PMU-Based ROCOF Measurements: Uncertainty Limits and Metrological Significance in Power System Applications

In modern power systems, the Rate-of-Change-of-Frequency (ROCOF) may be largely employed in Wide Area Monitoring, Protection and Control (WAMPAC) applications. However, a standard approach towards ROCOF measurements is still missing. In this paper, we investigate the feasibility of Phasor Measurement Units (PMUs) deployment in ROCOF-based applications, with a specific focus on Under-Frequency Load-Shedding (UFLS). For this analysis, we select three state-of-the-art window-based synchrophasor estimation algorithms and compare different signal models, ROCOF estimation techniques and window lengths in datasets inspired by real-world acquisitions. In this sense, we are able to carry out a sensitivity analysis of the behavior of a PMU-based UFLS control scheme. Based on the proposed results, PMUs prove to be accurate ROCOF meters, as long as the harmonic and inter-harmonic distortion within the measurement pass-bandwidth is scarce. In the presence of transient events, the synchrophasor model looses its appropriateness as the signal energy spreads over the entire spectrum and cannot be approximated as a sequence of narrow-band components. Finally, we validate the actual feasibility of PMU-based UFLS in a real-time simulated scenario where we compare two different ROCOF estimation techniques with a frequency-based control scheme and we show their impact on the successful grid restoration.Comment: Manuscript IM-18-20133R. Accepted for publication on IEEE Transactions on Instrumentation and Measurement (acceptance date: 9 March 2019

A Passive Islanding Detection Method for Neutral point clamped Multilevel Inverter based Distributed Generation using Rate of Change of Frequency Analysis

Presently renewable energies have taken a special place in the world and most of the Distributed Generations (DGs) used in the interconnected power system are utilized, renewable energy resources. Due to the DG’s advantages, including use of renewable energy such as, clean nature, does not pollute environment and having endless nature the use of these renewable resources to produce electrical energy in the world are increasing in day to day life. One problem with such Distributed generators is an unintentional islanding phenomenon. Islanding occurs when a Distributed Generation continues to energize an isolated part of a power system even after it was disconnected from the main grid, which is surrounded by unpowered lines. Since islanding can cause hazardous conditions for people and equipment which is connected to it. As per IEEE 1547 DG Interconnection standards, islanding should be quickly detected within 2 seconds, by protective relays and inverters that are part of the DG system. In this paper, a new passive method to identify islanding states has been proposed, based on the rate of change of frequency analysis (ROCOF) for a multilevel inverter based solar distributed generation systems. This method is efficient for both connecting DGs to the network with or without the Inverter. This method is more efficient than the existing methods and reducing the Non Detection Zone (NDZ), which is the disadvantage of existing passive methods and also clearly differentiating between the Islanding and Non-islanding events. The simulation results, which are carried on the MATLAB/Simulink environment shows the performance of the proposed method

Anti-Islanding Protection of Distributed Generation Based on Social Spider Optimization Technique

Anti-islanding protection is one of the most important requirements for the connection of Distributed Generators in power systems. This paper proposes a Social Spider Optimization (SSO) algorithm to detect unintentional islanding in power systems with distributed generation. The SSO algorithm is employed to differentiate frequency oscillations in synchronous generator those caused by non-islanding events. The SSO algorithm is based on the forging strategy of social spiders, which generated vibrations spread over the spider web to determine the positions of preys or any other disturbances. The vibrations from the spider are used to detect the occurrence of islanding in the synchronous generator. The SSO algorithm has superior performance when tested with IEEE 34 bus distribution system. The taken test system is evaluated for different scenarios and load distribution. The proposed SSO algorithm detects the islanding and prevents the system from undue tripping and outages. Furthermore, this technique may apply to prevent the system from islanding and maintains the future Indian Distributed Generation (DG) system reliability

Passive Islanding Detection Technique for Integrated Distributed Generation at Zero Power Balanced Islanding

Renewable power generation systems have more advantages in the integrated power system compared to the generation due to fossil fuels because of their advantages like reliability and power quality. One of the important problems due to such renewable distributed generation (DG) system is an unintentional islanding. Islanding is caused if DG supplies power to load after disconnecting from the grid. As per the DG interconnection standards, it is required to detect the islanding within two seconds after islanding with the equipments connected to it. In this paper a new passive islanding detection method is presented for wind DG integrated power system with rate of change of positive sequence voltage (ROCOPSV) and rate of change of positive sequence current (ROCOPSC). The islanding is detected if both the values of ROCOPSV and ROCONSV are more than a predefined threshold value. The test system results carried on MATLAB shows the performance of the proposed method for various islanding and non islanding events with different power imbalances. The results conclude that, this method can detect islanding even at balanced islanding with zero non detection zone (NDZ)

