AC Power Research Articles

Long-Term Solar Power Generation Forecasting at Eastern West Large Scale Solar (LSS) Farm using Random Forest Regression (RFR) Method

Precise forecasting of power generation and demand is essential for effective resource allocation and energy trading in contemporary energy systems. Power forecasting accuracy has increased dramatically since Random Forest Regression (RFR) techniques were used. The study's primary objective is to forecast electricity generation in Malaysia's Eastern West region, with a concentration on solar energy. The research process entails gathering and examining pertinent factors, weather information, and historical power data. To evaluate the accuracy and predictive potential of RFR models, a specific power grid is used for training, validation, and testing. One of the anticipated results is the creation of an accurate model for power generation predictions, which will help to optimise energy operations and smoothly incorporate renewable sources. The paper examines the advantages, disadvantages, and best practices related to RFR-based power forecasting. The dataset, which spans the years 2019 to 2023, includes 30-minute interval records for the following variables: average output power, ambient temperature, PV module temperature, global horizontal irradiance, and wind speed. Using the RandomForestRegressor class from the scikit-learn library, the RFR model is implemented. In order to assess the model's overall fit, average deviation, and sensitivity to outliers, measures such as root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE) are used on the test set. The temperature, irradiance, and AC power output of PV modules are found to be strongly correlated.

Intelligent Type-2 Fuzzy Logic Controller for Hybrid Microgrid Energy Management with Different Modes of EVs Integration

The rapid integration of renewable energy sources (RES) and the electrification of transportation have significantly transformed modern energy infrastructures, emphasizing the need for efficient and flexible energy management systems. This study presents an intelligent, variable-fed, Type-2 Fuzzy Logic Controller (IT2FLC) designed for optimal management of Hybrid Microgrid (HMG) energy systems, specifically considering different modes of Electric Vehicles (EVs) integration. The necessity of this study arises from the challenges posed by fluctuating renewable energy outputs and the uncoordinated charging practices of EVs, which can lead to grid instability and increased operational costs. The proposed IT2FLC is based on comprehensive mathematical modeling that captures complex interactions among HMG components, including Doubly Fed Induction Generator (DFIG) units, photovoltaic (PV) systems, utility AC power, and EV batteries. Utilizing a yearly dataset for simulation, this work examines the HMG’s flexibility and adaptability under dynamic conditions managed by the proposed intelligent controller. A Simulink-based model is built for this study to replicate the dynamical operation of the HMG and test the precise and real-time decision-making capability of the proposed IT2FLC. The results demonstrate the IT2FLC’s superior performance, achieving a substantial cost avoidance of nearly $3,750,000 and efficient energy balance, affirming its potential to sustain optimal energy utilization under stochastic conditions. Additionally, the results attest that the proposed IT2FLC significantly enhances the resilience and economic feasibility of hybrid microgrids, achieving a balanced energy exchange with the utility grid and efficient utilization of EV batteries, proving to be a superior solution for optimal operation of hybrid grids.

Thermo-Fluid–Structural Coupled Analysis of a Mechanical Seal in Extended Loss of AC Power of a Reactor Coolant Pump

We proposed a numerical method to investigate the thermo-fluid–structural coupled characteristics of a mechanical seal of a reactor coolant pump (RCP), especially during extended loss of AC power (ELAP) operation. We developed a finite element program for the general Reynolds equation, including the turbulence effect to calculate the pressure, opening force, and leakage rate of fluid lubricant and the two-dimensional energy equation to calculate the temperature distribution of the fluid lubricant. We verified the accuracy of the developed program by comparing the simulated temperature distribution and leakage rate of this study with those of previous research. Heat conduction and elastic deformation due to pressure and temperature changes at the seal structure were analyzed using an ANSYS program. The results showed that temperature more significantly affected the elastic deformation of the seal structure near clearance than pressure both under normal and ELAP operating conditions. High temperature and pressure of the coolant under ELAP operating conditions deform the seal structure, resulting in a much smaller clearance of the fluid film than normal operating condition. However, even with a small clearance under ELAP operation, the leakage rate slightly increases due to the high internal pressure of the coolant. This research will contribute to the development of robust mechanical seals for RCPs by accurately predicting the characteristics of mechanical seals, especially when the RCP is operating under ELAP.

