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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: 20 September 2026 | Viewed by 8562

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Special Issue Editor

Dr. Yingyi Liu

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Guest Editor

School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
Interests: HVDC power transmission; electric field measurement; condition monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Building on the success of the first edition of “Power Systems and Energy Systems: Technologies and Applications”, we are excited to present the second edition, aiming to further highlight the latest research and innovations in the field.

Power systems are composed of large and complex networks, governed by physical laws in which unexpected and uncontrollable events may occur. This complexity has grown considerably in recent years due to increased renewable energy generation capacity and increased distributed generation. Therefore, the analysis, design and operation of current and future power systems requires effective approaches to solving different problems such as load flow parameters and location finding, filter design, fault locations, accident analysis, system recovery after blackouts, islanding detection, economic dispatch, unit commitment, etc. This evolution has been so frenetic that it has become necessary for engineers to update the material sufficiently to face the new challenges involved in managing the new generation of networks.

This Special Issue, on “Power Systems and Energy Systems: Technologies and Applications, 2nd Edition”, will contain the results of the most advanced and latest research, and will particularly focus on the development and practical considerations for power systems and next-generation power electronic techniques. The topics covered in this issue comprise, but are not limited to, ‎the following items:

Power systems;
Energy management systems;
HVDC power transmission;
Optical fiber sensing technology;
DC high current detection technology;
Acoustic emission detection technology;
Power electronics technologies in power systems.

Dr. Yingyi Liu
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

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Published Papers (6 papers)

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Research

32 pages, 6500 KB

Open AccessArticle

Harmonic Resonance Mechanism and Suppression Strategies for High-Voltage Cables with Frequency-Dependent Parameters

by Zhaoyu Qin, Yan Zhang, Yuli Wang, Ge Wang and Xiaoyi Cheng

Appl. Sci. 2026, 16(9), 4202; https://doi.org/10.3390/app16094202 - 24 Apr 2026

Viewed by 251

Abstract

The increasing integration of nonlinear loads in modern power systems has made harmonic pollution a critical challenge to the operational safety of power cables. This study develops a frequency-dependent high-voltage cable system model using the ATP-EMTP (Alternative Transients Program-Electro Magnetic Transient Program) electromagnetic transient simulation platform, systematically investigating the amplification mechanisms and propagation characteristics of grounding currents under multi-type harmonic disturbances. A frequency-dependent parameter correction model is established by integrating the conductor skin effect and the dielectric relaxation properties of the insulation layers. This model incorporates the multi-structure combination among conductors, insulation, and metallic screen. It effectively overcomes the limitations of conventional lumped-parameter models in higher frequency harmonic analysis. Key findings are as follows: (1) The combined influence of harmonic frequency and amplitude leads to a grounding current amplification of up to 445 times (at 1950 Hz with 30% distortion level). Notably, current-source excitation produces significantly greater amplification than voltage-source excitation. (2) The distributed capacitance of long-distance cables (>8 km) exacerbates resonance risks within specific frequency bands (750–1250 Hz), resulting in a maximum harmonic amplification factor of 34.73 (observed for the 17th harmonic in a 15 km cable). (3) The contribution of voltage-source harmonics diminishes to less than 5% of the total current at high frequencies (≥1250 Hz), indicating a pattern of current-dominated harmonic superposition. Full article

(This article belongs to the Special Issue Power Systems and Energy Systems: Technologies and Applications, 2nd Edition)

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18 pages, 3592 KB

Open AccessArticle

Vibration-Based Mechanical Fault Diagnosis of On-Load Tap Changers Using Fuzzy Set Theory

