A BP neural network incorporated regression forecast technique based upon fragment swarm optimization (PSO) is proposed to design the state of crucial components of wind turbine so regarding realize mistake identification and detection. Firstly, specification recognition is carried out on the collection and tracking information of the system, and parameters connected to fault detection are extracted. Then, the residual optimization issue is made use of to establish the forecast model of nonlinear state evaluation and semantic network combination, and the gearbox temperature level or generator bearing are input as criteria right into the semantic network combination model and single model specifically, and the precision of the design is mirrored by the examination index. Lastly, BP design and PSO-BP combined forecast model are developed respectively by using the actual operation data of wind ranch SCADA, and the mistake state is evaluated according to whether the anticipated residual exceeds the set threshold, so regarding keep an eye on the temperature level of wind turbine transmission and generator bearing. By contrasting the data videotaped prior to and after the failing and making the information prediction analysis, the speculative results show that the forecast model established in this paper is viable for the device element fault detection.

The purpose of this paper is to improve the fault early warning effect of smart grid equipment through multi-sensor information fusion technology. Therefore, based on the analytical model of power grid fault diagnosis, this paper considers the influence of distributed generation in distribution network on fault diagnosis, as well as the misoperation or refusal of protection and switch, and the false alarm or leakage of alarm signal. At the same time, in order to display the results of fault diagnosis accurately and intuitively, an analytical model of fault diagnosis of distribution network based on multi-source information fusion is proposed. Finally, this paper verifies the effectiveness of this method through an example application. This article uses the PEDL dataset for experimental research, Through the comparison of fault data, it can be seen that compared with existing methods, the method proposed in this paper achieves the highest goodness of fit for warning, indicating the best fault warning effect.When there is enough training set, the prediction accuracy of the fault set can reach over 99%, Based on experimental analysis, it can be concluded that the proposed power grid equipment model has higher accuracy and reliability compared to traditional models. And the model in this article integrates the real-time monitoring function of power grid equipment and the equipment fault warning function, which improves the practicality of the power grid equipment monitoring system.

In the context of global energy transformation, the construction of smart grids is becoming a novel vogue in the evolution of power systems. As the core node of the smart grid, the efficient operation of the intelligent substation relay protection system is essential to the safety and stability of the power system. However, with the expansion of power grid-scale and complexity, traditional relay protection systems need help with fault diagnosis accuracy and response speed. This study proposes a fault diagnosis scheme of an intelligent substation relay protection system based on Transformer architecture and migration training model, aiming at improving the intelligent level of fault diagnosis. By introducing the Transformer architecture, the model can efficiently process high-dimensional and nonlinear complex data of substations, significantly improving the accuracy of fault pattern recognition from 82% of the original model to 96%, and the response speed is also increased by 30%. At the same time, using transfer learning technology, the adaptability and generalization capabilities of the model in new scenarios have been significantly enhanced, reducing the dependence on a large amount of new data and accelerating the deployment of the model among different substations. The experimental results show that this scheme can quickly and accurately identify various fault types and effectively locate fault points. This study not only promotes the development of intelligent technology for power systems but also lays a solid foundation for the safe and stable operation of smart grids and the sustainable development of the power industry.

With the rapid development of renewable energy, microgrid cluster systems are gradually being applied. To promote the development of microgrid cluster scheduling technology, maximize economic benefits while reducing the operating cost required for microgrid scheduling, an optimized scheduling scheme is proposed by constructing a function to minimize the operating cost of microgrids. Then, chaos mutation and Gaussian mutation are applied to improve the moth-flame algorithm that easily falling into local optima. A microgrid cluster optimization scheduling model on the basis of the improved moth-flame algorithm is constructed. The experimental results showed that the operating cost in islanding mode was 4286.21 yuan after 160 iterations. After optimizing the scheduling, the operating cost was 3912.3 yuan, with a decrease of 8.7%. The improved moth-flame algorithm had a stable average loss value of 20% and an operating efficiency of 97.19% after 10–50 iterations, which was significantly higher than other intelligent algorithms. This indicates that the improved moth-flame algorithm has high reliability and effectiveness in microgrid cluster optimization scheduling. Therefore, the proposed model effectively optimizes the scheduling scheme of microgrid cluster, providing new solutions for the efficient utilization of smart grids and renewable energy in the future.

As global energy resources get more limited and environmental problems worsen, it is crucial to enhance energy efficiency and reduce energy consumption in end-use products. This research focuses on electric water heaters, a significant household energy consumer, and collects a large amount of data through questionnaires and analyzes the current usage patterns of water heater use, as well as the impact of the users’ personal characteristics and energy-saving consciousness on usage behaviors. It also evaluates the energy-saving potential under different scenarios, considering both consumer behaviors and product efficiency levels. Results indicate that a substantial number of users still purchase high-energy-consuming water heaters and fail to adjust temperatures according to their specific needs, resulting in considerable energy waste. Electric water heaters exhibit significant potential for energy savings, with the efficiency of the product and user behaviors identified as key factors influencing overall energy consumption. The study provides important insights into the usage behavior of electric water heaters and offers actionable recommendations for manufacturers and government agencies: advocating the use of certified energy-efficient water heaters, raising public awareness of energy efficiency in appliance use, etc., which is in line with the country’s goals of energy conservation and environmental sustainability.

