Voltage Phase Angle Research Articles

A comprehensive overview of the ADALINE method applied to rapid voltage sags detection in multi-motors drive systems

Several strategies have been developed for identifying power quality issues, monitoring them, and compensating for relevant disturbances. In this field, online estimate of amplitudes and phase angles of network voltages and currents is commonly used. The adaptive linear neuron (ADALINE)-based voltage sag detection algorithm with least mean square (LMS) adaptation allows for rapid convergence of estimate techniques based on artificial neural networks (ANN). This approach has the advantage of being straightforward to implement on hardware and based on simple calculations (essentially multiply and accumulate "MAC"). This paper gives a comparison of the performance of two ADALINE approaches ("with" and "without" error supervision) for detecting and estimating voltage dips. The described techniques and models of a two-coupled motor system were implemented in MATLAB/Simulink/SimPowerSystems to run simulations under various fault scenarios in order to create the three-phase voltage sag alarm signal. The simulation outcomes are presented and debated.

Sliding Mode Control of Two-Parameter Fourth-Order Chaos Model of Power System

As a complex nonlinear system, the power system in operation may experience chaotic oscillation which can threaten the stability of the system for unexpected events and uncertain factors. Initially, through considering factors such as the generators, the power system node voltage amplitude and phase angle, this brief establishes a fourth-order power system dynamic model and a two-parameter fourth-order power system mathematical model with load reactive power and mechanical input power as chaotic parameters to explore the mechanism and characteristics of chaos in the power system with the chaos theory. In addition, a sliding mode controller based on exponential reaching law for four state variables is designed, which is used to suppress the chaotic oscillation of the power system and guarantee the system can work stably at the target operating point within the chaotic parameter range. The designed controller can effectively suppress the chaos in power system and has an ideal performance.

Fully Distributed Second-Order Cone Programming Model for Expansion in Transmission and Distribution Networks

As the penetration level of distributed energy resources (DERs) is increasing at distribution level, the stakeholders at the distribution level are no longer pure consumers and they play a more active role. DERs can be used for transmission needs, and the technical impacts of them may propagate to the transmission level. To unlock the full benefits of the transmission-distribution coordination, this article proposes a second-order cone programming (SOCP) based expansion model for coordinated transmission and distribution networks by decomposition and parallelization. First, we formulate an SOCP-based subnetwork expansion problem related to voltage phase angle variables which is valid for both transmission and distribution networks. Then, a fully distributed decision-making framework is presented to solve the coordinated transmission-distribution expansion problem iteratively. Taking the total power output of each subnetwork as the interacting variable, we deploy the operation optimization of substations as the master problem and the expansion optimization of subnetworks as the subproblems in the modified iteration algorithm. The feasibility and optimality of the iteration are proved and numerically verified. Simulation results indicate the advantages of the coordinated transmission-distribution expansion which can prevent line congestion, decrease boundary power mismatches and accommodate more DERs.

Fault diagnosis algorithm for 3-phase passive rectifiers based on frequency-domain analysis for acceleration grid power supply in CFETR NBI system

The acceleration grid power supply (AGPS) is a crucial part of the Negative-ion Neutral Beam Injection system in the China Fusion Engineering Test Reactor, which includes a 3-phase passive (diode) rectifier. To diagnose and localize faults in the rectifier, this paper proposes a frequency-domain analysis-based fault diagnosis algorithm for the rectifier in AGPS. First, time-domain expressions and spectral characteristics of the output voltage of the TPTL-NPC inverter-based power supply are analyzed. Then, frequency-domain analysis-based fault diagnosis and frequency-domain analysis-based sub-fault diagnosis algorithms are proposed to diagnose open circuit (OC) faults of diode(s), which benefit from the analysis of harmonics magnitude and phase-angle of the output voltage. Only a fundamental period is needed to diagnose and localize exact faults, and a strong Variable-duration Fault Detection Method is proposed to identify acceptable ripple from OC faults. Detailed simulations and experimental results demonstrate the effectiveness, quickness, and robustness of the proposed algorithms, and the diagnosis algorithms proposed in this article provide a significant method for the fault diagnosis of other rectifiers and converters.

