Reactive Power Signal Research Articles

A gear fault diagnosis method based on reactive power and semi-supervised learning

Abstract In gearbox gear fault diagnosis based on motor current signals, the gear fault characteristic frequency component is often overshadowed by the fundamental frequency component of the current. In addition, the complex working conditions during actual production and use make it difficult to collect gear operation monitoring data containing labeled feature information. To address the above problems, a semi-supervised learning method based on reactive power signals is proposed for gear fault diagnosis of gearboxes. First, the method utilizes the Hilbert transform to process the current signal of the drive motor in the mechanical system, from which the reactive power is constructed. Then, the reactive power signal is analyzed by spectral analysis as a basis for gear fault diagnosis. Subsequently, the GAF-CNN-MTDL(Gramian angular field—convolutional neural network-mean teacher deep learning) fault diagnosis model is proposed to convert the reactive power signal into a two-dimensional image by using the GAF, and the semi-supervised training method of the average teacher is applied to input the fault dataset into the gear fault diagnosis model which is based on the CNN as the main backbone after the fault dataset has been divided into the labeled and the unlabeled dataset in accordance with a certain ratio. Finally, the gear fault dataset is used for method validation. The experimental outcomes demonstrate the method’s proficiency in effectively emphasizing the fault feature information pertaining to the gear part, and the introduced GAF-CNN-MTDL fault diagnosis model enables the utilization of a minimal number of labeled samples to achieve highly accurate gear fault diagnosis.

A Cross-Power Spectral Density Method for Locating Oscillation Sources Using Synchrophasor Measurements

This paper proposes a new measurement-based approach for locating the sources of forced or poorly damped oscillations in a power system. The approach is based on the concept of cross-correlation in the frequency domain known as cross-power spectral density (CPSD). CPSDs of synchronized voltage magnitude and voltage angle versus active power and reactive power signals obtained by phasor measurement units (PMUs) are computed using fast Fourier transform. The largest positive imaginary part of a CPSD is used as an indicator of the oscillation source. The type of an oscillation source is determined by comparing the spectral densities of active and reactive power. In addition, preprocessing of the signals is performed via variational mode decomposition for extracting the dynamic component of the signals. The proposed approach was able to successfully identify all submitted test cases in the IEEE-NASPI Oscillation Source Location Contest. Several case studies presented in this paper highlight the advantages of the proposed approach compared to the state-of-the-art dissipating energy flow method.

Current References Limitation Method Considering Voltage and Current Maximums for STATCOMs Providing Simultaneously Reactive Power and Current Harmonics

This article focuses the analysis on a static synchronous compensator (STATCOM) that operates simultaneously providing reactive power and several current harmonics of different frequencies (active power filter or active impedance functionalities). Being more specific, this article proposes a general limitation method that reduces the current references of the STATCOM whenever the current or voltage limit of the converter is exceeded. In a realistic operation scenario, the STATCOM’s operator may ask for certain reactive power and harmonic current reference signals, and depending on the actual grid voltage conditions (unbalances and harmonics), the required converter voltage and current may exceed their limit. Consequently, the proposed limitation method protects the STATCOM system from malfunctioning when the voltage limit is exceeded and from damages when the current limit is exceeded. Hence, this article first proposes a limitation method oriented to a STATCOM that operates solely exchanging reactive power. Second, the limitation method is generalized for a STATCOM that controls reactive power as well as several current harmonics. Then, simulation and experimental results are provided to validate the proposed limitation method, under a rich variety of working conditions. Finally, a conceptual analysis of the method is carried out confronting it against several possible limitation alternatives.

High performance decoupled active and reactive power control for three-phase grid-tied inverters using model predictive control

