Safe Operation Of System Research Articles

Feasibility verification of green-power-supplied industrial parks considering peak shaving performances

Industrial parks configured with renewable energy have received extensive attention due to their importance in achieving carbon neutrality in China. However, national or regional policies also set up various constraints for such industrial parks to guarantee the safe and stable operation of the power system, among which the peak shaving ability of the industrial park is the top concern. This paper proposes a quantitative description for peak shaving ability and provides the feasibility verification method for the configuration scheme of renewable energy and energy storage in the industrial park. An actual industrial park scheme in Inner Mongolia is taken as an example, and detailed calculations and simulations are displayed to prove the effectiveness of the proposed method.

A hybrid model for deep learning short-term power load forecasting based on feature extraction statistics techniques

Accurate and reliable load forecasting can ensure the safety and economy of power system operation. To improve the accuracy of short-term power load forecasting, this paper adopts feature extraction combined with a variety of machine learning methods,(Empirical wavelet decomposition (EWT), Convolutional neural network (CNN) model, Recurrent neural network (RNN) model, Long short-term memory network (LSTM) model, Bayesian optimization (BOA) algorithm). A new hybrid forecasting model, the EWT-CNN-S-RNN + LSTM model is proposed to predict power consumption. In this method, the fixed mode of EWT decomposition is used to extract statistical features, and the LSTM/RNN model is selected according to the statistical features. This method uses the Bayesian optimization (BOA) algorithm to optimize the parameters to solve the model gradient explosion problem. Based on the electricity consumption data of 2007 (Australian Energy Market Operator) and a total of 336 power load data, a good load forecasting result is obtained, which can provide indirect support for power regulation and sustainable development of electricity.

Malfunction diagnosis of main station of power metering system using LSTM-ResNet with SMOTE method

The power metering system is an important part of the smart grid for data acquisition and analysis. The fault state of the main station directly affects the stable and safe operation of the power metering system. Hinged on the real-world data supplied by the monitoring platform of the Metrology Center of Guangdong Power Grid Co., Ltd., we present a novel malfunction diagnosis method for the main station of the power metering system. The proposed method utilizes the synthetic mi-nority over-sampling technique (SMOTE) and designs a combined model of long short-term memory (LSTM) network and ResNet. SMOTE solves the sample imbalance problem. Furthermore, the combined LSTM-ResNet model employs LSTM to extract the time-dependent signal feature and exploits ResNet to optimize data flow. Consequently, the proposed LSTM-ResNet model improves training efficiency and malfunction diagnosis accuracy. The proposed diagnosis mthod is verifird on the real-world data, which proves the proposed method’s surpass traditional methods. A specific analysis of results and the practical application of the proposed method is also elaborated.

Error theory study on EKF-based SOC and effective error estimation strategy for Li-ion batteries

Accurate state-of-charge (SOC) estimation is crucial for ensuring the safe and reliable operation of battery management systems (BMS). Among the various algorithms used for SOC estimation in real-vehicle BMS, the extended Kalman filter (EKF) algorithm holds significance due to its adherence to optimal estimation principles and its property of insensitivity to initial values. By studying the relationship between error sources and SOC estimation errors, it becomes possible to develop targeted measures for enhancing the accuracy of SOC estimation based on the EKF. From a probabilistic perspective, this paper derives theoretical equations that establish the connection between SOC estimation errors and various error sources, including measured voltage, measured current, open circuit voltage curve, capacity, ohmic internal resistance, and polarization resistance. Furthermore, the paper analyzes the relationship among multiple error sources in generating SOC estimation errors. Building upon the outcomes of this theoretical analysis, a joint SOC estimation method that combines the EKF with Ampere-hour counting (AHC) is employed to identify errors. In scenarios where simultaneous faults occur in the current and voltage sensors, they are identified based on the slope and Euclidean distance of the two SOC trajectories, respectively. Subsequently, sensor faults correction and SOC compensation are implemented by leveraging simplified equations involving capacity and SOC increments and measured voltage and SOC estimation errors. In addition to addressing sensor faults, the paper also considers battery model parameter errors. By incorporating customized current pulse profile and theoretical equations relating to error sources and SOC estimation errors, a comprehensive error estimation of model parameters is achieved, capable of handling single and multiple errors. The derived simplification bridges the gap between error sources and SOC estimation errors, offering a novel approach for parameter sensitivity analysis and a theoretical foundation for quantifying these errors. The experimental results demonstrate the effectiveness and rapidity of the proposed method for identifying and correcting sensor faults and model parameter errors.

