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Low-frequency oscillations in power system degrade power quality and may trigger blackouts. This study identifies the source location of these oscillations using measurements from phasor measurement unit (PMU), offline credibility estimation and classification models. The performance of these classification models is ranked for each reported feature to use highly ranked models during the online stage. This proposed framework named as credibility search ensemble learning was tested and validated with promising results using western interconnection power system in North America (WECC-179). The reliability and robustness of the proposed framework were checked against measurement errors in PMUs as well as for practical topology change scenarios. Experimental results and performance comparison with average weight-based approach proved that the proposed approach is capable enough to predict the source location of oscillations with good accuracy. An interfacing tool, for MATLAB-WEKA, was developed and employed in this work for validation and testing of the proposed approach.

A dissipating energy-based technique is proposed to locate the source of forced oscillations (FOs) in power systems. The network and load information is incorporated into the developed algorithm and continuously updated using supervisory control and data acquisition (SCADA) measurement. The effect of electromechanical damping on system response in FO scenario is discussed; and therefore, the efficiency of the proposed technique to locate the source has been investigated. The proposed methodology is tested and verified for different simulation test cases and for different scenario viz. for single and multiple sources of disturbances. In the case of multiple sources of disturbances with different time of initiation of the disturbance, the proposed technique successfully locates all sources in their time durations of disturbance. Different load models have been evaluated for their impact on the success of the proposed algorithm in a real-time digital simulation environment. The proposed technique is successfully verified for the test cases reported by the IEEE PES Task Force on Oscillation Source Location.

A wide area measurement system (WAMS) is a technology developed to improve the stability of the power system in the past few decades, which provides a distributed control structure of a highly interconnected power system. However, the critical issues of security in WAMSs are rising to a new class of control problems due to the malicious attacks. This work studies the distributed model predictive control (DMPC) problem for wide area measurement power systems under malicious attacks. The malicious attacks model as time-varying data injection attacks which describe delayed input states. The traditional three-order model of an interconnection power system is modified to a distributed model with coupling control inputs. A sufficient condition to ensure that the closed loop system with asymptotic stability is obtained by using Lyapunov theorem and linear matrix inequality technology. An iterative DMPC algorithm is proposed to design the distributed controllers based on a cooperative control strategy. Finally, a simulation example of a three-machine nine-node power system is presented to verify the effectiveness of the proposed algorithm.

Challenges facing power system protection in a wide-area system are latency and full coverage of a wide-area disturbance. The complexity of large-scale power system configurations has led to challenges in the design of coordination and operating systems for protection relays. Local measurements used for primary and backup protection cannot consider wide system disturbances. A new wide-area-monitoring-system-based primary protection is presented for a complex power system involving double and single lines. It is based on describing the non-linear dynamic operation of the transmission lines during a fault in the form of a set of differential equations that are solved through paths movements in a phase diagram. The fault on the lines can be precisely recognised. The speed of the traditional communication media limits wide-area monitoring as backup protection. This study presents the primary protection scheme for double and single circuits in a wide area for the first time based on fourth-generation technology with low latency. The justification for applying the proposed scheme as primary protection in a wide-area-monitoring system is discussed. The number of relays on the studied configuration is reduced from 18 local relays to only 5 phasor measurement units for protecting the lines in the area.

Timely detecting disturbances and locating their sources are critical to the reliable operation of power grids. This capability enables operators to effectively diagnose disturbances over wide areas and earns time for remedial reactions. In this study, a travelling-wave based scheme, namely data-driven online distributed disturbance location (DODDL), is proposed to quickly detect disturbances and determine their geographic location in large-scale power grids when the grids’ topology is not available. The proposed DODDL scheme consists of two function blocks: (i) a singular spectrum analysis-based change-point detection method, which can quickly detect disturbances and determine their arrival time at distributed sensors, and (ii) a novel temporal scanning algorithm, which can accurately determine the geographic location of the disturbance source point. Utilising field measurement data sets recorded by the frequency disturbance recorders from the frequency monitoring network, it is shown that the DODDL scheme is not only quicker and more robust to grid non-homogeneity than existing approaches, but also can capture and locate more subtle and concealed disturbances.

Within future homes and electrical power networks, emphasis is being placed on intelligent, distributed measurement devices. In particular, the recognition of individual or aggregated loads through harmonic signature has been proposed as a useful way to enhance the value of home energy monitoring/control. Clearly, the cost of implementing such measurement devices is a major barrier to acceptance. In a recent project, a challenge was set to implement real-time software on an ARM® Cortex™ LPC1768 microcontroller platform (chip cost c. £4). The software must be capable of measuring a single-phase AC frequency, real and reactive power flows and provide a full breakdown of the voltage and current (and power) behaviour via harmonic analysis from DC to the 40th, in real-time with a new output every 20 ms. In addition, the algorithm must be capable of adapting the measurement when the frequency is not nominal (50 Hz) so that spectral leakage is minimised. It is found that the LPC1768 processor is capable of supporting such an algorithm when it is coded appropriately. This knowledge de-risks the proposed use of such cheap microcontrollers for these relatively complex tasks.

Over the last decade, the number of cyber attacks targeting power systems and causing physical and economic damages has increased rapidly. Among them, false data injection attacks (FDIAs) are a class of cyber-attacks against power grid monitoring systems. Adversaries can successfully perform FDIAs to manipulate the power system state estimation (SE) by compromising sensors or modifying system data. SE is an essential process performed by the energy management system towards estimating unknown state variables based on system redundant measurements and network topology. SE routines include bad data detection algorithms to eliminate errors from the acquired measurements, e.g. in case of sensor failures. FDIAs can bypass BDD modules to inject malicious data vectors into a subset of measurements without being detected, and thus manipulate the results of the SE process. To overcome the limitations of traditional residual-based BDD approaches, data-driven solutions based on machine learning algorithms have been widely adopted for detecting malicious manipulation of sensor data due to their fast execution times and accurate results. This study provides a comprehensive review of the most up-to-date machine learning methods for detecting FDIAs against power system SE algorithms.

An important issue for the growth and management of grid-connected photovoltaic (PV) systems is the possibility to forecast the power output over different horizons. In this work, statistical methods based on multiregression analysis and the Elmann artificial neural network (ANN) have been developed in order to predict power production of a 960 kWP grid-connected PV plant installed in Italy. Different combinations of the time series of produced PV power and measured meteorological variables were used as inputs of the ANN. Several statistical error measures are evaluated to estimate the accuracy of the forecasting methods. A decomposition of the standard deviation error has been carried out to identify the amplitude and phase error. The skewness and kurtosis parameters allow a detailed analysis of the distribution error.

Time synchronisation over power lines faces limitations in precision and range due to channel noise and asymmetry. This paper presents a high‐accuracy synchronisation system using Power Line Carrier Communication (PLCC) enhanced with a threshold‐limited sliding average algorithm. By integrating two‐way time transfer with robust PLCC encoding, the proposed method adaptively filters time‐difference fluctuations and compensates for path asymmetry. Experimental validation on a 900‐metre active power line under real‐world interference achieved a Time Deviation (TDEV) of 150 ns at 100,000 s. Comparative analysis demonstrates that the proposed approach attains precision comparable to that of the Precision Time Protocol (PTP) at 10% of the cost, while outperforming the Network Time Protocol (NTP) by three orders of magnitude. This work provides a cost‐effective, infrastructure‐free solution for smart grids, industrial automation, and other time‐critical applications, enabling sub‐microsecond accuracy without dedicated cabling.