This paper compares a single source boosted bipolar PWM half-bridge inverter with a single source boosted bipolar PWM H-bridge inverter. Using the H-bridge inverter as a benchmark, the paper draws a comparison between the two topologies by evaluating the system performance and providing a comparative analysis. Both inverters are fed by a single DC source and employ a boost converter to maintain constant output voltage level over a variable load range. PSIM simulation software is used to examine the system performance of half-bridge inverter and compares it against its H-bridge counterpart for various performance parameters. Simulations demonstrate that half-bridge inverter is more power efficient and yields lower THD due to its smaller number of switches in the inverter's bridge.
Transmission expansion planning (TEP) is crucial for maintaining the reliable and efficient operation of the power systems, particularly in the face of increasing electricity demand and the integration of renewable energy sources. This paper aims to investigate the application of unconventional high surge impedance loading (HSIL) lines in TEP and presents a comparative analysis of their outcomes against conventional line-based TEP approaches. Starting with a 17-bus 500 kV test system, which can operate well under normal operating condition as well as all single contingency conditions, the objective is to connect a new load located in a new bus, bus #18, to the existing test system via two approaches: using conventional lines and incorporating unconventional HSIL lines. By comparing the number of lines required for the conventional and unconventional approaches, maintaining identical conductor weight per circuit, the effectiveness of unconventional HSIL lines in TEP is evaluated where using only two unconventional HSIL lines is sufficient to connect 1250 MW load demand at bus 18 while three transmission lines are required when using the conventional line. Finally, a thorough economic analysis has been conducted on both TEP scenarios, revealing that implementing unconventional HSIL lines leads to remarkable cost savings and thus can be considered a promising option for TEP studies.
Due to thermal, electrical, mechanical, and chemical stresses, line-post insulators in the power system may degrade over time. The degradation process continuously gets exacerbated by the above-mentioned factors. Therefore, condition monitoring of line insulators must be frequently carried out. Optical cameras are considered the most accurate among existing technologies for detecting such defects. Computer vision techniques aided by optical cameras could automate faulty insulator identification. However, there is a limited size of the training data set obtained from real-world optical camera images. In this paper, we propose a generative approach to creating a massive amount of line-post insulator fault images through Deep Convolutional Generative Adversarial Networks (DCGAN). The additional training data obtained from DCGAN-based approach is shown to improve the accuracy of the insulator fault classification. In the case study, we show that with an increasing number of synthetic images created by DCGAN, the accuracy of the fault classification continuously improves. The ability to classify true faulty insulators has increased from 56% to 94%. The performance of the DCGAN-based approach is also compared with the random oversampling approach. The numerical results suggest that the DCGAN-based approach has the advantage of detection accuracy and a lower false positive rate.
The resilience of microgrids, which is crucial for maintaining a stable and reliable power supply, has become increasingly important in the face of rising energy demands and the growing threat of extreme weather events. Incorporating wave energy systems into the grid infrastructure can diversify energy sources, improve grid stability, and ensure uninterrupted electricity supply even during disruptive events. This paper presents an innovative study on the integration of wave energy into a modified IEEE 33-node distribution network, providing a unique perspective on the impacts and opportunities of harnessing marine renewable energy in distribution microgrids. To understand the impact of wave energy on grid resilience, service restoration is compared under fault conditions with different renewable generation portfolios along the coast of North Carolina. This research underpins the significant potential of wave energy as a reliable and sustainable energy source for future microgrids in coastal or island areas. The case studies revealed that by introducing wave energy, the use of hybrid renewable energy sources, can reduce the value of lost load and contribute to improved network resilience and more effective load-shedding management during faults.
Geomagnetic disturbances (GMDs) threaten the grid through geomagnetically induced currents (GICs), which saturate transformers, causing operational effects such as voltage stability issues, potentially leading to load curtailment and, in extreme cases, grid blackout. GMDs pose a severe threat to system reliability, and it is imperative to model the effects of GMDs in the reliability assessment. Hence, this paper proposes an integrated reliability assessment framework wherein the GMD effects are included by adding a GMD reliability module to the generally accepted reliability assessment framework. The paper addresses the first subprocess in the integrated framework - reliability modeling of GMDs. A way to characterize the GMD storms in the context of reliability analysis is shown by introducing three parameters (TTGMD, GMDT, and GMDC) that model the storms' frequency, duration, and intensity. Over 90 years of historical geomagnetic data was processed, and historical observations for TTGMD, GMDT, and GMDC were obtained. An automatic fitter procedure then fits the historical data to probability distributions culminating in the initial steps for developing a GMD-integrated reliability assessment framework.