Online viewpoint optimization method for high-accuracy intraoperative optical measurement in orthopedic surgeries: An active navigation approach

In robotic orthopedic surgeries, the optical tracking system (OTS) is an essential technology to obtain the high-accuracy relative pose between the patient and surgical instrument via positioning tools. Typically, the OTS viewpoint is manually placed by surgeons and remains stationary afterwards. However, due to the limited measurement volume (MV), adjustments made to either the patient’s body or surgical instruments may move positioning tools beyond the MV, potentially leading to measurement failure. To tackle this issue, this paper proposes an online viewpoint optimization method to adjust the OTS viewpoint actively. Considering the safety of the operation, the adjustment to the OTS is only implemented when necessary. In the presence of multiple moving positioning tools, the OTS’s real-time adjusting trajectory is optimized to ensure that the OTS consistently tracks all positioning tools, guaranteeing high-accuracy intraoperative navigation consistency. Tests and experimental results validate the OTS’s high dynamic accuracy. For all the experimental results, the mean Euclidean and angular distance errors are below 0.48 mm and 0.30˚ using the proposed method. The maximum Euclidean and angular distance errors are measured as 1.06 mm and 0.84˚. These experimental results demonstrate that the OTS can consistently acquire high-accuracy measurement data from all positioning tools when confronted with moving tools. This active navigation approach promises to maintain high-accuracy intraoperative navigation for the safe operation of surgical systems.

A particle finite element method based partitioned paradigm for the axial-flow-induced vibration analysis of NHR200-II fuel rod

As a newly designed 200 MW nuclear heating reactor (NHR200-II), flow-induced vibration (FIV) of the fuel rod has attracted extensive attention due to its slender shape and the hydrodynamic loads arose from the turbulent flow of the surrounding fluid. Fretting wear and/or damage of fuel rod induced by FIV would highly affect system operation and nuclear safety. In this article, a particle finite element method (PFEM) based partitioned paradigm (i.e., implicit finite element method for structure dynamics, PFEM for fluid flow, and unsteady Reynolds averaged Navier–Stokes for turbulence modelling) toward FIV problems was proposed, implemented, and validated. Axial FIV of a single NHR200-II fuel rod was then analyzed through this finite-element based framework. Vibration characteristics of the fuel rod against varied turbulent inflow velocities with a constant turbulence intensity Tv of 5% were discussed in detail. The results showed that horizonal displacement is larger than vertical displacement but both within the same order of magnitude. The effect of inflow velocity of 1.0–2.0 m/s on the dominant frequency is also captured. Besides, fluctuating horizonal pressure is identified as the main source of forced vibration. Therefore, reinforcements on the horizontal constraint are recommended to better eliminate the vibration and enhance the reactor safety.

