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Abstract Due to the wide deployment of phasor measurement unit, the real‐time assessment of transient stability based on machine learning shows great potential for development. In order to solve the problem of time‐consuming data generation of offline training in such methods and the difficulty of quickly updating the model after the grid changes, the paper proposes a method for transient stability assessment (TSA) of power systems based on active learning. Firstly, different operation conditions and different faults are considered to perform short‐time simulation (simulation to the instant of fault clearance) to generate unlabelled samples. After the careful selection of critical TSA features, a part of samples are randomly selected for long‐term simulation to label the transient status of these samples, and random forest is further trained to construct TSA model. Finally some data is selected in the remaining unlabelled samples with higher information entropy to label and retrain the model until the model accuracy no longer changes. The simulation on the test power system shows that the method proposed in this paper can effectively reduce the time of offline simulation, and greatly improve the efficiency of model, and is also robust to wide‐area noise.

Unintentional islanding detection is one the mandatory criterion that must be met by PV inverters before connecting them into the grid. Acceptable time for inverter for islanding detection is less than 2 seconds. In this paper voltage parameters after islanding occurrence and before turning off the inverter are analyzed. In order to simulate islanding state and perform measurements the testing system was build. Three different commercial PV inverters were tested. Measured signals were used to calculate voltage envelope, phasor, frequency and ROCOF. Collected data proved to be helpful to compere different inverters.

The development of modern power systems is directly related to changes in the traditional principles of management, planning, and monitoring of electrical modes. The mass introduction of renewable energy sources and control devices based on power electronics components contributes to changing the nature of the flow of transient and quasi-established electrical modes. In this area, the problem arises of conducting a more accurate and rapid assessment of the parameters of the electrical regime using synchronized vector measurement devices. The paper presents an extensive meta-analysis of the modern applications of phasor measurement units (PMUs) for monitoring, emergency management and protection of power systems. As a result, promising research directions, the advantages and disadvantages of the existing approaches to emergency management, condition assessment, and relay protection based on PMUs are identified.

Harmonic and interharmonic content in power system signals is increasing with the development of renewable energy generation and power electronic devices. These multiple signal components can seriously degrade power quality, trip thermal generators, cause oscillations, and threaten system stability, especially the interharmonic tones with positive damping factors. The first step to mitigate these adverse effects is to accurately and quickly monitor signal features, including frequency, damping factor, amplitude, and phase. This paper proposes a concise and robust index to identify the number of modes present in the signal using the singular values of the Hankel matrix and discusses the scope of its application by testing the influence of various factors. Next, the simplified matrix pencil theory is employed to estimate the signal component frequency and damping factor. Then their estimates are considered in the modified least-squares algorithm to extract the wideband multi-component phasors accurately. Finally, this paper designs a series of scenarios considering varying signal frequency, damping factor, amplitude, and phase to test the proposed algorithm thoroughly. The results verify that the proposed method can achieve a maximum total vector error of less than 1.5%, which is more accurate than existing phasor estimators in various signal environments. The high accuracy of the proposed method is because it considers both the estimation of the frequency number and the effect of signal damping.

The operation of modern power systems must meet stability requirements to guarantee the supply of electrical energy. One of these requirements is to ensure that the low-frequency oscillation modes have high damping ratios to avoid angular instability and future power system blackouts. Advances in phasor measurement units (PMUs) have contributed to the development and improvement of wide-area damping controllers (WADCs) capable of increasing the damping rates of the oscillation modes of the system, especially the inter-area modes. Nevertheless, the operation of WADCs is vulnerable to communication failures and cyber-attacks, and if not properly designed the WADC can affect the stability of the entire system. This research proposes a procedure for designing a WADC robust to permanent communication failures using a linear quadratic regulator (LQR) and genetic algorithms. Case studies conducted on an IEEE 68-bus test power system show the effectiveness of the WADC designed by the proposed procedure even when communication failures are occurring in the system. The use of genetic algorithms improves the convergence and results of the LQR-based method.

