Search Results (20)

Against the backdrop of high-proportion renewable energy grid integration, modeling accuracy for substations incorporating wind and solar power is critical. Traditional modeling methods rely on theoretical parameters and lack sufficient accuracy. This study uses the 154 kV/23 kV Yeonggwang Substation in Jeollanam-do, South Korea (connected to three wind farms and three solar power plants, with 35 Micro-Phasor Measurement Unit (μPMU) measurement points deployed) as a case study. It investigates three-phase detailed modeling using Power System Computer Aided Design (PSCAD) and μPMU data-driven calibration. Based on substation topology and equipment parameters, a simulation model encompassing main transformers, transmission lines, renewable energy units, and loads was established. A hierarchical calibration system of “data preprocessing—parameter identification—iterative correction” was constructed, employing an iterative optimization strategy of “main grid layer—renewable energy layer—load layer.” A multi-objective optimization function centered on voltage, current, and power was developed. Verification results show that after calibration, the mean relative error rates (MRE) for voltage, current, active power and reactive power are 2.46%, 2.57%, 2.52% and 3.96% respectively, with mean error reduction rates (MERRs) of 80%, 82.75%, 81.33%, and 74.94% compared to pre-calibration values. The uniqueness of the calibration method proposed in this study lies in its use of actual μPMU measurement data to drive PSCAD model parameter calibration, achieving precise matching with the actual characteristics of the substation. This provides a reference method for modeling and digital twin construction of similar substations, demonstrating significant engineering application value. Full article

The rapid integration of solar power into distribution grids has intensified challenges related to frequency instability caused by fluctuating renewable generation. These unexpected frequency variations are difficult to capture using traditional or supervised methods because they emerge from nonlinear, rapidly changing inverter grid interactions and often lack labelled examples. To address this, the present work introduces a unique, frequency-centric framework for unsupervised detection and root cause analysis of grid anomalies using high-resolution micro-Phasor Measurement Unit ($\mu$PMU) data. Unlike previous studies that focus primarily on voltage phasors or rely on predefined event labels, this work employs SpectralNet, a deep spectral clustering approach, integrated with autoencoder-based feature learning to model the nonlinear interactions between frequency, ROCOF, voltage, and current. These methods are particularly effective for unexpected frequency variations because they learn intrinsic, hidden structures directly from the data and can group abnormal frequency behavior without prior knowledge of event types. The proposed model autonomously identifies distinct root causes such as unbalanced loads, phase-specific faults, and phase imbalances behind hazardous frequency deviations. Experimental validation on a real solar-integrated distribution feeder in the UK demonstrates that the framework achieves superior cluster compactness and interpretability compared to traditional methods like K-Means, GMM, and Fuzzy C-Means. The findings highlight SpectralNet’s capability to uncover subtle, nonlinear patterns in $\mu$PMU data, offering an adaptive, data-driven tool for enhancing grid stability and situational awareness in renewable-rich power systems. Full article

The placement of micro phasor measurement units in an active distribution network can improve the monitoring performance of the system. However, the price and placement cost of micro phasor measurement units are high, so existing research mostly focuses on solving the problem of achieving global observability with the minimum number of micro phasor measurement units. However, the problems of frequent topology change in the distribution network and the increase in the number of faults that will be caused by the high proportion of distributed energy resources connected to the system may lead to the above placement scheme losing the observability of the system. The balance of maintaining the number of micro phasor measurement units placed while ensuring the monitoring stability of the system remains a crucial challenge. To address this issue, a micro phasor measurement unit placement method that considers the observability of multiple monitoring devices and node importance is proposed in this paper, which, on the one hand, fully considers the impact of the observability of existing monitoring devices on the micro phasor measurement units placement scheme and reduces the number of micro phasor measurement units that are used to achieve global observability, and, on the other hand, gives priority to placing the micro phasor measurement units at important nodes in the distribution network that are closely related to observation stability, improving the observation performance of the system. The superiority of the proposed μPMU method is verified in standard systems such as IEEE-33, IEEE-34, and P&G69, in which the number of required μPMUs is reduced by 20%, and the observation performance of the system is improved by 30% in special cases. Full article

