Real time intelligent oscillatory stability monitoring and coherent groups identification

Real time monitoring and control of low frequency oscillations is an important issue to be taken into consideration in modern interconnected power systems operation. A method to find the system small signal stability and coherent generators following a disturbance in real-time is proposed in this work The synchronously sampled data available from Phasor Measurement Units (PMUs) at a high sampling rate provides an opportunity to observe the system operation near to real-time. The first four cycles of post disturbance data comprising of bus voltage magnitudes and angles is measured from optimally placed PMUs. This data is fed to different Artificial Neural Networks to determine the damping index and coherent groups. The dimensionality reduction is performed using Principal Component Analysis. The suggested method is very fast and accurate in predicting the system damping and coherent groups in real-time for different operating conditions including topological variations and 3-phase faults. The efficiency of the proposed methodology is investigated on IEEE 39-bus test system.

Since phasor measurement units (PMU) were invented, there has been growing interest in developing methodologies for improving monitoring, protection and control of power systems in real time. In this study, the authors propose a new methodology, based on graph modelling, to identify coherent groups of generators in a real‐time fashion. The coherent groups are identified with instantaneous values measured from the system through PMUs, and the methodology needs neither setting the number of desired groups nor defining a threshold value since it is based on coupling factors between generators. Moreover, it is proposed a new method for the online definition of areas for islanding when this action is required as the latest emergency control method. The methodology assigns the non‐generation buses to the previously found generators coherent groups considering three criteria: electrical distance to the group of generators, topology, and operational constraints, which are verified by mean of an optimal power flow. The methodology is tested on the IEEE 39‐bus and IEEE 118‐bus test systems. Results show that the real‐time identification of coherent groups and the definition of areas allow the development of islanding strategies with promising results.

The oscillatory stability of the system is becoming a vital problem to be taken into consideration in modern interconnected power systems operation. The information about poorly damped modes and their source should be known to the operator in real time to apply any control actions. But the conventional methods are usually time consuming and offline. In this paper, an intelligent Wide Area Measurement System (WAMS) based method employing Phasor Measurement Unit and Artificial Neural Network (PMU-ANN) is proposed, capable of identifying poorly damped low frequency oscillations and the responsible critical generator in real time under varying system operating conditions. The optimal PMU Placement is obtained using Integer Linear Programming (ILP). The input to ANN is the real time data obtained from PMU and output is the online mode related information like participation factor, damping of modes, and the most critical generator. The effectiveness of the proposed approach is investigated on IEEE 39-bus test system. The test results obtained for unseen operating conditions proves that the proposed method effectively monitors the power system oscillatory dynamics and identify the poorly damped modes and their source in real time with very less computational burden.

With the increase of renewables in the generation mix, the problem of low-frequency oscillations (LFOs) further escalates in modern power systems. This paper proposes a method for LFO modes monitoring and coherency identification using Phasor Measurement Units (PMUs) and Artificial Neural Network (ANN) in real time for a high wind-penetrated power system. The data for ANN training and testing is obtained from PMUs that are placed optimally in the system for complete system observability. The synchronously sampled PMU data is dimensionally reduced using Principal Component Analysis before using it to train the ANN. The proposed approach is verified on the IEEE New England benchmark system. The results obtained validate the effectiveness of the proposed approach in predicting system damping and coherency status with very less computational burden under varying operating conditions.

Power system monitoring and control in real time is a challenging task for modern power system due to large operational constraints. The deployment of phasor measurement units (PMUs) at key locations provides an opportunity for devising effective power system monitoring and control measures. In this study, a new method is proposed to determine the real-time transient stability status and identification of the coherent generator groups by predicting the rotor angle values following a large disturbance through radial basis function neural network. The first six cycles of synchronously sampled post-fault data measurements from PMUs consisting of rotor angles and voltages of generators are taken as the input to the neural network to predict the future state of the system. The proposed method can also determine the synchronism state of the individual machine in real time. The proposed scheme is demonstrated on the IEEE-39 bus test system at different operating conditions.