Finite control set-model predictive control (FCS-MPC) is employed in this paper to control the operation of a three-phase grid-connected string inverter based on a direct PQ control scheme. The main objective is to achieve high-performance decoupled control of the active and reactive powers injected to the grid from distributed energy resources (DER).The FCS-MPC scheme instantaneously searches for and applies the optimum inverter switching state that can achieve certain goals, such as minimum deviation between reference and actual power; so that both power components (P and Q) are well controlled to their reference values.In addition, an effective method to attenuate undesired cross coupling between the P and Q control loops, which occurs only during transient operation, is investigated. The proposed method is based on the variation of the weight factors of the terms of the FCS-MPC cost function, so a higher weight factor is assigned to the cost function term that is exposed to greater disturbance. Empirical formulae of optimum weight factors as functions of the reference active and reactive power signals are proposed and mathematically derived. The investigated FCS-MPC control scheme is incorporated with the LVRT function to support the grid voltage in fulfilling and accomplishing the up-to-date grid codes. The LVRT algorithm is based on a modification of the references of active and reactive powers as functions of the instantaneous grid voltage such that suitable values of P and Q are injected to the grid during voltage sag.The performance of the elaborated FCS-MPC PQ scheme is studied under various operating scenarios, including steady-state and transient conditions. Results demonstrate the validity and effectiveness of the proposed scheme with regard to the achievement of high-performance operation and quick response of grid-tied inverters during normal and fault modes.

Performance of a Shunt Active Power Filter for Unbalanced Conditions Using Only Current Measurements

Shunt active power filters (APFs) are used to address power quality issues such as harmonic distortion and power system imbalance. Many control systems for shunt APFs demand measurements of voltages and currents for the determination of instantaneous real and reactive power signals. From these signals, the reference currents of the shunt APF are calculated. A method of control for the APF shunt that only measures currents is proposed in this paper. The control method includes a modified technique to extract the positive and negative sequence components in the time domain. The reference currents in the shunt APF are calculated by the negative sequence components of the measured currents. Simulation and experimental results confirm the efficiency of this control scheme under ideal and non-ideal conditions.

A passive islanding detection method based on K-means clustering and EMD of reactive power signal

Abstract Distributed Generation (DG), though offers many advantages in making modern power systems smart ones, unveils many technical problems in terms of its control and operation. Islanding is one such problem which represents the loss of grid condition during which DG continues to feed the load. Due to its harmful effects, islanding has to be differentially detected from other power system transients in minimum possible time. Since the islanding event immediately affects the reactive power level in the system, such a reflection in reactive power signal at Point of Common Coupling (PCC) is utilized to extract the significant information regarding its occurrence. Hence a novel islanding detection method based on the signatures extracted from reactive power using Empirical Mode Decomposition (EMD) is proposed. The effectiveness of the proposed method is tested in different power system models developed in MATLAB/SIMULINK and the simulation results demonstrate the efficiency of the method in islanding detection within two cycles time Also, the results of various test cases of load parameter combinations evidence that this method has very near zero Non-Detection Zone (NDZ) with no effect on power quality. Further, the proposed method is validated through extensive simulations on IEEE 14 bus and applicability on PV systems are tested using modified IEEE 14 bus model. By suitably setting the detection threshold, the proposed method can distinguish islanding event from the non-islanding ones. For making the system autonomous, K means clustering is combined with EMD for the detection and classification of islanding from other power system transients.

Classification and Detection of Faults in Induction Motor using Dwt with Deep Learning Methods under the Time-Varying and Constant Load Conditions

This article proposed a method to detect the faults in multi-winding induction motor using Discrete Wavelet transform combined with Deep Belief Neural Network (DBNN). This technique relies on the instantaneous reactive power signal decomposition, from which detail coefficients and wavelet approximations are extracted which are termed as features. In order to obtain a robust diagnosis, this article proposed to classify the feature vectors extracted from DWT analysis of power signals using DBNN (Deep Belief Neural Network) to distinguish the motors state. Subsequently, in order to validate the proposed approach, a three phase squirrel cage induction machine is simulated under MATLAB software. To check the effectiveness of the proposed method of fault diagnosis the motor is simulated in different simulation environments like time varying load and constant load condition. Promising results were obtained and the performance of DBNN i.e. 99.75% accuracy. The proposed algorithm is compared with various other state-of-art methods and the comparison proves its robustness in diagnosing the fault in motors.

