Optimization Strategies for Enhancing Performance and Reliability of MV Switchgear

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056

Volume: 13 Issue: 04 | Apr 2026 www.irjet.net p-ISSN: 2395-0072

Optimization Strategies for Enhancing Performance and Reliability of MV Switchgear

Shamli Satish Wakchaure1 , Dr. S.P. Mogal 2 , Dr. D.V. Kushare 3

1Department of Mechanical Engineering, KBT College of Engineering, Nashik, Maharashtra, India

2,3 Professor, Department of Mechanical Engineering, KBT College of Engineering, Nashik, Maharashtra, India

Abstract - Medium-voltage (MV) switchgear is an essential component of today's electrical power distribution networks; however, the increasing loads, reduced dimensions, and environmental constraints have made it harder to guarantee its reliability, safety, and thermo-electrical efficiency. Thanks to the recent progress in Multiphysics simulations (electromagnetic, thermal, fluid, and mechanical analysis), significant improvements in the design process have been achieved. The current paper provides a thorough survey of state-of-the-art studies related to Multiphysics analysis and optimization of medium-voltage switchgear devices. Such aspects as electro-thermal interaction, heat generation calculation, insulation characteristics (SF6 alternatives), condition monitoring, reliabilityevaluation,anddigitalization trends are discussed. The case study describes how using CFD inputs along withmachinelearningalgorithmscanhelpreveal key factors influencing the temperature distribution such as materialproperties, busbar arrangement,andairflows.Other advancementsindigitalizationofswitchgears,environmentally sustainable insulation gases, thermal optimization methods, and reliability assessment of MV and GIS switches are presented. This review reveals promising research directions in combining Multiphysics modeling, explainable machine learning, and experimental validation.

Key Words: Medium-voltage switchgear, Multiphysics simulation, Electro-thermal analysis, SF₆ alternatives, Condition monitoring, Busbar geometry.

1. INTRODUCTION

Medium Voltage (MV) switchgear is a crucial part of the electrical power system infrastructure that deals with the switching,protection,andcontrolofelectricalnetworks.The demandforcompactsubstationswithahighercurrentrating and better reliability has created a substantial amount of thermal, electrical, and mechanical stress on the modern switchgearsystem.Ofthese,theriseinthetemperatureof the current-carrying parts along with contacts is well identifiedtodayasoneoftheprincipalcausesofagingofthe insulationmaterials[1].Conventionalmethodsofdesigning switchgearbasicallydependonintuitivecalculationsusing equations,tolerancespecificationsaccordingtostandards, and simplified analytical calculations. While these approachesguaranteeacceptableperformance,theyare,in general,insufficientforthecorrectcalculationsoflocalized heating effects or the internal complexities of

electromagnetic losses, heat flow, or air flow movements within the switchgear compartment, for instance, for the detection of inefficient thermal spots or the emergence of hotspots, as pointed out in [2]. In order to overcome the mentionedchallenges,theanalysismethodofMultiphysics fieldcouplinghasproventobeusefulfortheassessmentand optimizationofswitchgears.TheMultiphysicssimulationis capable of simulating the electrical, thermal, and fluid dynamicphenomena,andhenceithelpsthesimulation to estimatethetemperaturedistributionandlossissuesforthe switchgears [3], [5]. The simulation has been effectively carried out on high voltage as well as medium-voltage switchgears [6]. Apart from simulation studies, reliability and stability analyses have gained significance due to the growing complexities in power systems. Probabilistic and reliability analyses have been used to assess operational risks,modesoffailure,andsystemstabilitywhicharerelated toswitchgearlayouts[7],[8].Thesereferencesarespecial sincetheystressthesignificanceofsophisticatedmonitoring and prediction methods for safe and uninterruptible operation.

Recently,therehavealsobeenapproachesconcentratingon thestudyofswitchgearoperatingunderabnormalorfaulty conditions, where thermal stress becomes increasingly serious. Numerical simulations and experimental results have proved that temperature increase due to a faulty condition can accelerate the degradation of contacts and insulationfailureunlessitishandledproperly[9].Therefore, condition monitoring methods relying on temperature increase factors/indicators and thermal signs have also emergedtoidentifytheearlydeteriorationofconnectionsof switchgears[10],[11].Althoughconsiderableadvancements havebeenmadeinMultiphysicssimulationusingCFD,most oftheexistingconventionaldata-drivenmodels,whichcould be applied for thermal prediction, were not interpretable fromaphysicalstandpoint.Toresolvethis,newresearchhas emerged utilizing interpretable machine learning frameworks,whichintegratedMultiphysicssimulationusing CFD with AI, allowing for accurate and interpretable prediction of temperature rise [1]. As far as the problems mentioned,thisreviewpapergivesanoveralldescriptionof the modern developments in the simulation, thermal analysis, and insulation studies, as well as the reliability assessmentoftheMVswitchgear.Asacasestudy,thispaper chooses the use of the interpretable machine learningassisted Multiphysics CFD model introduced in [1] and synthesizes research trends, challenges, and future

