Operating Conditions Of System Research Articles

Parametric effects of ring-shaped phase change module on the temperature regulation of ultra-low carbon shallow geothermal ventilation system

As one of the most representative geothermal systems, shallow geothermal ventilation (SGV) system has been widely used with the advantages of simple configuration and low operation cost. However, this system is characterized by relatively large temperature fluctuations at the outlet in many application scenarios, which will reduce the thermal comfortable of the delivery outlet air temperature. The existing model development of horizontal buried-pipe SGV system tends to ignore the vertical buried-pipe part of the air inlet and outlet, and only considers the heat transfer model of horizontal buried-pipe, which may affect the accuracy of the developed model to a certain extent. Based on these, this study proposes a SGV system integrating the ring-shaped phase change material (RSPCM) module, and develops the dynamic heat transfer model considering the vertical part of the air inlet and outlet of the system. In this study, firstly, a relevant field experimental testbed is built and the developed dynamic model is verified with good agreement based on the experimental data, which has a maximum relative error of 4.83 %. In addition, the effects of RSPCM and its different parameters on the thermal performance of SGV system are investigated based on the developed model. The results show that RSPCM arranged at the air outlet can regulate the outlet temperature fluctuation of the SGV system and improve the comfort of the air supply temperature to a certain extent. When the inlet air velocity is 1 m/s, the temperature fluctuation at the outlet is 1.73 °C for the system comprising RSPCM, while the corresponding temperature fluctuation at the outlet is 2.10 °C for the system without RSPCM. The effect of thermal conductivity on the PCM's temperature regulation ability is not obvious enough for the proposed system application. Therefore, in practice, by increasing the thermal conductivity of RSPCM is not an optimal and feasible way when considering the economic cost. The RSPCM thickness is increased to a certain level and then this measure will not have much effect to improve the system's thermal performance. For this system structure and operating conditions, a more suitable RSPCM thickness should be about 20 mm, and the RSPCM with a phase-change temperature of 24 °C has a relatively good temperature regulation capability. The RSPCM has a better temperature regulation capability for the system outlet temperature when it is located at the outlet position. This study provides a novel research possibility for the application of SGV in buildings, and can also provide theoretical guidelines for designers and constructors in practice to apply SGV systems.

Research and implementation of frequency control strategy of islanded microgrids rich in grid-connected small hydropower

Due to the weak network architecture of mountainous power grid rich in grid-connected small hydropower, once the small hydropower units operate independently as a network when the upper power grid fails and loses power, the reliability of the power supply could be effectively improved. To cope with the problem of insufficient frequency regulation capacity of small hydropower units and the large risk of separation fault, this paper proposes a piecewise variable coefficient PID control strategy which improves the speed regulation control strategy of hydropower units, and a joint frequency regulation strategy of hydropower energy storage is designed to optimize the system operation state. Based on the MATLAB simulation platform, a hydropower energy storage microgrid model of mountainous power grid was built to verify the effectiveness of the segmented variable parameter control strategy. Using the proposed control strategy, the hydropower intelligent control terminal was developed and put into application in a certain area of Guangdong. Through simulation analysis and engineering applications, the practicality of the application of device engineering is verified to have good application and promotion value for a multi-hydropower mountainous power grid.

Machine learning-based prediction of operation conditions from plasma plume images of atmospheric-pressure plasma reactors

A technique was proposed in this paper to monitor the key operating conditions of a plasma abatement system, which are the concentration of the carbon-containing process gas and the treatment flowrate, from a plasma plume image acquired using an inexpensive color camera. The technique is based on the observation that the shape and color of the plasma plume vary with the variations in the specific energy input and plasma gas composition. In addition, because these variations are marginal and it is challenging to identify an analytical relationship between these variations and the operating conditions, the prediction model is obtained in a data-driven manner. Specifically, the model was composed of a set of convolutional autoencoders (CAEs) and a dense neural network. Furthermore, it was trained only with images captured under normal operation so that (1) images captured under abnormal operations could be identified based on the reconstruction error of the trained CAEs and (2) predictions are made only on normal images. As a demonstration, methane was tested as a process gas, and oxygen was used as a reaction agent in a nitrogen-rich environment. The test results showed that the optimized model could predict the treatment flowrate and process gas concentration with 96% probability within ±3.08 slpm and ±300 ppm, respectively.

