This paper introduces a new method for implementing Unified Power Flow Controllers (UPFCs) in the static transmission expansion planning (TEP) problem. The optimization process in obtaining the suitable number and position of lines and UPFCs and adjustment of UPFCs” parameters will be carried out in two separate optimization levels. The first level suggests the appropriate numbers and positions of lines and UPFCs., and the second level deals with adjusting UPFCs parameters based on power loss reduction. With respect to the mixed integer non-linear nature of the TEP problem considering UPFCs., two meta-heuristic optimization algorithms are utilized for the two levels to reach the optimal solution. This study has implemented the proposed model on Garver 6-bus and IEEE 24-bus test systems. Under different scenarios., the obtained results confirm the efficacy of the introduced approach.
Solar Energy from photovoltaic (PV) systems are among the most used alternative for power generation. Maximum Power Point Tracking (MPPT) algorithms must be applied to it to extract the maximum power from PV panels. Usually, these algorithm requires PV voltage and current sensors to track maximum power. This paper proposes a modified current observer with hybrid Perturb & Observe (P&O) and Particle Swarm Optimization (PSO) based Maximum Power Point Tracking (MPPT) control algorithm. This observer is designed to estimate the current with precise error under harsh environmental conditions. The robust observer ensures that the MPPT algorithm does not converge to false MPP tracking. This observation-based current sensing reduces the overall cost of the PV system, algorithmic burden, time complexity, and internal sensor complexity. The proposed observer drives the error faster than the conventional luenberger observer and eliminates the wrong direction drive. The error between the actual and observed value is less than 5 % at any point. The proposed system is verified using simulations in SIMULINK.
The present paper investigates the evaluation of flutter analysis of horizontal axis (100m) wind turbine blade (HW AT), with DU-97-W-300 aerofoil cross section using mathematical model supported by standard Finite Element Analysis. The FEA codes along with PK - method is carried out for finding flutter solutions whereas Double Lattice Method is used for finding matrices and aerodynamic loads. Galerkian equations are utilized to solve the problems associated with the equations of motion. The study analyses the coupled mode flutter of rigidly fixed, flexible aerofoil. The results presented in this paper shows the significant impact of first torsion and third flapwise bending modes in the motion of blade structure. Further the study focuses on the improper isolation of torsional natural frequencies which resulted in poor flutter performance of the blades associated with the lowering of critical velocity into the operational ranges.
A methodology is proposed for the improvement of electric distribution system resilience to high-impact, low-probability catastrophic events. An approach for dynamic network reconfiguration and coordination of distributed energy resources is introduced to assist in restoration efforts. The problem is formulated as a mixed-integer linear program that minimizes generation costs, the cost of lost load, and costs associated with equitable load shedding, while respecting operational limits of generation, loads, and the network. Constraints are imposed on binary switching variables to ensure equitable load shedding in emergency situations. Numerical validation of the proposed approach is conducted on an example distribution feeder, and case studies are performed to analyze the impact of various parameters in the optimization problem formulation.
This study replaces traditional heating and cooling methods in single family homes with appropriately sized geothermal heat pumps and battery energy storage systems. Presented is the hardware and software configuration used for real-time monitoring and control of the simulated distribution feeder using the Real-Time Digital Simulator (RTDS). Four-quadrant meter implemented for single-phase residential electric utility feeder support regarding suitable heat pump load shift as well as the absorption and injection of real and reactive power with the use of synchrophasors. This study helps achieve the goals set forth by the Climate Leadership and Community Protection Act (CLCPA) in New York State (NYS) for the transformation of the energy system with a high penetration of inverter-based distributed energy resources in the distribution system.
High penetrations of residential solar PV can cause voltage issues on low-voltage (LV) secondary networks. Distribution utility planners often utilize model-based power flow solvers to address these voltage issues and accommodate more PV installations without disrupting the customers already connected to the system. These model-based results are computationally expensive and often prone to errors. In this paper, two novel deep learning-based model-free algorithms are proposed that can predict the change in voltages for PV installations without any inherent network information of the system. These algorithms will only use the real power (P), reactive power (Q), and voltage (V) data from Advanced Metering Infrastructure (AMI) to calculate the change in voltages for an additional PV installation for any customer location in the LV secondary network. Both algorithms are tested on three datasets of two feeders and compared to the conventional model-based methods and existing model-free methods. The proposed methods are also applied to estimate the locational PV hosting capacity for both feeders and have shown better accuracies compared to an existing model-free method. Results show that data filtering or pre-processing can improve the model performance if the testing data point exists in the training dataset used for that model.
The electric power system is a critical infrastructure for the well-being of modern society. Therefore, its efficient and reliable operation is of paramount importance. Information and communication technology (ICT) has increasingly been integrated into the electric power system to assist system operators in making more informed decisions, enabling the efficiency and reliability of the power system to be properly managed. However, it has been found that interdependency issues can arise, i.e. problems in the ICT system can cause problems in the power system, and vice versa. This paper will discuss ICT and power system interdependencies and the state-of-art in modelling cyber-physical power systems (CPPS).
