Vijay Pakka

Dr Vijayanarasimha Hindupur Pakka

Job: Senior Lecturer in Engineering & Sustainable Development (VC2020)

Faculty: Computing, Engineering and Media

School/department: School of Engineering and Sustainable Development

Research group(s): Institute of Energy & Sustainable Development (IESD), Engineering & Physical Sciences Institute (EPSI)

Address: IESD, Queens Building, 黑料网, Leicester, LE1 9BH UK

T: +44 (0)116 207 8835

E: vpakka@dmu.ac.uk

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Personal profile

Vijay Pakka is a Senior Lecturer in Electrical Power Systems & Smart Grids, based within the School of Engineering & Sustainable Development. He undertakes research in the area of Smart Grids specifically on the topics of distributed resource integration, microgrid operation and control, voltage regulation, distribution system modelling, electricity markets, deregulated power systems, demand response strategies and network stability analysis. He has a strong background in power system analysis and works with a range of modelling and analysis tools such as multiagent systems, statistical models, machine learning algorithms and metaheuristic techniques. He has special interests in stochastic dynamical systems and Bayesian network theory. He teaches electronics and electrical principles and power system analysis to undergraduate and postgraduate students along with research supervision of Doctoral and MSc students.

Vijay has previously worked as a Senior Research Fellow on the EPSRC funded AMEN (Agent-based Modelling of Electricity Networks) project that sought to make a significant and original contribution to improve the international profile and national strategic impact of energy modelling in the UK. It addressed in the context of the electricity network, perceived general weaknesses in whole energy systems modelling - from the closely related standpoint of complex systems research - in the areas of end-use behaviour, technology dynamics, and energy in industry. Prior to this project, Vijay worked on the million-£ CASCADE (Complex Adaptive Systems Cognitive Agents and Distributed Energy) project funded by EPSRC which focussed on agent based modelling of the UK energy system from a socio-economic and technical prespective.

Prior to joining 黑料网, Vijay completed his PhD in computer Science from the University of Southampton, UK and his Masters from Indian Institute of Science, Bangalore, India. His PhD thesis was on the investigation of Complexity issues in Gene Regulatory Networks using Machine Learning and Stochastic Analysis Techniques and his MSc was on using Neural Networks and Support Vector Machines for locating Faults in Distribution Networks. Vijay has a background in Electrical & Electronics Engineering with focus on Power System Analysis, Smart Grids, Electricity Markets and Trading, Power Electronics, Machine Learning Techniques and Statistical Analysis. Vijay has a Higher Certificate in Statistics from the Royal Statistical Society.

