排序方式： 共有135条查询结果，搜索用时 0 毫秒
81.
The increased deployment of renewable energy in existing power networks has jeopardized rotational inertia, resulting in system degradation and instability. To address the issue, this paper proposes a demand response strategy for ensuring the future reliability of the electrical power system. In addition, a modified fuzzy logic control topology-based two-degree-of-freedom (fractional order proportional integral)-tilt derivative controller is designed to regulate the frequency within a demand response framework of a hybrid two-area deregulated power system. The test system includes thermal power plants, renewable energy sources (such as wind, parabolic trough solar thermal plant, biogas), and electric vehicle assets. To adaptively tune the controller''s coefficients, a quasi-opposition-based harris hawks optimization (QOHHO) algorithm is developed. The effectiveness of this algorithm is compared to other optimization algorithms, and the stability of the system is evaluated. The results demonstrate that the designed control algorithm significantly enhances system frequency stability in various scenarios, including uncertainties, physical constraints, and high penetration of renewables, compared to existing work. Additionally, an experimental assessment through OPAL-RT is conducted to verify the practicality of the proposed strategy, considering source and load intermittencies. 相似文献
82.
Yi Su Member IEEE Jiashen Teh Senior Member IEEE Qian Luo Kangmiao Tan Jiaying Yong 《电力系统保护与控制》2024,9(4):83-95
The urban power grid (UPG) combines transmission and distribution networks. Past studies on UPG congestion mitigation have primarily focused on relieving local congestion while ignoring large-scale energy transfer with safety margins and load balancing. This situation is expected to worsen with the proliferation of renewable energy and electric vehicles. In this paper, a two-layer congestion mitigation framework is proposed, one which considers the congestion of the UPG with flexible topologies. In the upper-layer, the particle swarm optimization algorithm is employed to optimize the power supply distribution (PSD) of substation transformers. This is known as the upper-layer PSD. The lower-layer model recalculates the new PSD, known as the lower-layer PSD, based on the topology candidates. A candidate topology is at an optimum when the Euclidean distance mismatch between the upper-and lower-layer PSDs is the smallest. This optimum topology is tested by standard power flow to ascertain its feasibility. The optimum transitioning sequence between the initial and optimum topologies is also determined by the two-layer framework to minimize voltage deviation and line overloading of the UPG considering dynamic thermal rating. The proposed framework is tested on a 56-node test system. Results show that the proposed framework can significantly reduce congestion, maintain safety margins, and determine the optimum transitioning sequence. 相似文献
83.
Zao Tang Member IEEE Jia Liu Member IEEE Jielong Ni Jimiao Zhang Member IEEE Pingliang Zeng Member IEEE Pengzhe Ren Tong Su Member IEEE 《电力系统保护与控制》2024,9(6):19-29
With significant expansion in wind farm capacity, wake disturbances from upstream wind turbines have emerged as a detrimental factor, adversely affecting the generated power of downstream units. However, the conventional power prediction models usually neglect the wake effect between adjacent wind turbines. To bridge this gap, this paper proposes a novel power prediction model that considers the wake effect and its boundary layer compensation, to enable joint spatial and temporal wind power prediction for wind farms. Firstly, a two-dimensional convolutional neural network is adopted to extract the key features and reconstruct wind power prediction data. Secondly, utilizing historical data, a long short-term memory algorithm is employed to investigate the correlation between elemental characteristics and wind data. Subsequently, a 3D-Gaussian Frandsen wake model that accounts for the wake effect and boundary layer compensation in wind farms is developed to precisely cal-culate the spatial wind speed distributions. Consequently, these distributions allow the power outputs of wind tur-bines in wind farms to be estimated more accurately via the rotor equivalent wind speed. Finally, several case studies are conducted to validate the effectiveness of the proposed method. The results demonstrate that the suggested approach yields favorable outcomes in predicting both wind speed and wind power. 相似文献
84.
