In modern power systems, as distributed generation resources increase and newer distribution and transmission lines are added, technology changes have led to complexity in the operation and control of the grid.
This complexity can result in a lack of monitoring and appropriate control, which may lead to catastrophic failure of the network known as a blackout.
Blackouts occur due to a series of outages in the system. The power network’s growth sees advanced longer paths to meet the existing demand, whereby the congestion and complexity in the network has pushed the grid to be enhanced for proper monitoring and control by Wide Area Monitoring Protection and Control (WAMPAC), an enabler of the Smart Grid, which is a bidirectional network that can heal itself in case of any failure.
In developing countries like India, distributed generation has increased to meet the demand. Due to this growth, complexity has risen, which has led to the incomplete monitoring of the network. The conventional monitoring and protection systems are unable to identify the fault accurately and time synchronisation is lacking .
Also, in these systems, the dynamic performances are not accurately captured. Due to these drawbacks, the outage is cascaded and thereby leads to catastrophic failure. The Northeast blackout during 14 August 2003 was the world’s largest power outage ever witnessed, affecting an estimated 10 million people in Ontario and 45 million people in eight US states.
The reason behind this outage was that the operators were unaware of the alarm to redistribute the power which had been overloading the transmission lines [2, 14]. This was not the last blackout to cause global concern.
The third largest electricity producer and consumer across the globe, India, was severely affected by cascaded outages on 30–31 July 2012. Some 620 million people were affected in nearly 18 states of the nation – by far the largest power outage ever experienced even compared to the previous one in 2001 affecting nearly 230 million people [3,15].
The reason for the blackout was that at 02:35 IST on 30 July the circuit breaker on the 400kV Bina-Gwalior line tripped, the fault spread to the Agra-Bareilly transmission section and the failure cascaded along the grid.
The shortage at that moment was 32MW. The power was restored after 15 hours. Then again on July 31, at 13:02, due to a relay problem near Taj Mahal, the line was tripped, and failure cascaded the grid. The event of the 2012 blackout is shown in Fig 1. The reasons behind the failure were huge demand and high loading; weak inter-regional power transmission corridors to multiple outages; lack of response by State Load Dispatch Centre to the instructions given by RLDC for reducing the over-draw, so States were drawing more power.
As the current increases, it produces more heat in the conductor and flashover occurs with the nearby objects as the sag increases; the protective relays detect this overcurrent and trip the faulted line . The load is now shared with the other lines and if they do not have extra capacity to carry this extra load, the line trips causing a cascading failure. In such case either the load from other generators should be shared or the line should be load shed until the impact does not cause any failure to the remaining part of the network.
The power control centre should ensure that the power supply is reliable and balanced. To aid this, a computer system with backups and alarm systems are available. Various power flow modelling tools are available to analyse the state of the system so that power distribution is possible . The traditional energy management system (EMS) is used for online monitoring of the power flow and used to assess the security of the network. This uses the measurement from the Supervisory Control and Data Acquisition system (SCADA) to avail the state estimation. And the offline dynamic studies are performed.
This EMS does not have the ability to capture the system dynamics when the system is subjected to large disturbances. The off-line studies do not have the capability to overcome such issues and this initiates a cascading of failure, which finally leads to a blackout.
By incorporating the WAMPAC system the early warning of small and large instabilities is recognised and thereby the system can be protected from vulnerable effects . To establish the WAMC, the phasor measurement unit needs to be placed in all the buses. Due to the high cost of the unit, it must be placed optimally.
Overview of the Indian power grid
The Power Grid Corporation of India has a wide network, which operates around 134,018 circuit km of transmission lines at 800/765kV, 400kV, 220kV and 132kV
India is a member of the Very Large Power Grid Operators, which is a non-profit organisation started in 2004 to investigate fundamental issues of common interest to its members.
The issues related to the planning and development of the transmission system in India is managed by the Power System wing of Central Electricity Authority (CEA). The All India power supply position in terms of energy and monthly peak demand is published by CEA. The total installed capacity of the Indian Regional Grid is shown in Fig 7.