Design and Implementation of an Integrated Control Scheme for GaN-Based Multiple Power Converters

In response to the rapid changes in the international energy environment, developing renewable energy (RE)-based distributed generation (DG) and various smart micro-grid systems is crucial for creating a robust electric power grid and new energy industries. In this aspect, there is an urgent need to develop hybrid power systems suitable for coexistent AC and DC power grids, integrated by high-performance wide ban gap (WBG) semiconductor-based power conversion interfaces and advanced operating and control strategies. Due to the intrinsic feature of variation in RE-based power generation, the design and integration of energy storage devices, real-time regulation of power flow, and intelligent energy control schemes are key technologies for further promoting DG systems and micro-grids. This paper investigates an integrated control scheme for multiple GaN-based power converters in a small- to medium-capacity, grid-connected, and RE-based power system. This is the first time that a complete design case demonstrating three GaN-based power converters with different control functions integrated with a single digital signal processor (DSP) chip to achieve a reliable, flexible, cost effective, and multifunctional power interface for renewable power generation systems is presented. The system studied includes a photovoltaic (PV) generation unit, a battery energy storage unit, a grid-connected single-phase inverter, and a power grid. Based on system operation condition and the state of charge (SOC) of the energy storage unit, two typical operating modes and advanced power control functions are developed with a fully digital and coordinated control scheme. Hardware of the GaN-based power converters and digital controllers are designed and implemented. The feasibility and effectiveness of the designed controllers and overall performance of the proposed control scheme are verified with results from simulation and experimental tests on a 1-kVA small-scale hardware system.

Calculation of Parameters of Prism Deflector for Laser Scanner

The prism deflector which is a multifaceted prism with reflective facets is the most common scanning element that allows to quickly fill a wide scanning area with laser radiation pulses along one coordinate. The parameters of a prismatic deflector are related to the characteristics of the laser radiation of the scanned area parameters and of the deflector position, and are also limited by various factors, such as safety requirements or scanning time. The aim of this work was to analyze the relationship between the external operating conditions of a laser scanning system and the internal design parameters of a prism deflector.A variant of calculating of laser pulses frequency by the number of spots and their overlap coefficient is considered. A method for calculating of a prism deflector characteristics based on external parameters, such as the angle of incidence of radiation to the facet and the width of the Gaussian beam with dimensions that are safe for а human eye is proposed. Рrismatic deflectors parameters are proposed depending on the number of reflecting facets. Dependence of a deflector size on the angle of radiation incidence to the reflecting face is shown.When designing a prismatic deflector of a laser scanner used to fill a certain scanning area with the required angular size σ by varying such parameters as the number of faces m and the feed angle α it is possible to achieve optimal deflector characteristics and scanning mode for the task.

Adaptive Assessment of Power System Transient Stability Based on Active Transfer Learning With Deep Belief Network

Transient stability assessment (TSA) based on deep learning has recently attracted broad attention. The offline training of deep learning model typically requires a large number of labeled samples, and the training process is very time-consuming. Moreover, it is difficult to ensure that the trained model is suitable for all the operation conditions of power systems. In order to dramatically reduce the computation cost, and further enhance the adaptability of the model, an innovative method is proposed based on the active transfer learning (TL). First, the deep belief network (DBN) is designed as the base model and trained with a large number of labeled samples to obtain better evaluation performance for TSA. Second, when the topologies or operation conditions change substantially, active learning with information entropy is exploited to select the minimum number of the most informative samples, which effectively reduces the time of samples generation. Third, Maximum Mean Discrepancy (MMD) is calculated to select different transfer schemes. Only the last BP layer needs to be fine-tuned for the scenarios with a low MMD, but for those with high MMD, the entire network needs to be fine-tuned. The transfer time is greatly shortened on the premise of ensuring the transfer effect. The comprehensive experimental results of three power systems illustrate the effectiveness of the proposed method. Note to Practitioners—This research is motivated by automatic adaptive assessment of power system transient stability. The main objective is to realize online fast and continuous prediction with limited samples. This kind of demand is very normal in the industrial systems where it is expensive, time-consuming and difficult to obtain labeled samples. This paper proposes an active transfer learning method to select critical samples and update the deep learning model, which greatly reduces the time of samples generation and improves the updating efficiency of model. The proposed method is also applicable to make classification prediction or to address regression problems of industrial scenarios based on deep learning models.

