Stable Operation Of Power System Research Articles

Framework for Transmission Line Fault Detection in a Five Bus System Using Discrete Wavelet Transform

The transmission line is an essential part of the power system and since itis exposed to the harsh environmental conditions, it is susceptible to opencircuit and short circuit faults. As the occurrence of failures affect the qualityof the electric power supplied, identifying the fault and clearing it quickly iscritical for the reliable power system operation. Fault Diagnostics of a multibus system is crucial for Power system stable operation. In this paper, a five-bus system is taken up for the study and twelve different types of faults withvarying fault durations are applied in transmission lines at different locations.Then Discrete Wavelet Transform (DWT) is applied to the receiving endcurrent signals of the line and derived wavelet coefficients are used to identifythe type of fault. Simulation and extraction of wavelet coefficients are done in MATLAB Simulink and results are presented. The Model Developed of a fiveBus system for Fault Diagnostics and Simulation can be used as Frameworkfor Fault Classification and Localization. The derived Wavelet Transformproves to be appropriate for the Framework developed.

Utility-Scale Shared Energy Storage: Business models for utility-scale shared energy storage systems and customer participation

Due to climate change, supply scarcity, and society’s desire to expand access to electricity and improve energy-system resilience, there has been an increasing demand to invest in and use renewable energy sources (RESs) that are environmentally friendly, efficient, sustainable, and affordable. This has diversified and decentralized energy sources and increased their penetration. However, the variability and intermittency of RESs has introduced new challenges to power system operators, such as the requirement for high ramping rates when solar energy declines in the afternoon, known colloquially as the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">duck curve</i> , voltage and frequency fluctuations, and reduced overall system inertia. In response to these challenges, energy storage systems (ESSs) (devices such as batteries, energy management, and energy conditioning) have become crucial components to the reliable and stable operation of modern power systems. ESSs can tackle the aforementioned challenges to seamlessly integrate RESs into the power grid. They can help system operators to smooth the output power of RESs and provide grid services, such as voltage and frequency regulation, peak load shaving, phase balancing, energy arbitrage, expansion deferral, and so on.

Optimal Placement and Sizing of Distributed Generators Based on Multiobjective Particle Swarm Optimization

To solve the problems of environmental pollution and energy consumption, the development of renewable energy sources becomes the top priority of current energy transformation. Therefore, distributed power generation has received extensive attention from engineers and researchers. However, the output of distributed generation (DG) is generally random and intermittent, which will cause various degrees of impact on the safe and stable operation of power system when connected to different locations, different capacities, and different types of power grids. Thus, the impact of sizing, type, and location needs to be carefully considered when choosing the optimal DG connection scheme to ensure the overall operation safety, stability, reliability, and efficiency of power grid. This work proposes a distinctive objective function that comprehensively considers power loss, voltage profile, pollution emissions, and DG costs, which is then solved by the multiobjective particle swarm optimization (MOPSO). Finally, the effectiveness and feasibility of the proposed algorithm are verified based on the IEEE 33-bus and 69-bus distribution network.

Combination weighting-based method for access point optimization of offshore wind farm

With the fast development of offshore wind power, the fluctuation of its output has an increasingly significant impact on the secure and stable operation of power system. Aiming at the large-scale offshore wind farms (OWFs) integrated, a method for selecting optimal access point is proposed. The capability of wind power accommodation, voltage stability, transmission network vulnerability and construction cost are considered as evaluation indexes. Based on the theory of information entropy and fuzzy analytic hierarchy process, taking the minimum deviation of the comprehensive weight and maximum of the compressive weight value as the optimization goal, the combination weighting model is established to determine the index weight. Simulation result of Shandong Power grid of China demonstrate the effectiveness of the proposed method for selecting the access point of the OWF.