This paper proposes a correction technique for bad data and high-precision analysis based on micro-phasor measurement unit (μPMU) data for a stable and reliable smart substation. First, a high-precision wide-area monitoring system (WAMS) with 35 μPMUs installed at Korea’s Yeonggwang substation, which is connected to renewable energy sources (RESs), is introduced. Time-synchronized μPMU data are collected through the phasor data concentrator (PDC). A pre-processing program is implemented and utilized to integrate the raw data of each μPMU into a single comma-separated values (CSV) snapshot file based on the Timetag. After presenting the technique for identification and correction of event, duplicate, and spike bad data of μPMU, causal relationships are confirmed through the voltage and current fluctuations for a total of five states, such as T/L fault, tap-up, tap-down, generation, and generation shutdown. Additionally, the difference in active power between the T/L and the secondary side of the M.Tr is compared, and the fault ride through (FRT) regulations, when the fault in wind power generation (WP), etc., occurred, is analyzed. Finally, a statistical analysis, such as boxplot and kernel density, based on the instantaneous voltage fluctuation rate (IVFR) is conducted. As a result of the simulation evaluation, the proposed correction technique and precise analysis can accurately identify various phenomena in substations and reliably estimate causal relationships. Full article

With the growing concern over climate change and energy security, the Government of India expedited enhancing the share of renewable energy (RE) derived from solar, wind and biomass sources within the energy blend. In this paper, a techno-economic and environmental analysis of a microgrid-integrated electric vehicle charging stations fueled by renewable energy is proposed for a typical area in the State of Karnataka, South India. The power transaction with the grid and the sell-back price to the national grid were investigated. Carbon emissions were also assessed, and 128,406 CO₂ kg/Yr can be saved in the grid-connected mode. Also, in this work, different scenarios such as injecting active power, reactive power, and active and reactive power, and injecting active and absorbing reactive power to the grid are comprehensively assessed. Out of four types, type 3 (inject real and reactive power) provides significant reduction in power losses by up to 80.99%. The synchrophasor-technology-based monitoring method is adopted in order to enhance the microgrid system’s overall performance. The execution times for different cases with distributed generators (DGs) and electric vehicle charging stations (EVCSs) for conventional systems and micro-phasor measurement units (µPMU) were observed to be 19.07 s and 5.64 s, respectively, which is well accepted in the case of online monitoring. Full article

Modern solutions for precise fault localisation in Low Voltage (LV) Distribution Networks (DNs) often rely on costly tools such as the micro-Phasor Measurement Unit ($\mu$PMU), which is potentially impractical for the large number of nodes in LVDNs. This paper introduces a novel fault detection technique using a distribution network digital twin without the use of $\mu$PMUs. The Digital Twin (DT) integrates data from Smart Meters (SMs) and network topology to create an accurate replica. In using SM voltage-magnitude readings, the pre-built twin compiles a database of fault scenarios and matches them with their unique voltage fingerprints. However, this SM-based voltage-only approach shows only a 70.7% accuracy in classifying fault type and location. Therefore, this research suggests using the cables’ Currents Symmetrical Component (CSC). Since SMs do not provide direct current data, a Machine Learning (ML)-based regression method is proposed to estimate the cables’ currents in the DT. Validation is performed on a 41-node LV distribution feeder in the Scottish network provided by the industry partner Scottish Power Energy Networks (SPEN). The results show that the current estimation regressor significantly improves fault localisation and identification accuracy to 95.77%. This validates the crucial role of a DT in distribution networks, thus enabling highly accurate fault detection when using SM voltage-only data, with further refinements being conducted through estimations of CSC. The proposed DT offers automated fault detection, thus enhancing customer connectivity and maintenance team dispatch efficiency without the need for additional expensive $\mu$PMU on a densely-noded distribution network. Full article

With the increasing integration of energy sources and the growing complexity of distribution networks, it is crucial to monitor and early detection of topological changes to ensure grid stability and resilience. Current methods, for optimizing the placement of micro Phasor Measurement Units ($\mu$PMUs) focus on achieving observability and efficient monitoring. These algorithms aim to minimize the number of $\mu$PMUs needed while maintaining system observability or meeting criteria for observability. However, they may not consider all real-world constraints and uncertainties. In this study, we introduce a strategy for placing $\mu$PMUs with the objective of enhancing observability and monitoring capabilities. Our proposed algorithm employs a technique that makes optimal decisions at each step to approximate the global optimum. To determine the locations for $\mu$PMUs our algorithm takes into account parameters such as network structure, key nodes, and system stability. One distinguishing feature is its adaptability to distribution networks, including changes, in topology or potential device failures. Unlike classical approaches, our algorithm can continuously provide optimal placement solutions even in evolving network conditions. We have demonstrated that our suggested method achieves better results in terms of observability value and the required number of $\mu$PMUs compared to the state-of-the-art. By strategically placing $\mu$PMUs, operators can improve system observability, quickly detect and locate faults, and make informed decisions for effective network operations. This research helps improve optimal placement strategies for $\mu$PMUs by providing practical and effective solutions to improve distribution network reliability, resilience, and performance in the face of changing dynamics. Full article

