Browsing by Author "Yarramsetty, C."

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A hybrid model of convolutional neural network and an extreme gradient boosting for reliability evaluation in composite power systems integrated with renewable energy resources
(Springer Science and Business Media Deutschland GmbH, 2025) Yarramsetty, C.; Moger, T.; Jena, D.
This paper introduces an approach that enhances the computational efficiency of reliability assessment for composite power systems by integrating machine learning (ML) techniques with sequential monte carlo simulation (SMCS). Integration of renewable energy resources (RERs) into power systems is increasing at a rapid pace. Evaluating the reliability of composite power systems is helpful in identifying any deficiencies in their operation. As power systems operation becomes more fluctuating and stochastic, it is necessary to update the tools used to analyse reliability. In this paper, SMCS is used as a conventional method, as it provides results by taking chronological nature of RERs. However, SMCS is highly computational. ML models fit for solving complex problems that require computational power. ML techniques, such as convolutional neural network (CNN) and hybrib models of Convolutional and Extreme Gradient Boosting (ConXGB), and Convolutional and Random Forest (ConRF) are proposed to determine the expectation of load curtailment and minimum amount of load curtailments. The proposed technique is applied on test system IEEE RTS-79. Results indicate the ConvXGB method is fast and accurate in computing composite reliability indices. For instance, it achieved a Loss of Load Probability (LOLP) of 0.0025 and an Expected Demand Not Supplied (EDNS) of 0.1850 MW, compared to SMCS’s LOLP of 0.0021 and EDNS of 0.1794 MW while reducing computational time from 12900 to 5414 s. These results confirm the proposed method’s speed and accuracy, making it a robust solution for modern power system reliability evaluation. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.
A Novel Transformer-Based Approach for Reliability Evaluation of Composite Systems With Renewables and Plug-in Hybrid Electric Vehicles
(Institute of Electrical and Electronics Engineers Inc., 2025) Yarramsetty, C.; Moger, T.; Jena, D.; Rao, V.S.
This paper proposes a novel hybrid framework that integrates machine learning (ML) techniques with Sequential Monte Carlo Simulation (SMCS) to enhance the reliability assessment of modern power systems incorporating renewable energy resources (RER) and plug-in hybrid electric vehicle (PHEVs) integration. While PHEVs can leverage RER to significantly reduce greenhouse gas emissions, the increased energy demand from large PHEVs fleets poses potential challenges to power system reliability. To address these issues, this research presents an advanced mixed-integer linear programming (MILP) based algorithm for optimizing EV charging. The algorithm prioritizes clean energy utilization through intelligent power allocation strategies while considering cost-revenue trade-offs. A probabilistic model is developed to account for factors such as driving distance, charging times, locations, battery state of charge, and charging needs of PHEVs. The proposed approach is tested on the IEEE RTS-79 test system and evaluates multiple ML architectures, including Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Transformer models, often combined with boosting algorithms, across three scenarios: base case, uncontrolled charging, and intelligent charging. Results highlight that ML-based approaches, particularly the Transformer model, achieve computational time reductions of up to 49% compared to traditional SMCS methods while maintaining comparable accuracy. The Transformer model identified 1,788 loss-of-load states compared to 1,510 actual instances, requiring only 176 minutes of computation. Among all models, the BiLSTM with Adaptive Boosting (BiLSTM+AB) achieved the lowest overestimation, exceeding actual instances by just 256 states. Performance metrics such as Loss of Load Probability (LOLP) and Expected Demand Not Supplied (EDNS) validate the effectiveness of the proposed ML approaches in balancing accuracy and computational efficiency. © 2013 IEEE.
Advances in Composite Power System Reliability Assessment: Trends and Machine Learning Role
(Institute of Electrical and Electronics Engineers Inc., 2025) Yarramsetty, C.; Moger, T.; Jena, D.; Rao, V.S.
This paper provides a detailed review of reliability assessment methods for composite power systems, focusing on integrating renewable energy and advanced computational approaches. The study classifies existing research into three main areas: investigation studies, planning and optimization studies, and efficient evaluation studies. Findings indicate that machine learning techniques are increasingly important in improving accuracy and computational performance in reliability analysis. A comparative examination of conventional and Machine Learning (ML)-based methods demonstrates that deep learning models, such as Convolutional Neural Networks, offer substantial reductions in computational time while maintaining reliability assessment precision. The review also identifies key research gaps, such as the need for realistic test systems and enhanced hybrid ML strategies. Additionally, recommendations are proposed to address these challenges and strengthen the effectiveness of future reliability evaluation methodologies. The insights presented in this study aim to support researchers and industry professionals in developing more efficient and scalable reliability assessment models for evolving power systems. Â© 2013 IEEE.
Advances in Composite Power System Reliability Assessment: Trends and Machine Learning Role
(Institute of Electrical and Electronics Engineers Inc., 2025) Yarramsetty, C.; Moger, T.; Jena, D.; Rao, V.S.
This paper provides a detailed review of reliability assessment methods for composite power systems, focusing on integrating renewable energy and advanced computational approaches. The study classifies existing research into three main areas: investigation studies, planning and optimization studies, and efficient evaluation studies. Findings indicate that machine learning techniques are increasingly important in improving accuracy and computational performance in reliability analysis. A comparative examination of conventional and Machine Learning (ML)-based methods demonstrates that deep learning models, such as Convolutional Neural Networks, offer substantial reductions in computational time while maintaining reliability assessment precision. The review also identifies key research gaps, such as the need for realistic test systems and enhanced hybrid ML strategies. Additionally, recommendations are proposed to address these challenges and strengthen the effectiveness of future reliability evaluation methodologies. The insights presented in this study aim to support researchers and industry professionals in developing more efficient and scalable reliability assessment models for evolving power systems. © 2013 IEEE.
