This paper proposes an error vector magnitude (EVM) measurement method for a wideband code-division multiple-access source using a real-time oscilloscope. The EVM values are extracted from the measured waveform using a signal processing that finds the appropriate carrier phase and symbol timing. Measurement uncertainty is also evaluated based on a Monte Carlo simulation, where the errors from the real-time oscilloscope and the signal processing are taken into account. The measured EVM of a source at 900 MHz is (0.2586 ± 0.0040)%, (0.2617 ± 0.0060)%, and (0.2543 ± 0.0078)% at 95% confidence level when the real-time oscilloscope has a bandwidth of 2, 4, and 20 GHz, respectively.

Advanced controllers have been widely introduced in power system-related research to alleviate the uncertainties of renewable sources. The performance of the controllers should be verified in a realistic environment before the controllers are used in practical applications. This study focuses on voltage regulation of wind farms in voltage drop cases where communication failures occur and discusses modifications done on the online wind farm management system. The modified management system is designed to respond to a voltage drop when the outer reference signal from the upper-level controller is absent due to technical problems, especially communication errors. Such a situation has not been considered in most of the management systems in the literature. The predictable situations in real fields have been further verified under the hardware-in-the-loop simulation environment RTDS ^TM . During communication failure, the proposed method can respond automatically to voltage recovery, along with a designed objective function. A comparative analysis of actual control modes was also conducted to prove the relevance and effectiveness of the proposed method.

Power system operators have recently introduced some AI-based techniques in load prediction, fault diagnosis, scheduling, and maintenance. Operators require a grid analysis that includes wind turbines to mitigate impacts by environmental factors. Among them, a method for modeling wind turbines that reflects the dynamic characteristics and their output characteristics is receiving attention. Recently, a data-based power curve modeling method has been adopted for a simplified model that characteristics as much as possible. However, the simplified EMT simulation is difficult to reflect the output characteristics according to nonlinear wind conditions accurately. This paper proposes a wind turbine model based on artificial neural network techniques using real supervisory control and data acquisition (SCADA) data from a wind farm. The proposed strategy derive the similar to real output value through the trained wind turbine model in various wind scenarios. For the verification of the proposed strategy, the case study was conducted using a real-time digital simulator (RTDS).

With the increase in the penetration rate of renewable energy sources, a machine-learning-based forecasting system has been introduced to the grid sector to improve the participation rate in the electricity market and reduce energy losses. In these studies, correlation analysis of mechanical and environmental variables, including geographical figures, is considered a crucial point to increase the prediction’s accuracy. Various models have been applied in terms of accuracy, speed calculation, and amount of data based on a mathematical model that can calculate the wake; however, it can be difficult to derive variables such as air density, roughness length, and the effect of turbulence on the structural characteristics of wind turbines. Furthermore, wake accuracy could decrease due to the excessive variables that come from the wake effect parameters. In this paper, we intend to conduct research to improve prediction accuracy by considering the wake effect of wind turbines using supervisory control and data acquisition (SCADA) data from the Dongbok wind farm. The wake divides the wind direction into four parts and then recognizes and predicts the affected wind turbine. The predicted result is the wake wind speed and its conversion to power generation by applying a power curve. We try to show the efficiency of machine learning by comparing the wake wind speed and the power generation in the wake model. This result shows the error rate using evaluation metrics of regression, such as mean squared error (MSE), root mean squared error (RMSE), and weighted absolute percentage error (WAPE), and attempts to verify power system impact and efficiency through a real-time digital simulator (RTDS).

When a lot of surplus power occurs in wind power system, an output limit is implemented to directly or indirectly curtail the output to maintain a balance between the supply and demand of the power system. The curtailment process of a large-scale wind farm causes loss of power and mechanical loads. Resultantly, imbalanced curtailments often occur, resulting in unilateral burdens for the owners of wind farms. Considering the curtailment issue, the study for minimizing system loss of power plants is required in terms of operational efficiency. This paper proposes an algorithm to achieve flexible control during the actual power curtailment process in a wind farm, considering the wake effect. Here, the Monte Carlo method was adopted to calculate the curtailment weight in wind farms by using power loss terms. In addition, an equivalent model of a real wind farm was implemented and simulated through real simulation computer-aided design (RSCAD) software. This paper verified the effectiveness of the proposed method by applying the curtailment communication signal to a real-time digital simulator (RTDS). The results showed a reduction in the computational loading of individual wind turbine curtailment values with the decline of the total effective power loss.

With the increased usage of different types of renewable energy based on variable output characteristics, power curtailments have become a major problem in grid operation. The advanced controllers in distribution networks are significant in reducing the uncertainties caused by renewable generators. Along with this, at the operational level, the determination of the appropriate amount of curtailment for the stability and economics of the system was taken into account The paper proposes an adaptive curtailment method in order to improve the accuracy of the power flow coming from the distribution network, with the signal created according to the system operator based on the response of the electric vehicle (EV). The method covers the economic loss caused by the curtailment process through the compensation profits. The main determinant reference proposal concerns the sensitivity and ability to increase the stability and economic feasibility of the interconnected power system. The benefits generated by the EV charging are provided to wind turbine operators in the scenario to compensate for excessive constraints. The whole method is applied in the IEEE-69 bus system to verify the effect with MATLAB.