Insulation aging diagnosis and defect location of crosslinked polyethylene cable in the distribution network based on radio frequency identification

With the expansion of power grid construction, the regulatory requirements of various equipment are increasing. Further, the safe and stable operation of the distribution network plays a crucial role in the entire power system. In particular, the cables in DNs are prone to aging during long-term operation due to various factors, such as electricity and heat. Therefore, the effective diagnosis of their operating status is necessary. Crosslinked polyethylene (XLPE) is widely used as the main insulation material for cables in power systems. In this study, the microscale and macroscale performance of the molding process of XLPE cables were compared. Wireless radio frequency identification (RFID) technology was used to diagnose the aging and locate the defects of XLPE cables in the DN. The experiment noted the average insulation crosslinking degree of 88.93% and 87.98% for the suspension chain- and tower-type XLPE cables, respectively. The insulation-melting and crystallization temperatures of the suspension chain-type XLPE cable were approximately 110 °C and 92 °C, respectively, and the crystallinity was approximately 36.5%. The insulation-melting and crystallization temperatures of the tower-type XLPE cable were approximately 105 °C and 88 °C, respectively, and the crystallinity was approximately 34%. Using the wireless RFID technology, the precise positioning of the local aging and insulation damage defects in XLPE cables can be achieved. The error in locating the cable defects using the high-frequency phase spectrum of the cable was less than 0.5 m. Therefore, the study on the manufacture of XLPE cables has significant impact on the physical and chemical properties of their insulation layer. Further, the cable inspection using the wireless RFID technology provides a guarantee on the safe operation of the power system.

Research on the control strategy of LCL grid-connected inverters based on improved auto disturbance rejection.

The grid-connected inverter is the key to ensure stable, reliable, safe, and efficient operation of the power generation system; the quality of the grid-connected output current waveform directly affects the performance of the entire power generation system. To improve the anti-interference performance and reduce the output current harmonic content of the grid-connected inverter, an improved control strategy that combined repetitive control (RC) and auto disturbance rejection control (ADRC) is designed in this paper. Firstly, decoupled the ADRC to realize the individual adaptation between tracking performance parameters and anti-interference performance parameters of the controller, through which the difficulty of adjusting parameters is reduced. Secondly, the control approach is devised by adding RC to ADRC. To demonstrate the effectiveness of the proposed method in this paper, detailed experimental studies are conducted using proportional integral control, traditional ADRC, and the proposed method under normal power grids, weak power grids, and periodic disturbances. And dynamic performance simulation experiment is done to verify the dynamic performance of the self-disturbance rejection controller before and after the addition of RC links. The results indicated the effectiveness and feasibility of the proposed method. Finally, after simulation, the steady state and dynamic performance are conducted on a hardware testing platform. The impacts of the obtained results indicate the effectiveness and feasibility of the control algorithm proposed, the ability to suppress intermediate frequency disturbances is improved, the bandwidth of the auto disturbance rejection controller is expanded, and the harmonic content of the output current is depressed.

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.

Dam System and Reservoir Operational Safety: A Meta-Research

Dams are critical infrastructure necessary for water security, agriculture, flood risk management, river navigation, and clean energy generation. However, these multiple, and often conflicting, objectives introduce complexity in managing dam operations. In addition, dam infrastructure has been evolving as complex systems-of-systems with multiple interacting components and subsystems, all susceptible to a wide range of uncertainties. Such complexities and uncertainties have triggered extensive research initiatives focused on dam systems and reservoir operational safety. Focusing on the latter, this paper meta-researches (conducts research-on-research) previously published studies to identify the critical research gaps and propose future research directions. In this respect, this paper first performs a quantitative analysis of the pertinent literature, using text mining and subsequent topic modeling, to identify and classify major and uncover latent topics in the field. Subsequently, qualitative analysis is conducted to critically review the identified topics, exploring the concepts, definitions, modeling tools, and major research trends. Specifically, the study identified seven topics: optimization models; climate change; flood risk; inflow forecasting; hydropower generation; water supply management; and risk-based assessment and management. The study also presents three main research gaps associated with the limitations in modeling concepts, modeling tools capabilities, and the lack of resilience-guided management of dam operational safety. Overall, this study presents a road map of the currently available dam and reservoir operational safety research and associated knowledge gaps, as well as potential future research directions to ensure the resilience of such critically important infrastructure, especially in the age of climate change.

