A Recurrent Neural Network-Based Method for Dynamic Load Identification of Beam Structures

The determination of structural dynamic characteristics can be challenging, especially for complex cases. This can be a major impediment for dynamic load identification in many engineering applications. Hence, avoiding the need to find numerous solutions for structural dynamic characteristics can significantly simplify dynamic load identification. To achieve this, we rely on machine learning. The recent developments in machine learning have fundamentally changed the way we approach problems in numerous fields. Machine learning models can be more easily established to solve inverse problems compared to standard approaches. Here, we propose a novel method for dynamic load identification, exploiting deep learning. The proposed algorithm is a time-domain solution for beam structures based on the recurrent neural network theory and the long short-term memory. A deep learning model, which contains one bidirectional long short-term memory layer, one long short-term memory layer and two full connection layers, is constructed to identify the typical dynamic loads of a simply supported beam. The dynamic inverse model based on the proposed algorithm is then used to identify a sinusoidal, an impulsive and a random excitation. The accuracy, the robustness and the adaptability of the model are analyzed. Moreover, the effects of different architectures and hyperparameters on the identification results are evaluated. We show that the model can identify multi-points excitations well. Ultimately, the impact of the number and the position of the measuring points is discussed, and it is confirmed that the identification errors are not sensitive to the layout of the measuring points. All the presented results indicate the advantages of the proposed method, which can be beneficial for many applications.

Aggregation Method of Distributed Load Resources Based on Non-intrusive Load Identification

Because the power system contains a large number of user-side adjustable load resources, it can effectively enhance the operational flexibility of the power system and realize the safe, economical and efficient operation of the power grid by aggregating and modeling all kinds of resources and participating in the interactive response of the system as a whole. In this paper, a user-side adjustable load resource aggregation method based on non-intrusive load identification is proposed, which aims to obtain the load response potential of various users without intruding into the users, thus providing important support for power grid dispatching. Specifically, starting from the basic attributes of electrical equipment, considering the influence of numerical features such as current, harmonics, power, and V-I trajectory image features on load identification, the deep learning algorithm is used to deeply fuse the numerical features and image features in high-dimensional space, and then the fused advanced features are supervised by the Softmax classification algorithm, so as to effectively identify different types of electrical equipment. Finally, a bottom-up aggregation strategy is adopted to aggregate and model all kinds of load resources under the same station, so as to realize the accurate evaluation of the response ability of station resources. The simulation results of a numerical example verify the correctness and effectiveness of the proposed method.

Nonintrusive Industrial Load Identification Combined with Random Forests and Steady-State Waveform

Nonintrusive industrial load identification can accurately acquire the operation data of each load in the plant, which is the benefit of intelligent power management. The identification method of the industrial load is complicated and difficult to be realized due to the difficulty in collecting transient data for modeling, and high-precision measuring equipment is required. Aiming at this situation, the article proposes a nonintrusive industrial load identification method using a random forest algorithm and steady-state waveform. Firstly, by monitoring the change of the industrial load power state, when the load changes and becomes stable, the steady-state waveform is extracted. Due to different electrical characteristics of industrial loads, the current waveform of loads is different to some extent. We can construct characteristic data for each industrial load to construct its own current steady-state waveform. Then, using the high-dimensional data of the steady-state waveform as the sample data, the bootstrap sampling method and the CART algorithm in the random forest algorithm are used to generate multiple decision trees. Finally, the industrial load types are identified by voting multiple decision trees. The actual operating load data of a factory are used as the sample data in the simulation, and the effectiveness and rapidity of the proposed identification algorithm are verified by the combined load method simulation comparison. The simulation results show that the accuracy of the proposed identification algorithm is more than 99%, the identification time is 3.36 s, which is much higher than that of other methods, and the operation time is less than that of other methods. Therefore, the proposed identification algorithm can effectively realize the nonintrusive industrial load identification.

Non-intrusive Load Identification Method based on SAGOA-GMM Algorithm

Non-intrusive Load Identification play an important role in daily life. It can monitor and predict grid load while statistics and analysis of user electricity information. Aiming at the problems of low non-intrusive load decomposition ability and low precision when two electrical appliances are started and stopped at the same time, a new type of clustering and decomposition algorithm is proposed. The algorithm first analyses the measured power and use DBSCAN to filter out the noise of the collected data. Secondly, the remaining power points are clustered using the Adaptive Gaussian Mixture Model (AGMM) to obtain the cluster centres of the electrical appliances, and finally correlate the corresponding current waveform to establish a load characteristic database. In terms of load decomposition, a mathematical model was established for the magnitude of the changing power and current. The Grasshopper optimization algorithm (GOA) is optimized by introducing simulated annealing (SA) to identify and decompose electrical appliances that start and stop at the same time. The result of the decomposition is checked by the current similarity test to determine whether the result of the decomposition is correct, thereby improving the recognition accuracy. Experimental data shows that the combination of DBSCAN and GMM can can identify similar power characteristics. The introduction of SA makes up for the weakness of GOA and gives full play to the advantages of GOA's high identification efficiency. Finally, the test is carried out through the load detection data of the simultaneous start and stop of the two equipment. The test results show that the proposed method can effectively identify the simultaneous start and stop of two loads and can solve the problem of low recognition rate caused by the similar load power, which lays the foundation for the development of non-intrusive load identification in the future.