My research has so far focused on the power system dynamics, stability and control, and smart grid. Currently, the research group is working on the data-driven modeling and cyber-physical systems-based control algorithm in power system.
My research has so far focused on the power system dynamics, stability and control, and smart grid. Currently, the research group is working on the data-driven modeling and cyber-physical systems-based control algorithm in power system.
Power System Transient Stability Enhancement
As transient stability is a critical issue in power system reliability, the objective of this thesis is to develop a control strategy which will introduce necessary compensation using effective control of FACTS devices input references to maintain system stability during post-fault conditions. The major motivation of this research is to maintain transient stability of an interconnected grid via the real-time and optimal adjustment of flexible AC transmission systems (FACTS) devices controller input references in post-fault conditions. The control coordination law is derived based on real-time requirements, in terms of time as well as computation capabilities to have a feasible solution.
This work is devoted to the development of new schemes for transient stability enhancement using FACTS (flexible alternating current transmission system) devices, and the control coordination of power systems with FACTS devices in a transient state of post-fault scenario. The key objectives of the research reported in the thesis are, through online control coordination based on the models of power systems having FACTS devices, those of maximizing and restoring system transient stability following a disturbance or contingency.
The new schemes are developed with a new MPC-based TCSC controller in which the application of MPC methodology to power systems is applied which is represented by detailed dynamic modeling and coordinated with power system primary controllers i.e. exciter and prime-movers.
Port-Controlled Phasor Hamiltonian Modeling and IDA-PBC Control of Solid-State Transformer
This research presents an application of interconnection and damping assignment passivity-based control (IDA-PBC) principle to the port-controlled phasor Hamiltonian (PCPH) model of the solid-state transformer (SST) (comprising of three stages, namely, ac/dc rectifier, dual active bridge converter, and dc/ac inverter). A PCPH model of SST is established for each individual stages using the dynamic phasor concept. In comparison with other PBC approaches, IDA-PBC offers an additional degree of freedom to solve the partial differential equations. According to the target of the controller design at each stage, the desired equilibrium point of the system is obtained. The closed-loop system performance achieves regulation of constant output dc-bus voltage and unity input power factor. Large-signal simulation results for the full system validate the simplifications introduced to obtain the controller and verify the proposed controller. Robustness of the controller is demonstrated with 20% load disturbance and 10% input disturbance. For validation of the proposed approach and its effectiveness, hardware-in-loop simulation is carried out using Opal-RT and dSPACE simulators
Secured Energy Trading using Blockchain technology
The rapid development of large data and the internet is driving the whole social form and infrastructure changes. With the development and advancement in energy market, distributed power system, energy storage, electric vehicle and demand side response there is an urgency to come up with a solution which has no central trust entity assuring transparent, irrevocable electronic transactions among persons or machines. A decentralized blockchain approach would overcome many of the problems associated with the centralized approach. The Blockchain technologies are well known fundamental of cryptocurrencies (e.g. Bitcoin), but offer many other possible application areas. At its core, blockchain is a peer-to-peer (P2P) distributed ledger that is cryptographically secure, immutable (extremely hard to change), and updateable only via consensus or agreement among peers.
Research related to electric vehicles (EV) is mainly focused on the hardware such as battery charging method, and still there is a lack of software research such as billing system that needs to be developed realistically. The result of charge measured in the charging EV can be different from the charged amount claimed to be charged in the charging system (CS). However, due to the potential security and privacy issues caused by untrusted and opaque energy markets, it becomes a great challenge to optimally schedule the charging behaviors of EVs with distinct energy delivery preferences of the CSs. In this research work, a novel EV participation charging scheme is proposed for a decentralized blockchain using smart contracts. Firstly, a permissioned blockchain system is introduced to implement secure charging services for EVs with the execution of smart contracts. Furthermore, based on the smart contract designed each EVs individual needs from CS is satisfied while maximizing its utility.
