Search

For data-driven dynamic stability assessment (DSA) in modern power grids, DSA models generally have to be learned from scratch when faced with new grids, resulting in high offline computational costs. To tackle this undesirable yet often overlooked problem, this work develops a light-weight framework for DSA-oriented stability knowledge transfer from off-the-shelf test systems to practical power grids. A scale-free system feature learner is proposed to characterize system-wide features of various systems in a unified manner. Given a real-world power grid for DSA, selective stability knowledge transfer is intelligently carried out by comparing system similarities between it and the available test systems. Afterward, DSA model fine-tuning is performed to make the transferred knowledge adapt well to practical DSA contexts. Numerical test results on a realistic system, i.e., the provincial GD Power Grid in China, verify the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

• A novel hybrid model and data-driven voltage stability online assessment approach is proposed. • The parameters in the model-driven method are modified based on the response data. • To improve accuracy, impedance difference is included in the data-driven input data. • The proposed method combines the clear mechanism of the model-driven and high accuracy of the data-driven. With the continuously increasing integration of renewable energy sources into power grids, the dynamic response of a power system is becoming more complex. For example, the interaction between the dynamic loads and low-voltage ride-through of renewable energy generators makes the voltage response more rapid and unpredictable. Ensuring the accuracy and speed of traditional voltage stability assessment methods is difficult. This study developed a novel hybrid model and data-driven voltage stability assessment approach. First, the equivalent parameters of a power system were calculated based on the measured data, and the parameters were constantly modified based on the response data. To further improve the accuracy of the approach, a data-driven method was introduced to correct the assessment results using a Thevenin equivalent-based assessment. The difference between the Thevenin and system impedances, which better reflects the system stability, was included in the data-driven input data. Finally, by combining the clear physical mechanism of the model-driven method and high accuracy of the data-driven method, the final the assessment process was a serial combination of the model- and data-driven methods. The effectiveness of the method was verified using an IEEE New England 10-generator 39-bus test system and a 100-bus actual system in China. The results showed that the method developed was more accurate and had higher robustness under data loss and noise conditions than other methods. [ABSTRACT FROM AUTHOR]

Currently, the increasing wind power penetration, with consequent randomness and variability, presents great challenges to power system planning and operation. Probabilistic power flow (PPF) has been developed to calculate the power flow under uncertain circumstances. However, the current wind power models are subject to specific probability distributions, limiting their accuracies in wider applications. Additionally, the cumulant method (CM)-based PPF, if nonlinear relationship is considered in, would face an impractically high computational complexity. To address these problems in modeling and cumulant calculation, this article proposes a novel generalized density/distribution fitting method (GDFM) combining with the Copula function to establish a joint probability model for wind power generation. A special impulse- mixed probability density (IMPD) integration method is also introduced to derive the input cumulants from the model. Finally, a fast cumulant method (FCM) is proposed to reduce the computational burden of output cumulant calculation while retaining a high accuracy in a nonlinear context. Case study on the IEEE-118 test system validates the effectiveness of the proposed methods, and a real application to a provincial power grid in China provides some useful power flow risk information for decision making. The whole FCM-based PPF scheme can be helpful for future power flow examination in power system planning and operation. [ABSTRACT FROM AUTHOR]

• A weak bus identification index is derived to identify the critical bus when power flow is unsolvable. • An extended system considering new bus type is proposed to improve power flow solvability. • A weak bus identification and bus type switching based framework is established to provide adjustment measures efficiently and automatically. • The method could provide a suitable solution automatically for unsolvable power flow in a variety of scenarios, retains stable convergence and has potential in large-scale power grids. The power flow solvability is greatly challenged by the growing pressure on power grid induced by the rapid development of economy. The approaches of restoring power flow solvability should be profoundly studied. Most of adopted power flow adjustment methods are based on manual interference which require extensive human resources with inefficiency. Thus, a power flow adjustment strategy based on a weak bus identification and a new bus type conversion is proposed. A weak bus identification index is defined and expressed when power flow has no solution. We propose a novel bus type and a concept of the power shortage to restore power flow solvability. The generator action set is determined by calculating the left eigenvector of the Jacobian matrix. At last, we establish a linear programming model to determine the generator output adjustment. Numerical results are obtained in 3 test systems and an actual system in China. Compared with other methods, the proposed method is verified to have a higher success rate of restoring power flow solvability and has robustness in various scenarios. [ABSTRACT FROM AUTHOR]

• A local power loss ∆ P estimation approach for frequency emergency control is proposed, and the coordination of the distributed loads is achieved by the edge intelligence and IoT technologies. • The proposed LFC method can achieve accurate and coordinated load shedding while reducing the communication burden. • The cyber-physical architecture and implementation of an industrial LFC system are elaborated, discussing practical issues. • Base on the IEEE 24-bus system and east China power System, numerical simulations, hardware experiments, and comparative studies are conducted. Developing effective strategies to arrest grid frequency drop in case of severe contingencies is an important requirement. While distributed responsive loads for primary frequency control have been studied in the literature, the coordination of the distributed loads relies on high-speed communication or centralized frequency threshold setting. They might be cost-prohibitive or not sufficiently fast in severe contingencies, like the fault of ultrahigh voltage direct current (UHVDC) lines in China. In this paper, a local power loss ∆ P estimation approach for frequency emergency control is proposed, and the coordination of the distributed loads is achieved by the edge intelligence and Internet-of-Things (IoT) technologies. The proposed approach can enable the distributed loads to provide fast and accurate frequency support to power grids. The parameter-setting in the control center and decision-making at the edge level for the proposed approach are described in detail. Based on a typical IEEE test system and a real bulk power system in China, numerical simulations and hardware experiments are conducted to verify the frequency support performance of the proposed approach. Further, a direct load frequency control system named Grid Sense implemented in China is explicitly described, and the real-world issues for implementing the proposed approach are analyzed. [ABSTRACT FROM AUTHOR]