Electromechanical Coupling Dynamic Characteristics of the Dual-Motor Electric Drive System of Hybrid Electric Vehicles

The electric mode is the main operational mode of dual-motor hybrid electric vehicles (HEVs), so the reliability of the dual-motor electric drive system (DEDS) is particularly important. To research the electromechanical coupling mechanism of the DEDS of HEVs, firstly, considering the time-varying mesh stiffness of gears and the nonlinear characteristics of inverters, an electromechanical coupling dynamics model of the DEDS was established, including the permanent magnet synchronous motor (PMSM) and the gear transmission system. Then, the electromechanical coupled dynamic characteristics of the DEDS in the single-motor and dual-motor drive modes were analyzed under steady-state and impact load conditions, respectively. The results show that the motor stator current frequency is modulated by the complicated gear meshing frequency, and the operation state of the gear transmission system can thus be monitored in the stator current. Impact load causes the instantaneous torsional vibration of the transmission system dominated by the first-order natural frequency, and the vibration characteristic frequency appears in the form of a side frequency in the stator current signal; moreover, compared with the single-motor drive mode, the speed synchronization error in the dual-motor drive mode will aggravate torsional vibration in the gear system. The impact energy of the gear system caused by external impact load can be suppressed by reducing the speed synchronization error.

범죄자의 자기통제력과 전자감독 순응도와의 관계 연구

The purpose of this study is to investigate the association between the low self control and the compliant attitude of probationers under electronic monitoring in South Korea. It is imperative to examine the relationship between self control of offenders and law-abiding behavior of probationers by utilizing the revised general theory of crime due to the henious crimes that probationers had commiitted recently while they are under EM. Revised general theory crime has been used in order to examine and check whether or not the level of bonding between probation officers and offenders under EM is key moderator for explaining the association between the self control and compliant attitude of probationers. This study has employed Hirsch's revised theory of self-control. Compared the original Gottfredson and Hirschi’s self-control theory(1990), the revised self-control theory emphaized the importance of social control factors. This study used ‘2013 sex offender electronic monitoring system Operational status survey’ of korean institute of criminology and justice data and a total of 410 cases were used for the analysis. SPSS was used as an analysis tool, and Hayes(2013)' PROCESS macro was used to examine the moderating effect. The result showed that the self-control of electronic monitoring probationer and compliance with the electronic monitoring system had a positive relationships while the social bonding between probation officers and offenders under EM had not been identified. Further policy implications will be discussed.

Data-driven power system security assessment using high content database during the COVID-19 pandemic

As the coronavirus disease (COVID-19) broke out in late 2019, the electricity sector was significantly impacted. Hence, the effects of the pandemic and restricting measures in power system operation are investigated during pandemic circumstances. The secure operation of the power system is a fundamental requirement. Appropriate procedures should be taken to mitigate these effects and ensure the power system's security. Accordingly, in this study, the authors determine that the COVID-19 pandemic can change the system's operating conditions in the first stage. Since data-driven security assessment methods require the training database to learn about Security constraints, this paper proposes an efficient database generation strategy respecting the consequences of the COVID-19 outbreak. The proposed strategy provides a training set with high information content compatible with the operating conditions. To this end, the method consists of a characteristics extraction approach and updating scheme. The characteristics should be extracted to represent the operating conditions of the system.Further, the similarity of intervals is compared using characteristics in updating scheme. The copula-based sampling approach is provided to generate the random samples. The proposed strategy generates a database for data-driven methods. Therefore, it can be utilized in various applications of security assessment. Real-world data is mapped to the IEEE 39-bus system to illustrate the framework efficiency. The outcomes indicate that a classification using the proposed strategy outperforms conventional methods in terms of evaluation metrics.© 2017 Elsevier Inc. All rights reserved.

