltage equations, 2) Maximum power transfer (MPT) through a line, 3) MPT theorem/maximum loadability limit, 4) P-V curve, 5) Energy function and 6) Jacobian matrix can be found in the literature. VSIs assist in identifying weak lines and buses, voltage collapse proximity, maximum loadability, voltage stability margin, and prompting countermeasures against voltage instability. This article examines 78 different VSIs based on their concepts, assumptions, thresholds, and formulae. Additionally, mathematical proofs for 29 indices have been elaborated upon so that researchers may familiarize themselves with the underlying concepts of the other existing indices and find newer indices in modern power systems composed of intermittent power generation and dynamic loads. Also, a novel qualitative comparative analysis of VSIs is tabulated. Further, a novel framework has been proposed to aid researchers in shortlisting VSIs for a particular application. This article intends to serve as a meaningful reference to various researchers and individuals working in domains like Distributed Generation (DG) placing and rating, Voltage Stability Assessment (VSA), and power system planning.

This paper proposes a new multi-objective stochastic market settlement model for a coupled energy and reactive power ancillary service, named as MOSMS-CERP model, for a prospective grid integration of wind energy. It is a comprehensive two-stage scenario-based stochastic optimization model to incorporate the system uncertainties associated with wind power generations along with load demands. In this multi-objective framework, two competing objective functions as the expected total payment functions for energy and reactive power ancillary services are simultaneously minimized while satisfying all the system operating constraints including technical as well as market driven constraints. An efficient hybrid payment strategy is designed for energy market as well as reactive power ancillary service market for creating a competitive and fair market auction. A pay-as-bid payment mechanism is adopted for all thermal generating units whereas a pay-at-MCP (Market Clearing Price) is adopted for all hydro-powered generating units and wind farms for the energy market settlement. However, the ancillary service market of reactive power is settled using uniform price payment mechanism. It is a unique market settlement model, which combines three significant complexities such as stochastic nature, multi-objective constrained optimization, and meta-heuristics. The proposed MOSMS-CERP model is examined by applying five well-known, recent, and advanced meta-heuristic methods such as Non-dominated Sorting Genetic Algorithms (NSGA-II & I-NSGA-III), Multi-Objective Keshtel Algorithm (MOKA), Hybrid Fuzzy Multi-Objective Evolutionary Algorithm (HFMOEA), and Multi-Objective Particle Swarm Optimization (MOPSO) for producing the sets of Pareto-optimal solutions. A fair statistical comparison is carryout for these five meta-heuristic methods based on No Free Launch Theorem. All simulations are conducted on a standard IEEE 24-bus RTS with a doubly fed induction generator (DFIG) based wind farm on a DIgSILENT Power Factory software platform. The obtained simulation results ensure the robustness and effectiveness of the proposed MOSMS-CERP model.

An adaptive control approach for a three-phase grid-interfaced solar photovoltaic system based on the new Neuro-Fuzzy Inference System with Rain Optimization Algorithm (ANROA) methodology is proposed and discussed in this manuscript. This method incorporates an Adaptive Neuro-fuzzy Inference System (ANFIS) with a Rain Optimization Algorithm (ROA). The ANFIS controller has excellent maximum tracking capability because it includes features of both neural and fuzzy techniques. The ROA technique is in charge of controlling the voltage source converter switching. Avoiding power quality problems including voltage fluctuations, harmonics, and flickers as well as unbalanced loads and reactive power usage is the major goal. Besides, the proposed method performs at zero voltage regulation and unity power factor modes. The suggested control approach has been modeled and simulated, and its performance has been assessed using existing alternative methods. A statistical analysis of proposed and existing techniques has been also presented and discussed. The results of the simulations demonstrate that, when compared to alternative approaches, the suggested strategy may properly and effectively identify the best global solutions. Furthermore, the system’s robustness has been studied by using MATLAB/SIMULINK environment and experimentally by Field Programmable Gate Arrays Controller (FPGA)-based Hardware-in-Loop (HLL).

This paper introduces a novel approach to enhance the control algorithm for a single-phase shunt active power filter(SAPF) by integrating a new technique into a 5-level cascaded multilevel inverter (MLI) with Photo Voltaic (PV) array integration. Due to the integration of non-linear loads in the grid, such as computers, variable speed drives, and other solid-state equipment, non-sinusoidal currents are drawn, introducing harmonics that distort the voltage and current waveforms. It is essential to mitigate these harmonics, and integrating Shunt Active Power Filters (SAPFs) gives the grid the ability to inject active power. In order to improve power quality and achieve active and reactive power balance, this integration requires an imperative control method. This work extracts the fundamental component of the load current and efficiently handles grid distortions such as DC offset, phase shift, and harmonics using a phase lock loop (PLL) based on a Modified Notch Filter Second Order Generalised Integrator (MNFSOGI). Using MATLAB/Simulink software modelling, experimental validation utilising the OPAL-RT real-time data simulator verifies the proposed system's operation. The control algorithm, which has been painstakingly designed and verified, allows feed-forward current estimate from the PV array, synchronization template obtained from the grid voltage signal, and estimation of both fundamental and non-linear load currents. The performance of the MNFSOGI-PLL is additionally compared with that of the conventional E-PLL, MAF-PLL and DSOGI-PLL techniques. By attaining power balance in multilevel inverters under distorted grid conditions, this research substantially contributes to improving power quality. According to simulation and experimental results, the Total Harmonic Distortion (THD) of the grid current is found to be 1.50% in normal grid and 3.96% in distorted grid which is less than 5%, by IEEE-1547 standards. Moreover, the limitations of the presented multilevel inverter system are highlighted by the disadvantages of traditional 2-level inverters, which include increased harmonics, restricted voltage levels, and poorer power quality.

