Data-Driven Method Based Wind Power Characteristic Analysis and Climbing Identification

Under the background of the construction of “three types and two networks”, the introduction of a large number of heterogeneous intelligent terminals, ubiquitous open networks, interactive real-time control services and wide area interconnected power systems have transmitted the network security risks to all aspects of the power system. The security defense capability of power system is not only the superposition of security defense equipment, but also the excellent network security team. The continuous improvement of the level of network security is inseparable from the accumulation of network security attack and defense technology and practical experience, as well as practical tools and equipment with the characteristics of the power industry. In order to meet the increasingly severe security protection challenges, actively implement the security protection requirements of the power information system, ensure the safe and stable operation of the power system, build a network shooting range suited to the attributes of the power industry, and continuously improve the ability of network security protection, consolidate the foundation of digital transformation to ensure the safe and stable operation of power system and reliable power supply.The safe and stable operation of the information system needs to carry out comprehensive and in-depth vulnerability troubleshooting. In view of the lack of power business loophole mining and security event verification shooting range environment, this paper studies the power security shooting range construction technology.

Power grid is developing towards the direction of intelligence. Nevertheless, the giant grid scale, complicated structure and external natural factors result in the potential of large-scaled power failure. Traditional black start power gives priority to hydropower. In areas with shortage of hydro power resources, suitable black start power is insufficient. The key of black start is able to realize the safe and stable operation of wind power and energy storage cogeneration system. Due to the combination of wind turbines and energy storage is a small system, energy storage, as a black start power source, starts the wind turbines together to supply power for the remote load. Therefore, it is necessary to run the balance and stable operation of a small system. In this paper, PSCAD simulation software is employed to conduct modeling of wind power and energy storage system, and the self-starting process of wind storage cogeneration system is analyzed by using resistance sensor as the load. The result verifies the wind power storage system's initiation process.

The accuracy of wind power forecasting has a very important influence on the safe and stable operation of power system. However, wind power prediction is very difficult, especially under the environment of massive data. This paper presents a novel multi-step ahead wind power prediction model based on recurrent neural network (RNN) with long short-term memory (LSTM) unit or gated recurrent unit (GRU) to improve the accuracy. Firstly, an overall forecasting framework for wind power with diverse forms of optional hybrid models is proposed. Moreover, an innovative LSTM/GRU-based forecasting model is developed with a wind speed correction process using numerical weather prediction (NWP) data. Through the application of GRU network, the correction process can provide corrected wind speed at the predicted moment as an input of the forecasting model. Finally, the experimental results demonstrate the superiority of the proposed RNN approaches as compared to the ARIMA method and SVM method while the main distinctions between the LSTM and GRU network are also illustrated.

The damage of arc to gas insulation protection will affect the safe and stable operation of power system seriously in the field of high voltage insulation. Therefore, it is a great theoretical value and practical significance to study the mechanism of gas discharge and find out the law of gas discharge under different conditions for ensuring the safe and stable operation of power system. In order to study the variation law of arc channel complexity under different voltage levels, this paper adopts the discharging experiment of needle-plate electrode, adds different high voltage to the needle-plate electrode under other conditions unchanged, carries out many experiments, and collects five groups of discharging channel plans of needle-plate gap under different voltage. The are discharge channel image in the experiment is restored to the three-dimensional image from the space angle, and the fractal dimension of arc discharge channel under different voltage pressure is calculated by box dimension method of fractal dimension. The results show that the complexity of discharge channel decreases with the increase of voltage level, that is, the lower the voltage level and the larger the fractal dimension.

It is of great significance for the safe and stable operation of power system to master the fluctuation characteristics of wind power output. On the basis of analyzing a large number of field measured data, a weighted mixed Gaussian probability model is proposed to simulate short-time wind power fluctuation characteristics of wind farm cluster, that evaluation indices to reflect the short-time maximum fluctuation of wind power output and maximum likelihood estimation algorithm based on Expectation Maximization (EM) to estimate model parameters are put forward. This model is compared with various other kinds of probability distribution model and the simulation results show that the weighted mixed Gaussian probability model possesses the highest precision, so as the effectiveness of the weighted mixed Gaussian probability model is verified.

Very-short-term wind power prediction is of great significance for the safe and stable operation of power system. As a well-known time series model, vector autoregressive (VAR) model is very suitable for very-short-term forecasting, and it can consider the temporal and spatial correlation among multiple wind farms to improve the forecasting accuracy. However, when there are a large number of wind farms, VAR model is prone to over-fitting. Slow calculation speed is also the problem of VAR model. This paper adds regularization terms in estimating coefficients to obtain a sparse coefficient matrix, which helps to avoid over-fitting. Online VAR model is used to improve the calculation speed, and a specific improved online VAR method is proposed for forecasting wind power output from new-built wind farms. This method is tested in a specific case, and the prediction accuracy and calculation speed are better than the traditional VAR model. In addition, the improved online VAR method can accurately predict wind power output of new-built wind farms with little historical data.

