Voltage Error Research Articles

A Novel PSO Based Fuzzy Controller for Robust Operation of Solid-State Transfer Switch and Fast Load Transfer in Power Systems

This paper proposes a novel particle swarm optimization (PSO) algorithm based fuzzy logic controller (FLC) for improving the performance of automatic solid-state transfer switch (SSTS) with respect to transfer time. The proposed technique generates adaptive membership functions (MFs) of voltage error and rate of change of voltage error for input and output based on the fitness function formulated by the PSO. An optimal PSO-based FLC (PSOF) fitness function is further employed to tune and minimize the mean absolute error (MAE) to improve the performance of the load transfer in a short duration. Results obtained from the proposed PSOF are compared with those obtained with the conventional FLC to validate the developed controller. It is observed that the proposed PSOF optimized controller can transfer the load faster than the conventional FLC controller. The accuracy of the developed PSOF is illustrated and investigated via simulation tests for SSTS in the IEEE 9-bus system. It can be concluded that the PSOF controller is better than the only fuzzy controller in all tested cases in terms of transfer time.

A Breaker Failure Detection Method Based on Nonlinear Parameter Estimation for Modular Hybrid DC Circuit Breakers

Modular hybrid dc circuit breakers (DCCBs) have been widely employed to protect multiterminal high-voltage dc (MT-HVdc) grids. However, breaker failures still pose a challenge to the protection of MT-HVdc grids, especially for a single module failure of breakers. To this end, a breaker failure detection method based on nonlinear parameter estimation is presented. This proposed method consists of three parts. The first is a signal preprocessing method that eliminates the effect of the RC circuit of the hybrid DCCB. The second is an offline optimal parameter acquisition method that has an improved numerical model of the metal–oxide varistor (MOV) and combines the Levenberg–Marquardt (LM) algorithm with the particle swarm optimization (PSO) algorithm. The third is an online breaker voltage error calculation that estimates the breaker voltage based on the improved numerical MOV model to detect the partial failure of the modular hybrid DCCB. The performance of the proposed algorithm is evaluated by both power system computer aided design (PSCAD)/electro-magnetic transient design and control (EMTDC) tests and field tests. The study results prove the effectiveness of the proposed method for the partial failure of the modular hybrid DCCB considering different faulty conditions and the aging of MOVs.

Electric power metering with its low quality

The issue of electricity metering with low quality is being considered. Special attention is paid to checking the connection metering scheme with low quality of electrical energy. It is shown that a specific circuit design can affect the accuracy of metering of electrical energy in case of poor quality of electrical energy. In distribution networks intended for power supply of industrial enterprises, reactive power is also active. If the consumer has compensating devices, the directions of active and reactive power can be opposite. It is noted that current transformers, selected taking into account the short-circuit current or the characteristics of relay protection, do not provide metering accuracy due to the increased transformation ratio, forcing the installation of an additional set of current transformers or transferring the meters to another point in the network. Thus, the current transformation ratio of the current transformer is somewhat different from the nominal one, and the secondary current vector forms a certain angle with the primary current vector. The voltage error is manifested in a slight decrease in the secondary voltage under load. The angular error is characterized by a certain angle between the vectors of the primary and secondary stress. The value of the errors depends on the load power of the transformer. The maximum allowable voltage drop of the transformer determines the accuracy class. A rated power is set for each accuracy class. Thus, the voltage transformer, depending on the load, can operate in different accuracy classes. Changing the accuracy classes leads to errors in the measurements of metering devices and requires appropriate correction. The article reveals the reasons for the occurrence of such errors, which makes it possible to develop methods and, as a consequence, create devices to prevent the described errors of metering devices.

NUMERICAL VOLTAGE RELAY ENHANCEMENT BY INTEGRATED DISCRETE WAVELET TRANSFORM

In today’s advanced technology and fast past energy demands, dependency on electrical power systems have reached the peak for daily human function in all life aspects. From high-voltage to low-voltage, big machines to a very articulated chipset, voltage optimization plays an important role in work operation and energy saving of electrical devices. Therefore, the importance of voltage relay in power systems is very significant to ensure normal operation, power system efficiency, power elements protection, and load safety. However, power system failures occur all the time due to numerous reasons which may lead to voltage variation (over-voltage or under-voltage). Such a variation can disrupt the essential elements of the power system and harm consumer load. Therefore, as network distribution cannot assure the same exact voltage at all day times, the need to optimize voltage relay has been a major issue in power system reliability and protection enhancements over the past few years. This paper aims to develop a unique technique to implement and test a new wavelet-based algorithm to modern numerical voltage relay to expedite voltage fault detection and precisely distinguish harmful voltage fluctuation levels. This enhancement will advance voltage waveform analysis to detect over-voltage/under-voltage phenomena, improve relay-tripping accuracy and relay response time. That is, wavelet transform has shown the ability to advance voltage relay operation with special properties of waveform de-noising, data extraction in an adjusted window, and data decomposition. Simulation of the Discrete Wavelet transform bank filters implemented into numerical voltage relay using MATLAB/Simulink has confirmed promising results as a quick voltage error detection based on few waveform data samples, faster data compiling process, and less complexity compared to typical conventional numerical voltage relay.

