Recent Advances in Electrical & Electronic Engineering - Volume 18, Issue 4, 2025

Under the "Dual Carbon" strategy, the new-type power system integrates various complex equipment, especially the addition of wind power, photovoltaic power generation, distributed energy storage and other systems, which not only brings clean and efficient energy to the power system, but also leads to potential risks in the power grid.

Traditional methods for assessing the risks of power systems mainly focus on the characteristics of traditional power grids. With the emergence of new features in the new power system, these traditional assessment methods are no longer effective, which could affect the safe and stable operation of the power system. To address this issue, this article proposes a new vulnerability index system for power systems. It also introduces the analytic hierarchy based on Moody's order graph (MAHP) quantification analysis method, and develops the Tabular data risk assessment methodology (TabRAM) risk assessment model based on an improved Transformer method.

MAHP is a comprehensive evaluation method combining Moody's Chart and Analytic Hierarchy (AHP), which has high comprehensiveness and operability. TabRAM uses Bayesian neural networks (BNN) to complete prior data fitting and model complex feature dependencies and potential causal mechanisms on tabular data. The network is trained using an improved Transformer, and after pre-training, it can approximate new prior probabilistic inference in a forward pass to achieve prediction and classification tasks for new datasets.

After multiple rounds of iterations and parameter adjustments, the model achieved the desired performance and was successfully applied to the risk assessment of the new power system.

According to the experimental results, the proposed TabRAM model demonstrates superior performance in the field of risk assessment for the new-type power system, as compared to traditional deep learning and machine learning algorithms.

Under the "Dual Carbon" strategy, the new-type power system integrates various complex equipment, especially the addition of wind power, photovoltaic power generation, distributed energy storage and other systems, which not only brings clean and efficient energy to the power system, but also leads to potential risks in the power grid.

Traditional methods for assessing the risks of power systems mainly focus on the characteristics of traditional power grids. With the emergence of new features in the new power system, these traditional assessment methods are no longer effective, which could affect the safe and stable operation of the power system. To address this issue, this article proposes a new vulnerability index system for power systems. It also introduces the analytic hierarchy based on Moody's order graph (MAHP) quantification analysis method, and develops the Tabular data risk assessment methodology (TabRAM) risk assessment model based on an improved Transformer method.

MAHP is a comprehensive evaluation method combining Moody's Chart and Analytic Hierarchy (AHP), which has high comprehensiveness and operability. TabRAM uses Bayesian neural networks (BNN) to complete prior data fitting and model complex feature dependencies and potential causal mechanisms on tabular data. The network is trained using an improved Transformer, and after pre-training, it can approximate new prior probabilistic inference in a forward pass to achieve prediction and classification tasks for new datasets.

After multiple rounds of iterations and parameter adjustments, the model achieved the desired performance and was successfully applied to the risk assessment of the new power system.

According to the experimental results, the proposed TabRAM model demonstrates superior performance in the field of risk assessment for the new-type power system, as compared to traditional deep learning and machine learning algorithms.

Accurate short-term load forecasting is an important guarantee for the safety, stability, economic and efficient operation of power systems.

In order to effectively improve the forecasting accuracy, this paper proposes a hybrid forecasting model of convolutional neural network (CNN) and bi-directional long short-term memory (BiLSTM) neural network considering load time-varying trend mapping model (M).

Firstly, the time-varying features of the load curve are analyzed, and a mapping model is established to characterize the load time-varying trend. The features of the load time-varying trend are extracted, and they are quantified into a mathematical model. Secondly, the feature set is reconstructed through data migration. Then, the reconstructed feature set is input into the CNN-BiLSTM hybrid model. In the hybrid model, CNN is used to extract the features from data again to form a new feature vector, and then the feature vector is input into BiLSTM for forecasting.

The power load data set from the New England in United States is used to simulate and verify the correctness and validity of the proposed method.

