Capacity of a battery is a measure of the amount of charge, in Ampere hours (Ah), stored in the battery. With the battery ageing processes, the capacity of the battery reduces. Consequently, it is essential to have an accurate knowledge of battery capacity in order to perform crucial battery management tasks. For example, in the field of battery reuse, the estimated residual capacity of batteries will help in determining a suitable secondary application for the retired battery pack. Existing literature on offline capacity estimation are often based on low-discharge or 1C rate discharge of the battery. This means that a minimum of one hour wait is required before an estimate of capacity is made. In this paper, a novel approach is proposed for fast offline estimation of battery capacity using the knowledge of the open-circuit voltage (OCV) - state of charge (SOC) curve of the battery. The proposed approach consists of a novel method to estimate the battery OCV by applying a current profile that is optimized to reduce the uncertainty in OCV estimation. For capacity estimation, a constant current pulse is applied for a short duration. The capacity estimation accuracy is theoretically derived as a function of this current duration. The proposed fast capacity estimation approach is shown to be able to estimate the battery capacity within as short as 1 minute duration with an estimation error standard deviation of 0.02 Ah.
The impacts of high voltage (HV) insulators on equivalent circuit parameters of wireless power transfer (WPT) system is evaluated and analyzed in this study. Flat spiral coils are inserted under the HV insulators and convey power to charge monitoring devices' battery at the top of the power line towers. All the external metal objects over the magnetic flux path are identified, and their material type, relative permeability, and conductivity are explained. An equivalent circuit for the WPT system is specified, and its parameters are calculated with and without the presence of external metal objects. In this sense, 3-D finite element method (FEM) simulations are conducted in ANSYS Maxwell. The variation of the magnetic flux within the airgaps is displayed, the eddy current induction initiation over the external metal objects is shown, and changes in the equivalent circuit parameters of the WPT system are clearly discussed. The results of the simulation study are then validated through experimental studies by means of fabricated flat spiral coils.
Power grids are critical cyber-physical systems that employ advanced Information and Communication Technologies (ICTs), e.g., Wide Area Measurement Systems (WAMSs), to deliver the energy to end users reliably and efficiently. WAMSs are used to collect real-time data from Phasor Measurement Units (PMUs) to improve the operator's situational awareness, as well as to enhance real-time monitoring and control of power systems. The WAMS, however, is vulnerable to cyber-attacks due to the susceptibility of its components-such as PMUs and Phasor Data Concentrators (PDCs)-and the lack of embedded security mechanisms in its communication protocols. Some more-destructive cyber-attacks, such as malware injection, can propagate themselves into the components of a WAMS through the communication network. Thus, in such attacks, an attacker can compromise a larger number of components, resulting in more-severe consequences. Therefore, investigating the propagation of cyber-attacks in WAMSs and devising effective countermeasures for this problem are of paramount importance. On this basis, this paper initially develops a model to analyze cyber-attack propagation in WAMS. Then, the impacts of the attacker's capability and the network operator's defensive ability on attack propagation are investigated in detail. Such a study can elucidate the required security measures and defensive strategies to prevent the spread of cyber-attacks in WAMSs. Finally, a Learning-Based Framework (LBF) is developed to estimate the attacker's capability. The simulation results corroborate the effectiveness of the proposed LBF in estimating the attacker's capability.
The concept of virtual synchronous generators (VSGs) has been developed in the literature as an effective approach for controlling power-electronics-based wind power generation. Therein, the converters are controlled to emulate the classic synchronous generators with droop characteristics and to regulate their inertia and damping for better dynamic response. In this paper, a new droop controller is proposed for VSGs which has the capability to regulate the converter frequency after disturbances for maximum power harnessing. The performance of the proposed controller is evaluated through simulation studies carried out in MATLAB/Simulink. It is demonstrated that the new droop controller improves the capability of the VSGs for maximum power point extraction by enabling smooth transition between different operating modes after a frequency disturbance in the grid.
The advent of wide-area measurement systems (WAMSs) in modern power systems enables deployment of wide-area damping controllers (WADCs) to effectively deal with dominant oscillation modes. However, the reliance of WAMSs on information and communication technologies (ICTs) exposes WADCs to potential cyber attacks. To come up with effective countermeasures, extensive knowledge about the existing vulnerabilities and possible cyber attacks is required. On this basis, this paper presents an attack model against WADCs by exploiting the vulnerabilities in the time-alignment strategies utilized by phasor data concentrators (PDCs) to aggregate the stream of phasor measurements. In this attack, the adversary manipulates data timestamp of phasor measurement units (PMUs) to compromise PDCs functionality, causing dropout in the WADC's critical phasor measurements data. To obtain the targeted PMUs and manipulated timestamp, a stochastic mixed-integer linear (MIL) model is developed from adversary prospective considering uncertainty of communication delay. The time-domain dynamic study on two-area Kundur test system demonstrates that the developed attack can jeopardize the WADC performance and even cause system instability.
