Volume 24, Issue 3

In this paper, the benefits of an offshore wind transmission backbone grid for the east coast of the United States are discussed. It is explained why this is a more structured approach than a traditional radial grid where each wind farm project has its own export cable connection onshore. In this study, it was revealed that following an individualistic approach that Europe has followed so far in the wind offshore transmission, strategically, is more costly and time-consuming, including long-lasting permission processes. However, States by following a more collectivistic approach and by working synergistically towards implementing a backbone grid, it may be possible to reduce costs and save time.

Air Insulated substations (AIS) experience over-voltages like lightning, switching and temporary over-voltages. The voltage distribution across air insulated surge arrester (AISA) is highly non-uniform and its non-uniformity increases with increase of rated voltage. To achieve non-uniformity voltage factor within permissible limits, special grading shields like window based shields are proposed. In the present study, 420 / 800 kV surge arrester models with composite insulator housing have been considered to analyze voltage distribution across the metal-oxide (MO) blocks. The electrostatic field and voltage distribution analyses are carried out on AISA by considering various shield designs using numerical techniques. Instead of single grading shield, multiple voltage grading-cum-electric field controlled shields are adopted to achieve uniform electric field and optimized non-uniformity voltage factors in the order of ±8 % irrespective of rated voltage of surge arrester. An electrical model is also developed using PSPICE software to calculate voltage distribution across MO blocks. The validity of the network model developed through PSPICE is validated by comparing with the results obtained from numerical analysis. Different configurations of grading shields are proposed and effect of various parameters like rated voltage i.e. 420/800 kV, height of shield, diameter of shield and type of shield like high voltage, floating, LT shield etc. on non-uniformity voltage factor and electric field distribution of surge arrester are analyzed.

Renewable energy generation systems connected to utility grid require perfect synchronization to grid which is one of the most important issues that needs to be taken into consideration. This paper proposed a hardware-accelerated implementation for the decoupled double synchronous reference frame phase-locked loop (DDSFR-PLL) for grid synchronization in grid-connected converters in weak grid that suffers from phase voltage-unbalance, variable phase and frequency conditions. Since the transformations and filtering of this method is computationally intensive and needs to be executed as fast as possible by the microcontroller unit (MCU) and Due to the presence of other current and voltage regulation loops in the same interrupt service routine (ISR) with high frequency rate, a hardware-based acceleration using the STM32G4x4 MCU built-in filter, filter math accelerator (FMAC) and coordinate rotation digital computer (CORDIC) is used to speed the execution time. This study addresses the description, derivation and implementation of the both DQ-PLL and DDSRF-PLL algorithms. The performance of both pure-software and accelerated implementation is demonstrated, compared and run on a three-level active neutral point clamped (ANPC) converter board. In proposed method, CPU load dropped from 80.5% by using the conventional software implementation to 23.6% (70% load reduction).This reduction in CPU load enables the addition of more features and more advanced current and voltage control algorithms.

Integrating renewable resources with existing power system are rapidly increasing day by day, becoming an effective way to rural electrification where distribution system extension is not economically feasible. This research aims to design such an optimal grid-connected hybrid renewable energy system (HRES) modelled using solar, wind energy, diesel generator, battery storage, thermal loads, thermal load controller, and boiler. This system is designed to meet the load demand requirement of chintalayapalle village, Andhra Pradesh, India. The techno-economic, sensitivity analysis and cost-effective optimal configuration of this system have been performed using HOMER software. Totally eight configurations have been designed here and the optimal configuration have been identified based on the minimum net present cost (NPC), lowest cost of energy (COE), and the highest renewable fraction (RF). Results obtained shows that the optimally configured system is more environmentally friendly because of less carbon emission. Also, it would be more cost-effective when wind power becomes the main energy source and combined with moderate capacity of solar photovoltaic and battery.

In this work, an adaptive power management algorithm is developed for a multi-source dc microgrid system. The proposed algorithm is tested with a dc microgrid system consisting of PV, wind, batteries, supercapacitors, and variable load. The PV and wind energy systems are operated in maximum power point tracking mode to extract maximum energy from the available wind power and solar irradiation. The proposed algorithm has considered wide variation in weather conditions like zero wind velocity, zero solar irradiation, wind gust, and dynamic load change at the dc bus. For these cases, the proposed algorithm maintains the dc bus voltage constant without using a communication link. The efficacy of the proposed algorithm is tested using MATLAB-Simulink. Also, the feasibility and effectiveness of the proposed algorithm are validated using OPAL-RT control platform-based experimental studies.

The real time transient stability assessment is the important steps of the dynamic security evaluation of power systems. To do this, intelligent systems (ISs) such as neural networks as an effective method to accurately and quickly estimate the critical clearing time (CCT) has been widely used. But, choosing the proper inputs for these systems remains a major challenge for researchers, still. Variables related to energy functions such as minimum kinetic energy, the slope of the variation of the minimum kinetic energy and maximum potential energy contain useful information in estimating CCT. Accordingly, in this paper, these variables are used as the IS inputs. However, the time-domain simulation of the power system response to obtain these inputs is time consuming. To be able to use the energy function-based inputs for real time stability assessment, in addition to the main IS used to estimate CCT, another ISs are used. By those ISs, a very limited period of system response is simulated to obtain proper inputs of the main IS. The method is simulated on the 10-generator New England test system. Simulation results show, the CCT can be found by simulation just 0.05 s of the considered power grid.