Long-Term Solar Power Generation Forecasting in the Eastern Coast Region of Malaysia using Artificial Neural Network (ANN) Method

Accurate prediction of power demand and generation is crucial for modern energy systems to efficiently allocate resources and facilitate energy trading. The integration of artificial intelligence (AI) and machine learning techniques has significantly improved the precision of power forecasting. This study focuses on the application of Artificial Neural Networks (ANN) for forecasting power generation in the Eastern Coast region of Malaysia, with a specific emphasis on solar power. The research methodology involves collecting and analyzing historical power data, weather data, and relevant variables. ANN models are trained, validated, and tested on a selected power grid to assess their accuracy and predictive capabilities. The expected outcomes aim to include the development of a precise power generation forecasting model, providing valuable insights for decision-makers to optimize energy operations and seamlessly integrate renewable sources. Additionally, the study explores potential challenges, limitations, and best practices associated with ANN-based power forecasting. The dataset covers the period from 2020 to 2023, with variables such as average output power, ambient temperature, PV module temperature, global horizontal irradiance, and wind speed recorded at 30-minute intervals. The architecture of the ANN model, implemented using the Keras framework, is described as a Sequential model with layers utilizing the 'ReLU' activation function. Model evaluation employs metrics like root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE) on the test set, offering insights into the model's overall fit, average deviation, and sensitivity to outliers. Results reveal strong correlations between PV module temperature, irradiance, and AC power generated.

Magnetic sensors for contactless and non‐intrusive measurement of current in AC power systems

AbstractThis paper reports the results of an investigation into the use of magnetic sensors for measuring AC currents and subsequently, estimating AC current phasors in low‐ and medium voltage AC power systems. Tunnelling magnetoresistive (TMR) sensor of high sensitivity and a wide range was used for the magnetic field measurement around AC conductor. The sensor was calibrated to overcome the inequality in the sensed magnetic field due to various aspects such as the distance from the source, minute structural variations, the magnitude of the source current, and presence of harmonics. Performance was tested for accuracy at lower frequencies such as 1, 2, 5 and 10 Hz as well as at higher frequencies such as 2nd, 3rd, 4th and 5th harmonics of the fundamental frequency. The percentage total vector error (TVE) was calculated for current phasors with input current magnitudes varying from 5 to 1500 A of various frequencies and was compared with the actual current as well as with the outputs of a high accuracy conventional core‐wound donut type current transformer (CT). The measurement accuracy corresponding to magnitude, phase and TVE during laboratory and field applications validated the suitability of TMR sensor for contactless and non‐invasive AC current measurement.

Case Studies of Telecommunication Problems Caused by Conducted Disturbance in AC Power Lines

Case Studies of Telecommunication Problems Caused by Conducted Disturbance in AC Power Lines

Integration flexibility of renewable distributed energy resources in active distribution networks: A two-module data-driven characterization method

Characterizing the integration flexibility of renewable distributed energy resources (RDERs), which describes the feasible region of RDER accommodations with which the operational constraints of an active distribution network (ADN) can be respected, facilitates the ADN operations. However, traditional characterization methods face the challenge of concurrently dealing with inevitable AC power flow and numerous RDERs. To resolve this issue, this paper develops a novel characterization method that contains two interconnected modules: learning and updating modules. The two modules jointly and iteratively calculate the parameters to characterize the integration flexibility of RDERs (IFR). In the learning module, a new loss function is designed to train the IFR parameters using a training data set. In the updating module, two optimization problems are developed based on the optimization-based performance estimation of the trained parameters to generate more data points for performance improvement in the learning module. The effectiveness of the proposed method is corroborated in the IEEE 33-bus and IEEE 136-bus test systems.