Time synchronized phasors obtained using Phasor Measurement Units (PMU) spread across wide areas have revolutionized power system monitoring and control. These synchronized measurements must be accurate and fast in order to comply with the latest IEEE standards for synchrophasor measurements. The speed at which a PMU provides an output depends on the group delay associated with that PMU and the permissible group delay in-turn decides the utility of a PMU for either control or measurement application. Based on the group delay compensation techniques, in the literature, two individual types of PMUs, such as causal and non-causal PMUs have been introduced. This paper presents an approach where both causal and non-causal PMUs are combined in an integrated PMU architecture. This method not only illustrates the group delay performance of two PMUs in a single module, but also can be used for multiple functions. In this environment several PMU algorithms have been compared with respect to their group delays and their effect on the response time. Application of the integrated PMU architecture to a four-machine 10-bus power system has been demonstrated using a six-input PMU with three-phase voltage and current signals as inputs. Different causal compensation schemes are introduced due to the availability of voltage and current-based frequency and ROCOF signals. Impact of these compensation schemes on PMU accuracy is evaluated through the Total Vector Error (TVE) index. The influence of these compensation schemes on measurements like power and impedance is also investigated. Finally, outputs from the integrated PMU architecture are fed into a Power System Stabilizer (PSS) to control the small-signal stability performance of a power system during dynamic conditions. [ABSTRACT FROM AUTHOR]

The ramping trend of cheap and performant single board computers (SBC) is growingly offering unprecedented opportunities in various domains, taking advantage of the widespread support and flexibility offered by an operating system (OS) environment. Unfortunately, data acquisition systems implemented in an OS environment are traditionally considered not to be suitable for reliable industrial applications. Such a position is supported by the lack of hardware interrupt handling and deterministic control of timed operations. In this study, the authors fill this gap by proposing an innovative and versatile SBC-based open-source platform for CPU-independent data acquisition. The synchronized measurement unit (SMU) is a high-accuracy device able to perform multichannel simultaneous sampling up to 200 kS/s with sub-microsecond synchronization precision to a GPS time reference. It exhibits very low offset and gain errors, with a minimum bandwidth beyond 20 kHz, SNR levels above 90 dB and THD as low as −110 dB. These features make the SMU particularly attractive for the power system domain, where synchronized measurements are increasingly required for the geographically distributed monitoring of grid operating conditions and power quality phenomena. We present the characterization of the SMU in terms of measurement and time synchronization accuracy, proving that this device, while low-cost, guarantees performance compliant with the requirements for synchrophasor-based applications in power systems. [ABSTRACT FROM AUTHOR]

This paper presents a dynamic approach for the monitoring and estimation of electromechanical oscillatory modes in the power system in real time with less computational burden. Extensive implementation of phasor measurement units (PMU) and the utilization of advanced signal processing techniques help in identifying the dynamic behaviors of oscillatory modes. Conventional nonstationary analysis techniques are computationally weak to handle a larger quantity of data in real-time. This research utilizes the variational mode decomposition (VMD) for signal decomposition, which is highly tolerant to noise and computationally more robust. The predefined parameters of the VMD process are assigned using FFT analysis of the signal. The significant decomposed mode resembling the original signal is determined using the correlation coefficient method and used for low-frequency mode estimation. The spectral analysis techniques are used to determine the instantaneous mode shapes, which help to identify the source of oscillation in the power system network. The proposed methodology has been tested using signals obtained from two area Kundur system and actual PMU data recorded from Power System Operation Corporation (POSOCO) Limited of the Indian Power grid. The results confirm the superior viability and adaptability of the proposed approach. The performance comparison with other existing signal processing techniques used to estimate low-frequency modes is also presented to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Time Synchronization Attacks (TSAs) against Phasor Measurement Units (PMUs) constitute a major threat to modern smart grid applications. By compromising the time reference of a set of PMUs, an attacker can change the phase angle of their measured phasors, with potentially detrimental impact on grid operation and control. Going beyond traditional residual-based techniques in detecting TSAs, in this paper we propose the use of Graph Signal Processing (GSP) to model the power grid so as to facilitate the detection and localization of TSAs. We analytically show that modeling the state of the power system as a low-pass graph signal can significantly improve the resilience of the grid against TSAs. We propose TSA detection and localization methods based on GSP, leveraging state-of-the-art machine learning algorithms. We provide empirical evidence for the efficiency of the proposed methods based on extensive simulations on five IEEE benchmark systems. In fact, our methods can detect at least 77% more TSAs of significant impact and localize an additional 70% of the attacked PMUs compared to state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

