Safe Operation Of Power System Research Articles

Online recognition method of partial discharge pattern for transformer bushings based on small sample ultra-micro-CNN network

Oil impregnated paper bushing is the key equipment connecting a transformer and a power grid. Insulation deterioration may cause partial discharge, which poses a great threat to the safe operation of power systems. In order to realize the online diagnosis for partial discharge and automatic identification of insulation defects of transformer bushings, an ultra-micro-convolutional neural network with only more than 3000 parameters is designed, which adaptively extracts partial discharge characteristics based on small samples, so as to judge the defect category and the reasons. The accuracy rate can reach 97.1%, the computational complexity is lower, the real-time performance is stronger, and it can be easily deployed on various embedded platforms.

Risk Assessment of Power Systems Considering CVaR

In view of the randomness of component fault and load, considering the deficiency of traditional single reliability index, this paper introduces the Conditional Value-at-risk method based on Monte Carlo simulation, and constructs the probability model of risk assessment considering the forced outage rate of unit and line. In order to quantify uncertainty caused by random factors, the value-at-risk and conditional value-at-risk of power system security was proposed and used as a grid security index in the model combined with reliability index, which can accurately describe secure operation level in power system under different operation parameters, and reflect the severity of the accident and weakness and the grid, for further research on safe operation of power system to offer reference and decision-making basis.

Evaluation of Breakdown Voltage and Water Content in Transformer Oil Using Multi Frequency Ultrasonic and Generalized Regression Neural Network

Power transformer is regarded as one of the crucial part of electrical power transmission and distribution system. The quality of transformer oil can directly affect the operation of the power transformer, and breakdown voltage (BDV) and water content are the two main parameters of transformer oil quality. Monitoring the BDV and water content of transformer oil is considered as an important method to evaluate the safe operation of power systems. This work proposes the measurement of BDV and water content in transformer oil using multi frequency ultrasonic and generalized regression neural network (GRNN). The BDV and water content of all 210 samples were firstly tested according to the traditional testing methods and the multi frequency ultra-sonic technology, separately. And then the 210 samples were randomly divided into training sets and test sets. The obtained multi frequency ultrasonic data were set as the input of GRNN, and the BDV and water content as the output of GRNN. Moreover, the 20-fold-cross-validation was incorporated to obtain the best smoothing factor δ for GRNN. Finally, the GRNN model was trained by the training sets with δ =4.54 and was evaluated with the test sets. All results show that the lower BDV or the higher water content of the sample will cause greater ultrasonic sound attenuation, and the prediction accuracy of the prediction model for BDV and water con-tent in oil is up to 95%. It provides a new method for evaluating the health of transformer oil.

Application of Intelligent Sensor Technology in Steady State Operation of Power System

Wireless sensor network technology is a new monitoring technology to ensure the stable and safe operation of power system. The network has many advantages: light weight, corrosion resistance, high temperature resistance and so on, which can effectively solve the measurement problems that conventional detection technology can not afford. With its unique physical properties, the sensor technology has a relatively broad application prospect in power system. This paper focuses on the main development achievements of wireless sensor technology, and analyzes its application in power system. This paper focuses on the key technologies in the implementation of WSN. This paper discusses the routing of WSN, low power algorithm of nodes, channel deployment, antenna selection, power supply and network security. At the same time, this paper implements the low-power algorithm of the node. The node can run stably for a long time without outside interference. In this paper, through the research on the key technology of theory, the WSN technology is applied to the field investigation and verification of distribution communication network and transmission line condition monitoring, which can be used as the main technical means of the last kilometer network in the power communication terminal network planning. WSN has a broad application prospect in power system.

Optimal Microgrid–Interactive Reactive Power Management for Day–Ahead Operation

The replacement of conventional generation sources by DER creates the need to carefully manage the reactive power maintaining the power system safe operation. The principal trend is to increase the DER volume connected to the distribution network in the coming years. Therefore, the microgrid represents an alternative to offer reactive power management due to excellent controllability features embedded in the DER, which enable effective interaction between the microgrid and the distribution network. This paper proposes a microgrid–iterative reactive power management approach of power-electronic converter based renewable technologies for day-ahead operation. It is designed to be a centralised control based on local measurements, which provides the optimal reactive power dispatch and minimise the total energy losses inside the microgrid and maintain the voltage profile within operational limits. The proposed optimal-centralised control is contrasted against seven local reactive power controls using a techno-economic approach considering the steady–state voltage profile, the energy losses, and the reactive power costs as performance metrics. Three different reactive power pricing are proposed. The numerical results demonstrate the optimal microgrid–interactive reactive power management is the most suitable techno-economic reactive power control for the day–ahead operation.