Flexible Step-Voltage Regulator for Unbalanced Distribution Networks

This article presents a new device named flexible step-voltage regulator (FSVR) based on a multiwinding transformer for unbalanced distribution networks. While the traditional step-voltage regulator (SVR) can be used to control the voltage magnitude only, the proposed device can be used to control both voltage magnitude and phase angle for each phase independently. This allows greater flexibility during voltage control in unbalanced distribution networks. A mathematical model of the FSVR is derived for implementation in unbalanced power flow analysis. Simulation studies for IEEE 13 bus and IEEE 34 bus test networks demonstrate the effectiveness of the FSVR over the traditional SVR in reducing real power losses and voltage imbalance in the distribution network. A scaled-down model of the FSVR is designed in the lab to demonstrate the proof of concept.

Distribution System State Estimation Algorithm with Improved Angular Reference Treatment

The efforts to build smarter grids with automatic control and intelligent functionalities for Distribution Systems (DSs) rely upon correctly determining the operational condition of these circuits, which is the purpose of a State Estimator (SE). DSs are complex and unbalanced networks, and in the last years several studies have evaluated the use of SEs in DSs. Despite some good theoretical results, one incorrect assumption is usually made: considering the Reference Bus (RB) to have balanced voltage phase angles. This paper proposes a three-phase state estimation algorithm that enables a correct treatment of the RB, which allows the determination of the unbalance at this bus as part of its iterative process, without relying on Phasor Measurement Units or external information that can be imprecise. Simulation results on IEEE 34 and 123 bus test feeders show the feasibility of the algorithm and the effects of the RB’s mistreatment on the SE accuracy.

1선 지락 사고시 배전선로 접지저항에 따른 유도전동기 토크 특성 변화에 관한 연구

Customers should be provided with good quality of electricity for stable load operation. Various failures can occur from substations and distribution lines to consumers. The most common failure in distribution lines is single-line ground fault. For distribution lines, grounding resistance is stipulated according to the length of the line and installation conditions. When single-line ground fault occurs, the magnitudes and phase angles of voltage and current change asymmetrically, resulting in unbalance. Three-phase induction motors among customer facilities are greatly affected by these voltage and current imbalances. Imbalance of current and voltage caused by a fault can have a great effect on the torque of the motor.BRIn this study, we analyzed how the pulsating torque of an induction motor appears according to the ground resistance of the line during single-line ground fault in a distribution line. It was confirmed that the higher the ground resistance of the distribution line, the lower the amplitude of the pulsating torque.

FFDSOGI-PLL-based DSTATCOM for Power Quality Enhancement

This paper proposes a control technique based on a frequency-fixed dual second-order generalized integrator phase-locked loop (FFDSOGI-PLL) for a Distribution Static Compensator (DSTATCOM). The goal is to mitigate power-quality (PQ) issues in a three-phase distribution network with a weak grid. The phase angle, symmetrical components, and positive sequence of supply voltages were estimated using the FFDSOGI-PLL even in a polluted grid environment. The active component magnitude of the fundamental load current was extracted using SOGI, which also reduces the PQ issues by generating compensating current. The effectiveness of the proposed FFDSOGI-PLL-based DSTATCOM was verified for functions like load balancing, power factor correction (PFC), harmonics reduction, etc. This was done in MATLAB/SIMULINK under steadystate and dynamic conditions. The simulation results show that the FFDSOGI-PLL-based control algorithm has faster convergence and quick dynamic response. Hence, the DSTATCOM performance is satisfactory for mitigating PQ issues with the FFDSOGI-PLL-based control technique.

Using Artificial Neural Network to Speed Up the Study of the State of Electrical Systems

The objective of this article is to use Artificial Intelligence (AI) method to study the state of electrical networks. The state of a network is described by its state variables, such as the voltage at the buses of a network or the currents flowing in the sections of a network. Power flow analysis is a basic tool for planning an electrical power system. Power flow is a non-linear problem. Therefore, the solution to this type of problem can be found using iterative numerical methods or Artificial Intelligence methods. In this paper, the method of Artificial Neural Network (ANN) has been used to predict voltage magnitudes and voltage phase angles of the I3E 14 bus system and a real network of Morocco ONEE 24 bus system. After testing several Neural Network models, it has been possible to propose an architecture with one hidden layer and Levenberg-Marquardt backpropagation as a training function. The results obtained by using this method have been positive and precise compared with the results obtained by the reference method Newton-Raphson. This means that this method can be used to get results in real time, in case of lack of data from the studied system and also where the deterministic methods do not converge.