Research group affiliations

Institute of Energy & Sustainable Development

Institute of Sustainable Futures

Publications and outputs

dc.title: Surface Water Quality Assessment of the Arkavathi Reservoir Catchment and Command Area, India, through Multivariate Analysis: A Study in Seasonal and Sub-Watershed Variations dc.contributor.author: Surendra Kumar, Jyothi Roopa; Pakka, V. H. dc.description.abstract: The Arkavathi River, one of the major tributaries of the Cauvery River in southern India, is a major source of drinking water and agricultural irrigation to villages and townships in the region. Surface water quality distribution and characteristics of the Arkavathi Reservoir catchment and command area were evaluated using multivariate statistical analysis on 29 water quality parameters collected across 30 monitoring stations over a two-year, three-season period. Factor analysis (FA), agglomerative hierarchical clustering (AHC), analysis of variance (ANOVA) and t-tests were used to reveal strong links between parameters and to reveal significant variations in their concentration levels with respect to monsoon seasons and sampling locations across the sub-watersheds. Results from factor analysis showed strong groupings of specific parameters across seasons, while results from clustering revealed distinct clusters of sampling points around the river, upstream from the reservoir (where human activity is high), in the command area downstream from the reservoir (where irrigation activity is similar), in hilly regions towards the northeast of the study area and in the scrubland regions. Based on multivariate analysis findings, specific recommendations are made for water quality improvement in the reservoir catchment and command area. dc.description: open access article
dc.title: Loss Sensitivity and Voltage Deviation Index Based Intelligent Technique for Optimal Placement and Operation of Distributed Generators dc.contributor.author: Bhatt, D.V.; Bhatt, Y.H.; Pakka, V. H. dc.description.abstract: Distribution system is the crucial part of the power system which needs to be monitored for quality since it is the last stage in delivering electricity to consumers. The main aim of suppliers is therefore to maintain excellent quality of supply with minimal support at the network level. This includes dealing with issues around voltage regulation and minimization of real power losses on the network which are reflected in a uniform voltage profile through various load points on the feeders. However, with increase in decentralization, Distributed Generation (DG) becomes the focal point of distribution networks and can be used to achieve the above mentioned objectives. DGs are used in this paper to minimize the losses along with an improvement in the voltage profile. The location of these DG units on the network is addressed as an optimization problem using the loss sensitivity index and voltage deviation index. The size of power injected by DG units is obtained with using an intelligent approach that combines Genetic Algorithm (GA) with neural networks. Different cases of DG in terms of real and reactive power injected are considered resulting in an improvement in voltage profile. The loss sensitivity and voltage deviation indices show the best result for the location of DG needed to minimize the power loss along with improvement in voltage regulation. This approach is tested on a 13-bus distribution network with different types of loads placed throughout the feeder. dc.description: The Publisher's final version can be found by following the DOI link.
dc.title: Voltage Stability Index and Voltage Deviation Improvements using Intelligent Algorithms for Capacitor Sizing and Placement dc.contributor.author: Bhatt, Y.H.; Bhatt, D.V.; Pakka, V. H. dc.description.abstract: Voltage stability of each bus of an electrical distribution system along with the deviation of voltage magnitudes from the tolerance limits are two of the most common but significant issues in the present day distribution networks. This work is carried out with the objective of identifying the optimal location and ideal sizes of capacitors to be used in a radial distribution network for alleviating the above problems. An intelligent two-stage methodology is used that employs genetic algorithm and neural networks in order to achieve this objective. By analyzing the load flow study of the base case of load profile, voltage deviation and voltage stability indices are calculated for each node of the system. From these indices, the candidate buses are identified for the capacitor allocation in stage one of the methodology. Then, the combination of two artificial intelligent algorithms based on modern learning methods are employed to identify the ideal sizes of the capacitors for operation throughout the day. This methodology is implemented on a 33 bus radial distribution network. The results of these intelligent algorithms predict the ideal sizes of capacitors with optimal locations, providing a stable system with a smooth voltage profile across the entire duration of the day. dc.description: The Publisher's final version can be found by following the DOI link.
dc.title: Improved occupancy monitoring in non-domestic buildings dc.contributor.author: Ekwevugbe, Tobore; Brown, Neil; Pakka, V. H.; Fan, Denis dc.description.abstract: Measuring occupancy can facilitate energy efficiency in non-domestic buildings, when control systems are able to adjust heating and cooling based on demand rather than fixed schedules. The variable 鈥渙ccupancy profile鈥� itself is rarely considered as a control system parameter in building energy management systems (BEMS), and this is largely because reliably measuring occupancy in the past has been too difficult, expensive, or a mixture of both. Occupancy detection is possible using e.g. CO2 sensors, passive infra-red (PIR) detectors, which can provide a basic trigger for services, but the actual occupancy count, and therefore the expected load on building services, requires a step change in instrumentation. Advanced occupancy sensors developed from a heterogeneous multisensory fusion strategy offer this, improving control system performance, e.g. turning off services out of hours, and not over-ventilating, saving energy, while not under-ventilating during occupancy, benefitting comfort and health. While this is the case, there is a shortage of any systematic methodology for developing robust and reliable occupancy monitoring systems from heterogeneous multi-sensory sources. In this paper we describe an innovative sensor fusion approach utilising symmetrical uncertainty (SU) analysis and a genetic based feature selection for building occupancy estimation. dc.description: The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
