Operation Of Electric Power Systems Research Articles

Insulator defect detection in transmission line based on an improved lightweight YOLOv5s algorithm

As one of the most important components on the transmission line, the insulator is prone to failure, which affects the safe operation of electrical power system. Hence, it is crucial to accurately detect the insulator defects for further maintenance in time. Recently, with the development of artificial intelligence and target detection algorithms, the insulator defect detection has received more and more attention. However, there are still existing some difficulties: insufficient samples and low detection accuracy. To improve the accuracy of insulator defect detection, this paper proposes an auto-detection method based on an improved lightweight YOLOv5s model. First, this paper introduces the basic network frame of YOLOv5s and proposes an improved algorithm by utilizing the GIoU loss function, Mish activation function, and CBAM module. Then, performs data enhancement in the insulator dataset to enhance the robustness of the model. Finally, trains and tests the improved YOLOv5s model, and compares it with traditional target detection algorithms. Compared with traditional target detection algorithms, the AP value of the proposed algorithm in detecting insulator defects can be improved by 5 %. The results demonstrate the improved algorithm proposed in this paper can effectively identify and position the insulator defects.

Spatial correlation learning based on graph neural network for medium-term wind power forecasting

With the increasing penetration of wind power in power grid, accurate and reliable wind power forecasting is of great significance for the economic operation and safe dispatching of electrical power system. In practice, there exists complex spatial correlation between wind power variables, which brings great challenges to the accurate forecasting of wind power. However, traditional deep learning-based methods mostly focus on temporal feature while ignoring the spatial correlation between wind power variables, leading to low forecasting accuracy. To explore the spatial correlation among wind power variables and extract temporal features simultaneously, we propose a double attention-based spatial–temporal neural network (DA-STNet). First, graph attention network is employed to explore the spatial correlation between wind power variables based on a graph constructed by maximal information coefficient, which can consider the combined influences of multivariate on the wind power output. Then, by incorporating causal reasoning and data-driven element-wise attention measures, a novel temporal attention layer is proposed to extract the temporal feature of wind power sequences. Comprehensive experiments were conducted on one self-collected and one public dataset with three different multi-steps ahead forecasting tasks, and the experimental results demonstrated that the performance of proposed DA-STNet is superior to the existing methods on both real-world datasets. In the 24 h ahead experiment on the NWWPF dataset, the MSE of our model can reach as low as 0.136 and MAE can be decreased to 0.275.

Распределенная система станций подзарядки для энергообеспечения группы БпЛА мультикоптерного типа

A steady trend towards the development of electric power systems leads to their continuous enlargement and sophistication. As a result, new ways of their control appear. In this regard, the existing models and complexes for adequacy assessment may work inadequately and ineffectively in terms of the obtained results adequacy. To assess the current state of the existing models and complexes, we reviewed and analyzed the domestic and foreign software and computer systems. In particular, we considered mathematical models of minimizing the power shortage. This work is based on the problem of modifying mathematical models of minimizing the power shortage used in adequacy assessment of the electric power systems of one of the complexes under consideration. As a modification of mathematical models, it is proposed to exclude the existing method of using the line capacities and start using correct accounting for the maximum permissible active power flow in controlled sections. The experimental part reflected in the paper concerns the testing of options for models to minimize the power shortage, as well as the proposed modifications on various systems, including those consisting of three and seven reliability zones with a variable number of controlled sections and power lines included in them. The results of the study have shown that the proposed modifications are efficient and can be used in the future. The authors also obtained the most adequate results in terms of the physical laws of electric power system operation due to the model of minimizing the power shortage with quadratic losses which takes into account the limitations of power transmission over controlled sections.

Mathematical Simulation for Right of Way Corridor for Overhead Transmission Line

Transmission lines play a vital role in the operation of a reliable electrical power system and that is why the transmission line system is considered as a lifeline system for power supply. The transmission line traverses across the length and breadth of the country, and it crosses all the natural reserves like forests, rivers, vegetation, mountains, etc., to make the system effective, Right of Way (ROW) plays a decisive role in the transmission line. But in today’s growing population and economy, land acquisition for the construction of transmission lines is a major bottleneck. This leads to huge compensations for the utilities and a threat to the natural resources. This paper attempts to arrive at a mathematical simulation for effective ROW by studying different codal provisions and utility practices. The approach is made to make efficient use of the land available without compromising any relevant parameters for ROW.