by Zhaoyu Qin, Feng Lin, Xiaoyi Cheng, Sasa Kong and Qingxiang Hu

Appl. Sci. 2026, 16(4), 1766; https://doi.org/10.3390/app16041766 - 11 Feb 2026

Viewed by 524

Abstract

On-load tap changers (OLTCs) are critical components of power transformers. In recent years, condition monitoring technologies for OLTCs based on vibration signals have attracted increasing research interest. However, practical applications still face several challenges, including background noise interference, insufficient characterization of transient signals, signal complexity, difficulty in detecting subtle anomalies, and ambiguous associations between fault modes and signal features. To address these issues, this paper proposes an OLTC acoustic fingerprint feature recognition method based on multidimensional phase-space trajectory analysis. First, an OLTC fault simulation platform was established, in which typical mechanical faults—such as fastener loosening, contact wear, and insufficient spring energy storage—were physically simulated. Corresponding vibration signals were then acquired under different operating conditions. Considering the independence of vibration characteristics at different locations of the distribution transformer, a blind source separation method based on endpoint detection was employed to separate OLTC vibration signals from the operational noise of the transformer body. Given the nonlinear and chaotic characteristics of OLTC vibration signals, phase-space reconstruction was introduced for signal analysis. Based on the reconstructed phase space, characteristic patterns and geometric feature parameters corresponding to different mechanical states of the OLTC were extracted. Furthermore, a two-dimensional membership function was constructed using the phase-space trajectories, and fuzzy inference based on predefined fuzzy rules was applied to compute representative feature parameters. A feature parameter database was subsequently established to enable OLTC condition identification. Experimental results demonstrate that the proposed diagnostic model can effectively classify and identify OLTC fault conditions using vibration signals, achieving an average classification accuracy exceeding 91.25%. The proposed method provides an effective non-intrusive approach for online monitoring and mechanical fault diagnosis of OLTCs without interrupting normal transformer operation. Full article

(This article belongs to the Special Issue Power Systems and Energy Systems: Technologies and Applications, 2nd Edition)

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25 pages, 8031 KB

Open AccessArticle

A Dual-Optimized Hybrid Deep Learning Framework with RIME-VMD and TCN-BiGRU-SA for Short-Term Wind Power Prediction

by Zhong Wang, Kefei Zhang, Xun Ai, Sheng Liu and Tianbao Zhang

Appl. Sci. 2026, 16(3), 1531; https://doi.org/10.3390/app16031531 - 3 Feb 2026

Cited by 2 | Viewed by 421

Abstract

Precise short-term forecasting of wind power generation is indispensable for ensuring the security and economic efficiency of power grid operations. Nevertheless, the inherent non-stationarity and stochastic nature of wind power series present significant challenges for prediction accuracy. To address these issues, this paper proposes a dual-optimized hybrid deep learning framework combining Spearman correlation analysis, RIME-VMD, and TCN-BiGRU-SA. First, Spearman correlation analysis is employed to screen meteorological factors, eliminating redundant features and reducing model complexity. Second, an adaptive Variational Mode Decomposition (VMD) strategy, optimized by the RIME algorithm based on Minimum Envelope Entropy, decomposes the non-stationary wind power series into stable intrinsic mode functions (IMFs). Third, a hybrid predictor integrating Temporal Convolutional Network (TCN), Bidirectional Gated Recurrent Unit (BiGRU), and Self-Attention (SA) mechanisms is constructed to capture both local trends and long-term temporal dependencies. Furthermore, the RIME algorithm is utilized again to optimize the hyperparameters of the deep learning predictor to avoid local optima. The proposed framework is validated using full-year datasets from two distinct wind farms in Xinjiang and Gansu, China. Experimental results demonstrate that the proposed model achieves a Root Mean Square Error (RMSE) of 7.5340 MW on the primary dataset, significantly outperforming mainstream baseline models. The multi-dataset verification confirms the model’s superior prediction accuracy, robustness against seasonal variations, and strong generalization capability. Full article

(This article belongs to the Special Issue Power Systems and Energy Systems: Technologies and Applications, 2nd Edition)

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17 pages, 692 KB

Open AccessArticle

Recursively Updated Probabilistic Model for Renewable Generation

by Wei Lou, Shen Fan, Zhenbiao Qi, Cheng Zhao, Hang Zhou and Yue Yang

Appl. Sci. 2025, 15(19), 10546; https://doi.org/10.3390/app151910546 - 29 Sep 2025