Efficiency improvement of the air-cooled packaged water chiller unit with three-phase alternating current control technology

A three-phase alternating current control technology (TPACCT) was proposed and applied to the compressor of an air cooled packaged water chiller unit (ACH) to maintain the stable operation of the air-conditioning system and reduce energy consumption. The proposed TPACCT mainly uses a Single-Chip microcomputer to perform the calculations of the self-developed program, and uses a silicon controlled rectifier (SCR) component to trigger the voltage phase angle of ACH. Due to the drop of the controlled voltage, the current value is reduced, and the corresponding ACH active power also drops. This study attempts to reach the dynamic balance of voltage and current of the compressor in the air-conditioning system under heavy-load operating conditions to obtain the best control parameters. This study also conducted a long-term day-to-day experiment of the compressor, mainly analyzing the cumulative energy consumption of ACH when operating in a single zone, and comparing the energy-saving effect of the proposed TPACCT with the case operating under mains electricity. Results show that the cumulative energy consumption is about 5% lower than that of mains electricity mode. Different energy-saving strategies are provided for air-conditioning systems that need to be maintained under heavy-duty operation for a long time.

Economic dispatch of active distribution network considering admissible region of net load based on new injection shift factors

With the increase of proportion of renewable energy sources, the variability and uncertainty in active distribution network (ADN) are significantly increased, which brings more challenges on ADN operation. To address this problem, a new economic dispatch model for ADN considering admissible region of net load is proposed. An affine policy is applied to allocate the net load disturbance to generators, and an affinely adjustable robust optimization model is established. The proposed model co-optimizes the generator base points, participation factors, reserve and admissible region based on preemptive goal programming. To facilitate the solution, the new injection shift factors (ISFs) are proposed. The proposed ISFs are derived based on a linear power flow expression which considers both voltage magnitude and phase angle, and they especially fit for ADN. The proposed ISFs can eliminate the uncertain equality constraints, then the proposed model can be easily recast as a deterministic problem that can be efficiently solved by a commercial solver. The validity and effectiveness of the proposed model are illustrated on a modified IEEE 33-node distribution network and a modified IEEE 69-node distribution network.

Research on splitting criterion of the power system with wind-storage system integrated

As the last defense line of the power system, splitting control is of great significance to the safety and stability of power grid. However, with the increasing penetration of wind power, photovoltaic, energy storage and other power electronic power sources in the grid, the transient stability of the power system is seriously affected. Meanwhile, the operation characteristics of power electronic power source are very different from that of traditional synchronous generator. The integration of power electronic power source affects the characteristics about out-of-step oscillation, and ultimately affects the safe implementation of splitting control. In order to split the power system more adaptably, a new splitting control method is proposed in this paper. Firstly, the equivalent external characteristic model of the wind-storage system is derived, and the influence of the wind-storage system on the out-of-step center is analyzed. Secondly, according to the out-of-step oscillation characteristics when the wind-storage system is integrated, line weak-connection index(LWcI) is proposed to evaluate the stability margin of the branch. Thirdly, according to the trajectory difference about LWcI-Δθ in all branches when it is unstable and stable, we propose the splitting control criterion based on LWcI-Δθ and the phase angle of voltage about the branch. Finally, the effectiveness and adaptability of the splitting criterion proposed in this paper are verified by simulation analysis of the improved IEEE-39 system.

Development of electrical power transmission system linear hybrid state estimator based on circuit analysis techniques

Electrical power systems are in rapid evolution, necessitating advanced monitoring, control, and protection aspects connected to an accurate knowledge of the state of the grid. State estimation is one-way such knowledge could be achieved by estimating voltage magnitudes and phase angles at all buses in electrical networks. Electric utilities are deploying advanced Phasor Measurement Units (PMUs) in grids with conventional Remote Terminal Units (RTUs). Integrating measurements from the two types of devices for state estimation is inevitable. In the past, hybrid state estimators have been based on nonlinear models associated with various challenges, such as using nonlinear state estimation algorithms with high computational time, slow convergence, and initialization problems. For this reason, there is a need to develop hybrid linear mathematical models that eliminate issues posed by nonlinearity models. This paper presents a novel mathematical formulation that gives a linear measurement model for hybrid state estimation based on the standard steady-state branch model. The developed model is then tested in MATLAB using three standard transmission test cases (IEEE 14-BUS, IEEE 30-BUS, and IEEE 57-BUS) with available measurements subjected to different errors. Notably, the performance of the developed model based on two different state estimation algorithms, Weighted Least Square (WLS) and Weighted Least Absolute Value (WLAV), are compared. The simulation results obtained for voltage magnitudes and phase angles for all buses in the three scenarios compare well with reference values. Using Normalized Cumulative Error (NCE) as a performance index, the developed linear measurement model gives less cumulative error than the conventional nonlinear model. With measurements subjected to bad data (such as negative values for measured variables), the developed model performs better using WLAV than the WLS estimation algorithm. The computational time for all three networks is notably less when using the WLAV algorithm than the WLS algorithm.