Parameter identification of lithium-ion battery pseudo-2-dimensional models using genetic algorithm and neural network cooperative optimization

The electrochemical model parameters of a lithium-ion battery are important indicators of its state-of-health, and many previous studies have proposed methods for identifying them. These identification methods must solve highly nonlinear optimization problems with many local optima. Hence, metaheuristic approaches are often employed. Most metaheuristics take a way to abandon worse solutions and make the most use of better solutions only. Such inefficient use of data leads to local optima problem in metaheuristics. To overcome these limitations, this paper proposes a novel parameter identification method in which a neural network cooperates with a genetic algorithm. The proposed method adopts an 1-dimensional convolutional neural network to learn the dynamics between the known input current and the corresponding simulated voltage. Although estimated parameters cause large output voltage errors, they are useful for building an electrochemical model and can be used to recommend highly probable parameter candidates. We clearly show through simulation and experiment that the electrochemical model parameters are identified more accurately and reliably compared with various existing results, owing to the high data efficiency of the proposed method.

Accurate Morphology Characterization Using Atomic Force Microscopy via Vertical Drift Correction and Illusory Slope Elimination

Abstract Thanks to the ability to perform imaging and manipulation at the nanoscale, atomic force microscopy (AFM) has been widely used in biology, materials, chemistry, and other fields. However, as common error sources, vertical drift and illusory slope severely impair AFM imaging quality. To address this issue, this paper proposes a robust algorithm to synchronously correct the image distortion caused by vertical drift and slope, thus achieving accurate morphology characterization. Specifically, to eliminate the damage of abnormal points and feature areas on the correction accuracy, the laser spot voltage error acquired in the AFM scanning process is first utilized to preprocess the morphology height data of the sample, so as to obtain the refined alternative data suitable for line fitting. Subsequently, this paper proposes a novel line fitting algorithm based on sparse sample consensus, which accurately simulates vertical drift and slope in the cross-sectional profile of the topographic image, thereby achieving effective correction of the image distortion. In the experiments and applications, a nanoscale optical grating sample and a biological cell sample are adopted to perform topography imaging and distortion correction, so as to verify the ability of the proposed algorithm to promote AFM imaging quality.

Fault Tolerant Sliding Mode Controller Design Subject to Sensor Faults for Output Voltage Regulation of a Self-Excited Induction Generator

This paper aims to improve the performance of the output voltage regulation of the Self-Excited Induction Generator (SEIG) with fixed capacitor-thyristor controlled reactor (FC-TCR) structure in case of sensor faults. A sliding mode controller (SMC) is designed to generate the triggering angle of the FC-TCR to regulate the output voltage. The performance of proposed SMC is firstly tested without sensor faults and high success in tracking the reference is obtained. Since an additive fault occurs on the sensor, SMC performs well tracking dynamic but is on the incorrect reference. Therefore, Radial Basis Function Neural Network (RBFNN) is trained accurately with the 2% mean squared error (MSE) by taking the stator currents, triggering angle and generator speed as inputs, whereas the output voltages of the SEIG are outputs. Further, fault detection architecture is well designed with a voltage error index calculated by subtracting the sensor output from the estimated output. The threshold based selector reconfigures the controller well. The simulation results show that in case of sensor faults and various loading conditions, proposed fault tolerant SMC remains the actual output voltage of the SEIG at real reference with maximum 2.2% steady state error in a finite time.

Research on battery SOH estimation algorithm of energy storage frequency modulation system

Based on the experimental data, the life characteristics of lithium-ion battery were analyzed. According to the experimental data statistics, the battery life changes with the number of cycles, the performance of the battery after repeated use is studied, and the changes of battery capacity, discharge efficiency, energy efficiency, internal resistance and other parameters with the battery life are explored. The mathematical model of battery life and battery usage times is established by curve fitting, and the SOH of battery is predicted according to this model. Finally, the SOH of Li ion battery is estimated according to the voltage curve fitting method. Firstly, the battery charging curves under different discharge rates and different SOH are normalized, and the charging voltage error after normalization calculation is compared. According to the existing experimental data, the SOH estimation algorithm of 92Ah lithium-ion battery is verified, the estimation accuracy of voltage curve fitting method is verified, and the estimation results of SOH are analyzed.