Detection of Current Transformer Saturation Based on Machine Learning

One of the tasks in the operation of electric power systems is the correct functioning of the protection system and emergency automation algorithms. Instrument voltage and current transformers, operating in accordance with the laws of electromagnetism, are most often used for information support of the protection system and emergency automation algorithms. Magnetic core saturation of the specified current transformers can occur during faults. As a result, the correct functioning of the protection system and emergency automation algorithms is compromised. The consequences of current transformers saturation are mostly reflected in the main protections of network elements operating on a differential principle. This work aims to consider the analysis of current transformer saturation detection methods. The problem of identifying current transformer saturation is reduced to binary classification, and methods for solving the problem based on artificial neural networks, support vector machine, and decision tree algorithms are proposed. Computational experiments were performed, and their results were analyzed with imbalanced (dominance of the number of current transformer saturation modes over the number of modes with its normal operation) and balanced classes 0 (no current transformer saturation) and 1 (current transformer saturation).

A Deep Learning Approach for the Automated Classification of Geomagnetically Induced Current Scalograms

During geomagnetic storms, which are a result of solar wind’s interaction with the Earth’s magnetosphere, geomagnetically induced currents (GICs) begin to flow in the long, high-voltage electrical networks on the Earth’s surface. It causes a number of negative phenomena that affect the normal operation of the entire electric power system. To investigate the nature of the phenomenon and its effects on transformers, a GIC monitoring system was created in 2011. The system consists of devices that are installed in the neutrals of autotransformers at five substations of the Kola–Karelian power transit in northwestern Russia. Considering the significant amount of data accumulated over 12 years of operating the GIC monitoring system, manual analysis becomes impractical. To analyze the constantly growing volume of recorded data effectively, a method for the automatic classification of GICs in autotransformer neutrals was proposed. The method is based on a continuous wavelet transform of the neutral current data combined with a convolutional neural network (CNN) to classify the obtained scalogram images. The classifier’s performance is evaluated using accuracy and binary cross-entropy loss metrics. As the result of comparing four CNN architectures, a model that showed high GIC classification performance on the validation set was chosen as the final model. The proposed CNN model, in addition to the main layers, includes pre-processing layers and a dropout layer.

Analysis and Suggestions for the Self-blast Problem of Glass Insulators on Transmission Lines

By virtue of many advantages such as good self-cleaning ability, outstanding mechanical performance, and excellent anti-vibration-fatigue ability, the glass insulators have been widely used in various alternating-current and direct-current transmission lines. However, the self-blast incidents of glass insulators have frequently occurred in transmission lines with various voltage levels, seriously affecting the safe operation of transmission lines. Herein, the main causes of self-blast problem of glass insulators are summarized and analysed from different perspectives, and some corresponding suggestions have been proposed to reduce the self-blast of glass insulators. In addition, the relevant prospects are provided for the future research work about the glass insulators. The viewpoints in this article have important theoretical and practical significance for improving the safety and reliability of transmission lines and ensuring the stable operation of electrical power systems.

Optimal Unit Commitment and Generation Scheduling of Integrated Power System with Plug-In Electric Vehicles and Renewable Energy Sources

The integration of wind energy sources and plug-in electric vehicles is essential for the efficient planning, reliability, and operation of modern electric power systems. Minimizing the overall operational cost of integrated power systems while dealing with wind energy sources and plug-in electric vehicles in integrated power systems using a chaotic zebra optimization algorithm (CZOA) is described. The proposed system deals with a probabilistic forecasting system for wind power generation and a realistic plug-in electric vehicle charging profile based on travel patterns and infrastructure characteristics. The objective is to identify the optimal scheduling and committed status of the generating unit for thermal and wind power generation while considering the system power demand, charging, and discharging of electric vehicles, as well as power available from wind energy sources. The proposed CZOA adeptly tackles the intricacies of the unit commitment problem by seamlessly integrating scheduling and the unit’s committed status, thereby enabling highly effective optimization. The proposed algorithm is tested for 10-, 20-, and 40-generating unit systems. The empirical findings pertaining to the 10-unit system indicate that the amalgamation of a thermal generating unit system with plug-in electric vehicles yields a 0.84% reduction in total generation cost. Furthermore, integrating the same system with a wind energy source results in a substantial 12.71% cost saving. Notably, the integration of the thermal generating system with both plug-in electric vehicles and a wind energy source leads to an even more pronounced overall cost reduction of 13.05%. The outcome of this study reveals competitive test results for 20- and 40-generating unit systems and contributes to the advancement of sustainable and reliable power systems, fostering the transition towards a greener energy future.