Wide-area security assessment based on informative variables of power system

In a power system, lack of efficacious information about operating conditions is a key challenge for power network operator. Especially, when this weakness coincides with increasing consumers' dependency on electric power, operating in adjacent of security constraints, and also occurrence of critical disturbances in the power system. In these circumstances, the role of secure operation, which contains High Information Content (HIC) about the operating space, becomes more prominent. However, power system variables of the operating space are monitored by dispatcher using Phasor Measurement Units (PMUs). Therefore, by identification of the informative variables, the power system can be operated in a low-dimensional space of security region. Indeed, by extraction of hidden information based on Entropy theory, descriptor variables of security region are recognizable. For this purpose, behavior of power system loadability is studied in the face of stressing power network in a low dimensional D-parametric space. Then, the informative variables are accessible using the measurement of information content of the power system variables in the operating space. Thus, the power system operator just by tracking a few number of descriptor variables guarantees a safe operation of power system even by occurrence of critical disturbances.

Micro-Cantilever Capacitive Sensor for High-Resolution Measurement of Electric Fields

Electric-field (EF) microsensors with high resolution are crucial for the realization of real-time dynamic status monitoring of Ubiquitous Power Internet of Things (UPIoT), which is of great significance for the safe operation of power system and early-stage diagnostics of power equipment. Distortion-free and high-resolution measurement of electric fields is also important in applications such as meteorological monitoring and aerospace launch. Existing electric-field measurement methods are always costly, bulky, and low in resolution. We presented a capacitive EF sensor with small size and high resolution. The presented sensor is based on piezoelectric effect, and a micro cantilever structure is realized using microfabrication. Simulation and experimental results indicate that the presented sensor has a resolution of about 45 V/m and a measurable magnitude of over 1.5 MV/m. Owing to the characteristics of small size, low cost and low power consumption, the EF sensor is suitable for applications of large-scale sensing arrays.

Vulnerability assessment of power system based on Theil entropy of branch potential energy

To achieve the safe operation of power system and prevent cascading failures, a vulnerability assessment method of power system is proposed based on the state correlation of branch fault. Firstly, a weighted correlation network based on the branch potential energy is established by using the state transition relationship of the two-level cascading failures. Secondly, the vulnerability assessment model of power system is constructed by applying the Theil entropy theory into the weighted correlation network. Finally, the simulation results on IEEE_39 bus system and an actual power system show that the model can not only evaluate the vulnerability of power system effectively but also find the vulnerable branches in power network more accurately.

Research on interruptible load supply curve simulation based on industrial classification

Recently, with the adjustment of industrial structure, peak power load is getting sharper in China. Interruptible Load Management (ILM) enables power system to gain deterministic access to Interruptible Load (IL) provided by power consumers, which can use a more economical way to ensure real-time balance and safe operation of power system. This paper proposes a simulation method of interruptible load supply curve of lager industrial power consumers based on industrial classification, which can predict capacity of interruptible load that lager industrial power consumers are willing to provide under different interruptible load prices. An example towards a China’s eastern province shows that, if government wants to acquire interruptible load capacity of 10 million kW, the interruptible price should be priced higher than 0.7 RMB/kWh.

A Single-Terminal Fault Location Method for HVDC Transmission Lines Based on a Hybrid Deep Network

High voltage direct current (HVDC) transmission systems play an increasingly important role in long-distance power transmission. Realizing accurate and timely fault location of transmission lines is extremely important for the safe operation of power systems. With the development of modern data acquisition and deep learning technology, deep learning methods have the feasibility of engineering application in fault location. The traditional single-terminal traveling wave method is used for fault location in HVDC systems. However, many challenges exist when a high impedance fault occurs including high sampling frequency dependence and difficulty to determine wave velocity and identify wave heads. In order to resolve these problems, this work proposed a deep hybrid convolutional neural network (CNN) and long short-term memory (LSTM) network model for single-terminal fault location of an HVDC system containing mixed cables and overhead line segments. Simultaneously, a variational mode decomposition–Teager energy operator is used in feature engineering to improve the effect of model training. 2D-CNN was employed as a classifier to identify fault segments, and LSTM as a regressor integrated the fault segment information of the classifier to achieve precise fault location. The experimental results demonstrate that the proposed method has high accuracy of fault location, with the effects of fault types, noise, sampling frequency, and different HVDC topologies in consideration.

Development and Realization of a Computer Monitoring System Based on a Power Network

Background: To solve an electric power enterprise for a safe operation of power systems. Methods: A data acquisition technology based on multi-thread mechanism of data collection technology is proposed; its application may provide a data acquisition rate of 1832/min and an update cycle of approximately 30 s to ensure efficient and reliable performance on a large-scale electric power realtime data acquisition. Results: A power state index diagnosis algorithm is designed. Conclusion: The on-line real-time diagnosis of the current operating state for the power system is realized, and its importance is given.

