An Integrated Industrial Engineering Optimization Framework for Maintenance Scheduling at a 400 kV EHV Substation: A Case Study of MPTCL Saikheda, Madhya Pradesh, India

Abstract

This paper presents an integrated four-phase Industrial Engineering (IE) optimization framework for maintenance scheduling at the 400 kV Saikheda Extra High Voltage (EHV) substation of Madhya Pradesh Power Transmission Company Limited (MPTCL), India. As Indian state transmission utilities grapple with ageing asset fleets, escalating corrective maintenance costs, and stringent regulatory compliance requirements under the Central Electricity Authority (CEA) and Madhya Pradesh Electricity Regulatory Commission (MPERC), evidence-based maintenance optimization has become operationally imperative. The proposed framework integrates Failure Modes, Effects and Criticality Analysis (FMECA), work measurement and Lean process redesign, Mixed Integer Linear Programming (MILP)-based maintenance scheduling, and Discrete Event Simulation (DES) for validation. Using five years of CMMS data (FY2019–FY2023) comprising 3,614 work orders, 823 unplanned corrective maintenance events, and cost data in Indian Rupees (?) from MPTCL\'s SAP Plant Maintenance module, the framework was calibrated and validated over 18 months. Results demonstrate statistically significant improvements across all measured KPIs: a 29.8% reduction in annual maintenance cost (?456 Lakhs to ?320 Lakhs), a 36.5% improvement in SAIDI (328.6 to 208.4 customer-min/yr), a 39.6% reduction in unplanned corrective events, and an equipment availability improvement to 97.1%—all within MPERC\'s performance standards. The study represents the first application of an integrated IE optimization framework to an Indian state transmission utility substation, with all cost parameters in INR and all constraints aligned to Indian regulatory requirements.

Introduction

This study focuses on optimizing maintenance operations at the 400 kV Saikheda Substation operated by Madhya Pradesh Power Transmission Company Limited (MPTCL), a critical node in the power transmission network of Madhya Pradesh. As India's power sector rapidly expands and integrates more renewable energy, the reliability of Extra High Voltage (EHV) transmission infrastructure becomes increasingly important. The 22-year-old Saikheda substation has experienced rising maintenance costs and increasing equipment failures due to aging assets, making traditional time-based maintenance practices inefficient and costly.

The research proposes a comprehensive Industrial Engineering (IE)-based optimization framework that integrates Failure Modes, Effects, and Criticality Analysis (FMECA), Reliability-Centered Maintenance (RCM), Lean process improvement, Mixed Integer Linear Programming (MILP), and Discrete Event Simulation (DES). The framework aims to improve maintenance scheduling, resource allocation, workforce productivity, and overall system reliability while complying with regulatory requirements.

The study had five main objectives: analyzing existing maintenance practices, developing an MILP-based maintenance scheduling model, prioritizing equipment testing using FMECA and RCM principles, optimizing workforce and inventory allocation, and validating improvements using real operational data from the Saikheda substation.

A review of recent international research showed that techniques such as predictive maintenance, machine learning, optimization models, Lean maintenance, and reliability-centered approaches can significantly reduce failures, maintenance costs, and labor hours. However, no previous study had applied an integrated IE framework tailored to Indian EHV substations and their regulatory environment.

The case study revealed substantial operational inefficiencies. Activity sampling showed that only 55.5% of maintenance time was productive, while administrative waiting, permit processing, tool retrieval, and idle time consumed a large portion of resources. Value Stream Mapping identified several sources of waste, including lengthy permit approvals, manual documentation processes, lack of pre-staged maintenance kits, and delayed data entry.

Failure analysis of 823 unplanned corrective maintenance events between FY2019 and FY2023 showed a 43% increase in failures, indicating that the asset fleet had entered the wear-out phase of its lifecycle. FMECA identified the most critical failure modes as:

• SF6 gas pressure loss in GIS circuit breakers.
• Numerical relay misoperation.
• Transformer winding insulation breakdown.
• Circuit breaker spring mechanism fatigue.
• Increased contact resistance in breakers.

Maintenance expenditure rose from ?343 lakhs to ?456 lakhs over five years, corresponding to a 7.4% annual growth rate, significantly exceeding regulatory allowances. Emergency corrective maintenance costs increased even faster, and preventable inefficiencies were estimated to cost approximately ?302 lakhs over the study period.

To address these challenges, the researchers developed a MILP optimization model that minimizes total maintenance costs while balancing labor costs, downtime risks, and residual failure risks. The model incorporates practical constraints such as regulatory maintenance requirements, N-1 grid reliability standards, outage windows approved by system operators, workforce capacity limits, and budget restrictions.

Reliability analysis using Weibull distributions confirmed that critical assets such as transformers and GIS circuit breakers exhibit wear-out failure behavior, supporting the need for condition-based and risk-prioritized maintenance instead of fixed calendar schedules.

Conclusion

This paper has presented, implemented, and validated the first integrated Industrial Engineering optimization framework applied to a specific Indian state transmission utility EHV substation—MPTCL Saikheda 400 kV—with all cost parameters in INR and all constraints aligned to Indian regulatory requirements (CEA, Grid Code 2023, MPERC). The four-phase framework combining FMECA, work measurement and Lean process redesign, MILP scheduling, and DES simulation validation produced statistically significant, consistent improvements across nine measured KPI dimensions. Key outcomes include: a 29.8% reduction in annual maintenance cost (?136 Lakhs/yr savings); a 36.5% SAIDI improvement meeting MPERC\'s 250 min/yr performance standard; a 39.6% reduction in unplanned corrective events; and 97.1% equipment availability surpassing MPERC\'s 96% target. Zero N-1 redundancy violations were recorded during 18 months of post-implementation monitoring, compared to four during the five-year baseline. The framework is parametrically generalizable to other MPTCL substations and, with appropriate regulatory recalibration, to state transmission utilities across India. Future research directions include multi-site MILP extension to MPTCL\'s complete 400 kV network, ML-based predictive maintenance integration with the MILP scheduler, renewable energy-aware dynamic scheduling, multi-year lifecycle cost optimization incorporating asset replacement, and real-time digital twin simulation linked to live SCADA telemetry.

References

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Copyright

Copyright © 2026 Pravesh Chadar, Dr. Akash Tomar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.