Energy-optimal DNN model placement in UAV-enabled edge computing networks

Unmanned aerial vehicle (UAV)-enabled edge computing is emerging as a potential enabler for Artificial Intelligence of Things (AIoT) in the forthcoming sixth-generation (6G) communication networks. With the use of flexible UAVs, massive sensing data is gathered and processed promptly without considering geographical locations. Deep neural networks (DNNs) are becoming a driving force to extract valuable information from sensing data. However, the lightweight servers installed on UAVs are not able to meet the extremely high requirements of inference tasks due to the limited battery capacities of UAVs. In this work, we investigate a DNN model placement problem for AIoT applications, where the trained DNN models are selected and placed on UAVs to execute inference tasks locally. It is impractical to obtain future DNN model request profiles and system operation states in UAV-enabled edge computing. The Lyapunov optimization technique is leveraged for the proposed DNN model placement problem. Based on the observed system overview, an advanced online placement (AOP) algorithm is developed to solve the transformed problem in each time slot, which can reduce DNN model transmission delay and disk I/O energy cost simultaneously while keeping the input data queues stable. Finally, extensive simulations are provided to depict the effectiveness of the AOP algorithm. The numerical results demonstrate that the AOP algorithm can reduce 18.14% of the model placement cost and 29.89% of the input data queue backlog on average by comparing it with benchmark algorithms.

Characterisation of resilience metrics in full-scale applications to interdependent infrastructure systems

Critical infrastructure (CI) plays a crucial role in our daily life and CI resilience is nowadays a priority for policymakers and a strategic objective for operators. This trend prompted a considerable research effort on measuring resilience, which proposed several different metrics aiming to capture resilience properties of a CI system, both generic and problem-specific. Selecting the appropriate metric for a given application, based on properties, major strengths, or weaknesses of existing metrics, is not trivial. To this end, this paper first performs a systematic literature review of resilience metrics suitable for application to networked infrastructure systems, where a representative subset of different types of metrics is selected. We advance the state-of-the-art by defining a few new metrics to partly cover the gaps in the extant literature. The final set of resilience metrics is rigorously compared by looking at their behaviour when the properties of the system vary. In particular, we evaluate the ability of each metric to assess the influence of the system's structural and operational conditions on its resilience profile. The proposed and implemented evaluation procedure uses the DMCI tool (Dynamic Functional Modelling of Vulnerability and Interdependencies of CIs) to simulate the dynamics of an interdependent networked infrastructure system. A large portion of the transportation network connecting Italy and Switzerland is used as the empirical setting.

Toward Interpretability in Fault Diagnosis for Autonomous Vehicles: Interpretation of Sensor Data Anomalies

To guarantee the safety and reliability of autonomous driving applications, it is indispensable to construct a proper fault diagnosis framework tailored to autonomous vehicles. Fault diagnosis aims to provide essential information about the system operational status and its interpretation facilitates decision-making and mitigates the potential operation risks. In the present work, interpretability issue in fault diagnosis for autonomous vehicles is discussed from the sensor data analytics perspective. Environmental impact is first evaluated using the noise energy as a measure to interpret the impact on sensor data caused by the environment. A signal quality indicator is proposed and Savitzky–Golay filters are applied for online denoising, acting as a countermeasure to mitigate the impact and to enhance the data quality. Then, the adversarial learned denoising shrinkage autoencoder (ALDSAE), an adversarial learning neural network, is constructed for sensor data anomaly detection. It considers an adversarial training technique to improve the performance of the anomaly detector. A residual explainer specific to the ALDSAE model is employed to calculate the contribution of each input feature to the anomaly score in order to interpret the anomaly detection results. Several experiments with the collected data from an autonomous vehicle in a real test field are implemented to validate the effectiveness of the proposed approaches. Results show that the area under the ROC curve (AUC_ROC) of the proposed ALDSAE is over 20% higher in average than several traditional anomaly detectors, and the mean explanation accuracy of the residual explainer achieves similar performance with the widely employed kernel Shapley additive explanation (SHAP) with more than 99% reduction in mean response time.