The operation of smart grids heavily relies on secure and accurate meter measurements provided by phasor measurement units (PMUs). Therefore, the optimal PMU placement (OPP) aiming to achieve the complete system observability of smart grids with as few PMUs as possible has been extensively investigated. Although many existing studies have focused on the OPP, few of them are concerned with the placement order of PMUs. To protect as many buses as possible in smart grids when installing PMUs in stages owing to high cost, this paper proposes the attack-resilient OPP strategy which places PMUs in order by using reinforcement learning guided tree search, where the sequential decision making of reinforcement learning is utilized to explore placement orders. The least-effort attack model is carried out to screen vulnerable buses such that the buses adjacent to these buses can be placed PMUs in advance to reduce the state space and action space of the large-scale smart grid environment. Based on that, the reinforcement learning guided tree search approach is used to explore the key buses which need placing PMUs, where the repeated exploration of the agent is avoided by tree search. Then, a reasonable placement order of PMUs is obtained according to the action sequence the proposed method provides. Finally, the effectiveness of the proposed method is verified on various IEEE standard test systems and the comparison results with existing methods are provided. [ABSTRACT FROM AUTHOR]

The need for low cost phasor measurement unit (PMU) devices in power system demands less complex measurement algorithms that can be implemented on inexpensive processing platforms. Unlike the widely used DFT, the Goertzel algorithm, having low complexity is not explored much for synchrophasor estimation. An efficient integration of zero crossing detection (ZCD) with Goertzel technique is presented in this work for the design of low cost synchrophasor devices. An extensive analysis has been done in the design of pre-filtering stage, selection of suitable window length, sampling rate, and other parameters to exploit the benefits of proposed algorithm. The metrological characterization of the proposed algorithm is delineated by showing the different test results following the IEEE synchrophasor standards. The proposed method exhibits the lowest computational time in comparison to the state-of-the-art methods with similar low computational complexity. Simulations and experimental results are obtained by using Raspberry Pi 3 model B+ board. • Kaiser window-based filter is analyzed for low cost and accurate operation of ZCD. • A harmonic removal technique is proposed for accurate Goertzel operation. • A novel integration of ZCD, Goertzel and harmonic remover are explained in detail. • Proposed method's metrological characterization is analysed. • Proposed method is capable of estimating the harmonic phasor. [ABSTRACT FROM AUTHOR]

With the access to a large number of intermittent and fluctuating new energy sources in the low-voltage distribution network, the complex relationship between producers and consumers makes the node voltage stability and other problems in the power system increasingly prominent. Aiming at the voltage stability problem caused by the intermittent and fluctuation of the new power system, based on the high-precision measurement data of PMU, this article used the model predictive control algorithm to realize regional voltage optimization and improve the stability of node voltage. First, the voltage sensitivity matrix is calculated using PMU measurement data and grid structure state information. Second, active power, reactive power, and voltage were taken as the input of the MPC algorithm, and the optimal compensation voltage instruction was obtained by rolling optimization, and the node voltage was compensated by SVG and SVC. Finally, a simulation experiment was carried out on the MATLAB/Simulink simulation platform, and the experimental results verified the correctness and effectiveness of the proposed control method.

Abstract The power system stability and reliability are stimulated by the faults on the transmission line. Many researchers have explored the performance of the transmission system under various kinds of faults. Specifically, the arrival of expeditious and effective data acquisition systems with high rate of sampling has set down the foundation for successful real-time monitoring. Using the LabVIEW and the data acquisition system’s of National Instruments (NI), virtual systems have been developed for obtaining optimal paradigmatic data with appropriate characterization and quality transmission. The primary objective of the work is to perceive and comprehend the transmission line faults with the aid of synchronized phasor measurements obtained from the phasor measurement unit (PMU) as well as protecting the system using auto-reclosing signal. The developed algorithms include phaselet coefficients for perception as well as comprehension. In order to increase the accuracy, particle swarm optimized extreme learning machine technique has also been used for comprehension. A protection scheme is employed using auto-reclosing to minimize the power loss and quick reconnection the power line in case of temporary fault. Developed algorithms have been validated on a practical laboratory transmission line using NI PMU. As the LabVIEW platform has been used for simulations, it is composed of visual displays such that the system operator can efficiently perform the planning and control decisions.

