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Quickly recognizing the real-time operating states will be helpful to identify the instantaneous and permanent power loss of the renewable energy station, so as to realize the continuous operation under the influence of the instantaneous disturbances caused by faults. This paper proposes a state recognition method for renewable energy units based on sparse stacked auto-encoder (SSAE) feature extraction and improved k-nearest neighbor (KNN) algorithm. The characteristics of this method is that the electrical parameters of the unit port are collected directly without relying on the unit’s supervisory control and data acquisition (SCADA) system, whose acquisition speed is too slow to meet the recognition accuracy requirement, and that the unit operation states can be recognized quickly and accurately. Firstly, operation states of renewable energy unit are divided, and the framework for the unit’s state recognition is proposed. Moreover, improved strategies for state recognition of renewable energy unit are proposed. Finally, the power system analysis software package (PSASP) is used to obtain the electrical parameters of renewable energy units and the improved KNN algorithm is used to recognize operation states after extracting features based on SSAE. By comparing the method proposed with the traditional KNN algorithm, the effect of the proposed method for states recognition is shown to be the best, with an accuracy of 98.16% and computing time of 50ms. The results show the validity of the proposed method.

The power flow of the distributed network (DN) with high penetration of distributed PV changes significantly, such as bidirectional power flow and larger deviation of voltage magnitude, under the variation of PV power output, and the number and the position of critical nodes would change accordingly. Hence, a data-driven nodes clustering and critical node identification method is proposed in this paper. Since the relationship between different nodes are nonlinear, the autoencoder method is firstly applied to obtain the unified features of the nodes connected with different number of branches. Using the unified features, the Frechet distance is calculated to demonstrate the similarity between any two nodes. The improved affinity propagation (AP) clustering algorithm is also proposed to classify the nodes into different clusters, and the number the clusters and the critical nodes in different clusters could be obtained automatically. The electrical distance is considered in the improved AP clustering algorithm, so that nodes in different branches with similar features are avoided to be classified into one cluster. The simulations are carried out on the IEEE 123-bus system and an actual DN system, the effectiveness and efficiency of proposed critical node identification method are verified.

Background: The hand motor function is lost and activities in daily life (ADLs) are affected due to some illnesses such as stroke and hemiplegia. As a coping way, we present a wearable rehabilitation glove with the bionic actuator for restoring the hand function and the motor control ability lost by stroke patients. Methods: The soft pneumatic bionic actuator (SPBA) is developed on the basis of the research of human hand bone structure and finger joint characteristics and a series of tests are conducted. Besides, we built the rehabilitation glove system based on the proposed SPBAs to verify the availability due to typical gesture, mirror therapy (MT) and grasping experiment for irregular objects. Result: The bending angle of SPBA can reach 260°. The output force of it can reach 5.1N with 0.25 MPa air pressure input. The maximum variance of the bending angle can be concluded at 5.1° in MT. The grasping experiments of the glove worn on the hand or not shows the proposed glove is flexible, the grip force is large and achieve stable grasping of objects. Conclusion: The designed SPBA is satisfied with the requirements of rehabilitation training and the proposed glove restore the normal hand motion of patients in ADLs.

Since doubly-fed induction machine pumped storage hydro (DFIM-PSH) unit can adjust active power flexibly through adjustable-speed operation, it has frequency regulation capability in both generating and pumping modes. In order to explore the frequency regulation capability of DFIM-PSH unit under different working conditions, this paper develops a frequency control module for DFIM-PSH unit in pumping mode, which is quite different from that in generating mode, and then optimizes the frequency control parameters aimed at minimizing the frequency deviation in multiple operating conditions. Based on the dynamic model of the DFIM-PSH unit, the system frequency response model is built to analyze the influence of parameters on frequency dynamic characteristics. An optimization method of frequency control parameters is developed based on improved particle swarm optimization algorithm to maximize its frequency regulation capability under different operating conditions while ensuring safe and stable operation. Finally, a four-machine two-zone power system model with a DFIM-PSH unit is simulated, and the simulation results show that the proposed strategy can make the DFIM-PSH unit have great frequency regulation performance in a wide range of operating conditions.