The integration of photovoltaic (PV) panels in power distribution systems (PDSs) has provided the grid with the capability to regulate voltage through the injection or absorption of reactive power. The deployment of information and communication technologies (ICTs), which is required for this voltage regulation scheme, has made the PDS prone to a variety of cyber attacks, e.g., false data injection (FDI) attacks. To counter these attacks, this paper proposes a data-driven detection framework to identify FDI attacks against voltage regulation of PV-integrated PDS. To regulate the voltage at a desired location, e.g., the point of common coupling (PCC), the voltage measurements are sent to a centralized controller and the calculated control signals are transmitted back to PVs to be added to their internal control schemes. During this transmission of data, an attacker manipulates the measurement data and launches an FDI attack leading to an unacceptable voltage profile and operation of protection systems. To detect these attacks, a machine learning (ML)-based framework based on support vector machine (SVM) is developed in this research. In this regard, a dataset of different operating points, e.g., loading conditions, is collected to train this supervised framework. The performance of the trained framework for attack detection has been compared with other supervised and unsupervised ML-based techniques in the case of FDI attacks against modified IEEE 33-bus PDS. The obtained results demonstrate the superior performance of the proposed framework in detecting FDI attacks.
The low-voltage DC distribution systems are comprised of various components with a wide range of power ratings. In such systems, compared with unipolar configurations, bipolar structures bring considerable superiority in terms of efficiency, safety, and compatibility. However, the scarcity of protection methods has restricted the expansion of bipolar DC grids. In line with this trend, this paper presents a dynamic series regulator (DSVR) for the protection of sensitive loads in bipolar DC distribution systems. The proposed DSVR injects dynamic voltage for a set of selected sensitive loads to suppress the probable voltage abnormalities in the system. Therefore, it provides desirable satisfaction of voltage quality metrics in bipolar DC power systems. The proposed DSVR is composed of a multi-active bridge converter with one primary and two secondary windings followed by two full-bridge DC-DC converters. To evaluate the feasibility and effectiveness of the proposed concept, various simulations of several case studies are carried out in the PLECS (Plexim) software.
The low inertia of microgrids, particularly when they operate in islanded mode, complicates the balancing of load and generation. As a result, islanded microgrids are exposed to various instability conditions, e.g., ones that result in large frequency deviations. To address this issue, this paper develops a cooperative control scheme based on a robust fractional order technique for regulating the frequency of an islanded microgrid which includes a variety of generation units, such as a generator, battery energy storage systems (BESSs), a wind turbine, and photovoltaic (PV) panels. The proposed controller is able to stabilize the microgrid in the presence of uncertainties, such as parametric uncertainties and disturbances. The performance of the proposed controller has been validated through simulations, and through comparison, it has been demonstrated that it outperforms optimal proportional integral derivative (PID) and fractional-order proportional-integral-derivative (FOPID) controllers.
Wind power is becoming a key player of the world's energy landscape thanks to its cleanliness, abundance and huge potential. At the same time, it attracts an increasing attention when it comes to its efficient production and financial viability, which otherwise may restrict its development in the foreseeable future. Indeed, enhancing energy production efficiency to reduce costs and improve the ability of the system to resist faults are research areas of great interest. With the advancement of machine learning and artificial intelligence along with increasing computational power at hand, reinforcement learning enables achieving an optimal control solution in an application environment after continuous attempts and updates. In this paper, a novel solution based on reinforcement learning is applied to the control of wind farm. An intelligent agent is designed to explore the environment, and after training, it effectively maintains the necessary balance between power generation and load, which in turn regulates the wind farm grid frequency when enough wind is available. The trained agent is tested under different loads, realistic wind fields, as well as fault scenarios. All simulation results show that the agent accurately understands the environment and load requirements, mitigates the impact of faults, and thus, improves the stability of the grid frequency.
In this study, a wind turbine design is proposed that complies with the Turkish grid code. The proposed design consists of the control algorithms of the grid connection regulations of the squirrel cage asynchronous generator (SCIG) and full-scale power converter (FSPC) configuration with 5 MW grid output power, which is called the type 4 wind turbine (WT). The FSPC design based on the 2-level full bridge IGBT model will be explained step by step under three main parts and a case study is conducted for the grid codes. First of all, the control of SCIG by the rectifier module of FSPC with the indirect field oriented control algorithm is explained. In the second part, the energy transferred to the DC busbar with the rectifier modules of FSPC is transferred to the grid with the help of control algorithms created on the inverter according to the grid requirements. Finally, simulation studies of FSPC control algorithms of wind turbines, which will provide solutions to Turkish electrical grid regulations, is carried out.