Abstract Real time monitoring and control of a modern power system has achieved significant development since the incorporation of the phasor measurement unit (PMU). Due to the time-synchronized capabilities, PMU has increased the situational awareness (SA) in a wide area measurement system (WAMS). Operator SA depends on the data pertaining to the real-time health of the grid. This is measured by PMUs and is accessible for data analytics at the data monitoring station referred to as the phasor data concentrator (PDC). Availability of the communication system and communication delay are two of the decisive factors governing the operator SA. This paper presents a pragmatic metric to assess the operator SA and ensure optimal locations for the placement of PMUs, PDC, and the underlying communication infrastructure to increase the efficacy of operator SA. The uses of digital elevation model (DEM) data of the surface topography to determine the optimal locations for the placement of the PMU, and the microwave technology for communicating synchrophasor data is another important contribution carried out in this paper. The practical power grid system of Bihar in India is considered as a case study, and extensive simulation results and analysis are presented for validating the proposed methodology.

THE PURPOSE. To consider the problem of detecting changes in a power grid topology that occurs as a result of the power line outage / turning on. Develop the algorithm for detecting changes in the status of transmission lines in real time by using voltage and current phasors captured by phasor measurement units (PMUs) are placed on buses. Carry out experimental research on IEEE 14-bus test system. METHODS. This paper proposes a method from the field of artificial intelligence such as machine learning in particular "Deep Learning" to solve the problem. Deep Learning arises as a computational learning technique in which high level abstractions are hierarchically modelled from raw data. One of the means to effectively extract the inherent hidden features in data are Convolutional Neural Networks (CNNs). RESULTS. The article describes the topic relevance, offers to apply the method for detecting status of lines using a CNN classifier. The combination of different CNN architectures and the number of time slices from the moment of line status change are used to detect the power grid topology. The effectiveness of the joint use of PMUs and CNN in solving this problem has been proven. CONCLUSION. A solution for the line status change detection in the transient states using a CNN classifier is proposed. A high accuracy of the line status detection was obtained despite the influence of noise on measurement data. A change in the network topology is detected at the very beginning of the transient state almost instantly. It will allow the operator several times during the first seconds to identify the line state in order to make sure that the decisions made are correct.

• According to the analysis of terminal measurement data, a voltage control model of distribution network without accurate modeling is proposed. • A distribution network voltage state estimation method based on AM-HNN model is proposed. • This method is adapted to a variety of scenarios, overcoming the shortcomings of other methods and thus improving estimation accuracy. • Taking the distribution network data of different scenes as an example, the effectiveness and universality of this model are verified. A large amount of distributed energy access increases the risk of voltage fluctuation and off-limit voltage. Accurate voltage estimation is more and more important to ensure the safety of distribution networks. With the introduction of phasor measurement unit (PMU), multi-source measuring data with different time scales and measuring precision exist in distribution networks. How to fully integrate multi-source measurement data to achieve fast and accurate state estimation of distribution networks is an important prerequisite for efficient operation decision making. In this paper, a distribution network voltage state estimation method based on AM-HNN model is proposed. The attentional mechanism (AM) is introduced to mine the influence of PMU measurement data on voltage state estimation, and the feature weight is modified adaptively. The highway neural network (HNN) was used to fit the mapping relationship between the injected power of grid nodes and the voltage of key nodes. Taking the distribution network data of different scenes as an example, the superiority of the proposed optimal measurement model is verified by comparing with various voltage estimation methods. [ABSTRACT FROM AUTHOR]

• Accuracy analysis of Class M PMU devices under fault conditions with a real-time digital simulator. • Analysis of fault location errors resulted from many well-known single-end and double-end expressions utilizing Class M synchrophasor data. • Justification that Class M PMUs could be used for fault location on overhead transmission lines provided that the fault lasts for at least four fundamental frequency cycles. • Some comparison with class P PMUs in terms of transient performance and associated total vector errors. • Investigation of a real fault case on a 500 kV line recorded by phasor measurement units. Synchrophasor measurements are normally used to represent steady states and electromechanical transients. However, a number of methods have been proposed to utilize phasor measurement unit (PMU) data for locating faults on transmission lines. Most of the studies focus on pure mathematical simulations with almost no consideration of the real PMU behavior. In this research, a lab testbed with production-grade PMUs is utilized to dig further into the problem of phasor-based fault location. The PMUs are configured as "Class M" defined in IEC/IEEE 60255-118-1. The applicability of class M PMUs for fault location is shown by both lab tests and numerical experiments with real-field phasors recorded during a single line-to-ground fault on a 500 kV transmission line. For the latter, the fault location error turned out to be well within 1 % although both PMUs were connected to metering current transformers. The obtained numerical results correlate well with data reported in another research discussing real-world fault cases on 500 kV lines, all captured by class M PMUs, thus giving grounds for practical PMU-based fault location applications. [ABSTRACT FROM AUTHOR]