dc.title: Design and Analysis of Electrical Distribution Networks and Balancing Markets in the UK: A New Framework with Applications dc.contributor.author: Pakka, V. H.; Rylatt, R. Mark dc.description.abstract: We present a framework for the design and simulation of electrical distribution systems and short term electricity markets speci铿乧 to the UK. The modelling comprises packages relating to the technical and economic features of the electrical grid. The 铿乺st package models the medium/low distribution networks with elements such as transformers, voltage regulators, distributed generators, composite loads, distribution lines and cables. This model forms the basis for elementary analysis such as load 铿俹w and short circuit calculations and also enables the investigation of effects of integrating distributed resources, voltage regulation, resource scheduling and the like. The second part of the modelling exercise relates to the UK short term electricity market with speci铿乧 features such as balancing mechanism and bid-offer strategies. The framework is used for investigating methods of voltage regulation using multiple control technologies, to demonstrate the effects of high penetration of wind power on balancing prices and 铿乶ally use these prices towards achieving demand response through aggregated prosumers.
dc.title: Advanced Occupancy Sensing for Energy Efficiency in Office Buildings dc.contributor.author: Ekwevugbe, Tobore; Brown, Neil; Pakka, V. H.; Fan, Denis dc.description.abstract: Control systems for Heating, Ventilation and Air-conditioning (HVAC) in non-domestic buildings often operate to fixed schedules, assuming maximum occupancy during business hours. Since lower occupancies usually mean less demand for HVAC, energy savings could be made. Air quality sensing, often combined with temperature sensing, has performed sufficiently in the past for this if maintained properly, although sensor and control failures may increase energy use by as much as 50%. As energy costs increase, building controls must meet increasingly stringent environmental requirements, increases in building services complexity, and reduced commissioning time, all placing ever higher demands on sensing, with a standing requirement to improve reliability. Sensor fusion offers performance and resilience to meet these demands, while cost and privacy are key factors which are also met. This paper describes a neural network approach to sensor fusion for occupancy estimation. Feature selection was carried out using symmetrical uncertainty analysis, while fusion of sensor features used a back-propagation neural network, with occupant count accuracy exceeding 74%.
dc.title: Exploring Smart Grid Possibilities: A Complex Systems Modelling Approach dc.contributor.author: Rylatt, R. Mark; Snape, J. Richard; Allen, P.; Ardestani, B. M.; Boait, Peter John; Boggasch, E.; Fan, Denis; Fletcher, G.; Gammon, Rupert; Lemon, Mark; Pakka, V. H.; Savill, M.; Smith, Stefan; Strathern, M.; Varga, Liz dc.description.abstract: Smart grid research has tended to be compartmentalised, with notable contributions from economics, electrical engineering and science and technology studies. However, there is an acknowledged and growing need for an integrated systems approach to the evaluation of smart grid initiatives. The capacity to simulate and explore smart grid possibilities on various scales is key to such an integrated approach but existing models 鈥� even if multidisciplinary 鈥� tend to have a limited focus. This paper describes an innovative and flexible framework that has been developed to facilitate the simulation of various smart grid scenarios and the interconnected social, technical and economic networks from a complex systems perspective. The architecture is described and related to realised examples of its use, both to model the electricity system as it is today and to model futures that have been envisioned in the literature. Potential future applications of the framework are explored, along with its utility as an analytic and decision support tool for smart grid stakeholders. dc.description: Complex Systems Research Centre, Cranfield University & Ostfalia, Fakult盲t Versorgungstechnik, EOS 鈥� Institut f眉r energieoptimierte Systeme Open Access article
dc.title: An NSGA-II based Multi-Objective Approach for Distribution System Voltage Control dc.contributor.author: Pakka, V. H.; Rylatt, R. M. dc.description.abstract: The aim of this work is to offer a voltage control strategy for distribution networks that experience voltage unbalance due to single phase and unbalanced loads and voltage rise due to high penetration of Distributed Generation units. The objectives are minimization of voltage imbalance on each node and total power losses on the entire network. The control of node voltages by Distributed Generation units has potential to clash with the more traditional method of voltage control adopted by Distribution Network Operators namely, tap changing voltage regulators and shunt capacitors. We look at a coordinated method of voltage control that solves the multi-objective optimization problem of voltage profile improvement and power loss reduction using a Pareto optimal and elitist evolutionary optimization algorithm called Non-dominated Sorting Genetic Algorithm II (NSGA-II). The study system is the IEEE 123 bus distribution test feeder which is highly unbalanced and includes most of the elements of a real network.
dc.title: Real-time Building Occupancy Sensing for Supporting Demand Driven HVAC Operations dc.contributor.author: Ekwevigb