Efficient Grid Research Articles

Neural Network Based Power Meter Wiring Fault Recognition of Smart Grids Under Abnormal Reactive Power Compensation Scenarios

This paper explores the challenges of detecting wiring anomalies in three-phase, four-wire energy metering devices, especially when large amounts of reactive power compensation are involved. Traditional methods, such as the hexagon phasor diagram technique, perform well under standard loads, but struggle to adapt to new situations, such as over- or under-compensation. To overcome these limitations, this paper proposes a hybrid approach that combines mechanism-based knowledge with data-driven technologies, including backpropagation neural networks (BPNNs). This method improves the accuracy and efficiency of anomaly detection and can better adapt to a dynamic power environment. The result is improved universality of anomaly detection, which helps to achieve safer, more accurate, and more efficient smart grid operation in complex situations.

Stability Assessment and Reactive Power Compensation Strategies for Grid-Connected Wind Power Generations

With the acceleration of global economic integration and the rapid increase in population, the reserves of mineral resources on the earth are becoming increasingly scarce, and non-renewable resources are becoming increasingly scarce. Technological advances have significantly reduced the cost of photovoltaic and wind power, improved energy storage efficiency and smart grid technology, and solved the intermittent problem of new energy. How to boost the utilization rate of new energy and ensure its secure and reliable operation during grid connection is an important challenge that the current development of electric power technology must tackle. To serve as a guide for the creation of new wind power, this article analyses the two most widely used reactive power compensation devices, studies the method of restoring the stable state through reactive power compensation after the grid-connected wind power becomes unstable, and introduces several control methods for power compensation using reactive power compensation devices. The main objective of this article is to summarize the current mainstream stability assessment methods in the world and compare their benefits and drawbacks.

Automated Rooftop Solar Panel Detection Through Convolutional Neural Networks

Transforming the global energy sector from fossil-fuel based to renewable energy sources is crucial to limiting global warming and achieving climate neutrality. The decentralized nature of the renewable energy system allows private households to deploy photovoltaic systems on their rooftops. However, inconsistent data on installed photovoltaic (PV) systems complicate planning for an efficient grid expansion. To address this issue, deep-learning techniques, can support collecting data about PV systems from aerial and satellite imagery. Previous research, however, lacks the consideration for ground truth data-specific characteristics of PV panels. This study aims to implement a semantic segmentation model that detects PV systems in aerial imagery to explore the impact of area-specific characteristics in the training data and CNN hyperparameters on the performance of a CNN. Hence, a U-Net architecture is employed to analyze land use types, rooftop colors, and lower-resolution images. Additionally, the impact of near-infrared data on the detection rate of PV panels is analyzed. The results indicate that a U-Net is suitable for classifying PV panels in high-resolution aerial imagery (10 cm) by reaching F1 scores of up to 91.75% while demonstrating the importance of adapting the training data to area-specific ground truth data concerning urban and architectural properties.

Volt-VAr Management System

In this paper, the newly developed system has been integrated with auxiliary systems such as GIS (Geographic Information System), SCADA-DMS, and Field Device Controllers. It monitors the active and reactive power flow of feeders at TEİAŞ (Turkey's Electricity Transmission Corporation) transformer stations, while enabling the remote monitoring and automatic switching of reactors, capacitors, and MCR (Magnetic Controlled Reactors). Optimized reactive power and voltage management with the VVMS (Volt-VAr Management System) improves the power factor, reduces technical losses, and lowers energy costs. It also prevents low or high voltage issues through voltage regulation, thereby improving power quality. Additionally, reactive power management performs necessary calculations to ensure that inductive and capacitive limits are not exceeded, and the relevant equipment can be controlled remotely or via protection relays. The system aims to reduce energy losses and voltage drops, providing more efficient grid management.

Research on Fault Prediction and Health Management of Grid-Connected Inverter based on Data Drive

Grid-connected inverter is the core component of photovoltaic power generation system, and its stable operation is very important to the overall performance of the system. However, the inverter is easily influenced by environmental factors and its own complexity, which leads to frequent failures and affects power generation efficiency and power grid stability. Firstly, an innovative fault prediction model of grid-connected inverter is constructed by using machine learning and deep learning technology. The prediction accuracy and robustness of the model are improved by integrating the prediction results of basic learners such as decision tree and support vector machine (SVM). The experimental results show that the integrated learning model achieves an accuracy of 0.95 and a F1 score of 0.94 under the optimal parameter configuration. At the same time, the Long Short Term Memory Network (LSTM) model is introduced to effectively capture the complex nonlinear relationship and time dependence in the data, which further improves the prediction performance, and the highest accuracy rate can reach 0.96. Based on the fault prediction model, a comprehensive health management strategy of grid-connected inverter is formulated in this paper. The strategy framework includes four links: data collection and monitoring, fault prediction and evaluation, preventive maintenance plan formulation, fault early warning and response. By monitoring the key parameters of the inverter in real time and comparing with the fault prediction model, the early warning and timely response of the fault can be realized. The experimental results show that after the implementation of health management strategy, the failure rate of inverter is significantly reduced, the response time of fault warning is significantly shortened, and the operation reliability is significantly improved.