Modelling of the power network for monitoring
The industry faces more challenges such as the need for more power, reduction in emissions, reliability of supply, and energy efficiency solutions. So there is a need to change over from the traditional grid to a modernised one where the renewable sources are decentralised, and the demand side management is achieved by incorporating more flexible alternating current transmission system (FACTS) controllers such as SVC, TCSC, STATCOM, etc. The percentage usage of WAMPAC is depicted in Fig 8.
The monitoring of the power network is basically achieved by the PMU, which gives the Wide Area information. Some of the applications of Wide Area Monitoring System are Phase Angle Monitoring, Voltage Stability Monitoring, Line Thermal Monitoring, Power Oscillation Monitoring, Power Damping Monitoring, etc. Power System Stability is a property of the system that enables it to remain in operating condition under normal state and regain its state of equilibrium after being subjected to a disturbance .
Stability is influenced by the dynamics of rotor angle and power angle relationships.
The stability is mainly categorised into Rotor Angle Stability and Voltage Stability. An equivalent part of the power system is modelled based on the PMU measurement. It is called the system identification and is an integral part of the Wide Area Monitoring and Control. The Kundur’s two-area four-machine power system is used as a test system as shown in Fig 9 (a). The pseudo-random binary signal applied to the input of the PSS of area 1 is shown in Fig 9 (b).
In a wide-area monitoring system, for system identification purposes as well as for prediction of system states, neural network based dynamic modelling is carried out. A Recurrent Neural Network (RNN) has been proved to be very effective for dynamic system identification. It is important to tune the weights of RNN, so that optimal values of those weights are obtained, and the neural network can effectively predict the system states. Particle Swarm Optimisation (PSO) is generally used to tune the weights of a neural network.
PSO, though very effective, sometimes gets trapped into a local optimum and fails to reach the global optimum . An advanced version of PSO, which includes Quantum Infusion (PSO-QI) can be very effective to avoid being trapped in a local optimum. In this work, PSO-QI is used to optimise an RNN, which is used to predict the speed deviation of a generator. It compares the actual measured signal with the predicted signal and shows how the best fitness is gradually reaching its optimum value in each iteration.
The Phasor Measurement Unit, modelled in MATLAB SIMULINK as shown in Fig 9, is installed on both the ends of a transmission line of a small power system. The PMU receives input from the instrument transformers. PMUs are more significant in the Wide Area Monitoring System as it is capable of measuring time synchronised voltage and current phasor in addition to the frequency with respect to the GPS time. The output of the Current and Potential transformer is given as input to PMU. An anti-aliasing filter is used to limit the bandwidth of a signal. The 2nd order Butterworth band pass filter with a centre frequency f0 = 50 Hz and bandwidth Δf = 10Hz. The analogue to digital converter is a device which converts the voltage to discrete form. It consists of a pulse generator, quantizer and sample, and hold circuit.
The Global Positioning System (GPS) receiver is used for time stamping. The pulse generator generates 1,000 pulses for the sample rate of 20 samples per cycle with 1 second as input and sampled with respect to the output of the pulse generator. The quantizer reduces the error at an interval of 5 seconds which represents the clock. An output signal is generated with its phase in phase with the input signal representing the Phaselocked Loop (PLL). The DFT is recursive and non-recursive. The sequence analyser is used to obtain the positive sequence phasor.
The stability of the monitored area is seen through the output of the PMU. The real-time information from PMU and automated control predicts the status of the network and responds to the issues, thereby avoiding power outages and power quality issues etc.
Wide Area Monitoring of Power Network
A Pseudo-Random Binary Signal (PRBS) disturbance is applied at the PSS input of generator 1 shown in Fig 9 (b). The speed deviation signal is captured. From the PRBS input and speed deviation output, the generator model is identified using Matlab System Identification Toolbox. Here a non-linear estimation technique is utilised. It is based on the Hammerstein-Wiener model. The output shown in Fig 10 compares the actual and estimated speed deviation. It shows a good match. However, there is still scope for improvement. The system modelled is a two area system, each having two identical round rotor generators. The positive sequence voltage and the active power from Area 1 to Area 2 are shown in Fig 11.