The continuous increase in load demand and high penetration of renewable energy sources have driven electric distribution systems to operate close to their voltage stability boundaries. Hence monitoring voltage stability limits in distribution networks is essential to determine any required preventive or corrective remedial action. This paper presents a new method for voltage stability assessment of distribution networks using local measurements. In contrast to existing methods in the literature, which rely on phasor measurement units (PMUs) that are not usually available in distribution systems, to estimate Thevenin Equivalent (TE) for the power system, the proposed method uses the available measurements from smart meters without exclusively relying on PMUs to determine TE. The system equivalent is then used to derive a voltage stability index (VSI). The operating and security constraints at the node of interest are represented in the complex voltage plane which also allows the consideration of additional constraints such as the maximum and minimum voltages. The proposed method is implemented to a 17-Bus test system. The proposed indicator shows a linear behavior making it easier to estimate the distance to voltage instability and the magnitude of the required remedial action. Margins to maximum loadability can be easily estimated. The required reactive support to keep a specified level of voltage stability can also be determined with high accuracy.
The transient stability analysis (TSA) is a major numerical function in Energy Management System for large-scale power transmission system planning and evaluation. Ideally, the trajectory of system dynamics, such as bus voltage magnitude and generator phase angle, can be predicted to forecast issues and disturbances based on a time-domain solution of differential and algebraic equations (DAEs). However, the rise of system scales and complex machine models requires advanced computing techniques to achieve even faster than real-time (FTRT) criteria. For accelerating the execution, High-Performance Computing (HPC) on traditional CPU-based supercomputing clusters has been widely investigated for decades. Recently, as heterogeneous computing was introduced to power system domain areas, general-purpose computing devices such as graphics processing units (GPU) have been deployed to enhance computational performance and program portability further. This paper reviews the trials within the last 15 years on developing parallel TSA applications regarding mathematical formulations, programming approaches, and performance. We also address the future of TSA computation and its potential to improve the efficiency of FTRT executions.
This article presents the application of numerical integration methods, such as forward Euler (FE), backward Euler (BE) and trapezoidal rule (TR), based on discrete Norton equivalent models (DNEM) through companion-circuit branches RL, for a representation of systems of linear equations for the solution of power systems of any scale in the time-domain (TD). This approach allows obtaining a matrix relationship from the companion-circuit analysis (CCA), which is mainly composed of a symmetric in structure and particularly sparse conductance matrix. The solution of this matrix is exploited using two sparse matrix $LU$ and $LDU$ decomposition processes. The resulting unified methods consist of CCA-LU and CCA-LDU, which are applied to the solution and analysis of the 2383-bus modified power system under fault conditions. The performance of the method is compared in terms of CPU time.
The aim of distribution networks is to meet their local area power demand with maximum reliability. As the electricity consumption tends to increase every year, limited line thermal capacity can lead to network congestion. Continuous development and upgradation of the distribution network is thus required to meet the energy demand, which poses a significant increase in cost. The objective of this research is to analyze distribution network topologies and introduce a topology reconfiguration scheme based on the cost and demand of electricity. Traditional electrical distribution networks are static and inefficient. To make the network active, an optimal dynamic network topology reconfiguration (DNTR) is proposed to control line switching and reconnect some loads to different substations such that the cost of electricity can be minimized. The proposed DNTR strategy was tested on a synthetic radial distribution network with three substations each connecting to an IEEE 13-bus system. Simulation results demonstrated significant cost saving in daily operations of this distribution system.