The Urban Agglomerations of Middle and Lower Reaches of the Yangtze River Economic Belt (UAMLYREB) have experienced rapid and intense urbanization over the past decades with natural ecosystems being converted to impervious surfaces. Thus, impervious surfaces are recognized as critical parameters when considering the effect of urbanization on water quality. While understanding how the threshold of impervious surfaces affects water quality has been a hot topic, there has been little quantitative analysis on how such thresholds change during rapid urbanization periods across large urban areas. To remedy this deficiency, this article made use of remotely-sensed impervious surface area data and in situ water quality monitoring observations for the period 2000 to 2018 to quantitively derive the temporal variation in the thresholds of the percentage of the impervious surface area (PISA) when inferring the relationship between PISA and a set of water quality indicators for a selection of watersheds within the UAMLYREB. We employed segmented regression model to derive the nonlinear relationship between PISA, the water quality indicators, and the PISA-related thresholds. Our results indicate that PISA may be considered a useful water quality indicator over watershed spatial scales. We also found that the threshold effects differed between water quality indicators (DO, CODMn, NH3-N), where, except for NH3-N, the indicators showed a PISA threshold of 30.08 to 42.34%, with slight variations over the study period. These results imply that maintaining PISA to be around 30% in watershed areas may be sufficient to mitigate against water quality degradation during the urbanization process.

How to tackle the order production priority in production scheduling is a key issue for make-to-order enterprises. Some approaches of determining the order production priority have been proposed from different angles such as linear programming, entropy weight and analytic hierarchy process. Nevertheless, under the supply chain environment, the determination of order production priority becomes a complex problem, and traditional approaches have their limitations. Hence, in this paper, a new evaluation index system of order production priority is established under supply chain environment, and then an evaluation model of order priority based on Kriging model is proposed to determine the order production priority. The performance of proposed model is demonstrated by simulation experiments. The results show that the proposed model is suitable for the problems of small samples and it is a feasible, effective evaluation model for order priority. Compared to other models, the proposed model has improved the evaluation precision of order priority. Meanwhile, the proposed model performs more reliable and stable and it augments the methods for order production priority.

The multi-objective evolutionary algorithm based on decomposition (MOEA/D) uses a fixed neighborhood size and allocates the same algorithm resources for all sub-problems. This approach makes it harder to effectively optimize the sub-problems in different periods of time, slows the convergence of the algorithm and reduces the quality of the decomposition. This paper proposes an adaptive neighborhood adjustment strategy designed to solve this problem. The neighborhood size of each generation of different subproblems can be adjusted adaptively, and limited algorithm resources can be allocated more efficiently to balance the convergence and diversity of the algorithm. In the algorithm performance comparison experiment, this paper compares the proposed algorithm with the MOEA/D, MOEA/D-GR, MOEA/D-DU and MOEA/D-DN in ZDT and DTLZ series test problems. The experimental results show that the proposed algorithm can efficiently allocate limited algorithm resources, improve algorithm convergence, and achieve better overall performance of the decomposition set.

Accurate calculation of water/steam temperature and pressure in the solar field of direct steam generation (DSG) parabolic trough is essential to power dispatch and control. However, it is very difficult to achieve satisfied accuracy in limited time because of the existence of two-phase flow that has complex dynamic characteristics. In this work, a new hybrid model is proposed, which is based on the heat transfer mechanism enhanced by artificial neural network. This hybrid model has two characteristics as follows. (1) A heat-transfer and hydrodynamic coupling steady-state mechanism model as a prior model, which is used to quickly obtain the prior values including mechanism factors that are then used as input to an artificial neural network (ANN) model. (2) An ANN model consisting of two four-hidden-layer back propagation (BP) networks. The inlet pressure and outlet temperature of solar field are modelled in the first four-layer neural network, and then the outlet temperature of solar field is analyzed in the second four-layer neural network to improve the simulation accuracy. This model is verified by the data from DISS plant on 26 June 2001, and can be used for control studies and dispatching optimization. The calculation results show that the mean absolute percentage error (MAPE) of predicted inlet pressure and outlet temperature are 0.48% and 1.14%, respectively, which are better than the results of BP model, general regression neural network (GRNN) model, hybrid model with one BP network mentioned in this paper, and the dynamic simulation model mentioned in the literature.

Transistors in three-phase voltage-source inverter often suffer from open-circuit failures due to the lifting of bonding wires caused by thermic cycling, resulting in performance degradation with ripple torque and current harmonics. Current-spectral-analysis based methods are widely applied to failure diagnosis; however, high calculation consumption and complex implementation limit their application in some real-time occasion. In this paper, a simplified Fourier series method is proposed by the product between reconstructed phase currents and reference signals. Meanwhile, a novel normalized method for DC and fundamental components of simplified Fourier series are proposed to locate twenty-one transistor open-circuit faults. Numerical results show that the proposed Fourier series method coincides with that of Fast Fourier Transform. Experimental results and the comparison with previous methods show high efficiency and merits of its application to transistor open-circuit fault location in the voltage-source inverter.