Dynamic hierarchical partitioning method of flexible resource for virtual power plant based on event triggering

Abstract With the advantage of efficient aggregation of flexible resources on the demand side, virtual power plants have been widely considered as one of the effective solutions to solve the lack of large-scale access systems and regulation capacity of new energy. Aiming at the problem of how the flexible resources of the virtual power plant can effectively participate in the optimal operation of hierarchical zoning of the power grid, this paper takes the aggregation of flexible resources of the virtual power plant to alleviate multiple line overload as an example. By deducing the transfer factor matrix under the linearized AC power flow model, an event-triggered hierarchical zoning method of flexible resources of virtual power plants considering resource effectiveness is proposed. The effectiveness of the algorithm is verified in several network examples, which provides a new idea for the virtual power plant to participate in the optimal operation of various levels and regions of the power grid.

Augmented SPWM based Hybrid Output Converter for Renewable Energy and Nano-Grid Application

This study suggests a novel Augmented Sine PWM(ASPWM) based Hybrid Output Converter that can concurrently power AC and DC loads. Two boost converters are used in this configuration, and the AC voltage is the differential voltage tapped between the two source nodes (of the MOSFETs) of the individual converters. This converter plays a different role in providing power to AC and DC loads than conventional boost converter. Conventional boost requires a minimum of two stages to step up output DC voltage and inverter stage to convert DC to AC power. This novel hybrid output converter (HOC) skips all stages of boosting the output voltage and next stage of inverter also. HOC consists of two boost converts pumped by two VM(voltage multiplier) stages to get high voltage DC output. This HOC has two inputs, supplied by renewable like solar PV cells and has two outputs to supply AC and DC loads simultaneously. This dual input HOC integrates two renewable inputs for standalone nano grid application. The principle of operation of HOC is augmented sinusoidal pulse width modulation with AC reference signal is shifted by 1800for individual boost converter and augmented by dc offset. The circuit has been extensively simulated in PSIM’22 Matlab Simulink and an experimental prototype of 110W is tested in the laboratory.

Power Regulation of a Three-Phase L-Filtered Grid-Connected Inverter Considering Uncertain Grid Impedance Using Robust Control

Uncertain grid impedance is often common in power distribution networks; therefore, it is crucial to design an efficient controller in this situation. An issue that frequently occurs is the problem of unpredictable grid impedance, which can cause voltage fluctuations, power quality problems, and potential damage to equipment. This work provides a systematic control strategy to tackle these issues by supplying well-regulated power from a DC source to an AC power grid. A linear matrix inequality (LMI)-based robust optimal control is proposed in this paper to provide stability to the inverter system without offset error at the output side. The convergence time to steady state is minimized by solving the LMI problem to maximize the eigen value of the closed-loop system with the inclusion of the uncertainty of the filter parameter and grid impedance. Furthermore, the uncertainties in this study include the potential variation of values for the filters and the grid's impedance. These uncertainties occur because the grid impedance can fluctuate fast in the event of a fault or termination of a transmission line, while the filter's impedance can also be affected by changes in operating temperature. The simulation study of this proposed control includes a comparison between wide and narrow uncertainty ranges, as well as a performance comparison under uncertain parameters. Furthermore, this approach exhibits a lower power ripple in comparison to existing PI control method.

A Low Frequency Power Amplifier Design Based on Output CapacitorLess Circuit

This low-frequency power amplifier utilizes the AT89S51 microcontroller as the main control chip. The system can be divided into several modules: power amplification circuit module, pre-amplification module, band-stop filtering module, protection circuit module, volume control module, power supply voltage, AC voltage, and AC current testing module, A/D conversion module, power module, and single-chip microcomputer subsystem control module. We chose class AB audio power amplifiers. To ensure circuit stability and higher fidelity, we selected the TDA2030 integrated chip to build the OCL power amplifier circuit, preventing efficiency from being too low. Through the control of single-chip microcomputer subsystem, it can measure and display output power, the supply power of the DC power source, and overall efficiency. This system utilizes electronic switches connected with band-stop filters, with the band-stop frequency ranging from 40～60Hz.

