Powering frequently utilised DC loads like LEDs, laptops, and adjustable DC motor drives is where the DC microgrid truly shines. The DC microgrid, on the other hand, is constrained by substantial voltage differences between each converter and an unequal distribution of current among the converters, which leads to a lot of circulating current. The suggested distributed droop controller effectively lowers the constraints and provides an improved response in both transient and steady states. The distribution of currents from the equality line is also quantified on the inequality curve, which resembles the Lorenz curve in economics. In order to store extra power and then give it back to the bus, energy storage devices are also incorporated into DC buses. In this case, specific controller regulates the charging and discharging modes. While transitioning from charging to discharging and vice versa, the controller is likewise put to the test. The DC system is designed to accomplish the targeted purpose as a consequence.
This paper presents the characterization of an unbalanced three-phase system based on the analysis of the Voltage Unbalance Factor (VUF) defined by the IEC standard concerning its effect on the performance of the induction motor. It is shown that for a given VUF, there are infinite values of the positive and negative sequence voltage that would impact the performance of a three-phase induction motor differently. Finally, an alternative for characterizing the unbalanced voltage system considering its effect on the induction motor performance is proposed.
En este documento se presenta una metodología de diseño de una red de telecomunicaciones con estándar IEC61850-7-420, para soportar los procesos requeridos en Redes Inteligentes y Generación Distribuida. Mediante el uso de poderosas herramientas de simulación de redes de telecomunicaciones como OPNET y MATLAB, se desarrollan y modelan los principales elementos de una "smartgrid". Esta prueba de concepto simula el comportamiento de un sistema con despacho óptimo y una respuesta a la demanda en tiempo real de donde se extraen estadísticas que permiten evaluar la posible implementación del sistema de telecomunicaciones.
Integrating smart grids with the renewable energy present in Colombia expands the country's energy matrix and strengthens it through distributed generation in non-interconnected areas. Therefore, integrating distributed generation sources is of great importance to meet the growing demand, security of electricity supply, and system efficiency. The integration of distributed generation sources needs several management algorithms, control schemes, and an adequate environment to verify the proper functioning. This paper shows the implementation in an emulated microgrid environment based on Solar, Wind, Water, Biomass, and Diesel generation. Each generation source and control was implemented on a Raspberry PI 3 and linked with a server to manage the microgrid. Implementation schemes, control algorithms, and microgrid testbed are displayed throughout the paper.
This paper aims to investigate the use of machine learning techniques for PV power forecasting. In this study, a bibliometric review was conducted to establish a baseline, which involved the use of a SARIMA model as a statistical model, XGBoost model for machine learning, and LSTM for deep learning. These models were trained and evaluated using a sliding window technique and a validation set. The study’s development utilized the generation data collected from May 2018 till February 2023. The dataset underwent preprocessing to fill in the gaps in the data and to analyze trends, seasonality, and stationarity. The study compared three models and found that all of them could predict PV power generation. However, the XGBoost model outperformed the other two in training and validation, resulting in an R-squared value of 0.99, an nMAE of 0.35%, and an nRMSE of 0.55%, indicating that its predictions were the closest to actual values. The study’s limitations and potential areas for future research were discussed at the end.
An improved interleaved Ćuk power converter called in short –"Interleaved Two"– is designed and presented in this article. It is investigated and compared to a similar interleaved Ćuk power converter named as –"Interleaved One"– . The result is that the "Interleave Two" topology requires less reactive elements than the "Interleaved One" to achieve same performance, it will offer the possibility to reduce its size and economic cost. The operational principle and modeling is presented thoroughly. Moreover, simulation results comparing steady state time, output voltage, input current ripple and efficiency are presented. Additionally, in order to validate the performance of the "Interleaved Two" converter experimental results are included.
In this paper, we use Genetic Algorithms to estimate the parameters of an Induction Motor. The identification is performed using data obtained from a reference model that considers core losses and utilizes parameters previously determined through no-load and blocked rotor tests. Based on these results, the model parameters with adjustable values are estimated using Genetic Algorithms. The proposed optimization function for the Genetic Algorithms aims to minimize the weighted error of currents and speed, obtained between the data from the reference model and the simulation results of the model with adjustable parameters. The results show that when the induction motor starts by driving a load proportional to the square of its speed, the mean squared error in parameter estimation is less than 2.5 %, using a population of 20 individuals. These results, although preliminary, allow us to conclude that obtaining appropriate parameters for induction motors operating online is possible.