A peak output voltage with a unity modulation index is desired at the output of the multilevel inverters with boosting capability. The operation of the inverter at a lower modulation index leads to lower boosting in the fundamental component of the output voltage, and hence an underrated inverter performance is achieved. Recently, an 11-level WE-Type inverter with 1.25 boosting was proposed. It was found that the level formation was hindered due to improper capacitor voltages if the modulation index was greater than 0.94, thus rendering the boosting capability to 1.175. In this work, a modified pulse width modulation is implemented on this inverter, which will enhance its capability of maximum boosting of 1.25 by making it operational at the unity modulation index. The analysis of the work is presented and supported by simulation results in MATLAB/Simulink environment.
Online monitoring of electric power components in smart grids is of great importance to enhance reliability. Fault detection at primary levels in distribution transformers, the chief components to maintaining the integrity of modern power networks, prevents following significant destructive damages and high costs of failures in smart grids. Data-driven structure in smart grids provides accessibility to data related to the condition of transformers in data centers. Frequency response analysis (FRA), an efficient and sensitive technique to identify transformer defects, can be utilized in online monitoring. However, a trustworthy and consistent code for interpreting frequency responses has not yet been proposed by standards. This study proposes a self-organizing map (SOM) neural network as an intelligent interpreter using appropriate feature groups obtained from suitable statistical indices (SIns). In order to distinguish the severities and locations of disk space variation (DSV) defects as common faults in transformers, an experimental setup including 20 kV windings of a 1.6 MVA distribution transformer and an impedance analyzer are provided. The promising performance of SOM in detecting DSV faults with 100% accuracy shows that the proposed method is capable of identifying faults using high dimensional and nonlinear FRA data sets.
Flywheel energy storage systems (FESS) are playing increasingly important roles in areas such as wind power fluctuation smoothing and grid frequency regulation due to their fast charging and discharging characteristics. In this paper, we propose a distributed control method applied to power distribution for flywheel energy storage systems. The total power is allocated according to the amount of energy that remains in the flywheel unit, and a dynamic average consistency algorithm is used to estimate the average reference power and the average energy. For local current control of individual flywheel, proportional integral (PI) algorithm is used, where the reference current is obtained from the power distribution. The feasibility and effectiveness of consistency are verified by numerical simulations.
This paper proposes a modified control algorithm for three phase squirrel cage induction motor. The proposed algorithm is based on the static or steady state model of a vectorcontrolled drive with rotor flux orientation and controls the machine dynamics over one fundamental cycle. The algorithm proposes current mode control on the direct axis and voltage mode control on the quadrature axis, unlike, state of the art vectorcontrolled drive where current mode control is used on both the axes. This offers two advantages. First, the control algorithm becomes independent of the rotor time constant, which is always varying, thereby reducing the dependency on rotor time constant of the motor. Second, one proportional integral controller is reduced in the control algorithm. The proposed scheme is validated through simulation using a fractional horse power squirrel cage induction motor. The proposed technique is also compared with the conventional vector control.
Simulation of current distributions in groups of cables directly in the time domain using finite element method software is a computationally expensive task. The time to carry out these simulations in the time domain increases as the structures become large and complex, and the frequencies involved become in the order of power frequencies. In contrast, the time to solve these same simulations in the frequency domain is relatively low. One issue with frequency domain simulations is the inclusion of nonlinear elements. This paper proposes a routine that combines finite element method software and circuit solvers. Our approach drastically reduces simulation times and can include nonlinear elements. First, we use CST Studio Suite to compute the frequency-dependent nodal admittance matrix of a group of cables. The ATP-EMTP does not have an interface to work directly with frequency-dependent data. Therefore, for the simulation of short cables, we compute an equivalent multi-conductor pi circuit, which has a nodal admittance matrix similar to the nodal admittance matrix calculated by the CST Studio Suite. The resulting pi circuit is coded into a custom PCH file that the ATP-EMTP recognizes as one of its native transmission line models, the coupled pi circuit. We use this methodology to simulate a phase-to-ground fault in an electrical panel within a ship. Our methodology shows the distribution of fault currents among cables, shieldings, and the hull.