This paper is devoted to a comparative analysis of thermal ageing tests of Thermally Upgraded Kraft (TUK) and Nomex-910 papers dipped in palm kernel oil methyl ester (MEPKO), at 110, 130, and 150 °C, for 96 h each. After ageing, paper breakdown voltage (BDV), oils’ dissipation factor, dielectric constant and oxidation stability were determined. Nomex’s BDV was greater than TUK BDV. However, the dissipation factor of the Nomex samples is slightly higher than that of TUK, and the dielectric constant has the opposite behaviour. In addition, the decay content of Nomex/MEPKO was greater than with TUK/MEPKO. These experimental findings indicate that Nomex can be mainly used in high voltage, and TUK in medium voltage power transformers respectively, to allow judicious use of their individual characteristics and money savings.

Power transformer is an important part of the entire power grid and the normal operation of the power transformer can ensure the normal operation of the entire power grid. The oil in the transformer plays a non-negligible role in the transformer. There are a lot of machine learning methods to predict oil temperature of power transformer. The work of this paper is to predict the oil temperature based on support vector regression machine (SVM) with three-phase power load, while particle swarm optimization (PSO) is employed for the model parameter optimization. As there are many influential factors for oil temperature prediction, confidence intervals are introduced to determine the prediction results. The experimental results show that the prediction accuracy reaches 90 with 85% confidence level. For the sample points falling outside the prediction interval, they can be regarded as the abnormal transformer status in time. The experimental results verified that the proposed oil temperature prediction method for power transformers based on modified SVM is effective and feasible.

In this article, a better optimization method known as the Taguchi method is implemented for optimizing the designing parameters of PV integrated Shunt Active power filter (SAPF). The effect of these parameters on the performance of SAPF is also studied with the help of this method. This paper also signifies the benefits of using the Taguchi method for optimization over the existing particle swarm optimization (PSO) algorithm. SAPF is a very popular device for power quality improvement through harmonics compensation. In non-conventional energy sources, solar is most widely used dues to its cost-effectiveness over other sources. Hence, PV integrated SAPF is considered for study for this article. Incremental conductance (IncCond) based maximum power point tracking (MPPT) technique is used to extract the maximum power from PV panel. Performance of this PV Integrated SAPF depends on many factors like reference current generation techniques, switching pulse generation technique and selection of SAPF designing parameters, etc. To get the optimum performance from SAPF, designing parameters like gains of PI-controller, coupling impedance, and dc-link capacitor need to be optimized. PV integrated SAPF is modeled and simulated in Matlab/Simulink. SAPF parameters are optimized by using both the proposed Taguchi method and the existing PSO algorithm and their results are analyzed. Simulation results are also verified using a laboratory-developed experimental setup.

Power generation using renewable energy sources is becoming popular in current scenario due to their capability of reduction in carbon emission as well as reliable power supply to consumers. However, power generation from a single renewable energy source cannot able to provide stable power. Hence, integration of renewable energy sources-based power generations needs to be established for providing continuous power supply to consumers. Solar energy and wind power are two major available sources on earth, hence, photovoltaic (PV) and permanent magnet synchronous generator (PMSG) based wind power generation system is considered in this paper. On the other hand, many loads are AC loads that required conversion from DC to AC such as inverters. Generally, multilevel inverters can able to provide quality supply to loads than normal inverters. However, the number of switches used in the multilevel inverter is more and also increasing size and complexity by increasing the level of the inverter. Therefore, an aligned multilevel inverter is implemented for a PV-Wind-based stand-alone system that has a smaller number of switches. Both solar irradiance and wind speed are depending on weather conditions and are unstable, hence a battery bank is incorporated with the help of a bidirectional DC to DC converter to provide an uninterrupted power supply to loads. A novel controller for 7 levels aligned inverter is implemented in this paper to supply quality power to loads under various scenarios. The proposed Aligned Multilevel Inverter needs half of the switches when compared to existing multilevel inverters as diode-clamped, flying-capacitor, and cascaded H-Bridge topologies. The hardware-in-loop is developed with OPAL-RT to present results under various case studies.

With the deepening of multi-energy flow coupling and the diversification of energy utilization demand, integrated energy system (IES) puts forward higher requirements for the economy and reliability of capacity allocation scheme. However, there is a complex relationship between economy and reliability, which makes it difficult for them to achieve coordinated optimization. Therefore, an economic and reliability optimal capacity allocation method of IES based on improved co-evolution algorithm is proposed. Firstly, the timing scenario reduction method of “wind power-electrical load” based on ordered clustering algorithm and K-means clustering algorithm is proposed. Aiming at minimizing the expected annual cost of construction investment, operation and maintenance, carbon emission and energy purchase cost, and constrained by equipment operation and energy flow balance, a multi-scenario economic operation model is constructed. Considering the dissatisfaction degree of comprehensive energy consumption, the energy supply reliability evaluation model based on Markov chain Monte Carlo (MCMC) method is established. On this basis, the IES multi-objective optimal capacity allocation model is established. Further, based on the orthogonal test method, the influence factors of decision variables on IES economy and reliability are calculated, and the evolution subpopulation of economy and reliability is established, and then the optimization model’s solution method based on improved co-evolution algorithm is proposed. Finally, an example simulation analysis is carried out combined with the actual data of a certain region. The simulation results show that the proposed model can cooperatively improve the economy and reliability of IES capacity allocation scheme, and provide a reference for the optimal capacity allocation of equipment in IES.