This article presents a comprehensive collection of formulas and calculations for hand-crafted feature extraction of condition monitoring signals. The documented features include 123 for the time domain and 46 for the frequency domain. Furthermore, a machine learning-based methodology is presented to evaluate the performance of features in fault classification tasks using seven data sets of different rotating machines. The evaluation methodology involves using seven ranking methods to select the best ten hand-crafted features per method for each database, to be subsequently evaluated by three types of classifiers. This process is applied exhaustively by evaluation groups, combining our databases with an external benchmark. A summary table of the performance results of the classifiers is also presented, including the percentage of classification and the number of features required to achieve that value. Through graphic resources, it has been possible to show the prevalence of certain features over others, how they are associated with the database, and the order of importance assigned by the ranking methods. In the same way, finding which features have the highest appearance percentages for each database in all experiments has been possible. The results suggest that hand-crafted feature extraction is an effective technique with low computational cost and high interpretability for fault identification and diagnosis.

In underground cables of power systems, the maximum temperature of the cable is a crucial factor in determining its capacity. According to standards, the permissible operating temperature for the XLPE cable conductor under steady-state conditions is 90 °C – a limit that should not be exceeded. Exceeding this temperature may lead to a thermal breakdown in the cable insulation, thereby resulting in interruption of the electrical power supply. Many factors affect the cable temperature, particularly through the processes of heat dissipation and diffusion from the cable into its surroundings. These factors include soil types and compositions, cable installation configuration, and thermo physical properties; therefore, accurate analysis of these factors is crucial for cable loading. In this study, the finite element method (FEM) is employed to predict the cable temperature considering different soil compositions and to present a new approach for the thermal analysis of an underground cable system. The novel approach considers various environmental conditions including single-layer and multi-layer soil types, homogeneous and non-homogeneous soil compositions, two configuration types – flat and trefoil – as well as two types of backfill materials, specifically sand-cement mixture backfill (SCMB) and fluidized thermal backfill (FTB), and dry zones to offer deeper insight into a thermal analysis. Given that the FEM requires the construction of a complex geometric model within an optimal operating condition to obtain results with high accuracy—a process that can often be complex as well as not adaptable because it depends on constant mathematical calculation—This paper presents a novel approach FEM-BPNN that uses an adaptive Backpropagation neural networks (BPNN) model as its mainstay. The proposed BPNN model exploits historical data from FEM to refine its predictive power, therefore, increasing its efficiency and accuracy. Furthermore, the model is subject to an optimization process, adjusting and refining its internal parameters in response to new data, with the ultimate goal of improving the predictive model capabilities for the temperature of underground power cables. The results underscored the high performance of FEM in the simulation, and it was observed that FEM yielded results closely aligned with those of the IEC standard. Moreover, the proposed FEM-BPNN demonstrated exceptional accuracy, achieving a low RMSE score of 0.008. It also exhibited impressive performance in the linear regression analysis, with an R2 value of 0.99. These metrics collectively signify the robustness and efficacy of the model.

Abstract Emerging sub-synchronous interactions (SSI) in wind-integrated power systems have added intense attention after numerous incidents in the US and China due to the involvement of series compensated transmission lines and power electronics devices. SSI phenomenon occurs when two power system elements exchange energy below the synchronous frequency. SSI phenomenon related to wind power plants is one of the most significant challenges to maintaining stability, while SSI phenomenon in practical wind farms, which has been observed recently, has not yet been described on the source of conventional SSI literature. This paper first explains the traditional development of SSI and its classification as given by the IEEE, and then it proposes a classification of SSI according to the current research status, reviews several mitigation techniques and challenges, and discusses analysis techniques for SSI. The paper also describes the effect of the active damping controllers, control scheme parameters, degree of series compensation, and various techniques used in wind power plants (WPPs). In particular, a supplementary damping controller with converter controllers in Doubly Fed Induction Generator based WPPs is briefly pronounced. This paper provides a realistic viewpoint and a potential outlook for the readers to properly deal with SSI and its mitigation techniques, which can help power engineers for the planning, economical operation, and future expansion of sustainable development.