Grid System Research Articles

Enhancing of single-stage grid-connected photovoltaic system using fuzzy logic controller

The power generated by photovoltaic (PV) systems is influenced by environmental factors. This variability hampers the control and utilization of solar cells' peak output. In this study, a single-stage grid-connected PV system is designed to enhance power quality. Our approach employs fuzzy logic in the direct power control (DPC) of a three-phase voltage source inverter (VSI), enabling seamless integration of the PV connected to the grid. Additionally, a fuzzy logic-based maximum power point tracking (MPPT) controller is adopted, which outperforms traditional methods like incremental conductance (INC) in enhancing solar cell efficiency and minimizing the response time. Moreover, the inverter's real-time active and reactive power is directly managed to achieve a unity power factor (UPF). The system's performance is assessed through MATLAB/Simulink implementation, showing marked improvement over conventional methods, particularly in steady-state and varying weather conditions. For solar irradiances of 500 and 1,000 W/m<sup>2</sup>, the results show that the proposed method reduces the total harmonic distortion (THD) of the injected current to the grid by approximately 46% and 38% compared to conventional methods, respectively. Furthermore, we compare the simulation results with IEEE standards to evaluate the system's grid compatibility.

Consortium blockchain based secure and efficient data aggregation and dynamic billing system in smart grid

In a smart grid, collected electricity consumption periodically from smart meters allow entities to bill the customers, power company to operate the grid successfully, and users to control the use of their appliances. However, energy consumptions of users should be protected since the data provides the user’s daily habit that an adversary uses the data to extract useful information about the users. Moreover, users’ identities should not be disclosed to untrusted entities since the untrusted entities map identities to their real identities. In this paper, we propose a system that protects users’ data privacy using multi-pseudorandom identities and a randomization technique. Moreover, the proposed work provides fast authentication for smart meters to send their readings to data aggregators. Furthermore, the proposed work is based on consortium blockchain to eliminate a single point of failure and provides transparency of messages and operations. In addition, we use dynamic billing and pricing mechanism for the users to see their bills.

Federated Deep Learning for Solving an Image Classification Problem on a Desktop Grid System

Federated Deep Learning for Solving an Image Classification Problem on a Desktop Grid System

An Intelligent Big Data Security Framework Based on AEFS-KENN Algorithms for the Detection of Cyber-Attacks from Smart Grid Systems

An Intelligent Big Data Security Framework Based on AEFS-KENN Algorithms for the Detection of Cyber-Attacks from Smart Grid Systems

Microgrid based on ICA and improved cuckoo search algorithm optimizing scheduling methods

Abstract The optimal operation of microgrids can provide a strong guarantee for the efficient utilization of distributed power and renewable energy. To solve the important problem of optimized operation of microgrids with a large proportion of distributed power sources, a microgrid optimal scheduling and operation method is proposed in the paper. This method first establishes a microgrid model containing distributed power sources and uses the ICA algorithm to screen out the key factors in the economy of the grid system. Then, taking the construction and operation cost of the microgrid as the objective function, it proposes to solve the model based on the ICA and the optimized cuckoo search algorithm. The method is able to effectively schedule the microgrid objective function based on the conditions, with the ability to quickly solve the scheduling problem and greatly reduce the system’s electricity cost. It can greatly increase the utilization rate of photovoltaic power generation. The method has a small amount of computation, small storage, and simple operation. An empirical study is carried out on the designed microgrid optimal scheduling scheme.

Study on Optimization of Placement Method for Transmission Line Monitoring Devices Based on Probability of Meteorological Disasters

Abstract To ensure the reliability and safety of the power grid system, it is of great significance to carry out electric meteorological monitoring for transmission channels. In this paper, a method for optimizing the placement of transmission line monitoring devices based on the probability of meteorological disasters is proposed. By considering the climatic characteristics near the transmission line, the probability of occurrence of adverse meteorological faults for each transmission tower in the transmission line is analyzed and calculated by using existing meteorological and GIS elevation data. Meanwhile, the historical failure data and voltage levels of transmission lines are combined to achieve the optimization of the installation position of monitoring devices in transmission channels. This method was applied to a 220 kV transmission line for practical analysis, achieving dense monitoring in areas with high failure rates and sparse monitoring in areas with low failure rates.