Viewed by 1061

Abstract

The Gaussian Mixture Model (GMM) is commonly used to formulate the probabilistic model for quantifying uncertainties in renewable generation. However, traditional static probabilistic models may not efficiently adapt and learn from newly forecasted and measured data. In this paper, we propose a recursively updated probabilistic model that leverages a recursive estimation method to update the parameters of the GMM based on continuously arriving data of renewable generation. This recursive modeling approach effectively incorporates new observations while discarding outdated samples, enabling the tracking of time-varying uncertainties in renewable generation in an incremental manner. Furthermore, we introduce an extra calibration stage to enhance the long-term accuracy of the probabilistic model after a large number of incremental updates. The main contribution is to address the potential degradation of performance caused by suboptimal incremental updates accumulated over time. Numerical tests demonstrate that the proposed model achieves 5–10% higher log likelihood in characterizing renewable generation uncertainties compared to purely recursive models, while reducing computational time by three to four orders of magnitude (1000 to 10,000 times) relative to conventional EM. These results highlight the proposed model’s suitability for real-time probabilistic modeling of renewable generation, with potential applications in system operation. Full article

(This article belongs to the Special Issue Power Systems and Energy Systems: Technologies and Applications, 2nd Edition)

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26 pages, 4894 KB

Open AccessArticle

Energy Management Strategy for Hybrid Electric Vehicles Based on Experience-Pool-Optimized Deep Reinforcement Learning

by Jihui Zhuang, Pei Li, Ling Liu, Hongjie Ma and Xiaoming Cheng

Appl. Sci. 2025, 15(17), 9302; https://doi.org/10.3390/app15179302 - 24 Aug 2025

Cited by 3 | Viewed by 4447

Abstract

The energy management strategy of Hybrid Electric Vehicles (HEVs) plays a key role in improving fuel economy and reducing battery energy consumption. This paper proposes a Deep Reinforcement Learning-based energy management strategy optimized by the experience pool (P-HER-DDPG), aimed at improving the fuel efficiency of HEVs while accelerating the training speed. The method integrates the mechanisms of Prioritized Experience Replay (PER) and Hindsight Experience Replay (HER) to address the reward sparsity and slow convergence issues faced by the traditional Deep Deterministic Policy Gradient (DDPG) algorithm when handling continuous action spaces. Under various standard driving cycles, the P-HER-DDPG strategy outperforms the traditional DDPG strategy, achieving an average fuel economy improvement of 5.85%, with a maximum increase of 8.69%. Compared to the DQN strategy, it achieves an average improvement of 12.84%. In terms of training convergence, the P-HER-DDPG strategy converges in 140 episodes, 17.65% faster than DDPG and 24.32% faster than DQN. Additionally, the strategy demonstrates more stable State of Charge (SOC) control, effectively mitigating the risks of battery overcharging and deep discharging. Simulation results show that P-HER-DDPG can enhance fuel economy and training efficiency, offering an extended solution in the field of energy management strategies. Full article

(This article belongs to the Special Issue Power Systems and Energy Systems: Technologies and Applications, 2nd Edition)

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28 pages, 5408 KB

Open AccessArticle

Optimization and Evaluation of the PEDF System Configuration Based on Planning and Operating Dual-Layer Model

by Tianhe Li, Pei Ye, Haiyang Wang, Weiyu Liu, Xinyue Huang and Ji Ke

Appl. Sci. 2025, 15(14), 7776; https://doi.org/10.3390/app15147776 - 11 Jul 2025

Cited by 1 | Viewed by 944

Abstract

The photovoltaic, energy storage, direct current, and flexibility (PEDF) system represents a crucial innovation for transforming buildings into low-carbon energy sources. Although it is still in the early stages of scalable demonstration, current research and practice related to PEDF lack comprehensive studies on optimizing and evaluating system capacity configuration across various scenarios. Capacity configuration and energy scheduling are crucial components that are often treated separately, leading to a missing opportunity to leverage the synergy among key interactive devices. To address this issue, this paper proposes an optimization and evaluation framework for the PEDF system that employs a dual-layer model for planning and operating. This framework precisely configures the PEDF topology, load, photovoltaic, energy storage, and critical interactive devices, while integrating economic, environmental, and reliability objectives. The effectiveness of the proposed model has been validated in optimizing capacity configurations for newly built office buildings and existing commercial settings. The results indicate that for new office buildings, schemes that prioritize low-carbon initiatives are more effective than those that focus on reliability and economy. In existing commercial buildings, reliability-focused schemes outperform those that prioritize economy and low carbon, and all three are significantly better than pre-configuration schemes. The proposed framework enhances the theoretical understanding of PEDF system planning and evaluation, thereby promoting broader adoption of sustainable energy technologies. Full article

(This article belongs to the Special Issue Power Systems and Energy Systems: Technologies and Applications, 2nd Edition)

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