This paper presents the design of a computational tool with Artificial Intelligence (AI) to find behavior patterns in consumption and the optimum point of operation of the system by using data mining and the computational algorithm for monitoring demand management of the freezing water system of the central building of the Universidad Autónoma de Occidente (UAO). The results showed the optimization of energy systems in buildings, employing big data analytics and computational tools, which were fundamental bases for the development of the algorithm that allowed the design of the methodology to optimize the point of operation of the loads that make up the freezing water system of the central building performing efficient management and operation of the energetic resources. The authors hope that the developed methodology in this project can be applied in other types of facilities to allow more efficient electricity use.
Energy management allows planning operators to determine how renewable energy, such as solar, is used to meet generation and consumption goals. Several management strategies can be designed to improve energy quality, reducing the power grid dependence and producing ambient benefits and economical profits. These strategies must be previous assessed by means of simulation models in order to determine their reliability and viability. The proposed simulation model allows users to implement, monitoring, analyze and evaluate various energy management strategies for microgrid power systems, based in photovoltaic (PV) generation and industrial consumption forecasts. Improved and customized methods of PV generation forecast, load prediction and battery management are developed and integrated to the simulating system. A case study is presented to assess the performance, capabilities and outcomes.
This article highlights current methodologies for optimization in energy-intensive processes controlled by Electric Motor Drive Systems (EMDS). Methodological intervention proposals such as technological substitution, demand management, and operational control offer benefits in terms of energy sustainability, but uncertainty in evaluating the impact of the results remains unresolved. In this study, the process load factor indicator is used to support the analysis and prioritization of intervention routes and energy monitoring. Additionally, a case study is presented, evaluating the possibilities for intervention and energy savings in an EMDS system for pumping, along with the use of an integrated energy efficiency indicator for monitoring the system's energy performance.
A two-phase sixth order (2P6O) non-isolated boost converter is introduced in this article with an improved operation and an innovative design that considers an interleaved switching strategy for the transistors. The result is that this 2P6O converter achieves outstanding performance. In this article, the 2P6O converter is compared against the well-known and very competitive traditional interleaved boost converter for a design exercise with similar performance and equivalent switching ripples. The 2P6O contains more passive components, but the design showed that those passive components are smaller in terms of stored energy. The introduction of the additional inductor and capacitor in the 2P6O converter was compensated by the fact that all energy-stored elements became smaller. This advancement can provide more compact, efficient, and economical solutions in various power electronics applications. Experimental results are provided to demonstrate the principle of the proposition.
This paper introduces a modified double dual boost converter with the same voltage and current transfer ratio as the cascade type. However, the proposed converter can use substantially smaller capacitances and be applied in different applications such as distributed generation and microgrids. This paper presents the steady-state analysis of the proposed converter in continuous conduction mode. The ripples on capacitor voltage are deeply analyzed to provide design guidelines. A comparison of the proposed converter against the traditional double dual boost converter is discussed. The capability of effectively reducing the output voltage ripple, even with small individual capacitances, is evaluated via simulation. Finally, experimental results are provided to verify the principle of the proposed converters in different operating conditions.
The effect of the transmission line on the harmonic distortion of both voltage and current is characterized, and recommendations are proposed to mitigate the effects of the distortion generated by the non-linear load. Effects of the use of CCVTs in voltage measurement with high voltage harmonics are also studied and taken under consideration.
Smart Transformer (ST)-based Meshed Hybrid Microgrids (MHM) present advantages concerning the performance of conventional AC-DC microgrids (MG). Integrating the ST in MHM and Optimal Power Flow (OPF) algorithms is a suitable alternative for managing microgrids with high penetration of distributed energy resources. Thus, equivalent models of converters associated with the MG and ST are required to facilitate the formulation and solution of the optimization problem. This paper proposes an equivalent power flow model in ST-based MHM to formulate an optimal power management algorithm for day-ahead operation. The management algorithm delivers the optimal operating points of each of the MG converters to allow power control on both the AC and DC sides. The feasible solution of the OPF is verified in a simulation model in Matlab® Simulink® and its implementation in Real-time on the OPAL-RT platform to compare the results and validate the accuracy of the static models of the OPF. According to the tests performed, the accuracy of the ST equivalent model for Optimal Power Flow for day-ahead operation problems of MHM was verified based on Software-In-the-Loop (SIL) approach.