Enhancing power quality in solar-wind grid-connected systems through soft computing techniques

This work intends to improve estimates of solar and wind energy generation through the application of resilient backpropagation control and substantial power evolution strategy (SPES) algorithms. In comparison to particle swarm optimization and genetic algorithms, the main goal is to minimize predicting mistakes. These methods increase grid reliability by lowering total harmonic distortion (THD) and improving power quality when integrated with the IEEE-9 bus standard. In order to evaluate the hybrid system's transient and steady-state reactions, the study also highlights the importance of bolstering operation and control. A revolutionary deep learning-based approach is also suggested for predicting wind and solar hybrid energy. The power grid's efficiency and dependability in handling renewable energy sources have significantly improved, according to the results.

Machine learning‐based multivariate forecasting of electric vehicle charging station demand

AbstractThe exponential rise of electric vehicles (EVs) is transforming the global automobile industry, driving a shift towards greater cleanliness and environmental sustainability. EV charging stations (EVCSs) play a pivotal role in this massive transition towards EVs, where accurate forecasting of EVCS demand is crucial for seamlessly integrating EVs into existing power grids. Most of the existing research mainly concentrates on univariate forecasting, neglecting the multiple factors influencing EVCS demand. Hence, this study offers a comparative analysis of different algorithms for univariate forecasting and multivariate forecasting, where the multivariate scheme incorporates metadata such as charging time, greenhouse gas savings, and gasoline savings. The experimental results indicate the superiority of the multivariate scheme over the univariate forecasting. For multivariate forecasting, the gated recurrent unit (GRU) has outperformed other models such as categorical boosting (Catboost), recurrent neural network (RNN), long short‐term memory (LSTM), extreme gradient boosting (XGBoost), random forest, convolutional neural network (CNN), CNN + LSTM, and LSTM + LSTM. The results of this study emphasize the significance of using the GRU model for multivariate forecasting with metadata during normal and noisy scenarios to yield more reliable and accurate predictions. This approach enhances decision‐making, policy development, and efficient grid integration in the growing EV sector.

Real-time Error Compensation Transfer Learning with Echo State Networks for Enhanced Wind Power Prediction

Accurate wind power forecasting is essential for efficient energy management and grid stability, enabling energy providers to balance supply and demand, optimize renewable energy integration, reduce operational costs, and enhance power grid reliability. The Echo State Network (ESN) is widely used for modeling nonlinear dynamic systems due to its simple and rapid training process. However, ESNs can be prone to system errors, leading to inaccurate models when handling high-order nonlinear complexities. To overcome this, we developed the Error Compensation Transfer Learning Echo State Network (ETL-ESN), which combines a computing layer based on ESN and a compensation layer using transfer learning. Our model identifies error auto-correlation as a key factor that increases variance in ESN predictions, and addresses this with an error compensation layer to reduce system errors. We further leverage transfer learning to prevent overfitting within the error domain. Extensive experiments using real-world wind power data demonstrate that the ETL-ESN model reduces training time from 65 s to 2 s compared to LSTM, while lowering MAE by up to 95%. The ETL-ESN achieves a 95% to 98% improvement in prediction accuracy across different turbines compared to traditional models. The code and datasets used in this study are available at GitHub repository https://github.com/zhuyingqin/Error-Transfer-ESN for further research and replication.

