Micro-phasor Measurement Units Research Articles

Hybrid Approach-Based Placement of Micro-Phasor Measurement Units in Active Distribution Networks

The placement of monitoring devices like micro-Phasor Measurement Unit (μPMU), which works 24 hours, at all nodes in the distribution system, is contributing to temperature rise. This temperature rise can be reduced by placing the μPMUs at selected nodes. Therefore, this paper depicted a hybrid approach for μPMUs optimal placement in the distribution system. Optimal placement recognises the placement set containing the least number of μPMUs with optimum redundancy with which the phasors of voltage, as well as the current, can be calculated at each bus of the system. Constraints such as communication link unavailability, zero injection buses, and network reconfiguration are considered in the optimal μPMU placement formulation. The proposed approach overcomes the downside of Integer Linear Programming (ILP) which provides a single optimal location set. The proposed approach automatically assigned μPMUs to buses connected to radial buses, established by the degree of each bus, and reduces the computational concern. Provided algorithm efficiency is checked out with IEEE 40 bus feeder and IEEE 85 bus feeder system under Matlab/Simulink environment.

A Fuzzy Decision-Based Multiobjective Planning for Microphasor Measurement Unit Placement in Power Distribution Infrastructure

A Fuzzy Decision-Based Multiobjective Planning for Microphasor Measurement Unit Placement in Power Distribution Infrastructure

An intelligent Island detection scheme to enhance grid resilience

AbstractThe importance of strengthening grid resilience has grown with the increase in environmental destruction and modern power grid complexity, as a consequence of power outages inflicted by human intrusion and extreme weather events. Micro-grids (MGs) have proven to be a viable alternative in such circumstances. However, these occurrences are highly unpredictable, resulting in unintended islands of MGs with negative consequences. As a response, alerting its distributed generations about unintended island is indeed a crucial issue for enhancing grid resilience with MG. Therefore, it is essential to develop a technique for the efficient and accurate detection of unintended islands. There has been an increase in the use of micro-phasor measurement units (µ-PMUs) in MG. In the perspective of this, using an efficient µ-PMU, the research provides a method for finding unintended islands in a MG. The µ-PMU analyses the solar generator bus voltage and analyzes it with symmetrical components for island identification. This study introduces a µ-PMU based Fortescue-transform and random forest algorithm method for rapid detection of unintended islanding in distribution generation system. The approach monitors voltage phasor of zero and negative sequence, calculating angular sum over time to distinguish islanding event from other disturbance. Using Matlab/Simulink, the proposed method is evaluated on the IEEE-34 node distribution network. Multiple simulations provide validation for the method’s resilient performance. The methodology proposed has a detection time of 20 ms.

Tractable Data Enriched Distributionally Robust Chance-Constrained Conservation Voltage Reduction

This paper proposes a tractable distributionally robust chance-constrained conservation voltage reduction (DRCC-CVR) method with enriched data-based ambiguity set in unbalanced three-phase distribution systems. The increasing penetration of distributed renewable generation not only brings clean power but also challenges the voltage regulation and energy-saving performance of CVR by introducing high uncertainties to distribution systems. In most cases, the conventional robust optimization methods for CVR only provide conservative solutions. To better consider the impacts of load and PV generation uncertainties on CVR implementation in distribution systems and provide less conservative solutions, this paper develops a data-based DRCC-CVR model with tractable reformulation and data enrichment method. Even though the uncertainties of load and photovoltaic (PV) can be captured by data, the availability of smart meters (SMs) and micro-phasor measurement units (PMUs) is restricted by cost budget. The limited data access may hinder the performance of the proposed DRCC-CVR. Thus, we further present a data enrichment method to statistically recover the high-resolution load and PV generation data from low-resolution data with Gaussian Process Regression (GPR) and Markov Chain (MC) models, which can be used to construct a data-based moment ambiguity set of uncertainty distributions for the proposed DRCC-CVR. Finally, the nonlinear power flow and voltage dependant load models and DRCC with moment-based ambiguity set are reformulated to be computationally tractable and tested on a real distribution feeder in Midwest U. S. to validate the effectiveness and robustness of the proposed method.

Fault Detection and Localisation in LV Distribution Networks Using a Smart Meter Data-Driven Digital Twin

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 (μPMU), 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 μPMUs. 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 μPMU on a densely-noded distribution network.