Demand side management with wireless channel impact in IoT-enabled smart grid system

Demand Side Management (DSM) is a vital issue in smart grids, given the time-varying user demand for electricity and power generation cost over a day. On the other hand, wireless communications with ubiquitous connectivity and low latency have emerged as a suitable option for smart grid. The design of any DSM system using a wireless network must consider the wireless link impairments, which is missing in existing literature. In this paper, we propose a DSM system using a Real-Time Pricing (RTP) mechanism and a wireless Neighborhood Area Network (NAN) with data transfer uncertainty. A Zigbee-based Internet of Things (IoT) model is considered for the communication infrastructure of the NAN. A sample NAN employing XBee and Raspberry Pi modules is also implemented in real-world settings to evaluate its reliability in transferring smart grid data over a wireless link. The proposed DSM system determines the optimal price corresponding to the optimum system welfare based on the two-way wireless communications among users, decision-makers, and energy providers. A novel cost function is adopted to reduce the impact of changes in user numbers on electricity prices. Simulation results indicate that the proposed system benefits users and energy providers. Furthermore, experimental results demonstrate that the success rate of data transfer significantly varies over the implemented wireless NAN, which can substantially impact the performance of the proposed DSM system. Further simulations are then carried out to quantify and analyze the impact of wireless communications on the electricity price, user welfare, and provider welfare.

Investigation on implementing the swarm nano grid system for effective utilization of solar-powered agro-industries

The agro-industry is the backbone of the global economy, even in the twenty-first century. The agro-industry would not be what it is now without irrigation. The production and use of renewable energy in this sector of the agricultural economy have also expanded rapidly in recent years. Base-load power production and conversions dominate the literature. This research examines user concerns. The swarm nano grid system fixes this. The pulse width modulation (PWM) sinusoidal inverter converted stable DC power from the bidirectional DC-DC converter into sinusoidal AC voltage for the irrigation pump induction motor. Solar panels, batteries, and converters are costly, but they pay off. The nano grid distributes excess power generated during low demand to local loads. This technique works well when the load can be disconnected from the power grid. MATLAB is used to keep an eye on the reliability and efficiency of the induction motor. In the first simulation, solar power generation is modeled using the MATLAB Simulink software in two distinct modes. A PWM sinusoidal inverter that is driven by solar energy is what provides power to the 5.67 kW induction submersible motor. The simulation result provides a conceptual model of how induction motors powered by renewable energy sources function in practice.

Detecting False Data Injection Attacks Using Machine Learning-Based Approaches for Smart Grid Networks

Current electricity sectors will be unable to keep up with commercial and residential customers’ increasing demand for data-enabled power systems. Therefore, next-generation power systems must be developed. It is possible for the smart grid, an advanced power system of the future, to make decisions, estimate loads, and execute other data-related jobs. Customers can adjust their needs in smart grid systems by monitoring bill information. Due to their reliance on data networks, smart grids are vulnerable to cyberattacks that could compromise billing data and cause power outages and other problems. A false data injection attack (FDIA) is a significant attack that targets the corruption of state estimation vectors. The primary goal of this paper is to show the impact of an FDIA attack on a power dataset and to use machine learning algorithms to detect the attack; to achieve this, the Python software is used. In the experiment, we used the power dataset from the IoT server of a 10 KV solar PV system (to mimic a smart grid system) in a controlled laboratory environment to test the effect of FDIA and detect this anomaly using a machine learning approach. Different machine learning models were used to detect the attack and find the most suitable approach to achieve this goal. This paper compares machine learning algorithms (such as random forest, isolation forest, logistic regression, decision tree, autoencoder, and feed-forward neural network) in terms of their effectiveness in detecting false data injection attacks (FDIAs). The highest F1 score of 0.99 was achieved by the decision tree algorithm, which was closely followed by the logistic regression method, which had an F1 score of 0.98. These algorithms also demonstrated high precision, recall, and model accuracy, demonstrating their efficacy in detecting FDIAs. The research presented in this paper indicates that combining logistic regression and decision tree in an ensemble leads to significant performance enhancements. The resulting model achieves an impressive accuracy of 0.99, a precision of 1, and an F1 score of 1.

DGGSTools: An Open Source Python Package for the Manipulation of Vector and Raster Datasets in the rHEALPix Discrete Global Grid System

DGGSTools: An Open Source Python Package for the Manipulation of Vector and Raster Datasets in the rHEALPix Discrete Global Grid System

A Design and Optimization of A Renewable EnergyBased Smart Microgrid for Rural Electrification: Analysis of the Simulink Model for Different Loads

The rapid growth of renewable energy technologies has provided an opportunity to address the electrification challenges faced by rural communities. This research paper presents the design and optimization far smart micro grid system that integrates renewable energy sources to provide reliable and sustainable electricity to rural areas. The objective is to analyze the performance of the Simulink model under different load conditions, considering the system's stability, efficiency, and economic viability.

