Energy Informatics最新文献

When power communication equipment malfunctions, the stability and safety of the power grid are compromised. This is due to the health status of the equipment. The safety and stability of the power grid will be impacted if dispatchers take too long to identify the fault’s origin and kind, which will disrupt the power communication system’s regular operations. In order to solve the problem of poor health status of power communication system due to the inability to timely determine and deal with the faults of power communication equipment, the study proposes the construction of health status recognition of power communication equipment with prediction model based on term frequency-inverse document frequency and cosine similarity. The model firstly extracted the fault information of power communication equipment and builds the fault knowledge graph. Secondly, the study identified and built a prediction model for the health status of power communication equipment based on term frequency-inverse document frequency and cosine similarity model. The outcomes revealed that the training model had the highest accuracy and the lowest loss rate when the learning rate was set to 1 × 10⁻⁵. When the iterations was set to 70, the training and test sets had the highest accuracy and the lowest loss rate. When the model utilized in the study was compared to other models with varying numbers of samples in the dataset, it performed well in terms of runtime and fault diagnosis accuracy. The model developed by the study improves the accuracy of fault extraction and recognition and can better ensure the normal operation of power communication equipment.

Successful adaptation to climate change requires resilient, reliable, and efficient energy systems. To unlock energy efficiency potentials in buildings, an intelligent, user-centered approach is vital. However, this requires handling diverse data on the energy system. Therefore, technologies for harmonizing, storing, and visualizing data, as well as managing physical devices and users are needed. This work assesses existing and required Information and Communication Technologies (ICT) for intelligent building energy system operation. We propose an intermediate architecture based on Internet of Things (IoT) core principles and feature insights from its implementation within the Living Lab Energy Campus (LLEC) at Forschungszentrum Jülich. We present an approach for integrating existing ICT components, such as building energy metering and central Heating, Ventilation and Air Conditioning (HVAC) management, and propose a comprehensive data collection and distribution infrastructure. We establish IoT-enabled applications for energy system monitoring, user engagement, advanced building operation, and device identification and management. We evaluate our ICT setup through functional and performance assessments. We find that heterogeneous data can be reliably collected, distributed, and managed using standardized interfaces, state-of-the-art databases, and cutting-edge software components. For the buildings operated through the ICT infrastructure, data transmission availability is above 98.90 %, mean time to repair (MTTR) is less than 2.68 h, and mean time between failures (MTBF) is in the range of 242.67 h to 1092.00 h, evaluated over a period of three months. Our approach promotes the early real-world adoption of intelligent building control prototypes and their sustainable development. We demonstrate the proposed ICT setup through an experimental study that applies a cloud-based Model Predictive Controller (MPC) to a real building space. Our results provide a comprehensive discussion of the required ICT setup for intelligent building energy system control in real-world environments, and highlight important design strategies that reduce the conceptual overhead and facilitate implementation in similar projects.

Recent advances in Artificial Intelligence (AI) have generated both excitement and concern within the power sector. While AI holds significant promise, enabling improved forecasting of renewable energy generation, enhanced grid resilience, and better supply-demand balancing, it also raises critical issues around transparency, data privacy, accountability, and fairness in power distribution. Despite the growing body of research on AI applications in power systems, there is a lack of structured understanding of the key socio-technical matters of concern (MCs) surrounding its integration. This paper addresses this gap by conducting a systematic literature review combined with qualitative text analysis to identify and synthesize the most prominent socio-technical concerns in the academic discourse. We analyzed a curated sample of peer-reviewed papers published between 1987 and 2024, focusing on high-impact journals in the field. Our analysis reveals four major categories of concern: (1) Operational Concerns-relating to AI’s reliability, efficiency, and integration with existing grid systems; (2) Sustainability Concerns-centered on energy consumption, environmental impact, and AI’s role in the energy transition; (3) Trust Concerns-including transparency, explainability, cybersecurity, and ethics; and (4) Regulatory and Economic Concerns-covering issues of accountability, regulatory compliance, and cost-effectiveness. By mapping these concerns into a cohesive analytical framework, this study contributes to the literature by offering a clearer understanding of AI’s sociotechnical challenges in the power sector. The framework also informs future research and policymaking efforts aimed at the responsible and sustainable deployment of AI in power systems.

In the bilateral negotiated electricity market, the existence of abnormal data in the real-time node electricity prices poses a great threat to the stability and reliability of the power system. This paper introduces a synchronous phasor anomaly detection method considering the influence of recording deviation. This paper comprehensively analyzes the causes of abnormal real-time node electricity price data collected by PMU in the bilateral negotiated electricity market. A weighted time–frequency transform is proposed. The estimator achieves the accurate synchronous phasor measurement of real-time node electricity price data by creatively combining the frequency discretization and online signal frequency detection technology. It also successfully minimizes the interference of recording deviation on synchronous phasor measurement. Comparing the estimated value of the SCADA system with the measured value of the PMU is part of the anomaly detection process.The experimental results prove the effectiveness of this method. It achieves a high level of accuracy and minimum error when processing the real-time node electricity price data. This method can accurately identify various anomalies, such as those related to node voltage, phase angle and power. In addition, this method has high detection accuracy and greatly improves the reliability of power system anomaly detection. This method not only provides reliable data for transaction decisions and operational evaluations in the power market but also enhances the power system’s safety and stability through timely detection of potential issues.