Techno-Economic Analysis of Grid-Connected Highway Solar EV Charging Station

AbstractSolar electric vehicle (EV) charging stations offer a promising solution to an environmental issue related to EVs by supplying eco-friendly electricity. Herein, we designed and analyzed a grid-connected highway solar EV charging station for 2022, 2030, and 2050 under two scenarios: Current policy scenario with restricted grid sales and policy mitigation scenario allowing grid sale. Future systems consider changes in EV charging station, grid CO2 emissions, carbon prices, and renewable costs. In 2022, high PV and battery costs led to a grid-only system. By 2030 and 2050, significant reductions in PV costs enabled systems with substantial PV capacity, especially under the policy mitigation scenario. Economic analysis showed that enabling grid sales reduces net present cost (NPC) by 40% in 2030 and 35% in 2050 compared to the current policy scenario, with significantly lower levelized cost of energy. CO2 emissions from EV operation are projected to be 44% and 3% of 2022 levels by 2030 and 2050, respectively. The policy mitigation scenario further reduces CO2 emissions by 22% in 2030 and 25% in 2050 compared to the current policy scenario. Sensitivity analyses reveal that increased grid sale capacity leads to higher renewable penetration and lower NPC but also increased grid congestion, highlighting the need for efficient grid management. Policymakers should consider revising regulations to support higher PV penetration and manage grid congestion. This study supports the transition to renewable systems and underscores the importance of policy measures in achieving sustainable energy goals.

Optimization of crop tissue culture technology and its impact on biomolecular characteristics

Modern agriculture relies on crop tissue culture technology for fast propagation, genetic improvement, and protection of plant species. Conventional media like Murashige and Skoog (MS) usually lack optimal conditions for embryogenesis, necessitating the development of improved media tailored to specific crop requirements. In this article, we introduce an Efficient Grid Identified-Deep Feedforward Neurons (EGI-DFFN) to identify the ideal nutrient and vitamin levels of the crop plants for improving crop tissue culture aimed to improve crop plant growth in a lab setting by predicting callogenesis rate (CGR), embryogenesis rate (EGR), and somatic embryo number (SEN), shoot regeneration rate (SRR), rooting rate (RR).Different concentrations of ionic macronutrients, bio-molecular, and vitamins of the crop plant are the input to the predictive model, which is collected through the laboratory Callus Induction Experiment (CIE). Z-score normalization is used to preprocessing the CIE data to ensure consistent scales across different input features and improve model training performance. DFFN used discriminates to predict complex relationships and interactions between CGR, EGR, SEN, SRR, and RR with EGI tuning. The EGI-DFFN model has significantly improved crop tissue culture growth by accurately predicting the CGR, EGR, SEN, SRR, and RR respectively. The EGI-DFFN model enhances understanding of how ionic macronutrients and vitamins impact plant growth. It identifies optimal concentrations of the biomolecular to enhance somatic embryo formation and plantlet development, providing insights for optimizing crop tissue culture conditions for optimal growth outcomes.

Enhancing Photovoltaic Power Forecasting through Hybrid Deep Learning Models: A CNN-RNN Approach for Grid Stability and Renewable Energy Optimization

This paper addresses the critical need for accurate photovoltaic (PV) power generation predictions to ensure efficient grid integration and management, especially considering the variability and intermittency of solar power. By exploring advanced deep learning techniques, including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and a hybrid CNN-RNN model, the study aims to enhance the accuracy and reliability of solar power forecasts. The CNN model achieved an accuracy of 0.84, while the RNN reached 0.94, with the highest accuracy of 0.99 attained by the hybrid CNN-RNN model. These models provide vital tools for mitigating fluctuations in solar power output, improving grid stability, and optimizing energy distribution. The study contributes to the advancement of renewable energy forecasting, helping to ensure a more sustainable and reliable energy future, while also supporting efforts to reduce CO2 emissions and combat climate change.

Constructing Efficient Mesh-Based Global Grid Systems with Reduced Distortions

Recent advancements in geospatial technologies have significantly expanded the volume and diversity of geospatial data, unlocking new and innovative applications that require novel Geographic Information Systems (GIS). (Discrete) Global Grid Systems (DGGSs) have emerged as a promising solution to further enhance modern geospatial capabilities. Current DGGSs employ a simple, low-resolution polyhedral approximation of the Earth for efficient operations, but require a projection between the Earth’s surface and the polyhedral faces. Equal-area DGGSs are desirable for their low distortion, but they fall short of this promise due to the inefficiency of equal-area projections. On the other hand, efficiency-first DGGSs need to better address distortion. We introduce a novel mesh-based DGGS (MBD) which generalizes efficient operations over watertight triangular meshes with spherical topology. Unlike traditional approaches that rely on Platonic or Catalan solids, our mesh-based method leverages high-resolution spherical meshes to offer greater flexibility and accuracy. MBD allows high-resolution polyhedra (HRP) to be used as the base polyhedron of a DGGS, significantly reducing distortion. To address the operational challenges, we introduce a new hash encoding method and an efficient barycentric indexing method (BIM). MBD extends Atlas of Connectivity Maps to the BIM to provide efficient spatial and hierarchical traversal. We introduce several new base polyhedra with lower areal and angular distortion, and we experimentally validate their properties and demonstrate their efficiency. Our experimentation shows that we achieve constant-time operations for high-resolution MBD, and we recommend polyhedra to be used as the base polyhedron for low-distortion DGGSs, compact faces, and efficient operations.