Matlab Applications for Power Flow Analysis

In this paper a brief review has been done on MATLAB Applications for power flow analysis with realistic loads . The classical algorithms for power flow Gauss Seidel and Newton Raphson method has been implemented under MATLAB codes, Simpower, simulink ,and fuzzy logic environment. Simscape™ Electrical™ can perform a power-flow, or load-flow, analysis for an AC, DC, or mixed AC and DC electrical power transmission system modeled using the Simscape three-phase electrical domain. A load-flow analysis allows to determine the voltage magnitudes, voltage phase angles, active power, and reactive power of the electrical system in steady-state operation. For a given steadystate operating point, the load-flow data reveals the:Voltage magnitude and voltage phase angle at each bus,Active and reactive power generation for each generator that supplies the grid,Active and reactive power that flows to each load that places demand on the grid , the data is used to determine ideal operating conditions or estimate the response of your system to hypothetical situations.

Recycling Newton–Krylov algorithm for efficient solution of large scale power systems

Power flow calculations are crucial for the study of power systems, as they can be used to calculate bus voltage magnitudes and phase angles, as well as active and reactive power flows on lines. In this paper, a new approach, the Recycling Newton–Krylov (ReNK) algorithm, is proposed to solve the linear systems of equations in Newton–Raphson iterations. The proposed method uses the Generalized Conjugate Residuals with inner orthogonalization and deflated restarting (GCRO-DR) method within the Newton–Raphson algorithm and reuses the Krylov subspace information generated in previous Newton runs. We evaluate the performance of the proposed method over the traditional direct solver (LU) and iterative solvers (Generalized Minimal Residual Method (GMRES), the Biconjugate Gradient Stabilized Method (Bi-CGSTAB) and Quasi-Minimal Residual Method (QMR)) as the inner linear solver of the Newton–Raphson method. We use different test systems with a number of busses ranging from 300 to 70000 and compare the number of iterations of the inner linear solver (for iterative solvers) and the CPU times (for both direct and iterative solvers). We also test the performance of the ReNK algorithm for contingency analysis and for different load conditions to simulate optimization problems and observe possible performance gains.

Shunt active power filtering with reference current generation based on dual second order generalized integrator and LMS algorithm

For reducing the carbon emissions from utility sector, it is important to improve the efficiency of network and electrical loads. Current harmonics are responsible for the degradation of the system as well as load efficiency. The propagation of current harmonics into the grid can be prevented with shunt active power filters (SAPFs). Reference current generation (RCG) is critical for SAPF operation. This paper presents the control of SAPF with RCG based on least mean square (LMS) and dual second order generalized integrator (SOGI). The two SOGIs separately process grid voltage and load current to determine the respective fundamental in-phase component (FPC) and fundamental quadrature component (FQC). FPC and FQC of grid voltage are used to determine the phase angle of fundamental grid voltage. With LMS algorithm the distorted current can be decomposed into fundamental active (FA), fundamental reactive (FR) and harmonic components. However, the dynamic response of LMS based estimation is poor when the same learning rate is used for all components. FQC of load current, estimated with SOGI, can be processed by LMS algorithm to separate FA and FR components. RCG can be implemented with the computed phase angle and FA component. In LMS algorithm, as only two components are to be separated, two different learning rates can be easily specified. This results in faster dynamic response for LMS algorithm and has consequent positive impact on RCG and SAPF operation. Also, the computational complexity is considerably reduced. The performance of SAPF with the proposed extraction algorithm is analyzed for different operating conditions and comparative analysis is carried out.

A fast voltage sag detection method based on second order generalized integrator and delay transform method

Voltage sag is an important problem that causes power quality degradation, and the premise of solving the problem of voltage sag is to detect the depth of voltage sag accurately and quickly. Based on the past research on voltage sag detection, this paper proposes a voltage sag detection method that combines second order generalized integrator and delay transform method. Use the second order generalized integrator as the phase-locked loop and filter to extract the grid voltage phase angle and grid voltage fundamental wave, and then construct the quadrature component of the grid voltage fundamental wave by delaying a small angle. Square and square root calculation to quickly obtain voltage sag information. At the same time, the voltage sag information is used as the control signal of the second order generalized integrator to solve the problem of long response time of the second order generalized integrator. In this way, the interference of harmonics, zero-sequence and negative-sequence voltages on sag detection is excluded, and the rapidity of sag detection is ensured. Simulation and experiments show that the method can accurately and quickly determine the voltage sag depth.