With the comparison of the forecasting results between different load forecasting models, the results show that the forecasting accuracy of the proposed method is higher and it is verified that the load time-varying trend mapping method proposed can improve the forecasting accuracy of different models in varying degrees.

Accurate short-term load forecasting is an important guarantee for the safety, stability, economic and efficient operation of power systems.

In order to effectively improve the forecasting accuracy, this paper proposes a hybrid forecasting model of convolutional neural network (CNN) and bi-directional long short-term memory (BiLSTM) neural network considering load time-varying trend mapping model (M).

Firstly, the time-varying features of the load curve are analyzed, and a mapping model is established to characterize the load time-varying trend. The features of the load time-varying trend are extracted, and they are quantified into a mathematical model. Secondly, the feature set is reconstructed through data migration. Then, the reconstructed feature set is input into the CNN-BiLSTM hybrid model. In the hybrid model, CNN is used to extract the features from data again to form a new feature vector, and then the feature vector is input into BiLSTM for forecasting.

The power load data set from the New England in United States is used to simulate and verify the correctness and validity of the proposed method.

With the comparison of the forecasting results between different load forecasting models, the results show that the forecasting accuracy of the proposed method is higher and it is verified that the load time-varying trend mapping method proposed can improve the forecasting accuracy of different models in varying degrees.

The anti-interference requirements of miniaturized satellite navigation arrays are increasing. The anti-interference ability reducing of small aperture arrays caused by large mutual coupling and large main lobe width may be solved by adopting a quad-arrays antenna.

Research of multiple interference suppression method is the aim of the paper, and it makes the research of a small four-element satellite navigation array antenna and makes a related analysis of its multiple interference suppression ability.

The paper, focuses on the structural design of low-coupling polarized array antennas and makes an analysis of mutual coupling between array elements, the compensation technology based on auxiliary sources is proposed.

Experimental analysis of antenna narrowband interference suppression is made. Relevant anti-interference results on four-element antennae are also obtained. The anti-interference ability of four four-element antennae is summarized.

The anti-interference ability of -the four-element antenna is analyzed and summarized in the paper; related results show that a small four-element satellite navigation array antenna can be used in multiple interference suppression in the satellite navigation system.

The anti-interference requirements of miniaturized satellite navigation arrays are increasing. The anti-interference ability reducing of small aperture arrays caused by large mutual coupling and large main lobe width may be solved by adopting a quad-arrays antenna.

Research of multiple interference suppression method is the aim of the paper, and it makes the research of a small four-element satellite navigation array antenna and makes a related analysis of its multiple interference suppression ability.

The paper, focuses on the structural design of low-coupling polarized array antennas and makes an analysis of mutual coupling between array elements, the compensation technology based on auxiliary sources is proposed.

Experimental analysis of antenna narrowband interference suppression is made. Relevant anti-interference results on four-element antennae are also obtained. The anti-interference ability of four four-element antennae is summarized.

The anti-interference ability of -the four-element antenna is analyzed and summarized in the paper; related results show that a small four-element satellite navigation array antenna can be used in multiple interference suppression in the satellite navigation system.

The development of energy storage networks has facilitated the rapid expansion of new energy-based power systems. However, the emergence of large-scale energy storage devices has also led to a significant increase in energy data volumes. Federated learning provides a solution by allowing energy data owners to train AI models without sharing local energy data, which is particularly advantageous for handling heterogeneous data.

This paper explores the application of federated learning in managing energy data within distributed energy storage networks. Specifically, we leverage deep reinforcement learning algorithms to optimize the selection of device subsets, aiming to mitigate data bias caused by non-identically and independently distributed (non-IID) data while enhancing convergence rates.

To achieve our objectives, we employ deep reinforcement learning to dynamically select the optimal subset of devices in the federated learning process. Additionally, we introduce a reputation replay array mechanism to address the issue of free-rider users and ensure fair modeling without payment penalties. We analyze energy data characteristics within distributed energy storage networks and simulate unstructured short data fragments using datasets such as 20 Newsgroups and AG News.