An improved IPT-PLL technology for single-phase grid-connected inverters in complex power grid conditions

Aiming at the common problems of frequency variations and harmonics in complex power grids, an improved inverse Park transform phase locked loop (IPT-PLL) technology for single-phase converters suitable for micro grid systems is proposed. Firstly, in the phase detection of PLL, the α component of Park transformation is selected as the reference voltage, and its orthogonal component is constructed using a 1/4 fundamental period delay method. Secondly, Lagrange interpolation polynomials are introduced to approximate fractional delay to solve the problem of delay calculation errors caused by frequency changes. Thirdly, in order to compensate for the poor ability of traditional proportional integral (PI) regulators, multi resonant controllers are superimposed to suppress low order harmonic disturbances. Finally, the design method and system performance of the PI regulator and each resonant controller are analyzed theoretically. The experimental results show that the proposed improved IPT-PLL method has strong adaptability to complex power grids. It can significantly improve the tracking performance of power grid frequency, suppress the interference of low order harmonics and DC bias. And it has good dynamic and static performance.

The Effect of Infiltration Rate on Flood Hazards in Gorontalo City

Infiltration rate is a measurement of the physical properties of soil. The classification of infiltration rates according to ILRI (1974) based on the results of changes by Rickard and Cossens (1965) is an important characteristic to indicate land drainage conditions. The aim of this research is to determine the classification of infiltration and its extent. The research method uses a grid system by dividing the city area into 250 m x 250 m consisting of 108 grids with 46 grids representing the lowlands. The infiltration rate in Gorontalo City as a whole is classified as very slow 2189.44Ha or 32.81%, the meaning of this percentage shows that an infiltration rate of less than 2.5 cm/hour occurs in Gorontalo City. For the infiltration rate with a slow classification, the speed is 2.5– 15 cm/hour, covering an area of 2615.38 Ha or 39.17%.

A Stabilization Control Strategy for Wind Energy Storage Microgrid Based on Improved Virtual Synchronous Generator

In high-penetration renewable-energy grid systems, conventional virtual synchronous generator (VSG) control faces a number of challenges, especially the difficulty of maintaining synchronization during grid voltage drops. This difficulty may lead to current overloads and equipment disconnections, and it has an impact on the security and reliability of the system, as well as limiting the dynamic reactive power support capability of the system. To solve this problem, in this study, a wind–solar hybrid power generation system is designed with a battery energy storage device connected on the DC side, and proposes a low voltage ride-through (LVRT) control strategy for the grid-connected inverter based on an improved VSG. The control strategy employs an integrated current limiting technique combining virtual impedance and vector current limiting to ensure that the VSG exhibits good dynamic power support characteristics during symmetrical faults by adjusting the setpoint value of reactive power. At the same time, it maintains the synchronization and power angle stability of the VSG itself to achieve the goal of LVRT. Simulation results show that the proposed control strategy can effectively suppress the renewable power fluctuations (about 30% reduction in fluctuations compared to the conventional strategy) and ensure the safe and reliable operation of the renewable energy sources and VSGs during grid-side faults. In addition, it provides a given reactive power support and stable grid voltage control (voltage dips reduced by about 20%), which significantly enhances the LVRT capability of the hybrid wind–solar-storage generation system.

Electricity Theft Detection in Smart Grids Using Sarimax and OCR

This study proposes a novel approach to detect electricity theft in smart grids by combining SARIMAX (Seasonal Autoregressive Integrated Moving Average with Exogenous Factors) models with Optical Character Recognition (OCR) techniques. Electricity theft remains a significant challenge in smart grid systems, leading to revenue losses and operational inefficiencies. Traditional methods often fall short in accurately identifying and addressing such illicit activities. The integration of SARIMAX models leverages time-series data, capturing patterns and anomalies indicative of electricity theft. These models incorporate exogenous factors such as weather conditions, economic indicators, and historical consumption patterns, enhancing the detection accuracy. Additionally, OCR technology is applied to analyze meter readings and identify discrepancies or irregularities that may signal potential theft instances. The synergy between SARIMAX and OCR offers a comprehensive solution for electricity theft detection in smart grids. By harnessing advanced analytics and machine learning algorithms, this approach enables utility providers to proactively identify suspicious activities, mitigate revenue losses, and ensure the integrity of their grid infrastructure

A Review of Time Series forecasting Techniques: Predicting Solar energy using AI and Statistics Techniques