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056

Volume: 13 Issue: 04 | Apr 2026 www.irjet.net p-ISSN: 2395-0072

directionsforthedevelopmentofswitchgearsystemsthat arereliable,thermallyefficient,andintelligent.

2. LITERATURE REVIEW

Inordertocomprehendthecoupledmechanical,electrical, and thermal behaviours of switchgear, Multiphysics simulations have become essential. Matin et al. (2025) emphasized the importance of multi-physics modelling in operational reliability by developing an interpretable machinelearningmodelinconjunctionwithCFDanalysisto forecasttemperatureriseinmedium-voltageswitchgear[1].

Similar to this, Du et al. (2025) investigated Multiphysics coupling and structure optimization in flux-switching permanentmagnetlinearmotors,whichhasimplicationsfor switchgear design in situations where electromagneticthermal coupling is important [2]. The significance of integrating various physical domains for performance enhancement was highlighted by Hou et al. (2024) and Zhang et al. (2024), who demonstrated simulation-based multi-physical field analysis to optimize thermal managementinhigh-voltageswitchgear[5,6].Theviability ofcomputationaldesigninpredictinglocalizedheatingand stress points was confirmed by Huang et al. (2023) and Zhengetal.(2023),whoalsousedCOMSOLandothermultiphysicssimulationtoolstostudysmallswitchgearthermal behaviour [13,15]. All of these studies show that multiphysicssimulationiscrucialforswitchgearsystemdesign optimizationandreliabilityevaluation.

One important factor influencing insulation deterioration and operational life in switchgear components is temperature rise. Significant hotspots in busbars and connectionswerefoundwhenYangetal.(2024)examined temperaturerisecharacteristicsunderfaultconditions[9]. In order to provide a predictive maintenance framework, Kejani et al. (2024) suggested monitoring methods for medium-voltage switchgear connections based on temperatureriseindices[10,11].Experimentalresearchon electricaldisruptionbyVegaetal.(2024)demonstratedthe importanceofprecisethermalmodellinginavertingfailures [12]. Other studies, like Seker et al. (2023) and Wu et al. (2022), validated simulation models for real-world applicationsbybenchmarkingnumericalsimulationswith experimental data for medium-voltage switchgear, demonstrating a strong correlation [14,24]. Switchgear researchcontinuestoprioritizeinsulationperformance.In ordertohelpdesignhigh-voltageapplicationswithoptimal insulation,Bharanidharanetal.(2025)usedFEMtosimulate thedielectricbehaviourofSF6andN2[3].Withafocuson multi-physicscouplinginenvironmentalconditions,Xiaohan etal.(2021)investigatedtherelationshipbetweeinsulation performance and humidity [27]. Gao (2022) examined environmentally friendly gas mixtures, emphasizing problemswithmaterialcompatibilityinMVswitchgear[25]. Reliabilityanalysisandmonitoringofswitchingapparatuses playanessentialroleinensuringoperationalsafety.Astudy

conductedbyPranjicandVirticin2024exploredoperational reliabilitythroughMonteCarlosimulationfordifferenttypes of substations [7]. The application of switchgear and protection for ensuring power system stability was emphasized by Shukla and Zadokar in 2024 [8]. A study conductedbyKastelanetal.in2022discusseddigitalization in switchgear, including progress in condition monitoring andpredictivemaintenancetechniques[20].Massaoudietal. in2020,andMiljanovicetal.in2022,2023,proposedpartial discharge localization through UHF and HFCT methods, underscoring the need for signal processing and machine learningintegrationforassessment[23,30].Mechanicaland structural design of switchgear is equally important for reliability concerns, especially under fault currents and environmental stresses. Shah et al. (2022) conducted the structuralandmodalanalysisofcompositematerial-based GIS structures for seismic applications [21]. Weiwei et al. (2023) investigated environmental design modifications, which could ensure robustness in different climatic conditions[19].

3. RESEARCH GAPS AND FUTURE DIRECTIONS

 Lackoffullyunitedelectro-thermal-structural-fluid coupledmodels.