As the electric grid becomes increasingly cyber-physical, it is important to characterize its inherent cyber-physical interdepedencies and explore how that characterization can be leveraged to improve grid operation. It is crucial to investigate what data features are transferred at the system boundaries, how disturbances cascade between the systems, and how planning and/or mitigation measures can leverage that information to increase grid resilience. In this paper, we explore several numerical analysis and graph decomposition techniques that may be suitable for modeling these cyber-physical system interdependencies and for understanding their significance. An augmented WSCC 9-bus cyber-physical system model is used as a small use-case to assess these techniques and their ability in characterizing different events within the cyber-physical system. These initial results are then analyzed to formulate a high-level approach for characterizing cyber-physical interdependencies.
Unmanned aircraft vehicles (UAVs) are being utilized for a variety of applications which include: medical deliveries, agriculture analysis, spraying, photography, and military purposes. Many of these UAVs are powered by batteries. Battery-powered drones, despite their advantages, such as low cost and ease of maintenance, are unable to fly for long periods of time. To address the problem, this paper proposes a hybrid Fuel Cell-Photovoltaic (FCPV) power system combined with a bank of batteries. Including additional energy sources such as fuel cells and solar cells with the battery can provide longer flight times. The feasibility and analysis of an FCPV-powered fixed-wing aircraft are examined in this paper. This hypothesis will be validated based on the simulation results.
This paper presents a type of recurrent neural network (RNN), namely, long short-term memory (LSTM) for real-time anomaly detection and classification at power electronics dominated grids (PEDG) inverters network. The proposed approach classifies signal sequences representative of voltage, frequency and rate of change of frequency (ROCOF) sensed at inverters point of common coupling (PCC), to normal or anomalous types. This paper addresses the challenges of training the network for real-time detection of anomalies in power grid due to large context of events. Numerous disturbances were included in the simulated PEDG for creating an effective database to avoid the neural network algorithm overfitting. The neural network classification accuracy and loss proved its capability of effectively detecting anomalous grid clusters with abnormal voltage and frequency behavior resulting from compromised inverters and their sensors. The preliminary case studies validate the performance and functionality of proposed real-time anomaly detection method for PEDG.
Cyber-physical situational awareness (CPSA) is crucial for understanding the intricacies and relationships within the interconnected electric grid, especially with increasing distributed energy resource penetration. However, beyond developing the necessary cyber-physical data fusion techniques, another critical challenge to address is the multi-level, multi-owner data exchange process and its cybersecurity. Therefore, in this paper we explore the considerations for secure data exchange for performing CPSA analysis and present the current landscape alongside existing gaps, potential solutions, and next steps. Additionally, we present a case study related to the project team's CPSA sensor and implementation architecture development, called griDNA, and how these potential solutions could apply and form a secure data exchange framework.
Partial discharges (PD) diagnostics is widely acknowledged as a reliable method for the health estimation of the insulation. During the operation of electric motors (EM) in industry, environmental stresses are produced due to the high concentration of contamination, temperature, and relative humidity (RH). These stresses severely impact stator insulation degradation and PD diagnostics is a challenge under these conditions. This research presents a framework for correctly evaluating the impact of contamination in EM insulation under high temperature and RH levels on PD activity and assessing the PD severity level. For this purpose, environmental stresses are produced in a climate control chamber and laboratory experiments are carried out under different concentrations of the contamination (up to 0.5 kg/m3) at high temperature (90 °C)and RH (40% to 98%). From the experimental results, it has been observed that both temperature and RH have a significant effect on the PD activity produced in EM insulation under different contamination levels.
Stirling cycle cryocooler is an ideal cooling device for lightweight superconducting (SC) rotors because of its compact design and non-dependency on externally supplied liquid cryogen. Yet the challenge lies in operating the cryocooler during high-speed rotation. Though most components of the cryocooler are symmetrical for a balanced rotation, it is not designed for rotational environment because of the various moving and asymmetrical internal components, including its regenerator and the passive balancer. Several experimental attempts have been made to test the rotating cryocooler and the highest tested speed was 1,500 rpm with no performance deterioration. This paper discusses an experimental approach to assess the performance of the Sunpower GT cryocooler at higher rotation speeds. The performance is characterized through the no-load temperature of the cold tip of the cryocooler. The apparatus designed for the test is discussed in detail in which its mounting, balancing, heat rejection, electrical connection, and vacuum are explained thoroughly. A test that spun the cryocooler to 2000 rpm has been performed and has proved the functionality of the apparatus. The results from the test show that the cooling performance does not deteriorate with the rotational motion. In contrast, the rotation slightly improves the cooling capability because the rotation enhances the air-cooling effectiveness on the heat rejector. The potential improvements of the apparatus and future test plans are also introduced.
This paper presents a control system for a photovoltaic (PV) system that functions as a Flexible AC Transmission System (FACTS) device called a STATCOM, with the implementation of anti-islanding protection using Support Vector Machines (SVMs). The controller can provide round-the-clock voltage control, and the entire inverter capacity is used for STATCOM operation at night. In the case of system disturbances during the day, the inverter temporarily stops generating active power and offers all its capacity to STATCOM operation. The anti-islanding protection uses SVMs for its classification abilities and is implemented passively, without affecting power quality. The effectiveness of the proposed control methods for islanding is assessed through simulations, and the findings are presented in the paper.