Process control of charging and discharging of magnetically suspended flywheel energy storage system

Flywheel energy storage system (FESS) is an energy conversion device designed for energy transmission between mechanical energy and electrical energy. There are high requirements on the power capacity, the charging efficiency and the output precision of FESS. Active magnetic bearings are used to suspend the flywheel (FW) rotor of the FESS in air to eliminate friction. A high rotating speed of the flywheel can increase the power capacity but it also increases the disturbance load torque on the FW rotor. An observation control model of load torque is therefore proposed to mitigate the disturbance load torque acting on magnetically suspended FESS (MS-FESS) during the charging process. Moreover, for the discharging process of MS-FESS, a compound control model combing the sliding model control and the extend state observer is proposed to improve the response speed and the output voltage precision. Simulations and experiments are conducted to testify the control performances of proposed control models during the charging and discharging processes of a MS-FESS. The charging efficiency is improved by 17.6% with 46.6% reduction of the output voltage error after using the proposed control models for the charging and discharging process control. The proposed control method has high potential to be applied for process control of charging and discharging of practical MS-FESS.

Improved Voltage Flux-Weakening Strategy of Permanent Magnet Synchronous Motor in High-Speed Operation

This paper presents an improved voltage flux-weakening strategy of a permanent magnet synchronous motor (PMSM) in a high-speed operation. The speed control performance using voltage flux-weakening control is not affected by the motor parameters, so it is used in various motors for high-speed operations. In general, the voltage flux-weakening control uses voltage references to generate a flux axis current reference. However, there may be errors between the voltage reference and the actual voltage flowing into the motor. This causes an error in the current reference generation and reduces the efficiency of the inverter and motor due to the use of more current. In this paper, the problems that can occur due to voltage errors were analyzed through theoretical approaches and simulations, and improved voltage flux-weakening control to resolve these problems was presented. This method’s advantage is that the error between the voltage reference and the voltage applied to the motor can be minimized, and the target speed can be reached with minimum current. As a result, it was possible to increase the energy efficiency by reducing the amount of current flowing through the motor. The effect of the improved voltage-based flux-weakening control method was verified through simulations and experiments. As a result, the voltage errors were reduced by approximately 2.16% compared to the general method. Moreover, the current used in the field-weakening control region was reduced by up to 27.17% under the same torque condition.

Data-driven systematic parameter identification of an electrochemical model for lithium-ion batteries with artificial intelligence

Electrochemical models are more and more widely applied in battery diagnostics, prognostics and fast charging control, considering their high fidelity, high extrapolability and physical interpretability. However, parameter identification of electrochemical models is challenging due to the complicated model structure and a large number of physical parameters with different identifiability. The scope of this work is the development of a data-driven parameter identification framework for electrochemical models for lithium-ion batteries in real-world operations with artificial intelligence, i.e., the cuckoo search algorithm. Only current and voltage data are used as input for the multi-objective global optimization of the parameters considering both voltage error between the model and the battery and the relative capacity error between two electrodes. The multi-step identification process based on sensitivity analysis increases the identification accuracy of the parameters with low sensitivity. Moreover, the novel identification process inspired by the training process in machine learning further overcomes the overfitting problem using limited battery data. The data-driven approach achieves a maximum root mean square error of 9 mV and 12.7 mV for the full cell voltage under constant current discharging and real-world driving cycles, respectively, which is only 17.9% and 42.9% of that of the experimental identification approach.

Parameter identification method for lithium-ion batteries based on recursive least square with sliding window difference forgetting factor

In order to ensure battery management system (BMS) operating safely and reliably, it is of critical importance to accurately identify lithium-ion battery model parameters. A recursive least square parameter identification approach is proposed by variable forgetting factor with the difference between open circuit and terminal voltages in sliding window mode (SDFF-RLS). The contributions of the approach include: Firstly, a novel discrete form of second-order resistance-capacitance (RC) model for lithium-ion battery is derived by the difference between open circuit and terminal voltages. Secondly, self-adaptive selection of sliding window size is applied according to working conditions, which effectively response to data perturbations. On this basis, forgetting factor is obtained by utilizing mean square value of the difference between open circuit and terminal voltages within sliding window, which strengthens the amount of information available from new data. Besides, the model parameters are counter-solved using recursive least square (RLS) approach. Thirdly, the effectiveness of the proposed approach is validated by comprehensive experiments on three different working conditions. The experimental results demonstrate that the approach has the following strengths: (1) self-adaptive selection of sliding window size is able to improve efficiency of data utilization; (2) taking sliding window mode and the difference between open circuit and terminal voltages as the basis for forgetting factor contributes to promoting convergence speed of parameters; (3) the proposed SDFF-RLS algorithm makes it possible to identify the parameters accurately while keeping the terminal voltage error within a relatively narrow range.