Assessment of the state-of-the-art of solid-state transformer technology: design, control and application

The structure and principle of operation of conventional iron-and-copper conventional transformers has not changed for the past decades largely to its high efficiency and reliability. However, conventional transformers are generally heavy and limited to transformation of AC power only in a unidirectional pattern. However, due to the increasing integration of distributed generation to grid, bidirectional power flow within the present grid systems is now inevitable. This makes solid-state transformer (SST), as a more flexible, portable, and cost-effective alternative, to gain considerable attention in recently. The SST is designed using power electronic components and a high frequency transformer (HFT). As an energy router, SST is crucial to the deployment of internet energy system due to its compact size, improved controllability, resiliency, bidirectional power flow, and variety of applications. Various designs and control approaches have been proposed for the SST to suit various applications. Thus, this paper reviews the extent of research works carried out on the SST vis-à-vis classifications, design, control and applications. The review is centered on establishing the state-of-the-art and identify future research prospects in the design, control, and applications of SST.

Brief Analysis of the Location and Determination of Maximum Capacity of Distributed Generation in Electrical Systems Considering Demand Scenarios in Ecuador

This research focuses on evaluating the importance of the use of renewable sources through distributed generation and its implication in the operation of electrical systems given that its incorporation has a direct impact on the expansion of the capacity of the networks, the minimization losses, and the impact on end users, all supported by the growth of demand. Under this context, the study focuses on incorporating distributed generation (DG), taking scenarios of base, medium, and peak demand and the modeling of the network, and subsequently evaluating the service quality indices and operating costs in addition to the electrical variables of the system. For this purpose, the present work proposes an optimization model to be solved using the Matlab (2021b) computational program together with GAMS (37.1.0 Major release (11 November 2021)) and mixed-integer nonlinear programming, determining the optimal insertion and determination of the maximum capacity of distributed generators while complying with the technical restrictions of the system and applying optimal AC power flows. Localizing and determining maximum capacity for distributed generation (DG) in electrical systems are critical aspects of modern grid planning and operation. With the increasing penetration of renewable energy sources and the growing complexity of energy demand patterns, efficient integration of DG has become paramount for ensuring grid reliability and sustainability. In this context, the analysis of DG localization and capacity determination considering demand scenarios emerges as a critical area of research in electrical engineering. By employing advanced optimization techniques such as mixed-integer nonlinear programming (MINP), this research addresses the multidimensional challenges associated with DG deployment, including technical constraints, economic considerations, and environmental impacts. Understanding the contribution of this optimization approach to electrical engineering is fundamental for optimizing grid performance, enhancing renewable energy integration, and supporting the transition towards more resilient and sustainable energy systems. Consequently, investigating this optimization model represents a crucial step towards advancing the state-of-the-art in grid planning and facilitating the transition to a cleaner and more efficient energy future.

Power Flow Calculation for AC/DC Power Systems with Line-Commutated Converter–Modular Multilevel Converter (LCC-MMC) Hybrid High-Voltage Direct Current (HVDC) Based on the Holomorphic Embedding Method

To harness the strengths and mitigate the limitations of line-commutated converter (LCC) and modular multilevel converter (MMC) HVDC systems, the LCC-MMC hybrid HVDC system has been developed. This paper proposes a holomorphic embedding (HE)-based power flow calculation method for AC/DC power systems with the LCC-MMC hybrid HVDC system. Firstly, the methodology involves establishing the mathematical model of the LCC-MMC hybrid HVDC system and its control modes. Subsequently, the HE formulation for the constructed LCC-MMC model is derived using the HE method. The proposed method simplifies the HE formulation using substitute variables, making it adaptable to various control modes. The designed HE formulation facilitates the derivation of the germ solution and the calculation of high-order power series coefficients. A sequential solution flow chart compatible with AC power flow algorithms is constructed to enhance the practicality of the HE method for real-world large-scale systems. The accuracy and effectiveness of the proposed method are verified through numerical tests on the modified IEEE 14-bus, IEEE 57-bus, and large 2383-bus system. The proposed method provides a reliable and efficient solution for power flow analysis in hybrid HVDC systems.

