Corresponding author’s email address: durojaiye.koffa@fulokoja.edu.ng    1026 

ARID ZONE JOURNAL OF ENGINEERING, TECHNOLOGY & 

ENVIRONMENT 

 

ORIGINAL RESEARCH ARTICLE 

MACHINE LEARNING FRAMEWORK FOR PREDICTIVE MAINTENANCE OF 

DISTRIBUTION TRANSFORMERS IN RESOURCE-CONSTRAINED POWER 

SYSTEMS 

D.J. Koffa1* and S.O. Oyakhilome2 

 
1Department of Physics, Federal University Lokoja, Nigeria 

2Department of Electrical and Electronics Engineering, University of Benin, Nigeria. 

Corresponding author: durojaiye.koffa@fulokoja.edu.ng 

 
ARTICLE INFORMATION ABSTRACT 

Aging power infrastructure in developing nations faces challenges from increasing 

demand, renewable integration, and limited replacement capital. Nigeria's grid 

exemplifies these constraints, with 70% of transformers exceeding design lifetime 

and 15% annual failure rates causing substantial losses. Traditional time-based 

maintenance proves unsustainable, while existing predictive frameworks require 

unaffordable infrastructure. This study developed a lightweight machine learning 

framework for transformer failure prediction using standard Supervisory Control 

and Data Acquisition data. Four algorithms- Random Forest, Gradient Boosting, 

Support Vector Machine, and Long Short-Term Memory networks were compared 

using 60 months of data from 1247 transformers. Physics-informed feature 

engineering extracted degradation patterns from voltage, current, temperature, 

and load measurements. Random Forest achieved optimal performance with 

94.7% accuracy, 92.3% precision, and 91.8% recall for 30 to 90 day predictions, 

representing 62% improvement over threshold methods. The framework identified 

87% of critical failures while reducing false alarms by 64%. Economic analysis 

demonstrates a 260%t return on investment, an 8.3-month payback, and 8.5-

billion-naira annual savings. This research proves sophisticated predictive 

maintenance achieved excellent results in resource-constrained environments 

without massive investment, offering replicable solutions for developing utilities. 

Received: 16th October 2025 

Revised:  12th November 2025 

Accepted: 13th November 2025 

Keywords: 

Predictive maintenance 

Machine learning 

Power grid stability 

Resource-constrained systems 

Grid reliability 

 

© 2025 Faculty of Engineering, University of Maiduguri, Nigeria. All rights reserved. 

1.0 Introduction 

Electrical power grid evolution from centralized architectures to complex energy networks has transformed 

asset management globally, though progress remains uneven. While developed nations invest billions in smart 

infrastructure, developing countries contend with aging equipment exceeding design lifetimes, limited 

replacement capital, and expanding access demands (Eberhard et al., 2011). Unplanned outages cost 150 billion 

dollars annually worldwide, with developing nations bearing disproportionate burdens (Eaton, 2018). Nigeria 

exemplifies these challenges, despite its 13,000 megawatts installed capacity, actual availability remains below 

5,000 megawatts, with losses approaching 50% (Sambo et al., 2021). Distribution transformer fleets average 

28 years, serving twelve million customers with 15% annual failure rates (NERC, 2024). Each failure triggers 

service interruptions affecting thousands, emergency repairs costing one point eight million naira, and industrial 

losses. 

Current maintenance practices employ time-based preventive maintenance, scheduling overhauls at fixed 

intervals regardless of condition. Preventive maintenance costs 450,000 naira per transformer, while 

emergency repairs exceed 1.8 million naira, a four-fold premium (Bloom, 2005). Tropical factors—

temperatures reaching 42˚ Celsius, humidity exceeding 90%, harmattan dust, and salt spray—accelerate 

degradation beyond schedule assumptions. Predictive maintenance, enabled by sensors and machine learning, 

has revolutionized industrial asset management (Lee et al., 2014), reducing turbofan unscheduled removals by 

40% (Tahan et al., 2017). Power utilities report 50% failure reductions (Wang et al., 2020). Fassi et al. (2024) 

AZOJETE December 2025. Vol.21(4):1026-1038 

Published by the Faculty of Engineering, University of Maiduguri, Maiduguri, Nigeria. 

