PMU Devices Research Articles

Fault Detection, Classification and Localization Along the Power Grid Line Using Optimized Machine Learning Algorithms

Distributed energy generation increases the need for smart grid monitoring, protection, and control. Localization, classification, and fault detection are essential for addressing any problems immediately and resuming the smart grid as soon as possible. Simultaneously, the capacity to swiftly identify smart grid issues utilizing sensor data and easily accessible frequency and voltage data from PMU devices is a prerequisite of this task. Therefore, this paper proposes new methods using fuzzy logic and adaptive fuzzy neural networks as well as machine learning and meta-heuristic algorithms. First, line voltage is used by a fuzzy thresholding method to estimate when a transmission line defect would develop in less than 1.2 clock cycles. Next, features taken from frequency signals in the real-time interval are utilized to classify the type of error using machine learning systems (decision tree algorithm and random forest algorithm) optimized with wild horse meta-heuristic algorithm. To locate the precise problem location, we finally use a neural fuzzy inference system that is capable of adapting to new data. We employ a simulated power transmission system in MATLAB to test our proposed solutions. Mean square error (MSE) and confusion matrix are used to assess the efficiency of a classifier or detector. For the decision tree algorithm method, the detector attained an acceptable MSE of 2.34e−4 and accuracy of 98.1%, and for the random forest algorithm method, an acceptable MSE of 3.54e−6 and accuracy of 100%. Furthermore, the placement error was less than 153.6 m in any direction along the line.

Methods for optimal placement of PMU devices to assess the stability of the Azerbaijan power system

This article discusses the method of integer linear programming, a method based on estimating the rate of change in operating conditions when conditions become more complex or disturbances occur. In the first method, the criterion for placing PMUs in the energy system of Azerbaijan is to ensure full observability; the second method examines the rate of change in the relative angles of synchronous generators under large disturbances. Based on the obtained parameters, weak nodes are identified where it is necessary to install PMU devices.

A multi-objective optimal PMU placement considering fault-location topological observability of lengthy lines: A case study in OMAN grid

Using PMU data, the fault location algorithm has gradually been trended in the literature. Besides the optimality of the algorithm, the fault location application should directly receive the data of the faulty line from the PMU devices to have the best performance; using state estimation to create the input data of fault location algorithm not only wastes the golden time, but it also decreases the reliability. Given, it is not possible to install PMU devices in all buses of the system, it is better to install them for the lengthy lines having the most probability of the failure. Although various approaches have been presented in the literature to solve optimal PMU placement (OPP), the fault location observability has less been considered in OPP formulation Accordingly, this paper considers a new index, named fault location observability (FLO), as a second objective function in its OPP formulation. Using this index, in addition to minimizing PMU installation cost, the number of lengthy lines being directly observable are also maximized. Using the ɛ-constraint method, the multi-objective problem has been solved several times as single-objective optimizations. Given the constraints and objective functions are linear, the work presents a mixed integer linear programming (MILP) formulation for the placement problem. Using the GAMS under CPLEX solver, the global optimal solutions of MILP model corresponding to the various ɛ-constraints yields to a Pareto optimal front. The simulation results, next, implements the proposed method on the IEEE 300 bus test system as well as the practical OMAN grid to prove the effectiveness of the proposed method.

A Novel Security Methodology for Smart Grids: A Case Study of Microcomputer-Based Encryption for PMU Devices

Coordination of a power system with the phasor measurement devices (PMUs) in real time on the load and generation sides is carried out within the context of smart grid studies. Power systems equipped with information systems in a smart grid pace with external security threats. Developing a smart grid which can resist against cyber threats is considered indispensable for the uninterrupted operation. In this study, a two-way secure communication methodology underpinned by a chaos-based encryption algorithm for PMU devices is proposed. The proposed system uses the IEEE-14 busbar system on which the optimum PMU placement has been installed. The proposed hyperchaotic system-based encryption method is applied as a new security methodology among PMU devices. The success of results is evaluated by the completeness of data exchange, durations, the complexity of encryption-decryption processes, and strength of cryptography using a microcomputer-based implementation. The results show that the proposed microcomputer-based encryption algorithms can be directly embedded as encryption hardware units into PMU and PDC devices which have very fast signal processing capabilities taking into considerations the acceptable delay time for power system protection and measuring applications and quality metering applications which is 2 ms and 10 ms, respectively. While proposed algorithms can be used in TCP or UDP over IP-based IEEE C37.118, IEC 61850, and IEC 61850-90-5 communication frameworks, they can also be embedded into electronic cards, smartcards, or smart tokens which are utilized for authentication among smart grid components.

Estimation of Modal Parameters for Inter-Area Oscillations Analysis by a Machine Learning Approach with Offline Training

An accurate monitoring of power system behavior is a hot-topic for modern grid operation. Low-frequency oscillations (LFO), such as inter-area electromechanical oscillations, are detrimental phenomena impairing the development of the grid itself and also the integration of renewable sources. An interesting countermeasure to prevent the occurrence of such oscillations is to continuously identify their characteristic electromechanical mode parameters, possibly realizing an online monitoring system. In this paper an attempt to develop an online modal parameters identification system is done using machine learning techniques. An approach based on the development of a proper artificial neural network exploiting the frequency measurements coming from actual PMU devices is presented. The specifically developed offline training stage is fully detailed. The output results from the dynamic mode decomposition method are considered as reference in order to validate the machine learning approach. Some results are presented in order to validate the effectiveness of the proposed approach on data coming from recordings of real grid events. The main key points affecting the performance of the proposed technique are discussed by means of proper validation scenarios. This contribution is the first step of a more extended project whose final aim is the development of an artificial neural networks (ANN) architecture able to predict the system behavior (in a given time span) in terms of LFO modal parameters, and to classify the contingencies/disturbances based on an online training that has memory of the passed training samples.

