Explore the vast range of titles available.

Due to increasing complexity, uncertainty and data dimensions in power systems, conventional methods often meet bottlenecks when attempting to solve decision and control problems. Therefore, data-driven methods toward solving such problems are being extensively studied. Deep reinforcement learning (DRL) is one of these data-driven methods and is regarded as real artificial intelligence (AI). DRL is a combination of deep learning (DL) and reinforcement learning (RL). This field of research has been applied to solve a wide range of complex sequential decision-making problems, including those in power systems. This paper firstly reviews the basic ideas, models, algorithms and techniques of DRL. Applications in power systems such as energy management, demand response, electricity market, operational control, and others are then considered. In addition, recent advances in DRL including the combination of RL with other classical methods, and the prospect and challenges of applications in power systems are also discussed.

Smart grids are the developmental trend of power systems and they have attracted much attention all over the world. Due to their complexities, and the uncertainty of the smart grid and high volume of information being collected, artificial intelligence techniques represent some of the enabling technologies for its future development and success. Owing to the decreasing cost of computing power, the profusion of data, and better algorithms, AI has entered into its new developmental stage and AI 2.0 is developing rapidly. Deep learning (DL), reinforcement learning (RL) and their combination-deep reinforcement learning (DRL) are representative methods and relatively mature methods in the family of AI 2.0. This article introduces the concept and status quo of the above three methods, summarizes their potential for application in smart grids, and provides an overview of the research work on their application in smart grids.

With the development of a smart grid and smart home, massive amounts of data can be made available, providing the basis for algorithm training in artificial intelligence applications. These continuous improving conditions are expected to enable the home energy management system (HEMS) to cope with the increasing complexities and uncertainties in the enduser side of the power grid system. In this paper, a home energy management optimization strategy is proposed based on deep Q-learning (DQN) and double deep Q-learning (DDQN) to perform scheduling of home energy appliances. The applied algorithms are model-free and can help the customers reduce electricity consumption by taking a series of actions in response to a dynamic environment. In the test, the DDQN is more appropriate for minimizing the cost in a HEMS compared to DQN. In the process of method implementation, the generalization and reward setting of the algorithms are discussed and analyzed in detail. The results of this method are compared with those of Particle Swarm Optimization (PSO) to validate the performance of the proposed algorithm. The effectiveness of applied data-driven methods is validated by using a real-world database combined with the household energy storage model.

We present a novel transient fault detection and classification approach in power transmission lines based on graph convolutional neural network. Compared with the existing techniques, the proposed approach considers explicit spatial information in sampling sequences as prior knowledge and it has stronger feature extraction ability. On this basis, a framework for transient fault detection and classification is created. Graph structure is generated to provide topology information to the task. Our approach takes the adjacency matrix of topology graph and the bus voltage signals during a sampling period after transient faults as inputs, and outputs the predicted classification results rapidly. Furthermore, the proposed approach is tested in various situations and its generalization ability is verified by experimental results. The results show that the proposed approach can detect and classify transient faults more effectively than the existing techniques, and it is practical for online transmission line protection for its rapidness, high robustness and generalization ability.

Transient stability preventive control (TSPC), a method to efficiently withstand the severe contingencies in a power system, is mathematically a transient stability constrained optimal power flow (TSC-OPF) issue, attempting to maintain the economical and secure dispatch of a power system via generation rescheduling. The traditional TSC-OPF issue incorporated with differential-algebraic equations (DAE) is time consumption and difficult to solve. Therefore, this paper proposes a new TSPC method driven by a naturally inspired optimization algorithm integrated with transient stability assessment. To avoid solving complex DAE, the stacking ensemble multilayer perceptron (SEMLP) is used in this research as a transient stability assessment (TSA) model and integrated into the optimization algorithm to replace transient stability constraints. Therefore, less time is spent on challenging calculations. Simultaneously, sensitivity analysis (SA) based on this TSA model determines the adjustment direction of the controllable generators set. The results of this SA can be utilized as prior knowledge for subsequent optimization algorithms, thus further reducing the time consumption process. In addition, a naturally inspired algorithm, Aptenodytes Forsteri Optimization (AFO), is introduced to find the best operating point with a near-optimal operational cost while ensuring power system stability. The accuracy and effectiveness of the method are verified on the IEEE 39-bus system and the IEEE 300-bus system. After the implementation of the proposed TSPC method, both systems can ensure transient stability under a given contingency. The test experiment using AFO driven by SEMLP and SA on the IEEE 39-bus system is completed in about 35 s, which is one-tenth of the time required by the time domain simulation method.

Optimal needle trajectory selection is critical in biopsy procedures to minimize tissue damage and ensure diagnostic accuracy. Timely trajectory planning is essential, as it relies on preoperative CT imaging. Prolonged processing times increase the risk of patient movement, rendering the planned path invalid. Traditional methods relying on clinician expertise or slow algorithms struggle with complex anatomical modeling for structures such as blood vessels. We introduce a novel method that reframes trajectory planning as an optimal puncture site identification problem by leveraging optical principles and computer rendering. A 3D model of key anatomical structures is reconstructed from CT images and segmented using SegResNet (average Dice similarity coefficient of 0.9122). A virtual light source positioned at the target illuminates the space, assigning distinct absorption coefficients to tissues based on needle permissibility and risk. Diffuse reflection simulates needle angle, and accumulated absorption represents depth, capturing puncture constraints. This simulation generates a grayscale map on the skin surface, highlighting candidate puncture sites. Furthermore, we employ a random forest-based method to model clinician preferences. This model analyzes an RGB image derived from the grayscale distribution to automatically select the optimal path and determine the needle entry point. The experimental evaluation demonstrates an average computation time of just 1.905 s per sample, which is significantly faster than traditional methods that require seconds to minutes. Moreover, clinical assessment by a thoracic surgeon found that 78% of the recommended paths met clinical standards, with 0% deemed unsatisfactory. These findings suggest that our method provides a rapid, intuitive, and reliable decision-support tool, improving biopsy safety and efficiency.

