The Experts below are selected from a list of 293319 Experts worldwide ranked by ideXlab platform

Donghua Zhou - One of the best experts on this subject based on the ideXlab platform.

  • A Residual Storage Life Prediction Approach for Systems With Operation State Switches
    IEEE Transactions on Industrial Electronics, 2014
    Co-Authors: Xiangyu Kong, Donghua Zhou
    Abstract:

    This paper concerns the problem of predicting residual storage life for a class of highly critical systems with Operation State switches between the working State and storage State. A success of estimating the residual storage life for such systems depends heavily on incorporating their two main characteristics: 1) system Operation process could experience a number of State transitions between the working State and storage State; and 2) system's degradation depends on its Operation States. Toward this end, we present a novel degradation model to account for the dependency of the degradation process on the system's Operation States, where a two-State continuous-time homogeneous Markov process is used to approximate the switches between the working State and storage State. Using the monitored degradation data during the working State and the available system Operation information, the parameters in the presented model can be estimated/updated under Bayesian paradigm. Then, the posterior probabilistic law of the number of State transitions and their transition times are derived, and further, the formulation for the predicted residual storage life distribution is established by considering the possible State transitions in the future. To be solvable, a numerical solution algorithm is provided to calculate the distribution of the predicted residual storage life. Finally, we demonstrate the proposed approach by a case study for gyroscopes.

Zhijian Zhang - One of the best experts on this subject based on the ideXlab platform.

  • Deep Reinforcement Learning-Based Tie-Line Power Adjustment Method for Power System Operation State Calculation
    IEEE Access, 2019
    Co-Authors: Xu Huating, Qingping Zheng, Jinxiu Hou, Yawei Wei, Zhijian Zhang
    Abstract:

    Operation State calculation (OSC) provides safe operating boundaries for power systems. The operators rely on the software-aid OSC results to dispatch the generators for grid control. Currently, the OSC workload has increased dramatically, as the power grid structure expands rapidly to mitigate renewable source integration. However, the OSC is processed with a lot of manual interventions in most dispatching centers, which makes the OSC error-prone and personnel-experience oriented. Therefore, it is crucial to upgrade the current OSC in an automatic mode for efficiency and quality improvements. An essential process in the OSC is the tie-line power (TP) adjustment. In this paper, a new TP adjustment method is proposed using an adaptive mapping strategy and a Markov Decision Process (MDP) formulation. Then, a model-free deep reinforcement learning (DRL) algorithm is proposed to solve the formulated MDP and learn an optimal adjustment strategy. The improvement techniques of “stepwise training” and “prioritized target replay” are included to decompose the large-scale complex problems and improve the training efficiency. Finally, five experiments are conducted on the IEEE 39-bus system and an actual 2725-bus power grid of China for the effectiveness demonstration.

Xiangyu Kong - One of the best experts on this subject based on the ideXlab platform.

  • A Residual Storage Life Prediction Approach for Systems With Operation State Switches
    IEEE Transactions on Industrial Electronics, 2014
    Co-Authors: Xiangyu Kong, Donghua Zhou
    Abstract:

    This paper concerns the problem of predicting residual storage life for a class of highly critical systems with Operation State switches between the working State and storage State. A success of estimating the residual storage life for such systems depends heavily on incorporating their two main characteristics: 1) system Operation process could experience a number of State transitions between the working State and storage State; and 2) system's degradation depends on its Operation States. Toward this end, we present a novel degradation model to account for the dependency of the degradation process on the system's Operation States, where a two-State continuous-time homogeneous Markov process is used to approximate the switches between the working State and storage State. Using the monitored degradation data during the working State and the available system Operation information, the parameters in the presented model can be estimated/updated under Bayesian paradigm. Then, the posterior probabilistic law of the number of State transitions and their transition times are derived, and further, the formulation for the predicted residual storage life distribution is established by considering the possible State transitions in the future. To be solvable, a numerical solution algorithm is provided to calculate the distribution of the predicted residual storage life. Finally, we demonstrate the proposed approach by a case study for gyroscopes.

Nan Wang - One of the best experts on this subject based on the ideXlab platform.

  • Chance-constrained programming model for reference network with wind power integration
    2017 IEEE Conference on Energy Internet and Energy System Integration (EI2), 2017
    Co-Authors: Donglei Sun, Jinhong Yang, Xueshan Han, J. Wang, Xin Tian, Bin Yang, Zhang Lina, Nan Wang
    Abstract:

    With the large-scale integration of volatile wind power generations, power system Operation and planning face the problem of increasing injection fluctuations and uncertainties. In this paper, chance constraint expressions are used to characterize the wind power uncertainty, and then a new chance-constrained programming model for reference network with wind power integration is proposed to ensure the expected wind power utilization. In the proposed model, the objective function is minimizing total power generation cost and transmission investment cost, and the constraints are the safety Operation technical requirements under intact Operation State and the preconceived contingency Operation State. The sample average approximation (SAA) method is used to develop a deterministic approach for the proposed model. Numerical analysis shows the potential benefit of the proposed model.

Chen Xingtia - One of the best experts on this subject based on the ideXlab platform.

  • A Big Data Simplification Method for Evaluation of Relay Protection Operation State
    2015
    Co-Authors: Chen Xingtia
    Abstract:

    Online evaluation is an important mean to make sure relay protection system did the correct action when a fault occurs. Relay protection information system has Big Data characteristics, it's hard to use so much data directly for relay protection system online evaluation, because of data transfer blockage, unsynchronized data source, lack of intelligence analysis tools. Based on the demand of reducing the processing amount of relay protection information data, this paper proposed a State assessment data reduction ideas based on the process information of internal protection, constructed simplified data index set that could reflect the characteristics of relay protection Operation State, and built relay protection online evaluation method using simplified data index. By using simplified index and evaluation model, it can provide enough information for master station, and use less information transmission to realize State analysis. While ensuring the effect of evaluation, this method reduces upload amount of data and evaluation time, provides a reference for the further use of Big Data in power transmission and transformation aspects. The proposed method is verified by a typical 220 k V substation, which has proved the superiority on reducing upload amount of data and fast evaluation of relay protection devices.