Communication based loss-of-mains protection method by frequency correlation

Due to the increasing penetration of distributed generation (DGs) in the distribution network in high numbers and proportions, and its conspicuous impact on power system stability. This occurs during a wide system disturbance in the power system, the DGs will start to disconnect from the main source in large proportions. This will further affect the power system stability and causes damages to its components and DGs. This thesis investigates in the reliability, security, and efficiency of satellite and internet communications, specifically for loss of mains (LOM) protection and exploring the strengths, the weaknesses, the feasibility of each type of communications, and the requirements of communication system components. By using communications network to send Phasor Measurement Unit (PMU) data to DGs protection equipment that are connected at remote areas all over UK, the LOM protection can be improved, obtain synchronization, precision, and coordination among power protection components. Satellite communication is chosen as it makes a better communication method when it comes to the installation, construction, urban disruption, time saving, and the installation and annual cost on every participant. However, the high latency issue is approached and solved by making a few changes in the communication protocol format and the data requirements to reduce the effect of latency to a level that can be tolerated. This thesis presents the development of a novel LOM protection method based on communication and frequency correlation. The stability and sensitivity assessment will show that this method is highly secure and reliable. It can also withstand a communication delay of 120ms without causing any nuisance tripping, and have a relay response to LOM operation of a maximum of 1s. The thesis also presents a novel method in time delay estimation that has been developed for power system applications. This method is called the Linear Trajectory Path (LTP) and its performance fulfils the LOM synchronisation requirements by succeeding in determining the time delay between the two data streams within the tolerated estimation error of ±100ms.Due to the increasing penetration of distributed generation (DGs) in the distribution network in high numbers and proportions, and its conspicuous impact on power system stability. This occurs during a wide system disturbance in the power system, the DGs will start to disconnect from the main source in large proportions. This will further affect the power system stability and causes damages to its components and DGs. This thesis investigates in the reliability, security, and efficiency of satellite and internet communications, specifically for loss of mains (LOM) protection and exploring the strengths, the weaknesses, the feasibility of each type of communications, and the requirements of communication system components. By using communications network to send Phasor Measurement Unit (PMU) data to DGs protection equipment that are connected at remote areas all over UK, the LOM protection can be improved, obtain synchronization, precision, and coordination among power protection components. Satellite communication is chosen as it makes a better communication method when it comes to the installation, construction, urban disruption, time saving, and the installation and annual cost on every participant. However, the high latency issue is approached and solved by making a few changes in the communication protocol format and the data requirements to reduce the effect of latency to a level that can be tolerated. This thesis presents the development of a novel LOM protection method based on communication and frequency correlation. The stability and sensitivity assessment will show that this method is highly secure and reliable. It can also withstand a communication delay of 120ms without causing any nuisance tripping, and have a relay response to LOM operation of a maximum of 1s. The thesis also presents a novel method in time delay estimation that has been developed for power system applications. This method is called the Linear Trajectory Path (LTP) and its performance fulfils the LOM synchronisation requirements by succeeding in determining the time delay between the two data streams within the tolerated estimation error of ±100ms

Smart Distributed Generation System Event Classification using Recurrent Neural Network-based Long Short-term Memory

High penetration of distributed generation (DG) sources into a decentralized power system causes several disturbances, making the monitoring and operation control of the system complicated. Moreover, because of being passive, modern DG systems are unable to detect and inform about these disturbances related to power quality in an intelligent approach. This paper proposed an intelligent and novel technique, capable of making real-time decisions on the occurrence of different DG events such as islanding, capacitor switching, unsymmetrical faults, load switching, and loss of parallel feeder and distinguishing these events from the normal mode of operation. This event classification technique was designed to diagnose the distinctive pattern of the time-domain signal representing a measured electrical parameter, like the voltage, at DG point of common coupling (PCC) during such events. Then different power system events were classified into their root causes using long short-term memory (LSTM), which is a deep learning algorithm for time sequence to label classification. A total of 1100 events showcasing islanding, faults, and other DG events were generated based on the model of a smart distributed generation system using a MATLAB/Simulink environment. Classifier performance was calculated using 5-fold cross-validation. The genetic algorithm (GA) was used to determine the optimum value of classification hyper-parameters and the best combination of features. The simulation results indicated that the events were classified with high precision and specificity with ten cycles of occurrences while achieving a 99.17% validation accuracy. The performance of the proposed classification technique does not degrade with the presence of noise in test data, multiple DG sources in the model, and inclusion of motor starting event in training samples

Study on the effectiveness of commercial anti‐islanding algorithms in the prospect of mass penetration of PVs in low‐voltage distribution networks