In order to cope with the increasing energy demand and achieve the “double carbon “goal of China’s 14th Five-Year Plan,” combined with hydrogen energy storage technology, it has the characteristics of zero pollution, high efficiency and rich source. In the context of reducing energy consumption and the vigorous development of hydrogen energy storage technology, a multi-objective optimization configuration model with economy, energy consumption index and carbon emission index is proposed, which takes into account the working characteristics of the hydrogen energy storage system, and the exothermic heat release from the electrolysis tanks and fuel cells when they are working to provide the loads with an additional heat source of the Combined Cooling, Heating and Power (CCHP) system, to reduce energy consumption and carbon emission. Finally, taking a region as an example, a multi-objective optimization algorithm based on decomposition is used to solve the model, so as to obtain a series of alternatives with better optimization effect. At the same time, the two-way projection method based on interval intuitionistic fuzzy information is used to make decisions, and the scheme that optimizes the economy, energy consumption index and carbon emission index is obtained, which verifies the feasibility of the system proposed in this paper.

In the context of global low-carbon development, reducing rural residential carbon emissions is the key to implementing emission reduction policies. In order to reduce carbon emissions from rural housing, a carbon emission classification method based on residential life cycle assessment is proposed based on the characteristics of rural housing in China. Its innovation lies in achieving precise analysis of carbon emissions from multiple stages of residential design, construction, and use. Secondly, introducing a lifecycle based emission reduction planning strategy to achieve a new pattern of low-carbon emission reduction in rural residential areas. Taking a rural residential building as a case study, in the early stage of implementing emission reduction, the mean values of the initial carbon emissions corresponding to building energy consumption, energy consumption, and resident living habits were 689, 691, and 683, with standard deviations of 81, 79, and 84. After implementing emission reduction plans, the values decreased to 686, 674, and 631, respectively, with standard deviations reduced to 28, 32, and 13. It was evident that emission reduction planning not only significantly reduced the mean carbon emissions but also substantially decreases their variability, enhancing the stability of carbon emissions. This research contributed to a deeper understanding of the carbon emissions from rural residential life cycles and provides theoretical support and data references for the formulation and implementation of more scientific and effective emission reduction planning. Simultaneously, it promoted low-carbon development in rural areas of China, achieving a harmonious coexistence of economic and social development with environmental protection and contributing to global low-carbon development.

Aiming at the consumption problem caused by the increasing scale of wind power and photovoltaic (PV) grid-connected, a multi-energy co-generation system is constructed with wind power, PV, concentrated solar power (CSP), and thermal power; in addition, in order to reduce the impact of the prediction errors of wind power, PV, and loads on the system’s economic operation, the photovoltaic and thermal power plants are used to provide the system's backup capacity together, and the opportunity constraint model of the reliability of the backup capacity is established, so as to satisfy the system reliability constraints at a certain higher confidence level; finally, a sampling-based deterministic transformation method is introduced to simplify the model. The model is simplified by introducing a sampling-based deterministic transformation method of opportunity constraint; finally, a stochastic optimal dispatch model of the combined wind-photovoltaic-CSP-fire system, which accounts for the conversion of electricity and heat, is constructed with the objective of minimising the integrated operating cost of the combined system, and the effectiveness of the proposed model is analysed by simulation verification.

Under the background of increasing system service data, it is difficult to trace the faults of power dispatching system. The current fault tracing method has some problems, such as low precision, low efficiency and difficult troubleshooting. To solve this problem, a fault tracing method based on data partition hybrid sampling method and multiple incremental regression tree algorithm is proposed. In this paper, it first uses the hybrid sampling method of data partition and dynamic selection technology to detect the business anomaly, and then applies the clustering algorithm and the information difference graph model to realize the fault tracing of system components. The experimental results showed that the F-metric value and geometric mean value of the study design method were 0.964 and 0.685, respectively. In addition, normalized discounted cumulative gains were observed in the top 10, and the mean average precision of the top 10 was 0.752 and 0.186, respectively. The proposed method can effectively improve the fault tracing efficiency of power grid operation and maintenance personnel, and provide strong data support for the safety maintenance of power grid dispatching system.

Aiming at the complex structure, numerous equipment, intricate control and protection logic, as well as the existence of numerous unmodeled dynamics and black-box device models in photovoltaic (PV) grid-connected systems, a modeling method based on Particle Swarm Optimization Neural Network (PSO-NN) is proposed to address the inability of pure mechanism models to accurately simulate their operational dynamics. Utilizing the differences in active power response waveforms under Voltage-Frequency (Vf) control, Power-Reactive Power (PQ) control, and Droop control as criteria for control strategy identification, a PSO-NN model is constructed for PV grid-connected systems, with inputs comprising temperature, humidity, light intensity, voltage, and frequency disturbances, and outputs being active and reactive power. To validate the model's effectiveness, a PV grid-connected system model is built in a self-developed simulation software and connected to an IEEE 14-bus distribution network for simulation verification. The results demonstrate that the proposed PV grid-connected model can effectively identify the types of Vf control, PQ control, and Droop control strategies, and accurately reflect the dynamic response characteristics of active and reactive power under various voltage and frequency disturbances.