The model uncertainties and the heterogeneous energy states burden the effective aggregation of electric vehicles (EVs), especially coupling with the real-time frequency dynamic of the electrical grid. Integrating the advantages of deep learning and reinforcement learning, deep reinforcement learning shows its potential to relieve this challenge, where an intelligent agent fully considers the individual state of charge (SOC) difference of EV and the grid state to optimize the aggregation performance. However, existing policies of deep reinforcement learning usually provide deterministic and certain actions, and it is difficult to deal with the increasing uncertainties and randomness in modern electrical systems. In this paper, a probability-based management strategy is proposed with continuous action space based on the deep reinforcement learning, which provides fine-grained energy management and addresses the time-varying dynamics from EVs and electrical grid simultaneously. Moreover, an optimization based on the proximal policy is further introduced to clip the policy upgradation speed to enhance the training stability. The effectiveness of proposed energy management structure and policy optimization strategy are verified on various scenarios and uncertainties, which demonstrates advantageous performance in the SOC management and frequency maintenance. Besides the performance merits, the training procedure is also presented revealing the evolution reason for the proposed approach.

In this paper, a fractional order LQG benchmark is proposed for the control performance assessment of fractional order control systems. Similar to the conventional LQG benchmark, the fractional order LQG performance benchmark curve is determined by the numerical calculation method, which avoids the calculation of the complex interaction matrix. The fractional order process model is discretized via fractional order calculus. Meanwhile, the fractional order integral is introduced into the conventional LQG cost function. Then solving the linear quadratic Gaussian problem under the fractional order model and fractional control, the optimal input and output variances are determined for different weighting factors and the performance curves can be achieved. The comparison between fractional order LQG and the conventional LQG shows the improvement of the proposed benchmark under the same condition. The proposed benchmark can provide a more direct and superior reference standard to evaluate the performance of fractional order control system. Finally, a case study of fractional order PID(FO-PID) controller in industrial heating furnace temperature control experiment with model matching and model mismatch conditions is used to verify the effectiveness of the proposed benchmark.

Nowadays, more and more electric machines are demanded to operate under wide ranges of speed and load conditions. This paper deals with the operating performance enhancing method for doubly salient electromagnetic machine (DSEM) under light load condition. With the finding that the power loss can be decreased with reduced field current, and there's regulated space for the distribution of field current and armature current under light load condition. A novel control method with field current inner loop and speed outer loop is proposed for DSEM, the system driving efficiency is desired to be improved. Meanwhile, the cogging torque which is a main ripple source of DSEM torque can be decreased with the reduced filed current, so the output torque performance can be enhanced as well. In addition, the armature reaction of the proposed control method is researched, and the influence on the filed current control during commutation process is theoretically analyzed. The experiments on a 12/8-pole DSEM validate the correctness and flexibility of the proposed control method.

As the degree of nonlinearity in high-speed links becomes more and more serious, traditional decision feedback equalization (DFE) which is based on linear time-invariant assumption can no longer eliminate the inter-symbol interference effectively. In this paper, the shortcomings of traditional DFE structure for nonlinear high-speed links are first analyzed. Then, the multi-bit response (MBR) method is proposed to accurately construct the bit stream responses of the nonlinear high-speed links. Lastly, a novel DFE structure based on the MBR method is presented to improve the signal quality of the nonlinear high-speed links. The accuracy of the proposed method is verified by the simulation based on a nonlinear high-speed link model. Compared with traditional DFE, the proposed DFE structure can significantly improve the signal quality of the nonlinear high-speed links. As the degree of nonlinearity increases, the advantage of the proposed DFE becomes more prominent.

As a typical representative of distributed model predictive control, distributed dynamic matrix control (DDMC) is able to satisfy the basic control requirements for large-scale systems. However, the constraints and disturbances in actual industrial process usually lead to the slow set-point target tracking, large overshoot and weak anti-interference ability of the system. Therefore, the relevant requirements may not be met for some complex industrial processes. The existing distributed PID type dynamic matrix control (PID-DDMC) method can improve the control performance, but it maybe not accurate enough in some cases. Based on this background, this article introduces fractional order PID (FOPID) into distributed dynamic matrix control, and proposes a distributed fractional order PID type dynamic matrix control (FOPID-DDMC) algorithm. To compare with the conventional PID control, it expands the control and parameter setting range of the controller, and makes the control effect of the controller more accurate. Furthermore, the coupling effect among subsystems is dispelled by adopting the Nash optimal theory, and information interaction between the subsystems through network communication is realized, thereby, completing the optimization of the whole large-scale system. Finally, through a numerical simulation example and a level-temperature control process, the feasibility of the proposed algorithm is demonstrated by comparing with the traditional DDMC and PID-DDMC.