Print ISSN: 1596-2490, Electronic ISSN: 2545-5818 

https://doi.org/10.63958/AZOJETE/2025/21/04/012 

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demonstrated physics-informed machine learning for power converters, while Elkateb et al. (2024) showed 

that Internet of Things sensors with machine learning reduce downtime by 34%. 

Transformer-specific machine learning applications have matured substantially. In a study, Li et al. (2023) 

developed wireless temperature and vibration sensors enabling early fault prediction. Also, Bhuiyan et al. 

(2024) achieved 97% accuracy in transformer fault classification using artificial neural networks with dissolved 

gas analysis data, extending to real-time monitoring systems (Bhuiyan et al., 2025). Song et al. (2023) introduced 

attention mechanisms improving model interpretability, while Zhou et al. (2024) combined Bayesian 

optimization with convolutional neural networks for operation state prediction. Advanced ensemble methods 

integrating Random Forest, Gradient Boosting, and deep reinforcement learning demonstrate superior 

accuracy for industrial Internet of Things predictive maintenance (Li et al., 2024). Deep learning architectures 

prove particularly promising, with convolutional neural networks and Long Short-Term Memory networks 

capturing complex temporal patterns (Cao et al., 2024; Hossain et al., 2024). Interestingly, existing frameworks 

often exceed developing nations resources. Published frameworks assume 2 to 5 million naira per transformer 

for comprehensive monitoring with dense sensors, high-bandwidth infrastructure, and cloud analytics requiring 

subscriptions (Xing et al., 2023; Vatsa et al., 2024). State-of-the-art deep learning requires hundreds of 

thousands of labeled failures, exceeding utilities' documented histories (Du et al., 2023). Akinbulire et al. (2020) 

documented that 73% of Nigerian substations lacked reliable internet in 2019, while Kherif et al. (2024) noted 

sophisticated equipment requires unavailable expertise.  Where frameworks demand comprehensive sensors, 

this study maximizes six basic Supervisory Control and Data Acquisition parameters. Where cloud 

architectures impose subscriptions, a lightweight model runs on commodity servers costing 450,000 naira. 

The hypothesis: domain knowledge and algorithmic sophistication compensate for infrastructure gaps, enabling 

predictive maintenance without massive investment. 

This investigation contributes to predictive maintenance in resource-constrained power systems in five ways. 

First, physics-informed feature engineering bridges the gap between limited sensor data and high prediction 

accuracy, achieving performance comparable to comprehensive monitoring systems. Second, comparative 

evaluation across Random Forest, Gradient Boosting, Support Vector Machines, and Long Short-Term 

Memory networks guides practitioners in selecting techniques for resource-constrained deployments. Third, 

economic analysis quantifies actual financial savings using Nigerian operational costs rather than generic 

assumptions. Fourth, the framework provides a replicable blueprint adaptable by other utilities with 

implementation guidelines and computational requirements. Fifth, empirical evidence proves artificial 

intelligence applications in critical infrastructure need not require massive capital; sophisticated predictive 

maintenance achieves excellent results using existing infrastructure when properly designed. Research focuses 

on transformer failure prediction in 11kv to 4415v distribution networks, addressing three-phase oil-filled 

transformers rated between three hundred and five hundred kilovolt-amperes. The dataset encompasses 5 

years of operational history for 1247 transformers, capturing Nigerian operating conditions with missing 

values, sensor errors, and ambiguous failure classifications—authentic testing conditions distinguishing this 

work from laboratory studies or simulations. 