The wide application of power electronic devices brings an increasing amount of undesired harmonic and interharmonic tones, and accurate harmonic phasor estimation under a complex signal input is an important task for smart grid applications. In this paper, an optimization of least-square dynamic harmonic phasor estimators, considering multi-interference and harmonic frequency variance, is proposed. A comprehensive error index (CEI) composed of the fundamental-leakage-led harmonic amplitude estimation error, harmonic mutual interference, out-of-band interference, and harmonic frequency deviation is employed. The largest CEI part of least-square algorithms using three different signal decomposition models is analyzed for the first time, and variables to reduce this error component are then introduced using singular value decomposition. With the CEI and defined variables, a minimum-error estimation of harmonic phasors under various interference and harmonic frequency change is discussed. Numerical tests are performed, and the test results show that after the proposed optimization is applied to least-square algorithms, the harmonic phasor estimation errors are considerably reduced, especially for low-order harmonics. We also show the possibility of choosing desired optimal phasor filter design by balancing the measurement accuracy and data latency. For example, when the window length is set to three nominal cycles, the proposed optimization can yield both good accuracy and fast measurement speed for estimating harmonic phasors under multi-interference and harmonic frequency variance.

Power system stability is a task that every system operator (SO) is required to achieve daily to ensure an uninterruptible power supply. Especially at the transmission level, for each SO it is of utmost importance to ensure proper exchange of information with other SOs, mainly in case of contingencies. However, in the last years, two major events led to the splitting of Continental Europe into two synchronous areas. These events were caused by anomalous conditions which involved in one case the fault of a transmission line and in the other a fire outage in proximity to high-voltage lines. This work analyzes these two events from the measurement point of view. In particular, we discuss the possible impact of estimation uncertainty on control decisions based on measurements of instantaneous frequency. For this purpose, we simulate five different configurations of phasor measurement units (PMUs), as characterized by different signal models, processing routines, and estimation accuracy in the presence of off-nominal or dynamic conditions. The objective is to establish the accuracy of the frequency estimates in transient conditions, more specifically during the resynchronization of the Continental Europe area. Based on this knowledge, it is possible to set more suitable conditions for resynchronization operations: the idea is to consider not only the frequency deviation between the two areas but also to take into account the respective measurement uncertainty. As confirmed by the analysis of the two real-world scenarios, such an approach would allow for minimizing the probability of adverse or even dangerous conditions such as dampened oscillations and inter-modulations.

The increase in renewable energy sources (RESs) in distribution grids is a major driver for achieving green energy goals worldwide. However, RES power inverters affect power quality, increase power losses, and, in certain cases, may cause power interruptions due to harmonics, deterioration of the rate of change of frequency, and inability to rapidly react in grid faults. Today, phasor measurement units (PMUs) are the ultimate tools for real-time monitoring of distribution grids’ health, and they enable several data-driven added-value services such as fast and automated fault detection, isolation, and recovery; state estimation; power quality monitoring; dynamic events analysis, etc. The present paper proposes an open hardware and software PMU platform, which is low cost, high performance, expandable, and, in general, suitable for research and innovation activities. The system is based on two processor modules (a digital signal processor from Texas Instruments TMS320c5517, and a microprocessor System-in-Package from Octavo Systems OSD3358), two local databases of 64 Gbytes each, GPS module, 5G modem interface, as well as analog and signal conditioning circuits to interface three-phase power voltage and current signals. The entire hardware design, schematics, and instrumentation components, as well as all firmware and software functions are completely open source. Pilot operation of the prototype design has been installed in three medium-/low-voltage substations in Cyprus, as well as twelve substations in Spain and Italy.