Our experiments show that our proposed model outperforms FedAvg and TiFL on the 20 Newsgroups and AG News datasets, especially under non-iid conditions. Our model significantly reduces communication rounds by up to 47% and 39%, respectively. It also maintains high accuracy and resilience against dishonest nodes, ensuring the quality of the training model.

Our research concludes that combining federated learning with deep reinforcement learning not only solves the problems of data management and privacy protection in distributed energy storage networks, but also promotes the sustainable development of new energy systems.

The development of energy storage networks has facilitated the rapid expansion of new energy-based power systems. However, the emergence of large-scale energy storage devices has also led to a significant increase in energy data volumes. Federated learning provides a solution by allowing energy data owners to train AI models without sharing local energy data, which is particularly advantageous for handling heterogeneous data.

This paper explores the application of federated learning in managing energy data within distributed energy storage networks. Specifically, we leverage deep reinforcement learning algorithms to optimize the selection of device subsets, aiming to mitigate data bias caused by non-identically and independently distributed (non-IID) data while enhancing convergence rates.

To achieve our objectives, we employ deep reinforcement learning to dynamically select the optimal subset of devices in the federated learning process. Additionally, we introduce a reputation replay array mechanism to address the issue of free-rider users and ensure fair modeling without payment penalties. We analyze energy data characteristics within distributed energy storage networks and simulate unstructured short data fragments using datasets such as 20 Newsgroups and AG News.

Our experiments show that our proposed model outperforms FedAvg and TiFL on the 20 Newsgroups and AG News datasets, especially under non-iid conditions. Our model significantly reduces communication rounds by up to 47% and 39%, respectively. It also maintains high accuracy and resilience against dishonest nodes, ensuring the quality of the training model.

Our research concludes that combining federated learning with deep reinforcement learning not only solves the problems of data management and privacy protection in distributed energy storage networks, but also promotes the sustainable development of new energy systems.

Current work is associated with the deployment of synchronized chaotic Secure Hash Algorithm (SHA256) random number generators along with their application in image encryption.

This study aimed to design a perfect communication system to encrypt and decrypt the images via image processing. We introduced a new sliding mode scheme for master-slave four-dimensional (4D) Lorenz-Stenflo systems to deal with the synchronization problem. Then, a new synchronized dynamic secure hash algorithm (SHA-256) was implemented to generate the dynamic random numbers.

An image encryption and decryption mechanism was implemented by using synchronized chaotic master-slave systems corresponding to the generated hash values. Here, the master systems were used for encryption, and the slave systems were responsible for the decryption mechanism. The complete design of this paper was implemented in a Python environment.

The demonstration of simulation results includes the state responses, strange attractors, and synchronization error responses of master-slave chaotic systems with appropriate switching functions and control input.

Current work is associated with the deployment of synchronized chaotic Secure Hash Algorithm (SHA256) random number generators along with their application in image encryption.

This study aimed to design a perfect communication system to encrypt and decrypt the images via image processing. We introduced a new sliding mode scheme for master-slave four-dimensional (4D) Lorenz-Stenflo systems to deal with the synchronization problem. Then, a new synchronized dynamic secure hash algorithm (SHA-256) was implemented to generate the dynamic random numbers.

An image encryption and decryption mechanism was implemented by using synchronized chaotic master-slave systems corresponding to the generated hash values. Here, the master systems were used for encryption, and the slave systems were responsible for the decryption mechanism. The complete design of this paper was implemented in a Python environment.

The demonstration of simulation results includes the state responses, strange attractors, and synchronization error responses of master-slave chaotic systems with appropriate switching functions and control input.

The phase-locked loop (PLL) is widely used to estimate synchronization information such as magnitude and phase angle of the grid voltage, which is essential for grid-connected power generation from distributed renewable energy sources. However, the harmonics in the grid often affect the accurate extraction of fundamental positive sequence, resulting in phase deviations.

