Detection of high impedance faults (HIF) has been one of the biggest challenges in the power distribution network. The low current magnitude and diverse characteristics of HIFs make them difficult to be detected by over-current relays. Recently, data-driven methods based on machine learning models are gaining popularity in HIF detection due to their capability to learn complex patterns from data. Most machine learning-based detection methods adopt supervised learning techniques to distinguish HIFs from normal load conditions by performing classifications, which rely on a large amount of data collected during HIF. However, measurements of HIF are difficult to acquire in the real world. As a result, the reliability and generalization of the classification methods are limited when the load profiles and faults are not present in the training data. Consequently, this paper proposes an unsupervised HIF detection framework using the autoencoder and principal component analysis-based monitoring techniques. The proposed fault detection method detects the HIF by monitoring the changes in correlation structure within the current waveforms that are different from the normal loads. The performance of the proposed HIF detection method is tested using real data collected from a 4.16 kV distribution system and compared with results from a commercially available solution for HIF detection. The numerical results demonstrate that the proposed method outperforms the commercially available HIF detection technique while maintaining high security by not falsely detecting during load conditions.
Load-shedding is an effective control strategy for realization of the power supply-demand balancing. The balancing supply and demand is a crucial issue for power system stability. In this paper, based on IEC 61850 protocols, a supervisory control and data acquisition (SCADA) platform of dynamic load-shedding (DLS) technology in microgrid (MG) is built. Integrating of SCADA and IED (intelligent electronic device), the proposed technology can fetch the information of power generation, power consumption, and MG structure in real time to evaluate the appropriate amount of load to be shed. The proposed DLS technology has been verified on the simulated MG in Qimei Island, Penghu, Taiwan. Compared with conventional methods, the simulation results verify that the proposed DLS provides the fast and precise load-shedding in different loading scenarios. The proposed method has the superiority in reducing the amount of load-shedding and shedding time.
With increasing adoption of residential PV systems, net load forecasting is gradually shifting from forecasting pure load to forecasting pure load with PV generation. This paper explicitly compares two methods of net load forecasting for systems with high behind-the-meter (BTM) PV penetration. The first method is an additive method, in which PV generation and pure load are forecasted separately and combined to produce a net load forecast. First, a disaggregation algorithm is applied to aggregate net load measurements of residential homes to separate the pure load and PV generation. Then, a long short-term memory (LSTM) model is used to forecast pure load and PV separately using the historical disaggregated pure load and PV, respectively, and weather factors. The results are combined to generate a net load forecast. The additive model is compared to a direct net load forecast from an LSTM model. Results show that over the five-month test horizon, the additive method decreases the root mean square error (RMSE), maximum absolute error, and mean absolute error (MAE) of the net load forecast by 6.13%, 3.63%, and 6.06% respectively, compared to the direct method.
In this research work, a new method which determines the individual harmonic voltage contributions of the EAF plants supplied from a point of common coupling (PCC) to the PCC voltage is presented. EAFs are one of the most significant sources of the harmonics, especially the uncharacteristic ones, therefore it is important to be able to discriminate the amount of individual contributions from the feeders of a PCC supplying multiple EAFs. The proposed method uses the relationship derived between the correlation coefficient of the PCC voltage and the feeder current waveforms and the harmonic voltage contribution of each plant supplied from the PCC. The main idea is based on the fact that harmonic voltage at the PCC is a result of the additive effect of voltage drops on the source side impedance of the power system caused by the individual feeder currents. After computing the Pearson correlation coefficients at each harmonic frequency using 10-cycle synchronized feeder current and PCC voltage waveforms, harmonic voltage contribution of the related feeder is obtained using the proposed method. This procedure can be repeatedly used to obtain the contribution of each EAF plant at each frequency component. Field measurements from a PCC supplying multiple EAF plants are used to verify the results and the performance is compared with the previously proposed methods. With a specified source side impedance by the utility, the proposed approach is a fast method of obtaining the harmonic responsibilities, since no real-time impedance measurements are required and no need for the measurements of the other feeders to compute the contribution of a specific feeder in contrast to some other methods. The proposed method can be easily adapted as a real time harmonic contribution detection tool for the power quality analyzers, all of which have synchronized voltage and current waveform measurements.