This paper presents a rules-based integrated fault detection, classification and section identification (I-FDCSI) method for real distribution networks (DN) using micro-phasor measurement units ($\mathsf{\mu }$PMUs). The proposed method utilizes the high-resolution synchronized realistic measurements from the strategically installed $\mathsf{\mu }$PMUs to detect and classify different types of faults and identify the faulty section of the distribution network. The I-FDCSI method is based on a set of rules developed using expert knowledge and statistical analysis of the generated realistic measurements. The algorithms mainly use line currents per phase reported by the different $\mathsf{\mu }$PMUs to calculate the minimum and maximum short circuit current ratios. The algorithms were then fine-tuned with all the possible types and classes of fault simulations at all possible sections of the network with different fault parameter values. The proposed I-FDCSI method addresses the inherent challenges of DN by leveraging the high-precision measurements provided by $\mathsf{\mu }$PMUs to accurately detect, classify, and sectionalise faults. To ensure the applicability of the developed IFDCSI method, it is further tested and validated with all the possible real-time events on a real distribution network and its performance has been compared with the conventional fault detection, classification and section identification methods. The results demonstrate that the I-FDCSI method has a higher accuracy and faster response time compared to the conventional methods and facilitates faster service restoration, thus improving the reliability and resiliency indices of DN. Full article

Faults in distribution networks occur unpredictably, causing a threat to public safety and resulting in power outages. Automated, efficient, and precise detection of faulty sections could be a major element in immediately restoring networks and avoiding further financial losses. Distributed generations (DGs) are used in smart distribution networks and have varied current levels and internal impedances. However, fault characteristics are completely unknown because of their stochastic nature. Therefore, in these circumstances, locating the fault might be difficult. However, as technology advances, micro-phasor measurement units (micro-PMU) are becoming more extensively employed in smart distribution networks, and might be a useful tool for reducing protection uncertainties. In this paper, a new machine learning-based fault location method is proposed for use regardless of fault characteristics and DG performance using recorded data of micro-PMUs during a fault. This method only uses the recorded voltage at the sub-station and DGs. The frequency component of the voltage signals is selected as a feature vector. The neighborhood component feature selection (NCFS) algorithm is utilized to extract more informative features and lower the feature vector dimension. A support vector machine (SVM) classifier is then applied to the decreased dimension training data. The simulations of various fault types are performed on the 11-node IEEE standard feeder equipped with three DGs. Results reveal that the accuracy of the proposed fault section identification algorithm is notable. Full article

Simultaneous real-time monitoring of measurement and parameter gross errors poses a great challenge to distribution system state estimation due to usually low measurement redundancy. This paper presents a gross error analysis framework, employing $\mu$PMUs to decouple the error analysis of measurements and parameters. When a recent measurement scan from SCADA RTUs and smart meters is available, gross error analysis of measurements is performed as a post-processing step of non-linear DSSE (NLSE). In between scans of SCADA and AMI measurements, a linear state estimator (LSE) using $\mu$PMU measurements and linearized SCADA and AMI measurements is used to detect parameter data changes caused by the operation of Volt/Var controls. For every execution of the LSE, the variance of the unsynchronized measurements is updated according to the uncertainty introduced by load dynamics, which are modeled as an Ornstein–Uhlenbeck random process. The update of variance of unsynchronized measurements can avoid the wrong detection of errors and can model the trustworthiness of outdated or obsolete data. When new SCADA and AMI measurements arrive, the LSE provides added redundancy to the NLSE through synthetic measurements. The presented framework was tested on a 13-bus test system. Test results highlight that the LSE and NLSE processes successfully work together to analyze bad data for both measurements and parameters. Full article

In the energy sector, distributed synchronism and a high degree of stability are necessary for all real-time monitoring and control systems. Instantaneous response to critical situations is essential for the integration of renewable energies. The most widely used standards for clock synchronisation, such as Network Time Protocol (NTP) and Precision Time Protocol (PTP), do not allow for achieving synchronised simultaneous sampling in distributed systems. In this work, a novel distributed synchronism system based on the Time-Sensitive Networking (TSN) standard has been validated for its integration in an architecture oriented towards the high-resolution digitisation of photovoltaic (PV) generation systems. This method guarantees a time stamping with an optimal resolution that allows for the analysis of the influence of fast-evolving atmospheric fluctuations in several plants located in the same geographical area. This paper proposes an enhanced micro-phasor measurement unit (µPMU) that acts as a phasor meter and TSN master controlling the monitoring system synchronism. With this technique, the synchronism would be extended to the remaining measurement systems that would be involved in the installation at distances greater than 100 m. Several analyses were carried out with an on-line topology of four acquisition systems capturing simultaneously. The influence of the Ethernet network and the transducers involved in the acquisition process were studied. Tests were performed with Ethernet cable lengths of 2, 10, 50, and 75 m. The results were validated with 24-bit Sigma-Delta converters and high-precision resistor networks specialised in high-voltage monitoring. It was observed that with an appropriate choice of sensors and TSN synchronism, phase errors of less than ±1 µs can be guaranteed by performing distributed captures up to 50 kS/s. Statistical analysis showed that uncertainties of less than ±100 ns were achieved with 16-bit Successive Approximation Register (SAR) converters at a moderate cost. Finally, the requirements of the IEEE C37.118.1-2011 standard for phasor measurement units (PMU) were also satisfied. This standard establishes an uncertainty of ±3.1 μs for 50 Hz systems. These results demonstrate the feasibility of implementing a simultaneous sampling system for distributed acquisition systems coordinated by a µPMU. Full article