Islanding detection scheme for distributed generation systems using modified reactive power control strategy

The reactive power control (RPC)-based technique is a promising solution for islanding detection. The existing RPC-based islanding detection techniques have limitations of injecting unwanted reactive power during normal operation, almost same detection time for various percentage of unbalance in load and false detection of islanding for small disturbances when steady-state frequency is close to its threshold limit. To overcome the first limitation, the phase angle (θ) between the actual and nominal point of common coupling (PCC) voltage is added to the reference reactive power signal of the controller. The angle (θ) has zero value during normal system operation and increases prominently after islanding. To obtain proportionately faster detection for various percentage of unbalanced load conditions, the positive sequence PCC voltage is used in the detection scheme. Owing to major power imbalance between generation and load at steady state, the frequency may come close to its threshold. Under this situation, even a small disturbance will drag the frequency beyond threshold and false islanding detection will occur. To overcome this problem, discrete wavelet transform analysis of θ is considered in the decision-making criteria. The proposed scheme is tested for islanding conditions as well as under various non-islanding events.

Reactive Power Compensation in Electric Arc Furnaces Using Prediction Intervals

This paper proposes a probabilistic method to model the uncertainty of reactive power compensation by static VAr compensators (SVCs) in electric arc furnaces (EAFs). The time-varying characteristics of EAF accentuate the voltage fluctuations and produce flicker in power lines as well as neighboring loads. In order to solve this issue, quick and accurate response of SVC within a half-cycle ahead is required. This paper proposes a nonparametric approach based on lower upper bound estimation method to construct prediction intervals (PIs) for the reactive power in EAFs. Due to the nonlinear nature of reactive power signals in EAFs, a set of PIs are produced and combined to find an optimal aggregated PI. The proposed prediction method provides a faster-than-real-time monitoring of SVC, which aims at high speed and efficient reactive power compensation. In order to find the most satisfying PIs with high coverage probability and low average width, an optimization algorithm is developed to improve the training process of neural networks. The appropriate performance of the proposed method is examined on the practical data gathered from the Mobarakeh Steel Company, Iran.

New reactive power calculation method for electric arc furnaces

In this study main reactive power calculation methods are evaluated and compared. Furthermore, a novel simple method that can be used for calculation of reactive power in electric arc furnace (EAF) plants is proposed. The focus here is on their application in the control system of static VAr compensators (SVCs). SVC can compensate only the fundamental harmonic component. So the reactive power signal used in the control system should be sensitive only to the main harmonic component. In addition, the reactive power calculation should be fast enough that SVC can follow the quick changes of the load to mitigate the flicker efficiently. To compare various reactive power calculation methods and to evaluate the proposed method, the simulated known loads are used beside huge amount of practical data recorded in Mobarakeh Steel Company in Isfahan/Iran. Results show the proposed method has the best performance for SVC control system.

Improvement of Dynamic Behavior of Shunt Active Power Filter Using Fuzzy Instantaneous Power Theory

Dynamic behavior of the harmonic detection part of an active power filter (APF) has an essential role in filter compensation performances during transient conditions. Instantaneous power (p?q) theory is extensively used to design harmonic detectors for active filters. Large overshoot of p?q theory method deteriorates filter response at a large and rapid load change. In this study the harmonic estimation of an APF during transient conditions for balanced three-phase nonlinear loads is conducted. A novel fuzzy instantaneous power (FIP) theory is proposed to improve conventional p?q theory dynamic performances during transient conditions to adapt automatically to any random and rapid nonlinear load change. Adding fuzzy rules in p?q theory improves the decomposition of the alternating current components of active and reactive power signals and develops correct reference during rapid and random current variation. Modifying p?q theory internal high-pass filter performance using fuzzy rules without any drawback is a prospect. In the simulated system using MATLAB/SIMULINK, the shunt active filter is connected to a rapidly time-varying nonlinear load. The harmonic detection parts of the shunt active filter are developed for FIP theory-based and p?q theory-based algorithms. The harmonic detector hardware is also developed using the TMS320F28335 digital signal processor and connected to a laboratory nonlinear load. The software is developed for FIP theory-based and p?q theory-based algorithms. The simulation and experimental tests results verify the ability of the new technique in harmonic detection of rapid changing nonlinear loads.