Fault Diagnosis in Gas Insulated Switchgear Based on Genetic Algorithm and Density- Based Spatial Clustering of Applications With Noise

As a kind of widely used switchgear in power system, the reliability of gas insulated switchgear (GIS) is very important for the safe operation of power systems. However, there is a lack of research on intelligent detection technology of mechanical state of GIS at present. A new method is urgently needed to improve the operability, effectiveness, and accuracy of fault detection in GIS. Aiming at the abnormal vibration signals generated by GIS faults, this article presents a fault diagnosis method (GA-DBSCAN) consisting of a feature selection method based on genetic algorithm (GA) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and a fault diagnosis method based on DBSCAN. First, this article analyzes the incentive force of GIS and discusses the characteristic frequency of response signal combining with the non-linear characteristics of a GIS system. Second, GA and DBSCAN are used to screen features for dimension reduction and get the optimized feature space, and DBSCAN-based classification is used to classify faults. Finally, optimized feature space is verified to be superior to the original feature space by typical classification method; the superiority and reliability of DBSCAN-based classification method under optimized feature space is verified by comparing with other classification methods. The proposed GA-DBSCAN approach can substantially increase the performance of the fault diagnosis method, which indicates that the method promotes development of intelligent detection technology of mechanical state in GIS.

Research on the application of target detection based on deep learning technology in power grid operation inspection

-External damage to power facilities caused by crane, excavator and other construction operations increases year by year, which will seriously threaten the safe operation of power system. It is an important measure to ensure the safe and reliable operation of power system to implement intelligent monitoring and early warning of power external breakdown through video and other non-contact observation means. The video data of power mainly comes from the fixed monitoring of helicopters, uavs and transformation poles and towers, which is characterized by large amount of data, complex scenes and serious environmental interference. The traditional target detection method usually selects the candidate area first, and then makes judgment based on the characteristics of human construction. The detection speed is slow and the accuracy is low, which makes it impossible to monitor the video data in real time, so as to make timely and accurate early warning and intervention fbr external damage. The target detection method based on deep learning optimizes or even eliminates the selection of candidate regions, which greatly speeds up the detection speed. By learning a lot of target samples through the deep neural network, the characteristics of high robustness are gradually fitted to make the target judgment more accurate. There are three key problems in introducing the target detection method based on deep learning into the power video detection: Firstly, the target detection method based on deep learning has a large amount of calculation and many parameters. In order to realize in-place operation on terminals with limited computing and storage capacity, it is necessary to find a practical method to simplify the network and reduce the amount of operational data in the detection process, which is the key to realize in-place operation and terminal operation of deep neural network. Secondly, for specific application scenarios, the effect of different target detection algorithms varies greatly, and there is a strong particularity of power video. Finding an effective target detection method is the key to improve the detection speed and accuracy. Finally, with the continuous development of deep learning, the structure of deep neural network changes with each passing day, and each has its own characteristics, which network structure is used as the feature extraction layer of target detection algorithm is the focus of research.

Online frequency security assessment based on an analytical model considering limiting module

Due to the increasing implementation of high voltage direct current (HVDC) and the integration of renewable resources, the power system frequency stability problems are drawing more attention in recent years. It has become more necessary to carry out online frequency security assessments to ensure the safe operation of power systems. Considering the low time-efficiency of simulation-based methods, analytical models, such as the frequency nadir prediction(FNP) model, are more suitable for online assessment which requires calculating the worst frequency deviation under contingencies. Based on the FNP model, the FNP-L model for online frequency security assessment is proposed in this paper. The proposed model implements security assessment by calculating and checking the frequency features, including the nadir time and frequency, followed by contingencies. The effect of the governor including the nonlinear constraints is approximated into polynomial functions so that results are given by solving multiple polynomial equations. Case studies are carried out over the New-England 39-bus system and a regional power grid, which show that the proposed model could achieve both high speed and high accuracy, and can therefore be applied in online security assessment.

Power System Load Frequency Active Disturbance Rejection Control via Reinforcement Learning-Based Memetic Particle Swarm Optimization

Load frequency control (LFC) is necessary to guarantee the safe operation of power systems. Aiming at the frequency and power stability problems caused by load disturbances in interconnected power systems, active disturbance rejection control (ADRC) was designed. There are eight parameters that need to be adjusted for an ADRC, which are challenging to adjust manually, thus limiting the development of this approach in industrial applications. Regardless of the theory or application, there is still no unified and efficient parameter optimization method. The traditional particle swarm optimization (PSO) algorithm suffers from premature convergence and a high computational cost. Therefore, in this paper, we utilize an improved PSO algorithm, a reinforcement-learning-based memetic particle swarm optimization (RLMPSO), for the parameter tuning of ADRC to obtain better control performance for the controlled system. Finally, to highlight the advantages of the proposed RLMPSO-ADRC method and to prove its superiority, the results were compared with other control algorithms in both a traditional non-reheat two-area thermal power system and a non-linear power system with a governor dead band (GDB) and a generation rate constraint (GRC). Moreover, the robustness of the proposed method was tested by simulations with parameter perturbations and different working conditions. The simulation results showed that the proposed method can meet the demand for the frequency deviation to stabilize to 0 in LFC with higher performance, and it is worthy of popularization and application.