Segmentation of data when analyzing the state of telecommunication systems

<span lang="EN-US">The identification of abnormal situations in information and telecommunication systems is considered, based on analyze statistical information of network traffic packages. The method of identifying an anomalous situation based on segmentation of data sample is proposed. The method is aimed at using classifying algorithms that have the best quality indicators on individual data segments. The proposed method will be useful for monitoring information security systems. The method registers of factors that affect the change in the properties of targeted variables. Impact detection allows you to generate data samples, depending on current and expected situations. On the example of the NSL-KDD dataset, there was a division of many data into subset, taking into account the influence of the factors on the range of values. The processing of factors is shown using the change point detection function in the time series. With its use, a division of data sample by the final number of non-intersecting measurable subsets has been made. The results of Accuracy, Precision, F-Measure, Recall for various classifiers are shown. The proposed method allows to increase the quality indicators of classification in continuously changing operating conditions of telecommunication systems.</span>

Fast Frequency Response Reserve Planning for Power Systems Considering Homogeneous Extreme Risks

To deal with the risk of frequency instability in the low-carbon emission power system, the fast frequency response reserve (FFRR) should be separately classified and rationally planned to ensure the reliability of the system. In FFRR planning, the frequency nadir under disturbances should be calculated quickly due to massive calculation in the probabilistic production simulation (PPS) to analyze the severity of disturbances. A set of corresponding analysis strategies need to be formulated due to the difficulty in accurately determining the loss of extreme events. In light of this, in this study, an analytical model of FFRR economics considering risk preference is proposed to evaluate the risk of rare and extreme events. The system operation state model is established based on the sequential Monte Carlo method, and the contingency set is sent into the proposed system frequency analytical model to obtain the frequency nadir. Considering the risk preference, a FFRR optimization planning and configuration method is proposed, in which the losses of extreme events can be appropriately accounted by considering the homogeneous extreme events separately. The risk preference essentially incorporates the operating experience into the calculation process, to avoid the homogenization of low-probability extreme events and improve the feasibility of the planning scheme. Simulation results show that the proposed FFRR planning and configuration scheme can avoid homogenizing small-probability extreme events, and the amount of FFRR demand is significantly increased when the operator concentrates on lower probability with high risk of extreme events.

Towards a Power Production from 100% Renewables: The Italian Case Study

The need to reduce greenhouse gas emissions is driving many actions to decarbonize the most impactful sectors. Among these, the energy sector accounts for almost one third of emissions. Increasing the penetration of renewable energy in the energy mix could easily reduce the emissions of this sector. Theoretically, the target to aim for would be 100% renewable energy production. However, the variable nature of power production from photovoltaic and wind systems, which are expected to play a key role in the energy transition, may pose several limitations to the effective penetration of renewable energy. Many concerns arise when one considers the large diffusion of renewable energy that would be required to meet green targets, and the operating conditions of other systems in charge of compensating for renewable energy variations. This study aims to investigate the potential impact of an increase in the amount of renewable energy installed in a country, particularly in Italy. A simplified approach has been used, based on the assumption of knowing the hourly demand and power generation mix, and multiplying the intermittent power generation by a certain factor. Although not accurate, this approach allows the authors to highlight some critical aspects regarding the potential surplus of renewable energy and the operating conditions of other energy sources. The results of this study may provide a useful basis for a preliminary system evaluation, in particular to assess the feasibility of surplus recovery and the operability of residual generation systems. In addition, it may be easily replicated in other countries for similar estimations.