A dual extended novel third-order generalized integrator (DENTOGI) is proposed and placed into the control outer loop of the PLL. This approach can be applied in the condition of the grid with DC offset voltage (DCOV). Additionally, the PLL is proposed by introducing moving average filter (MAF) and eliminating the proportional controller of the integration link to suppress high-frequency harmonics and speed up the response.

A comparative experiment using Matlab/Simulink is conducted to evaluate the proposed PLL against the conventional PLLs. The experimental results confirm that the proposed PLL exhibits superior filtering performance for DC offset and harmonic components when compared to the conventional PLL. Additionally, the proposed PLL demonstrates better dynamic adjustment capabilities.

The main contribution of this paper is to propose a phase-locked method that can eliminate DCOV and harmonic components, which can be applied in non-ideal power grid conditions. The proposed PLL based on DENTOGI and MAF can lock the grid phase stably without errors under the grid fault involving DCOV and harmonic disturbance, it also exhibits the characteristics of fast response.

The phase-locked loop (PLL) is widely used to estimate synchronization information such as magnitude and phase angle of the grid voltage, which is essential for grid-connected power generation from distributed renewable energy sources. However, the harmonics in the grid often affect the accurate extraction of fundamental positive sequence, resulting in phase deviations.

A dual extended novel third-order generalized integrator (DENTOGI) is proposed and placed into the control outer loop of the PLL. This approach can be applied in the condition of the grid with DC offset voltage (DCOV). Additionally, the PLL is proposed by introducing moving average filter (MAF) and eliminating the proportional controller of the integration link to suppress high-frequency harmonics and speed up the response.

A comparative experiment using Matlab/Simulink is conducted to evaluate the proposed PLL against the conventional PLLs. The experimental results confirm that the proposed PLL exhibits superior filtering performance for DC offset and harmonic components when compared to the conventional PLL. Additionally, the proposed PLL demonstrates better dynamic adjustment capabilities.

The main contribution of this paper is to propose a phase-locked method that can eliminate DCOV and harmonic components, which can be applied in non-ideal power grid conditions. The proposed PLL based on DENTOGI and MAF can lock the grid phase stably without errors under the grid fault involving DCOV and harmonic disturbance, it also exhibits the characteristics of fast response.

Smart vehicles are connected to the Internet of Things (IoT), bringing with them the potential to transform human existence in so-called “smart cities.” Intelligent vehicle architecture revolves around the Vehicular-AdhocNetwork (VANET). The goal of a VANET is to make driving more pleasant. VANETs' message-sharing capabilities contribute to improved traffic management, reduced congestion, and safer driving. However, VANETs' usefulness could be diminished by the spread of fraudulent or erroneous messages.

For better road safety and less congestion, it guarantees secure and accurate communication between vehicles and between vehicles and infrastructure. The security and privacy of a VANET, however, can be compromised by threats, including denial-of-service (DoS), replay, and Sybil attacks. These problems can cause a rogue node to send out faulty data throughout the system. We introduce a biometrics-blockchain (BBC) approach to ensure the safety of information exchanged between vehicles in a VANET and to preserve archival data in a tried-and-true environment. To protect the anonymity of users, the suggested framework makes use of biometric data to verify the identity of the sender.

As a result, the proposed BBC scheme creates a safe and reliable environment for vehicles in VANET, with the added benefit of identity tracing capabilities. To prove the effectiveness of the proposed framework, simulations were run in the urban mobility models OMNeT++, veins, and SUMO. Packet-delivery-rate (PDR), packet-loss-rate (PLR), and computing cost (CC) were used to assess the framework's efficiency.

The outcomes highlighted the superiority of our innovative model over conventional methods, such as PDR slightly increased to 8-10%, PLR decreased to 20-25%, and CC also reduced to 15-20% compared to state-of-art models.