تطبيق شبكات العصب الاصطناعي وأساليب المنطق الضبابي لتوقع الحمل على المدى القصير لدى شبكة كهرباء غرب ليبيا

This research paper investigates the application of artificial neural networks (ANNs) and fuzzy logic methods for short-term load forecasting in the Western Libyan Electric Network. Historical hourly load data collected during the year 2023 by the General Electricity Company of Libya (GECOL). The study aims to develop accurate and reliable load forecasting models to support efficient grid operation and resource planning. Historical load data and meteorological variables are utilized to train and evaluate the forecasting models. The performance of the ANNs and fuzzy logic methods is compared, and the suitability of each approach for load forecasting in the Western Libyan Electric Network is assessed. The results demonstrate the effectiveness of both ANNs and fuzzy logic methods in short-term load forecasting, providing valuable insights for electricity providers and policymakers in the region. Keywords: neural' networks, fuzzy logic, load forecasting

Nonlinear Ensemble Deep Learning Model for Energy Consumption Prediction with Bayesian Optimization

Accurate prediction of electric energy consumption is crucial for efficient load dispatching, energy utilization, and grid operation. Traditional statistical and classical machine learning methods struggle with the nonlinear nature of energy consumption data, often leading to higher prediction errors. Additionally, deep learning models using a single approach face challenges such as convergence to local minima and poor generalization. This paper proposes a nonlinear ensemble deep learning model for residential energy consumption prediction, incorporating Bayesian optimization for hyperparameter tuning. The model combines Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and 1D Convolutional Neural Networks (1D-CNN), leveraging their powerful nonlinear feature learning capabilities. A k-means clustering approach is used to preprocess and reduce variability in the data, enhancing the ensemble model's performance. The ensemble model was tested on real energy consumption data from two districts in Addis Ababa, showing significant improvements in prediction accuracy with lower MAE, RMSE, and MAPE values compared to single models and un-clustered data. The integration of clustering and Bayesian optimization further enhanced model generalizability and minimized overfitting, demonstrating the effectiveness of a nonlinear approach in capturing complex energy consumption patterns.

Vehicle-to-Grid: quantification of its contribution to security of supply through the F-Factor methodology

The increasing adoption of electric vehicles is expected to substantially raise electricity demand. This could require significant grid investment to maintain secure electricity supply, which has traditionally been provided through infrastructure upgrades. The potential of smart technologies like Vehicle-to-Grid (V2G) to contribute to security of supply has prompted the need to quantify their impact. We hypothesize that the F-Factor methodology can effectively quantify V2G’s security of supply contribution. Applying F-Factor analysis to V2G through optimization modeling and sensitivity studies, we find that key parameters like V2G charger ratings, EV battery capacities, and load profile peakiness significantly influence the results. We conclude that the F-Factor provides a valuable tool for assessing V2G’s potential to enhance security of supply, with implications for more efficient grid planning in the context of transport electrification.

An Efficient Progressive Grid Generation Method Considering Internal Quality and Boundary Grid Adjustment

Due to its elegant appearance and high structural efficiency, free-form grid structures are increasingly adopted in architectural design. However, it is a challenging task for engineers to generate a uniform and well-shaped grid on a free-form surface while considering the processing of both interior and boundary of grid, especially for composite surfaces. To generate well-shaped grids with uniform rods, regular cells and smooth visual effects over free-form surfaces, this paper develops an innovative triangular grid generation method capable of automatically managing grid boundary with extending initial surface. In the method, nodes of grid structure are considered to be zero mass particles and are progressively added to the extended surface from geometric center to the boundary of surface. A boundary-processing algorithm is then established to evenly distribute nodes on initial boundary curve, while ensuring that grid cells at the boundary do not exhibit elongation. A mechanical simulation system based on spring-mass model is improved to optimize spatial grid structure, rods of grid are regarded as linear springs, a surface attraction force and an anchoring force are performed on grid nodes. Moreover, the proposed method allows architects change grid direction and rods length easily, which can greatly improve the efficiency of design. Several case studies show that the method can effectively avoid distortion of grids and generate well-shaped grids that can meet aesthetic requirements.

