Abstract:The development of distributed renewable energy, such as photovoltaic power and wind power generation, makes the energy system cleaner, and is of great significance in reducing carbon emissions. However, weather can affect distributed renewable energy power generation, and the uncertainty of output brings challenges to uncertainty planning for distributed renewable energy. Energy systems with high penetration of distributed renewable energy involve the high-dimensional, nonlinear dynamics of large-scale complex systems, and the optimal solution of the uncertainty model is a difficult problem. From the perspective of statistical machine learning, the theory of planning of distributed renewable energy systems under uncertainty is reviewed and some key technologies are put forward for applying advanced artificial intelligence to distributed renewable power uncertainty planning.

Abstract:This study proposes a novel combined primary and secondary control approach for direct current microgrids, specifically in islanded mode. In primary control, this approach establishes an appropriate load power sharing between the distributed energy resources based on their rated power. Simultaneously, it considers the load voltage deviation and provides satisfactory voltage regulation in the secondary control loop. The proposed primary control is based on an efficient droop mechanism that only deploys the local variable measurements, so as to overcome the side effects caused by communication delays. In the case of secondary control, two different methods are devised. In the first, low bandwidth communication links are used to establish the minimum required data transfer between the converters. The effect of communication delay is further explored. The second method excludes any communication link and only uses local variables. Accordingly, a self-sufficient control loop is devised without any communication requirement. The proposed control notions are investigated in MATLAB/Simulink platform to highlight system performance. The results demonstrate that both proposed approaches can effectively compensate for the voltage deviation due to the primary control task. Detailed comparisons of the two methods are also provided.

Abstract:A conventional hybrid circuit breaker (HCB) is used to protect a voltage source converter-based high voltage direct current transmission system (VSC-HVDC) from a short circuit fault. With the increased converter capacity, the DC protection equipment also requires a regular upgrade. This paper adopts a novel type of HCB with a fault current limiter circuit (FCLC), and focuses on the responses of voltage and current during DC faults, which are associated with parameter selection. PSCAD/EMTDC based simulation of a three-terminal VSC-HVDC system confirms the effectiveness and value of HCB with FCLC, by using an equivalent circuit modelling approach. Laboratory experimental tests validate the simulation results. The peak fault current is reduced according to the current limiting inductor (CLI) increase, and can be isolated more quickly. By adopting parallel metal oxide arrester (MOA) with the main branch of HCB, voltage stresses across the breaker components decrease during transient and continuous operation, and less energy needs to be dissipated by the MOA. The remnant current for all cases is transmitted to power dissipating resistor (PDR) in the final stage, and the fault current is reduced to the lowest possible value. When the current from the main branch is transferred to the FCLC branch, transient voltage spikes occur, while smaller PDR is required to absorb current in the final stage.

Abstract:A conventional hybrid circuit breaker (HCB) is used to protect a voltage source converter-based high voltage direct current transmission system (VSC-HVDC) from a short circuit fault. With the increased converter capacity, the DC protection equipment also requires a regular upgrade. This paper adopts a novel type of HCB with a fault current limiter circuit (FCLC), and focuses on the responses of voltage and current during DC faults, which are associated with parameter selection. PSCAD/EMTDC based simulation of a three-terminal VSC-HVDC system confirms the effectiveness and value of HCB with FCLC, by using an equivalent circuit modelling approach. Laboratory experimental tests validate the simulation results. The peak fault current is reduced according to the current limiting inductor (CLI) increase, and can be isolated more quickly. By adopting parallel metal oxide arrester (MOA) with the main branch of HCB, voltage stresses across the breaker components decrease during transient and continuous operation, and less energy needs to be dissipated by the MOA. The remnant current for all cases is transmitted to power dissipating resistor (PDR) in the final stage, and the fault current is reduced to the lowest possible value. When the current from the main branch is transferred to the FCLC branch, transient voltage spikes occur, while smaller PDR is required to absorb current in the final stage.