2. Materials and Methods 

2.1 Context and Prior Work 

Maintenance evolved from reactive responses to preventive scheduling and predictive frameworks. The 1965 

Northeast Blackout, affecting thirty million people, demonstrated the need for proactive infrastructure 

management (Anderson and LeReverend, 2005). Preventive maintenance became dominant, with IEEE C57.91 

exemplifying time-based thermal aging models (IEEE, 1995). However, 30 to 50% of interventions addressed 

equipment showing no degradation, while 15 to 20% of failures occurred between inspections (Bloom, 2005). 

Condition-based maintenance using dissolved gas analysis provided early warning (Duval, 2002) and partial 

discharge detection enabled prediction weeks ahead (Stone et al., 2014), though labor intensity restricted 

application. Machine learning applications accelerated through the 2010s. Support Vector Machines achieved 

87% transformer assessment accuracy (Islam et al., 2017), while Random Forest demonstrated 92% accuracy 

(Abu-Elanien and Salama, 2015). LSTM frameworks achieved 91% F1-scores but required thousands of 

examples (Chen et al., 2019). Isolation Forest detected faults 42 days ahead (Mohammadi et al., 2018). 

Transformer monitoring advanced significantly: Bacha et al. (2012) achieved 994% accuracy, Susa et al. (2005) 

demonstrated 30% thermal monitoring improvement, and Chen et al. (2019) predicted circuit breaker failures 

with 89% recall.  

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Research predominantly assumes data abundance. Eberhard et al. (2011) found fewer than 30% of African 

utilities maintained 5-year digital databases, with quality issues 3 to 5 times higher than in developed nations 

(Kanchev et al., 2011). Akinbulire et al. (2020) documented 73% of Nigerian substations lacked internet 

connectivity. Gaps persist: developed-nation focus, limited resource-constrained guidance, rare comparative 

evaluations, and minimal deployment attention. 

2.2 Problem Formulation 

Consider a power distribution network comprising N equipment units indexed by i ∈ {1, 2, … , N}, where N 

= 1247 distribution transformers. For each unit i, we have time series operational data at discrete intervals t 

with a sampling period ∆t = 15 minutes. The operational state is characterized by: 

 Xi(t) = [x1(t), x2(t), … , xm(t)]T 1 

where m =  6 raw SCADA measurements: primary voltage Vp, secondary voltage Vs load current Il oil 

temperature Toil, ambient temperature Tamb, and load factor LF. Through feature engineering, this expands 

to m′ ≈ 78 derived features. 

The prediction task is a binary classification. For the prediction horizon Δth, we determine whether unit i fails 

within [t, t + Δth]: 
 y = {

1,   if unit i fails within [t, t + Δth] 
0 otherwise                                               

 2 

We investigate Δth ∈ {30,60,90}  days, balancing response time needs against prediction accuracy 

degradation. The objective learns mapping f: Rm′
→ {0,1} maximizing accuracy while accounting for 

asymmetric error costs: 

 f ∗ = arg min
f

[α ⋅ E[Lmiss(f)] + (1 − α) ⋅ E[Lfalse(f)] + λ ⋅ R(f)] 3 

where Lmiss represents missed detection costs, Lfalse represents false alarm costs, R(f) prevents overfitting, 

α ∈ [0,1] balances error types, and λ controls regularization. Economic analysis yields α ≈ 0.85, heavily 

weighting false negative minimization. 

Table 1 establishes the baseline cost parameters that drive the economic analysis throughout this study, 

including emergency repair expenses, planned maintenance costs, and hourly outage impacts. These values 

were derived from actual operational data provided by the distribution company and reflect the substantial 

economic penalty associated with reactive rather than proactive maintenance strategies. 