Smart Home Powered by Solar: IoT-based SEPIC Converter Control

Background: In this article, an IOT solution for managing and controlling a PV system with applications for the home is presented. A DC-DC SEPIC converter, a bidirectional converter, a PWM generator, and a single-phase voltage source inverter with active clamping are used for power conditioning. An output voltage control loop is implemented, and real-time online communication with an internet server is accomplished using the PIC microcontroller. Methods: The ESP8266MOD WIFI IOT module works with the Blynk mobile app. Experimental findings showing the proper operation of IOT features demonstrate the effectiveness of voltage regulation. In this article, frequency is taken into consideration as solar power is integrated with the grid. A user may easily control the DC-DC converter, load batteries, and monitor battery voltage through our userfriendly GUI interface. Results: In this paper, an IOT (Internet of Things) application was used to control and track the output of a modest PV power plant. The study shows the efficiency of the digitally controlled system developed to regulate the output voltage of the SEPIC DC-DC converter. Practical Significance: They further argue that the ESP8266 and microcontroller's versatility make them an appropriate instrument for remote and real-time monitoring and control of the proposed model from around the world. The energy that is produced by the sun is added to the grid so that it may be put to effective use and contribute to satisfying the rising demand for energy. Solar energy is the input that SEPIC uses to charge the batteries and for all of its other processes. In the event that solar photovoltaic (PV) panels are not available, the SEPIC DC-DC converter will derive its energy from AC power. Conclusion: In this article, an IOT (Internet of Things) application was used to control and track the output of a modest PV power plant. The research demonstrates the effectiveness of the digitally controlled system created to manage the output voltage of the SEPIC DC-DC converter. They further argue that the ESP8266 and microcontroller's versatility make them an appropriate instrument for allowing remote and real-time monitoring and control of the proposed model from any part of the world. The energy that is produced by the sun is added to the grid so that it may be put to effective use and contribute to satisfying the rising demand for energy. Solar energy is the input that SEPIC uses to charge the batteries and for all of its other processes. In the event that solar photovoltaic (PV) panels are not available, the SEPIC DC-DC converter will derive its energy from AC power.

A novel solution methodology for electrical vehicles charging schedule in coordinated with full flexible resources in microgrid

This paper proposes a new model for the day-ahead Electric Vehicle charging schedule in Microgrids (MGs) with flexible resources such as Wind Turbine (WT) and Photovoltaic (PV) electrical power, Combined Heat and Power (CHP), Energy Storage Source (ESS), and load Demand Responses (DR). The proposed Mixed Integer Nonlinear Multi-Objective Optimization (MINMO) model considers uncertainty properties with optimal operation objects. The cost function, and power loss minimization are the proposed model's objects for considering optimal operation objects. This model is solved by a three-level decomposition method in master-sub problem presentation and Binary-Continues PSO algorithm (BC PSO) to quickly calculate the optimal solution. In the first layer, the main constraint of electric vehicle aggregators (EVAs) is satisfied by the binary PSO algorithm. According to the top-layer solution, flexible recourses are managed to satisfy the operation objects by the Bi-level Continues PSO algorithm in the second layer. Then, after converging to the optimum solution of these two layers, Electric Vehicles (EVs) charging stations are solved at the last layer by algebraic equations. Finally, the efficiency of the proposed method is illustrated via its application in IEEE 33-bus MG system. The numerical results show that the proposed solution methodology reduces the runtime of the problem up to 95 % compared to intelligence-based and full Newton–Raphson AC power flow method. Also, numerical results illustrate that considering CHP and DR in addition to the other flexible resources reduces the operation cost and power loss up to 21 % and 5 % respectively. In top of that the minimum total cost for day-ahead EV charging schedule by the proposed model and solution methodology is 578,312.7$.

Design and development of efficient and compact 20 kW 325MHz solid-state amplifier for superconducting accelerator applications.

