Faults Detection for Power Systems

Non

The influence of water-based drilling fluid on mechanical property of shale and the wellbore stability

AbstractBecause of high cost and pollution of oil-based drilling fluid, the water-based drilling fluid is increasingly used now. However, bedding planes and micro-cracks are rich in shale formation. When water-based drilling fluid contacts formation rock, it causes the propagation of crack and invasion of drilling fluid, which decrease shale strength and cause wellbore instability. In this paper, we analyzed influence of water-based drilling fluid on shale strength and failure mode by mechanics experiment. Based on those experimental results, considering the effect of bedding plane and drilling time, we established modeling of wellbore stability for shale formation. The result from this model indicates that in certain azimuth of horizontal well, collapsing pressure increases dramatically due to shale failure along with bedding plane. In drilling operation, those azimuths are supposed to be avoided. This model is applicable for predication of collapsing pressure in shale formation and offers reference for choosing suitable mud weight

Digital Media and Psychological Well-being among Youth

Most of the previous research has been noting that daily screen time has several negative consequences among youth for instance foster loneliness and displace other well-being activities, such as sports or social activities. Meanwhile, media use also gives positive influences such as fulfil the needs for autonomy, competence, and relatedness or relieving stress and daily hassles. Like the flip of a coin, media may serves both positive and negative impacts into psychological health. Recent research has documented a rapid increase in the use of new technologies such as touchscreen or tablets on mental health issues, however little of the research shown the empirical evidence about its relation to the foremost psychological well-being (PWB) concept. Using data from 147 youth, we analyse the association between digital media use and PWB in a sample of 16 to 24-year-old Indonesian. The digital media use was examined from the screen activities duration and the compulsiveness of internet use. Our analysis shows that screen-time duration and compulsive internet use does not significantly associate with low lever of PWB, respectively. However, the current research found the associations between compulsive internet use and self-acceptance, one of the PWB dimension. Additionally, the finding suggests the presence of gender differences concerning the extent of media use. To sum up, the current findings can be explained as the variation of the possible effect of media on psychological risk in Indonesian Youth

Active arc suppression device based on voltage-source convertor with consideration of line impedance in distribution networks

Abstract In the non‐effectively grounding distribution system, residual current under single‐line‐to‐ground (SLG) fault threatens the safety of human being and power supply equipment. Active arc suppression device has been proved to be effective for SLG fault arc suppression when the line impedance is ignored. However, in practice, line impedance varies with the fault location and the load current flowing through the impedance brings about additional voltage drop, which increases the fault current and is not dealt with by the conventional methods. To achieve accurate SLG fault arc suppression with the existence of line impedance, the neutral‐to‐ground voltage reference for full ground‐fault current compensation is firstly derived and a detection method is then proposed. The pre‐fault and post‐fault line currents are used to eliminate the influence of load current on the line impedance voltage drop. A dual‐loop voltage and current controller is then designed. The prototype of active arc suppression device was developed. The results of simulation and prototype experiment validate the effectiveness of the proposed method

Short-Term Load Forecasting for Industrial Customers Based on TCN-LightGBM

Accurate and rapid load forecasting for industrial customers has been playing a crucial role in modern power systems. Due to the variability of industrial customers’ activities, individual industrial loads are usually too volatile to forecast accurately. In this paper, a short-term load forecasting model for industrial customers based on the Temporal Convolutional Network (TCN) and Light Gradient Boosting Machine (LightGBM) is proposed. Firstly, a fixed-length sliding time window method is adopted to reconstruct the electrical features. Next, the TCN is utilized to extract the hidden information and long-term temporal relationships in the input features including electrical features, a meteorological feature and date features. Further, a state-of-the-art LightGBM capable of forecasting industrial customers’ loads is adopted. The effectiveness of the proposed model is demonstrated by using datasets from different industries in China, Australia and Ireland. Multiple experiments and comparisons with existing models show that the proposed model provides accurate load forecasting results

Short-Term Load Forecasting for Industrial Customers Based on TCN-LightGBM

Accurate and rapid load forecasting for industrial customers has been playing a crucial role in modern power systems. Due to the variability of industrial customers' activities, individual industrial loads are usually too volatile to forecast accurately. In this paper, a short-term load forecasting model for industrial customers based on the Temporal Convolution Network (TCN) and Light Gradient Boosting Machine (LightGBM) is proposed. Firstly, a fixed-length sliding time window method is adopted to reconstruct the electrical features. Next, the TCN is utilized to extract the hidden information and long-term temporal relationships in the input features including electrical features, a meteorological feature and date features. Further, a state-of-the-art LightGBM capable of forecasting industrial customers' loads is adopted. The effectiveness of the proposed model is demonstrated by using datasets from different industries in China, Australia and Ireland. Multiple experiments and comparisons with existing models show that the proposed model provides accurate load forecasting results

FDTD analysis of transient fault induced travelling-wave propagation for multi-branch distribution networks

Many methods are available to analyze the process of the travelling-wave propagation. Among these methods, the Finite Difference Time Domain (FDTD) method has a distinct advantage in calculating dynamic process of the travelling wave propagation in the time domain and is thus applied to the field of power system protection for researching transient fault induced travelling-wave propagation. The novelty of this paper is that the attenuation law of the traveling wave signal affected by the fork junction in the multi-branch distribution network is summarized and the cause of failure in the fault location based on the incipient travelling wave front method in distribution networks is found