Table 1: Cost Parameters for Nigerian Power Grid Context 

Parameter Value (₦) Justification 

Unplanned outage/hour 2,500,000 Industrial customer losses 

Planned maintenance 450,000 Average preventive cost 

Emergency repair 1,800,000 Emergency callout + parts 

False alarm cost 85,000 Labor + transportation 

2.3 Feature Engineering 

The transformation from raw SCADA measurements to predictive features represents the most critical 

component, where domain knowledge compensates for data limitations. Raw measurements contain 

substantial redundancy and limited direct predictive power. However, trends and relationships carry rich 

diagnostic information. We construct physically meaningful derived quantities: 

 ΔT(t) = Toil(t) − Tamb(t) 4 

Temperature rise quantifies heat generation, with abnormally high values suggesting deteriorating cooling or 

developing faults. 

 
ρ(t) =

IL(t)

Irated
 5 

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Loading ratio normalizes current to rated capacity, with values consistently exceeding unity indicating chronic 

overloading. 

 
δVp(t) =

|Vp(t) − Vnominal|

Vnominal
× 100% 6 

Voltage deviation quantifies power quality and network instability. The thermal aging acceleration factor, 

derived from the Arrhenius equation: 

 
AF(t) = exp [

15000

383
−

15000

Toil(t) + 273
] 7 

calculates relative aging rate compared to reference conditions (IEEE C57.91). Integrating AF(t) over 

operational history, estimates consumed insulation life. 

We compute statistical aggregations over windows w ∈ 7,14,30,90 days: 

 

μx,w(t) =
1

w
∑ x(τ)

t

τ=t−w+1

 8 

 

σx,w(t) = √
1

w
∑ [x(τ) − μx,w(t)]

2
t

τ=t−w+1

 9 

 
trendx,w(t) =

x(t) − x(t − w)

w
 10 

The complete feature vector combines all elements: 

 z(t) = [x(t), xderived(t), μw(t), σw(t), trendw(t)]T 11 

resulting in dimension d ≈ 78. 

Figure 1 illustrates the complete feature engineering workflow, showing how raw SCADA data (voltage, 

current, temperature) undergoes transformation into physically meaningful derived quantities and statistical 

aggregates. The diagram captures the systematic process of converting basic measurements into features that 

reflect the underlying physics of transformer degradation, including thermal aging factors and loading patterns 

across multiple time windows. 

 
Figure 1: Feature engineering pipeline transforming raw SCADA measurements into a comprehensive feature 

vector through physical derivations and temporal aggregations. 

2.4 Machine Learning Algorithms 

2.4.1 Random forest 

Random Forest (Breiman, 2001) constructs ensembles of decision trees through bootstrap aggregating. 

Prediction aggregates across K trees: 

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 ŷ = mode{h1(z), h2(z), … , hK(z)} 12 

where K = 300. The method handles non-linear relationships naturally, requires minimal hyperparameter 

tuning, and demonstrates robustness to noisy measurements—critical for SCADA data quality. 

Feature importance quantifies contribution through mean decrease in impurity: 

 

I(xj) =
1

K
∑ ∑ p(t)

t∈Tk

K

k=1

⋅ Δi(t, xj) 13 

2.4.2 Gradient boosting 

Gradient Boosting (Friedman, 2001) constructs ensembles through sequential addition of weak learners: 

 

FM(z) = ∑ γmhm(z)

M

m=1

 14 

where M is the boosting iterations, γm is the learning rate, and hm are shallow decision trees. At each iteration: 

 FM(z) = Fm−1(z)γmhm(z) 15 

Gradient Boosting often achieves superior accuracy with imbalanced classes, though at cost of increased 

training time. 

2.4.3 Support vector machine 

SVM (Vapnik, 1995) finds an optimal hyperplane that maximizes the margin between classes: 

 

min
w,b,ξ

1

2
||w||2 + C ∑ ξi

N

i=1

 16 

Subject to  

 𝑦𝑖(𝑤𝑇∅(𝑧𝑖) + 𝑏) ≥ 1 − ξ𝑖,         ξi ≥ 0 17 

RBF kernel were employed: 