Economic Model Predictive Control (EMPC) has recently been researched in many areas, especially in terms of energy. The development of advanced control algorithms has promoted the cost reduction of marine energy generation and improved the efficiency and reliability of marine current turbines. By reducing the loss on the marine turbine system, better economic indicators can be achieved. Based on this, the maximum marine power can be captured such that the entire marine current turbine system can obtain maximum economic benefits. In this paper, it is mainly based on the economic model predictive control framework to optimize the ocean current turbine power generation system. The system index is directly used for the objective function of the EMPC problem, as EMPC has the advantage of dealing with system constraints and cost functions. Compare the performance of the proposed control framework with the performance of commonly used basic MPC controllers, simulation results show that the proposed controller can reduce the loss of the turbine system and gearbox, indicating that more energy capture can be obtained for the system to achieve better economic effects.

The wind turbine is usually integrated into the power grid via back to back converter, and the inertia is decreased. Virtual synchronous machine (VSM) control was proposed to improve inertia of the wind farm. While for the doubly-fed induction generator (DFIG)-based wind farm with traditional control strategy, sub-synchronous resonance (SSR) has become a significant problem when it is connected to series compensation transmission lines. Therefore, the impacts of VSM control strategy for DFIG on SSR need to be investigated. In this paper, a detailed small-signal stability analysis model of the VSM control strategy is first established and eigenvalue analysis is then carried out. SSR in the wind farm with the VSM control under different series compensation levels, controller parameters and wind speeds is analyzed, and it is also compared to that in the wind farm with the conventional vector control strategy (VC). Simulations are performed in PSCAD/EMTDC to verify the result of SSR analysis.

To effectively study the dynamics of power systems with large-scale wind farms (WFs), an equivalent model needs to be developed. It is well known that back-to-back converters and their controllers are important for the dynamic responses of the wind turbine (WT) under disturbances. However, the detailed structure and parameters of the back-to-back converters and their controllers are usually unknown to power grid operators. Hence, it is difficult to build an accurate equivalent model for the WF using the component model-based equivalent modeling method. In this paper, a transfer function based equivalent modeling method for the WF is proposed. During modeling, the detailed structure and parameters of the WF are not required. The objective of the method is reproducing the output dynamics of the WF under the variation of the wind speed and power grid faults. A decoupled parameter-estimation strategy is also developed to estimate the parameters of the equivalent model. A WF that consists of 16 WTs is used to test the proposed equivalent model. Additionally, the proposed equivalent modeling method is applied to build the equivalent model for a real WF in Northwest China. The effectiveness of the proposed method is validated by the real measurement data.

The advantages of ultraviolet light-emitting diodes (LEDs) inserted with a p-AlGaN layer, whose Al mole composition is less than that of the last barrier, between the last barrier and the electron blocking layer have been investigated by using the Crosslight APSYS programs. The results show that the output power and the internal quantum efficiency of the proposed LEDs are improved. Furthermore, the efficiency droop is also mitigated effectively. Based on the analysis of electrical and optical characteristics, these improvements are mainly attributed to the relatively higher effective barrier height against the escape of electrons and an increased hole concentration in the quantum wells by inserting a hole reservoir near the active region. In addition, the optimized Al mole composition of this inserted layer has been also studied in detail, and the optimized Al mole composition has been achieved.

The advantages of blue InGaN light-emitting diodes (LEDs) with thickness-chirped barriers in the active region have been investigated by using the Crosslight APSYS programs. The results show that the output power of the proposed LED is increased 80% and the efficiency droop is decreased from 59% in conventional LED to 28% at the current of 250 mA. Based on the analysis of electrical and optical characteristics, these improvements are mainly attributed to the change of electrostatic field in the active region by using thickness-chirped barriers. In the even-numbered barriers, the fields are increased, which gets rid of more seriously bended valence band and results in decreased barrier heights for hole transport in the active region. Furthermore, the direction of electrostatic field in the last barrier is reversed to along the drift direction of holes, which not only can lead to upbended conduction band to rise the barrier height for electron escape but also can accelerate holes to increase the hole injection current. As a result, electrons blocking and holes injection are enhanced, and in turn, the performance of the proposed LED is improved.