An algorithm for forecasting day-ahead wind power via novel long short-term memory and wind power ramp events

Wind power predication is significant for the safety and stability of power system operation. Due to the power forecasting single model or combination model without considering the information fusion and the influence of forecasting error on power dispatching, forecasting results are not accurate enough. In the paper, a novel combination model for wind power forecasting with error evaluation parameter based on cross entropy theory is proposed. Wind power prediction is seen as information fusion, and the weights of different wind power prediction methods is set according to their cross levels. Besides, an error evaluation parameter is also proposed to estimate the different influences of positive and negative error on power scheduling. Application results in an actual wind farm show that the cross levels of different wind power prediction methods can be identified effectively and prediction precision is improved by the proposed method.

Prediction interval of wind power using parameter optimized Beta distribution based LSTM model

With the rapid development of the intermittent renewable energy power generations such as wind power and solar power and the new random load such as EV, the double randomness become a great threat the safe and stable operation of power system. It has become a hot issue raised increasingly concern from the academic and engineering fields that how to reduce the randomness and volatility of the two sides of the system by utilizing the spatial-temporal complementary characteristics of different energy resources and loads. In this paper, the evaluation model of complementary characteristics between power and load across time and space based on the Power Big Data theory is proposed. By simulating the complementarity of the power supply and load between Northwest China and European load centres from three dimensions: load-load complementarity, source-source complementarity and source-load complementarity, it proves there are great complementary benefit by interconnecting the grids with different regions across continents, which provides a theoretical basis for the next implementation of the Global Energy Interconnection.

Reliable wind power forecasting is essential for maintaining the safe and stable operation of power systems with high renewable energy penetration. This study proposes a short-term wind power forecasting model based on decomposition–optimization–prediction, integrating complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN), the improved football team training algorithm (IFTTA), and the bidirectional long short-term memory network model (BiLSTM). CEEMDAN is employed to decompose the non-stationary wind power sequence into relatively stable intrinsic mode functions (IMFs), thereby separating multi-scale fluctuation features. The IFTTA incorporates a dynamic probability allocation strategy and an adaptive parameter adjustment mechanism, which contributes to a better balance between global exploration and local exploitation. After optimizing the hyperparameters of BiLSTM using IFTTA, the prediction performance significantly improved. Validations were conducted on three datasets from Xinjiang, Ningxia, and Inner Mongolia, China, each containing 1440 samples (1152 for training and 288 for testing). Comparisons with the benchmark forecasting model demonstrate that the pro-posed model reduces the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) by at least 25.29%, 29.62%, and 20.66%, respectively. Correspondingly, the coefficient of determination (R2) was improved by at least 0.0069. This model provides an effective solution for short-term wind power prediction in practical engineering.

As power big data plays an increasingly important role in the operation, maintenance, and management of power systems, complex and covert false data attacks pose a serious threat to the safe and stable operation of the power system. This article first explores the characteristics of new power systems, and the challenges posed by false data attacks. The application of big data technology in power production optimization, energy consumption analysis, and user service improvement is then investigated. The article classifies typical attacks against the four stages of power big data systems in detail and analyzes the characteristics of the attack types. It comprehensively summarizes the attack detection technologies used in the four key stages of power big data, including state estimation, machine learning, and data-driven attack detection methods in the data collection stage; clock synchronization monitoring and defense strategies in the data transmission stage; data processing and analysis, data integrity verification and protection measures of blockchain technology in the third stage; and traffic supervision, statistics and elastic computing measures in the control and response stage. Finally, the limitations of attack detection mechanisms are proposed and discussed from three dimensions: research problems, existing solutions, and future research directions. It aims to provide useful references and inspiration for researchers in power big data security to promote technological progress in the safe and stable operation of power systems.

In the distributed load frequency control (DLFC) scheme, the compromised areas by attackers may endanger the stable and reliable operation of the entire power system. A credibility‐based secure DLFC strategy is in this work proposed to sustain the stable operation of the power system under false data injection attacks. In this secure DLFC method, the gradient‐variation‐based credibility metrics are defined to detect potential attacked measurements. Then, the communication isolation decisions are made based on the adaptive thresholds. To compensate the impact of the communication isolation of misbehaving areas, predictions of future measurements by using the average gradient variation of stored healthy data are feeded back to the area controller. Simulation results of a four‐area power system show the proposed secure DLFC can identify and cut off the communication of the misbehaving areas and still maintain the stability of the power system.

Transient stability assessment (TSA) plays an essential role in the safe and stable operation of power system. The traditional time-domain simulation method and direct method have great limitation in modeling and computing, which can not meet the need of fast and accurate assessment of power system stability. Therefore, combined physics model with data-driven algorithm, this paper proposes a physics-informed deep learning paradigm to solve this problem. In the data-driven algorithm, recurrent neural networks are trained to predict the rotor speed and power angle with the guiding of the power system’s physical information. Finally, the simulation on IEEE14 system is carried out by using PSS/E software, which proved that the proposed method can predict the power angle trajectory quickly and accurately.

In today's world, with the rapid development of green and clean energy, the comparison of wind power in power network has been gradually improved. However, wind power has also brought new challenges to the stable operation of power system due to its inherent volatility and randomness. In this paper, the economic dispatching model of power system including wind power grid connection is studied. On the basis of stochastic expectation and chance-constrained programming, the specific economic dispatch model with wind power integration is given. The core of this paper is that we simulate the degree of wind power output fluctuation by setting different confidence levels. Simulation results show that a larger confidence interval can achieve better security, while a smaller confidence interval can achieve better economy.