The need for Phasor Measurement Units (PMUs) is rising as renewable energy sources become more prevalent in power networks since the rate of change of frequency is being deteriorated. Appropriate and accurate network measurements are a requirement for the precise monitoring and control of the system. This paper presents a low-cost PMU development, the so-called PhasorsCatcher, for the frequency and rate of change of frequency measurements in power networks, using sufficient but straightforward modular and reconfigurable friendly technology for its implementation. The entire hardware design, schematics, and instrumentation components are shown. Moreover, the visualisation has been calibrated and verified through an experimentation set-up and the existing electrical and communication standards. [ABSTRACT FROM AUTHOR]

With a high proportion of new energy and power electronic equipment connected to the modern power system, a new type of subsynchronous oscillation (SSO) is triggered, which affects the safe and stable operation of the system. Accurate analysis and identification of the SSO source becomes the key to studying this problem. This article presents a novel method to analyze SSOs. First, multisynchrosqueezing transform is applied to the signal to get the concentrated time–frequency analysis results. Then, the ridge tracking method is utilized to identify and reconstruct the subsynchronous component signals. The method of dissipating energy flow is applied to locate the source and sink of the SSO in the subsynchronous component signals. The performances of the proposed method are verified using electromagnetic transient simulations in a three-wind farms system and a modified IEEE 10-generator 39-bus New England power system. [ABSTRACT FROM AUTHOR]

In this research work, an admissible searching‐based algorithm A‐star and binary search tree is proposed to unravel the optimal phasor measurement unit (PMU) placement problem (OPPP) for achieving full interconnected power network observability considering contingencies. The proposed method is an advanced version of the best‐first search method that finds minuscule paths to the best solution. Moreover, it is an optimal and complete search method. Optimal signifies that the proposed method is guaranteed to unravel the minimum cost from origin to the terminus point and Complete denotes that it is going to determine all the possible paths that are available to us from the origin to the destination. The binary tree is used for this purpose as its structural design is well‐matched with the proposed method. Integrating an A‐star algorithm and binary search tree enhances the optimization and performance toward unraveling the OPPP. Also, for more than one solution of OPPP, redundancy measurement is taken into consideration. The proposed method is applied on standard IEEE‐14, ‐30, ‐118, New England‐39, and northern regional power grid (NRPG) 246‐bus test systems. Furthermore, to check the scalability of the proposed method, the Polish 2383‐bus system is considered. The obtained experimental results were compared to established procedures published in peer‐reviewed journals, and the proposed methodology outperformed the other comparison approaches. Thus, the proposed method successfully accomplishes complete observability of the power networks. [ABSTRACT FROM AUTHOR]

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In realistic power system state estimation, the distribution of measurement noise is usually assumed to be Gaussian while many researcher have verified that it can be non-Gaussian. In this paper, a new robust state estimator based on exponential absolute value function is proposed to address the non-Gaussian measurement noise and outliers. The influence function, a robust statistics tool, is used to obtain the state estimates to reduce its computational burden. A state estimation mean squared error formula of the proposed robust estimator is derived which can be used as a reference in the wide area monitoring system design or upgrade. Simulation results obtained from the IEEE 30-bus, 118-bus and 300-bus systems verify the effectiveness and robustness of the proposed robust estimator. [ABSTRACT FROM AUTHOR]

A two-stage machine learning-based approach for creating synthetic phasor measurement unit (PMU) data is proposed in this article. This approach leverages generative adversarial networks (GAN) in data generation and incorporates neural ordinary differential equation (Neural ODE) to guarantee underlying physical meaning. We utilize this approach to synthetically create massive eventful PMU data, which would otherwise be difficult to obtain from the real world due to the critical energy infrastructure information (CEII) protection. To illustrate the utility of such synthetic data for subsequent data-driven methods, we specifically demonstrate the application of using synthetic PMU data for event classification by scaling up the real data set. The addition of the synthetic PMU data to a small set of real PMU data is shown to have improved the event classification accuracy by 2 to 5 percent.

Information technology expressively improves remote electricity measurement and monitoring. Integrating Dynamic Thermal Current Rating (DTCR) software packs with the exclusive phasor measurement-based Wide Area Measurement (WAM) framework, the remote Transmission Lines (TLs) current rating can be measured. WAM is used for data acquisition from different sensors, and also allows data transmissions and processing for which sensor cloud system (SCS) plays a vital role. DTCR with phasor-measurement based WAM framework is mainly used to analyze and determine the current ratings of overhead TLs using weather condition estimation or prediction methods. However, the recent study suggests that the accuracy of the DTCR has become an issue in the smart grid of Sarawak Energy Berhad (SEB). Hence, this article studies and discusses the relevant models and systems, and then proposes an improved thermal pi (π) model for the transmission line thermal model of DTCR software in WAM Framework. The performance of the improved π model will be distinguished from the existing thermal model. The weather factors that bring a substantial impact on the current rating is also considered, where the relevant data is monitored via different weather sensors. Besides, this study also focuses on calibrating the DTCR through phasor measurement in the WAM system, as well as the field measured data. All the data is collected from relevant sensors, and a detailed comparative analysis is provided based on the proposed model for the sake of improving the reliability of the system. The performance analysis of the thermal models is evaluated using Matlab software-based numerical analysis.