Abstract:With the rapid development of flexible interconnection technology in active distribution networks (ADNs), many power electronic devices have been employed to improve system operational performance. As a novel fully-controlled power electronic device, energy storage integrated soft open point (ESOP) is gradually replacing traditional switches. This can significantly enhance the controllability of ADNs. To facilitate the utilization of ESOP, device locations and capacities should be configured optimally. Thus, this paper proposes a multi-stage expansion planning method of ESOP with the consideration of tie-line reconstruction. First, based on multi-terminal modular design characteristics, the ESOP planning model is established. A multi-stage planning framework of ESOP is then presented, in which the evolutionary relationship among different planning schemes is analyzed. Based on this framework, a multi-stage planning method of ESOP with consideration of tie-line reconstruction is subsequently proposed. Finally, case studies are conducted on a modified practical distribution network, and the cost–benefit analysis of device and multiple impact factors are given to prove the effectiveness of the proposed method.

Abstract:To improve location speed, accuracy and reliability, this paper proposes a fault location method for distribution networks based on the time matrix of fault traveling waves. First, an inherent time matrix is established according to the normalized topology of the target distribution network, and a post-fault time matrix is obtained by extracting the head data of initial waves from traveling wave detection devices. A time determination matrix is then obtained using the difference operation between the two matrices. The features of the time determination matrix are used for fault section identification and fault distance calculation, to accurately locate faults. The method is modified by considering economic benefits, through the optimal configuration of detection devices of traveling waves when calculating fault distances. Simulation results show that the proposed method has good adaptation with higher fault location accuracy than two other typical ones. It can deal with faults on invalid branches, and the error rate is under 0.5% even with connected DGs.

Abstract:This paper investigates automatic generation control (AGC) of a realistic hybrid four-control area system with a distinct arrangement of thermal units, gas units and additional power generation. A proportional-integral-double derivative cascaded with proportional-integral (PIDD-PI) controller is employed as secondary controller in each control area for robust restructured AGC considering bilateral transactions and contract violations. The Harris Hawks algorithm is used to determine the optimal controller gains and system parameters under several scenarios. Electric vehicle (EV) aggregators are employed in each area to participate fully along with thermal and gas units to compensate for the unscheduled system demand in the local area. A comparison of non-cascaded controllers such as PI-PD, PD-PID and the proposed PIDD-PI proves the superiority of the last. The effect of the decline in inertia is closely examined because of the sudden outage of a generating unit while at the same time considering the change in area frequency response characteristics and area control error. EV fleets make significant contributions to improving the system dynamics during system inertia loss. The use of EVs in the presence of a wind energy-supported grid can provide a stable efficacy to the power grid. Numerous simulations with higher load demands, stochastic communication delays in presence of the WTG plant, and violations in system loadings and changes in gas turbine time constants in the absence of WTG demonstrate the robustness of the proposed control approach.

Abstract:Health condition monitoring of induction motors is important because of their vital role and wide us in a variety of industries. A stator inter-turn fault (SITF) is considered to be the most common electrical failure according to statistical studies. In this paper, an algorithm for the detection of an SITF is presented. It is based on one of the blind source separation techniques called principal component analysis (PCA). The proposed algorithm uses PCA to discriminate between the faulty components of motor current signatures and motor voltage signatures from other components. The standard deviation of one of the decomposed vectors is used as a statistical SITF criterion. The proposed criterion is robust to non-fault conditions including voltage quality problems and large mechanical load changes as well as harmonic contaminants in the voltage supply. In addition, with a straightforward and low computational burden in the fault detection process, the proposed method is computationally efficient. To evaluate the performance of the proposed method, large numbers of practical and simulation scenarios are considered, and the results confirm the good performance, high degree of accuracy, and good convergence speed of the proposed method.

Abstract:Direct wind power purchase for large industrial users is a meaningful way to improve wind power consumption and decrease industrial production costs. Short-term wind power fluctuations may lead to large-scale wind power curtailment problems. To promote use of wind energy, a demand side control method is proposed based on output regulator theory for a grid-connected industrial microgrid with electrolytic aluminum loads to continuously track and respond to wind power fluctuations. The control model of the EALs and the dominant frequencies of the wind power fluctuation signals are analyzed and incorporated into the demand side control plant. The feedback control signals with active power deviations on the tie-line are used to design the demand side controller. Simulations are conducted for an actual industrial microgrid to validate the feasibility and effectiveness of the proposed method. The results demonstrate that the proposed controller based on output regulator theory is able to effectively track wind power fluctuations.