Risk Decision for a Port Shore Power Supply System Based on Cumulative Prospect Theory and an Improved Gray Target

Considering carbon neutral development strategies, ecological ports and green growth concepts have become the future development trend of ports. As an important technology for green port construction, the stable operation of port shore power systems is particularly important. However, as a new field of transmission system, the development of port shore power is also facing many new risks and challenges. For example, the shore power system has security risks as well as technical and economic difficulties. There are also problems such as the lack of regulations on shore power systems. To ensure the safe and stable operation of a port grid system, this paper proposes a risk decision method for a transmission system in a market environment based on interval number and cumulative prospect theory. Based on the constructed risk evaluation index system of a port shore power supply system, a risk decision model based on cumulative prospect theory and an improved gray target risk model (CPT-IGT) are constructed considering the uncertainty and ambiguity of the index attributes and weight information as well as the risk attitude held by decision-makers; these models are developed with improved interval gray number and cumulative prospect theory. Finally, a case study applied to the risk decision of a port shore power supply system verified the reasonableness and effectiveness of the method. The results show that the proposed method has some advantages in dealing with language terms, representing language uncertainty, and reflecting risk decisions in different environments. The research can provide a theoretical basis for the risk assessment of a port power system in the future and provide suggestions for the green growth of the port.

Load frequency control strategy for islanded multimicrogrids with V2G dependent on learning‐based model predictive control

AbstractSystem reliability and stability can be significantly improved by the interconnected operation of multimicrogrids, and electric vehicles (EVs) provide a more flexible solution for frequency control, which also present challenges for frequency control. Therefore, a load frequency control (LFC) strategy for multimicrogrids with vehicle to grid (V2G) dependent on learning‐based model predictive control (MPC) is proposed. First, a controller‐interconnected multimicrogrid topology is proposed; thus, a multimicrogrid consisting of microturbines (MTs), distributed power sources, and EVs and their random power constraints is established. Second, a control parameter adaptive algorithm based on learning‐based MPC is designed. The real‐time frequency offset and EV station output power boundary are used as the state set, adjustable parameters of the MPC controller are used as the action set, and reward function is set with frequency deviation so that the adaptive adjustment of the weight parameters of the MPC controller is realised. Additionally, the improved MPC controller designed in this paper can transform the frequency control process into an optimization problem, which is well adapted to various random constraints in the control process. In addition, the deep deterministic policy gradient (DDPG)‐MPC double‐layer controller can prevent machine learning controller failure. Finally, the simulation results show that, compared with traditional control and MPC algorithms, the learning‐based MPC controller applied to the controller interconnection structure can exchange information between submicrogrids. Moreover, based on the experience accumulated in the prelearning process, the controller parameters can be updated according to the environmental state in real time, thereby significantly improving the robustness and rapidity of the multimicrogrid frequency control process. Meanwhile, compared with a traditional DDPG controller, the proposed controller with double‐layer coupling structure can better ensure the safe operation of the multimicrogrid system when the machine learning agent fails and cannot output actions normally.

Hybrid Energy Storage Power Allocation Strategy for Stabilizing Wind Power Fluctuations

In order to solve the problem of energy shortage, renewable energy such as wind power has developed rapidly worldwide. However, due to its time-varying and uncertain nature, wind power is prone to local fluctuations and operational safety hazards when connected to the power grid. In response to the stable grid connection problem of wind power generation, a hybrid energy storage system is proposed A method was proposed to achieve smooth power fluctuations in wind power generation using a model, and a charging and discharging control and allocation method for energy storage was proposed. Firstly, the preliminary power of hybrid energy storage was obtained using the model predictive control method, Then, an adaptive variational mode decomposition method was used to achieve energy distribution between batteries and supercapacitors. Finally, the measured data of a 100MW wind power plant in Xinjiang was analyzed as an example. The proposed method has been validated to not only achieve reasonable power allocation between hybrid energy storage systems, but also effectively reduce the impact of wind power generation on the power grid, achieving long-term safe operation of hybrid energy storage systems.

