Power System Failure Research Articles

Detection Method for Cascading Failure of Power Systems Based on Epidemic Model

Detection Method for Cascading Failure of Power Systems Based on Epidemic Model

Tracking moisture contents in the pollution layer on a composite insulator surface using hyperspectral imaging technology.

Electrical insulators used in transmission lines and outdoor substations are exposed to severe environmental pollution, which significantly increases the risk of power system failure, especially when the pollution layer is highly humid due to adverse weather conditions. The focus of this paper is to establish an effective method for assessing the moisture content (MC) in pollution layers as it serves as a crucial indicator for evaluating the risk of failure in insulators. Hyperspectral imaging (HSI) technology with a spectral range of 371.08-1037.89 nm was applied to determine significant changes in reflectance spectral characteristics in insulators during dynamic wetting and drying periods. Partial least squares regression (PLSR) models were utilized to evaluate the data presentation enhancement abilities of spectral transformation models and the data dimensionality reduction abilities of characteristic band selection methods. Furthermore, PLSR models were developed to calculate the MC along the pixel dimension to visually retrieve the dynamic wetting and drying processes of the pollution layer. The R-squared and root-mean-square error (RMSE) results in the cross-verification set and prediction set of the RE-RF(70%)-PLSR model with two characteristic bands with a wavelength of 543.28 nm and 848.01 nm were as follows: RCV2 = 0.9824, RMSECV = 0.0367, RP2 = 0.9818, RMSEP = 0.0369, respectively. This research contributes towards the visualization retrieval of the MC and offers an important technique for analyzing flashover evolution, optimizing insulator design, and preparing coating materials for insulators.

A Review on Simulation Models of Cascading Failures in Power Systems

A Review on Simulation Models of Cascading Failures in Power Systems

Search strategy and line association analysis of cascading failure accident chain in new energy power systems

As the penetration rate of new energy in the power system gradually increases and the complexity of cascading faults increases, it is of great significance for the power system to comprehensively explore the chain of cascading faults in the new energy power system and quickly determine the closely related lines in the cascading faults. In response to the lack of consideration in existing research of the changes in the importance of transmission lines after the introduction of new energy, this paper proposes a cascading failure prediction index that integrates the importance and operational status of transmission lines in new energy power systems and applies it to the search for cascading failures in new energy power systems. First, the development characteristics of cascading faults were analyzed, and the main factors influencing cascading faults were identified: the importance of the transmission line and operating status of the new energy power system. Based on these factors, a prediction index for cascading faults was established, and the accident chain was searched using this index. Then, the FP-growth algorithm was used to analyze the lines in the fault chain concentration, and based on the analysis results, the correlation relationship suitable for the cascading failure lines in the new energy power system was determined. Finally, a simulation was conducted on an IEEE 10 machine 39 node system containing new energy wind turbines, and the results verified the effectiveness of the proposed indicators and strategies.

Cyber–physical anomaly detection for inverter-based microgrid using autoencoder neural network

Contemporary cyber–physical systems have evolved into highly autonomous and distributed entities, enabled by cutting-edge control frameworks and advanced communication networks. This transformation has empowered these systems to efficiently accommodate inverter-based resources, harnessing the grid’s dynamic potential. However, the increased integration of cyber and communication infrastructure has exposed these systems to vulnerabilities stemming from both cyber and physical disturbances. Power system errors in the physical layer of microgrids and malicious attacks on the communication layer pose substantial threats to grid stability and load supply. In response to these challenges, this paper presents an innovative unsupervised learning method for cyber–physical anomaly identification. Leveraging an autoencoder-based neural network, this approach addresses the intricate interplay between the physical and cyber aspects of power system failures and false data injection within the communication network. The method’s efficacy is evaluated on an islanded inverter-based microgrid, representing a realistic and vulnerable testbed. The autoencoder error is introduced as a key performance metric, providing a robust and practical measure of the proposed detection technique’s effectiveness. Simulation results demonstrate the promising utility of this method, positioning it as a valuable tool for enhancing the resilience of contemporary cyber–physical systems against cyber–physical anomalies.