Abstract:In this paper, an overview of several strategies for fault ride-through (FRT) capability improvement of a doubly-fed induction generator (DFIG)-based wind turbine is presented. Uncertainties and parameter variations have adverse effects on the performance of these strategies. It is desirable to use a control method that is robust to such disturbances. Auto disturbance rejection control (ADRC) is one of the most common methods for eliminating the effects of disturbances. To improve the performance of the conventional ADRC, a modified ADRC is introduced that is more robust to disturbances and offers better responses. The non-derivability of the fal function used in the conventional ADRC degrades its efficiency, so the modified ADRC uses alternative functions that are derivable at all points, i.e., the odd trigonometric and hyperbolic functions (arcsinh, arctan, and tanh). To improve the efficiency of the proposed ADRC, fuzzy logic and fractional-order functions are used simultaneously. In fuzzy fractional-order ADRC (FFOADRC), all disturbances are evaluated using a nonlinear fractional-order extended state observer (NFESO). The performance of the suggested structure is investigated in MATLAB/Simulink. The simulation results show that during disturbances such as network voltage sag/swell, using the modified ADRCs leads to smaller fluctuations in stator flux amplitude and DC-link voltage, lower variations in DFIG velocity, and lower total harmonic distortion (THD) of the stator current. This demonstrates the superiority over conventional ADRC and a proportional-integral (PI) controller. Also, by changing the crowbar resistance and using the modified ADRCs, the peak values of the waveforms (torque and currents) can be controlled at the moment of fault occurrence with no significant distortion.

Abstract:As high amounts of new energy and electric vehicle (EV) charging stations are connected to the distribution network, the voltage deviations are likely to occur, which will further affect the power quality. It is challenging to manage high quality voltage control of a distribution network only relying on the traditional reactive power control mode. If the reactive power regulation potentials of new energy and EVs can be tapped, it will greatly reduce the reactive power optimization pressure on the network. Keeping this in mind, our reasearch first adds EVs to the traditional distribution network model with new forms of energy, and then a multi-objective optimization model, with achieving the lowest line loss, voltage deviation, and the highest static voltage stability margin as its objectives, is constructed. Meanwihile, the corresponding model parameters are set under different climate and equipment conditions. Ultimately, the optimization model under specific scenarios is obtained. Furthermore, considering the supply and demand relationship of the network, an improved technique for order preference by similarity to an ideal solution decision method is proposed, which aims to judge the adaptability of different algorithms to the optimized model, so as to select a most suitable algorithm for the problem. Finally, a comparison is made between the constructed model and a model without new energy. The results reveal that the constructed model can provide a high quality reactive power regulation strategy.

Abstract:Artificial intelligence (AI) can potentially improve the reliability of transformer protection by fusing multiple features. However, owing to the data scarcity of inrush current and internal fault, the existing methods face the problem of poor generalizability. In this paper, a denoising-classification neural network (DCNN) is proposed, one which integrates a convolutional auto-encoder (CAE) and a convolutional neural network (CNN), and is used to develop a reliable transformer protection scheme by identifying the exciting voltage-differential current curve (VICur). In the DCNN, CAE shares its encoder part with the CNN, where the CNN combines the encoder and a classifier. Based on the interaction of the CAE reconstruction process and the CNN classification process, the CAE regards the saturated features of the VICur as noise and removes them accurately. Consequently, it guides CNN to focus on the unsaturated features of the VICur. The unsaturated part of the VICur approximates an ellipse, and this significantly differentiates between a healthy and faulty transformer. Therefore, the unsaturated features extracted by the CNN help to decrease the data ergodicity requirement of AI and improve the generalizability. Finally, a CNN which is trained well by the DCNN is used to develop a protection scheme. PSCAD simulations and dynamic model experiments verify its superior performance.