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Fault trip of overload line transfers the power flow to other lines in the associated transmission section, which results in interlocking tripping accident. The key to prevent cascading tripping accidents is to quickly formulate a reasonable and effective power flow control strategy to eliminate line overload. In view of the defects of low accuracy of sensitivity method and too much calculation of optimization method, a double-layer optimal power flow control strategy of transmission section based on positive and negative comprehensive sensitivity is proposed. Considering the overall regulation effect of nodes on overload and heavy load line power flow, the positive and negative comprehensive sensitivity index is proposed, and then the power flow control strategy is quickly formulated by double-layer optimization method. In order to prevent the surge of computation caused by too large search space, a multi-objective selecting optimization model of the first layer scheduling nodes is established to optimize the scheduling nodes involved in the adjustment. On this basis, the second layer power flow regulation optimization model is constructed to obtain the accurate output adjustment of the scheduling nodes. The above model realizes the accurate dispatching of power flow out of limit on the transmission section. The simulation results of IEEE 39-bus system and a provincial power grid verify the rapidity and accuracy of the proposed method.

Aiming at the problem that traditional convolutional neural network (CNN) continuously stacks convo-lutional layers and pooling layers in order to obtain high accuracy, resulting in complicated model structure, long training time, and single data processing method, a optimized layered convolutional sub-health recognition algorithm of improved capsule network is proposed. Firstly, the original vibration data are transformed by wavelet denoising and wavelet packet denoising to better retain the useful information in the original signal for sub-health recognition. Secondly, CNN adopts the idea of layered convolution, parallelizes three convolution kernels of different scales, and carries on multi-angle feature extraction. Finally, the features extracted by the convolution kernels are input into the improved capsule network with pruning strategy for sub-health recognition. The improved capsule network can not only guarantee the accuracy, but also accelerate the sub-health recognition time, thus the problems of too compli-cated CNN structure and poor recognition effect are solved. Experimental results show that the proposed algorithm has high recognition accuracy and less recognition time.

Equipment maintenance decision is a very critical technology in power grid asset management. The traditional maintenance decision focuses on the analysis of the operation performance of the equipment itself, but lacks the analysis and description of the fuzziness of power equipment outage parameters. Therefore, this paper establishes the reliability assessment model based on equipment health condition monitoring, which makes the maintenance decision transition from equipment level to system level, and the fuzzy mathematics theory is introduced to establish a model describing the fuzziness of equipment failure rate parameter to make system reliability assessment and equipment maintenance decision scheme. Firstly, this paper proposes a fuzzy failure rate model of power transformer based on condition monitoring. Then the fuzzy parameters are combined with the conventional probabilistic reliability assessment method to establish the fuzzy probability hybrid reliability assessment model of the transmission system, and the fuzzy maintenance reliability benefit index is defined and deduced. Finally, a maintenance strategy of transformer based on fuzzy probability hybrid reliability assessment and fuzzy set theory is proposed, and the case study is carried out on a regional power grid in East China. The results show that the proposed model provides an improved maintenance strategy of power equipment. [ABSTRACT FROM AUTHOR]

Time-of-use (TOU) is an effective price-based demand response strategy. A reasonable design of TOU strategy can effectively reduce the peak-valley difference, and then produce a lot of benefits (such as delaying power grid investment, reducing interruption cost, and improving reliability). However, changing peak-valley period has a great influence on the peak-valley difference and power supply reliability of power system. Therefore, this paper aims to investigate the optimal modification of peak-valley period considering reliability loss under multiple TOU schemes. Firstly, this paper presents a clustering model and algorithm of optimal load curve based on a minimum error iteration method. Secondly, an optimal modification of peak-valley period based on a dynamic load point method is proposed, and the traditional peak-valley difference is replaced by the global peak-valley difference to calculate the objective function. Thirdly, this paper establishes a load–reliability relation fitting model based on the back propagation neural network. Finally, the effectiveness and correctness of the proposed method are investigated by the Roy Billinton test system and reliability test system. [ABSTRACT FROM AUTHOR]

