A MATLAB Code for Adaptive Fuzzy Campus Placement based Optimization Algorithm Ankeshwarapu Sunil, C. Vyjayanthi, Ch. Venkaiah 7th International Conference on Energy Power and Environment Icepe 2025, 2025 This paper presents a MATLAB code for Adaptive Fuzzy Campus Placement based Optimization Algorithm (AFCPOA), a novel approach designed to address the solutions to single and multi-objective optimization problems. Inspired by campus placement process for hiring students by visiting companies, AFCPOA integrates fuzzy logic principles with adaptive techniques to optimize the matching of candidates’ skills and preferences with available job positions, considering diverse criteria such as academic performance, skill proficiency, and job requirements. The paper provides an in-depth explanation of AFCPOA’s principles, including step-by-step calculations for single-variable minimization functions and implementation details for solving engineering optimization problems, such as economic load dispatch. Illustrative examples are provided to demonstrate the application of AFCPOA, and MATLAB code is included to facilitate implementation. This paper serves as a valuable resource for researchers, practitioners, and educators seeking to understand and implement AFCPOA.
Multi objective queue theory based optimal planning of rapid charging stations and distributed generators in coupled transportation and distribution network Vutla Vijay, Chintham Venkaiah, D. M. Vinod Kumar Energy Storage, 2024 Abstract The environment is adversely affected by greenhouse gas (GHG) emissions from conventional combustion engines. In this regard, electric vehicles (EVs) are a viable transportation option that benefit the environment in reducing GHG emissions. Although the installation of rapid charging stations (RCSs) helps to promote EVs, installing these at improper locations in the distribution network worsens the voltage profile, increases power loss, and energy loss while travelling from EV's current location to RCS. Furthermore, RCS installation cost and waiting time at RCS need to be considered. Therefore, a two‐stage optimal planning is proposed in this article to address the issues stated above. In the first stage, simultaneous optimal planning of RCS and distributed generators is done to minimize active power loss, voltage deviation, EV user cost and to maximize voltage stability index. In the second stage, optimal number of connectors are decided to minimize the installation cost and waiting time in queue at RCS. Here, queuing model is considered to determine the waiting time. A test network of coupled IEEE 33 bus distribution system and transport network is proposed to validate the proposed methodology. Multi objective Rao algorithm (MORA) is used to solve the formulated optimization problems, and results are compared with non dominated sorting genetic algorithm (NSGA‐II) algorithm.
Semi-Supervised Machine Learning Model for Sizing of Distributed Renewable Energy Sources Kuber Kushwaha, Ch. Venkaiah 2024 IEEE Students Conference on Engineering and Systems Interdisciplinary Technologies for Sustainable Future Sces 2024, 2024 This study presents a significant advancement in energy planning for grid-connected homes with plug-in electric vehicles (PEVs). A cutting-edge model has been developed to accurately size battery storage systems (BSS), small wind turbines (SWT), and solar photovoltaic panels (SPV). The model considers real-world factors like grid limitations and component degradation, resulting in more realistic outcomes. To tackle the complex problem, a semi-supervised machine learning algorithm approach was employed, combining unsupervised and supervised methods. This innovative algorithm outperforms traditional machine learning techniques and metaheuristic methods. By analyzing a wide range of configurations using both labeled and unlabeled data, the optimal setup to minimize electricity costs is identified. In addition, a real-time, rule-based, and efficient home energy management system is presented. The study is based on real data from Australia, including temperature, wind speed, solar radiation, load, and economic and technical information on solar, wind, batteries, and plug-in electric vehicles. The results demonstrate that the proposed model significantly outperforms the conventional Group Method of Data Handling (GMDH), marking a significant advancement in energy planning technology.
Power System Optimization using an Adaptive Fuzzy Campus Placement based Optimization Algorithm A Sunil, Vijay Saieesh, Ch Venkaiah Conference Proceedings 13th IEEE Power and Energy Society Innovative Smart Grid Technologies Asia Isgt Asia 2024, 2024 The use of optimization tools in complex problems is becoming increasingly crucial to overcome the challenging task of achieving efficient results. The Novel Adaptive Fuzzy Campus Placement based Optimization Algorithm (AFCPOA) is a new method for solving optimization problems that is based on campus recruiting process used in universities. In this study, two power system optimization problems, namely, Economic Load Dispatch (ELD) and Optimal Power Flow (OPF) have been tested on IEEE 30 bus test system. The main objective of ELD is scheduling generation units to lower costs while meeting system constraints, whereas OPF decides how to dispatch generating units to meet the demand for power at the lowest possible cost. The proposed method (AFCPOA) is applied on 16 Congress on Evolutionary Computation (CEC) benchmark test functions for validation and subsequently applied to two power system optimization problems ELD and OPF under MATLAB environment. The proposed AFCPOA method shows significant improvement in results compared with other methods for optimization problems.