This article proposes a new passive islanding detection technique for inverter-based distributed generation (DG) in microgrids based on local synchrophasor measurements. The proposed method utilizes the voltage and current phasors measured at the DG connection point (point of connection, PoC). In this paper, the rate of change of voltages and the ratio of the voltage and current magnitudes (VoI index) at the PoC are monitored using micro-phasor measurement units. The developed local measurements based decentralized islanding detection technique is based on the VoI index in order to detect any kind of utility grid frequency fluctuations or oscillations and distinguishing them from islanding condition. The simulation studies confirm that the proposed scheme is accurate, robust, fast, and simple to implement for inverter-based DGs. Full article

The integration of advanced measuring technologies in distribution systems allows distribution system operators to have better observability of dynamic and transient events. In this work, the applications of distribution grid measurement technologies are explored in detail. The main contributions of this review are: (a) a comparison of eight advanced measurement devices for distribution networks, based on their technical characteristics, including reporting periods, measuring data, precision, and sample rate; (b) a review of the most recent applications of micro-Phasor Measurement Units, Smart Meters, and Power Quality Monitoring devices used in distribution systems, considering different novel methods applied for data analysis; and (c) an input-output table that relates measured quantities from micro-Phasor Measurement Units and Smart Meters needed for each specific application found in this extensive review. This paper aims to serve as an important guide for researches and engineers studying smart grids. Full article

The wide area measurement system (WAMS) based on synchronous phasor measurement technology has been widely used in power transmission grids to achieve dynamic monitoring and control of the power grid. At present, to better realize real-time situational awareness and control of the distribution network, synchronous phasor measurement technology has been gradually applied to the distribution network, such as the application of micro multifunctional phasor measurement units (μMPMUs). The distribution network phasor data concentrator (DPDC), as a connection node between the μMPMUs and the main station, is also gaining more attraction. This paper first analyzes the communication network structure of DPDCs and μMPMUs and compares and analyzes the differences in the installation locations, functions, communication access methods and communication protocols of the phasor technology devices of the distribution network and the transmission network. It is pointed out that DPDCs not only need the functions of data collection, storage, and forwarding like transmission network PDCs, but also should be able to access more μMPMUs, and can aggregate the phasor data of the same time scale from μMPMUs by different communication methods. The communication protocol selected by DPDC should be expanded to support remote control, telemetry, fault diagnosis and other functions of distribution automation. The application requirements of DPDCs are clarified, and the key indicators of DPDCs are given as a method to evaluate the basic performance of DPDCs. Then, to address the problems of more μMPMU access, abnormal communication, and data collection with different delays that DPDC encountered, a DPDC that considers multiple communication methods is designed. Based on the Linux system and the libuv library, the DPDC is designed with event-driven mechanism and structured programming, runs multiple threads to implement multitasking, and invokes callbacks to perform asynchronous non-blocking operations. The DPDC test system and test methods are designed. The performance of the designed DPDC is evaluated through the test and the test results are analyzed. Lastly, its real-world application is disclosed, which further confirmed the value of our DPDC. Full article

One of the established unstable power swing (out-of-step) detection algorithms in micro grid/smart grid power systems uses a trajectory of apparent impedance in the R-X plane. However, this algorithm is not suitable for fast out-of-step conditions and it is hard to detect out-of-step conditions exactly. Another algorithm for out-of-step detection is using phasor measurement units (PMUs). However, PMUs need extra equipment. This paper presents the out-of-step detection algorithm using the trajectory of complex power. The trajectory of complex power and generator mechanical power is used to identify out-of-step conditions. A second order low pass digital filter is used to extract the generator mechanical power from the complex power. Variations of complex power are used to identify equilibrium points between stable and unstable conditions. The proposed out-of-step algorithm is based on the modification of assessment of a transient stability using equal area criterion (EAC). The proposed out-of-step algorithm is verified and tested by using alternative transient program/electromagnetic transient program (ATP/EMTP) MODELS. Full article