Utilization of Stockwell Transform and Random Forest Algorithm for Efficient Detection and Classification of Power Quality Disturbances

Power quality disturbances (PQDs) can lead to significant operational and financial losses in power systems. Accurate detection and classification of PQDs are essential for maintaining power quality and preventing power system failures. This research article introduces an innovative approach for the precise detection and classification of single- and multiple-state power quality disturbances (PQDs) using the Stockwell transform (ST) and a random forest classifier. To create realistic PQD signals, seventeen distinct classes are generated in accordance with IEEE Standard 1159, employing mathematical equations implemented in MATLAB software. The ST is employed to extract relevant features from the PQD signals, which are subsequently utilized as input for the random forest classifier. The classifier employs bootstrapping sampling to generate multiple training sets from the original dataset. Each training set is used to construct a decision tree by recursively partitioning the data based on significant features. To mitigate overfitting and enhance robustness, a random subset of features is selected at each node of the decision tree, thereby reducing tree correlation. The performance of the random forest classifier is compared with other widely utilized machine learning classifiers. The results exhibit the efficacy of the proposed approach in accurately detecting and classifying PQ events, highlighting its superiority over alternative methods.

Renewable energy time series regulation strategy considering grid flexible load and N-1 faults

As the proportion of renewable energy in electricity generation continues to increase, the uncertainty of renewable energy has brought significant challenges to power system security. Traditional renewable energy dispatching methods have less research on power system failure risk analysis due to economic considerations. In this paper, a renewable energy time series regulation strategy considering N-1 faults is proposed to improve the power system economy and security. In this strategy, by researching flexible load aggregation models, the time-series regulation model of aggregation loads is determined. Then the network N-1 security analysis divided into generator detection and branch detection is conducted. Determining possible N-1 faults of the system, the scheme minimizes the operating cost and grid risk. Adopting the methods above, the most desired renewable energy time-series regulation results are obtained. The results have shown that after adopting the proposed strategy in the improved system, the solar and wind curtailment volume is reduced by 38.10%, and the operating cost is reduced by 24.60%. In a daily cycle, the maximum power of all branch power overruns at the most risky moment is reduced by 20.36%, and the sum of the total power of all branch power overruns is reduced by 40.30%.

Improving fault tolerance in diagnosing power system failures with optimal hierarchical extreme learning machine

Improving fault tolerance in diagnosing power system failures with optimal hierarchical extreme learning machine

Data-driven approach to fuzzy inference system tuning for static security assessment of multi-area power systems with renewable energy

Energy market deregulation and increased integration of intermittent renewable energy resources (RE) have pushed modern power systems to operate close to their security limits, which if violated could be catastrophic. To prevent power system failure and damage, a fast and accurate security assessment tool is required. In this paper, a data-driven fuzzy inference system tuned by hybrid genetic-simulated annealing (HGSA-FIS) is proposed for quick static security assessment (SSA) of a power system under a single component failure. An existing composite security index is modified to make a reliable SSA by distinguishing between severe and non-severe operating scenarios and contingencies. The prediction performance of HGSA-FIS was evaluated on an RTS-GMLC test system with operating scenarios varying with RE generation, load demand, and tie-line flows. The findings demonstrate the robust performance of HGSA-FIS in making static security assessments under varying operating scenarios, exceeding ANFIS in most and ANN in a few scenarios.

The impact of electric vehicle charging infrastructure on the energy demand of a city

With the increased market penetration of electric vehicles (EVs), energy consumption is growing at a quicker rate. Electric vehicles are slowly but surely gaining a foothold in India’s rapidly expanding market. With more EVs on the road, there will be more demand for charging and more energy use in some places, which can put a lot of stress on the power grid. In the absence of sufficient planning, there is a risk of power system interruption and failure. Therefore, the need for energy in cities will shift in light of the advent of EVs in the next decade or two. Accordingly, Surat has been chosen as the research region for the planning of charging infrastructure in the current study. After reviewing the literature, primary data was acquired to estimate citizen travel demands. Vehicle registration statistics were compiled from the Regional Transport Office (RTO) for the purpose of EV forecasting. After collecting data, the EV population and demand for public charging stations by 2025 and 2030 were calculated. This study aims to meet the public charging demand in the city of Surat by analyzing the electric vehicle growth rate and vehicle forecast. This anticipated data was then utilized to compute the energy demand consumption for Surat city by calculating vehicle kilometers traveled. The demand for public charging comes out to be 42.4 MWh/day in 2025 and 358.05 MWh/day in 2030, respectively. The study’s objective is to determine energy demand per region in order to better understand grid planning due to the imbalance in electricity demand.

