For voltage sag position tracing (VSPT) through deep learning methods, model performance deteriorates rapidly under changed scenes. Moreover, time and effort are wasted in retraining numerous models for all different scenes. Therefore, a self-adaptive VSPT method which can response to changed scene is urgently needed. In this article, a deep transfer learning for self-adaptive VSPT under changed scenes is proposed. For accurate VSPT under original scene, a deep learning method via temporal iTransformer is presented, which can enhance local feature extraction capability while retaining the iTransformer’s global perspective. For self-adaptive VSPT under changed scenes, a deep transfer learning based on feature-decoupling is further presented. Here, domain invariant features are calculated via feature-decoupling module, and the difference between source domain features and target domain features is adaptively minimized via feature transference. We test the proposed method via simulation and experimental platform, verifying that the proposed deep transfer learning has satisfactory domain adaptability for self-adaptive VSPT under changed scenes.

For online ride-hailing platforms, choosing the right time to match idle vehicles with passengers is one of the most important factors affecting the platform's profit.On one hand, vehicles and passengers arrive dynamically, and an appropriate delayed matching may generate a highly efficient matching result with more values.On the other hand, different regions may have different states of supply (vehicles) and demand (passengers), and the matching time should be different.At this moment, we need an efficient matching time strategy that takes into account matching time and regional differences to maximize the platform's long-term profit.In this paper, we propose a dynamic matching time algorithm based on multi-agent reinforcement learning, which is called Multi-Region Differentiated Matching Decision.Firstly, we describe the order matching process and then model it as a decentralized partially observable Markov decision process (Dec-POMDP).Secondly, considering that there are regional differences in supply and demand, we divide the overall area based on historical data and propose an algorithm based on multi-agent reinforcement learning to realize multiregion differentiated dynamic matching.Finally, we conduct extensive experiments to evaluate our matching algorithm against benchmark algorithms in a real-world dataset.The experimental results show that our algorithm can outperform benchmark algorithms.

As an economical, low-carbon and convenient travel model, bike-sharing has become common in many cities around the world. However, the daily usage of shared bikes results in the dispatching problem, i.e., dispatching bikes to the specific destinations to satisfy riding demands. The bike-sharing platform can hire riders as workers and pay to incentivize them to accomplish the dispatching tasks. However, there exist multiple workers competing for the dispatching tasks, and they may strategically report their task accomplishing costs (which are usually private information only known by themselves) in order to make more profits, which may result in inefficient task dispatching results. In this paper, we first design a dispatching algorithm named GDY-MAX to allocate tasks to workers, which can achieve good performance . However, it cannot prevent workers strategically misreporting their task accomplishing costs. Regarding this issue, we further design a strategy proof mechanism under the budget constraint, which consists of a task dispatching algorithm and a worker pricing algorithm. We theoretically prove that our mechanism can satisfy incentive compatibility, individual rationality, budget constraint and a constant approximation ratio. Furthermore, we run extensive experiments to evaluate our mechanism based on a Mobike dataset. The results show that the performance of the proposed strategy proof mechanism and GDY-MAX is similar to the optimal algorithm in terms of the coverage ratio of accomplished task regions and the sum of task region value, and our mechanism has better performance than the uniform algorithm in terms of the total payment and the unit cost value.

As a green and low-carbon transportation way, bike-sharing provides lots of convenience in the daily life. However, how to dispatch bikes efficiently is a key issue in such a system. The bike-sharing platform can hire workers and pay to incentivize them to accomplish the dispatching tasks. However, there exist multiple workers competing for the dispatching tasks, and they may strategically report their task accomplishing costs (private information known by themselves) in order to make more profits, which may result in inefficient task dispatching. In this paper, we first design a dispatching algorithm named GDY-MAX to allocate tasks to workers. Furthermore, we design a strategy proof mechanism under the budget constraint to allocate tasks and determine the payments to workers. We theoretically prove that our mechanism can satisfy the properties of incentive compatibility, individual rationality and budget balance. Furthermore we run extensive experiments to evaluate our mechanism based on a Mobike dataset. The results show that our approaches can make better performance than benchmark approaches.

In this paper, a sequence-to-sequence deep learning architecture based on the bidirectional gated recurrent unit (Bi-GRU) for type recognition and time location of combined power quality disturbance is proposed. Especially, the proposed methodology can determine the type of each element in input sequence, which is different from existing sequence-to-sequence model employing encoder–decoder network. First, the input sequence is normalized and batched. Second, deep features are extracted from input sequence by constructing Bi-GRU recurrent neural network, where multiple Bi-GRU layers are stacked together in both forward direction and backward direction. Third, according to aforementioned extracted features, fully connected layer and Softmax are employed to calculate the corresponding probability indicating the category that each element in input sequence is classified to. Fourth, Argmax or Top_K operation is further integrated to determine the type of each element in input sequence by selecting the maximal probability. Finally, the type is recognized, and meanwhile, starting–ending times of disturbances are also located just at the moment when the type is changed. The proposed model is further validated and tested by synthetic signals and practical field signals, respectively. Experimental results demonstrate that the accuracy of type recognition is over 98% for 96 kinds of disturbances including single and combined disturbances with signal-to-noise ration being 20 dB. Besides, the starting–ending times are also located with the absolute error less than six sampling points when sampling frequency is 256 points per cycle with noisy environment.

In this paper, a novel bidirectional control principle of active tuned hybrid power filter (ATHPF) based on the active reactor using active techniques is proposed. The proposed control principle, in essence, is to continuously adjust the filter inductance of the active reactor by regulating active power filter (APF) output current in terms of its magnitude and direction. Therefore, the ATHPF using the bidirectional control principle can simultaneously supply different impedances at different selective suppressed harmonic frequencies. The bidirectional control principle can perform both the normal active tuning function and the abnormal active detuning function. To be specific, the normal active tuning function refers to the harmonic elimination with filtering performance independent of the deviation of passive filter parameters, while the latter, the abnormal active detuning function, refers to the flexible protection against harmonic over-current without losing two harmonic elimination and reactive power compensation at the occurrence of harmonic over-current. Experimental results verify the effectiveness of the ATHPF with the bidirectional control principle in selective harmonic elimination and flexible protection against harmonic over-current.