Search results

It is very indispensable for the various control centers of multi-transmission system owners (TSOs) grids to coordinate their reactive power optimization (RPO) efforts. However, such coordinated equilibrium point is comparatively hard to achieve unless one TSO control center could obtain all grids’ information in detail, which may lead to confidential issue and heavy communicating load. The purpose of this paper is to propose a solution to optimizing the reactive power control efforts among multi-TSOs grids with a mathematic interconnection model and reasonable communication cost.

Based on the interconnected power network equation, the stability-related optimum reactive power injection and the power-loss-related optimum reactive power injection were derived, respectively. Furthermore, according to the decomposition-and-coordination-based computing methodology, a coordinated RPO model for interconnected TSOs was designed, taking into consideration both the static voltage stability and economy.

The extreme values for the indicator L of power grid voltage stability and active power loss function were found and proved to be minimums. According to these extreme values, an expression for the reactive power injection at interconnected nodes between TSOs grids was obtained, and a coordinated strategy of RPO was established, which could take the static voltage stability and economy into consideration without confidential concern.

The existence of minimum values for indicator L of voltage stability and power loss was demonstrated, respectively. And the method presented in this paper can ensure the safety of information among different TSO grids, i.e. avoiding confidential issues. In particular, the coordinated control method can be implemented on the local power grid without knowing all of the parameters of its interconnection.

With the growing scale of power grids, integrated power grids often contain multiple areas. When the control centre of each regional grid conducts an assessment of local voltage stability, the calculation is always based on the local regional power grid model. However, less consideration is given to a detailed model of the entire network, which may lead to a large calculation error. Under the premise of ensuring the data and information security of Supervisory Control and Data Acquisition between different regional power grid operation control centres, the purpose of this paper is to reduce calculation error simply by using the data of a local power network.

According to the calculation methodology of “decomposition and coordination” and the power balance equation of an interconnected power grid, an improved radial equivalent independent (REI) equivalent method, which can reflect the dynamic characteristics of interconnected power grids to a certain extent, is proposed in this paper. A mathematical model of multi-area-grid L indicator synergic computing is derived as well.

With the calculation of Institute of Electrical and Electronics Engineers (IEEE) standard grids and an actual grid model, it is proven that the method proposed in this paper can significantly improve the accuracy of the regional power grid L indicator calculation and achieve the synergic computing of a multi-area power system L indicator, without an increase in data interaction among the regional power grids.

The indicator of voltage stability among multi-area was obtained by using the improved REI equivalent method with the change of the load participation factor. Particularly, the coordinated calculation method can be implemented on a local power grid without knowledge of all the parameters of its interconnection, which can avoid possible leakage of confidential data and information of the system owners.

The study aims to accomplish the short-term load forecasting for microgrids. Short-term load forecasting is a vital component of economic dispatch in microgrids, and the forecasting error directly affects the economic efficiency of operation. To some extent, short-term load forecasting is more difficult in microgrids than in macrogrids.

This paper presents the method of Dragonfly Algorithm-based support vector machine (DA-SVM) to forecast the short-term load in microgrids. This method adopts the combination of penalty factor C and kernel parameters of SVM which needs to be optimized as the position of dragonfly to find the solution. It takes the forecast accuracy calculated by SVM as the current fitness value of dragonfly and the optimal position of dragonfly obtained through iteration is considered as the optimal combination of parameters C and s of SVM.

DA-SVM algorithm was used to do short-term load forecast in the microgrid of an offshore oilfield group in the Bohai Sea, China and the forecasting results were compared with those of PSO-SVM, GA-SVM and BP neural network models. The experimental results indicate that the DA-SVM algorithm has better global searching ability. In the case of study, the root mean square errors of DA-SVA are about 1.5 per cent and its computation time is saved about 50 per cent.

The DA-SVM model presented in this paper provides an efficient and effective method of short-term load forecasting for a microgrid electric power system.

Considering the problem that the high recognition rate of deep learning requires the support of mass data, this study aims to propose an insulating fault identification method based on small data set convolutional neural network (CNN).

Because of the chaotic characteristics of partial discharge (PD) signals, the equivalent transformation of the PD signal of unit power frequency period is carried out by phase space reconstruction to derive the chaotic features. At the same time, geometric, fractal, entropy and time domain features are extracted to increase the volume of feature data. Finally, the combined features are constructed and imported into CNN to complete PD recognition.

The results of the case study show that the proposed method can realize the PD recognition of small data set and make up for the shortcomings of the methods based on CNN. Also, the 1-CNN built in this paper has better recognition performance for four typical insulation faults of cable accessories. The recognition performance is improved by 4.37% and 1.25%, respectively, compared with similar methods based on support vector machine and BPNN.