Enhancing PV distributed generator planning in medium‐voltage DC distribution networks: A multi‐design techno‐economic analysis with load demand response

AbstractDistributed generators have grown in significance for the technical and financial operation of electric power systems in recent years. The integration of these generators into the electrical distribution network (EDN) has experienced rapid growth, primarily driven by advancements in renewable energy sources (RESs), particularly photovoltaic distributed generators (PVDGs). With the increasing implementation of solar energy as a RES, designing the optimal integration of PVDGs into medium voltage direct current (MVDC) networks is crucial to comprehensively analyzing technical and economic factors. To address this issue, a new multiple objective function (MOF) is proposed, which combines various techno‐economic parameters such as total active power loss (APL), voltage deviation (VD), and the investment cost of PVDGs (ICPVDG) installed in the test system. The objective is to minimize the MOF simultaneously to achieve the optimal incorporation of PVDGs. This study aims to solve the allocation problems related to the location and size of PVDG units in the modified MVDC test systems IEEE 27 and 33‐bus. Simulation results demonstrate the superior accuracy and effectiveness of the equilibrium optimization algorithm (EOA) in achieving the minimum MOF. The utilization of multiple PVDG units reduces overall active power losses and improves voltage profiles across all buses.

Methodological Proposal: First Steps for the Implementation of Demand Management Programs in Colombia

The electrification of isolated or rural areas brings with it technical, economic, and social challenges that differentiate the operation of these networks compared to the traditional operation of large electrical power systems, such as the dependence of air or river transport on fuel for the supply of generation plants, the absence of individual measurement and the obsolete infrastructure of the distribution networks. Therefore, this paper proposes a hybrid methodology for studying a Colombian case, analyzing the development of programs for rational electricity use and energy efficiency in isolated areas. These first steps are related to the diagnosis of the current conditions of the power network, the identification of actors that can influence the regulation of the electricity service in the area, and the proposal of mechanisms that allow promoting the rational and efficient use of the electricity.

Identifying drivers and mitigators for congestion and redispatch in the German electric power system with explainable AI

The transition to a sustainable energy supply challenges the operation of electric power systems in various ways. Transmission grid loads increase as wind and solar power is often installed far away from the consumers. System operators resolve grid congestion via countertrading or redispatch to ensure grid stability. While some drivers of congestion are known, the magnitude of their impact is unclear, and other factors might still be unidentified.In this study, we conduct a data-driven investigation of congestion in the German transmission grid that reveals drivers and mitigators and quantifies their impact ex-post. Specifically, we used Gradient Boosted Trees and SHAP values to develop an explainable machine learning model for the hourly volume of redispatch and countertrade. As expected, wind power generation in northern Germany emerged as the main driver. Cross-border electricity trading, especially with Denmark, also plays an important role. German solar power has very little effect. Furthermore, our results suggest that run-of-river generation in the alpine region has a strong mitigating effect. Our results support the idea that market design changes, e.g., a bidding zone split, could contribute to congestion prevention.

Erosion characteristics of WCu/Ti3SiC2 electrical contact materials by arc discharge in various ambient atmospheres

Arc erosion performances of electrical contacts are vital to guarantee the stable operation of electric power systems. In this work, arc erosion performances of WCu/Ti3SiC2 materials in SF6, CO2, O2, and Ar were systematically researched. Detachment of silicon layers from Ti3SiC2 occur in the 4 atm, Ti3SiC2 plays an important role in protecting the composites. Severe splashings and cavities are found on the eroded surfaces in CO2 and O2. Based on the arc energy densities and composition changes, arc erosion mechanisms of WCu/Ti3SiC2 in various atmospheres are proposed.

Provision of ramp-rate limitation from distribution networks to transmission systems as preventive action towards frequency disturbances

The high proliferation of Converter-Interfaced Renewable Energy Sources (CIRES) poses several challenges to electric power system operators regarding frequency stability and control. Some of these issues are related with the high active power Ramp Rates (RRs) of CIRES, stemming from their inertia-less and stochastic nature. To deal with such problems, Transmission System Operators (TSOs) often commit excessive amounts of conventional spinning reserves, while in weak Transmission Systems (TSs) regulations are imposed, aiming to mitigate RRs of CIRES. Moreover, several technical studies have proposed control approaches for RR Limitation (RRL). Nevertheless, the influence of RRL on frequency quality and mainly the impact of RRL control capability of small-scale CIRES, connected to Distribution Networks (DNs), on frequency deviations have not been fully explored and quantified. To fill this gap, a detailed investigation is performed in this paper. For this purpose, simulations are conducted on a simplified System Frequency Response (SFR) model as well as on an analytical power system model. The latter is a modified version of the IEEE 9-bus Test System, consisting of three conventional Synchronous Generators (SGs) and three Medium Voltage (MV) DNs, which include CIRES equipped with SuperCapacitors (SCs), thus presenting RRL control capability. To thoroughly evaluate the impact of RRL on frequency dynamics, a set of metrics is introduced and frequency duration curves are created for various scenarios. Finally, the influence of several factors, such as the total system inertia, the RRL target value, the SC size, etc, is quantified by using the proposed indices.