To improve the accuracy and reliability of circuit breaker detection in power systems, this study proposes an intelligent detection instrument. The instrument addresses key issues found in traditional methods, such as limited real-time performance, inadequate data integration capabilities, and poor environmental adaptability. The instrument integrates multimodal data fusion technology to comprehensively analyze electrical parameters, mechanical characteristics, and environmental factors, enabling full awareness of the circuit breaker’s status. Additionally, this study optimizes the fault diagnosis algorithm, enhancing detection stability and robustness. By improving the model architecture, the computational burden is reduced, making the system more suitable for real-time monitoring and resource-constrained environments. Experimental results demonstrate that the intelligent detection instrument outperforms existing methods in terms of accuracy, detection efficiency, and anti-interference capabilities. It can more effectively identify the operational status of circuit breakers while maintaining high detection performance under complex operating conditions. Compared to traditional methods, the proposed solution shows significant advantages in reducing false alarms, optimizing detection speed, and improving environmental adaptability. Therefore, the study provides efficient and stable technical support for intelligent circuit breaker detection in power systems, laying a solid foundation for the development of smart grids.

Load frequency control (LFC) in power systems faces increasingly complex cyber-physical attack threats, while existing detection methods have limited capability to identify intelligent attacks. This paper constructs an LFC system model considering dynamic response characteristics and establishes a reinforcement learning-based method for generating multiple attack strategies, covering typical scenarios such as false data injection (FDI) and load switching attacks. A multi-model fusion attack detection framework is proposed, integrating (Long Short-Term Memory) LSTM supervised learning and autoencoder unsupervised learning algorithms, with an adaptive weight adjustment mechanism that dynamically optimizes detection strategies. Experimental results demonstrate that the fusion mechanism achieves 99.4% comprehensive identification accuracy across four system states, outperforming single supervised models (98%) and single unsupervised models (76.4%). Detection accuracy exceeds 99% for three different frequency characteristic attacks, with an average detection delay of only 0.12 seconds. The fusion mechanism effectively reduces false positive and false negative rates (FNRs), showing significant advantages in identifying and defending against unknown attacks, providing a new approach for LFC system security protection.

As the world’s energy structure is gradually changing, the automotive industry is shifting its focus to new energy vehicles in an effort to improve the performance and service life of the charging pile. To solve the problem that traditional models tend to fall into locally optimal solutions (i.e., the model optimization process stays in the non-optimal regional minimum) in complex parameter space, the study innovatively proposes a hybrid prediction model that combines the whale optimization algorithm with the gated recurrent unit-long short-term memory neural network. By introducing the whale optimization mechanism to globally optimize the key parameters of the neural network, the method improved the model’s ability to model complex time series data. Moreover, the method also effectively avoided the problem of traditional methods falling into local optimal solutions, thus improving the training efficiency and generalization ability while maintaining the model accuracy. It took only 21 s to complete the training of 600 samples, and the prediction accuracy was as high as 91%. In the four classes of fault classification experiments, the proposed model performs well in classification accuracy in all classes, showing strong multi-class fault recognition capability. Therefore, the fault prediction model developed in this study can accurately and effectively identify and predict charging pile faults, and shows high performance. This not only provides a strong theoretical foundation for the application of deep learning in charging pile fault prediction, but is also of great significance in terms of reducing operation and maintenance costs, supporting energy structure transformation, and promoting green development.

Accurately evaluating the error of voltage transformers in distribution networks is crucial for the safe operation of power systems and the fairness of electricity trade. This paper uses the connection relationship between distribution transformers and voltage transformers to predict the secondary voltage of voltage transformers through the secondary voltage of transformers, constructing a voltage transfer characteristic model between the two to achieve accurate evaluation of voltage transformer errors. To address the challenge of extracting complex nonlinear features from multivariate electrical data, a combined model of a self-attention mechanism and a graph convolutional network (GCN) is proposed. The self-attention mechanism captures global dependencies among power parameters, while the GCN effectively constructs the multivariate data structures in distribution networks. By integrating both approaches, the model can fully extract the intrinsic features of the data as well as the hidden dependency information between data points. Additionally, to prevent gradient vanishing as the combined model’s structure deepens, a multi-head residual structure is introduced to enhance the self-attention mechanism. Experimental results show that compared to a single model, the proposed combined model reduces the mean squared error by 82.35% and increases the coefficient of determination R² by 9.07%, demonstrating significant accuracy advantages in voltage transformer error evaluation.