Research on the possibilities of solution of the monitoring projects of the railway power supply system

The object of research is the monitoring parameters of the railway power supply system. PMUs power supply system recording devices are the basis of the WAMS transient monitoring system. An important aspect of WAMS elements is data synchronization, which is technologically ensured using satellite-based orientation systems.The study used a system analysis, a systematic approach to the problem, as well as methods of synchronization and systematization of data.Methods for solving problems of monitoring processes of the railway transport power supply system are investigated. Diagnostic functions, currently implemented using PMUs, WAMS, are actually concentrated and limited in the areas of power supply, and include monitoring of low-frequency fluctuations of system parameters. As well as identification of the emergency mode and determining the location of damage in the facilities of the power supply system. In addition, to use these functions, at the same time, even with the modern development of systems, it is necessary to ensure diagnostic functions on the scale of power systems, with minimizing the participation and influence of a person in the formation of the results of the assessment of modes. This is due to the fact that there is a need to expand diagnostic functions according to the parameters of the objects of the power supply system, the monitoring systems used, which will solve the problems of operational dispatch control of these objects.This ensures the resolution of operational dispatch tasks, assessing and predicting the state in the processing of information, its synchronization and systematization. As well as monitoring and synchronizing measurements over time, which significantly increases the level of operational dispatch control of the operating modes of objects of the power supply system. Compared with similar technologies, the new ones based on the use of PMU devices for data collection have an advantage over traditional SCADA technologies, which measured system objects without time synchronization.

An Adequate Mathematical Model of an Ultrahigh-Voltage Overhead Transmission Line Using Synchronized Phasor Measurements

Methodical errors of simplified models of steady state of ultrahigh-voltage overhead lines have been investigated. It has been established that the error of these models does not correspond to the accuracy of PMU measurements. A method and algorithm for improving the accuracy of modeling by the mode parameters collected from PMU devices have been proposed. Chain model of an overhead line proposed in this paper takes actual weather conditions into account. Application of the proposed approach makes it possible to increase the accuracy of calculating steady state and state estimation and identifying the electrical parameters of the overhead line. This improves the effectiveness of transmission line control in real time. Proposed method was applied for existing 500-kV ultrahigh-voltage overhead line.

Real-time multi-state classification of short-term voltage stability based on multivariate time series machine learning

The analysis of the significant amount of data collected by PMU devices in wide-area monitoring, protection and control (WAMPAC) applications has been a great challenge for assessing power system fast phenomena. This paper presents a novel methodology for real-time assessment of short-term voltage stability (STVS) under large disturbances with an approach based on data mining and machine learning. This methodology classifies off-line the power system stability in multiple operating states through the calculation of the maximal Lyapunov exponent and dynamic voltage indices. This allows identifying not only fast voltage collapses (FVC) but also fault-induced delayed voltage recovery (FIDVR) events. The multivariate time series data of the power system dynamic response are processed and transformed with a symbolic representation technique. This together with the operating state classification are used to train an intelligent machine based on Random Forest proposed for applying in real time to classify the post-disturbance operating state. This methodology is tested in the New England 39-bus system. The performance of the methodology to classify the STVS in real time was verified, obtaining a classification error less than 2% using a post-contingency data window of 0.58 s and less than one-third of all the bus voltage measurements. Results show the ability of the methodology to predict voltage stability problems, having enough time to carry out automatic control actions to prevent or mitigate problems.

Compliance Analysis of PMU Algorithms and Devices for Wide-Area Stabilizing Control of Large Power Systems

For the first time, IEEE Std. C37.118.1-2011 now provides metrics for PMU dynamic performance in terms of classes P and M filter designs. This paper attempts to determine whether fulfilling these requirements makes the PMU inherently well suited for stability control applications such as wide-area power system stabilizers (PSSs). In this aim, we considered two different frequency-adaptive approaches for class-P and -M compliance to ensure operation over a wide frequency range. The first is based on a finite-impulse response (FIR) with no overshoot in either the phase or the amplitude step responses, while the second is Kalman filter-based (EKF), which allows for a more refined out-of-band interference rejection at the cost of a phase step response with overshoot. These two approaches are benchmarked against Hydro-Quebec`s existing PSS requirements and the conclusion is that the total vector error-based response time is not indicative of the phase lag within the frequency band of interest, nor of the 3-dB bandwidth under sinusoidal amplitude/frequency modulation phenomena, which are key criteria when specifying PSS PMUs. Using simulated and field-recorded network fault responses, we also show that a class-M PMU is unsatisfactory for wide-area stabilizing control, unless its performance is improved during the fault period, which is not covered by Std. C37.118.1-2011.