Laser deicing for high-voltage composite insulator by high-power mid-infrared fiber laser

Ice disaster has always been a threat to the stable operation of electrical power systems, and it is a challenge to melt ice online for high-voltage composite insulator. Here, we proposed a safe and efficient method to deice a high-voltage composite insulator using high-power mid-infrared (mid-IR) laser. The laser source is a Tm-doped fiber laser, delivering a continuous-wave (CW) output power of 75 W at 1940 nm. Due to the strong vibration absorption of H2O molecule at 1940 nm, the mid-IR laser could be absorbed efficiently by ice/water with an absorption coefficient as high as 135 cm − 1. Indoor deicing experiment demonstrated that an 8-mm-diameter ice pillar could be cut off within 28 s under an incident optical intensity of 25 W/cm2. Due to the small penetration depth of 148 μm, the residual water film could prevent the laser from damaging the composite insulator. Our research results show that high-power mid-IR Tm-doped fiber laser is a potential scheme for online deicing high-voltage composite insulator.

Identification of topology changes in power grids via reduced admittance matrix

Frequent changes in power grid topology bring risks to the stable operation of power systems. It is essential to identify changes in the power grid topology quickly and accurately. This paper presents a novel method named network reduction-based topology change identification (NR-TCI) algorithm to identify topology changes in multi-machine power systems. The proposed algorithm can quickly identify power grid topology changes using only phasor measurement unit (PMU) data sampled during the system’s transient process. The NR-TCI algorithm uses the network order reduction method to reduce the order of a bus admittance matrix and then uses PMU measurement data to estimate the reduced admittance matrix by least square method. Finally, the reduced admittance matrix is adopted to find topological information, and the Sherman–Morrison formula is utilized to identify the topology changes. The effectiveness of the proposed NR-TCI algorithm is verified with a case study of a 3 machine 9 bus system in Matlab. In addition, the influence of PMU sampling frequency on the effectiveness of the proposed algorithm is also studied.

A Short-Term Power Output Forecasting Based on Augmented Naïve Bayes Classifiers for High Wind Power Penetrations

Renewable-power-generating resources can provide unlimited clean energy and emit at most minute amounts of air pollutants and greenhouse gases, whereas fossil fuels are contributing to environmental pollution problems and climate change. The share of global power capacity comprising renewable-power-generating resources is increasing. However, due to the variability and uncertainty of wind resources, predicting the power output of these resources remains a key problem that must be resolved to establish stable power system operation and planning. In this study, we propose an ensemble prediction model for wind-power-generating resources based on augmented naïve Bayes classifiers. To select the principal component that affects the wind power outputs from among various meteorological factors, such as temperature, wind speed, and wind direction, prediction of wind-power-generating resources was performed using multiple linear regression (MLR) and a naïve Bayes classification model based on the selected meteorological factors. We proposed applying the analogue ensemble (AnEn) algorithm and the ensemble learning technique to predict the wind power. To validate this proposed hybrid prediction model, we analyzed empirical data from the wind farm of Jeju Island in South Korea and found that the proposed model has lower error than the single prediction models.

Comparison of the Deep Learning Performance for Short-Term Power Load Forecasting

Electricity demand forecasting enables the stable operation of electric power systems and reduces electric power consumption. Previous studies have predicted electricity demand through a correlation analysis between power consumption and weather data; however, this analysis does not consider the influence of various factors on power consumption, such as industrial activities, economic factors, power horizon, and resident living patterns of buildings. This study proposes an efficient power demand prediction using deep learning techniques for two industrial buildings with different power consumption patterns. The problems are presented by analyzing the correlation between the power consumption and weather data by season for industrial buildings with different power consumption patterns. Four models were analyzed using the most important factors for predicting power consumption and weather data (temperature, humidity, sunlight, solar radiation, total cloud cover, wind speed, wind direction, humidity, and vapor pressure). The prediction horizon for power consumption forecasting was kept at 24 h. The existing deep learning methods (DNN, RNN, CNN, and LSTM) cannot accurately predict power consumption when it increases or decreases rapidly. Hence, a method to reduce this prediction error is proposed. DNN, RNN, and LSTM were superior when using two-year electricity consumption rather than one-year electricity consumption and weather data.