Robustness Analysis of an Urban Public Traffic Network Based on a Multi-Subnet Composite Complex Network Model.

An urban public traffic network is a typical high-order complex network. There are multiple types of transportation in an urban public traffic network, and each type has different impacts on urban transportation. Robustness analyses of urban public traffic networks contribute to the safe maintenance and operation of urban traffic systems. In this paper, a new cascading failure model for urban public traffic networks is constructed based on a multi-subnet composite complex network model. In order to better simulate the actual traffic flow in the composite network, the concept of traffic function is proposed in the model. Considering the different effects of various relationships on nodes in the composite network, the traditional cascading failure model has been improved and a deliberate attack strategy and a random attack strategy have been adopted to study the robustness of the composite network. In the experiment, the urban bus-subway composite network in Qingdao, China, was used as an example for simulation. The experimental results showed that under two attack strategies, the network robustness did not increase with the increase in capacity, and the proportion of multiple relationships had a significant impact on the network robustness.

Smart Grid Outlier Detection Based on the Minorization-Maximization Algorithm.

Outliers can be generated in the power system due to aging system equipment, faulty sensors, incorrect line connections, etc. The existence of these outliers will pose a threat to the safe operation of the power system, reduce the quality of the data, affect the completeness and accuracy of the data, and thus affect the monitoring analysis and control of the power system. Therefore, timely identification and treatment of outliers are essential to ensure stable and reliable operation of the power system. In this paper, we consider the problem of detecting and localizing outliers in power systems. The paper proposes a Minorization-Maximization (MM) algorithm for outlier detection and localization and an estimation of unknown parameters of the Gaussian mixture model (GMM). To verify the performance of the method, we conduct simulation experiments by simulating different test scenarios in the IEEE 14-bus system. Numerical examples show that in the presence of outliers, the MM algorithm can detect outliers better than the traditional algorithm and can accurately locate outliers with a probability of more than 95%. Therefore, the algorithm provides an effective method for the handling of outliers in the power system, which helps to improve the monitoring analyzing and controlling ability of the power system and to ensure the stable and reliable operation of the power system.

Multi-view broad learning system for electricity theft detection

Electricity theft poses a huge hazard to the economic efficiency of power companies and the safe operation of the power system. Analysis of smart grid data can help to identify abnormal electricity usage patterns of the thieves. However, existing models may suffer from underfitting issues due to the high dimensionality and imbalanced class distribution in the electricity dataset. To address these challenges and improve the performance of electricity theft detection, this study proposes a multi-view detection model based on broad learning system (BLS). First, a new multi-view framework is presented to map the raw power data into different sub-views, thereby reducing redundant electricity data features. Then, an adaptive weighting strategy based on the regional distribution of the data is developed. The optimized sub-views are obtained by considering the sample size and dispersion of the data. Finally, a power theft detection model is constructed by combining the region distribution weighted BLS and the multi-view rotation BLS. Comparative experiments on real-world electricity dataset demonstrate the superiority of our proposed approach.

SEP Optimization of Power System Location and Timing Module Based on MLE-KNN Synthesis Algorithm

To ensure the safe operation of the power system, it is necessary to monitor and manage all kinds of power facilities. Real-time power monitoring can play a role in the early warning of geographical disasters. At the transmission end, the state detection and fault location of transmission lines are related to the normal operation of the power system. The precise time synchronization of the power system can be guaranteed through the satellite positioning time service, so that the time of various power operation actions is consistent. The accuracy can reach several microseconds, which provides a basis for the analysis of power grid operation. In this paper, by installing BeiDou functional modules in power equipment and developing the BeiDou operation platform for the distribution network, the precise management of power timing and positioning is realized by docking with the geographic information systems platform of the power grid. The three-dimensional sphere error probability (SEP) algorithm is selected to evaluate the positioning error data. Based on the three traditional SEP calculation methods, the maximum likelihood estimation algorithm is optimized and improved, which can achieve nanosecond synchronization accuracy. It can also be applied to other precision of synchronization scenarios of the power system to meet the development needs of the future clock system of the power system. Through the simulation of experimental data and comparison with other algorithms, the accuracy is verified and the reliable estimation of parameters is realized. Considering the information loss caused by dimension reduction evaluation, the simulation analysis in this paper is compared with other methods, and the accuracy is significantly improved. The power system positioning and timing module developed in this paper can not only be used in the power system but also has a certain reference and application value for other devices in the same industry that need timing and positioning services.

