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With the rapid development of inverter-based generators (IGs), power grid is faced with critical frequency stability challenges because the existing IGs have no inertia. To equip IGs with inertial response, researchers have proposed several virtual inertia control methods, which can be classified into two categories: virtual synchronous generator (VSG) control and droop control based on rate of change of frequency (ROCOF-droop control). In this paper, the comparison between both virtual inertia control methods is conducted from three perspectives: mathematical model, output characteristic and small-signal stability. State-space models are firstly built to analyze the control mechanism of VSG control and ROCOF-droop control methods. Simulation and eigenvalue analysis are conducted to study the transient responses and oscillation characteristics of both methods, which is helpful to understand the advantages and limitations of existing virtual inertia control methods. Finally, the obtained theoretical results are validated through realtime laboratory (RT-LAB) hardware-in-loop simulation platform.

Doubly Fed Induction Generator (DFIG) and Permanent Magnet Synchronous Generator (PMSG) are converter-interfaced wind turbines, which have much more complex short circuit behaviour after grid voltage drops. The research on short circuit current contributions provided by the wind turbines is very important for power system planning and relay protection's setting. Here, the Low Voltage Ride Through (LVRT) test data in north China are used to study the short circuit behaviour of DFIGs and PMSGs. The characteristics of two featured quantities, fundamental frequency component and peak current, are investigated. Unlike simulation or theoretical analysis, the field test data of real wind turbines from different manufacturers help utility operators to better understand the fault characteristics of converter-interfaced wind turbines.

According to the character of active distribution network that it could actively control and manage distributed energy resources, this paper puts forward a distribution network reconfiguration method for active distribution network. Because of the random power of distributed generation driven by renewable energy, this paper regards power of controllable load and electrical energy storage as controllable value and takes the effect of V2G(Vehicle-to-grid)into consideration, thus establishes a multi-objective optimization model. Based on knowledge of Graph Theory, this paper proposes a reconfiguration strategy containing island partition, and uses a hybrid algorithm of Prim algorithm and chaos particle swarm optimization to solve the model and optimize reconfiguration project. The validity of the proposed method of active distribution network reconfiguration is verified by simulation results of IEEE 33-node distribution system.

Partial discharge in the transformer may generate noise. In view of this characteristic, it is proposed to apply the audio detection method to the diagnosis of partial discharge. Discharge faults in the transformer were simulated, and a method for diagnosis of these faults based on audio is studied. Three models for typical partial discharges are designed, namely the plate electrode discharge, the pin plate electrode discharge and the surface discharge. The audio produced by these discharges was recorded and hence an abnormal audio database was built. Using wavelet packet algorithm, audio features of partial discharge are extracted, and then the neural network algorithm is used to distinguish one kind of discharges from others successfully.

Audio method is useful for fault diagnosis as some mechanical failures in power transformers may cause abnormal sound. Some metal elements are loose or even fall in the transformer tank, which may cause them to collide with each other and make an abnormal sound. A model is developed to simulate the impact in transformers and the sound of the impact is recorded. The wavelet packet algorithm is used to extract the audio characteristics, and then the neural network algorithm is used for fault diagnosis. The characteristics of the sound are analyzed, and the sound of the impact is identified, so that it can be diagnosed whether there is an impact fault caused by loose elements in the transformer.

In China, large-scale wind power are usually integrated to the power system via long transmission lines, which is one of the most important development and utilization mode. With the increasing of installed capacity in wind power integration area, power quality problems, especially voltage unbalance rise. In this paper, an improved automatic voltage control (AVC) framework is proposed and the three-phase reactive power optimization model is developed considering wind power integration. Therefore, the goals of voltage profile and negative-sequence voltage depression are achieved. Finally, the efficiency are verified via a case study in North China.

Wind power forecasting (WPF) is an effective way to eliminate the negative impacts and enhance the market competitiveness of wind power. Probabilistic WPF gives the prediction intervals of possible output power, which may largely improve the confidence level and accuracy of the wind power forecasts. Considering the fluctuation characters of wind power, a combination scheme of different Gaussian kernels is used to establish the Gaussian Process Regression model, which is used to predict the mean value and the confidence interval of output power by taking wind speed and wind direction as input variates. Via the case analysis of a real wind farm in North China, results show that, the proposed Gaussian kernel scheme can well describe the fluctuation process of wind power, and the uncertain intervals produced by the established probabilistic WPF model basically able to cover the measured power. Compared with a typical statistical WPF model based on neural network, the improvement of monthly forecasting precision is up to 1.8 %.