Jianquan Zhu Senior Member IEEE Kai Zeng Jiajun Chen Wenmeng Zhao Wenhao Liu Wenkai Zhu 《电力系统保护与控制》2024,9(5):42-53
This paper studies the rolling security-constrained unit commitment (RSCUC) problem with AC power flow and uncertainties. For this NP-hard problem, it is modeled as a Markov decision process, which is then solved by a transfer-based approximate dynamic programming (TADP) algorithm proposed in this paper. Different from traditional approximate dynamic programming (ADP) algorithms, TADP can obtain the commitment states of most units in advance through a decision transfer technique, thus reducing the action space of TADP significantly. Moreover, compared with traditional ADP algorithms, which require to determine the commitment state of each unit, TADP only needs determine the unit with the smallest on-state probability among all on-state units, thus further reducing the action space. The proposed algorithm can also prevent the iterative update of value functions and the reliance on rolling forecast information, which makes more sense in the rolling decision-making process of RSCUC. Finally, numerical simulations are carried out on a modified IEEE 39-bus system and a real 2778-bus system to demonstrate the effectiveness of the proposed algorithm. 相似文献
85.
Jiaqi Yu Yong Li Senior Member IEEE Jianghu Wan Feng Zhou Mingmin Zhang Lihua Cao 《电力系统保护与控制》2024,9(6):58-70
This paper introduces a tri-state modulation technique for a soft-switching bidirectional DC-DC con-verter (BDC). This method maintains the soft-switching condition and introduces a freewheeling interval that reduces the rise and fall times of the inductor current, effectively suppressing inductor current ripples. Additionally, the tri-state modulation provides an extra degree of freedom, enabling optimization for reduced operating losses. The paper details the operation principles of tri-state modulation in both buck and boost modes and discusses optimization strategies for minimizing losses. An experimental setup is developed to validate the tri-state modulation approach, where switching waveforms and efficiency are measured. The experimental results con-firm that the proposed method achieves soft-switching conditions, suppresses inductor current ripples, and provides higher efficiency compared to conventional hard-switching BDC and typical soft-switching BDC. 相似文献
86.
Muhammad Noman Guojie Li Senior Member IEEE Muhammad Waseem Khan Keyou Wang Bei Han 《电力系统保护与控制》2024,9(6):122-142
Fully harnessing the ocean wave''s renewable energy resources could benefit coastal countries. However, ocean wave energy harvesting systems encounter several challenges, i.e., marine uncertainties, long-distance mainte-nance, power fluctuations, irregular wave currents, non-linear generator dynamics, turbine limitations, cost optimization, and power smoothing issues. To overcome these challenges, this paper proposes a new multi-stage con-trol design approach for performance evaluation of the os-cillating water column (OWC)-based ocean wave energy conversion (OWEC) system. The first stage optimizes the Wells turbine by implementing an efficient airflow control strategy. It achieves maximum power-harvesting ability by eliminating stalling phenomena. In the second stage, we investigate the robustness of the permanent magnet syn-chronous generator-based OWEC system by designing adaptive back-stepping controllers, taking into account the Lyapunov stability theory. It accomplishes precise speed regulation for optimal power extraction while delivering reduced delay response and percentage errors. To ensure the OWEC system''s availability, the third stage incorporates fault-ride-through capabilities. It executes a fault reconfig-urable control for a parallel converter configuration, elimi-nating only the faulty leg instead of the entire power con-verter. In the fourth stage, a supercapacitors-based energy management system achieves power smoothing, even when the OWC plant output power fluctuates. We accomplish this by implementing a model predictive control strategy. Finally, the Matlab/Simulink results verify that the presented mul-ti-stage control for the OWC OWEC system is an effective design approach, offering an optimal, robust, reliable, and power-smoothing solution. 相似文献
87.