This paper evaluates the characteristic parameters of the permanent regime of power quality in an electrical system within a service industry focused on metal-mechanic engineering solutions, encompassing the design, manufacture, and repair of structural components for mining equipment. The significance of this study arises from the escalating power quality issues attributed to the company's technological processes heavily reliant on non-linear electrical loads. The investigation encompasses a comprehensive survey of the company, delineating and contrasting the measured and computed variables against acceptable thresholds stipulated by prevailing regulations. The measurements and analyses were conducted on a 3000 kVA PCC transformer with a designated voltage of 13.8 kV/460 V and ten transformers with capacities ranging from 10 kVA to 250 kVA, catering to individual circuits. Within the scope of the study, challenges pertaining to voltage fluctuations, current imbalances, and current harmonic distortions emerged in nine out of the ten individual circuit transformers, whereas the PCC exhibited no discernible issues concerning the quality of electrical power. The outcomes underscore that within an industrial electrical system, there exists the possibility that the quality parameters of electrical power align with normative thresholds at the PCC level. However, such conformity may not extend to individual circuits, potentially giving rise to power quality complications, subsequently curtailing the operational lifespan of equipment, and inducing energy losses within the industrial setting.
This paper examines the behavior in total losses and the efficiency of induction motors when supplied with frequency converters. The analysis of tests conducted in the laboratory on two induction motors of different power and efficiency classes is presented, following the methods 2-3A and 2-3C of the IEC60034-2-3 technical specification. Furthermore, the effect of increased switching frequency of the converter on the losses of both motors was experimentally evaluated. The results reveal an increase in losses when operating with the frequency converter compared to losses under balanced sinusoidal conditions. While both methods demonstrate this increase, method 2-3C estimated higher total losses, and therefore lower efficiency, than method 2-3A. Additionally, it was observed that the increase in the switching frequency of the frequency converter leads to lower losses compared to those obtained with lower switching frequencies. The increase in total losses due to supply with frequency converters must be taken into account when calibrating motor protections and when assessing efficiency improvements using frequency converters.
Electric motors represent an essential pillar in industrial processes; therefore, its continuous operation continues to be a priority for end users aiming to avoid unexpected outages. This paper proposes an indicator of motor degradation when subjected to detrimental motor conditions at the supply voltage. For that purpose, experimental measurement campaigns on a squirrel cage induction motor have been performed under ideal power supply conditions as well as in overvoltage conditions, then, an assessment based on signal processing techniques from the current waveform frequency spectrum has been carried out, then, the proposed methodology is then validated in real conditions with 15 kW motors, supplied with overvoltage. The results reveal that the proposed indicator responds adequately to the assessed disturbance in such a way that it can be a predictive tool once validated with other existing disturbances in power systems.
The IEEE 1584-2002 standard provided guidelines for applicable techniques to determine arc current, incident energy, and electrical arc limits. Therefore, this article presents the development of a software tool for calculating incident energy in electrical arc events, which helps determine the risk label and appropriate Personal Protective Equipment (PPE) for electrical risk mitigation based on the 2018 revision of IEEE 1584.
In recent years, artificial intelligence has focused on addressing various issues related to global technologies and needs. Its contributions in areas such as computer vision, sequential signal processing, natural language processing and system optimization using artificial intelligence-based algorithms stand out. This article presents the simulation of the solar photovoltaic system of the Universidad Autónoma de Occidente, covering the two stages of construction. In addition, a storage system connected to each set of solar panels, linked to the inverter, is introduced. The simulation allows an analysis of the most relevant variables for the system: solar radiation, ambient temperature, voltage generated and current generated.
In this work, we propose the design and modeling of a lithium-ion battery system for an electric vehicle, evaluating its performance through simulations. The vehicle model is developed associated with a typical trajectory within the city of Cuenca to determine speed and power requirements. With this information and a detailed modeling of battery charging and discharging, their behavior is determined considering both conventional and regenerative braking. The indicators of State of Charge (SOC) and State of Health (SOH) are used for this study. Additionally, a series of tests are presented to obtain a precise analysis of the electric vehicle’s range, considering charge and discharge cycles that can affect the battery’s health and lifespan.
