A clamping type DC circuit breaker with short fault isolation time and low energy dissipation

The development of DC grids faces challenges from DC fault protection. The conventional DC circuit breaker (DCCB) employs metal-oxide varistor (MOV) to isolate the faulted line, in which the fault isolation process is coupled with the energy dissipation process. In this study, a clamping type DCCB (CTCB) uses internal capacitors to clamp the converter voltage is proposed. Thanks to the proposed configuration, fault isolation and energy dissipation are decoupled, resulting in a fast fault isolation and low energy dissipation compared to the conventional DCCB. The working principle of the proposed CTCB is presented and verified in a DC grid simulation model. A comparison is made with the traditional DCCB. The fault isolation time can be reduced by 34.5 %. The dissipated energy can be reduced by 17.4 %. The energy dissipation power can be reduced by 76.2 %

Coordinated control of DC circuit breakers in multilink HVDC grid

High voltage DC grid is developing towards more terminals and larger transmission capacity, thus the requirements for DC circuit breakers (DCCB) will rise. The conventional methods only use the faulty line DCCB to withstand the fault stress, while this paper presents a coordination method of multiple DCCBs to protect the system. As many adjacent DCCBs are tripped to interrupt the fault current, the fault energy is shared, and the requirement for the faulty line DCCB is reduced. Moreover, the adjacent DCCBs are actively controlled to help system recovery. The primary protection, backup protection, and reclosing logic of multiple DCCB are studied. Simulation confirms that the proposed control reduces the energy dissipation requirement of faulty line DCCB by around 30–42 %, the required current rating for IGBTs is reduced, and the system recovery time reduced by 20–40 ms

Coordination method for DC fault current suppression and clearance in DC grids

The modular multilevel converter (MMC) based DC grid is considered as a future solution for bulk renewable energy integration and transmission. However, the high probability of DC faults and their rapid propagation speed are the main challenges of the development of DC grids. Existing research mainly focuses on the DC fault clearance methods, while the fault current suppression methods are still under researched. Additionally, the coordination method of fault current suppression and clearance needs to be optimized. In this paper, the technical characteristics of the current suppression methods are studied, based on which the coordinated methods of fault current suppression and clearance are proposed. At last, a cost comparison of these methods is presented. The research results show that the proposed strategies can reduce the cost of the protection equipment

Edaravone Guards Dopamine Neurons in a Rotenone Model for Parkinson's Disease

3-methyl-1-phenyl-2-pyrazolin-5-one (edaravone), an effective free radical scavenger, provides neuroprotection in stroke models and patients. In this study, we investigated its neuroprotective effects in a chronic rotenone rat model for Parkinson's disease. Here we showed that a five-week treatment with edaravone abolished rotenone's activity to induce catalepsy, damage mitochondria and degenerate dopamine neurons in the midbrain of rotenone-treated rats. This abolishment was attributable at least partly to edaravone's inhibition of rotenone-induced reactive oxygen species production or apoptotic promoter Bax expression and its up-regulation of the vesicular monoamine transporter 2 (VMAT2) expression. Collectively, edaravone may provide novel clinical therapeutics for PD

Fault Diagnosis of Power Transformers using Kernel based Extreme Learning Machine with Particle Swarm Optimization

To improve the fault diagnosis accuracy for power transformers, this paper presents a kernel based extreme learning machine (KELM) with particle swarm optimization (PSO). The parameters of KELM are optimized by using PSO, and then the optimized KELM is implemented for fault classification of power transformers. To verify its effectiveness, the proposed method was tested on nine benchmark classification data sets compared with KELM optimized by Grid algorithm. Fault diagnosis of power transformers based on KELM with PSO were compared with the other two ELMs, back-propagation neural network (BPNN) and support vector machines (SVM) on dissolved gas analysis (DGA) samples. Experimental results show that the proposed method is more stable, could achieve better generalization performance, and runs at much faster learning speed

