Current Research

The EPES Lab currently deals with the following research.

Cyber Security Issues and Solutions for Smart Grid and Microgrid Systems
A supervisory control and data acquisition (SCADA) system is an important component of a microgrid system for its proper functionality. There are several controllers involved in the microgrid system. For all of these control systems, necessary signals are collected through wireless sensors and sent to the SCADA where control decisions and commands are made and sent to control devices. As the SCADA is an internet-based control system, there is a high possibility of cyber-attacks at the SCADA as well as other control systems or signal loss in the process, which may significantly affect the controller performance of the overall microgrid system. If the controllers do not function properly, then the stability and power quality of the microgrid system will be deteriorated. Our research addresses the cyber security issues and investigates various means to tackle the cyber-attacks at the SCADA or other control systems of the microgrid and smart grid systems. We are applying various deep learning and machine learning based approaches for intrusion detection in the system.

Data Analytics for Smart/Microgrid Systems
The power grid is expected to become more dynamic and require constant decisions based on data streaming in from various sources at any given time. Examples of data include power system model data, market data, phasor measurement unit (PMU) data, smart meter data, etc. Big data in the electric power industry can be of large volume, high velocity, increasing variety, or all three. These data can be overwhelming if not properly managed and processed. Utilities require more powerful data analytics solutions to handle this extreme increase in data volume, variety, and velocity. Our research focuses on various data analytics applications to smart/microgrid problems. 

AC/DC Hybrid Microgrid Control
Hybrid AC/DC microgrids (HMGs) are becoming increasingly popular as they combine functionalities and usefulness of both AC and DC microgrids to reduce frequent energy conversions and to increase efficiency and reliability. Separate AC and DC microgrids have been developing in different aspects of control, power management, design, and planning over the past two decades. Although at the present time, AC microgrids are prevalent, it is foreseen that AC and DC microgrids will commonly and efficiently be utilized to interconnect energy resources and serve AC and DC loads at the distribution level. Our research focuses on developing new control methods to augment the dynamic performance of the hybrid microgrid system.

Load Forecasting and Scheduling for Smart Buildings
The load forecasting plays a pivotal role for buildings’ energy management system to schedule and control loads effectively while maintaining the reliability, stability and quality of the utilized power within the buildings. Our research focuses on exploring new methods based on data analytics, machine learning and artificial intelligence for forecasting and scheduling of residential loads. In particular, we are looking into data-driven machine learning based new approach to estimate the PV capacity based on predicted temperature, solar irradiance, humidity, heating degree days (HDD), cooling degree days (CDD), etc. Moreover, equation-based new prediction models for the loads are being implemented. 

Cyber-Attacks Detection and Mitigation Techniques for Internet of Electric Vehicles
The use of Electric Vehicle (EV) is growing rapidly due to its environmental benefits. However, the major problem of these vehicles is their limited battery, the lack of charging stations and the re-charge time. Introducing Information and Communication Technologies, in the field of EV, will improve energy efficiency, energy consumption predictions, availability of charging stations, etc. The Internet of Electric Vehicles (IoEV) is a complex system composed of vehicles, humans, sensors, road infrastructure and charging stations. All these entities communicate using several communication technologies (ZigBee, cellular networks, etc.). IoEV is therefore vulnerable to significant attacks such as DoS, false data injection, modification, etc. Hence, security is a crucial factor for the development and the wide deployment of IoEV. We are exploring novel means based on machine learning and artificial intelligence to detect cyber-attacks and mitigate their adverse impacts on the IoEV infrastructure.

Distributed Energy Resources
Distributed energy resources (DERs) are small-scale units of local power generation connected to the power grid at distribution level. The arrival of DER – a source of decentralized, community-generated energy – and its two-way flow of power is transforming the grid. DERs can include behind-the-meter renewable and non-renewable generation such as rooftop solar photovoltaic (PV) units, natural gas turbines, microturbines, wind turbines, fuel cells, battery energy storage, electric vehicles (EV) and EV chargers, controllable loads, and demand response applications. DERs may provide reliability during outages resulting from weather events, manage energy expenditures, meet customer desires to reduce their environmental footprint and/or support new evolving technologies. The impact of DERs on operating the power grid has been dramatic and exciting, resulting in both challenges and benefits to the entire grid spanning utilization, distribution, and transmission voltage ranges. Our research focuses on three major DERs such as PV, wind generator and energy storage systems.