Power system oscillations monitoring is a vital issue in operation of modern interconnected power systems. The existing methods for identifying the electromechanical modes are time-consuming and require modelling of the entire system that includes a large number of states and are performed offline. In this paper, an integrated Phasor Measurement Unit and Artificial Neural Network (PMU-ANN) based approach for online and real time monitoring of power system electromechanical oscillations is proposed. The placement of PMU is obtained using Integer Linear Programming (ILP). The data obtained from PMU is given as input to a multilayer Feedforward Neural Network (FFNN) and its output gives all the information related to the modes of the system and the mode ranking. The effectiveness of the proposed approach is investigated on IEEE 39-bus test system. The results show that the proposed approach is fast with less computational burden and is suitable for online and real time oscillations monitoring of the power systems under varying operating conditions.

The electrical power system is becoming more and more complex with the increasing demand of the electricity across the world, as the world is becoming the global village. The electrical power system must be reliable and safe enough to fulfil the energy requirements with the continuous supply of energy. The chances of power system's blackouts and outages are increasing with its increased complexity. So, there is a need of an efficient control system to make the power system more safe, efficient and reliable. Wide Area Monitoring, Protection and Control system (WAMPAC) is becoming an emerging technology for an efficient monitoring, protection and control of power system. Phasor measurement unit (PMU) is an integrated part of wide area monitoring, protection and control (WAMPAC) system, which can be used for monitoring, protection and control of complex electrical power systems. PMU gives the synchronized phasor measurements of voltage and current and can easily monitor and control even small disturbances in power system to protect the power system from any blackouts and outages before any fault occurs. In the proposed paper a PMU model is designed in MATLAB/Simulink and then PMU is used for power system protection and the comparative analysis are done with the conventional protection technique for an interconnected two-area network power system.

An artificial neural network (ANN) architecture for real-time estimation of available transfer capability (ATC) has been reported in this study. The real-time data obtained from phasor measurement unit (PMU) is utilised to generate target output (ATC) using the pattern search optimisation-based method. The set of information provided as input to the pattern search-based ATC optimiser along with its output forms the input and target output for ANN training. The input information consists of active and reactive power injected along with voltage and current vectors measured at PMU buses. The ATC optimiser is functional as long as ANN is under training. Once the ANN is trained, it receives input set directly from PMU and produces ATC values. PMU emulation is employed for archiving the PMU data. The proposed method is tested on modified IEEE 24-bus, IEEE 30-bus, and IEEE 118-bus test system. The proposed method has also been implemented on real-time digital simulator.

The wind integration is on the rise in modern grids to generate cleaner energy due to increasing environmental concerns. This further escalates the problem of low frequency oscillations (LFOs) in power system. Thus, real-time monitoring of oscillations becomes even more important in present interconnected power systems. The conventional methods are offline and time consuming. In this work, a wide-area based method employing Phasor Measurement Units (PMUs) and Artificial Neural Network (ANN) is proposed to predict the system oscillatory status in real-time. The PMUs are optimally placed using modified Integer Linear Programming. The PMU data is dimensionally reduced using Principal Component Analysis before using it to train the ANN. The ANN predicts the LFO related information like damping ratio and frequency and an index to access the mode’s localness. The proposed methodology is verified on IEEE New England benchmark system. The suggested method is very fast and accurate in predicting the required information in real-time for different operating conditions involving topological variations with very less computational requirement.

The efficient placement of Phasor Measurement Units (PMUs) is crucial to minimize their number while ensuring full power system observability. A power system is deemed observable when the voltage at every bus is known. This study proposes a topology transformation approach that combines a Zero-Injection Bus (ZIB) with one of its neighboring buses to achieve optimal PMU placement. The choice of the neighboring bus for combination with the ZIB significantly influences the outcome of the merging process. To identify the optimal candidate bus for merging with the ZIB, the proposed approach utilizes three selection principles aimed at determining the minimal number of PMUs necessary for complete system observability. This study also examines the scenario of power flow measurements, formulating the problem using Integer Linear Programming (ILP). The effectiveness of the proposed method is demonstrated through simulations conducted in MATLAB on various IEEE Bus systems, with a detailed explanation provided using the IEEE 14-Bus system. Comparative analysis reveals that the number of PMUs obtained with the suggested method is competitive with existing techniques. Furthermore, this study includes a comparison between the IEEE 30-Bus and IEEE 14-Bus systems by inducing faults at specific buses. This is done to verify whether PMUs placed at optimal locations ensure complete coverage of all buses in both systems. The comparison also extends to evaluating the cost implications of placing PMUs solely at these optimal locations.