The dynamic model of the system based on PSO-QI optimised RNN technique is more accurate to capture the speed deviations of the generator as shown in Fig 12. The best fitness gradually reaching its optimum value with each iteration is shown in Fig 13. It clearly shows that by using PSO QI the speed deviations are predicted accurately in a cost-effective manner and thereby the status of the system can be alarmed to the power system operator for necessary actions to be implemented within a short span of time for having a secured and reliable supply.
The power system indeed lacks monitoring and control in the voltage deviation, frequency deviation, communication delay, and natural disaster etc. Major blackouts that have occurred so far show that there is a need for better monitoring. The present energy management system sets back in the time delay and lacks synchronisation.
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BASIC DEFINITIONS REVEAL USEFUL INFORMATION
Wide Area Measurement System (WAMS)
The basic definition of a Wide Area Measurement System is an advanced measurement technology, which consists of advanced information tools, operational infrastructure, which facilitates the operation of the complex network by collecting data . It provides complete monitoring, control and protection. In this section, the chief components of the Wide Area Measurement System are explained. PMU is an enabler of WAMS which prevents the power network from any blackout. The SCADA, PMU, PDC, etc are explained. The picture of the WAMS is shown in Fig 2 and the components are described [Nalini, A et al. (2015)]. Some of the applications of WAMS are the detection of a loss in synchronism, temperature identification, and power system restoration.
Supervisory Control and Data Acquisition
Better known as SCADA, this is a computer-based automation and control system, which emerged to enable operations and control from a remote location. The control system is combined with data acquisition systems. The main functions of the SCADA are monitoring, data presentation, data acquisition, supervisory control, and alarm display. It consists of both hardware and software . The main components include Remote Terminal Units (RTU), Programmable Logic Controller (PLC), a telemetry system, data acquisition server, and Human Machine Interface (HMI). The computer gathers data and the signal is sent to the control unit.
The sensors are either analogue or digital and are interfaced with the system. Note that these are incapable of providing the dynamic state of the power system and the data received is also not time synchronised. The information provided by SCADA is a steady, low sampling density and non-synchronous. The dynamic state of the system is not provided so that immediate action cannot be taken in the case of failure. The block diagram of SCADA is shown in Fig 3. The Mater Terminal Unit is the main part of the SCADA system which is the server where all the communications, data from RTU, are managed and stored, while commands and interfacing with the operators are managed by the MTU.
Synchronised Phasor Measurement System (SPMS)
The SPM unit was firstly developed in the mid-1980s to measure the phasor of voltage, current and the local frequency, and its rate of changes. The SMPS consists of three main parts namely the Phasor Measurement Unit (PMU), Phasor Data Concentrator (PDC), and Communication system. The PDC gathers the data from several PMU and rejects the bad data and aligns the time stamps.
Notably, the PMU is an enabler of WAMS. It is a device which measures the phasor of the current and voltage of the connected bus. It makes use of the GPS receiver to collect the data from the buses located at various places. The collected data is sent to the control unit through the PDC and the PMUs provide time-synchronised data and high resolution for a WAM. By analogue, to digital converter the data samples are taken from the AC waveform and Discrete Fourier Transform is applied. The phasor representation of the voltage signals of two buses is depicted in Fig 4. With respect to the common reference axis, the voltages of different buses are compared and are monitored.
The PMU is a microprocessor-based device using the ability of the Digital Signal processors, which measures 50/60 HZ AC waveforms at a typical rate of 48-60 samples per cycle . The PMUs are optimally placed at different substations, which provide time-stamped positive sequence voltages and currents of all the monitored buses. For the full benefit of the SynchroPhasor Measurement the architecture involves PMUs, communication links, and PDC. The block diagram of PMU is shown in Fig 5, which comprises Anti-aliasing Filter, Analog to Digital Converter, Phasor Microprocessor, Phase Locked Oscillator, Modem and the GPS . The commercialisation of the GPS with accuracy of timing pulses in the order of 1 microsecond is made possible by many industries. By using this, a high degree of accuracy is achieved. The analogue input signals with respect to the voltage and current are received from the instrument transformer.