Power quality disturbances can be observed as sags, swells, transients, and harmonics, and can affect customers at varying levels of intensity. It is the responsibility of the utility to supply customers with power, however power quality disruptions can occur during distribution. Traditionally, only voltage information is used to conduct power quality monitoring at the distribution level. It is common to record the RMS values of the bus voltages and to identify abnormal operations based on when a sag or swell occurs. This paper proposes a tool consisting of an algorithm and an accompanying graphical user interface (GUI) that can display historical voltage bus data, analyze the data, and provide the user with information that details voltage behavior outside of a user-defined threshold. The GUI gives the user the interactive ability to import data and set the desired threshold. The algorithm then detects events in the imported data outside of the chosen threshold. It also provides the user with event durations, magnitudes, local maximums, and area. The efficacy of the algorithm was verified by comparing the output determined by the algorithm versus the conclusion drawn by a human observer. Additionally, this paper provides a brief overview of two power quality curves: the Computer and Business Equipment Manufacturers' Association (CBEMA) curve, and the Information Technology Industry Council (ITIC) curve. These curves have been utilized in past decades as the common mechanisms to identify voltage variations and the duration of disturbances. Although these curves have proven to have great merit for use as tolerance curves, they may not capture all the necessary details of the events for power quality characterization.
This paper studies the performance of a multifunctional inverter (MI) connected to a distribution grid in faulty operating conditions. The MI includes a conventional power generation from a photovoltaic source, an active filter for harmonics, power factor correction and phase balancing for non-linear and unbalanced loads connected at the same point of common coupling. A model of the MI is proposed, and the analysis in a CIGRE low voltage test grid is performed for fault conditions. The inverter operation is analyzed according to IEEE Std 1574:18. Therefore, the MI must be disconnected very fast for faults that produce severe voltage sags, to avoid damage. On the other hand, for faults that produce more shallow voltage sags, the MI must operate safely, complying with the fault ride-through standards.
This paper considers the problem of state of charge (SOC) estimation in rechargeable batteries. Traditionally, the SOC is estimated based on quantifiable measures such as current, voltage, or both. The current and voltage-based approaches are, in general, susceptible to several uncertainties as well as practical limitations. Meanwhile, the fusion of both approaches through nonlinear filtering techniques tends to preserve respective benefits and improves the overall SOC estimation process. However, there are no general solutions to the nonlinear filtering problem but only sub-optimal approximations, which compels the selection of appropriate filter to be a concern while designing SOC algorithms. Additionally, practical implementation of filter-based approaches would require high-precision storage systems to store the model parameters. For restrictive computational scenarios, the round-off errors could induce numerical instabilities. Therefore, this paper presents a novel table-based Kalman filter (TKF) that describes the usual nonlinear functional relationship between the open-voltage voltage (OCV) and the SOC through inflection points of the OCV-SOC curve. The table-based approximation is advantageous as the system model can be described linearly in terms of the table components. The resulting linear system model would allow us to apply the Kalman filter directly rather than its computationally expensive nonlinear variants. The results show that a 16-point table can have a maximum error of approximately 0.01. Further, it highlights that the TKF with 32 points has a comparable error performance to the state-of-the-art nonlinear filtering approach.
Fast internal detection and location in Shunt Ca-pacitor Banks (SCBs) can lead to the prevention of damages to other SCBs' elements and consequently avoid undesirable performance and effects in power system operation. This paper targets the performance of phasor-based algorithms of failure detection and fault location of SCBs. Being dependent on the fundamental phasor components which usually are calculated based on the Discrete Fourier Transform (DFT), the failure detection and fault location algorithms suffer from almost one-cycle delay. This paper provides sub-cycle phasor estimation based on the least-square technique. The proposed algorithm is evaluated for different configurations of SCBs considering different fuse protection designs. The proposed method provides a criterion for relay decision-making in the case of multiple faulty phases condition. The proposed method is designed to monitor and detect consecutive failures based on the existing data of commercial relays. Performance evaluations are conducted under different circumstances namely voltage unbalance conditions and multiple internal fault locations.
The false data injection attacks (FDIAs) targeting smart grids compromise the integrity of supervisory control and data acquisition systems (SCADA), posing a significant threat to the safe operation of the smart grids. The residual threshold method is usually used to detect whether the power system is under attack. However, the attack sequence carefully constructed by the attacker has concealed characteristics and can avoid false data detection mechanisms. Therefore, based on the unscented Kalman filter (UKF) state estimation, it is judged whether the power supervisory control and data acquisition system is attacked. The standard IEEE-14 node test system is used for simulation experiments, and the results show that this method can effectively estimate FDIAs for smart grids.
