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With the successful application of artificial intelligence technology in various fields, deep reinforcement learning (DRL) algorithms have applied in active corrective control in the power system to improve accuracy and efficiency. However, the "black-box" nature of deep reinforcement learning models reduces their reliability in practical applications, making it difficult for operators to comprehend the decision-making mechanism. process of these models, thus undermining their credibility. In this paper, a DRL model is constructed based on the Markov decision process (MDP) to effectively address active corrective control issues in a 36-bus system. Furthermore, a feature importance explainability method is proposed, validating that the proposed feature importance-based explainability method enhances the transparency and reliability of the DRL model for active corrective control. [ABSTRACT FROM AUTHOR]

In the context of the new power system, the widespread access to massive distributed new energy sources has led to the power distribution and consumption tasks characterized by multiple time scales, wide random distribution, and large demand differences, resulting in unpredictable resource peaks in the tasks computing resource demand curve. In view of this situation, a method of forecasting and dynamic balancing of computing resource demand for power distribution and consumption tasks based on state iteration was proposed: firstly, the tasks computing resource demand model was established under the analysis of the attributes and parameter demand of the power distribution and consumption tasks scenario. Secondly, on the basis of the short-term effectiveness prediction of the traditional Markov model, the firstorder difference of the state is used for data training to track the state fluctuation, and the historical state and the predicted state are used for state iteration, so as to avoid the convergence of long-term prediction. Finally, a dynamic balancing model is established according to the time-scale characteristics of cyclical and non-cyclical tasks, and the optimal configuration of load imbalance is achieved through the identification and adjustment of historical data and burst data. The simulation results show that the improved Markov model based on first-order difference and state iteration has the short-term accuracy of the traditional model and the long-term traceability of data fluctuations. The dynamic balancing model can combine the characteristics of historical data and burst data to effectively reduce the imbalance of resource demand, and show good ability to cope with resource imbalance deviation. [ABSTRACT FROM AUTHOR]

The ability of fast restoration reflects power system resilience and safety. This paper proposes a partitioning decision-making method for parallel restoration based on the label propagation algorithm and the cooperative game theory. The topological and physical characteristics of blackout systems are considered as the edge weight and the node importance simultaneously for formulating the partitioning model. By the label propagation algorithm, various labels representing different subsystems mark all buses. To accelerate the speed of convergence and prevent the label oscillation, this paper proposes a game strategy of cooperation between buses and subsystems by evaluating the Shapley value of buses. The partitioning constraints are integrated into each label propagation process to assess the feasibility of the partitioning strategy. Finally, case studies on the IEEE 39- and 118-bus test systems and an actual urban power system in South China verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Non-intrusive load identification can improve the interaction efficiency between the power supply side and the user side of the grid. Applying this technology can alleviate the problem of energy shortage and is a key technique for achieving efficient management on the user side. In response to the cumbersome process of manually selecting load features and the low accuracy of identification in traditional machine learning algorithms for non-intrusive load identification, this paper proposes a method that transforms the one-dimensional reactive electric signal of the load into a two-dimensional image using Gram coding and utilizes the Residual Attention Network (RAN) for load classification and recognition. By transforming the one-dimensional electrical signal into a two-dimensional image as the input to the RAN network, this approach retains the original load information while providing richer information for the RAN network to extract load features. Furthermore, the RAN network effectively addresses the poor performance and gradient vanishing issues of deep learning networks through bottleneck residual blocks. Finally, experiments were conducted on a public dataset to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Continued research into thermal barrier coatings (TBCs) for internal combustion engines has generated insights into the design of TBCs that can increase fuel conversion efficiency. In this work, two low thermal inertia TBCs (a commercially available Gen 1 material, Gadolinium Zirconate, and a proprietary Gen 2 material) were experimentally tested in gasoline compression ignition (GCI) on a light-duty single cylinder research engine. Compared to a metal baseline, a 250-micron thick Gen 1 coated piston produced an efficiency benefit of 0.4 percentage points (pp) at 6 and 10 bar net indicated mean effective pressure (IMEPn) and a 0.2 pp penalty at 15 bar IMEPn. The Gen 1 coating also showed an uHC and smoke penalty. An 80-micron thick Gen 2 coated piston showed an efficiency benefit of 0.9 pp at 6 bar IMEPn and 0.4 pp at 10 bar IMEPn, but an efficiency penalty of 0.5 pp at 15 bar IMEPn. The Gen 2 coating, which has a lower thermal inertia than the Gen 1 coating, displayed durability problems while the Gen 1 coating did not. A third piston was coated with a 120-micron thick Gen 2 coating everywhere except the high failure outer bowl region, which was coated with a 120-micron Gen 1 coating. This hybrid coating displayed good durability, an uHC emission penalty, a smoke benefit, an efficiency benefit of 0.1 pp at 6 bar IMEPn, an efficiency benefit of 0.4 pp at 10 bar IMEPn, and no change in efficiency at 15 bar IMEPn. These results indicate that a durable Gen 1/Gen 2 coating can provide an efficiency benefit to GCI combustion, and that further improvements on piston-spray interaction optimization and surface sealing of the TBC, as well as increasing the coated combustion chamber surface area (e.g. head and valves) can add further improve efficiency. [ABSTRACT FROM AUTHOR]