Wide Area Monitoring Protection and Control
With the advent of WAMPAC systems, the power network became more secure and the reliability of the power supply improved. To address the earlier catastrophic failures, some of the issues such as the system security indices should have been detected and monitored earlier; the critical real-time information, which triggers the dynamic security, needed to be analysed; and Wide Area information, control schemes and actions needed to be deployed . The structure of a simple WAMPAC system is shown in Fig 6. In this section, the Literature survey for Optimal Placement of Phasor Measurement Unit, Wide Area Monitoring, Wide Area Control and Wide Area Protection is presented.
 Anamitra Pal, Gerardo Sanchez-ayala, Virgilio Centeno and James Thorp. “A PMU Placement scheme ensuring real-time monitoring of critical buses of the network.” IEEE Transactions on Power Delivery 29(2) (2014): 510 -517.
 Atena Darvishi and Ian Dobson, “Threshold-Based Monitoring Of Multiple Outages With PMU Measurements of Area Angle.” IEEE Transactions on Power Systems, 2016, vol. 31, no. 3, pp. 2116 – 2124.
 Carlo Muscas, Marco Pau, Paolo Attilio Pegoraro, Sara Sulis, Ferdinanda Ponci and Antonello Monti, “Multiarea Distribution System State Estimation.” IEEE Transactions on Instrumentation and Measurement 64(5) (2015): 1140 – 1148.
 Chenine, Ullberg, Nordstrom, Wu and Ericsson, “A framework for wide-area Monitoring and Control Systems Interoperability and Cybersecurity Analysis.” IEEE Transactions on Power Delivery 29(2) (2014): 633 – 641.
 Christian Dufour and Jean Belanger, “On the Use of Real-Time Simulation Technology in Smart Grid Research and Development.” IEEETransactions on Industry Applications 50(6) (2014): 3963 – 3970.
 Dao Zhou, Jiahui Guo, Ye Zhang, Jidong Chai, Hesen Liu, Yong Liu, et.al, “Distributed Data Analytics Platform for Wide-Area Synchrophasor Measurement Systems.” IEEE Transactions on Smart Grid 7(5) (2016): 2397 – 2405.
 Deyu Cai, Regulski, Osborne and Terzija, “Wide Area Oscillation Monitoring using Fast Nonlinear Estimation Algorithm.” IEEE Transactions on Smart Grid 4(3) (2013):1721 – 1731.
 Dinesh, RG and Athula, DR, “Post-Disturbance Transient Stability Status Prediction Using Synchrophasor Measurements.” IEEE Transactions on Power Systems 31(5) (2016): 3656 – 3664.
 Sharda Tripathi and Swades De “Dynamic Prediction of Powerline Frequency for Wide Area Monitoring and Control” IEEE Transactions on Industrial Informatics 14(7) (2018): 2837 – 2846.
 Manas Kumar Jena, Bijaya Ketan Panigrahi, Subhransu Ranjan Samantaray “A New Approach to Power System Disturbance Assessment Using Wide Area Post Disturbance Records” IEEE Transactions on Industrial Informatics 14(3) (2018): 1253 -1261.
 Lianfang Cai, Nina F, Thornhill, Stefanie K
 Jongho Kim, Kiyoung Choi, Yonghwan Kim, Wook Kim, Kyungtae and Jungyun Choi “Delay Monitoring System with Multiple Generic Monitors for Wide Voltage Range Operation” IEEE Transactions on Very Large Scale Integration (VLSI) Systems 26(1) (2018): 37 – 49
 Nalini, A, Manivannan, S and Sheeba Percis, E, Intelligent Identification for Wide Area Monitoring in Power System” ARPN Journal of Engineering and Applied Sciences 10(20) (2015):9401 -9407
 Sheeba Percis, E, Arunachalam, P, Nalini, A and Shiyamala Rajam “Modeling and Intelligent Control of Hybrid Microgrid ina Wide Area System” International Journal of Pure and Applied Mathematics 120(6) (2018):11437 -11446
This article is republished with minor edits under the creative commons CC BY-NC-ND license. “Wide area monitoring system for an electrical grid” by Nalini Anandan, Sheeba Percis E, Sivanesan S, Rama S, T Bhuvaneswari. Department of EEE, Dr M.G.R Educational & Research Institute, India; and National Contracting Company PVT Ltd, India.