This paper presents a measurement-based electricity market structure to establish peer-to-peer (P2P) transactions along with imports from or exports to the upstream network. A key benefit of the proposed P2P market is that participants therein can fully express their proclivities by setting their individual preferences for buying and selling partners independently. Moreover, resulting P2P transactions satisfy power flow constraints of the underlying distribution system without needing an offline network model. Instead, we estimate a linear sensitivity model mapping bus voltages to injections using only online measurements collected from P2P market participants, which is then embedded as an equality constraint in an optimal power flow (OPF) problem. The OPF problem minimizes total cost of P2P transactions incurred to market participants capturing network usage fees, buying/selling preferences, net import/export cost, and operation cost. The optimal solution of the OPF problem comprises the P2P transactions (specifying partners, quantity, and price for each trade), the optimal dispatch, as well as locational marginal prices at buses where measurements are collected. Via numerical simulations involving a 22-bus test system, we demonstrate the effectiveness of the proposed method to establish P2P transactions that respect individual preferences; we also validate notable properties pertaining to the trade prices.
Accurate near real time monitoring is needed for Distribution Management Systems (DMS), and power flow-based methods are commonly used in practice for this purpose. However, near real time power flow results are not always accurate because of errors in load estimation, and which may result in poor voltage estimates on distribution feeders. This paper focuses on this issue and proposes a data analytics-based method to parse through data and identify factors/parameters that are unique and relevant to poor near real time power flow performance. The proposed method uses a clustering method (K-means clustering) to determine if we can separate the save cases that perform well from those that do not based on the selected parameters. In the second step, we propose to use a binary logistic regression method to identify unique features for the clusters having distribution feeders with a poor voltage estimate. Test results show that the proposed method can help us to identify unique features for the feeders having poor near real time power flow performance.
A 30kW laboratory scale model electric power grid was re-engineered to use more modern distributed data acquisition in the form of a Multi Agent System (MAS) with a laboratory-wide timing pulse to serve for measurement synchronization similar to GPS signals used in industrial synchrophasor equipment. The scale model power grid is useful in an electric power academic environment for teaching concepts, providing a hands-on tool for students, and to serve as a testbed for researching new ideas in the ever evolving “smart grid”. Various elements of the design of the MAS Smart grid are presented highlighting unique circuit designs, a state machine control algorithm, distributed timing pulse synchronization, and Smart Meter protection. Three test cases of the grid are presented highlighting autonomous generator connections, synchronized measurements, and power flows.
This work utilized a Reinforcement Learning (RL) agent to track for the Maximum Power Point (MPP) of a 200 kW Photovoltaic (PV) power system to supply power to a load of 150 kVA with a 0.8 lagging power factor at 120 $V_{\mathrm{r}ms}$ where the solar irradiance was changing randomly to a gaussian distribution in the range of 0 to 1000 W/m 2 . The implemented RL agent was trained using a Proximal Policy Optimization (PPO) algorithm that used a Fuzzy Logic reward system that was compared with the Perturb & Observe (P&O) algorithm, and another PPO agent trained with a simpler reward system. The implemented RL agent was able to outperform the P&O algorithm and PPO agent utilizing a simple reward system by outputting the highest mean PV power of 132.0 kW and regulated voltage of 117.6 $V_{rms}$ with little to no oscillations in MATLAB Simulink simulated for 10 seconds.
In this paper, we propose a comprehensive algorithm for identifying alternative solutions in power flow analysis. Our approach considers various power system conditions and grid sizes, from small to large-scale cases. By analyzing the Jacobian matrix and its singularity, we accurately detect the proximity to voltage collapse and identify alternative solutions. The results demonstrate the effectiveness of the algorithm in handling diverse scenarios and showcasing its capability in identifying alternative solutions in power flow analysis.
Availability of fine granularity electricity consumption data is critical for building energy management and designing efficient electric retrofits. Methods have been developed for producing year-long hourly electric load profiles of residential and commercial buildings without the need of direct smart meter measurements, which may incur privacy, data inaccuracy, and security concerns. Many of these techniques are built upon monthly utility bills, some leveraging multiple time-of-use intervals. This work proposed an adaptive building electric load profiling technique, which improves upon the limitations of existing work by introducing a transition period that is not always included in the utility bills, while also considering the impacts of seasonal weather changes. The proposed profiling method is tested on a gas-heated building and a fully electric building. Results show the gas-heated building exhibits better profiling errors compared to the fully electric building, whose electric load is more sensitive to environmental temperature changes, resulting in error outside of the acceptable error threshold during shoulder seasons. However, this may be acceptable as shoulder seasons do not meaningfully impact electric retrofits.