A Critical Review of Data-Driven Transient Stability Assessment of Power Systems: Principles, Prospects and Challenges

Transient stability assessment (TSA) has always been a fundamental means for ensuring the secure and stable operation of power systems. Due to the integration of new elements such as power electronics, electric vehicles and renewable power generations, dynamic characteristics of power systems are becoming more and more complex, which makes TSA an increasingly urgent task. Since traditional time-domain simulations and direct method cannot meet the actual operation requirements of power systems, data-driven TSA has attracted growing attention from both academia and industry. This paper makes a comprehensive review from the following four aspects: feature extraction and selection, model construction, online learning and rule extraction; and then, summarizes the challenges and prospects for future research; finally, draws the conclusions of this review. This review will be beneficial for relevant researchers to better understand the research status, key technologies, and existing challenges in the field.

Corrosion prediction of galvanized steel electrode in soil using deep learning‐based model

AbstractAccurate prediction for the corrosion status of grounding electrodes is critical for the safe and stable operation of power systems. However, the corrosion rate of grounding electrodes changes dramatically with the soil environmental parameters, making it hard to be precisely predicted. To address this problem, a deep learning method was proposed to numerically predict the corrosion rate of a galvanized carbon steel grounding electrode in this paper. The long‐short term memory method is selected as the modeling algorithm, and also chosen as the hidden layer for the Recurrent Neural Network, while the soil environmental parameters are used as input features. The predicted results match well with the experimental data when evaluated using different soil parameters, such as soil moisture, chloride (Cl–) concentrations, and sulfate (SO42–) concentrations. The threshold corrosion rate related to each parameter is obtained to estimate the corrosion rate with more accuracy.

Mobile Emergency Power Source Configuration Scheme considering Dynamic Characteristics of a Transportation Network

Recently, with the large scale of power grids and the increase in frequency of extreme weather, the safe and stable operation of power systems is facing great challenges. Therefore, mobile emergency power source (MEPS) are a promising and feasible way to deal with extreme weather and reduce economic losses. However, the current urban power grid and transportation network are closely coupled, and the congested traffic hinders the rapid configuration of MEPSs. Therefore, this paper proposes an MEPS configuration scheme considering real-time traffic conditions. Firstly, the dynamic road traffic index (DRTI) is defined, which can fully describe the dynamic characteristics of traffic. The wavelet neural network (WNN) is used to predict the traffic flow. Then, combined with the knowledge of graph theory, an A-Star algorithm (AS) is used to determine the optimal path. Secondly, the optimal installation location of MEPSs is determined by forward–backward sweep method in distribution network. Finally, the feasibility, accuracy, and time cost of the proposed method are verified by numerical simulations, which can meet the requirements of online application.

Integrating a softened multi-interval loss function into neural networks for wind power prediction

High-quality wind power interval prediction is an effective means to ensure the economic and stable operation of the electric power system. Comparing with single-interval prediction, multi-interval prediction is conducive to providing more uncertainty information to decision-makers for risk quantification. Existing multi-interval prediction methods require several independent forecasting models to generate prediction intervals (PIs) at different prediction interval nominal confidence (PINC) levels, which would lead to long training time and cross-bound phenomenon. This paper constructs a novel framework to simultaneously generate multiple PIs for wind power by integrating a proposed softened multi-interval loss function into neural networks. Firstly, the effectiveness of the proposed loss function is verified via simulation data, and the suitable training method and softening factor range are found. Then, five widely used neural networks are employed with both single-interval and multi-interval loss functions to carry out multiple interval prediction on two real-world wind power datasets. The results indicate that the proposed loss function can effectively avoid the cross-bound phenomenon and decrease the mean prediction interval width of PIs. In addition, the echo state network (ESN) with the proposed loss function exhibits the best forecasting performance among the investigated models for both point prediction and interval prediction.