In order to obtain films with better mechanical properties and fracture toughness, the (TiAlCrZrNb)- Six-N (x=0, 4%, 8%, 12% and 16%) high entropy ceramic nanocomposite films with different Si contents were deposited on Si substrate by magnetron sputtering technology. The effects of Si doping on the microstructure, mechanical properties! and fracture toughness of the films were investigated by X-ray diffractometry, scanning electron microscopy, high-resolution transmission electron microscopy and Nano indentation apparatus. The results show that the mechanical properties and fracture toughness of the films increase first and then decrease with the addition of Si element, which can be attributed to the formation of nanocomposite structures. When Si content is 4%, (TiAlCrZrNb)-Six-N film has the best comprehensive mechanical properties, and Us maximum hardness and elastic modulus are 22.7 GPa and 192.0 GPa, respectively. In this case, the fracture toughness reaches the best, and the radial crack length C as 6.760 µm, The K1C was 1.77 MPa • m1/2. [ABSTRACT FROM AUTHOR]

Temperature swing thermal barrier coatings (TBCs) have the potential to improve efficiency and performance through thermal management of the combustion chamber. However, the effects of combustion chamber deposits (CCDs) on the surface of the TBCs are unclear. Therefore, this paper analyzes the impact of CCD formation on a temperature swing TBC. A piston and a heat flux probe coated with a novel material are installed in a single-cylinder research engine at a low-load condition for 62.5 h to promote CCD growth. Every 12.5 h, the performance at a knock limited condition is assessed and thermophysical property measurements on the heat flux probe are performed. Net thermal efficiency increased by 0.4% absolute after 12.5 h relative to the baseline condition, but further CCD growth caused a dithering of efficiency between the 12.5 h and baseline points. The KLSA retarded consistently throughout this period. External property measurements with the coated heat flux probe showed an improvement in the thermophysical properties of the TBC/CCD layer. [ABSTRACT FROM AUTHOR]

The application of thermal barrier coatings (TBCs) for internal combustion engines has drawn more research attention in recent years due to the improved coating technology and advanced material properties. This paper presents the combination of thermal barrier coatings with an advanced combustion concept: gasoline compression ignition (GCI). This study aims to evaluate the coating performance with GCI combustion on Aramco's light-duty GCI engine. The thermodynamic engine model has been established and collaborated with CFD results to investigate thermal barrier coatings for GCI with a PPCI-diffusion combustion strategy primarily at a load of 23.5 bar IMEPg. Two real-world coating materials were pre-selected as Gen.1 and Gen.2 candidates. The investigation consists of three main topics: the effect of coating thickness on GCI, the effect of intake boost on the performance of traditional coatings and temperature swing coatings, and the effect of combustion chamber coverage on engine performance. The result shows that the TBC thickness became the key parameter to optimize for TBC with GCI combustion mode, and in the end, 250–500 microns is recommended based on the temperature limit of ceramic material. It was also found that the high level of intake boosting could significantly alleviate the charge heating penalty. The coverage of piston, engine head, and both intake and exhaust valves should be applied to achieve the highest efficiency gains. It was found that the gain ratio from each chamber component can be drastically different at different loads. [ABSTRACT FROM AUTHOR]

Methanol is a fuel that can be produced using renewable electricity, green hydrogen, and a carbon-neutral carbon source. As a low-carbon liquid fuel, methanol is a leading candidate as a drop-in replacement fuel for rapid decarbonization of the transportation sector. In this work, methanol is tested experimentally in mixing-controlled compression ignition using a single-cylinder research engine. Specifically, the impact of a double injection strategy and high exhaust gas recirculation (EGR) dilution on performance and emissions parameters are studied. As a single-carbon, oxygenated fuel, methanol has a near-zero sooting potential, meaning there is no soot-NOx tradeoff as EGR dilution is increased. EGR dilution effectively reduces NOx emissions without a significant combustion efficiency or thermal efficiency penalty until the global equivalence ratio exceeds 0.9, at which point, a significant combustion efficiency penalty is incurred in the form of increased CO emissions. If stoichiometric operation with high EGR dilution is considered, engine-out NOx emissions are low, even at very high loads, and NOx aftertreatment is simplified through the use of a three-way catalyst. However, a relative decrease in the net fuel conversion efficiency of up to 8.5% was observed, demonstrating an emissions-efficiency tradeoff. Under lean and stoichiometric conditions, no appreciable agglomeration of particulate matter was observed. [ABSTRACT FROM AUTHOR]