Time of use pricing strategy can not only reduce the load fluctuation but also improve the power system reliability. Generally, before carrying out a time of use pricing optimization, it needs to perform a period partition optimization for dividing peak‐valley periods. In existing researches, however, the period partition optimization and the time of use pricing optimization are implemented independently. Although the output of period partition optimization is used as the input of time of use pricing optimization, the bidirectional feedback effect between period partition optimization and time of use pricing optimization is not considered, which will lead to the existing time of use pricing optimization may not be the globally optimal solution. Therefore, this paper investigates a new optimization method for time of use pricing optimization with the consideration of bidirectional feedback effect, establishes a comprehensive customer satisfaction degree model considering the customer load proportion coefficient, and defines an elastic cofactor to describe the electricity price response difference of multiple types of customers. The Roy Billinton test system is used to verify the correctness and effectiveness of the proposed method in this paper. [ABSTRACT FROM AUTHOR]

Natural gas, as a clean fossil energy, has been widely used in power generation system by converting chemical energy into electrical energy through gas turbines. However, the natural gas pipeline network system (NGPNS) is comprised of many equipments, and the failure of any equipment will probably cause the outage of gas turbines. Therefore, this study proposes an analytical method which can incorporate the uncertainty of NGPNS into the reliability assessment of integrated energy system. Firstly, on the basis of an analytical technique, NGPNS's operating states and the corresponding state probabilities are simulated. Secondly, a state transferring and equivalence technique is proposed for transferring the unreliability of NGPNS to the involved outage of gas turbines. Thirdly, an equivalent reliability model of gas turbine is established for taking into account the uncertainty of NGPNS. Finally, the NGPNS is connected to the Roy Billinton Test System through the coupling of gas turbines between electrical and gas networks. The results of case studies verify the correctness and effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a maintenance strategy for overhead lines based on condition monitoring and system reliability assessment. A quantitative relationship between the condition monitoring data and failure rate of overhead lines is established. The system reliability is evaluated using monitoring data; the reliability benefit due to the maintenance of each line is quantified; and the maintenance ranking order is created using the benefit/cost ratio index. The proposed strategy can provide the largest reliability improvement at the lowest maintenance cost and within budget. Case studies were performed on a 182-bus, 5474-MW transmission system located in southwest China. [ABSTRACT FROM PUBLISHER]

This article presents a quantified method to evaluate an overall condition index of power equipment and its probability density distribution using monitored data. A special transformation is developed to normalize the monitored data of different parameters. The overall condition index can be easily calculated using the normalized parameters. To capture the uncertainty and randomness of the monitored data, the kernel density evaluation method is used to assess the probability distribution of the condition index. With the probability density function, the probability of power equipment operating in the normal condition can be assessed. The monitored data of two breakers for five monitored parameters are used in the case study. The results indicate that the proposed method can straightforwardly identify which breaker has a worse performance and, therefore, should be considered for maintenance first. [ABSTRACT FROM AUTHOR]

Information between interconnected power systems is difficult to share in real time, due to trade secrets and technical limitations. The regional power grid cannot timely detect the impact of changes in the operation mode of the external power grid on the regional reliability, due to faults, load fluctuations, power generation plan adjustments, and other reasons. How to evaluate the reliability of a regional power system under the conditions of information isolation is a difficult problem for the security of interconnected power systems. Aiming at this problem, an operational reliability evaluation method for an interconnected power system is proposed herein, which does not depend on external network information directly, but only uses boundary phasor measurement unit (PMU) measurement data and internal network information. A static equivalent model with sensitivity consistency was used to simplify the external network to ensure the accuracy of the reliability calculation of interconnected power systems. The boundary PMU measurement data were used to update the external network equivalent model online. The algorithm flow of the operation reliability assessment for the interconnected power grid is given. The results of an example based on the IEEE-RTS-96 test system show that the proposed method can track the equivalent parameters of the external network without depending on the actual topological information, and calculate the reliability index of the internal network accurately. [ABSTRACT FROM AUTHOR]

The growing penetration of wind power in a power system brings great challenges to system operation flexibility. For generation planning in presence of high wind power penetration, it is essential to take the operation flexibility of the system into account. Firstly, this paper developed the system operation flexibility metrics through considering the flexibility contribution of thermal generating units (TGUs) by operational state transition. Secondly, a planning model for the bundled wind-thermal-storage generation system (BWTSGS) that considers the operation flexibility constraints is proposed. The planning model is used to determine the power and energy rating of an energy storage system (ESS) as well as the type and number of TGUs. A daily scheduling simulation model of a BWTSGS is proposed to calculate the operation cost for the planning model and consider the sequential operation characteristics of the BWTSGS. Further, in order to accelerate the computation, a wind power sequential clustering technique based on the discrete Fourier transform (DFT) method is developed for improving the computational efficiency. Case studies have been conducted on a 1000-MW wind farm to demonstrate the validity and effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]