The overuse of fossil fuels and their detrimental effects on the environment must be considered in the current electrical energy crisis scenario. This promotes sustainable development by encouraging the use of renewable resources. The overall capacity of the energy systems was enhanced by adding such unpredictable renewable energy sources; however, the design of these hybrid systems is extremely important, which is why many academics have come to trust this area. Normally, in order to advance sustainable growth in the current electrical market, renewable resources like solar energy have been integrated into existing grid systems, but such systems have faced numerous challenges in terms of energy management. Energy management in solar-integrated electric grid systems is challenging because of a number of factors, such as temperature, wind speed, air pressure, and precipitation, that have erratic and volatile properties, and all such factors impact solar energy generation. Further, due to this, the power grid may become unstable if all of these unstable variables are disregarded, as voltage fluctuations may arise. In order to handle all of this, solar energy prediction is necessary so that the required actions can be taken based on the data available for renewable energy. Hence this paper reviewed the articles on statistical and AI-based solar irradiance forecasting in order to provide classifications for employees based on the situation. Keywords: Solar Energy, Statistical and AI-based Solar Irradiance Forecasting.

Energy 4.0: AI-enabled digital transformation for sustainable power networks

The considerable developments in green technology have changed several industries, including the energy business and power sector. These advances have transformed power generation and distribution. Artificial intelligence (AI), machine learning, computer vision, blockchain, and the internet of things (IoT)have advanced significantly in recent years. These advances have transformed the energy business and expanded its applications. In this concise yet comprehensive analysis, energy and power systems are examined from an eco-friendly perspective and emerging research trends are highlighted. The increasing use of renewable energy sources necessitates infrastructure restructuring, which presents the potential for grid development and AI usage for intermittent and distributed generation. The integration of AI, IoT, and blockchain technologies into energy and power systems offers opportunities to improve efficiency, sustainability, and dependability. Application-specific research is comprehensively examined to investigate the present state-of-the-art and limitations of various technologies. The focus in this study is the importance of implementing a complete policy and planning framework to support a significant and efficient shift toward advanced grid systems. The convergence of AI, information and communication technology , and energy systems offers unparalleled prospects affecting the trajectory of energy generation, distribution, and consumption in the future. This study makes a valuable contribution to the wider academic discussion on utilizing digital transformation to create sustainable and intelligent energy ecosystems by integrating insights acquired from several fields.

Catalytic Pyrolysis of Polypropylene for Cable Semiconductive Buffer Layers.

With the progress of the power grid system, the coverage area of cables is widening, and the problem of cable faults is gradually coming to affect people's daily lives. While the vast majority of cable faults are caused by the ablation of the cable buffer layer, polypropylene (PP), as a common cable buffer material, has pyrolysis properties that critically impact cable faults. Studying the semiconductive buffer layer of polypropylene (PP) and its pyrolysis properties allows us to obtain a clearer picture of the pyrolysis products formed during PP ablation. This understanding aids in the accurate diagnosis of cable faults and the identification of ablation events. In this study, the effects of temperature and catalyst (H-Zeolite Standard Oil Corporation Of New York (Socony) Mobil-Five (HZSM-5)) content on the PP thermolysis product distribution were studied by using an online tubular pyrolysis furnace-mass spectrometry (MS) experimental platform. The results showed that PP/40% HZSM-5 presented the highest thermolytic efficiency and relative yield of the main products at 400 °C.

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.

Investigating the impact of X-ray irradiation on the SF6 gas discharge in GIS equipment

ABSTRACT Gas Insulated Switchgear (GIS) is extensively utilized in power grid systems. However, the existence of numerous photoelectrons in the SF6 gas during X-ray non-destructive testing in ultra-high-voltage (UHV) GIS equipment may induce gas streamer discharge in a strong electric field. To address this issue, in this work, the impact of X-ray irradiation on the SF6 gas discharge in GIS equipment was systematically analyzed. Firstly, one GIS engineering mode with a three-dimensional electric field was established, and a physical GIS model was established by the Geant4 and Garfield++ codes. Secondly, the electrons’ ionization, and multiplication processes in SF6 gas during the X-ray irradiation were studied. Finally, a test platform with an equivalent electric field tube, which had the same intensity as in the GIS equipment, was established to verify the physical GIS model. X-ray beams with 0.1 MeV, 0.16 MeV, 0.3 MeV, and 1.25 MeV energy were used during the test process. From our analysis, it was concluded that X-ray irradiation can cause an electron avalanche in SF6 gas and the intensity of the avalanche is low, which is far away from the appearance of the streamer discharge.