Mechanism analysis and mitigation strategies for oscillatory instability of synchronous generators affected by voltage source converters

The electric power systems are experiencing a transformation to be characterized by power electronic devices, for example, voltage source converters (VSCs). However, the large-capacity VSC will interact with the synchronous generators (SGs) and could lead to low-frequency oscillation (LFO). It becomes a hurdle for the secure and stable operation of modern electric power systems. Therefore, this paper analyses the oscillatory instability mechanism of the SG with the penetration of grid-forming (GFM) and grid-following (GFL) VSC. The distinctive transfer function model of the SG-VSC system is first developed. Then, through parameter sensitivity and transfer function analysis, the impact of three types of VSC is investigated, encompassing GFM-VSC and GFL-VSCs with two conventional reactive control schemes. It proves that the voltage control of the VSC can be detrimental to the damping torque of the SG. According to the theoretical analysis, a supplementary compensator is proposed to suppress the LFO induced by the VSC. Finally, case studies in a two-area test system validate the analytical results and robustness of the proposed enhancement method.

Detection of Abnormal Vibration Dampers on Transmission Lines in UAV Remote Sensing Images with PMA-YOLO

The accurate detection and timely replacement of abnormal vibration dampers on transmission lines are critical for the safe and stable operation of power systems. Recently, unmanned aerial vehicles (UAVs) have become widely used to inspect transmission lines. In this paper, we constructed a data set of abnormal vibration dampers (DAVDs) on transmission lines in images obtained by UAVs. There are four types of vibration dampers in this data set, and each vibration damper may be rusty, defective, or normal. The challenges in the detection of abnormal vibration dampers on transmission lines in the images captured by UAVs were as following: the images had a high resolution as well as the objects of vibration dampers were relatively small and sparsely distributed, and the backgrounds of cross stage partial networks of the images were complex due to the fact that the transmission lines were erected in a variety of outdoor environments. Existing methods of ground-based object detection significantly reduced the accuracy when dealing with complex backgrounds and small objects of abnormal vibration dampers detection. To address these issues, we proposed an end-to-end parallel mixed attention You Only Look Once (PMA-YOLO) network to improve the detection performance for abnormal vibration dampers. The parallel mixed attention (PMA) module was introduced and integrated into the YOLOv4 network. This module combines a channel attention block and a spatial attention block, and the convolution results of the input feature maps in parallel, allowing the network to pay more attention to critical regions of abnormal vibration dampers in complex background images. Meanwhile, in view of the problem that abnormal vibration dampers are prone to missing detections, we analyzed the scale and ratio of the ground truth boxes and used the K-means algorithm to re-cluster new anchors for abnormal vibration dampers in images. In addition, we introduced a multi-stage transfer learning strategy to improve the efficiency of the original training method and prevent overfitting by the network. The experimental results showed that the mAP@0.5 for PMA-YOLO in the detection of abnormal vibration dampers reached 93.8% on the test set of DAVD, 3.5% higher than that of YOLOv4. When the multi-stage transfer learning strategy was used, the mAP@0.5 was improved by a further 0.2%.

Interval forecasting system for electricity load based on data pre-processing strategy and multi-objective optimization algorithm

Electricity load prediction is of great significance to the development of the power market and stable operation of power systems. In recent years, scholars in this field have only considered point forecasting, which ignores the inevitable prediction bias and uncertain information. To fill this gap, this study proposes an interval prediction system consisting of an advanced data reconstruction strategy, a multi-objective optimization algorithm based on the theory of non-negative constraints, and an outstanding interval forecasting model fitted by the predicted fluctuation characteristics. Moreover, this study theoretically proves that the weight assigned by the optimization algorithm is the Pareto optimal solution. Empirical data with 30 min intervals from Queensland, Australia are selected as samples for research. The results not only demonstrate the superiority of the proposed model but also provide effective technical support for power grid operation and dispatch by quantifying changes in the prediction results caused by uncertainties.

