Abstract:Parallel operation of inverter modules is the solution to increase the reliability, efficiency, and redundancy of inverters in microgrids. Load sharing among inverters in distributed generators (DGs) is a key issue. This study investigates the feasibility of power-sharing among parallel DGs using a dual control strategy in islanded mode of a microgrid. PQ control and droop control techniques are established to control the microgrid operation. P-f and Q-E droop control is used to attain real and reactive power sharing. The frequency variation caused by load change is an issue in droop control strategy whereas the tracking error of inverter power in PQ control is also a challenge. To address these issues, two DGs are interfaced with two parallel inverters in an islanded AC microgrid. PQ control is investigated for controlling the output real and reactive power of the DGs by assigning their references. The inverter under enhanced droop control implements power reallocation to restore the frequency among the distributed generators with predefined droop characteristics. A dual control strategy is proposed for the AC microgrid under islanded operation without communication link. Simulation studies are carried out using MATLAB/SIMULINK and the results show the validity and effective power-sharing performance of the system while maintaining a stable operation when the microgrid is in islanding mode.

Abstract:Stable and safe operation of power grids is an important guarantee for economy development. Support Vector Machine (SVM) based stability analysis method is a significant method started in the last century. However, the SVM method has several drawbacks, e.g. low accuracy around the hyperplane and heavy computational burden when dealing with large amount of data. To tackle the above problems of the SVM model, the algorithm proposed in this paper is optimized from three aspects. Firstly, the gray area of the SVM model is judged by the probability output and the corresponding samples are processed. Therefore the clustering of the samples in the gray area is improved. The problem of low accuracy in the training of the SVM model in the gray area is improved, while the size of the sample is reduced and the efficiency is improved. Finally, by adjusting the model of the penalty factor in the SVM model after the clustering of the samples, the number of samples with unstable states being misjudged as stable is reduced. Test results on the IEEE 118-bus test system verify the proposed method.

Abstract:This paper proposes Phasor Measurement Unit (PMU) based adaptive zone settings of distance relays (PAZSD) methodology for protection of multi-terminal transmission lines (MTL). The PAZSD methodology employs current coefficients to adjust the zone settings of the relays during infeed situation. These coefficients are calculated in phasor data concentrator (PDC) at system protection center (SPC) using the current phasors obtained from PMUs. The functioning of the distance relays during infeed condition with and without the proposed methodology has been illustrated through a four-bus model implemented in PSCAD/EMTDC environment. Further, the performance of the proposed methodology has been validated in real-time, on a laboratory prototype of Extra High Voltage multi-terminal transmission lines (EHV MTL). The phasors are estimated in PMUs using NI cRIO-9063 chassis embedded with data acquisition sensors in conjunction with LabVIEW software. The simulation and hardware results prove the efficacy of the proposed methodology in enhancing the performance and reliability of conventional distance protection system in real-time EHV MTLs.

Abstract:The large-scale popularization of electric vehicles (EVs) brings the potential for grid frequency regulation. Considering the characteristics of fast response and adjustment of EVs, two control strategies of automatic generation control (AGC) with EVs are proposed responding to two high frequency regulating signals extracted from area control error (ACE) and area regulation requirement (ARR) by a digital filter, respectively. In order to dispatch regulation task to EVs, the capacity of regulation is calculated based on maximum V2G power and the present V2G power of EVs. Finally, simulations based on a two-area interconnected power system show that the proposed approaches can significantly suppress frequency deviation and reduce the active power output of traditional generation units.

Abstract:Big data technology is more and more widely used in modern power systems. Efficient collection of big data such as equipment status, maintenance and grid operation in power systems, and data mining are the important research topics for big data application in smart grid. In this paper, the application of big data technology in fast image recognition of transmission towers which are obtained using fixed-wing unmanned aerial vehicle (UAV) by large range tilt photography are researched. A method that using fast region-based convolutional neural networks (Rcnn) convolutional architecture for fast feature embedding (Caffe) to get deep learning of the massive transmission tower image, extract the image characteristics of the tower, train the tower model, and quickly recognize transmission tower image to generate power lines is proposed. The case study shows that this method can be used in tree barrier modeling of transmission lines, which can replace artificial identification of transmission tower, to reduce the time required for tower identification and generating power line, and improve the efficiency of tree barrier modeling by around 14.2%.