Computation of steady-state operating conditions of a DFIG-based wind energy conversion system considering losses

Computation of steady-state operating conditions of a DFIG-based wind energy conversion system considering losses

An online power system transient stability assessment method based on graph neural network and central moment discrepancy

The increasing penetration of renewable energy introduces more uncertainties and creates more fluctuations in power systems. Conventional offline time-domain simulation-based stability assessment methods may no longer be able to face changing operating conditions. In this work, a graph neural network-based online transient stability assessment framework is proposed, which can interactively work with conventional methods to provide assessment results. The proposed framework consists of a feature preprocessing module, multiple physics-informed neural networks, and an online updating scheme with transfer learning and central moment discrepancy. The t-distributed stochastic neighbor embedding is used to virtualize the effectiveness of the proposed framework. The IEEE 16-machine 68-bus system is used for case studies. The results show that the proposed method can achieve accurate online transient stability assessment under changing operating conditions of power systems.

Fault Diagnosis of HV Cable Metal Sheath Grounding System Based on LSTM

At present, the metal sheath of high voltage (HV) cables generally adopts the cross-bonded grounding method, which brings many types of faults and challenges the monitoring and diagnosis of the operation status of the cables. In order to effectively diagnose various types of faults in the metal sheath grounding system of HV cables, this paper proposes a fault diagnosis method for the metal sheath grounding system of HV cables based on long and short-term memory (LSTM). Firstly, the grounding system model of HV cable metal sheath is established. Secondly, the sheath currents of four faults are analyzed. Based on the sheath current amplitude ratio and phase difference of the same loop and the same grounding box, 14 feature vectors reflecting the operation state of the metal sheath grounding system are constructed. Then, the operation state of 18 kinds of metal sheath grounding systems is simulated, and the fault database is established. Finally, the LSTM algorithm is used to accurately identify the fault of HV cable grounding system. The results show that the LSTM algorithm can effectively diagnose and identify the faults of the HV cable metal sheath grounding system, and the accuracy rate is 100%.

Analysis of the Influence of Nodal Reactive Powers on Voltages in a Power System

The paper deals with finding power system nodes where reactive powers have the greatest influence on system voltages. The problem to be solved is important in reactive power planning. Its proper solution indicates in which nodes new sources of reactive power should be installed in order to achieve the assumed goals in the aforementioned planning. So far, the problem formulated earlier has not been satisfactorily resolved. The paper presents an original method, which, based on the entire history of the system operation states, allows a solution to the problem mentioned above to be found. The proposed method assumes the use of measurement data of nodal-voltage magnitudes and nodal reactive power. Correlational relationships between the above-mentioned quantities are investigated. The paper shows that the considered correlational relationships are not linear. In this situation, Kendall’s rank correlation coefficient is used to evaluate the strength of these correlational relationships. Analysis of the strongest relationships allows us to identify those nodal reactive powers that have the greatest influence on the voltages in the power system. The results of the analysis are the basis for determining the location of additional reactive power sources in the power system, which is very essential in reactive power planning. The proposed method is relatively easy to implement and does not require complicated calculations. The paper additionally shows that failure to use the entire spectrum of representative system states when solving the problem under consideration can adversely affect the result.

Assignment Approach for Electric Vehicle Charging Using Traffic Data Collected by SUMO

Consumption habits are changing due to the development of new technologies around renewable energy, environmental awareness, and new incentive policies. Smart grids are seen as an effective way to accommodate more renewable energy, achieve better control of demand, and improve the operating conditions of the electrical system. However, electric vehicles, which are an environmentally friendly alternative, have very high market penetration and require efficient electrical management at charging stations. Among the factors that have a significant impact on electrical energy consumption are traffic conditions, which can seriously impact the efficiency of electric vehicles. Therefore, the focus is on developing charging infrastructure and reducing vehicle waiting time by optimally allocating electric vehicles to charging stations. To this end, an optimization approach is presented, based on the traffic conditions collected by the SUMO simulator. This approach enables each vehicle to be assigned to the appropriate station while maintaining its battery state of charge at a higher level.