Transfer Learning for Renewable Energy Systems: A Survey

Currently, numerous machine learning (ML) techniques are being applied in the field of renewable energy (RE). These techniques may not perform well if they do not have enough training data. Additionally, the main assumption in most of the ML algorithms is that the training and testing data are from the same feature space and have similar distributions. However, in many practical applications, this assumption is false. Recently, transfer learning (TL) has been introduced as a promising machine-learning framework to mitigate these issues by preparing extra-domain data so that knowledge may be transferred across domains. This learning technique improves performance and avoids the resource expensive collection and labeling of domain-centric datasets; furthermore, it saves computing resources that are needed for re-training new ML models from scratch. Lately, TL has drawn the attention of researchers in the field of RE in terms of forecasting and fault diagnosis tasks. Owing to the rapid progress of this technique, a comprehensive survey of the related advances in RE is needed to show the critical issues that have been solved and the challenges that remain unsolved. To the best of our knowledge, few or no comprehensive surveys have reviewed the applications of TL in the RE field, especially those pertaining to forecasting solar and wind power, load forecasting, and predicting failures in power systems. This survey fills this gap in RE classification and forecasting problems, and helps researchers and practitioners better understand the state of the art technology in the field while identifying areas for more focused study. In addition, this survey identifies the main issues and challenges of using TL for REs, and concludes with a discussion of future perspectives.

短時間フリッカ測定機能を備えたバンク逆潮流事故記録装置の配備とその計測事例

With the increase in the amount of interconnection of renewable energy, a transformer reverse power flow has occurred. In the past, transformer reverse power flow was restricted, but with the revision of the law in May 2013, it has permitted to operate the system on the premise of transformer reverse power flow by installing the necessary protection device. Along with this, the bank reverse power flow fault recorder is installed at the distribution substation for power system failure recording and analyzing at the time of transformer reverse power flow. On the other hand, the occurrence of voltage flicker phenomenon due to renewable energy has become a problem in recent years. This voltage flicker has the property of changing rapidly, and it is difficult to measure with the conventional ΔV10 flicker meter. Therefore, we implemented a short-time flicker measurement function that can measure voltage flicker with rapidly changing in the bank reverse power flow fault recorder. The outline and measurement examples are introduced.

Enhanced solar systems efficiency and reduce energy waste by using IoT devices

The best choice for producing power in an ecologically friendly way is renewable energy. In recent years, environmental preservation has grown to be a serious issue. Solar energy production is a well-liked renewable energy source with a cheap cost and minimal environmental impact. A massive PV system is complex for human workers to handle. On the other hand, the IoT makes it possible to continuously monitor and analyse data, giving consumers a clear real-time picture of their system's performance and identifying solar power system failure or problematic areas. A web interface-based prototype device is built as part of this research that can be integrated into any level system that can monitor an individual panel's voltage and current, alerting if any failure occurs. The results of this research show an alarming situation for one of the PV power generation farms connected in monitoring system. The efficiency of farm 2 is observed poor because few panels are found defected and not producing power.