Control method of applied voltage for fabricating micro-pattern using near-field electrohydrodynamic direct-writing technology

In the process of fabricating micro-patterns using the near-field electrohydrodynamic (EHD) direct-writing technology, the voltage error and the dynamic responsiveness of applied voltage have an effect on the jetting shape and the direct-writing pattern. To solve these problems, an iterative control method was proposed to compensate the voltage error between the expected voltage and the actual voltage of the near-field EHD direct-writing setup, which was controlled by the idea of iteration, and a control method based on feedforward and feedback was proposed to enhance the dynamic response of applied voltage in the process of the near-field EHD direct-writing micro-pattern, which was controlled by the idea of the feedforward and feedback. Software algorithms of the voltage error compensation and improved dynamic response of applied voltage were implemented in the near-field EHD direct-writing setup. The experimental results show that the proposed method of voltage error compensation can effectively improve the precision of applied voltage and the presented control method of improving dynamic response capability can effectively shorten the delay time of applied voltage. Finally, two groups of contrast experiments were carried out in the near-field EHD direct-writing equipment, and the experimental results demonstrate that the proposed control methods can effectively improve the jetting shape and the quality of the direct-writing micro-pattern.

Intelligent Multiobjective NSBGA-II Control of Power Converters in DC Microgrids

In this article, we develop a novel multiobjective controller to regulate the power converters of a class of dc microgrids connected to nonlinear constant power loads and linear resistive loads. The suggested control approach uses the nondominating sorting binary genetic algorithm (NSBGA-II) to directly design the on/off switching signal of the converters without using the pulsewidth modulation technique. The multiobjective controller minimizes the tracking error of the dc bus voltage and at the same time tries to reduce the total number of switching actions. Thereby, the developed controller tracks the desired reference with a reduced converter switching action and power loss by using a proper Pareto solution. Moreover, by employing the NSBGA-II algorithm, it is feasible to involve the switching frequency in the design procedure to enhance the performance. Exploiting the binary genetic algorithm instead of the conventional genetic algorithm (GA) turns a continuous surface searching into a binary one, which not only makes it more compatible with the nature of the power converter control but also decreases the online computational burden. To illustrate the superiority of the proposed approach, real-time OPAL results are provided.

Design and Analysis of Multi-Device Interleaved Boost Converter Driving an Induction Motor

Electric vehicles are the future of mobility solutions. The electric vehicles are driven by an electric motor with the help of a power electronic interface. The power electronic interface needs to be designed in an efficient way both in mechanical and electrical aspects. This paper proposes the concept of design, simulation and analysis of a 10 kW Multi-Device Interleaved DC-DC Boost Converter (MDIBC) to drive a 4 kW Induction Motor. The motor is driven from the MDIBC through an inverter with SPWM technique. The variation in DC link voltage due to motor is controlled and stabilized to give a constant DC of 400 V. MDIBC consists of semi-controlled switches topology excited by Phase Shifted PWM technique to reduce the ripple current in interleaving inductors. The dual loop control methodology using PI controller is adopted to reduce the ripple in input inductor current and DC link voltage. The open loop simulation and closed loop simulation are done in MATLAB Simulink environment. The simulation results show that the overshoots and steady state error in inductor currents and output voltage are reduced in addition with reduction in current and voltage ripples.

Modeling of the Rail Type Electromagnetic Launch System With Superconducting Pulsed Power Supply Considering Resistance

In the rail type electromagnetic launch system, when the projectile accelerates in orbit, it is affected by not only the electromagnetic force, but also various resistances, such as sliding friction, air resistance and so on. These resistances affect the characteristics of the pulsed power supply (PS). Nowadays, in order to analyze and design the pulsed PS conveniently, researchers usually ignore the influence of various resistors, and use fixed loads including resistors and inductors to simulate the rail load module, which will cause large errors. So, the mathematical model of the orbital electromagnetic launch system with superconducting is established, including the pulsed PS model with the multiple high-temperature superconducting pulsed power transformer (HTSPPT) and the dynamic rail load model considering various forces. The simulation results indicate that the dynamic rail load model considering various forces can reduce the error of capacitor voltage, load current and other parameters, the features of pulsed PS can be studied more precisely.