Efficient grid management: smart forecasting of short-term power load using PSO-LSTM

Recent load forecasting techniques combining machine learning models and hyperparameter optimization algorithms have shown success for short-term load forecasting (STLF) task, but they often require complex programming, higher computational costs, and greater parameter tuning. In this paper, we introduce an improved STLF model that combines Long Short-Term Memory (LSTM) neural network with Particle Swarm Optimization (PSO) for enhanced performance. In the proposed approach, the number of hidden neurons in different LSTM layers, learning rate and the number of iterations for training are optimized using the PSO algorithm. To validate the effectiveness of this method, meteorological data and historical load data from a real-world power grid are used as input. The experimental results reveal PSO significantly enhances hyperparameter tuning for LSTM neural networks, leading to improved predictive modelling. The PSO-LSTM model performed better than the LSTM model by more than 20% (in terms of Mean Absolute Error), and showed low sensitivity to hyperparameters. Comparative analysis with alternative approaches from the literature further validates the PSO-LSTM’s effectiveness in STLF. Additionally, the model achieved stable multi-step prediction capabilities, with average errors of 3.6445 for MAE, 4.6509 for RMSE, and 4.6519 for MAPE over a 1–4 day ahead lead times. This study highlights PSO-LSTM’s enhanced robustness and accuracy in power load prediction while addressing hyperparameter tuning challenges through self-optimization.

Smart Internet of Things Power Meter for Industrial and Domestic Applications

Considering the widespread presence of switching devices on the power grid (including renewable energy system inverters), network distortion is more prominent. To maximize network efficiency, our goal is to minimize these distortions. Measuring the voltage and current total harmonic distortion (THD) using power meters and other specific equipment, and assessing power factor and peak currents, represents a crucial step in creating an efficient and stable smart grid. In this paper, we propose a power meter capable for measuring both standard electrical parameters and power quality parameters such as the voltage and current total harmonic distortion factors. The resulting device is compact and DIN-rail-mountable, occupying only three modules in an electrical cabinet. It integrates both wired and wireless communication interfaces and multiple communication protocols, such as Modbus RTU/TCP and MQTT. A microSD card can be used to store the device configuration parameters and to record the measured values in case of network fault events, the device’s continuous operation being ensured by the integrated backup battery in this situations. The device was calibrated and tested against three industrial power meters: Siemens SENTRON PAC4200, Janitza UMG-96RM, and Phoenix Contact EEM-MA400, obtaining an overall average measurement error of only 1.22%.

Smart Grid Technologies: Advancements and Applications in Nigeria

This study explores the impact of smart grid technologies on the modernization of power grids in response to evolving energy demands and the integration of renewable energy sources in Nigeria. It aims to empirically assess advancements in smart grid technologies, focusing on four key objectives: eval_uating the impact of smart meters on energy consumption and peak demand reduction, analyzing the effectiveness of Advanced Distribution Management Systems (ADMS) in enhancing grid reliability and efficiency, assessing the role of demand response programs in balancing supply and demand, and examining the integration and management of Distributed Energy Resources (DERs) within smart grids. The research employs a quantitative methodology, collecting and analyzing data from utility companies that have implemented smart grid technologies. Key findings reveal that smart meters significantly reduce energy consumption and peak demand by providing real-time monitoring, while ADMS improves grid reliability and operational efficiency through enhanced fault detection and automated control. Demand response programs effectively balance energy supply and demand, reducing peak loads and energy costs. The integration of DERs increases renewable energy utilization and grid stability but also presents challenges related to variability in power output and management complexity. The study concludes that smart grid technologies play a crucial role in achieving a more resilient and efficient power grid, though addressing challenges related to DER integration and system management is essential for maximizing their benefits.

Optimal Reconfiguration of Bipolar DC Networks Using Differential Evolution

The search for more efficient power grids has led to the concept of microgrids, based on the integration of new-generation technologies and energy storage systems. These devices inherently operate in DC, making DC microgrids a potential solution for improving power system operation. In particular, bipolar DC microgrids offer more flexibility due to their two voltage levels. However, more complex tools, such as optimal power flow (OPF) analysis, are required to analyze these systems. In line with these requirements, this paper proposes an OPF for bipolar DC microgrid reconfiguration aimed at minimizing power losses, considering dispersed generation (DG) and asymmetrical loads. This is a mixed-integer nonlinear optimization problem in which integer variables are associated with the switch statuses, and continuous variables are associated with the nodal voltages in each pole. The problem is formulated based on current injections and is solved by a hybridization of the differential evolution algorithm (to handle the integer variables) and the interior point method-based OPF (to minimize power losses). The results show a reduction in power losses of approximately 48.22% (33-bus microgrid without DG), 2.87% (33-bus microgrid with DG), 50.90% (69-bus microgrid without DG), and 50.50% (69-bus microgrid with DG) compared to the base case.