Direct current (DC) can be converted to alternating current (AC) utilizing switches like MOSFETs and IGBTs. Available inverter topologies can be broadly categorized either as two-level inverters or multilevel inverters (MLIs). Particularly for high-power applications, multilevel inverters provide several benefits over conventional two-level inverters. These benefits include the almost sinusoidal output voltage waveforms that the multilevel inverter can generate and the minimal overall harmonic distortion. Additionally, voltage and power levels can be scaled up easily with modular designs without any issues regarding voltage sharing between Power Semiconductors. The cascaded H-Bridge inverter is an excellent example of such scalability. This paper presents the simulation and hardware implementation of a cascaded single-phase MLI with three, five, and seven levels. In addition, the complexity of the circuit topologies, the precision of the output voltage, and the total harmonic distortion are compared in the above three topologies of multilevel inverters. Simulations carried out in LTSPICE, SAM, and MATLAB Simulink are used to derive the results.
This paper discusses power generation, transmission, distribution, storage, and power consumption, as well as power flow control and energy management in extraterrestrial microgrids that will be critical for deep space habitation. It also highlights the modeling, operational constraints and environmental challenges associated with extraterrestrial microgrids. To assess the effects of external disturbances, simulation results for different disturbance scenarios are shown using a space habitat model through the NASA-funded Resilient Extraterrestrial Habitats Institute (RETHi) 1 1 This work is supported by NASA STRI Resilient Extraterrestrial Habitats Institute (RETHi) under grant number 80NSSC19K1076..
Pumped storage hydropower (PSH) stores electrical energy as gravitational potential energy. Water is pumped from a lower elevation reservoir to a higher one and later flows back to the lower reservoir through a turbine. For areas with naturally large elevation changes, PSH has been an effective way to store excess energy produced from renewable sources. However, areas that have relatively small elevation changes require man-made height differences for PSH. Water towers could provide the required height differences. Here, three different water tower designs with varying pipe and Pelton Wheel Turbine nozzle diameters are examined numerically to determine an optimal configuration for energy storage. Maximum water level and Pelton Wheel blade angle are held constant. The results suggest that each water tower has a maximum energy capacity that can be achieved with multiple combinations of pipe and nozzle diameter.
This paper presents an Artificial Intelligence (AI)-based frequency trajectory prediction scheme to enhance power electronics-dominated grid's (PEDG)s resiliency. The proposed approach incorporates two main modules: i) Data mining module, and ii) real-time AI-based frequency trajectory prediction module. The data mining module provides a comprehensive dataset for a wide range of system frequency and rate of change of frequency (ROCOF). This approach encompasses all practical ranges for total system inertia, vast range of load disturbances and system reactance-to-resistance (X/R) ratio. In this module, Bayesian regularization algorithm is used to compensate dataset size limitations, resolving noisy training dataset issues and cover any potential uncertainties. The AI-enabled neural network (ANN) module is incorporated in the grid following inverter's (GFLI)s control loop to adjust their pre-defined power references in real-time during grid disturbances. The functionality of the proposed approach is validated via real-time testing of a 14-bus PEDG network on an OPAL-RT platform. Obtained results attest to the theoretical methodology validation and the robustness of the proposed approach.
This paper presents a Bayesian regularization based artificial neural network (BRANN) corrective actions for resilient operation of power electronic dominated grids (PEDG) with compromised sensors of grid-forming (GFM) or grid-following (GFL) inverters. GFM inverters play a significant role in governing the voltage and frequency stability of upcoming PEDG. Thus, spoofing the GFM inverter sensors' readings for their feedback control systems can jeopardize the PEDG resilient operation. This paper presents a framework that corrects the spoofed sensors' readings in the primary controller of the GFM inverter in PEDG. A neural network is trained via Bayesian regularization to realize the proposed corrective actions for sensors readings and consequently enhancing the resiliency of PEDG under sensor attacks. The trained neural network is integrated with GFM inverter's primary control loop to provide real-time corrective action whenever intrusive sensor behavior is detected. Summary of the proposed framework is provided in this paper. The simulated case studies are provided in the paper to validate the theoretical expectations.
This paper presents preliminary technical and economical analysis for using second-life batteries, retiring out of electric vehicles, in residential solar storage in Pakistan. To achieve this, an optimal battery control is designed to reduce the electricity costs to a residential user by the use of batteries. The optimal battery control requires day ahead load forecasting of the residential load, which for brevity, we assume is available, and do not present results for. In formulating the optimization problem, and its solution, we make the assumption that second-life batteries cost 50% of the new battery costs. Preliminary results for three cases (a) without PV and battery energy storage, (b) PV only, and (c) PV + optimized battery storage are compared. It is shown that due to a competitive grid feed-in tariff, solar PV installation alone can provide sizable savings for users.