 Limitedaccuracytopredicthotspotsinthevicinity ofcontactsandjoints

 Insufficient long-term experimental validation underhigh-currentconditions

 Inadequate datasets to train AI-based monitoring algorithms

 Limitedunderstandingoflong-termperformanceof SF₆-freeinsulationsystems

 Incompletecouplingofarcdynamicsandpressure increase, as well as the associated mechanical degradationoftheenclosure.

Addressingthesegapsenablesthedevelopmentofreliable, compact,andenvironmentallysustainablenext-generation switchgear.

4. METHODOLOGY

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5. CASE STUDY REVIEW

Machine Learning–Assisted Thermal Prediction in MV Switchgear

Reliabletemperaturerisepredictionis a basicneedinthe design of medium voltage (MV) switchgears because temperatureoverstresshasadirectimpactonthereliability oftheequipment.Although traditionalsolutionsinvolving experimental temperature rise tests as well as CFD simulationscanefficientlyprovideaccuratesolutions,these processes involve high computational costs that make it difficult to apply in the design stage to search for optimal solutions. To overcome these problems, researchers proposed an innovative machine learning-based thermal predictionsolutioncombinedwithCFDanalysisbyMatinet al.[1].

For the study cited in this article, an air-insulated MV switchgearsystemsimulationwasconductedtoanalysethe correspondingcoupledelectric-thermal-fluidphenomenon under rated conditions. Sources of heat generation were identified as Joule losses in conductors and contact resistances at busbar connections. The simulation model accountedforconductioninsolidparts,naturalconvection in the enclosure, and radiation from the enclosure to simulate the system's thermal behaviour under steady conditions. The simulation results are verified with temperature rise tests under conditions prescribed in internationalstandards[1].

The generated data set was then utilized to train an interpretable ML model with the capability of estimating maximumtemperaturerise atcritical spotsinswitchgear. UnliketraditionalMLmodelsthatfunctionasa"blackbox," thedevelopedmethodwasmoreconcernedwithopacityand successfully measured the effect of input parameters on thermal characteristics. Analysis of importance to the features revealed that the main parameters causing hot spots were the magnitude of the current, resistance, and conductor geometry, aligning with thermal switchgear designprinciplesidentifiedbyexistingresearch[1].

The mission-ready model demonstrated prediction capabilitiescomparabletofullCFDsimulationsintermsof accuracy, yet with less computational cost. This made it possible to quickly test various design configurations withouthavingtorelyonrepeatedfull-scale⅜simulations. It was shown that interpretable machine learning can contributetoCFD-basedMultiphysicssimulationstoallow for fast thermal analysis with maintained physical understanding of the heat generation and dissipation mechanisms[1].

Thebroaderoutlookofthiscasestudyisthatitaddressesa scalableframeworkforsimulation-drivenswitchgeardesign. ThereisastrongpotentialfortheintegrationofCFD-based

multi-physicsmodellingwithexplainablemachinelearning in early-stage design optimization, reliability assessment, and future digital switchgear applications using real-time monitoringandpredictivemaintenancestrategies[1].

6. CONCLUSIONS

Inthisreviewpaper,recent advancementsinthermaland multi-physics analyses for medium voltage-switchgear, especially temperature rise effects acting as factors controlling reliability and operational life, have been discussed.

Temperature increase is always found to be an important performance-limitingfactorintheexistingliteratureforthe degradationofinsulations,contactwear,andoverallsafety ofthesystem.

AcasestudyanalysisrevealstheintegrationofMultiphysics CFDsimulationsandmachinelearningmodelsiscapableof enablinganaccuratetemperatureforecastwhileremaining physicallytransparent.

Comparedtotraditionalempiricalapproachesandblackbox AImodels,interpretativemachinelearningmethodsimprove explainability, which makes it easier to understand the prominentthermalvariablesandprocesses.

Reviews of literature have found that combined electrothermal-fluidmodellingwillhelptoimprovetheaccuracyof thermal analysis and facilitate the rational designoptimizationofthevariouscomponentswithinaswitchgear relay.

Advances in the area of insulation materials, conductor layout, and enclosures have been found to decrease the temperatureriseandenhancetheoperationalreliability. The adoption of digital monitoring systems and data analyticsindiagnosticprovideseaseofreal-timeassessment andpredictivemaintenancetechniques.

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Researchisanticipatedtobefocusedonphysics-informed explainabledata-driventechniquesinthecomingfutureto efficiently develop safe and sustainable next-generation mediumvoltageswitchgearsystems.

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