In an inductive power transfer (IPT) system, various magnetic materials have difference characteristics in enhancing the degree of coupling and misalignment tolerance between the primary and secondary coils. However, the iron losses in ferromagnetic materials may decrease the transfer efficiency of the IPT system significantly. Therefore, in this study, performance and misalignment tolerance of IPT systems with four types of ferromagnetic materials are investigated. Firstly, an equivalent circuit model of the IPT system is established and the variations of system efficiency with different ferromagnetic materials are also revealed. Then, a 3-D finite element analysis (FEA) model of the magnetic coupler is established. In addition, loss test platform is established to obtain the accurate loss coefficients. Finally, the transfer efficiency and magnetic field distributions are both investigated. The results show that the high efficiency and magnetic flux density can be achieved when nanocrystalline core with vertical laminations is used.
The asynchronous operation of a turbo-generator after the loss of field can avoid large-scale blackout and improve the reliability of the power systems. However, the turbo-generator would absorb large reactive power during asynchronous operation, and the increased stator currents can result in the increase of the leakage flux and loss in the end region of the turbo-generator. In order to study the losses of the end structural components during asynchronous operation, this paper presents a method combining the 2-D field-circuit coupled time-stepping finite element model (FCCTSFEM) with the 3-D transient electromagnetic field in the end region of the turbo-generator. The dynamic responses of the turbo-generator after loss of field are calculated by FCCTSFEM, and 3-D transient electromagnetic field and the losses in the end region of the turbo-generator are calculated based on the results of the dynamic response. From the detailed performance evaluations by the 3-D finite-element analysis, the flux density and loss distributions of the end structural components are compared. The regions with the maximum loss in the end structural components are found. The losses of the end structural components affected by the different materials of the metal shield are studied. The results could provide a theoretical basis for improving the asynchronous operating ability of the turbo-generator.
Due to increasing penetration of renewable energy-based distributed generation (DG), conventional distribution networks are transformed into their active form, where microgrids are considered fundamental building blocks. Optimal placement of DG units is the primary step towards microgrid planning. Optimally placed and sized DGs can reduce the total power losses in distribution networks by localizing the power supply to loads. In this paper, a DG optimal placement method by minimizing the total power losses is proposed, where Brute Force search algorithm and Backward Forward Sweep method are used to solve the optimization and load flow problems, respectively. IEEE 33 bus radial distribution test system is used to validate the proposed method in MATLAB. Several case studies are conducted, dispatchable and non-dispatchable DGs and capacitor banks are used in the optimal placement and sizing. The proposed method is proved to be effective by comparing with an existing method.
This paper introduces an adaptive Hamiltonian energy control strategy for the proposed fuel cell hybrid power electronics architecture. The proposed controller with added integrator action is based on the Hamiltonian-Lyapunov function. In contrast to previous work, the proposed control method aims to control the output current of the fuel cell, while the DC bus voltage is controlled by the supercapacitor. Moreover, integrators are built into the control structure to eliminate steady-state errors in current and voltage, respectively. The Lyapunov candidate function is chosen to demonstrate the large-signal stability of the whole system. The experimental results demonstrate the effectiveness and feasibility of the proposed control method.
Distribution power grids are rapidly changing from a centralized and unidirectional distribution network to a decentralized, bidirectional network that supports the penetration of large amounts of observable/unobservable distributed energy (DERs). The additional variability, uncertainty and scale of control variables generated by DER integration greatly increase the complexity of power system operation. This paper presents a method to improve the resiliency of distribution systems using increasing distributed energy resources (DERs). DERs and loads are aggregated into a switch-level equivalent model while retaining controllability information for real-time power outage management analysis after a power outage. Pre-fault smart meter measurements serve as input to a machine learning integrated cold load pickup (CLPU) model to obtain short-term load predictions, while optimal recovery analysis is carried out to determine restored network topology and utility control loads and DERs set-points.
