Power System Blackouts Research Articles

Self-Organized Criticality and Trend Analysis in Time Series of Blackouts for the China Power Grid

This paper proposes effective evidence on the correlation between trend and self-organized criticality (SOC) of the power outage sequence in China. Taking the data series of blackouts from 1981 to 2014 in the China power grid as the research object, the method of V/S is introduced into the analysis of the power system blackout sequence to demonstrate their prominent long-time correlations. It also verifies the probability distribution of load loss about blackout size in the China power grid has a tail feature, which shows that the time series of blackouts in the China power grid is consistent with SOC. Meanwhile, a kind of mathematical statistics analysis is presented to prove that there is a seasonal trend of blackouts, and the blackout frequency and blackout size have not decreased over time but have an upward trend in the China power grid, thereby indicating that blackout risk may be increasing with time. The last 34 years’ data samples of power failure accidents in the China power grid are used to test the proposed method, and the numerical results show that the proposed self-organized criticality and trend analysis method can pave the way for further exploration of the mechanism of power failure in the China power grid.

A robust optimization approach for protecting power systems against cascading blackouts

Due to uncertainties and the complicated intrinsic dynamics of power systems, it is difficult to predict the cascading failure paths once the cascades occur. This makes it challenging to achieve the effective power system protection against cascading blackouts. By incorporating uncertainties and stochastic factors of the cascades, a Markov chain model is developed in this paper to predict the cascading failure paths of power systems. The transition matrix of Markov chain is dependent on the probability of branch outage caused by overloads or stochastic factors. Moreover, a robust optimization formulation is proposed to prevent the cascading blackouts by optimal load shedding and generation control for multiple cascading failure paths with relatively high probabilities. Since each state on the cascading failure paths can be described by one convex set, the proposed robust optimization problem is equivalent to the best approximation problem in Euclidean space. Thus, an efficient numerical solver based on Dykstra’s algorithm is employed to deal with the robust optimization problem. In theory, we provide a lower bound for the probability of preventing the cascading blackouts of power systems. Finally, the proposed approach for power system protection is verified by a case study of IEEE 118 bus system.

A Case Study on Self Healing of Smart Grid With Islanding and Inverter Volt–VAR Function

Increased variability, and uncertainty getting induced in the power system with the growing penetration of renewable resources necessitates the need for power flow stability studies, analysis, and contingency responses to address various operational concerns. Utilization of Volt–VAR (VV) functions of inverters to mitigate voltage violations have been introduced in recent grid integration standards. All the new grid connected nonsynchronous generators are expected to provide a composite power delivery at continuous rated output power as well as they should participate in dynamic reactive power injection while maintaining the power factor within an acceptable range. The present case study is based on a voltage instability incident which led to partial blackout in state grid, which is also identified as one of the root causes of power system blackouts worldwide. The state power grid case study has been simulated using real-time power grid data collected from PMUs/SCADA system for all 400 and 220 kV nodes. The location for integrating solar PV renewable energy in state grid is identified using an analytical methodology, by deriving an evaluation index represented as improved power stability index. An approach for employing VV functionality of inverter based grid connected solar photovoltaic system, as a solution to address the voltage instability issue that occurred in the conventional grid, is elaborated. The article also illustrates islanding using generator coherency identification.

Preventing cascading failure of electric power protection systems in nuclear power plant

Cascading failure is the main cause of large blackouts in electrical power systems; this paper analyzes a cascading failure in Hanbit nuclear power plant unit two (2) caused by a circuit breaker (CB) operation failure. This malfunction has been expanded to the loss of offsite power (LOOP). In this study, current practices are reviewed and then the methodologies of how to prevent cascading failures in protection power systems are introduced. An overview on the implementation of IEC61850 GOOSE messaging-based zone selective interlocking (ZSI) scheme as key solution is proposed. In consideration of ZSI blocking time, all influencing factors such as circuit breaker opening time, relay I/O response time and messages travelling time in the communication network should be taken into account. The purpose of this paper is to elaborate on the effect of cascading failure in NPP electrical power protection system and propose preventive actions for this failures. Finally, the expected advantages and challenges are elaborated.