Analysis of Passive Islanding Detection Techniques for Double Line Fault in Three Phase Microgrid System

Microgrids are able to dispatch power to distribution systems with the advancement of power electronics-based inverters. As per IEEE-1547-2018 standards, Microgrid has to maintain voltage of 0.88 < V < 1.1 p.u (per unit) and frequency of 58.8< f_0 < 61.2 Hz and detect un-intentional faults in less than 2 seconds to bring Microgrid into islanding mode seamlessly. Unless these faults are detected and Microgrid is islanded, the system stability cannot be maintained and Microgrid cannot feed the connected loads. To detect these unsymmetrical faults, to bring the Microgrid to islanding mode and to be stable during non-islanding cases like loads switch on and throw off at Point of Common Coupling (PCC), a passive islanding detection method, Rate Of Change Of Voltage Phase Angle (ROCOVPA) is proposed. The methodology is simple. First, the voltage phase angle of generator bus and the grid is monitored. Then, absolute value is found and finally differentiated to get ROCOVPA and detect islanding. Also, this technique is compared with the widely used method of Rate Of Change Of Frequency (ROCOF) at different percentage active and reactive power mismatches. It also avoids nuisance tripping so that Microgrid's stability is maintained. This method is tested for un-symmetrical double line fault, for islanding cases and switch on or throw off, for non-islanding cases with linear and non-linear loads. In this method, the power quality is also not affected because of no perturbations during testing and the Non-Detection Zone (NDZ) is almost zero. The proposed method is verified by simulating islanding and non-islanding conditions in MATLAB/Simulink and by comparing with ROCOF method and found effective.

Power Data Quality Improvement Through PMU Bad Data Detection Based on Deep Complex Network

Phasor Measurement Units (PMUs) enable the switching of devices in various power signal modes. A jitter or glitch in a signal cause bad data and also the PMU data will spike due to a disturbance or a transmitting data mistake. As a result of these difficulties, PMU data suffer from different degrees of data quality problems. To detect the bad data, several approaches have been already utilized however it provides some disadvantages such as complexity due to the utilization of dual identical systems separately for analyzing both real and imaginary values of PMU. Likewise, the bad data due to the topology variations have not been optimally identified. To overcome these issues a Robust Bad Data Detection Technique has been proposed in which a Deep complex neural network (DCNN) is incorporated to process the complex number having both voltage magnitude and phase angle. Deep complex Networks are also proposed with the conjunction of topology processor and AC state estimator (SE). Moreover, instead of Batch normalization weight normalization is altered due to the fusion of recurrent timestamps for measuring voltage magnitude and phase angle. The comparative analysis is done in terms of accuracy , Bad data detection capability , bad data detection range and running time with existing techniques The proposed technique provides accuracy of about 99.5% which is higher than the existing techniques.

Partial Discharge Diagnosis in GIS based on Pulse Sequence Features and Optimized Machine Learning Classification Techniques

Partial discharge (PD) diagnostics in Gas Insulated Switchgear (GIS) is important for reliable and secure operation of electrical utilities. Different techniques were used for PD diagnosis in GIS. In this work, PD diagnosis in GIS is proposed based on PD pulse sequence. PD pulse sequence only requires the measurement of PD phase appearance and its corresponding instantaneous voltage. The PD diagnosis of various defect types is implemented using five optimized machine learning classification techniques: decision tree classification, ensemble methods, k-nearest neighbouring, Discriminant analysis, and Naïve Bayes classification. The features used for PD pulse sequence are the voltage change and phase angle change between successive PD pulses. Three scenarios are proposed for predicting the defect types in GIS. The first scenario is built based on the extracted features for two successive PD pulses, the second scenario is built based on the extracted features for three successive PD pulses, while the last scenario is built based on the extracted features for four successive PD pulses. The results illustrate the superior detecting accuracy of the second scenario with the proposed five ML classification techniques. The optimized ML classification techniques are implemented and carried out based on MATLAB software package. The ensemble classification method exhibited the highest accuracy for PD-based diagnosis in GIS with an overall accuracy of 97.1%.