Abstract:Remote monitoring of transmission lines of a power system is significant for improved reliability and stability during fault conditions and protection system breakdowns. This paper proposes a smart backup monitoring system for detecting and classifying the type of transmission line fault occurred in a power grid. In contradiction to conventional methods, transmission line fault occurred at any locality within power grid can be identified and classified using measurements from phasor measurement unit (PMU) at one of the generator buses. This minimal requirement makes the proposed methodology ideal for providing backup protection. Spectral analysis of equivalent power factor angle (EPFA) variation has been adopted for detecting the occurrence of fault that occurred anywhere in the grid. Classification of the type of fault occurred is achieved from the spectral coefficients with the aid of artificial intelligence. The proposed system can considerably assist system protection center (SPC) in fault localization and to restore the line at the earliest. Effectiveness of proposed system has been validated using case studies conducted on standard power system networks.

Abstract:This paper presents a design of robust intelligent protection technique using adaptive neuro-fuzzy inference system (ANFIS) approach to detect and classify the fault types during various faults occurrence in large-scale grid-connected wind farm. Also, it is designed to determine the fault location and isolate the wind turbine generators located in the faulted zone during fault occurrence and reconnect them after fault clearance. The studied wind farm has a total rating capacity of 120 MW, where it consists of 60 doubly fed induction generator (DFIG) wind turbines each has a capacity of 2 MW. Moreover, the wind farm generators are positioned in 6 rows, where each row consists of 10 generators. The impacts of fault type, fault location, fault duration, cascaded faults, permanent fault and external grid fault on the behaviours of the generated active and reactive power are investigated. Also, the impacts of internal and external faults in cases of different transition resistances are investigated. The simulation results indicate that, the proposed ANFIS protection technique has the ability to detect, classify and determine the fault location, then isolate the faulted zones during fault occurrence and reconnect them after fault clearance. Furthermore, the wind turbines generators which are located in un-faulted zones can stay to deliver their generated active power to the grid during fault period.

Abstract:The economy of distribution networks largely depends on the utilization rate of distribution network equipment. Most of the emerging intelligent power consumption technologies have a positive effect on equipment utilization and their use can save investment of distribution networks. In this paper, the influence of intelligent power consumption technologies on the utilization rate of distribution network equipment is reviewed. The evaluation methods and indexes are assessed first and then intelligent power consumption equipment with energy storage function, vehicle-to-grid (V2G) technology and time-of-use (TOU) tariff are reviewed respectively. It is concluded that these intelligent power consumption technologies and measures have great potential to improve utilization rate of distribution network equipment because of their effective improvement to power load. Meanwhile, recommendations on how to utilize these intelligent power consumption technologies to improve utilization rate of distribution network equipment are proposed.

Abstract:The recent development of phasor measurement technique opens the way for real-time post-disturbance transient stability assessment (TSA). Following a disturbance, since the transient instability can occur very fast, there is an urgent need for fast TSA with sufficient accuracy. This paper first identifies the tradeoff relationship between the accuracy and speed in post-disturbance TSA, and then proposes an optimal self-adaptive TSA method to optimally balance such tradeoff. It uses ensemble learning and credible decision-making rule to progressively predict the post-disturbance transient stability status, and models a multi-objective optimization problem to search for the optimal balance between TSA accuracy and speed. With such optimally balanced TSA performance, the TSA decision can be made as fast as possible while maintaining an acceptable level of accuracy. The proposed method is tested on New England 10-machine 39-bus system, and the simulation results verify its high efficacy.

Abstract:To deal with the high dimensionality and computational density of the Optimal Power Flow model with Transient Stability Constraints (OTS), a credible criterion to determine transient stability is proposed based on swing curves of generator rotor and the characteristics of transient stability. With this method, the swing curves of all generator rotors will be independent one another. Therefore, when a parallel computing approach based on the MATLAB parallel toolbox is used to handle multi-contingency cases, the calculation speed is improved significantly. Finally, numerical simulations on three test systems including the NE-39 system, the IEEE 300-bus system, and 703-bus systems, show the effectiveness of the proposed method in reducing the computing time of OTS calculation.