This paper presents the implementation of an intelligent algorithm for improving the reliability and suitability of a photovoltaic (PV) system. These renewable energy systems are extremely susceptible to power grid transients and their operation may suffer drastically during faults located within the solar arrays, power electronics, and the inverter. Thus, it is significantly important to develop an intelligent mechanism to detect any type of fault or abnormalities at the shortest possible time and provide security for the solar system. In order to accomplish that, an adaptive neuro-fuzzy inference system (ANFIS) is developed to distinguish between normal, and faulty operation of a grid-connected PV system. A large dataset from real-time laboratory experiment using TBD125x125-36-P PV module, which includes the current, and voltage characteristic of PV is extracted, preprocessed and used in the training of the machine learning algorithm. The performance of the proposed intelligent fault detection scheme is also compared with other popular machine learning algorithms, where ANFIS have demonstrated outstanding results, with accuracy rate of 95.4%. Furthermore, the proposed technique is significantly more robust, straightforward, and requires less implementation time compared to other machine learning techniques such as, K nearest neighbor, decision tree, Naïve Bayes, Ensemble, linear discriminant analysis, support vector machine, and finally neural network. Thus, the developed ANFIS based intelligent technique will enhance the reliability of the PV system through minimizing the maintenance cost, saving time and energy.
With the emergence of renewable energy sources, the power grid all over the world is going through a paradigm shift. The inverter based renewable energy sources (IRES) are replacing the conventional rotating synchronous generators and this trend is expected to continue in coming years. Consequently, the grid strength is decreasing, which can pose significant challenges on the grid stability, especially during integration of IRESs. This paper presents a thorough analysis on the integration challenges of IRESs in weak grids. The paper presents a comprehensive investigation on the impacts of factors such as available fault level at the point of interconnection (POI), feeder length and grid nature on the POI voltage and maximum allowable power injection by IRES. This is followed by maximum allowable IRES power injection sensitivity analysis to quantify the impacts of the aforementioned factors. The paper concludes with a thorough case study on the IEEE 39-bus test system to numerically show the impact of diminishing grid strength on the integration of IRESs.
This paper describes the basic concepts and advantages of more electric aircraft (MEA). Shaft-line-embedded starter/generator is one of the key technologies to enhance the comprehensive performance of MEA. Switched reluctance machines (SRM) have no permanent magnets and are suitable for high speed which makes them perform well at high temperatures and high-speed operation. Besides, the concentrated stator windings have strong fault tolerance. So it possesses good application prospects in shaft-line-embedded integrated aero-engine starter/generator system to satisfy the high-temperature and high-speed operation. To broaden the application of SRM in high-temperature environments and achieve the efficient transmission of airborne energy, this paper aims at high-temperature applications in aero-engine which is over 350°C. Lastly, the direct instantaneous torque control method was used to reduce the torque fluctuation during the motoring process. It demonstrates that the high-temperature environment is beneficial to reduce the torque ripple of the SRM.
This paper concerns with the design of experiments aimed at determining whether the critical flicker frequency (CFF) is subject to change when the observer is exposed to pulsating sounds. The paper describes the work done in designing the experiments and also reveals data from a preliminary campaign. Data do not form a basis to prove a direct link between pulsating sound and CFF, but can be used to better adjust the conditions before the full campaign.
The variable speed doubly fed induction generators (DFIGs) are the future of hydrogenating plants owing to their flexible nature and speedy response. The new digitalized power scenario needs attention on cyber-attacks and measures suggested as per IEC Standard 62433 (2019): Security for Industrial Automation and Control Systems. Different system responds differently to cyber-attacks. This work focus on the impact analysis of speed and DC link voltage sensor attacks on grid connected large hydrogenating unit, of capacity 250MW. MATLAB simulations are carried out for False Data Injection (FDI) and Denial of Service (DoS) attacks and analyze the system behavior. The simulation results demonstrate that the system is affected severely under different attack scenarios.