Experimental End-To-End Delay Analysis of LTE Cat-M With High-Rate Synchrophasor Communications

Micro-Phasor Measurement Units (μ-PMUs) are devices that permit monitoring voltage and current in the distribution grid with high accuracy, thus enabling a wide range of smart grid applications, such as state estimation, protection and control. These devices need to transmit the synchronous measurements of voltage and current, also known as synchrophasors, to the power utility control center at a high rate. The use of wireless networks, such as LTE, to transmit synchrophasor data is becoming increasingly popular. However, synchrophasors are included in small frames and it would be more efficient to use low-power cellular solutions, such as LTE cat-M. In this work, we present experimental research on the deployment of a μ-PMU with the ability to connect over a commercial LTE cat-M network. The deployed μ-PMU is built with off-the-shelf hardware, such as Arduino microcontrollers, and is used to transmit data—compliant with the IEEE C37.118.2 standard—at a variable rate from 1 frame/s to 80 frames/s. A detailed network performance analysis is carried out to show the suitability of LTE cat-M to support μ-PMU communications. Experimental results on performance indicators, such as delay and jitter, are reported. The effect of the LTE cat-M access mechanism on the time distribution of frame arrivals is also thoroughly analyzed.

Optimized Fault Location Identification in Power Distribution Systems With Inverter-Interfaced Distributed Generations

This article proposes a fault location identification method for radial power distribution systems with inverter-interfaced distributed generations (IIDGs). The proposed method is not restricted to any specific types of data. It can fully utilize all available data types in power distribution systems to enhance the accuracy of results. The dataset may include synchronized voltage and current measurements collected by micro-phasor measurement units (micro-PMUs) and/or unsynchronized measurements collected by legacy measuring devices, active and reactive power measurements, pseudo measurements of load values, and virtual measurements based on Kirchhoff's laws. The proposed method explicitly considers uncertainties in the dataset and is applicable to different load types. The article proposes a mathematical model for representing IIDGs in fault location identification applications that does not require any synchronized sampled data or during the fault period data collection. Simulation results on IEEE 34-node feeder demonstrate the accuracy of the proposed method.

Microgrid Differential Protection Based on Superimposed Current Angle Employing Synchrophasors

The inclusion of multi-energy distributed generators (DGs), especially inverter-interfaced generators, presents challenges in the microgrid's protection strategy and operational constraints. The work for designing primary protection mechanism for microgrid is continuously progressing. In this regard, this article presents a positive sequence superimposed current differential angle (SCDA) based protection scheme employing only positive sequence current phasor from both ends of the line. The proposed scheme collects current phasor measurements from the low-cost micro-phasor measurement units ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula> PMUs). The performance of the proposed scheme is analyzed through simulations on a 7-node microgrid in grid-connected and isolated mode operation under various fault and no-fault situations, and also considering the effect of DG penetration level and DGs types. The simulations are also carried out in the IEEE-33 node network considering C37.118.1 standards-compliant PMUs. Furthermore, a real-time control hardware-in-loop (RTC-HIL) testing is performed to further validate the scheme for practical implementation.

A co-optimized approach for state estimation based micro phasor measurement unit allocation in power distribution infrastructure

Due to the highly complex and dynamic electrical characteristics of the 3-phase unbalanced power delivery networks (PDNs), it is challenging for utilities to observe the network entirely. This impediment necessitates the PDNs to become intelligent nowadays in the presence of various smart devices. Micro-phasor measurement units (µPMUs) are expected to play a crucial role in observing the smart power distribution system due to their micro-seconds resolution and millidegree precision accuracy. The placement of µPMUs in PDNs is also very challenging due to their associated costs. So, allocating a minimal number of µPMUs in PDN with fulfilling the topological observability can elucidate the issue of real-time visibility of the entire system. This paper presents a novel co-optimized placement strategy of µPMUs (CPMP) in PDNs that minimizes the µPMU numbers and the estimation error in the buses other than the µPMU assigned nodes. This work also displays a novel dynamic heuristic distribution system state estimation (DDSSE) to estimate the system's unknown states by using only the information of bus voltage phasors at the µPMU specified buses. The DDSSE is used to estimate the root mean square error (RMSE) in estimated voltage magnitudes and angles. Besides this, the efficacy of the grey-wolf optimizer (GWO) is presented in this work to solve the newly formulated placement problem. The results are validated on an Indian 19-bus and 34-bus unbalanced radial distribution system (URDS), and a detailed comparative study of different heuristic algorithms is also analyzed in this work.