Improvements in power system control and protection is achieved by utilizing real time synchronized phasor measurements. The trend in recent years is the steady increase of Phasor Measurement Unit (PMU) installations worldwide for various applications those targeted for State Estimation enhancement. This paper solves the optimal placement of phasor measurement units for complete observability using Integer Linear Programming (ILP) solution methodology. ILP is used to determine the optimal number and location of PMUs needed to make the network completely observable. This method is tested on Thirunelveli network and complete system observability after placing optimum number of PMUs has been verified.

This paper presents the concept of the prediction of phasor measurement unit (PMU) data, which are unavailable due to one reason or another at the central control room, using the 3-layered feedforward back-propagation neural network (BPNN). BPNN are used to determine hidden pattern in a process, using the historical data of that process. Work presented in this paper shows that, change in the voltage at PMU buses due to change in the system operating condition can be identified using the artificial neural network, and this information of changing voltage pattern can be used to reconstruct the missing measurement data, by making use of the voltage measurement of remaining PMU buses. The concept is initially tested on an IEEE 39-bus (New England) test system and then finally verified on northern regional Indian power grid, using the PMU measurement of 21st April 2014 between 08:36 AM to 09:36 AM.

Recently, the electric power systems are operated relatively close to their operational limits due to worldwide deregulated electricity market policies. The power systems are being operated with high stress, and hence sufficient voltage stability margin is necessary to be managed to ensure secure operation of the power system. A particle swarm optimization-based support vector machine (SVM) approach for online monitoring of voltage stability has been proposed in this paper. The conventional methods for voltage stability monitoring are less accurate and highly time-consuming consequently, infeasible for online application. SVM is a powerful machine learning technique and widely used in power system to predict the voltage stability margin, but its performances depend on the selection of parameters greatly. So, the particle swarm optimization is applied to determine the parameter settings of SVM. The proposed approach uses bus voltage angle and reactive power load as the input vectors to SVM, and the output vector is the voltage stability margin index. The effectiveness of the proposed approach is tested using the IEEE 14-bus test system, IEEE 30-bus test system and the IEEE 118-bus test system. The results of the proposed PSO-SVM approach for voltage stability monitoring are compared with artificial neural networks and grid search SVM approach with same data set to prove its superiority.

The accurate real-time state estimation is needed to perform real-time monitoring and control. However, the current power system uses SCADA technology as a tool for monitoring and control. SCADA existing measurement device has a few disadvantages: low accuracy, low sampling rate in measuring the measurements data, and asynchronous measurements between instrument. Due to those disadvantages, state estimation resulted has low accuracy, and not in real-time. These disadvantages can be minimized by installing several phasor measurement unit (PMU). IEEE-30 bus test system is applied to simulate the proposed technique. The result shows, the required PMU should be installed in power system that already has 116 conventional SCADA measurements is 6. The location of these PMUs are in buses 2, 10, 12, 15, 25, and 27. That result is achieved in 320 iteration of genetic algorithm (GA). In this paper, mean absolute error (MAE) is used to asses the state estimation accuracy. The value of $\overline {MAE} $ system with PMU is 7.20718X10−6. While, the value of $\overline {MAE} $ system without PMU is 0.110162054.

This paper aims in presenting the optimal placement of the Phasor Measurement Unit (PMU) of an IEEE-5 bus system. In this paper a simplest scheme for dynamic vulnerability assessment based on Power System Loss Index has been proposed for Optimal PMU placement and which is compared against the Multi Criteria Decision Making (MCDM) Techniques namely Analytical Hierarchy Process (AHP), Fuzzy AHP approach and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approach. MCDM helps in finding the best solution among the multiple alternatives for the placement of PMU which is based on the weighing factor. But this MCDM has neglected the dynamic operation of the system. In the proposed scheme, the probabilistic nature of network dynamics is performed by Monte Carlo Simulation to iteratively evaluate the system performance for probable input parameter variations such as load variation, generation variation and list of credible contingencies to assess the vulnerability index of the system. Newton — Raphson load flow analysis is performed for each contingency and the power system losses in various parts of the networks are observed. The vulnerability Index is calculated based on total Power System Loss (PSL). Based on the index, the vulnerable regions in the power system network are identified and clustered with the help of Data clustering algorithm. The PMUs have to be located in the most vulnerable regions to prevent the system from blackouts and to take corrective control actions. This proposed simple approach is tested on IEEE-5 Bus test systems. The test result shows that PSL index is effective in identifying the vulnerable regions for optimal PMU placement. The findings of the PMU location are compared against MCDM techniques.