Transformer fault diagnosis based on Improved Particle Swarm Optimization to support Vector Machine

Power transformer fault diagnosis exerts a vital part in the safe operation of power system. The PSO-SVM based on transformer fault diagnosis still has some shortcomings, such as slow convergence speed and easy to fall into local optimization. This dissertation proposes a transformer diagnosis method based on Improve Particle Swarm Optimization to support Vector Machine (MPSO-SVM). Adding disturbance to Particle swarm optimization (PSO) to disturb the position of such precocious particles, so as to get rid of local optimum. The case analysis represents that the diagnostic accuracy of MPSO-SVM is higher than that of PSO-SVM and Generalized Regression Neural Network (GRNN), and MPSO-SVM can effectively promote the fault diagnosis performance of transformer.

Analysis of winding temperature field under dynamic variable load of oil-immersed transformer

The research on the temperature field of the transformer winding under dynamic variable load is of great significance for ensuring the safe operation of power systems. This paper takes an SSP-360000/500 transformer as the research object, establishes a 2-D simulation model, and uses the finite volume method to analyze the high voltage winding and low voltage winding temperature distribution of the transformer under dynamic variable load. The simulation calculation results have been fully verified by the experimental data to make the successful prediction of the overall temperature and hot spot temperature position of forced oil circulation transformers with a guided structure. The results show that the most significant temperature raise occurs at the secondary end of the winding. In the case of dynamic variable load. The temperature raise of the winding becomes larger as the load increases, but before the rated load is in the stable temperature rise range, it can run safely for a long time. However, during overload operation, the average temperature raise of the high voltage winding may exceed its limit, the insulation material is damaged.

Analysis of critical condition for dry and wet growth icing on insulators

Icing on the insulators of transmission lines threats the safe operation of power system. Most of the existing researches focus on the discharge and flashover characteristics of ice-covered insulators. There is a lack of research on the growing process of insulator icing. In this paper, the critical condition for the dry and wet growth icing on insulators is taken as the research object. The calculation formulas of the freezing time of droplets and the collision interval time between adjacent water droplets are derived, based on which, the parametric characterization of different insulator icing types is proposed. By deriving the expression of the local collision efficiency β3, the distribution of water film on the insulator surface is obtained, which is used to distinguish the dry and wet growth icing on insulators. It is found that the critical condition of dry and wet growth icing is the condition that make the area covered with water film decrease to zero. With the decrease of temperature and droplets’ size, the area covered with water film decreases from the periphery of the droplet collision region to the centre, which is consistent to the observation test result on Xuefeng Mountain.

Wind Power Ramp Event Forecasting Based on Feature Extraction and Deep Learning

In order to improve the accuracy of wind power ramp forecasting and reduce the threat of ramps to the safe operation of power systems, a wind power ramp event forecast model based on feature extraction and deep learning is proposed in this work. Firstly, the Optimized Swinging Door Algorithm (OpSDA) is introduced to detect wind power ramp events, and the extraction results of ramp features, such as the ramp rate, are obtained. Then, a ramp forecast model based on a deep learning network is established. The historical wind power and its ramp features are used as the input of the forecast model, thereby strengthening the model’s learning for ramp features and preventing ramp features from being submerged in the complex wind power signal. A Convolutional Neural Network (CNN) is adopted to extract features from model inputs to obtain the coupling relationship between wind power and ramp features, and Long Short-Term Memory (LSTM) is utilized to learn the time-series relationship of the data. The forecast wind power is used as the output of the model, based on which the ramp forecast result is obtained after the ramp detection. Finally, the wind power data from the Elia website is used to verify the forecast performance of the proposed method for wind power ramp events.

Research on Computer-based Smart Grid Dispatching Control System and Power System Safe Operation Strategy

In today’s world, social and economic development is getting faster and faster, and people are increasingly demanding energy. Among many energy sources, electricity is one of the most widely used energy sources. People’s social life and economic development are coming. The more you can’t leave the power. In this situation, the safe operation of the power grid system is particularly important. The emergence and development of the smart grid dispatch control system based on computer has greatly improved the safety, stability and operational efficiency of the power grid system.