Photovoltaic (PV) Power Generation Systems- Penetration of Photovoltaic (PV) power to the grid is increasing very rapidly. Energy regulatory body is imposing much stricter grid code due to this high penetration of the PV power. Grid connected PV system encounters different types abnormalities at the time of grid faults. When the fault appears in the grid side, the point of common coupling (PCC) voltage will go very low which causes the DC link voltage goes very high for power balancing. This high DC link voltage may damage the inverter. Also, the voltage sag will force the PV system to be disconnected from the grid according to grid code. And, shutdown of large PV plant may have adverse effect in power system operation. Our research focuses on applying different control methodologies to counteract the effect of faults in the grid side and hence preventing the voltage sag in grid side. Improving voltage sag by appropriate control methods will enhance the low voltage ride through (LVRT) capability of the PV plant. Moreover, we are focusing on the development of intelligent control based inverters and DC-DC choppers for the PV system.

Wind Turbine Generator Systems- Wind energy is getting increasing acceptance day by day. However, wind generators have stability issues during network faults. Moreover, due to random variation of wind speed, output power, voltage and frequency of wind generators fluctuate. Our research focus is to explore appropriate control means to maintain transient stability, power quality and fault ride through capability of wind generator systems. Moreover, development of a cost-effective wind turbine generator system aiming at increasing the power capacity of a wind generator is currently being investigated.

Energy Storage Systems- Energy storage is an essential element to provide constant and smoothed power to the customers. Among several energy storage technologies, we are mainly focusing on the modeling and simulation of battery energy storage, supercapacitor energy storage, superconductive magnetic energy storage (SMES) and fuel cell systems. These energy storage devices are being integrated to the wind generator and PV power generation systems.
 Effects of Geomagnetic Induced Current (GIC) on Transformers
Geomagnetically induced current (GIC) is a naturally occurring phenomenon initiated by solar activity. Sunspots (relatively cool areas shielded by complex magnetic fields) can give rise to solar flares and coronal mass ejections (CMEs). The CME carries its own currents and magnetic fields that are capable of affecting the Earth’s magnetic field. Charged particle movement in the conductive ionosphere increases the current flows in the electrojets, which are currents in the order of millions of amperes located more than 100 km above the Earth’s surface. These electrojet currents induce quasi-dc voltages in transmission lines that, in turn, drive the flow of GIC s wherever there is a path for them to flow. In power systems, GICs are quasi-dc currents and consequently cause saturation of transformers. This in turn results in a nonlinear operation of the transformer and in a significant increase of the exciting current. All this may then lead to the generation of harmonics in the electricity, unnecessary relay tripping, increased reactive power demands, voltage fluctuations and drops, and even a black-out of the whole system. Transformers may be overheated and, in the worst case, be permanently damaged. Our research focuses on exploring new means to minimize the adverse effects of GIC on transformers.

Communication Delay Issues of Smart Grid and Microgrid Systems

Communication delay occurs in smart power grid for various reasons. However, communication delay has adverse effects on control systems and consequently the power grid. Those delays in the system cannot be avoided. Some means or algorithm can be developed to minimize negative effects of time delays. Our research focuses on minimization of negative effects of such delays in a smart gird system. So far, the prediction method has been used to minimize the negative effects of time delay. Other novel and suitable control means are being explored to minimize negative effects of time delays with a view to realizing a reliable and secure smart grid system.

 Research Facilities

The EPES Lab is equipped with the latest computers to perform research. The following softwares are available in the Lab.

  • Matlab/Simulink
  • LabVIEW

Moreover, the Lab members have access to the Energy Conversion Laboratory of the ECE Department, which is equipped with the modern generators, motors, transformers, wind turbine and photovoltaic trainer, smart grid electrical generation trainer, various meters, oscilloscopes, etc. Therefore, the students have the scope to perform hardware based research also.

Smart Grid Electrical Generation Trainer

Smart Grid Electrical Generation Trainer

Wind Turbine and Photovoltaic Trainer

Wind Turbine and Photovoltaic Trainer