Asynchronous generator replacement scheme with internal capacitive reactive power compensation

The relevance of using compensated induction machines in the generator mode of autonomous power supply systems is determined by their ability to provide a stable and reliable power supply even in the absence of an external grid. The research aims to develop a procedure for determining the characteristics of an equivalent circuit of compensated induction generators that considers the mutual influence between the main and additional phase windings of the armature. The theory of an idealised induction machine with the representation of electromechanical energy conversion processes and basic physical parameters in the form of equations of electrical equilibrium and drive motion was used to obtain the calculated characteristics of the generator’s operating modes of this class. The generalised system of differential equations made it possible to calculate dynamic and static processes for symmetrical and asymmetrical modes at given machine parameters under different methods of excitation and voltage stabilisation under variable load. For the practical implementation of modelling the parameters of an induction generator, the equations of electrical equilibrium of the stator and rotor circuits for the symmetrical steady-state mode are used, which is a special case of the generalised mathematical model of the machine for both transient and steady-state processes with constant parameters of the substitution scheme. Consideration of the structural and functional features of compensated induction generators was made possible by using a mathematical model with the armature winding divided into two coaxial or spatially offset half-windings. In this case, the number of calculation equations doubles. The use of an autotransformer scheme for switching on the stator phase half-windings to capacitors required the introduction of an additional differential equation for the electrical equilibrium of the stator phases. This made it possible to substantiate the values of active and inductive resistances used in the replacement circuit of an induction generator with internal capacitive reactive power compensation. The mutual inductive coupling of the main and additional half-windings of the generator stator phase windings due to both the operating magnetic flux and the magnetic fluxes of dissipation was considered. The practical significance of the obtained results is to increase the efficiency and stability of the power supply, which helps to reduce losses and improve the operation of electric power systems

Numerical Overcurrent Power Relay Enhancement Based on Integrated Discrete Wavelet Transform

As electrical power demands increases, the need for advanced protection relays has surged as a vital element in the power distribution system. That is, to ensure the continuity of power supply and eliminate abnormal faulty status, relays with different types and functions play a major role in electrical power system operation and protection as will. In the power system, current variation faults may accrue for many reasons, which may harm power system components, load, and jeopardize human safety. However, in now a day digital current relay, a large number of samples are collected and processed in order to continuously monitor and detect faults. This large amount of samples needs a bigger storage data real state and longer time to compile, optimization is needed to improve current relay reliability and accuracy with much fewer samples. Therefore, as this work aim toward optimizing the current relay, a substantial algorithm is needed for better current waveform analysis. As a result, the Wavelet transform algorithm with its wide spectrum feature of waveform analysis and decomposition process will be used as an enhancement tool. Using MATLAB/Simulink, a simulation, the unique characteristics of Wavelet transform (de-noising, mining, and decomposition) will be implemented into the modern numerical current relay to test the ability to advance fault detection based on less number of current waveform data. As this evaluation was compared with a typical conventional numerical overcurrent relay, a promising result in terms of less response time and compiling process was achieved.

Modelingand method for assessing the efficiency of the power system

The work is dedicated to addressing the issues related to assessing the efficiency of modern electrical power systems, especially as an increasing number of consumers desire to actively influence the electricity supply schedule, providing conditions to electricity producers. The research can be divided into two main segments: the development of a model for assessing the efficiency of electrical power system operation and the development of a method for analyzing the performance of the energy system, which is based on a model of changes in the quality indicators of the energy system. The proposed model takes into account properties of the energy system such as the probability of uninterrupted network operation, normalized frequency of electrical power, the volume of consumed electrical energy, and the electrical energy supplied by the transmission organization. It is expected that this model will help provide a more accurate assessment of the efficiency of energy system operation under normal operating conditions. Furthermore, a method for analyzing the efficiency of energy system operation has been developed, which is based on a model of changes in the quality indicators of the energy system. A unique featureof this method is the introduction of a new comprehensive indicator of energy system efficiency, which is determined using a convolution method of partial indicators, namely: the volume of electrical energy supply, the quality of electrical energy supply,and the efficiency of electrical energy supply. The reliability of the model was confirmed through the calculation of the daily electric supply and load schedule.

