Shared energy storage is an energy storage business application model that integrates traditional energy storage technology with the sharing economy model. Under the moderate scale of investment in energy storage, every effort should be made to maximize the benefits of each main body. In this regard, this paper proposes a distributed shared energy storage double-layer optimal allocation method oriented to source-grid cooperative optimization. First, considering the regulation needs of the power side and the grid side, a distributed shared energy storage operation model is proposed. Second, a distributed shared energy storage double-layer planning model is constructed, with the lowest cost of the distributed shared energy storage system as the upper-layer objective, and the lowest daily integrated operation cost of the distribution grid-distributed new energy stations as the lower-layer objective. Third, a double-layer iterative particle swarm algorithm combined with tide calculation is used to solve the distributed shared energy storage configuration and distribution grid-distributed new energy stations’ economic operation problem. Finally, a comparative analysis of four scenarios verifies that configuring distributed shared energy storage can increase the new energy consumption rate to 100% and reduce the net load peak-valley difference by 61%. Meanwhile, distributed shared energy storage operators have realized positive returns.

The prediction of cold load in ice-storage air conditioning systems plays a pivotal role in optimizing air conditioning operations, significantly contributing to the equilibrium of regional electricity supply and demand, mitigating power grid stress, and curtailing energy consumption in power grids. Addressing the issues of minimal correlation between input and output data and the suboptimal prediction accuracy inherent in traditional deep-belief neural-network models, this study introduces an enhanced deep-belief neural-network combination prediction model. This model is refined through an advanced genetic algorithm in conjunction with the “Statistical Products and Services Solution” version 25.0 software, aiming to augment the precision of ice-storage air conditioning load predictions. Initially, the input data undergo processing via the “Statistical Products and Services Solution” software, which facilitates the exclusion of samples exhibiting low coupling. Subsequently, the improved genetic algorithm implements adaptive adjustments to surmount the challenge of random weight parameter initialization prevalent in traditional deep-belief networks. Consequently, an optimized deep-belief neural-network load prediction model, predicated on the enhanced genetic algorithm, is established and subjected to training. Ultimately, the model undergoes simulation validation across three critical dimensions: operational performance, prediction evaluation indices, and operating costs of ice-storage air conditioners. The results indicate that, compared to existing methods for predicting the cooling load of ice-storage air conditioning, the proposed model achieves a prediction accuracy of 96.52%. It also shows an average improvement of 14.12% in computational performance and a 14.32% reduction in model energy consumption. The prediction outcomes align with the actual cooling-load variation patterns. Furthermore, the daily operational cost of ice-storage air conditioning, derived from the predicted cooling-load data, has an error margin of only 2.36%. This contributes to the optimization of ice-storage air conditioning operations.

In order to solve the problem that the fault classification accuracy of the main bearing of the wind turbine is not high due to the unbalanced vibration signal data of the main bearing of the wind turbine under the background of noise, this article proposes a double-layer fault diagnosis model for the main bearing of the wind turbine that combines the auxiliary classifier generation adversarial network (ACGAN) and the deep residual shrinkage network (DRSN). First, the wind turbine main bearing data is sent into the ACGAN to learn the distribution features of fault data, and a particular type of fault data is generated to expand the original dataset to achieve balance conditions, and then the expanded dataset is sent to the DRSN to reduce noise to improve the fault classification accuracy. The simulation results show that, compared with the traditional deep learning model, the model proposed in this article can significantly improve the classification accuracy of the main bearing fault of wind turbines under noise conditions, and also has a strong diagnosis ability in a state of datasets with different loads.

Due to the harsh environment, the maintenance of offshore wind turbines (OWTs) is very difficult and the failure rate is very high. Improving the condition monitoring technologies will be beneficial to promote the performance of OWTs. Motivated by the application advantages of wireless sensor networks (WSNs) in environment monitoring, a wireless network based condition health monitoring system for OWTs is proposed, which is composed of different types of wireless sensor nodes. Sensor nodes are deployed on the key components of OWTs based on the fault feature analysis on the different components. In order to guarantee the reliabilities of information collection and communication of the sensor nodes, the principles of the deployment and communication of the sensor nodes are presented and the cluster-based distributed wireless sensor network topology is adopted to increase the efficiency of the system. An example is given to illustrate the proposed method of the condition health monitoring for OWTs.

This paper proposes a reliability analysis method for the condition monitoring network of wind turbine blade based on wireless sensor networks. Two critical factors which play significant roles in the reliability evaluation of the monitoring network are focused on, that is, the reliability of communication links and the reliability of sensor nodes. Firstly, with the established reliability models for sensor nodes and communication links the method of establishing reliability simulation model of monitoring network is presented based on Monte Carlo method. Secondly, according to the analysis of the intra-cluster reliabilities of the tree topology and the mesh topology, the topology selection principle of the sensor network for a single blade is proposed. Finally, the influence of maintenance cycle and communication interference on the overall reliability of the monitoring network is illustrated and the proper maintenance cycle is achieved. The solution to communication interference is put forward with the data retransmission measure. The overall reliability of the network is improved effectively by adopting the one-time data retransmission measure. Our work is expected to provide the guidance in theory and technology for constructing the high-performance condition monitoring and control system for wind turbine blades.

This paper proposes an optimal mobile sensor-scheduling algorithm for recovering the failure sensors in hybrid wireless sensor networks (WSNs). To maintain a guaranteed coverage over the area of interest, spare mobile sensors in WSNs will be activated to replace the failure sensors. The optimal scheduling problem is formulated into two optimization problems, one of which precisely determines the minimum value of the largest distance required to travel for mobile sensors, while the other one gives the optimal dispatch for mobile sensors to minimize the total travel distance. Furthermore, a distributed suboptimal scheduling, which only requires the local matching information of mobile sensors, is developed as well. Both regular and random network topologies are provided to illustrate the proposed algorithms in the simulation.