 
𝐾(𝑧𝑖 , 𝑧𝑗) = 𝑒𝑥𝑝(−𝛾 ||𝑧𝑖, −𝑧𝑗||

2
 

18 

2.4.4 LSTM networks 

LSTM (Hochreiter and Schmidhuber, 1997) addresses vanishing gradients through gating: 

 ft = σ(Wf ⋅ [ht−1, zt] + bf) 19 

 it = σ(Wi ⋅ [ht−1, zt] + bi) 20 

 Ct = ft ⊙ Ct−1 + it ⊙ tanh(WC ⋅ [ht−1, zt] + bC) 21 

 ot = σ(Wo ⋅ [ht−1, zt] + bo) 22 

 ht = ot ⊙ tanh(Ct) 23 

Our architecture comprises two LSTM layers (sixty-four and thirty-two units) with dropout, followed by a 

dense layer and sigmoid output. 

3. Materials and Method 

3.1 Data Collection 

Operational data was obtained from Abuja Electricity Distribution Company covering 60 months (January 

2020-December 2024) for 1,247 distribution transformers (300-500 kVA, 11kV/415V). SCADA records 6 

measurements at 15-minute intervals: primary voltage, secondary voltage, load current, oil temperature, 

ambient temperature, and load factor, totaling 5.24 billion measurements. 

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Failure events were identified from maintenance records and incident reports, classified by type: winding 

insulation breakdown, bushing failure, tap changer malfunction, core fault, or external damage. The dataset 

contains 183 documented failures (14.7% annual failure rate). 

Table 2 summarizes key characteristics of the operational dataset used throughout this research, including the 

number of monitored transformers, observation duration, and failure event frequency. The statistics confirm 

that the dataset reflects real-world conditions with typical data quality issues while providing sufficient failure 

examples for meaningful machine learning model training. 

Table 2: Dataset Summary Statistics 

Attribute Value 

Total transformers 1,247 

Observation period (months) 60 

Total failure events 183 

Annual failure rate 14.7% 

Average age (years) 28.3 

Data completeness 87.4% 

Missing data (average) 12.6% 

3.2 Data Preprocessing 

Outlier detection employed physical bounds checking and z-score thresholding (|z|  >  4), removing 1.3% of 

measurements. Timestamp inconsistencies were corrected through interpolation. Duplicates were removed 

(0.8% of observations). 

Missing data imputation varied by gap duration: linear interpolation for gaps under 2 hours (87% of instances), 

seasonal decomposition for two to twenty-four hours, and forward fill up to 7 days for persistent failures. 

This achieved mean absolute percentage errors of 3.7% for voltage, 4.2% for current, and 2.1% for 

temperature. 

Feature normalization employed robust scaling: 

 
x̃ =

x − median(x)

Q3(x) − Q1(x)
 24 

Time-series segmentation created training samples through sliding 90-day windows with 7-day stride, labeled 

positive if failure occurred within the prediction horizon. This generated approximately 15,000 samples. 

SMOTE addressed class imbalance during training. 

3.3 Model Training 

Dataset partitioning: training set (January 2020-December 2022, 70% transformers), validation set (January-

June 2023, 15%), test set (July 2023-December 2024, 15%). 

Hyperparameter optimization employed Bayesian optimization with 100 iterations per algorithm, optimizing 

F1-score on the validation set. 

Table 3 presents the optimal hyperparameter configurations identified through 100 iterations of Bayesian 

optimization for each of the 4 machine learning algorithms. These settings represent the best-performing 

combinations on the validation dataset and were subsequently used for final model training and test set 

evaluation. 

Training used scikit-learn (Random Forest, Gradient Boosting, SVM) and TensorFlow/Keras (LSTM) with fixed 

random seeds. SMOTE achieved 5:1 negative to positive sample ratio. Five-fold stratified cross-validation 

provided robust performance estimates. Total training time was approximately 8 hours on a standard 

workstation. 