A TWO STAGE MODEL FOR DAY-AHEAD ELECTRICITY PRICE FORECASTING: INTEGRATING EMPIRICAL MODE DECOMPOSITION AND CATBOOST ALGORITHM

Electricity price forecasting is crucial for the secure and cost-effective operation of electrical power systems. However, the uncertain and volatile nature of electricity prices makes the electricity price forecasting process more challenging. In this study, a two-stage forecasting model was proposed in order to accurately predict day-ahead electricity prices. Historical natural gas prices, electricity load forecasts, and historical electricity price values were used as the forecasting model inputs. The historical electricity and natural gas price data were decomposed in the first stage to extract more deep features. The empirical mode decomposition (EMD) algorithm was employed for the efficient decomposition process. In the second stage, the categorical boosting (CatBoost) algorithm was proposed to forecast day-ahead electricity prices accurately. To validate the effectiveness of the proposed forecasting model, a case study was conducted using the dataset from the Turkish electricity market. The proposed model results were compared with benchmark machine learning algorithms. The results of this study indicated that the proposed model outperformed the benchmark models with the lowest root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and correlation coefficient (R) values of 8.3282%, 5.2210%, 6.9675%, and 86.2256%, respectively.

Quenching chaos in a power system using fixed-time fractional-order sliding mode controller

The aim of this paper is to study the unwanted chaotic oscillation that can severely affect the reliable and safe operation of electrical power systems. The dynamical behavior of a benchmark three-bus nonlinear electrical power system model is explored using modern nonlinear analysis methods, where the Lyapunov exponents spectrum, bifurcation diagram, power spectral density and bicoherence are used to investigate the chaotic oscillation in the power system. The analysis shows the existence of critical parameter values that may drive the power system to an unstable region and can expose the system to bus voltage collapse and angle divergence or blackout. To eliminate the chaotic oscillation, a fractional-order fixed time sliding mode controller has been used to control the power system in a finite time that can be predetermined by the designer. The Lyapunov theorem has been used to prove the stability of the controlled power system. The results confirm the superiority, robustness, and effectiveness of the suggested control algorithm.

Generalized Normal Distribution Optimization Algorithm for Economic Dispatch with Renewable Resources Integration

In an electric power system operation, the main goal of economic dispatch (ED) is to schedule the power outputs of committed generating units efficiently. This involves consideration of relevant system equality and inequality constraints to meet the required power demand at the lowest possible operational cost. This is a challenging optimization problem for power system operators that can be dealt with efficient meta-heuristic algorithms. This article uses a recent meta-heuristic approach named the generalized normal distribution optimization (GNDO) algorithm to achieve near-optimal solutions. The efficacy of the proposed GNDO algorithm is validated through experimentation on three distinct test power system networks: one with three thermal units, the second one with six thermal-unit, and the third one with ten thermal units. The algorithm's performance is also assessed on a power network with renewable energy sources. All analyses of the four test cases are conducted on the MATLAB/SIMULINK platform. Finally, this article also compares the obtained results with other literature-reported strategies, genetic algorithm (GA), particle swarm optimization (PSO), whale optimization algorithm (WOA), flower pollination algorithm (FPA), and bald eagle search (BES) algorithm. It is evident from the simulated cases that the employed GNDO algorithm exhibits superior performance for two cases and competitive performance for the remaining cases in achieving the lowest operation costs and power losses.

Optimal frequency support method for urban microgrids of building prosumers

High penetration of renewable energy sources (RES) is raising a number of technical challenges for the sustainable and reliable operation of electric power systems. To this end, the need for ancillary services provided by the building sector to the electric power system is becoming particularly significant; with frequency support being one among them. Urban microgrids and especially those of them hosting building prosumers will have a key role in frequency support as they comprise flexible components that can be exploited to this end. In this paper, detailed models for the accurate simulation of frequency support mode of operation are developed. Frequency support in microgrids of buildings is enhanced by properly utilizing the flexibility of building prosumers. Frequency is also controlled by optimally regulating building local generation units and their adjustable electrical loads in a flexibility-based way and taking into consideration gas emission limitation. Emphasis is placed on the enhancement of the computational efficiency of the proposed support method by suitably exploiting smart power dispatch techniques. The developed method is validated through the simulation of a realistic complex urban microgrid.