The abnormality of valve cooling system in converter station is sometimes concealed in the fluctuation of normal monitoring signal and difficult to be identified. The adequacy of cooling capacity under some extreme conditions is difficult to assess. There are some problems such as few data sources and low information dimension, from the point of view of big data application. The corresponding algorithm model has simple logic and insufficient application of artificial intelligence technology. To solve the above problems, a cooling capacity modeling and early warning method for converter valves based on multi-source data is proposed. Based on the analysis of the operation principle of valve cooling system and the analysis of various state detection methods, the definition of cooling capacity is quantified and a multi-dimensional evaluation model is established. Based on multi-dimensional time series trend analysis and correlation analysis algorithm, a defect early warning model of valve cooling system is established. The validity of the model is verified by applying the model to the analysis of the monitoring data of valve cooling system at Suidong Station.

DC de-icing technology is one of the main methods to prevent ice coating on transmission lines. The two star-connected cascaded STATCOMs connected by neutral point can realize DC de-icing and dynamic reactive power compensation. This paper firstly introduces the operating principle of DC de-icing device, then studies the design of three-phase voltage source rectifier (VSR) control system. To solve the problem of limiting DC de-icing current value under traditional control method, this paper proposes equalization control and saturation modulation control method。Finally, a real-time simulation platform based on NI and RT-LAB is built to simulate. Results show that the de-icing current value is significantly improved, and the correctness of the scheme is verified.

To identify the type of partial discharge signal quickly and accurately for the safe operation of equipment, a method of partial discharge feature extraction and discharge recognition based on S transform and genetic algorithm optimization probabilistic neural network (GA-PNN) is proposed in this paper. Firstly, the time-frequency diagram and time-frequency matrix A of partial discharge signal are obtained by S-transform, to reduce the dimension of the matrix and remove the Gauss noise at the same time, the time-frequency matrix B of the main characteristics of partial discharge is extracted from A by S-transform time-frequency diagram and spectral kurtosis algorithm. Then singular value decomposition of time-frequency matrix B is carried out to extract appropriate number of singular values as eigenvectors to be the input of probabilistic neural networks (PNN), and genetic algorithms (GA) is used to optimize the parameters of PNN to realize the identification of partial discharge signals. Results show that the proposed method can identify the type of cable partial discharge very well, and has a better recognition effect than GA-BP.

High-voltage complex cable lines fault location process is complicated,and the fault pinpointing method and accuracy need to be improved. The positioning method based on time difference of arrival (TDOA) is presented in this paper. Mathematical model for multi-sonic TDOA is established. And the objective function of the optimal solution of nonlinear equations for two-dimensional fault location is derived. The objective function is optimized by Broyden-Flether-Goldfarb-Shanno (BFGS) quasi-Newton algorithm. The algorithm has fast convergence speed and facilitates digital processing. The validity of the method is verified by MATLAB simulation analysis and positioning experiments. The proposed fast and accurate method can be used for high-voltage complex cable lines fault pinpointing.

At present, the massive access of microgrid has already had an important impact on the safe operation of distribution network. Based on this background, this paper proposes a bi-level energy management model for active distribution network considering microgrid access. The model includes distribution network level and microgrid level. The distribution network level is optimized from three perspectives: the lowest network loss, the highest voltage quality and the lowest power fluctuation. At the microgrid level, the lowest total cost in the operation cycle is taken as the optimization objective. By using second-order cone relaxation and linearization methods, the upper and lower models are transformed into second-order cone programming and mixed integer linear programming respectively. Finally, the simulation results of an improved IEEE-33 example show that the model can balance the interests of both distribution network and microgrid. The operation safety of distribution network is improved while the operation cost of microgrid group is reduced.

The augmented linearized state matrix model is commonly used in the small signal analysis of power systems. However, the mathematical implementation of this model is complicated for implementation, which brings inconvenience to small-signal stability analysis. In view of this, this paper presents a new type of power system linearization model based on matrix pencil. The eigen-analysis techniques including shift-invert transformation, sensitivity analysis, controllability and observability analysis based on the new model are given. The new model, which is with higher sparsity, is easy to implement and is suitable for small signal analysis of large-scale power systems. The correctness of the new model is verified with typical system examples.

Aiming at the capacity optimization of islanded microgrid, this paper establishes a capacity optimization model considering the load demand management based on multi-scale uncertainty coupling. The influences of multi-scale uncertainty coupling are considered as follow. At long-term scale, the stochastic energy network calculus theory is used to deal with the energy balance problem. At short-time scale, the uncertainty of source and load prediction error is considered at the power balance level, and load demand management is introduced, by controlling the interruptible load and transferable load, the load characteristics can be optimized. the operation cost is reduced and the system stability is increased. The two-layer optimization model is established and analyzed from the aspects of energy constraint and power balance, and the proposed model is solved by DE algorithm with the minimum investment cost. By planning the capacity optimization of wind turbines/photovoltaics/controllable sources and energy storage systems in a regional microgrid system, the rationality and effectiveness of the proposed models and methods are verified.