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Table 3: Hyperparameter Optimal Values 

Algorithm Parameter Optimal 

Random Forest n estimators 300 

 max depth 18 

Gradient Boosting n estimators 250 

 learning rate 0.05 

SVM C 12.5 

 gamma 0.05 

LSTM units layer1 64 

 dropout 0.2 

3.4 Performance Metrics 

Model performance was evaluated using multiple complementary metrics to provide a comprehensive 

assessment of predictive capability. These metrics address different aspects of classification performance, with 

particular emphasis on the asymmetric costs associated with false negatives (missed failures) and false positives 

(unnecessary maintenance) in predictive maintenance applications. All metrics were computed on the held-

out test set comprising 15% of transformers not used during model training or hyperparameter optimization. 

Table 4 summarizes all evaluation metrics employed in this study, their formulas, ranges, and interpretation in 

the context of transformer failure prediction. 

Table 4: Performance Metrics table 

Metric Formula Range Interpretation 

Accuracy (TP+TN)/(TP+TN+FP+FN) [0, 1] Overall correctness can be 

misleading with imbalanced data 

Precision TP/(TP+FP) [0, 1] Fraction of predicted failures that are 

correct; minimizes false alarms 

Recall TP/(TP+FN) [0, 1] Fraction of actual failures detected; 

minimizes missed failures 

Specificity TN/(TN+FP) [0, 1] Fraction of healthy transformers 

correctly identified 

F1-Score 2×(Prec×Rec)/(Prec+Rec) [0, 1] Harmonic mean balancing precision 

and recall 

AUC-ROC Area under ROC curve [0.5, 1] Threshold-independent separability; 

0.5=random, 1.0=perfect 

PR-AUC Area under PR curve [0, 1] Focuses on minority class; robust to 

class imbalance 

Confusion Matrix 2×2 contingency table N/A Complete classification breakdown; 

basis for all metrics 

4. Results and Discussion 

4.1 Model Performance 

Table 5 presents comprehensive metrics. It was observed that Random Forest achieved the highest 

performance of (F1 = 0.920), followed by Gradient Boosting (0.912). Additionally, SVM and LSTM showed 

lower performance of 0.876 and 0.831, respectively. All machine learning approaches substantially outperform 

the threshold baseline, with Random Forest improving the F1-score by 62%. Also, Ensemble methods 

outperform SVM, suggesting that bagging/boosting effectively handles SCADA data noise. Conversely, LSTM 

underperformance likely reflects insufficient training data. 

Figure 2 presents the ROC curves for all four machine learning algorithms alongside the threshold baseline 

method, with the area under each curve quantifying overall classification performance. The plot clearly shows 

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that Random Forest and Gradient Boosting achieve AUC values exceeding 0.97, substantially outperforming 

both the SVM and LSTM while the traditional threshold approach lags far behind. 

 

Figure 2: ROC curves comparing all models. Random Forest and Gradient Boosting achieve AUC : 0.97. 

Given the substantial class imbalance inherent in failure prediction tasks, precision-recall analysis provides a 

more informative performance assessment than ROC curves alone (Figure 3). The Random Forest model 

maintains consistently high precision even as recall increases, achieving a PR-AUC of 0.891. Figure 3 displays 

precision-recall curves that are particularly relevant for the imbalanced dataset, where failures represent only 

14.7% of observations. However, Random Forest demonstrates robust performance by sustaining high 

precision across the entire recall spectrum, indicating that the model reliably identifies true failures without 

generating excessive false alarms. 

 

Figure 3: Precision-Recall curves. Random Forest maintains high precision across recall range (PR-AUC = 

0.891). 

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Table 5: Performance Metrics on Test Set 

Model Acc Prec Rec F1 ROC PR MCC 

Random Forest 0.947 0.923 0.918 0.920 0.976 0.891 0.889 

Gradient Boost 0.943 0.908 0.916 0.912 0.971 0.885 0.878 

SVM 0.921 0.867 0.885 0.876 0.945 0.832 0.818 

LSTM 0.898 0.821 0.842 0.831 0.918 0.778 0.762 

Threshold 0.743 0.512 0.634 0.567 0.721 0.502 0.445 

Random Forest confusion matrix: 168 of 183 failures correctly predicted (91.8% recall), 1,007 of 1,064 non-

failures correctly classified (94.6% specificity), yielding 57 false alarms. For every 100 transformers monitored, 

the system correctly flags approximately 14 of 15 failures while generating 4.5 false alarms. 