The rapid growth of wind power has created a significant challenge to the stability of wind farms (WF) and their connected power grids. Due to the complexity on detailed WF modeling, an aggregated model which retains the required level of accuracy is necessary to be developed for theoretical studies and industrial applications. However, the full parameter identification of the WF aggregated model and the correlated parameter estimation are still two crucial issues which restrained the practical application of the WF aggregated models. To solve these issues, a dynamic equivalent modeling approach which is able to identify the full parameter of the aggregated WF model meanwhile considering parameter correlation is proposed in the paper. A primary WF aggregated model (PWAM) is firstly developed with traditional capacity-weighted method to reduce the number of identified parameters. Then, the invalid parameters which are the root-cause of the PWAM’s output error is screened out with the Hilbert-Huang Transform (HHT) marginal spectrum. Moreover, the invalid parameters are divided into four categories with the correlation analysis and tuned with a well-designed multistage parameter calibration algorithm. The accuracy and robustness of the aggregated WF model were verified with a modified IEEE 39-bus system through several disturbances.
In this paper, a cascaded extended state observer- based sliding mode control (SMC) is proposed for the typical microgrid (MG) interfaced dc-dc boost converter. For a given MG system, the power fluctuation is unpredictable, which may cause an unstable dc-bus voltage. In that situation, all the dc-bus connected electronic loads cannot work normally. In order to maintain a stable dc-bus voltage, additional power sources with a suitable power converter and the advanced controller are necessary. Especially, the controller for the power converter plays a crucial role in reducing the power fluctuation and regulating the dc-bus voltage. In this paper, to improve the ability of the converters to reject external power disturbance, a cascaded extended state observer is designed to estimate the lumped disturbance more accurately. Besides, the continuous SMC method is adopted to provide a feedback control loop. The corresponding simulation results could highly validate the effectiveness of the proposed control method.
The electrical power required in an electrified car park can be significant, so it appears necessary a rational design of the distribution system for the recharging sockets, in order to obtain an expandable and cost-effective system. This paper deals with the designing criteria to electrify an EV park suggesting a load-driven approach. The modularity of the configuration allows for easier management, maintenance and effective supervision. Essential optimizations of the constitution and management of an EV parking are the need to simplify power wallboxes and the provision of a low-cost recharge, based on a simple “topping up” of the batteries while the car is parked. These measures will certainly favor the extension of parking spaces to thousands of electric vehicles.
This article presents a small-signal stability analysis tool for large-scale power systems with high penetration of inverter-based resources (IBRs). Firstly, a network transfer function matrix (NTFM), which represents the information of the system topology, transmission lines, loads, IBRs locations, etc., is derived to model the entire power system network. Secondly, small-signal perturbation method is applied to obtain the sequence impedance/admittance models of the block-box IBRs considering the frequency cross-coupling effects. With the obtained NTFM as well as IBR models, a multi-input, multi-output (MIMO) feedback system is constructed, and the generalized Nyquist criterion (GNC)-based stability method is employed to analyze the stability of the entire power system. Furthermore, based on the developed stability analysis method, sensitivity analysis is conducted on an unstable case to identify which parameter has a high impact on the system stability. Different test cases based on a modified IEEE 14-bus system as well as a reduced 240-bus WECC system are studied to verify the proposed stability analysis tool.
This work provides an electromagnetic transient model for low-voltage surge protective devices connected to the dc side of electric vehicle charging stations with a maximum operating voltage of 1000 V. An equivalent circuit model is developed based on standard impulse voltage ($1.2/50\ \mu\mathrm{s}$) and impulse current ($8/20\ \mu\mathrm{s}$ and $10/350\ \mu\mathrm{s}$) experiments. The proposed model reproduces quite accurately the response of the combination type surge protective device under study in terms of sparkover voltage, residual voltage, and energy absorption, as illustrated through ATP-EMTP simulations. The developed simulation model can be an effective tool for surge protection and insulation coordination studies for electric vehicle charging stations.