Soft open point (SOP) is an important equipment to improve the flexibility of distribution networks. Conventional fault reconfiguration methods assume determinant values for loads and distributed generator outputs, which may lead to a reconfiguration solution that cannot satisfy operational constraints of distribution networks. This paper proposes a fault reconfiguration method for distribution networks with SOPs considering the uncertainties of photovoltaics and loads. Firstly, a fast probabilistic power flow method based on voltage sensitivity for distribution networks with SOPs is proposed. Secondly, the fault reconfiguration model considering uncertainties of photovoltaics and load is formulated to minimize load shedding and network losses. A genetic algorithm is adopted to solve the model. Finally, simulations on the 33-bus and 69-bus distribution systems are given to verify the effectiveness of the proposed method.

Aiming at the problem that the traditional black-start auxiliary service pricing method is not applicable due to the fluctuation of distributed power generation output, this paper proposes a black-start auxiliary service pricing strategy for distribution network with distributed power generation. Aiming at rapidly recovering more important loads, the load recovery range and recovery sequence during black start in each time period are optimized, and the quality of the black start auxiliary service that the power generator can provide on that day is determined; According to the load recovery time, load grade, load recovery amount and other agreement contents signed by the black-start auxiliary service, the calculation of the black-start auxiliary service and the calculation of the default compensation fee of the black-start auxiliary service in the distribution network containing the distributed power source are determined, the black-start auxiliary service signing time optimization model is established to obtain the maximum expected total compensation fee of the power generator, and the best recovery time signing scheme between the power generator and each user is formulated. Finally, the total compensation fee is divided into capacity fee and usage fee, and the capacity fee is shared by evaluating the black-start capability of each distributed power source. According to the actual black start of the distributed power supply output level to share the use fee. Taking IEEE 69-bus system as an example, the steps and basic features of this strategy are illustrated.

The increasing penetration of uncontrollable distributed generators (NDGs) exacerbates the risk of voltage violations in active distribution networks (ADNs). Soft open point (SOP) can realize accurate power flow control and continuous voltage regulation. Centralized control strategies of SOP are difficult to meet the requirement of fast voltage and reactive power control because of the heavily computational and communicational burdens. Local voltage control based on the real-time measurements has a fast response to the frequent distributed generators (DGs) fluctuations. In this paper, an optimization method of local control strategy of SOP based on kriging metamodel is proposed. Taking minimum network losses as the objective function, the metamodel for local control of SOP is constructed based on the kriging methods. Then, the operation strategies of SOP are developed by calculating the optimal weighted vector based on real-time measurements. The proposed local voltage control strategies of SOP based on only local measurements can quickly response to the frequent DGs fluctuations and lighten the computation burdens with large networks. Case studies under different scenarios on the IEEE 33-node system are conducted to verify the effectiveness of the proposed method, and the results show that the proposed method can effectively solve the problems of voltage deviation and voltage fluctuation caused by high penetration of DGs.

The traditional energy-consuming DC electronic load has the disadvantages: large volume, large mass, limited space, large energy loss and power battery characteristics cannot be realized. Aiming at the above problems, combining energy-feedback DC electronic load and simulation battery technology, an energy-feedback charging load scheme for field testing of off-board chargers is proposed. The volume and weight of the electronic load are reduced significantly, the energy-feedback efficiency can reach 90% or more. And the actual power battery characteristics can be realized. Meanwhile, the control strategy is designed, which can realize four typical control modes including constant input voltage, constant input current, constant input power and constant resistance. The working parameters can be adjusted within a certain range. Finally, the feasibility and effectiveness of the proposed scheme are verified by MATLAB/Simulink simulation.

The power market’s policies on wind power forecasting and imbalance settlement have an important impact on the wind power business within its operation scope. The wind power forecasting and imbalance settlement mechanism in North America electricity market have reference significance for the construction of China’s power market. This paper introduces the wind power forecasting mechanism and imbalance settlement method of nine North American power markets, and studies the influence of various operation policies on wind power providers from two aspects of wind power forecasting mechanism and imbalance settlement mechanism. The enlightenment to the construction of China’s power market is as follows: adopting a reasonable unified wind power forecasting mechanism, establishing a complete wind power imbalance settlement mechanism, and carrying out renewable energy participation plan.