4.2 Feature Importance 

Analysis of feature contributions reveals that thermal parameters dominate the predictive signal, with the top 

ten features accounting for 73% of the model's discriminative power (Figure 4). Notably, temporal aggregations 

consistently outrank instantaneous measurements, confirming that degradation trends carry more diagnostic 

value than snapshot conditions. Similarly, Figure 4 ranks features by their importance scores derived from the 

Random Forest model's mean decrease in impurity metric. The 30-day mean oil temperature rise emerges as 

the strongest predictor, followed by load factor variance and thermal aging acceleration, demonstrating that 

temperature-related indicators provide the most reliable early warning signals for transformer failures. 

 

Figure 4: Feature importance ranking. Thermal parameters dominate; top 10 features capture 73% of 

predictive power. 

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Aggregated temporal features consistently outrank instantaneous measurements, confirming that trends 

matter more than absolute values. Thermal parameters dominate top positions, while electrical parameters 

rank lower, reflecting that voltage quality is inherently variable in Nigerian grids, so voltage deviations carry 

less diagnostic value. This suggests deployment can prioritize temperature sensing. 

4.3 Economic Analysis 

Table 6 details the cost comparison over 5 years per transformer. Predictive maintenance requires ₦275,000 

capital cost but yields ₦7.9M annual net savings, totaling ₦39M over 5 years (260% ROI, 8.3-month payback). 

Figure 5 shows ROI remains positive across a wide parameter range. For the 1,247 transformer fleet: annual 

prevented failures increase from 183 to 67 (63.4% reduction), saving 11,136 outage hours. Emergency repair 

costs drop from ₦330M to ₦121M annually, protecting ₦8.1B revenue. First-year net benefit reaches ₦8.25B; 

five-year NPV totals ₦35.8B 

Table 6: Cost Comparison (5-year, per transformer, thousands ₦) 

Component Reactive Predictive Savings 

Capital costs 0 275 (275) 

Annual inspections 340 170 170 

Emergency repairs 1,440 360 1,080 

Planned maintenance 135 405 (270) 

Unplanned outages 9,600 2,400 7,200 

Planned outages 0 600 (600) 

5-Year Total 63,450 24,425 39,025 

 

Figure 5: Sensitivity analysis. ROI remains positive across all reasonable scenarios; outage cost prevention 

dominates economic benefit. 

5. Conclusion 

This research demonstrates that machine learning-based predictive maintenance delivers transformative 

reliability improvements in resource-constrained power systems without massive capital investment. Working 

within Nigerian distribution network constraints—aging equipment, limited SCADA coverage, sparse failure 

data, restricted budgets—a lightweight framework achieved 94.7% accuracy predicting transformer failures 

thirty to ninety days ahead. The Random Forest classifier identified 87% of critical failures while reducing false 

alarms by 64%. Economic analysis demonstrates a 260% return on investment with an 8.3-month payback, 

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translating to 8.5-billion-naira annual savings. These findings prove that careful algorithm selection, domain-

informed feature engineering, and practical deployment strategies enable developing nation utilities to achieve 

performance comparable to well-resourced counterparts without sophisticated infrastructure. Future 

research should investigate transfer learning for cross-utility adaptation, uncertainty quantification, multi-

equipment coordination, federated learning, hybrid physics-machine learning models, edge computing 

implementation, and explainable artificial intelligence, ensuring equitable reliability improvements. 

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power sector. Energy Reports, 6: 2531-2538. 

Anderson, PM. and LeReverend, BK. 2005. Industry experience with special protection schemes. IEEE 

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