In this paper, a method of insulation fault recognition based on CNN with small data set is proposed, which can solve the difficulty to realize insulation fault recognition of cable accessories and deep data mining because of insufficient measure data.

The purpose of this paper is to form a numerical simulation method for permeability coefficient that can consider the characteristics of gravel gradation and further explore the effects of indoor test factors and gradation characteristics on the permeability coefficient of gravel.

The random point method is used to establish the polyhedral gravel particle model, the discrete element method (DEM) is used to construct the gravel permeability test sample with gradation characteristics and the finite element method is used to calculate the permeability coefficient to form a DEM-computational fluid dynamics combined method to simulate the gravel seepage characteristics. Then, verified by the indoor test results. Based on this method, the influence of sample size, treatment method of oversize particles and the content of fine particles on the permeability coefficient of gravel is studied.

For the gravel containing large particles, the larger size permeameter should be used as far as possible. When the permeameter size is limited, the equal weight substitution method is recommended for the treatment method of oversized particles. Compared with the porosity, the pore connectivity has a higher correlation with the permeability coefficient of the sample.

Insufficient consideration of the movement of gravel particles in the seepage process is also an issue for further study.

The simulation method described in this paper is helpful for qualitative analysis, quantitative expression of pore size and makes up for the defect that the seepage characteristics in pores cannot be observed in laboratory tests.

Noise abatement is one of the key techniques for Partial Discharge (PD) on-line measurement and monitoring. However, how to enhance the efficiency of PD signal noise suppression is a challenging work. Hence, this study aims to improve the efficiency of PD signal noise abatement.

In this approach, the time–frequency characteristics of PD signal had been obtained based on fast kurtogram and S-transform time–frequency spectrum, and these characteristics were used to optimize the parameters for the signal matching over-complete dictionary. Subsequently, a self-adaptive selection of matching atoms was realized when using Matching Pursuit (MP) to analyze PD signals, which leading to seldom noise signal element was represented in sparse decomposition.

The de-noising of PD signals was achieved efficiently. Simulation and experimental results show that the proposed method has good adaptability and significant noise abatement effect compared with Empirical Mode Decomposition, Wavelet Threshold and global signal sparse decomposition of MP.

A self-adaptive noise abatement method was proposed to improve the efficiency of PD signal noise suppression based on the signal sparse representation and its MP algorithm, which is significant to on-line PD measurement.

A green innovation strategy is an important step for enterprises to balance economic and environmental. As the executors of strategic decisions, the attitude and capabilities of senior managers determine the effectiveness of implementing green innovation. Therefore, this paper aims to explore the relationship between executive compensation incentives and green innovation.

Based on the data of heavily polluting enterprises listed in China's A-share market from 2015 to 2020, this study constructs an OLS model with fixed effects of time and industry, and uses the mediation three-step method to verify the correlation between executive compensation incentives, innovation openness and green innovation. Meanwhile, the grouping regression was used to test the moderating effect of environmental regulation on executive compensation incentives.

The empirical results show that executive salary incentives promote green innovation and equity incentives inhibit green innovation; the openness breadth partially mediates the relationship between salary incentives, equity incentives and green innovation, while the openness depth only partially mediates the relationship between equity incentives and green innovation; and environmental regulation positively moderates executive incentives.

Due to sample selection and variable measurement, the study lacks certain generality. Therefore, future research needs to further analyze the internal factors affecting green innovation from multiple dimensions.

This study provides a new evidence for analyzing how executive compensation measures affect green innovation, and further enhances the mediating mechanism of open innovation.

This study has significant theoretical implications for examining the intra-firm factors that affect green innovation.

This paper aims to improve the alignment accuracy of large components in aircraft assembly and an evaluation algorithm, which is based on manufacture accuracy and coordination accuracy, is proposed.

With relative deviations of manufacturing feature points and coordinate feature points, an evaluation function of assembly error is constructed. Then the optimization model of large aircraft digital alignment is established to minimize the synthesis assembly error with tolerance requirements, which consist of three-dimensional (3D) tolerance of manufacturing feature points and relative tolerance between coordination feature points. The non-linear constrained optimization problem is solved by Lagrange multiplier method and quasi-Newton method with its initial value provided by the singular value decomposition method.

The optimized postures of large components are obtained, which makes the tolerance of both manufacturing and coordination requirements be met. Concurrently, the synthesis assembly error is minimized. Compared to the result of the singular value decomposition method, the algorithm is validated in three typical cases with practical data.

The proposed method has been used in several aircraft assembly projects and gained a good effect.

This paper proposes a method to optimize the manufacturing and coordination accuracy with tolerance constraints when the postures of several components are adjusted at the same time. The results of this paper will help to improve the quality of component assemblies.