Smart vehicles are connected to the Internet of Things (IoT), bringing with them the potential to transform human existence in so-called “smart cities.” Intelligent vehicle architecture revolves around the Vehicular-AdhocNetwork (VANET). The goal of a VANET is to make driving more pleasant. VANETs' message-sharing capabilities contribute to improved traffic management, reduced congestion, and safer driving. However, VANETs' usefulness could be diminished by the spread of fraudulent or erroneous messages.

For better road safety and less congestion, it guarantees secure and accurate communication between vehicles and between vehicles and infrastructure. The security and privacy of a VANET, however, can be compromised by threats, including denial-of-service (DoS), replay, and Sybil attacks. These problems can cause a rogue node to send out faulty data throughout the system. We introduce a biometrics-blockchain (BBC) approach to ensure the safety of information exchanged between vehicles in a VANET and to preserve archival data in a tried-and-true environment. To protect the anonymity of users, the suggested framework makes use of biometric data to verify the identity of the sender.

As a result, the proposed BBC scheme creates a safe and reliable environment for vehicles in VANET, with the added benefit of identity tracing capabilities. To prove the effectiveness of the proposed framework, simulations were run in the urban mobility models OMNeT++, veins, and SUMO. Packet-delivery-rate (PDR), packet-loss-rate (PLR), and computing cost (CC) were used to assess the framework's efficiency.

The outcomes highlighted the superiority of our innovative model over conventional methods, such as PDR slightly increased to 8-10%, PLR decreased to 20-25%, and CC also reduced to 15-20% compared to state-of-art models.

Compared with the traditional power system, the large-scale access of distributed energy resources in the new power system has a great impact on the structure and operation mode of the power grid, and it is also more susceptible to device-level and network-level FDI attacks.

In order to improve the accuracy and precision of detecting false data injection attacks in distributed energy resources integration into distribution networks and to further explore time series modeling methods for measurement data, it is helpful for the FDIAs detection method to be widely adopted and applied in new power systems.

To address false data injection attacks on distributed energy resource integration into distribution grids within new power systems, a data-driven time series anomaly detection method is employed. Firstly, time-aware shapelets are extracted from time series data, and then the shapelet evolution graph is constructed to capture the correlation between the shapelets. Finally, time series representation vectors are learned using segment embeddings derived from the shapelet evolution graph through the DeepWalk algorithm. These representation vectors are then input into a BO-XGBoost anomaly detector, facilitating the detection of FDIAs.

After multiple rounds of parameter tuning, the parameters of Shapelet quantity (K=40) and segment length (L=4) achieved an accuracy of 92.8% in FDIA detection. Comparative experimental results with different algorithms indicate that, compared to other unsupervised learning methods, this approach exhibits an accuracy improvement of 20-40%. In the case of BO-XGBOOST, it achieves a 5% increase in accuracy compared to the unmodified XGBOOST.

The experimental results indicate that this method can effectively detect false data injection attacks on the integration of distributed energy resources into distribution grids within new power systems. This model significantly enhances detection accuracy and precision while also imparting physical significance to the dynamic evolution of time series models.

Compared with the traditional power system, the large-scale access of distributed energy resources in the new power system has a great impact on the structure and operation mode of the power grid, and it is also more susceptible to device-level and network-level FDI attacks.

In order to improve the accuracy and precision of detecting false data injection attacks in distributed energy resources integration into distribution networks and to further explore time series modeling methods for measurement data, it is helpful for the FDIAs detection method to be widely adopted and applied in new power systems.

To address false data injection attacks on distributed energy resource integration into distribution grids within new power systems, a data-driven time series anomaly detection method is employed. Firstly, time-aware shapelets are extracted from time series data, and then the shapelet evolution graph is constructed to capture the correlation between the shapelets. Finally, time series representation vectors are learned using segment embeddings derived from the shapelet evolution graph through the DeepWalk algorithm. These representation vectors are then input into a BO-XGBoost anomaly detector, facilitating the detection of FDIAs.