Composite Power System Reliability Evaluation Using Artificial Neural Networks
(Institute of Electrical and Electronics Engineers Inc., 2023) Yarramsetty, C.; Moger, T.; Jena, D.
This paper uses Deep learning and Monte Carlo Simulation (MCS) to speed up composite power system reliability evaluation. Due to recurring optimum power flow (OPF) solutions, reliability evaluation approaches for large integrated power grids are computationally demanding. Machine learning can avoid OPF in reliability assessment by identifying system states as successful or failed. They can only assess energy and power indices by solving OPF for all failures. This research calculates minimal load curtailments without OPF, except during training. Power, energy, probability, and frequency indices are evaluated. This paper presents a neural network based classification technique and linear regression based regression for evaluating composite power systems reliability. The proposed framework is illustrated through IEEE RTS 79 and 96 test systems. Findings show that the proposed method calculates composite system reliability indices accurately and efficiently. Â© 2023 IEEE.
Impact of Electric Vehicles on Power Systems Reliability
(Institute of Electrical and Electronics Engineers Inc., 2023) Yarramsetty, C.; Sawant, S.; Moger, T.; Jena, D.
The depletion of fossil fuel supplies and global climate change are two major challenges with gasoline automobiles. This worry has resulted in rising interest in electric cars in recent years. Plug-in Hybrid Electric Vehicles (PHEVs) are becoming regarded as a viable alternative to petrol automobiles. PHEVs are powered by both petrol and electricity. However, widespread integration of PHEVs would significantly increase the strain on the power system, threatening the reliability of the current power supply. The evolution of a probabilistic model for a PHEV is described in this paper. PHEVs are designed with driving distance, charging periods, and battery state of charge in consideration. A sequential Monte Carlo simulation approach is presented as a method for determining the reliability of power systems that are integrated with PHEVs. IEEE RTS 79 is utilised in the experiments. This research evaluates well-known generation system reliability metrics. Â© 2023 IEEE.
Impact of Wind Energy on Reliability of Generation System
(Institute of Electrical and Electronics Engineers Inc., 2023) Sawant, S.; Yarramsetty, C.; Moger, T.; Jena, D.
In power systems planning studies, reliability evaluation plays an important role. Due to increasing penetrations of renewables in power systems, it became essential to analyze system behavior under different conditions. This paper studies two cases: adding wind energy into the modern power system and replacing conventional generators with wind energy generation. These two cases are considered by changing wind energy penetration. In the suggested method, states of conventional generator units and wind farms are combined utilizing sequential monte carlo simulation. Weibull distribution is employed to make the estimates for the hourly wind speeds. The Copula approach simulates correlated random variables that represent the correlation between wind farms. Loss of Load Expectation (LOLE) and Loss of Energy Expectation (LOEE) are considered to evaluate the effect of wind energy generation on system reliability. The proposed approach of reliability evaluation of the generation system is examined on IEEE-RTS 24 bus system. Â© 2023 IEEE.
Reliability Evaluation ofÂ Composite Power Systems Using Machine Learning Techniques
(Springer Science and Business Media Deutschland GmbH, 2025) Yarramsetty, C.; Moger, T.; Jena, D.
Ensuring consistent electricity supply to consumers necessitates a reliable composite power system. Traditional methods struggle to manage the complexity of modern power systems, underscoring the need for advanced data mining approaches like machine learning (ML). This research evaluates the performance of various ML techniques, including K-Nearest Neighbor (KNN), Linear Classifier (LC), Ensemble methods (EN), and Neural Networks (NN), in assessing the reliability of composite power systems. Numerical simulations on IEEE RTS 79 and 96 test systems demonstrate that while EN exhibits perfect accuracy, other ML techniques achieve comparable results. However, computational efficiency varies significantly among these techniques. LC, in particular, outperforms others in terms of computational speed, making it a promising choice for real-time reliability assessment. This study provides valuable insights into the strengths and limitations of different ML techniques for reliability evaluation, guiding the selection of appropriate methods for diverse power system applications Â© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Role of Battery Energy Storage in Enhancing the Reliability of Wind-Integrated Power Systems
(Institute of Electrical and Electronics Engineers Inc., 2023) Sawant, S.; Yarramsetty, C.; Moger, T.; Jena, D.
The rising use of fossil fuels has led to a dramatic rise in atmospheric carbon dioxide levels. Renewable energy sources like wind and solar are actively pursued worldwide as a possible solution. However, the intermittent nature of wind power challenges the electric grid's reliability and stability. Battery energy storage is becoming more popular to ensure a steady and stable supply of electricity to combat this problem. This research employs Sequential Monte Carlo Simulation (SMCS) to analyze time series data on wind patterns and load levels to assess the dependability of wind and energy storage systems. It proposes an operational strategy for battery and wind cooperation to optimize the renewable energy sources usage. The study evaluates the impact of various factors such as wind energy dispatch limits, battery energy storage charging and discharging rates, and storage capacities on the reliability of the system. Effect of cycle ageing of battery is also evaluated. The proposed model of generation system reliability evaluation is tested on the IEEERTS 24-bus system. The study concludes that the proposed operational strategy for battery and wind cooperation can significantly improve system reliability and reduce the dependence on fossil fuels. Â© 2023 IEEE.