The closed-form motion equation of the redundant actuation parallel robot with joint friction is established by an extended application of the Udwadia–Kalaba modeling. Based on the cascading nature of the Udwadia–Kalaba equation, the motion equation of the parallel robot is obtained in a hierarchical manner. The redundant actuation parallel robot is segmented into several leg subsystems, which are connected by the kinematic constraints. The constraint forces between the subsystems can be calculated by the Udwadia–Kalaba equation. In virtue of the derived constraint forces, the explicit joints friction models, described by Coulomb friction and Stribeck friction, are formulated separately. There are no auxiliary variables (such as Lagrange multipliers or pseudo-generalized speeds) needed in the method. The established dynamic modeling technique evades the curse of dimensionality when dealing with Moore–Penrose inverse. A 2-DOF redundant actuation parallel robot is chosen to demonstrate the method.

Linear power flow (LPF) is necessary for robust and fast centralized control of active distribution networks (ADNs). With penetration of distributed generators (DGs) into ADN, voltage-controlled nodes are becoming more common, to maintain a normal voltage profile in the distribution network. Existing three-phase LPF formulations under a rectangular coordinate system cannot cope with local voltage-controlled nodes, and does not consider multi-slack-node features of three-phase distribution network, detailed control characteristics and loss participation features of DGs. Here, a general three-phase LPF under a polar coordinate system is presented to address these issues. The proposed method can account for various connection ZIP loads, transformers, and single-phase or three-phase DGs. The detailed control model of the DGs and the distributed slack bus are taken into account. The effectiveness and advantages of the proposed method are validated with balanced 33, 70, 84, 119, and 874-node networks and modified IEEE 13, 34, 37, and 123 unbalanced networks.

In weak power system with large-scale wind power integration, three-phase unbalance occurs frequently. Voltage unbalance may lead to more losses and heat to power system and deteriorate operation performance of wind turbine generators. In this paper, the voltage unbalance mechanism is investigated based on a field test case in North China. The technical features of three-phase voltage are extracted from field test data. An equivalent three-phase model is proposed, based on which, the theoretical derivation of voltage unbalance factor(VUF) is investigated and impacts of wind power on VUF are analyzed. Finally, the field practice model is built on matlab/simulink platform and the simulated three-phase voltage and VUF are compared with field test data.

In order to ensure the micro-grid with distributed power to operate in a more economical, flexible and environmental way as well as the superiority of distributed power can be given full play. Aimed at the scheduling of the optimization of micro-grid, the paper propose the multi-objective optimization model that electric vehicles will be both a load and power generation unit at the same time, which aims to maximize the economic benefit and minimize the environmental cost. Using the non-objective sorting genetic algorithm NSGA-to solve the multi-objective optimization problems and obtain the Pareto front-end methods, finally, getting the best dispatch strategy of micro-grid. The validity of the proposed model, dispatch strategy and algorithm is proved by the examples.

At present, the stability of virtual synchronous generator (VSG) is mainly studied in micro-grid or single grid-connected VSG, and the stability of multiple grid-connected VSGs still lacked the research. This paper builds the small signal models of multiple grid-connected VSGs, and the influence of VSG control parameters (active power droop coefficient, virtual inertia, etc.) on the characteristic roots and stability of the system are discussed. Besides, the stability difference between multiple grid-connected VSG and single VSG are compared. Finally, the correctness of small signal modeling and results analysis are verified by simulation. The results of this paper show that when the parameters of multiple VSG grid-connected systems are the same, the stability of multiple VSGs is basically the same as that of single VSG system, while the system oscillation mode may have a significant difference when there are some differences in VSG parameters.

In order to stabilize the engine speed, increase the dynamic performance, save the energy, this paper presents a speed sensing control method for an EFI engine. The best operating points of an engine were discussed according to the static characteristics of an engine. Based on the best operating points and the load, the engine speed could be controlled by a double-closed loop control method, which could obtain the feedback signal from the engine speed and the throttle position. The model of a 161kW diesel engine and the control method were built, and the simulation was calculated based on the best working conditions. The results showed that the method could maintain the actual speed of an engine close to the goal speed, and achieve the requirements of the maximum torque, maximum power and the most economical output using the best operation points.

This paper presents a method of using nonlinear decision function to improve the performance of AdaBoost with SVM based weak learners. Compared with the existing AdaBoostSVM methods,this method, named ERBF-AdaBoostSVM, has advantages of higher hate rate and better generalization performance. This method also provides non-linear separator in the weak learner space and classifies accurately more examples. Experimental results demonstrated that ERBF-AdaBoostSVM achieve better generalization performance and higher hate rate than the existing SVM and AdaBoostSVM methods.