After multiple rounds of parameter tuning, the parameters of Shapelet quantity (K=40) and segment length (L=4) achieved an accuracy of 92.8% in FDIA detection. Comparative experimental results with different algorithms indicate that, compared to other unsupervised learning methods, this approach exhibits an accuracy improvement of 20-40%. In the case of BO-XGBOOST, it achieves a 5% increase in accuracy compared to the unmodified XGBOOST.

The experimental results indicate that this method can effectively detect false data injection attacks on the integration of distributed energy resources into distribution grids within new power systems. This model significantly enhances detection accuracy and precision while also imparting physical significance to the dynamic evolution of time series models.

In response to the need for improved performance in electric machines, this paper introduces and evaluates a novel hybrid excitation partitioned stator flux-switching (HEPSFS) machine. This design optimizes output torque while considering power density, torque density, and overall efficiency.

The primary objective is to enhance electromagnetic torque production by minimizing flux leakage to the inner-stator core by creating an auxiliary air-gap in the inner-stator tooth. Additionally, a partitioned stator design is adopted to accommodate armature and field windings without space conflicts, allowing for increased windings and permanent magnets (PMs) to maximize torque density and flux regulation capability.

A comparative analysis is performed with a conventional HEPSFS (CHEPSFS) machine to evaluate the proposed design. Both machines share the same design dimensions and winding configuration to ensure a fair assessment. Finite Element Method (FEM) simulations using ANSYS Maxwell software are conducted to validate the results.

The analysis reveals that the proposed HEPSFS (PHEPSFS) machine outperforms the conventional counterpart. It exhibits higher torque output, torque density, power density, and efficiency while minimizing torque ripple. Moreover, at a current angle of 0 degrees, the PHEPSFS machine shows substantial percentage improvements compared to the CHEPSFS machine: a 285% increase in torque output, a 281.39% rise in power density, a 283.82% enhancement in torque density, and a 9.14% boost in efficiency. Furthermore, the PHEPSFS machine design reduces torque ripple by an impressive 59.63% compared to the CHEPSFS machine design.

The study concludes that the PHEPSFS design effectively optimizes torque performance, making it a promising advancement in HEPSFS machines.

In response to the need for improved performance in electric machines, this paper introduces and evaluates a novel hybrid excitation partitioned stator flux-switching (HEPSFS) machine. This design optimizes output torque while considering power density, torque density, and overall efficiency.

The primary objective is to enhance electromagnetic torque production by minimizing flux leakage to the inner-stator core by creating an auxiliary air-gap in the inner-stator tooth. Additionally, a partitioned stator design is adopted to accommodate armature and field windings without space conflicts, allowing for increased windings and permanent magnets (PMs) to maximize torque density and flux regulation capability.

A comparative analysis is performed with a conventional HEPSFS (CHEPSFS) machine to evaluate the proposed design. Both machines share the same design dimensions and winding configuration to ensure a fair assessment. Finite Element Method (FEM) simulations using ANSYS Maxwell software are conducted to validate the results.

The analysis reveals that the proposed HEPSFS (PHEPSFS) machine outperforms the conventional counterpart. It exhibits higher torque output, torque density, power density, and efficiency while minimizing torque ripple. Moreover, at a current angle of 0 degrees, the PHEPSFS machine shows substantial percentage improvements compared to the CHEPSFS machine: a 285% increase in torque output, a 281.39% rise in power density, a 283.82% enhancement in torque density, and a 9.14% boost in efficiency. Furthermore, the PHEPSFS machine design reduces torque ripple by an impressive 59.63% compared to the CHEPSFS machine design.

The study concludes that the PHEPSFS design effectively optimizes torque performance, making it a promising advancement in HEPSFS machines.