Hybrid Predictor and Field-Biased Context Pixel Selection Based on PPVO

Most pixel-value-ordering (PVO) predictors generated prediction-errors including −1 and 1 in a block-by-block manner. Pixel-based PVO (PPVO) method provided a novel pixel scan strategy in a pixel-by-pixel way. Prediction-error bin 0 is expanded for embedding with the help of equalizing context pixels for prediction. In this paper, a PPVO-based hybrid predictor (HPPVO) is proposed as an extension. HPPVO predicts pixel in both positive and negative orientations. Assisted by expansion bins selection technique, this hybrid predictor presents an optimized prediction-error expansion strategy including bin 0. Furthermore, a novel field-biased context pixel selection is already developed, with which detailed correlations of around pixels are better exploited more than equalizing scheme merely. Experiment results show that the proposed HPPVO improves embedding capacity and enhances marked image fidelity. It also outperforms some other state-of-the-art methods of reversible data hiding, especially for moderate and large payloads

Obtuse Angle Prediction and Factor Evaluation for Image Reversible Data Hiding

A pixel-based pixel-value-ordering (PPVO) has been used for reversible data hiding to generate large embedding capacity and high-fidelity marked images. The original PPVO invented an effective prediction strategy in pixel-by-pixel manner. This paper extends PPVO and proposes an obtuse angle prediction (OAP) scheme, in which each pixel is predicted by context pixels with better distribution. Moreover, for evaluating prediction power, a mathematical model is constructed and three factors, including the context vector dimension, the maximum prediction angle, and the current pixel location, are analyzed in detail. Experimental results declare that the proposed OAP approach can achieve higher PSNR values than PPVO and some other state-of-the-art methods, especially in the moderate and large payload sizes

Dynamic Economic Dispatch Model of Microgrid Containing Energy Storage Components Based on a Variant of NSGA-II Algorithm

With the development of microgrid, in order to improve the economy of the microgrid and intelligent service of electric power marketing, the proper management of the output of micro-source in microgrid and power exchange between grids is an urgent problem to be solved. Considering the interests of multiple stakeholders, such as users, power grids, renewable energy and battery, a dynamic economic dispatch model of microgrid is proposed in this paper based on time-of-use power price mechanism. Using a variant of Non-Dominated Sorting Genetic Algorithm (NSGA)-II algorithm, at the same time, an external penalty function is introduced to deal with the constraint conditions, which is convenient for solving multi-objective optimization models. Based on the data of load forecasting and renewable energy output in microgrid, the function of battery and time-of-use power price mechanism is considered to optimize the output of controllable micro-source in the system, in order to achieve the optimization of microgrid dispatch. The model established in this paper considers the overall economic optimization of multi-objective and multi-interest groups within the microgrid, and hence, can get a more comprehensive and reasonable scheduling scheme. It provides effective help for the operation of micro grid system, and realizes the electric power marketing for demand side, so as to provide help for improving the power marketing’s economy and intelligent service

MGF-Based Mutual Approximation of Hybrid Fading: Performance of Wireless/Power Line Relaying Communication for IoT

Wireless and power line communications (PLC) are important components of distribution network communication, and have a broad application prospect in the fields of intelligent power consumption and home Internet of Things (IoT). This study mainly analyzes the performance of a dual-hop wireless/power line hybrid fading system employing an amplify-and-forward (AF) relay in terms of outage probability and average bit error rate (BER). The Nakagami-m distribution captures the wireless channel fading; whereas the PLC channel gain is characterized by the Log-normal (LogN) distribution. Moreover, the Bernoulli-Gaussian noise model is used on the noise attached to the PLC channel. Owing to the similarity between LogN and Gamma distributions, the key parameters of probability density function (PDF) with approximate distribution are determined by using moment generating function (MGF) equations, joint optimization of s vectors, and approximation of LogN variable sum. The MGF of the harmonic mean of the dual Gamma distribution variables is derived to evaluate the system performance suitable for any fading parameter m value. Finally, Monte Carlo simulation is used to verify the versatility and accuracy of the proposed method, and the influence of the hybrid fading channel and multidimensional impulse noise parameters on system performance is analyzed

An Improved Unit-Linking PCNN for Segmentation of Infrared Insulator Image

To segment the infrared insulator image efficiently, an improved Unit-linking PCNN algorithm, which makes improvements on both the linking coefficient b and the standard for choosing the best segmented image, is proposed in this paper. The relationship of the gray value of each neuron is used to determine the linking coefficient b and MSE, which consider the relationship between the gray value of the original image and the segmented image, is used to determine the best segmented image. The proposed algorithm is tested on both the standard test images and the aerial infrared images and the results show that the proposed algorithm gives better segmentation of the target image and better vision effect and less time are needed to get the best one