Induction Motors Broken Rotor Bars Diagnosis Through the Discrete Wavelet Transform of the Instantaneous Reactive Power Signal under Time-varying Load Conditions

—In this article, a method based on the application of the discrete wavelet transform to the instantaneous reactive power signal, for diagnosing the occurrence of broken rotor bars ininduction motors operating under time-varying load conditions, is presented. This method is based on the decomposition of the instantaneousreactive power signal, from which wavelet approximation and detail coefficients are extracted. The energy evaluation of known bandwidths permits to define a fault severity factor. This method has been tested through the simulation of an induction motor using a mathematical model based on the winding-function approach. These simulation results are complemented by experimental tests conductedon an induction motor with several faulty rotors that can be interchanged and both simulation and experimental results have shown the effectiveness of the proposed method for broken rotor bars diagnosis in induction motors even under time-varying load conditions.

Design of Three-Phase Multi-Function Energy Meter Based on Measurement Chip RN8302

According to the development status of the energy meter, we put forward a design of a Three-phase multi-function energy meter based on professional measurement chip RN8302 and High-performance single chip microcomputer MSP430 in this article. It has the characteristics which are low-power consumption, high-precision and electricity anti-theft. It could also real-time measured current, voltage, active power, reactive power, power factor, over-voltage and over-current signal. The design also has the functions of surge prevention, lightning prevention and voltage protection, improving the overall safety and reliability of the system.Use the Low-voltage power line carrier communication, there is no need to build another peripherals,saving the cost of grid investment. The article also gave the overall design scheme and the main hardware principle Schematic.

On-line neuro-expert monitoring system for Borssele Nuclear Power Plant

A new method for an on-line monitoring system for the nuclear power plants has been developed utilizing the neural networks and the expert system. The integration of them is expected to enhance a substantial potential of the functionality as operators support.The recurrent neural network and the feed-forward neural network with adaptive learning are selected for the plant modeling and anomaly detection because of the high capability of modeling for dynamic behavior. The expert system is used as a decision agent, which works on the information space of both the neural networks and the human operators. The information of other sensory signals is also fed to the expert system, together with the outputs that the neural networks generate from the measured plant signals. The expert system can treat almost all known correlation between plant status patterns and operation modes as a priori set of rules.From the off-line test at Borssele Nuclear Power Plant (PWR 480 MWe) in the Netherlands, it was shown that the neuro-expert system successfully monitored the plant status. The expert system worked satisfactorily in diagnosing the system status by using the outputs of the neural networks and a priori knowledge base from the PWR simulator. The electric power coefficient is simultaneously monitored from the measured reactive and active electric power signals.

00/00814 Mechanism of lithium storage in low temperature carbon

An output feedback variable structure controller is designed for a thyristor-controlled series compensator (TCSC) in order to improve the damping characteristic of a power system. Physically measurable real power and reactive power signals near TCSC locations are used as the inputs to the variable structure controller. These input signals are employed to construct the switching hyperplane of the proposed variable structure TCSC controller. To demonstrate the effectiveness of the proposed variable structure TCSC controller, time-domain simulations are performed on a power system with a TCSC. It is concluded from the simulation results that system damping can be significantly improved by the proposed variable structure TCSC controller.

Design of an output feedback variable structure thyristor-controlled series compensator for improving power system stability

An output feedback variable structure controller is designed for a thyristor-controlled series compensator (TCSC) in order to improve the damping characteristic of a power system. Physically measurable real power and reactive power signals near TCSC locations are used as the inputs to the variable structure controller. These input signals are employed to construct the switching hyperplane of the proposed variable structure TCSC controller. To demonstrate the effectiveness of the proposed variable structure TCSC controller, time-domain simulations are performed on a power system with a TCSC. It is concluded from the simulation results that system damping can be significantly improved by the proposed variable structure TCSC controller.

Microprocessor-based active and reactive power measurement

A dual microprocessor-based system is designed and constructed to provide active and reactive power signals for control applications. The algorithm is based on the Fourier series technique. The design of the associated hardware and software is described, and the results of static and dynamic tests for various load changes are compared with standard measuring devices.

Suppression of self-excited oscillations in series-compensated transmission lines by excitation control of synchronous machines

The self-excited oscillations are likely to occur in highly series compensated transmission lines having low resistance, particularly in long-distance bulk-power EHV transmission lines. This paper proposes a measure to suppress the self-excited oscillations in which the excitation of a synchronous machine is controlled by a reactive power (Q) signal. This paper also discusses the theoretical analyses of this suppression and the results of testing using a small generator.