Power system fault detection and notification with Wi-Fi

In this study, a prototype model was designed and implemented for power system failure. A mobile application was developed for notification of faults that may occur in power systems. The line current data was transmitted with Wi-Fi via module on mobile application and so negative impacts of the production sector was eliminated and the ordinary flow of life by minimizing the duration of power outages due to technical failures were prevented. By measuring the current in the prototype, it was possible to determine a short circuit situation, and to warn the operator with the mobile application on the mobile phone, and then to quickly dispatch them to the defective area. Protection in the power system is carried out more healthily with instant data flow with the mobile application

Dynamic and quantitative risk assessment of Cruise ship pod propulsion system failure: An integrated Type-2 Fuzzy-Bayesian approach

Azipod propulsion has revolutionized the future of ship propulsion and maneuvering operations with over 20 years of experience, eventually improving ship safety, reliability, energy efficiency, and overall vessel performance. However, it consists of technically complex machinery systems; its breakdown can result in many potential failures with severe repercussions. In this work, an accident-based dynamic risk assessment study of the Cruise ship pod propulsion system has been carried out. It is based on the real-ship accident of the Norwegian Star, which resulted in the loss of ship propulsion. When historical accident statistics are limited, traditional methods fail to address the uncertainty, dynamic environment, and quantitative risk assessment problems. The goals include avoiding worst-case scenarios by illustrating the progression of dynamic change in risk and establishing the Bayesian network framework for dealing with uncertainty more effectively. Interval type-2 Fuzzy expert system is integrated with the dynamic Bayesian network and bow-tie model for quantitative risk assessment, which laid the groundwork for the subsequent hazard identification and quantitative risk assessment. This accident network summarizes how pod machinery operational failures can cause accidents. The estimated sensitive machinery nodes such as Main motor failure, propulsion system, and power system failure are the leading causes of pod propulsion system failures on board cruise vessels. Risk analysts and decision-makers in the marine and offshore engineering industry can use the suggested model as a decision support tool to considerably reduce and eliminate potential failures, improving the ship's safety and system reliability.

Fault Detection and Diagnosis in Spacecraft Electrical Power Systems

The ability to accurately identify and isolate failures in the electrical power system (EPS) is critical to ensure the reliability of spacecraft. This paper proposes a novel solution to the problem of fault detection and diagnosis in direct current (DC) electric power systems for spacecraft. Autonomous operation becomes essential during deep space missions that lack the ability to monitor and control the spacecraft from ground locations. The current state of EPS fault supervision is insufficient to guarantee highly reliable operation. To solve this issue, a combination of model-based and knowledge-based techniques are used in a hierarchical framework to improve the diagnostic performance of the system. Noise, disturbances, and modeling errors are considered in the design of the fault detection system. Practical considerations related to spacecraft flight hardware and software are accounted for in the system design for flight applications. To assess the functionality of the design, a wide array of failures are simulated in a series of experiments. The experiments showed that the technique improved the capability of the autonomous system by increasing the number of fault types diagnosed. The significance of this study is to provide a framework capable of advanced diagnostics of an EPS with little to no interaction from human operators.

Intelligent identification algorithm and key point detection of abnormal vibration of transmission tower based on machine learning

Abstract In some special geological areas, the inclination and displacement of transmission line towers are relatively common, which should be analyzed from various factors. Transmission towers are the support structure for overhead transmission lines and play a pivotal role in the safe operation of the power grid. Transmission lines are widely distributed. Transmission poles and towers are generally built in areas with poor geological conditions such as goaf, river bed and slope. Under severe weather conditions, the force on the tower may be affected to a certain extent, causing the tower to tilt, deform, or even collapse, and thus causing a wide range of power system failures, which have a great impact on people’s production and life. Based on this objective problem, this paper has mainly studied the intelligent recognition methods and key technologies based on machine learning. In the experimental study of support vector machine (SVM) model based on machine learning, the average training time of genetic algorithm support vector regression (GA-SVR) was the longest, reaching 1.462 s. The average training duration of double chain quantum genetic algorithm-least squares support vector regression (DCQGA-LSSVR) was the shortest, with 0.156 s. The average pose error of double chain quantum genetic algorithm-support vector regression (DCQGA-SVR) was the smallest, only 0.136, while the average attitude error of genetic algorithm-least squares support vector regression (GA-LSSVR) was the highest, reaching 0.45. Therefore, it is of the great significance to analyze abnormal vibration of the transmission towers based on machine learning method.