Interactive Distribution Expansion and Measurement Planning Considering Controlled Partitioning

To model interactions among system expansion, con-trolled partitioning (CP), and observability, an integrated multi-period framework is developed in this paper for simultaneous planning of distribution systems and measurements with CP con-straints (DSMP-CP) in unbalanced multi-phase networks. The proposed framework, employing the network AC model, is formu-lated as a mixed-integer quadratically-constrained programing problem. This problem minimizes investment costs (including the costs of feeder reinforcement, distributed generation (DG) place-ment, and allocation of micro-phasor measurement units and their measuring channels) and operation costs (including the costs of DG operation, energy purchased from the upstream grid (UG), re-dispatching of DG and UG injections, and energy curtailment). A phase-based observability function is also introduced to ensure the observability of all phases of buses. Furthermore, the concept of zero-injection (ZI) property is proposed for ZI groups considering physical connections and mutual impedances among phases to en-hance observability efficiency and avoid suboptimal solutions. To enhance problem tractability and eliminate linearization errors, a duality-based solution approach is proposed employing an exact-ness loop with recursive re-linearization. The performance of the proposed framework is evaluated by testing it on the unbalanced IEEE 111-node and 256-node distribution test systems.

Integrated Fault Detection, Classification and Section Identification (I-FDCSI) Method for Real Distribution Networks Using μPMUs

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 (μPMUs). The proposed method utilizes the high-resolution synchronized realistic measurements from the strategically installed μPMUs 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 μPMUs 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 μPMUs 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.

A Static State Estimation Scheme in Microgrid Utilizing μPMU Measurements

The operation of power systems needs uninterrupted and precise wide-area real-time monitoring for secure and automated control of power networks, therefore, there is a requirement of robust state estimation (SE) by the smart control centre. An iterative free static state estimation (IFSSE) scheme is developed using the linear measurement model (LMM) in this paper for microgrids (MGs) integrating distributed generation energy resources (DGERs). The developed scheme of IFSSE utilizes M-estimator (ME) concept, which is combined with the linear static state estimation (SSE) approach in order to provide robust estimation. The developed IFSSE scheme is iteration-free due to the implementation LMM that is achieved from placing the microphasor measurement units (μPMUs) and separate bad data processing (BDP) is not required for this proposed scheme. The proposed scheme's performance has been verified by employing it on modified IEEE-14 bus test systems (BTS) integrating DGERs and modified IEEE 30 BTS integrating DGERs then comparison has been done by taking two cases, i.e., linear SSE without BDP and linear SSE with BDP.

μPMU-based islanding detection method in power distribution systems

This paper focuses on the use of micro-phasor measurement units (μPMUs) for the prompt detection of unplanned islanding conditions. The investigated method is based on the monitoring of voltage and current phasors at distributed generation (DG) buses equipped by μPMUs. The difference between two consecutive phasors (voltage phasors and current phasors) are monitored until a perturbation occurs. When a perturbation is detected, the first phasor, called pre-disturbance phasor, is kept fixed and the second one, called post-disturbance phasor, is continuously updated with each new sample arrival. The differences between the pre-disturbance and post-disturbance phasors are calculated and summed up for a predefined time window. When this exceeds a predefined threshold, the island condition of the related bus is detected. The presented procedure can distinguish the islanding condition from other perturbations, such as motor starting, faults, and capacitor switching. The performance of the proposed procedure, as well as its sensitivity to the system conditions and predefined values, are analyzed considering a 7-bus distribution system and the IEEE 33-bus test system, both with synchronous generators and inverter-based DGs.

Power Distribution System Characterization With Active Probing: Real-World Testing and Analysis

Power distribution systems are traditionally characterized using passive means such as parameter estimation and topology identification. This work investigates an active way to test and characterize dynamically changing distribution systems. The method actively perturbs local distribution networks with programmable pulses using a Grid Resonance Probe (GRP) and observes the response using a micro-phasor measurement unit ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> PMU). After applying advanced data analytics, the perturbation and response measurements can reveal characteristics of distribution systems, such as feeder parameters and topologies. To validate the proposed approach, three case studies are executed on a real distribution system, including visualization, feeder connection identification, and feeder impedance estimation. Real data processing considerations and sensitivity analysis are also discussed. The proposed testing and analysis approach is expected to provide engineers a new solution to measure, parameterize, and characterize distribution systems.

Optimal Micro-PMU Placement for Improving State Estimation Accuracy via Mixed-integer Semidefinite Programming

Micro-phasor measurement units (µPMUs) with a micro-second resolution and milli-degree accuracy capability are expected to play an important role in improving the state estimation accuracy in the distribution network with increasing penetration of distributed generations. Therefore, this paper investigates the problem of how to place a limited number of µPMUs to improve the state estimation accuracy. Combined with pseudo-measurements and supervisory control and data acquisition (SCADA) measurements, an optimal µPMU placement model is proposed based on a two-step state estimation method. The E-optimal experimental criterion is utilized to measure the state estimation accuracy. The nonlinear optimization problem is transformed into a mixed-integer semidefinite programming (MISDP) problem, whose optimal solution can be obtained by using the improved Benders decomposition method. Simulations on several systems are carried out to evaluate the effective performance of the proposed model.

