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The inverter dead time effect is non-negligible for the control performance of sensorless synchronous reluctance motor (SynRM) drives at low speeds. In this paper, a reduced-order observer-based sensorless control method for SynRM drives combined with the adaptive linear neurons (ADALINE)-based dead-time compensation is proposed. The reduced-order observer-based sensorless control method is presented, for which is parameter tuning is easy. On this basis, the dead-time compensation strategy using ADALINE filters is proposed to reduce the voltage harmonics effect on the estimation performance of the reduced-order observer. With ADALINE filters, the sixth current harmonic can be successfully filtered out by compensating the voltage directly or fitting the current to compensate the voltage. In this way, the low-speed estimation performance of the reduced-order observer is improved. The effectiveness of the proposed method is verified on a 3 kW SynRM experimental platform. [ABSTRACT FROM AUTHOR]

There are usually noise problems when the position sensorless control of a synchronous reluctance motor (SynRM) is carried out by high-frequency (HF) signal injection method. Due to the special structure, the cross-saturation effect of the SynRM is particularly serious, resulting in reduced position observation accuracy. In this paper, a variable-angle random HF voltage injection strategy with cross-saturation effect compensation is proposed for position sensorless SynRM drives. Random number generation based on the chaotic mapping method is used to generate random HF voltage signals with different frequencies for injection; the current power spectral density (PSD) distribution is extended and the HF noise can be reduced. A control strategy based on variable-angle square-wave injection is proposed to suppress the cross-saturation effect. By measuring the position error curves of different loads off-line and polynomial fitting the curves, the position error is achieved by combining with the corresponding signal demodulation algorithm. The proposed method does not require additional hardware resources and can maintain high control accuracy and robustness. Finally, the effectiveness of the proposed sensorless control strategy is verified on a 3 kW SynRM experimental platform. [ABSTRACT FROM AUTHOR]

Although traditional fault diagnosis methods are proficient in extracting signal features, their diagnostic interpretability remains challenging. Consequently, this article proposes a conditionally interpretable generative adversarial network (C-InGAN) model for the interpretable feature fault diagnosis of bearings. Initially, the vibration signal is denoised and transformed into a frequency domain signal. The model consists of the two primary networks, each employing a convolutional layer and an attention module, generator (G) and discriminator (D), respectively. Latent code was incorporated into G to constrain the generated samples, and a discriminant layer was added to D to identify the interpretable features. During training, the two networks were alternately trained, and the feature mapping relationship of the pre-normalized encoder was learned by maximizing the information from the latent code and the discriminative result. The encoding that represents specific features in the vibration signal was extracted from the random noise. Ultimately, after completing adversarial learning, G is capable of generating a simulated signal of the specified feature, and D can assess the interpretable features in the vibration signal. The effectiveness of the model is validated through three typical experimental cases. This method effectively separates the discrete and continuous feature coding in the signal. [ABSTRACT FROM AUTHOR]

The deployment of a green power alternative within an isolated network, powered by renewable energy sources, in the "Three North" region of China can facilitate the substitution of high-energy-consuming industrial loads with green power. However, an inadequate power supply configuration may lead to economic and reliability issues. To address this problem, effective capacity allocation within the green power alternative isolated network is proposed. The capacity allocation process starts with the design of a network structure that aligns with local conditions. Subsequently, a capacity allocation model is developed, considering economic factors, renewable energy utilization efficiency, and system reliability. The gray wolf optimizer is enhanced to establish a capacity allocation method for the green power alternative isolated network. This method is then employed to simulate and assess the performance of the network. The results indicate that the green alternative isolated grid can successfully facilitate green power substitution, satisfying the energy requirements of the loads. [ABSTRACT FROM AUTHOR]

This research presents an interconnected operation model that integrates carbon capture and storage (CCS) with power to gas (P2G), tackles the challenges encountered by integrated electricity-natural gas systems (IEGS) in terms of energy consumption and achieving low-carbon economic operations, and formulates a DRL-based, physically model-free energy optimization management strategy for IEGS, designed to lower operational costs and carbon emissions. Initially, the CCS-P2G interconnected IEGS system undergoes mathematical modeling. Subsequently, the system's uncertainty in optimal scheduling is formulated as a Markov decision process, with the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm facilitating real-time scheduling decisions. Comparative analysis across various scenarios demonstrates that the model offers superior low-carbon economic benefits and enhanced environmental sustainability. Further analysis validates that the optimized scheduling strategy proposed herein advantages in achieving low-carbon financial objectives, convergence speed, and system learning performance, as evidenced by training the model with historical data and the comparative analysis of the DQN and DDPG algorithms. • "Integrates CCS and P2G in IEGS for optimal renewable use." • "Reduces wind and solar curtailment effectively." • "Enhances system's low-carbon economy significantly." • "Utilizes surplus renewable energy for hydrogen production." • "Demonstrates CCS-P2G synergy in minimizing carbon emissions." [ABSTRACT FROM AUTHOR]

Aiming to amplify the renewable energy consumption capacity, this study delineates the development of an off-grid Renewable Energy Large-Scale Hydrogen Production System (H2-RES). The system was optimized for economic efficiency and safety, promising a reduction in both the investment cost for grid connection and the overall cost of hydrogen production from electrolytic water. We presented a comprehensive mathematical model for each H2-RES unit and designed a control strategy to enhance energy optimization and management. An intelligent energy scheduling policy empowered by the DDPG algorithm is introduced for optimal decision-making in continuous state and action spaces. Comparative analysis with traditional control policies, PSO, and DQN algorithms underscores the superior economic efficiency, enhanced renewable energy consumption, and safe operation facilitated by DDPG. These findings underscore the academic and engineering potential of DDPG in the energy dispatch of H2-RES. [ABSTRACT FROM AUTHOR]