Bidirectional AC-DC Converter Simulation for V2X Power Transmission Hari Krishna Nelluru, Ch. Venkaiah, Senthilnathan Thangavelu 2024 IEEE 4th International Conference on Sustainable Energy and Future Electric Transportation Sefet 2024, 2024 This paper primarily focuses on the implementation of V2X technology in Electric Vehicles (EV). The pivotal role of power electronics and its control mechanisms is emphasized to enable this technology. We employ a bidirectional totem pole Power Factor Correction (PFC) to convert AC power into DC power, facilitating both the charging and discharging of the EV battery. A detailed exploration of V2X operation and the control intricacies of the AC-DC converter is presented. The converter exhibits a maximum efficiency of 98.5% across various operational modes, with a power factor of 0.995 during charging. Additionally, the paper provides a comparative analysis of different AC-DC converters. Bidirectional PFC plays a pivotal role in aligning with the regulatory mandates and fostering the development of an advancing smart grid ecosystem.
Investigation of Power Flow Analysis in Networked Microgrids Ankeshwarapu Sunil, Chintham Venkaiah International Conference on Smart Systems for Applications in Electrical Sciences Icsses 2023, 2023 In this paper, the power flow analysis of the Networked Microgrids (NMGs) is analyzed for different case studies of benchmark test systems. Multiple Microgrids (MGs) operating in combination enable high penetration of Distributed Energy Resources (DERs), which lowers electricity costs and improves the resilience and stability of the power network. This article's benchmark test system for networked MGs connects and manages four separate MGs. An important technique for operational and planning studies of NMGs is power flow analysis (PFA). Each MG uses tie lines to work alone after initially operating as a group employing all of the MGs. The bus voltages, phase angles, active power loss, reactive power loss, iterations and computational time for NMGs were analyzed.
Multi-Layer Model Classifier for Cyberattack Detection in Smart Electric Grid Sourabh Singh, Ch. Venkaiah 5th International Conference on Energy Power and Environment Towards Flexible Green Energy Technologies Icepe 2023, 2023 In the Smart Grid, communication lines and physical open access points are always prone to cyber-attacks, and electric theft is the most common one. To detect electricity theft, researchers have developed several advanced machine learning models. However, existing work has not explored the problem of data imbalance properly, which is one of the significant challenges in electricity consumption data. This paper aims to compare various data balancing techniques and present an integrated theft detection model. This paper presents a multi-layer model for detecting fraudulent consumers in the smart grid. The detection process starts with data preparation steps, which include data interpolation, outlier handling, and data standardization. The next crucial step is handling data imbalance. Various techniques are tested, and AdaSys performs better than others. The model is being trained on a balanced dataset and validated on a real imbalanced dataset for realistic results. For higher performance, a two-layer model is chosen for electricity theft detection. The first layer consists of three heterogeneous machine learning models, and an Artificial Neural Network (ANN) model is used for the second layer. The first layer's probabilistic prediction serves as input to the second layer, which makes the final prediction. Experimental results confirm that multilayer model classifiers perform better than individual classifiers for detecting cyber-attacks on real consumption datasets.
Adaptive Fuzzy Campus Placement Based Optimization Algorithm Ankeshwarapu Sunil, Vijay Saieesh ATS, Venkaiah Chintham 5th International Conference on Energy Power and Environment Towards Flexible Green Energy Technologies Icepe 2023, 2023 A novel Adaptive Fuzzy Campus Placement Optimization Algorithm (AFCPOA) is developed for solving unconstrained optimization problems. The proposed optimization algorithm is based on the concept of campus placement procedure adopted for offering a job to a student by an employer visiting campus for hiring students seeking employment. Fuzzy models are considered to depict written test and interview process. The performance of the proposed algorithm was tested on 10 benchmark optimization test functions and compared with other existing algorithms. Subsequently, the proposed algorithm is applied on IEEE 33 bus radial distribution system for optimal placement and sizing of Distributed Generators (DGs) to mitigate active power losses and voltage deviation. It is observed from the results that the proposed algorithm is more effective in comparison with existing algorithms.
Optimal Power Dispatch of Multiple DGs Using a Hybrid Algorithm for Mitigating Voltage Deviations and Losses in a Radial Distribution System with Economic Benefits Ankeshwarapu Sunil, Chintham Venkaiah, D. M. Vinod Kumar Distributed Generation and Alternative Energy Journal, 2023 In this research, a meta-heuristic-based hybrid algorithm was used to optimize the power dispatch of numerous Distributed Generators (DGs) in a Radial Distribution System (RDS) for hourly fluctuating seasonal loads in order to reduce losses and voltage variations while also saving money. With hourly seasonal load changes, renewable DGs like PV, Wind, and Hybrid (PV+Wind) were used. The HA is proposed in this paper as a way to achieve successful outcomes by merging two meta-heuristic algorithms. The findings of the HA are compared with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Shuffled Frog Leap Algorithm (SFLA), and Jaya Algorithm (JA) when they are applied to a standard IEEE 33 bus RDS and PG&E 69 bus RDS. According to the test findings simulated in the MATLAB environment, Hybrid Algorithm (HA) beat GA, PSO, SFLA, and JA in terms of optimal power dispatch of numerous DGs to minimise losses and voltage variations, as well as the cost-benefit analysis of renewable DGs energy generation.
