Estimating Critical Clearing Time of Grid Faults Using DA of State-Reduction Model of Power Systems

This paper proposes a critical clearing time (CCT) estimation method by the domain of attraction (DA) of a state-reduction model of power systems using sum of squares (SOS) programming. By exploiting the property of the Jacobian matrix and the structure of the boundary of the DA, it is found the DA of the state-reduction model and that of the full model of a power system are topological isomorphism. There are one-to-one correspondence relationships between the number of equilibrium points, the type of equilibrium points, and solutions of the two system models. Based on these findings, an expanding interior algorithm is proposed with SOS programming to estimate the DA of the state-reduction model. State trajectories of the full model can be transformed to those of the state-reduction model by orthogonal or equiradius projection. In this way, CCT of a grid fault is estimated with the DA of the state-reduction model. The calculational burden of SOS programming in the DA estimation using the state-reduction model is rather small compared with using the full model. Simulation results show the proposed expanding interior algorithm is able to provide a tight estimation of DA of power systems with higher accuracy and lower time costs.

A User-Transformer Relation Identification Method Based on QPSO and Kernel Fuzzy Clustering

User-transformer relations are significant to electric power marketing,power supply safety,and line loss calculations.To get accurate user-transformer relations,this paper proposes an identification method for user-transformer relations based on improved quantum particle swarm optimization(QPSO)and Fuzzy C-Means Clustering.The main idea is:as energymeters at different transformer areas exhibit different zero-crossing shift features,we classify the zero-crossing shift data from energy meters through Fuzzy C-Means Clustering and compare it with that at the transformer end to identify user-transformer relations.The proposed method contributes in three main ways.First,based on the fuzzy C-means clustering algorithm(FCM),the quantum particle swarm optimization(PSO)is introduced to optimize the FCM clustering center and kernel parameters.The optimized FCM algorithm can improve clustering accuracy and efficiency.Since easily falls into a local optimum,an improved PSO optimization algorithm(IQPSO)is proposed.Secondly,considering that traditional FCM cannot solve the linear inseparability problem,this article uses a FCM(KFCM)that introduces kernel functions.Combinedwith the IQPSOoptimization algorithm used in the previous step,the IQPSO-KFCM algorithm is proposed.Simulation experiments verify the superiority of the proposed method.Finally,the proposed method is applied to transformer detection.The proposed method determines the class members of transformers and meters in the actual transformer area,and obtains results consistent with actual user-transformer relations.This fully shows that the proposed method has practical application value.

Efficient utilization of wind power： Long-distance transmission or local consumption？

Excess wind power produced in wind-inten- sive areas is normally delivered to remote load centers via long-distance transmission lines. This paper presents a comparison between long-distance transmission, which has gained popularity, and local energy consumption, in which a fraction of the generated wind power can be locally consumed by energy-intensive industries. First, the challenges and solutions to the long-distance transmission and local consumption of wind power are presented. Then, the two approaches to the utilization of wind power are compared in terms of system security, reliability, cost, and capability to utilize wind energy. Finally, the economic feasibility and technical feasibility of the local consumption of wind power are demonstrated by a large and isolated industrial power system, or supermicrogrid, in China. The coal-fired generators together with the shortterm interruptible electrolytic aluminum load in the supermicrogrid are able to compensate for the intermit- tency of wind power. In the long term, the transfer of high- energy-consumption industries to wind-rich areas and their local consumption of the available wind power are beneficial.

Load Shedding and Restoration for Intentional Island with Renewable Distributed Generation

Due to the high penetration of renewable distributed generation(RDG),many issues have become conspicuous during the intentional island operation such as the power mismatch of load shedding during the transition process and the power imbalance during the restoration process.In this paper,a phase measurement unit(PMU)based online load shedding strategy and a conservation voltage reduction(CVR)based multi-period restoration strategy are proposed for the intentional island with RDG.The proposed load shedding strategy,which is driven by the blackout event,consists of the load shedding optimization and correction table.Before the occurrence of the large-scale blackout,the load shedding optimization is solved periodically to obtain the optimal load shedding plan,which meets the dynamic and steady constraints.When the blackout occurs,the correction table updated in real time based on the PMU data is used to modify the load shedding plan to eliminate the power mismatch caused by the fluctuation of RDG.After the system transits to the intentional island seamlessly,multi-period restoration plans are generated to optimize the restoration performance while maintaining power balance until the main grid is repaired.Besides,CVR technology is implemented to restore more loads by regulating load demand.The proposed load shedding optimization and restoration optimization are linearized to mixed-integer quadratic constraint programming(MIQCP)models.The effectiveness of the proposed strategies is verified with the modified IEEE 33-node system on the real-time digital simulation(RTDS)platform.

Evaluation of lightning-induced overvoltage on a 10 kV distribution line based on electromagnetic return-stroke model using finite-difference time-domain

Accurate simulation of lightning-induced overvoltage for overhead distribution lines is helpful to prevent lightning trip accidents.An electromagnetic return-stroke model was used to represent lightning and then a 3D finite-difference time-domain(FDTD)method was adopted to simulate the lightning-induced overvoltage on a distribution line without a field-line coupling model.How lightning-induced overvoltage behave for different ground conductivity and varying distance between the distribution line and the lightning channel was analysed.The results showed that the overvoltage waveforms at the centre point of the line corresponding to lightning strikes on the lossy ground and an ideal ground(σ=∞)were similar;however,the peak amplitudes of the waveform were affected by soil conductivity at a close distance.The relationship between magnitude of the overvoltage and distance can be described by a second-order exponential decay equation.Finally,the overvoltage calculated using the proposed model was compared with those obtained based on Agrawal's model and measurements made using the newly developed intelligent insulator on site.From these comparisons,it could be concluded that the FDTD method with the electromagnetic return-stroke model produces reasonably accurate results of the attenuated oscillation waveform,which can better reproduce the overvoltage on operational distribution lines.