This paper progresses on the development of the discrete electromechanical oscillation control (DEOC). The DEOC approach is based on the step-wisely control of electronically-interfaced resources' (EIR) power output and aims to significantly reduce the amplitude of multiple oscillatory modes in power systems. The theoretical formulation of the problem and the proposed solution is described. This work addresses the issues of a nonlinear grid representation and favorable reduction of control actions from EIRs, as well as their impact on the DEOC performance. Simulations on a 9-bus system validate the effectiveness of the proposed control even when highly load scenarios are considered.
Electric vehicles (EVs) have been ‘just around the corner’ for over 100 years, but it appears that they finally may be on the near horizon for wide scale deployment. At least, this may be said considering recent developments in California. This paper describes a state resolution in California to prohibit the sale of gasoline powered automobiles and light trucks by 2030, and the expected impact of this resolution on electric power, energy, and load factor. The approach taken for this impact assessment is statistical and probabilistic with particular attention to the statewide annual load factor. Data on expected EV deployment are given for 2023 - 2030. Since the power system load factor is a ratio of two quantities, the statistics of the load factor are characterized as a newly developed probability density function that models the system load factor as a ratio distribution. Some results, examples and preliminary conclusions are drawn.
Distributed generators (DGs), especially inverter based renewable generation sources, are becoming prevalent in power distribution grids to meet increasing demand and harness renewable energy. However, DGs of intermittent nature such as solar power can lead to significant voltage fluctuations. Traditional mechanical voltage regulating equipment such as on-load tap changers (OLTCs) and capacitor banks (CBs) are unable to handle these fast-changing fluctuations. This paper proposes a new optimal cooperative control-based method for controlling DG inverters to adjust reactive power generation or consumption of DGs. The optimization is based on local information and the information received from other nodes with or without reactive power sources. The objective function is to minimize voltage fluctuation and reactive power output since additional reactive power output could cause accelerated aging of inverters. Results based on simulation studies have shown that the proposed method can achieve smoother voltage profiles.
The occurrence of cyber and physical disturbances in power systems is increasing, leading to increased public focus on cyber-physical architectures. It has been observed that disturbances can propagate between cyber and physical systems, highlighting the need to study their interdependencies. In this paper, we present an approach to improve the characterization of cyber-physical interdependencies through modeling techniques. These improved assessments of dependencies can then help optimize system design to improve functional resilience. To achieve this goal, we transform the cyber-physical architecture into a graph and apply bio-inspired network analysis using bipartite network methods to characterize the system during disturbances. Moreover, we apply a DeepWalk-based method to cluster the components based on their interdependencies. A WSCC-9 bus system is used for numerical study and quantification.
In this paper, a highly compact, low power $(\leq 10)$ , high frequency (2 MHz) isolated active clamp forward converter, comprising a coreless Printed Circuit Board-based transformer is proposed. To decrease the size of converter, high switching frequency is considered for the forward topology which lead to decrease in inductor, capacitor and transformer size. How-ever, highly switch loss due to hard switching is an important constraint of forward topology to increase frequency. In this paper, the active clamp circuit is added to forward topology to achieve zero voltage switching and decrease switching loss drastically. Due to zero voltage switching, the proposed converter can operate in high frequency. The principle of active clamp forward converter is described in this paper. Another constraint to increase the switching frequency of forward converter is trans-former core losses. In this paper, coreless PCB-based transformer is proposed and implemented to be utilized in the structure of the active clamp forward converter. Instead of classic core-based transformer, using a PCB-based transformer as the power transmitter has increased the efficiency due to elimination of core hysteresis loss. The equivalent circuit, transfer function and input impedance of PCB-based coreless transformer are presented in high frequency. Finally, an experimental prototype of the active clamp forward converter which is comprised of a coreless transformer is implemented. The experimental results of proposed converter are presented to evaluate the theoretical analysis and performance.