An efficient and compact, 20 kW solid-state power amplifier (SSA) at 325MHz has been designed and developed in-house, using single stage combining. It comprises of 24 nos. of 1 kW power amplifier (PA) modules, a 24-way Wilkinson power combiner and divider, and other peripheral systems. The typical gain and conversion efficiency of the PA modules at 1.0 kW output is 21.7 dB and 66.6%, respectively. It is demonstrated that overall power gain and AC to RF efficiency of this SSA at 20 kW is 88.5 dB and 54.8%, respectively, which matches closely with the design estimates. The harmonic content in the RF output is < -40 dBc for all the harmonics. The results of the Monte Carlo simulation are also presented, showing lower bound on combining efficiency with a degree of confidence if magnitude and phase data for 24 inputs are randomly chosen from a normal distribution's pre-defined interval. The salient features of this SSA include power density of 12.7 kW/m3, AC to RF efficiency of 54.8% at 20 kW, and guaranteed output of 20 kW with one failed PA module and 18.1 kW under two failed PA modules condition.

Design of Laser-Powered Opto-Electrical Current Transformer and Its Application in Electrical Automation of Substations

The novel power equipment represented by the Opto-Electrical Current Transformer (OECT) possesses unparalleled advantages compared to traditional Current Transformers (CTs), rendering it with vast potential in power systems. In the design of OECT, this study employs laser power supply and focuses on the overall perspective, particularly the design of the laser power supply module and the optical fiber digital transmission module. The laser power supply module selects a high-power semiconductor laser to provide power to the circuit, incorporating line filters, thermistor resistors, and a laser overcurrent protection circuit. An automatic temperature control circuit based on PI regulation is utilized to stabilize the working temperature of the laser diode (LD). The photoelectric conversion device PPC-6E achieves a maximum photoelectric conversion efficiency of 40%, and the MAX639 DC–DC conversion chip ensures stable output voltage. The optical fiber digital transmission module adopts a circuit composed of multiple NOR gate logic for electro-optical conversion, a low-power driving circuit based on the 74LVC1G07 chip, and the HFBR-2412 photoelectric converter to simplify the circuit board design. To achieve a higher communication baud rate, a quartz multimode optical fiber with a diameter of 62.5 μm is chosen. In the experimental phase, a simulation of high-power AC power with equal turns is conducted. The linearity/temperature test results demonstrate that the designed OECT in this study meets expectations. In the electrical automation test of substations, a comparison between traditional CTs and the designed OECT is carried out using traditional protection algorithms and optical differential protection algorithms. The results indicate that, compared to traditional protection algorithms, the optical differential algorithm using the designed OECT achieves faster protection times.

An Improved Model for AC Power From Grid Connected Photovoltaic Systems and Comparison With Large-Scale Hourly Measured Data

An Improved Model for AC Power From Grid Connected Photovoltaic Systems and Comparison With Large-Scale Hourly Measured Data

Grid control strategy of wind turbines based on LADRC voltage control

Traditional grid-controlled wind turbines are required to rely on AC power grid operation, but when the power grid fluctuates, it will cause a series of oscillation instability problems in wind turbines, such as low-frequency oscillation, subsynchronous oscillation, etc. The safe and stable operation of the crisis power grid needs to be ensured. In this study, the virtual synchronous machine is supposed to be adopted as the main control strategy for grid-type control, transforming the traditional system into a more robust and reliable one. Additionally, a converter model of the grid-type control network is set up. By implementing grid-type control, voltage support and frequency support can be offered without relying solely on AC power grid operation. This approach effectively addresses issues caused by preventive such as reliance on wind turbines. The stability of the networked control system is dependent on maintaining stable system power. When there are fluctuations in system power, it impacts the stability of the networked control system. To mitigate these effects and enhance overall stability, this paper proposes using linear active disturbance rejection control (LADRC) based voltage and grid controls to suppress voltage fluctuations through linear active disturbance rejection techniques. The overall system performance is demonstrated through simulations conducted using Matlab/Simulink platform.