To mitigate the problems of demand-supply mismatch in the future grid the solution of renewable energy source (RES) integration results in a bidirectional flow of information and transactions, which are prone to different kinds of cyber attacks, especially in energy trading where the security of financial transactions is of most concern. Electric Vehicle (EV) having the advantage of mobility can play a significant role in maintaining demand-supply balance at any location unlike their peers (conventional compensator). For deciding entire system security, securing EVs charging-discharging transactions at all charging stations or connecting points is most important. The system can be made more secure against cyber-attacks with the introduction of the blockchain framework. Hence, in view of secured transactions, the paper focuses on the energy trading process between EVs and distribution network (DN) in a Byzantine based blockchain consensus framework. During peak load period DN initiates the energy trading process by demanding additional power from the EVs. This process of energy trading results in energy and information exchange which needs to be secured through blockchain from vulnerable attacks and threats. Possible scenarios of various cyber-attacks on different nodes of the system are visualized in the form of false data. To highlight the application of blockchain, the Byzantine general problem framework is used which states that for successful attack 33% of information is to be manipulated, in other words, decreasing the probability of attack confirms the system security. Numerical results based on various operating scenarios for the standard IEEE 33 bus system are in agreement with the Byzantine consensus problem indicating improvement in system security.
Data-driven modeling for prediction and optimization
This research gives glimpses of complexity associated with the physical modeling approach to predict load profile and temperature profile for a smart building by considering a system model of a typical smart building. To avoid this complexity, a data-driven approach for predicting the electricity consumption and temperature prediction of a smart building is proposed. A commercial smart building is considered as a representative case study, whose weekly electricity consumption profile is predicted using Gaussian Process Regression (GPR) which corresponds to the class of Bayesian approach, and a comparative study is carried out in order to highlight the issues associated with Polynomial Regression, Artificial Neural Network (ANN), Dynamic Mode Decomposition (DMD) and Hankeled DMD (HDMD). The prediction results obtained with different algorithms are validated with various test scenarios.
With the recent trends of smart cities, the development in the sector of Smart Buildings has emerged tremendously which consists of multiple layers coordinating and interacting with each other with the help of a building management system (BMS). This interaction of different layers in the smart building with the help of a communication channel leads to exposure of layers to vulnerabilities (cyber attacks) which may lead to anomalies condition. This kind of anomalies can be avoided by proper prediction of data and coordination among different layers of the building operation. However, to develop the model for prediction of data is quite time consuming and hence, the paper proposes the concept of Dynamic Mode Decomposition (DMD) for predicting data with help of past available data even in absence of system model. In this paper temperature profile of heating, ventilation, and air conditioning (HVAC) system in BMS is predicted with the help of available past data. Once the prediction of the temperature profile is achieved the machine learning algorithm is used to classify and identify the data as normal or anomalies condition. The two-fold contribution of the paper in the prediction of temperature using DMD where all system states may not be observable and classification of data using machine learning is validated considering different test scenarios and results show the effectiveness of the DMD method in the prediction of data as well as classification using a machine learning algorithm.
In the era of data-driven, the next state of the system can be predicted and controlled with the help of system data set even in the absence of system model knowledge. Dynamic Mode Decomposition (DMD) algorithm is one of the techniques for predicting system states by breaking data into its principal modes, derived from a compilation of training data. The principal modes are useful for finding the behavior of the system and for predicting its future states, even in a noisy environment. The paper focuses on predicting variation in the rotor angle during a severe fault on the power system with the help of DMD. The paper also proposes the enhancement in the rotor angle with the help of Model Predictive Control (MPC) technique. For prediction and enhancement of the rotor angle, the multi-machine and single-machine systems are considered in the paper and the results are obtained for various operating scenarios. The results show the effectiveness of the proposed technique for prediction and enhancement of the rotor angle in the case of multi-machine and single-machine system with the help of system data only.
Smart buildings are gaining popularity with the surfacing trend of smart grid and smart city. Effective energy management is a major aspect of the smart building management system that demands accurate prediction of building electrical energy consumption profile. The paper focuses on a data-driven approach to load profile prediction with the highlighted benefit of a model-free environment. The electricity consumption profile of a commercial smart building is predicted using Gaussian Process Regression (GPR), and a comparative study is carried out to highlight the issues associated with Polynomial Regression, Artificial Neural Network (ANN), Dynamic Mode Decomposition (DMD), and Hankeled DMD (HDMD). For testing the effectiveness of the proposed methodology, various test scenarios were conducted and from the result, it is observed that the HDMD and GPR are preferred techniques to provide reliable prediction, which is beneficial for arranging a specific demand response schedule to earn benefits like financial rewards and carbon footprint curtailment.