Dan Liu Qiufan Yang Yin Chen Xia Chen Senior Member IEEE Jinyu Wen Member IEEE 《电力系统保护与控制》2025,10(2):40-53
Energy storage with virtual inertia and virtual droop control has attracted wide attention due to its improved frequency stability with high penetration of renewable energy sources. However, there are significant spatial differences in frequency response. The location and capacity of energy storage are urgent issues to be resolved to support frequency. This study addresses the minimum investment of hybrid energy storage systems for providing sufficient frequency support, including the power capacity, energy capacity, and location of energy storage. A frequency response model is developed taking into account the network structure and frequency spatial distribution characteristics. In addition, a numerical computation method is provided for determining the frequency dynamic indices and calculating the output power of energy storage. Based on a simplified frequency response model, an optimal hybrid energy storage configuration method is proposed to optimize the control parameters, location, and capacity to satisfy the frequency dynamic constraints. This configuration method can exploit the potential of energy storage with different rates in different frequency support stages. To address the nonconvex drawback of this configuration, a numerical calculation method is provided based on the explicit gradient of the frequency and energy storage indices to enhance the computational efficiency. Simulations of a two-area system and the south-east Australian system verify the effectiveness of the proposed hybrid energy storage configuration method. 相似文献
88.
Genghong Lu Chi Wai Tsang Ho Nam Yim Chao Lei Siqi Bu Senior Member IEEE Winco K. C. Yung Senior Member IEEE Michael Pecht Fellow IEEE 《电力系统保护与控制》2025,10(2):25-39
Partial discharge (PD) activity is an indicator of insulation deterioration and by extension, the reliability of power lines. Existing data-driven methods, while helpful, treat PD detection as a binary classification problem, thereby failing to provide physical information (e.g., filter PD pulse), and often provide results that contradict physical knowledge. To tackle this challenge, this paper develops a physics-informed temporal convolutional network (PITCN) for PD diagnosis (i.e., PD detection and PD pulse filtering). During training, physical knowledge of the background noise and PD pulse identification is integrated into a learning model. Once the model is trained, the PITCN can automatically detect PD activity from time-series voltage signals with different background noises and filter PD pulses. Experimental results demonstrate that the developed PITCN outper-forms the rest of the data-driven methods implemented, and in particular, the Matthews correlation coefficient of PITCN surpasses the conventional temporal convolutional network by 0.21. 相似文献
89.
Wenmeng Zhao Tuo Zeng Zhihong Liu Lihui Xie Lei Xi Member IEEE and Hui M Member IEEE Hui M Member IEEE 《电力系统保护与控制》2024,9(4):39-50
The increasing use of renewable energy in the power system results in strong stochastic disturbances and degrades the control performance of the distributed power grids. In this paper, a novel multi-agent collaborative reinforcement learning algorithm is proposed with automatic optimization, namely, Dyna-DQL, to quickly achieve an optimal coordination solution for the multi-area distributed power grids. The proposed Dyna framework is combined with double Q-learning to collect and store the environmental samples. This can iteratively update the agents through buffer replay and real-time data. Thus the environmental data can be fully used to enhance the learning speed of the agents. This mitigates the negative impact of heavy stochastic disturbances caused by the integration of renewable energy on the control performance. Simulations are conducted on two different models to validate the effectiveness of the proposed algorithm. The results demonstrate that the proposed Dyna-DQL algorithm exhibits superior stability and robustness compared to other reinforcement learning algorithms. 相似文献
90.
Mohammed Y. Morgan Hatem F. Sindi Senior Member IEEE Hatem H. Zeineldin Senior Member IEEE Ahmed Lasheen 《电力系统保护与控制》2025,10(4):146-156
This paper employs artificial intelligence and machine learning techniques to predict the dominant oscillation modes in AC microgrids. The dominant modes are highly dependent on the droop gains and only slightly affected by the loading conditions. This paper utilizes the least absolute shrinkage and select operator (LASSO) algorithm to extract the key features contributing directly to dominant modes. The adaptive neuro-fuzzy inference system (ANFIS) is employed as a nonlinear regression technique to train a model that relates the system''s key features to the dominant modes of the AC microgrid. The data obtained from a 6-bus AC microgrid test system is used to train the LASSO-based ANFIS model. The results show that the proposed method can substantially reduce the data volume of the training set due to LASSO sparse feature. The precision of the proposed algorithm is determined by comparing its output to the modes determined by the derived small-signal model of the system. 相似文献