Study of Different Estimation Techniques for a Micro-Phasor Measurement Unit Implementation

Distribution power networks are very complex, due to the great number nodes, short distances, and small amplitude and phase angle differences between nodes, faster dynamics and lack of standard documentation. Thus, these complexities have raised the need to develop new Phasor Measurement Unit (PMU) called Micro-Phasor Measurement Unit (μPMU) with high accuracy and high precision. In this work, several estimation techniques have been investigated so far for improving effectiveness and accuracy of this micro-PMU.

Forecasting Aided Distribution Network State Estimation Using Mixed μPMU-RTU Measurements

Distribution network state estimation is the backbone of energy management systems, whose accuracy and adaptability are very important for the advanced application software in distribution networks. This article proposes a novel forecasting-aided state estimation method for distribution networks with the mixed measurements of microphasor measurement unit (μPMU) and remote terminal unit (RTU). First of all, the data imputation techniques for RTU with a longer update period are proposed to handle asynchronous characteristics and improve computational accuracy using historical and current measurements. Then, the measurement equations are built to process the different types of measurement data from μPMU and RTU. The cubature Kalman filter is adopted to ensure the numerical stability of state forecasting, measurement forecasting, and filter correction. Finally, the IEEE 33- and 37-node systems are applied to verify the effectiveness of the proposed method, which can get an accurate state under mixed measurement.

Optimal Micro-PMUs Placement with Channel Limits using Dynamically Controlled Taguchi Binary Particle Swarm Optimization

The two-directional flow of electrical power has necessitated real-time monitoring of the distribution system (DS), which can otherwise result in power system failure. A micro-phasor measurement unit (micro-PMU) is proficient monitoring equipment that is presently employed to keep track of the changes taking place in DS. However, due to the higher cost of micro-PMU, an optimal number of micro-PMUs with channel limits must be strategically placed in the system. Therefore, an optimization problem for the optimal placement of micro-PMU has been formulated and solved in the present work under different operating scenarios for complete observability with sufficient redundancy. The optimal locations of micro-PMUs with channel limits in DS are obtained using an innovative technique that combines the concepts of Taguchi method (TM) and dynamically controlled binary particle swarm optimization (DBPSO). The proposed micro-PMU placement algorithm has been tested and analyzed on standard IEEE-33 bus and IEEE-69 bus distribution feeders with two different S-shaped and V-shaped sigmoidal functions. The results demonstrate that the proposed technique utilizing V sigmoidal function provides a better solution for optimal micro-PMU placement problem. A comparative investigation of the presented work to the existing micro-PMUs placement techniques for the DS establishes the efficacy of the proposed technique.

Network Identification Using μ-PMU and Smart Meter Measurements

The network identification plays a very prominent role for the network operator to accomplish the various objectives such as state-estimation, monitoring, control, planning, and real-time analytics. The network structure varies from time-to-time and its details are often not available with the network operator. To address this issue, in this article, an alternating direction method of multipliers (ADMM) based framework is presented herein to identify the network topology and line parameters using smart meter and microphasor measurement unit (μ) measurements. The presented algorithm is divided into two sections 1) approximate parameter evaluation through regression, to extract the partial topology information and 2) complete network topology identification through the ADMM framework. This algorithm accomplishes the objectives of identifying the network configuration, branch parameters (e.g., conductance and susceptance), and change in branch parameters. Simulation results demonstrate the effectiveness of the presented algorithm on the benchmarked IEEE 13-bus and IEEE 123-bus feeders under various operating scenarios. Furthermore, the presented framework illustrates excellent network identification even with the presence of the stochastic nature of renewable power generation. The presented algorithm exhibits an excellent performance even with the consideration of noise in both measurements. In addition, the comparative performance is carried out on the benchmarked unbalanced IEEE 13-bus and balanced IEEE 33-bus feeders to highlight the efficacy of the presented framework over the state-of-art framework.

A Comprehensive review on the current trends in Micro-Phasor Measurement Units

Micro-Phasor Measurement Units are invaluable data sources that improve visibility in a variety of domains of application, including power generation, transmission, and distribution. It started out with the objective of acquiring phasor values at specific points across the network and synchronizing them to a standard time frame. The technology has evolved into an all-encompassing unit with applications in the fields of protection, control, and automation. In light of the fact that these units are now being used as a standard benchmark, it is imperative that they be improved. This study presents a comprehensive review into the world of Synchro-Phasor technology, covering all aspects of the production of μPMUs and their role in the modern power system. In addition, the current deployment status of this technology has also been discussed to further emphasize its emergence.