Short-term wind speed forecasting using deep reinforcement learning with improved multiple error correction approach

The safe and stable operation of wind power systems requires the support of wind speed prediction. To ensure the controllability and stability of smart grid dispatching, a novel hybrid model consisting of data-adaptive decomposition, reinforcement learning ensemble, and improved error correction is established for short-term wind speed forecasting. In decomposition module, empirical wavelet transform algorithm is used to adaptively disassemble and reconstruct the wind speed series. In ensemble module, Q-learning is utilized to integrate gated recurrent unit, bidirectional long short-term memory, and deep belief network. In error correction module, wavelet packet decomposition and outlier-robust extreme learning machine are combined to developing predictable components. An appropriate correction shrinkage rate is used to obtain the best correction effect. Ljung-Box Q-Test is utilized to judge the termination of the error correction iteration. Four real data are utilized to validate model performance in the case study. Experimental results show that: (a) The proposed hybrid model can accurately capture the changes of wind data. Taking 1-step prediction results as an example, the mean absolute errors for site #1, #2, #3, and #4 are 0.0829 m/s, 0.0661 m/s, 0.0906 m/s, and 0.0803 m/s, respectively; (b) Compared with several state-of-the-art models, the proposed model has the best prediction performance.

Risk assessment of power system oscillation instability based on geomagnetic storm events in the 24th solar cycle

When studying the harm of geomagnetic storms to the power system, the focus is often placed on the influence of transformers reactive power loss (GIC-Q) caused by geomagnetic induced current (GIC) on voltage fluctuation. GIC-Q, as a reactive load, can cause power flow changes, and the small disturbance stability is affected by the power flow distribution before disturbance, so GIC-Q connects geomagnetic storms with small disturbance stability. In this study, the probability distribution function of induced geoelectric fields is established, and the sample data is obtained by sampling. Combined with the GIC benchmark network model, multiple sets of GIC-Q in each substation are calculated and added to the system one by one to study the influence of geomagnetic storms on the oscillation characteristic parameters and establish the risk index of small disturbance instability. The result shows that the risk index value of oscillation instability in the occurrence of geomagnetic storms increases both in the AC system and the AC/DC hybrid system, but the increased range of risk value of local oscillation instability decreases with the addition of DC systems. The research results can provide a reference for evaluating the influence of geomagnetic storms on the stable operation of power systems.

Study of magnetic resonance coronary angiography based on functional magnetic resonance imaging

China's economic development depends on the stable operation of power system. Whether in industry or agriculture, electric power resources have covered all aspects of people's production and life. In particular, China's economy is developing at a high speed, and the demand for power resources is increasing day by day. Based on this actual demand, this paper takes 35kV Transmission Line as an example to analyze its safe operation and maintenance in detail, aiming to provide reference scheme for China's power industry and promote the healthy and stable operation of China's transmission line.

Short-Term Load Forecasting Based on Deep Learning Bidirectional LSTM Neural Network

Accurate load forecasting guarantees the stable and economic operation of power systems. With the increasing integration of distributed generations and electrical vehicles, the variability and randomness characteristics of individual loads and the distributed generation has increased the complexity of power loads in power systems. Hence, accurate and robust load forecasting results are becoming increasingly important in modern power systems. The paper presents a multi-layer stacked bidirectional long short-term memory (LSTM)-based short-term load forecasting framework; the method includes neural network architecture, model training, and bootstrapping. In the proposed method, reverse computing is combined with forward computing, and a feedback calculation mechanism is designed to solve the coupling of before and after time-series information of the power load. In order to improve the convergence of the algorithm, deep learning training is introduced to mine the correlation between historical loads, and the multi-layer stacked style of the network is established to manage the power load information. Finally, actual data are applied to test the proposed method, and a comparison of the results of the proposed method with different methods shows that the proposed method can extract dynamic features from the data as well as make accurate predictions, and the availability of the proposed method is verified with real operational data.

재생에너지 시나리오 모델링 기반 확률론적 정태 안정도 해석

As the proportion of large-scale variable power generation increases, it is necessary to secure the grid reliability and flexibility of the power system. In addition, it is necessary to analyze the probabilistic security analysis considering the output variability of renewable energy. In this paper, scenarios reflecting the characteristics of renewable energy were created through the renewable energy output scenario modeling based on Monte Carlo Simulation. The scenario is used as input data for calculation of steady state and AC contingency solution assumed power flow, and probabilistic security analysis of branch flow and bus voltage was performed on the practical power system. The proposed algorithm can be used for decision making to establish a stable power system operation and plan according to the large-scale variability power system connection by analyzing the weak branches and buses according to the fluctuations in the output of renewable energy.