Coordination control method to block cascading failure of a renewable generation power system under line dynamic security

The large application of renewable energy generation (REG) has increased the risk of cascading failures in the power system. At the same time REG also provides the possibility of new approaches for the suppression of such failures. However, the capacity and position of the synchronous generator (SG) involved in regulation limit the power regulation speed (PRS) of REG to the overload line which is the main cause of cascading failures, while the PRS of SG is related to the position and shedding power. REG and SGs have difficulty in achieving effective cooperation under constraints of system power balance. Particularly, the dynamic variation of line flow during power regulation causes new problems for the accurate evaluation of line thermal safety under overload. Therefore, a new strategy for quantitatively coordinating shedding power and power regulation to block cascading failures in the dynamic security domain is proposed in this paper. The control capability and dynamic security domain of the overload line are modeled, and the coordination control method based on power regulation is then proposed to minimize shedding power. The algorithm for the optimal control scheme considers the constraints of load capacity, power source capacity and bus PRS. The correctness of the proposed method is verified using case studies.

Stochastic Security-Constrained Economic Dispatch of Load-Following and Contingency Reserves Ancillary Service Using a Grid-Connected Microgrid during Uncertainty

In the context of the growing penetration of renewable power sources in power systems causing probabilistic contingency conditions, a suitable economic dispatch model is decisively needed. There is a lack of research in the field of probabilistic mathematical formulation considering the uncertainties due to the stochastic nature of renewables and contingency occurrence, as it is a very complex problem to be solved. The most appropriate model is the stochastic security-constrained economic dispatch (SSCED) model for optimized economic dispatch decisions during uncertainty. However, because of its complexity, it is rarely employed. This paper attempts to solve the complex SSCED problem in the presence of the uncertainty of resources and probabilistic contingency conditions, which is a novel effort in this regard. The SSCED is carried out over multiple periods to provide the load-following or contingency reserves. In the proposed SSCED, the uncertainty problem is addressed by modeling the stochastic wind energy power source by using “probability transition scenarios”. The uncertainty caused by probabilistic contingency conditions in the dispatch schedule is approximated using a “state-specific transition matrix”. The frequency control reserves in contingency conditions are co-optimized with energy, and stochastic security-constrained economic dispatch is achieved. The efforts are put forward to suggest a new market model in the presence of the uncertainty of renewable energy availability. Case studies are examined to show the potential technical and financial advantages of the proposed SSCED through co-optimization. Grid-connected microgrid owners offer frequency control ancillary services by providing load-following ramping reserves in the normal state and contingency reserves in the state of contingency. The probabilistic contingencies considered are generator failure and an underloading condition. A modified “IEEE 30 bus system” is considered a grid-connected microgrid for testing the proposed SSCED. The results show that the greater the flexibility of the resources, the greater the technical and economic benefits. The increase in ramping flexibility of a wind source results in almost an 8.1% reduction in operational costs compared to the base case. The contingency condition analysis shows that the presence of ramping reserves in the system enhances the power system performance, avoiding the cascading effects that ultimately cause a power system failure.

Adaptive Network Response to Line Failures in Power Systems

Transmission line failures in power systems propagate and cascade nonlocally. In this work, we propose an adaptive control strategy that offers strong guarantees in both the mitigation and localization of line failures. Specifically, we leverage the properties of network <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bridge-block decomposition</i> and a frequency regulation method called the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unified control</i> . If the balancing areas over which the unified control operates coincide with the bridge-blocks of the network, the proposed strategy drives the postcontingency system to a steady state where the impact of initial line outages is localized within the areas where they occurred whenever possible, stopping the cascading process. When the initial line outages cannot be localized, the proposed control strategy provides a configurable design that progressively involves and coordinates more balancing areas. We compare the proposed control strategy with the classical automatic generation control (AGC) on the IEEE 118-bus and 2736-bus test networks. Simulation results show that our strategy greatly improves overall reliability in terms of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N-k$</tex-math></inline-formula> security standard, and localizes the impact of initial failures in the majority of the simulated contingencies. Moreover, the proposed framework incurs significantly less load loss, if any, compared to AGC, in all our case studies.