An improved prediction method of subsequent commutation failure of an LCC-HVDC considering sequential control response

Subsequent commutation failure (SCF) can be easily generated during the first commutation failure (CF) recovery process in a line-commutated converter-based high voltage direct-current system. SCF poses a significant threat to the safe and stable operation of power systems, and accurate prediction of CF is thus important. However, SCF is affected by the operating characteristics of the main circuit and the coupling effects of sequential control response in the inverter station. These are difficult to predict accurately. In this paper, a new SCF prediction method considering the control response is proposed based on the physical principle of SCF. The time sequence and switching conditions of the controllers at different stages of the first CF recovery process are described, and the corresponding equations of commutation voltage affected by different controllers are derived. The calculation method of the SCF threshold voltage is proposed, and the prediction method is established. Simulations show that the proposed method can predict SCF accurately and provide useful tools to suppress SCF.

Anomaly Perception Method of Substation Scene Based on High-Resolution Network and Difficult Sample Mining

The perception of anomalies in power scenarios plays a crucial role in the safe operation and fault prediction of power systems. However, traditional anomaly detection methods face challenges in identifying difficult samples due to the complexity and uneven distribution of power scenarios. This paper proposes a power scene anomaly perception method based on high-resolution networks and difficult sample mining. Firstly, a high-resolution network is introduced as the backbone for feature extraction, enhancing the ability to express fine details in power scenarios and capturing information on small target anomaly regions. Secondly, a strategy for mining difficult samples is employed to focus on learning and handling challenging and hard-to-recognize anomaly samples, thereby improving the overall anomaly detection performance. Lastly, the method incorporates GIOU loss and a flexible non-maximum suppression strategy to better adapt to the varying sizes and dense characteristics of power anomaly targets. This improvement enables higher adaptability in detecting anomalies in power scenarios. Experimental results demonstrate significant improvements in power scene anomaly perception and superior performance in handling challenging samples. This study holds practical value for fault diagnosis and safe operation in power systems.

Intelligent Fault Diagnosis of Rolling Element Bearings Based on Modified AlexNet.

The reliable and safe operation of industrial systems needs to detect and diagnose bearing faults as early as possible. Intelligent fault diagnostic systems that use deep learning convolutional neural network (CNN) techniques have achieved a great deal of success in recent years. In a traditional CNN, the fully connected layer is located in the final three layers, and such a layer consists of multiple layers that are all connected. However, the fully connected layer of the CNN has the disadvantage of too many training parameters, which makes the model training and testing time longer and incurs overfitting. Additionally, because the working load is constantly changing and noise from the place of operation is unavoidable, the efficiency of intelligent fault diagnosis techniques suffers great reductions. In this research, we propose a novel technique that can effectively solve the problem of traditional CNN and accurately identify the bearing fault. Firstly, the best pre-trained CNN model is identified by considering the classification's success rate for bearing fault diagnosis. Secondly, the selected CNN model is modified to effectively reduce the parameter quantities, overfitting, and calculating time of this model. Finally, the best classifier is identified to make a hybrid model concept to achieve the best performance. It is found that the proposed technique performs well under different load conditions, even in noisy environments, with variable signal-to-noise ratio (SNR) values. Our experimental results confirm that this proposed method is highly reliable and efficient in detecting and classifying bearing faults.