In the coming years, distribution grids will be progressively flooded by renewable energy sources (RES) that will be interconnected with the main grid through power electronic converters. Photovoltaics (PVs) are one of the most promising renewable technologies even for densely built-up areas where space problems are inevitable. The high penetration prospect of PV facilities on low-voltage distribution networks raises questions regarding the necessity of advanced functions that will enable electronically coupled RES to support the operation of distribution grids and to enhance their reliability. In this context, the objective of this study is to investigate the effectiveness of various islanding prevention measures installed in commercial PV inverters, when multiple inverters are operating in parallel with a low-voltage distribution network (LVDN). Extensive experiments were performed under various PV penetration levels, linear/non-linear load and over/under voltage and over/under frequency conditions, as well as for various values of total harmonic distortion of the mains voltage. Further to the primary statistical analysis, the results were analysed in depth by advanced mathematical methods such as box plot and cluster analysis. The findings of this study indicate that commercial anti-islanding techniques present a high probability of failure in the case of multiple PV units at the same point of common coupling, calling for new and more advanced algorithms.European Commission, H2020, 65411

Real-time power system impedance estimation for DG applications: Using PV-inverter based harmonic injection method

On-line power system (PS) Thévenin equivalent impedance (TEI) estimation involves the reduction of the PS's complex circuit into a simple form that provides valuable insight into its state and behaviour. It finds application in numerous areas such as voltage stability monitoring and islanding detection. In the context of distributed generation (DG), on-line TEI estimation can be easily implemented in existing hardware to add functionality and improve the operation of power converters – the key components of DG systems. Two distinct methods of on-line PS TEI estimation exist. The passive method involves only measurement of voltage and current, whereas the active method involves injection of current into the PS and measurement of the response. This work is focused on the active method. Through a review of the available literature, limitations of past work are highlighted. It is shown that the nature of current injection varies greatly in different works and that evaluation of implementation performance is generally not thorough. Little consideration has been made of the effect of injection current level and frequency on the performance of on-line TEI estimation. Furthermore, the behaviour of the grid and its impact has not been thoroughly investigated. In this work, the active method is implemented in a three-phase PV-inverter and thoroughly tested in terms of its TEI estimation accuracy. Dependence of said accuracy on parameters such as the level of injected current and its frequency is shown to be high through tests performed on the live PS at two locations. These parameters are optimised such that TEI accuracy is maximised and the performance of the device is shown to be good compared to calibration equipment. The accuracy of PS TEI tracking is evaluated and quantified. Considerations are also made of the device's hardware limitations and their effect. A process by which a device's TEI estimation accuracy can be thoroughly evaluated is developed through this work. The behaviour of the PS's TEI is also investigated over long periods and characterised. It is found that the TEI remains steady around an average level in both test locations, with a low standard deviation. Consistency in results is found to be high between the two tests

AN INTELLIGENT PASSIVE ISLANDING DETECTION AND CLASSIFICATION SCHEME FOR A RADIAL DISTRIBUTION SYSTEM

Distributed generation (DG) provides users with a dependable and cost-effective source of electricity. These are directly connected to the distribution system at customer load locations. Integration of DG units into an existing system has significantly high importance due to its innumerable advantages. The high penetration level of distributed generation (DG) provides vast techno-economic and environmental benefits, such as high reliability, reduced total system losses, efficiency, low capital cost, abundant in nature, and low carbon emissions. However, one of the most challenges in microgrids (MG) is the island mode operations of DGs. the effective detection of islanding and rapid DG disconnection is essential to prevent safety problems and equipment damage. The most prevalent islanding protection scheme is based on passive techniques that cause no disruption to the system but have extensive non-detection zones. As a result, the thesis tries to design a simple and effective intelligent passive islanding detection approach using a CatBoost classifier, as well as features collected from three-phase voltages and instantaneous power per phase visible at the DG terminal. This approach enables initial features to be extracted using the Gabor transform (GT) technique. This signal processing (SP) technique illustrates the time-frequency representation of the signal, revealing several hidden features of the processed signals to be the input of the intelligent classifier. A radial distribution system with two DG units was utilized to evaluate the effectiveness of the proposed islanding detection method. The effectiveness of the proposed islanding detection method was verified by comparing its results to those of other methods that use a random forest (RF) or a basic artificial neural network (ANN) as a classifier. This was accomplished through extensive simulations using the DigSILENT Power Factory® software. Several measures are available, including accuracy (F1 Score), the area under the curve (AUC), and training time. The suggested technique has a classification accuracy of 97.1 per cent for both islanded and non-islanded events. However, the RF and ANN classifiers\u27 accuracies for islanding and non-islanding events, respectively, are proven to be 94.23 and 54.8 per cent, respectively. In terms of the training time, the ANN, RF, and CatBoost classifiers have training times of 1.4 seconds, 1.21 seconds, and 0.88 seconds, respectively. The detection time for all methods was less than one cycle. These metrics demonstrate that the suggested strategy is robust and capable of distinguishing between the islanding event and other system disruptions