Application of Automatic Code Generation Based on PSIM in Digital Control BUCK Circuit

In recent years, digital control has been widely used in the field of power electronics. Automatic code generation, as a graphical software writing method, has brought great convenience to the writing of electronic control software due to a series of advantages such as easy to write and easy to simulate and verify. Simcoder in PSIM is an automatic code generation software that enables the automatic generation of complete DSP control program projects. In this article, the current and voltage loops of a typical average current controlled BUCK circuit are designed as an example, and the use of PSIM and Simcoder is described in detail. Based on the main circuit and Matlab parameter calculations, the model is simulated and the self-generated code is downloaded into the TMS320F28335 to test the output voltage and current of the hardware circuit. The circuit output has the advantage of stable output voltage with small steady-state error and low voltage overshoot, demonstrating the practicality and efficiency of automatic code generation on this project.

Simple Capacitor Voltage Balancing for Three-Level NPC Inverter Using Discontinuous PWM Method With Hysteresis Neutral-Point Error Band

Three-level neutral-point-clamped (3L-NPC) voltage source inverters are widely used in many low- and medium-power applications. However, the 3L-NPC inverter has an inheritance issue of the neutral-point (NP) voltage unbalancing due to the deviation of dc-link capacitor voltages causing distortions for the output waveform quality. Generally, discontinuous pulsewidth modulation (DPWM) is used to diminish the stress on power transistors and prolong their lifespan, however; it is not capable of solving the issue of NP voltage unbalancing. Therefore, this article proposes a simple voltage balancing control based on DPWM with a hysteresis NP voltage band. The balancing control method is only activated once the capacitance voltage error exceeds the hysteresis band by generating a momentary offset on the opposite direction of the DPWM offset. In this way, both top and bottom capacitance voltages will converge within a predefined error band. Various hysteresis error bands are investigated by analyzing the power losses, total harmonic distortions, and common mode voltage. The advantages of this proposed method are its simplicity and ease of control while maintaining the features of DPWM. The effectiveness of the proposed method is validated using simulation and experimental results.

A novel control approach for harmonic compensation using switched power filter compensators in micro‐grids

This paper presents a new harmonic compensation system using filter compensator devices in a hybrid micro-grid (MG). In order to reduce the amplitude of current and voltage harmonics, two separate systems, including a switched power filter compensator (SPFC) and a switched active power filter (SAPF) have been used in the AC side of the MG. The gain coefficients of both SAPF and SPFC modules (with different controllers) using meta-heuristic algorithms are optimized and applied for three harmonic loops of voltage, current and controller error to obtain an optimal response for reducing the harmonic amplitude of current and voltage. The objective function, containing controller error, current and voltage harmonics is defined and followed by the optimized coefficients for the PID controller of each of the SAPF and SPFC modules. To find the best answer to this problem, the related gains using four meta-heuristic algorithms, including artificial bee colony (ABC), particle swarm optimization (PSO), harmonic search (HS), and differential evolution (DE) are optimized and compared. The simulation results show that using the proposed method, the total harmonic distortion of the system can be significantly reduced.

Determining the electrochemical transport parameters of sodium-ions in hard carbon composite electrodes

Sodium-ion batteries offer advantages over conventional Li-ion batteries, including cost and safety. However, much less is known about their operation and performance properties, particularly at the anode. The electron and ion transport in the active materials and composite electrode significantly impact battery performance. Understanding the changes in transport properties as a function of state-of-charge and state-of-health is essential for effective electrode design and performance assessment. In this work, the resistivity and diffusivity of sodium transport in hard carbon composite electrodes are studied at different states-of-health, using Galvanostatic Intermittent Titration Technique (GITT), Electrochemical Impedance Spectroscopy (EIS), and Electrochemical Potential Spectroscopy (EPS) in a stable 3-electrode test cell configuration. The reference electrode eliminated some voltage errors arising from the overpotentials on the counter electrode. The resistance contributions from the surface electrolyte interface, electrolyte transport in the electrode pores, and the charge transfer resistance are extrapolated from the impedance measurements and the diffusion coefficient from the GITT and EPS. The different techniques indicate similar trends in the diffusion coefficient during sodiation, desodiation, and ageing, although different orders of magnitude were observed between the EPS and GITT data. The accuracy of the parameters calculated using the different electrochemical techniques is discussed in detail.