This paper proposes a new approach for online power system transient security assessment (TSA) and preventive control based on XGBoost and DC optimal power flow (DCOPF). The novelty of this proposal is that it applies the XGBoost and data selection method based on the 1-norm distance in local feature importance evaluation which can provide a certain model interpretability. The method of SMOTE+ENN is adopted for data rebalancing. The contingency-oriented XGBoost model is trained with databases generated by time domain simulations to represent the transient security constraint in the DCOPF model, which has a relatively fast speed of calculation. The transient security constrained generation rescheduling is implemented with the differential evolution algorithm, which is utilized to optimize the rescheduled generation in the preventive control. Feasibility and effectiveness of the proposed approach are demonstrated on an IEEE 39-bus test system and a 500-bus operational model for South Carolina, USA.

With the continuous development of the electricity market, the electricity retailers, as the intermediaries between producers and consumers, have emerged in some of the liberalized electricity markets. Meanwhile, the electricity retailer faces many increasingly significant challenges from the complexities and uncertainties in both the supply and consumption sides. This paper applies a data-driven decision-making strategy via Advantage Actor-Critic (A2C) and Deep Q-Learning (DQN) for the electricity retailers. The retailers' profits and consumers' costs are both taken into account. This study verifies that the applied data-driven methods can handle the decision-making problem as well as promote the profitability of retailers in the electricity market. Furthermore, A2C is more appropriate than DQN in our simulation. The effectiveness of the applied datadriven methods is validated by using real-world data.

Planting structure adjustment (PSA) affects agricultural water saving, and is an essential part of water-saving agricultural construction. This study introduced virtual water theory and innovatively constructed a model to assess the water-saving effects of PSA in Shiyang River Basin over the past 38 years, explore the relationship between planting structure and water saving, and clarify the most water-saving planting structure. The results showed that the sown area of economic crops consistently increased as food crop areas decreased in the four counties (districts) from 1980 to 2017. Being considered a “big water consumer”, wheat has lost its dominant position. The water requirements of major crops in the four counties and districts showed an increasing trend. The total area proportion of vegetables, wheat, corn, and oil-bearing crops (Abbreviated as TPVWCO) directly determined the water-saving amount. The lower the TPVWCO, the better the water-saving effect. Taking 1980 as the reference year, the most water-saving years in Gulang, Liangzhou, Yongchang, and Minqin were 2007, 1981, 2008, and 2005, respectively. Taking 2007 as the reference year, there were no water-saving years available after that due to the higher TPVWCO. Taking into account food security, ecological and economic benefits, it was recommended to control the TPVWCO at 40% in the Shiyang River Basin in the future. The land vacated should be planted with cotton in Minqin, while the land vacated in the other three counties should be planted with fruits. The research results would provide scientific basis for optimizing the planting structure and managing agricultural water resources in inland river basins in arid regions.

This study aimed to explore physicians’ and pharmacists’ knowledge, attitudes, and practice (KAP) regarding the prevention and treatment of cardiovascular toxicity associated with cancer treatment. A multicenter cross-sectional study included physicians and pharmacists between April 2023 and June 2023. The study included 918 participants (514 physicians and 404 pharmacists). The average scores of knowledge, attitudes, and practice were 11.6 ± 3.39, 24.7 ± 2.6, and 26.3 ± 6.8 points. Sufficient knowledge was significantly associated with age ≥ 41 years (odds ratio (OR) = 2.745, 95% confidence interval (CI) 1.086–6.941, P = 0.033), male (OR = 2.745, 95% CI 1.150–2.223, P = 0.005), bachelor’s degree (OR = 0.084, 95% CI 0.013–0.533, P = 0.009), master’s degree and above (OR = 0.096, 95% CI 0.015–0.609, P = 0.013), physician occupation (OR = 7.601, 95% CI 1.337–43.207, P = 0.022), pharmacy department (OR = 18.858, 95% CI 3.245–109.57, P = 0.001), oncology department (OR = 4.304, 95% CI 2.426–7.634, P < 0.001), cardiology department (OR = 3.001, 95% CI 1.387–6.492, P = 0.005), hospitals located in Eastern China (OR = 1.957, 95% CI 1.120–3.418, P = 0.018), and hospitals located in Western China (OR = 3.137, 95% CI 1.783–5.518, P < 0.001). Positive attitudes were significantly associated with a senior professional title (OR = 2.989, 95% CI 1.124–7.954, P = 0.028) and hospitals located in Eastern China (OR = 0.424, 95% CI 0.257–0.698, P = 0.001), Western China (OR = 0.231, 95% CI 0.136–0.394, P < 0.001), and Southern China (OR = 0.341, 95% CI 0.198–0.587, P < 0.001). Proactive practice was significantly associated with male (OR = 1.414, 95% CI 1.029–1.943, P = 0.033), senior professional title (OR = 3.838, 95% CI 1.176–12.524, P = 0.026), oncology department (OR = 3.827, 95% CI 2.336–6.272, P < 0.001), and cardiology department (OR = 2.428, 95% CI 1.263–4.669, P = 0.008). Both physicians and pharmacists had positive attitudes toward the prevention and treatment of cardiovascular toxicity associated with cancer treatment, while their knowledge and practice were not as proactive.