Power system inter-area oscillations curtail the power transferring capabilities of the transmission lines in a large interconnected power system. Accurate identification of dominant modes and associated contributing generators is important to avoid power system failures by taking appropriate remedial measures. This paper proposes a multi-channel Improved Dynamic Mode Decomposition (IDMD) algorithm-based modal analysis technique using Synchrophasors measurement. First, a reduced-order dynamic power system model is estimated and using this model dominant oscillation modes, corresponding modes shapes, damping ratio, coherent group of generators, participation factors are determined. To improve the accuracy data stacking technique is used to capture detailed information of the system. An optimal hard threshold technique is utilized to select the most optimal model order to avoid uncertainties due to the presence of high level of measurement noise. The study results show that the proposed algorithm gives an accurate and robust solution even in systems having high level of noise in the measurement data. The performance of the proposed technique is tested on simulated data from two-area four-machine system and wNAPS 41-bus 16-generator system with PMU measurements corrupted with different levels of measurement noise. To further strengthen the viewpoint, the proposed method is validated on real-time PMU measurement from ISO New England data to validate the accuracy of the proposed work.

Synchronous phasor measurements have long been an important tool in power flow modelling and network state estimation as well as in many other tasks that support power grid observability. Classic phasor estimation algorithms are often preferred over complex alternatives. They are based on a variant of the Discrete Fourier Transform (DFT) while relying on a sampling frequency locked to the system nominal frequency. The spectral leakage caused by the small off-nominal frequency excursions in such settings introduces errors that are handled by the estimation algorithms to a different extent. Well-known three-point averaging filter (3P) is a low-cost spectral leakage reduction technique in one of the simplest estimators based on a single-bin DFT. However, it only partially removes the oscillating components. When an independent standard-based frequency tracking is available, the errors can be further compensated with minimal cost. We propose a frequency-corrected three-point estimator (F3P) to eliminate the induced spurious spectrum progressively with the increasing sampling rate while building on the analytical expression of the remaining error. We show performance benefits of the F3P under finite sampling rates when compared to the baseline 3P and to the classical Interpolated DFT (IPDFT) in static conditions. The F3P removes most of the leakage without the need of data resampling. In less ideal conditions with additive 50-dB noise, up to a 10-fold reduction of the 3P error is achieved. The error ratio of the reference IPDFT over the F3P is approximately 1.4 under the same conditions and largely independent of the frequency excursion. The presented simulation results prove the algorithm’s superior performance in most of the steady-state tests defined by the relevant Standard, whereas the dynamic performance can be brought to an acceptable level by the use of a single-cycle observation window, with the response times comparable to those of the IPDFT.

As the Distributed Generation (DG) is evolving, the distribution network has undergone tremendous change. The DG sources like wind and solar are intermittent in nature. Hence to estimate the Active distribution network’s (ADN) parameters (Voltage and Current) accurately with the DG variation, the traditional offline method like long-term smart distribution network planning (with off-line variant) method is not feasible in terms of network’s reliability and operation. In this work, an online voltage estimation technique using PMU (Phasor Measurement Unit) technology is proposed to enhance a distribution network’s reliable operation and real-time monitoring. The proposed estimation method does not require PMU units at all nodes in the network. The estimation of unknown states information can be achieved from available PMU unit’s data. The proposed methodology is economical and feasible for voltage estimation, and system observability as the number of PMU units required is less. The standard IEEE Distribution network systems are considered for checking the feasibility of the recommended technique. For the simulation of the case studies, MATLAB programming is used. A Forward and Backward sweep (FBS) load-flow algorithm is used to authenticate the proposed methodology.

Bad data may lead to performance degradation or even instability of a power system, which can be caused by various factors: unintentional PMU abnormalities, topology error, malicious cyber-attacks, electromagnetic interference, temporary loss of communication links, external disturbances, extraneous noise biases, etc. In order to develop a more resilient and reliable state estimation technique, this manuscript presents a novel two-step fault tolerant extended Kalman filter framework for discrete-time stochastic power systems, under bad data, PMU failures, external disturbances, extraneous noise, and bounded observer-gain perturbation conditions. The failure mechanisms of multiple phasor measurement units are assumed to be independent of each other with various bad data or malfunction rates. The benchmark IEEE standard test systems are utilized as a demonstrative example to carry out computer simulation studies and to examine different estimation algorithms. Experimental results demonstrates that the proposed second-order fault tolerant extended Kalman filter provides more accurate estimation results, in comparison with traditional first- and second-order extended Kalman filter, and the unscented Kalman filter. The proposed two-step fault-tolerant extended Kalman filter can serve as a powerful alternative to the existing dynamic power system state estimation techniques.

The increasing use of renewable generation, power electronic devices, and nonlinear loads in power systems brings more severe interharmonic tones to the measurand, which can increase estimation errors of P-class PMUs, cause misoperation of protection relays, and even threaten the stability of the power systems. Therefore, the performance of the P-class synchrophasor estimator under interharmonic interference should be evaluated and new estimation schemes that can improve the operational robustness are needed for various protection and control applications. In this paper, a synchrophasor estimator design for P-class PMUs that introduces singular value decomposition to the least square algorithm based on the Taylor series is proposed. By constructing an optimization with proposed adjustable parameters, finite impulse response filters with high transition band attenuation are designed. Numerical and experimental tests verify the proposed dynamic synchrophasor estimator performance and show an effective improvement in rejecting interharmonic tones, especially when a short time window and light computational burden are required.

Power quality (PQ) monitoring is important for both the utilities and also the users of electric power. The most widespread measurement instrument used for PQ monitoring is the PQM (Power Quality Monitor) or PQA (Power Quality Analyzer). In this paper we propose the usage of PMU data for PQ parameters monitoring. We present a new methodology of PQ parameters monitoring and classification based on PMU data. The proposed methodology is tested with real measurements performed in distribution system using dedicated PMU system.

Background: Smart grid systems require high-quality phasor measurement unit (PMU) data for proper operation, control, and decision-making. Missing PMU data may lead to improper actions or even blackouts. While the conventional cubic interpolation methods based on the solution of a set of linear equations to solve for the cubic spline coefficients have been applied by many researchers for interpolation of missing data, the computational complexity increases non-linearly with increasing data size. Methods: In this work, a modified recurrent equation-based cubic spline interpolation procedure for recovering missing PMU data is proposed. The recurrent equation-based method makes the computations of spline constants simpler. Using PMU data from the State Load Despatch Center (SLDC) in Madhya Pradesh, India, a comparison of the root mean square error (RMSE) values and time of calculation (ToC) is calculated for both methods. Results: The modified recurrent relation method could retrieve missing values 10 times faster when compared to the conventional cubic interpolation method based on the solution of a set of linear equations. The RMSE values have shown the proposed method is effective even for special cases of missing values (edges, continuous missing values). Conclusions: The proposed method can retrieve any number of missing values at any location using observed data with a minimal number of calculations.

The power grid is evolving into a smart grid due to the diverse energy generation and distribution. This complex grid has to be continuously monitored in real-time for its safe operation. Sensors known as phasor measurement units (PMUs) are used for obtaining health information pertaining to the grid in terms of time-synchronized voltage and current phasors. Measurements from several PMUs are sent through a synchrophasor communication network (SCN) to the phasor data concentrator (PDC). The PMUs, the PDC and the SCN together constitute the wide area measurement system (WAMS). Being an important constituent of the WAMS, the resiliency estimation of SCNs is paramount for their proper design. Resilience is a measure of the systems resistance to the disturbances or a measure of its ability to bounce back to a functional state in the event of failure. This paper presents a quantitative metric for estimating the resiliency of SCNs. Monte Carlo simulation (MCS) models are used to simulate random component failures, and the data is used for measuring the resiliency of the SCNs. A multi-objective genetic algorithm (GA) is used for optimizing the placement of PMUs and the PDC, to observe the power system with the minimum number of PMUs, and to simultaneously maximize the resilience. The practical power grid of West Bengal, India, is analyzed as a case study. This work can be a significant contribution to the power sector as it assists in the proper planning and placement of the communication infrastructure in a WAMS.