A dynamic-adaptive load shedding methodology to improve frequency resilience of power systems

Frequency stability is one of the major concerns for secured operation of a power system. A severe disturbance causes rapid frequency decline, which may instigate power system blackout in an extreme case. In emergency situations, load shedding is applied to arrest frequency excursion. Therefore, a load shedding scheme needs to be sufficiently adaptive to tackle sudden generation loss. Note that conventional load shedding schemes may fail to provide satisfactory frequency response in certain cases. To address this challenge, a new dynamic-adaptive load shedding mechanism is proposed in this paper. The proposed methodology considers the actual contingency size, frequency sensitivity due to load damping, frequency response and ranking of load buses according to voltage stability index. The method ensures that the actual load cut is sufficiently close to the required load shedding to keep system frequency within the given acceptable limit. To do so, it ranks the load buses within a zone and dynamically selects appropriate feeders for load shedding. It also proportionally applies higher amount of load shedding to relatively weaker buses for taking care of the voltage stability. To examine the performance of the proposed scheme, it is applied to the IEEE 39 bus test system that is divided into six zones. It is found from simulations that for 600 MW, 700 MW and 900 MW generation loss events in zone-3, the required amounts of load shedding are 430 MW, 531 MW and 730 MW respectively to keep system frequency above 49.1 Hz. However, these quantities increase to 450 MW, 550 MW and 749 MW respectively to retain system frequency above 49.2 Hz. Furthermore, the impact of 400 MW generation loss in zone-1 is explored. It is found that after shedding additional loads in zone-1 and zone-2, the risk of tie line overloading is mitigated that eventually assures tie line security. In addition, performances of the proposed algorithm are compared to that of an adaptive technique and a semi-adaptive technique. It is noticed that the proposed methodology provides better results than the existing methods. Therefore, it can be articulated that the developed scheme ensures the correct amount of load shedding to maintain satisfactory frequency response and voltage stability.

Abnormality Detection of Cast-Resin Transformers Using the Fuzzy Logic Clustering Decision Tree

Failures of cast-resin transformers not only reduce the reliability of power systems, but also have great effects on power quality. Partial discharges (PD) occurring in epoxy resin insulators of high-voltage electrical equipment will result in harmful effects on insulation and can cause power system blackouts. Pattern recognition of PD is a useful tool for improving the reliability of high-voltage electrical equipment. In this work, a fuzzy logic clustering decision tree (FLCDT) is proposed to diagnose the PD concerning the abnormal defects of cast-resin transformers. The FLCDT integrates a hierarchical clustering scheme with the decision tree. The hierarchical clustering scheme uses splitting attributes to divide the data set into suspended clusters according to separation matrices. The hierarchical clustering scheme is regarded as a preprocessing stage for classification using a decision tree. The whole data set is divided by the hierarchical clustering scheme into some suspended clusters, and the patterns in each suspended cluster are classified by the decision tree. The FLCDT was successfully adopted to classify the aberrant PD of cast-resin transformers. Classification results of FLCDT were compared with two software packages, See5 and CART. The FLCDT performed much better than the CART and See5 in terms of classification precisions.

Voltage Stability Augmentation using Shunt FACTS

With the ever increasing demand of power, the major concern that has aroused is the problem of voltage instability. Due to voltage instability several major power system failures and blackouts occur. Voltage stability thus becomes a necessity. For this FACTS devices like SVC, STATCOM, etc. are used. Load Flow analysis and Continuation Power Flow Analysis is done to identify the weak buses and FACTS devices are installed in these weak buses to enhance the voltage stability. This paper presents a network formulation of IEEE 30 Bus test system using MATLAB and PSAT software and then comparing the effect of SVC and STATCOM for voltage stability enhancement.

Web Application based Electric Load Forecasting using Machine Learning

Electricity plays an important role in many activities supporting all kinds of developments. To supply adequately and efficiently the demand required can protect the electric power system blackout. Nowadays we see that companies or industries working on a large scale usually consumes enormous amount of electric power, which leads to high opertional costs and this has been recognized as a main challenge in terms of economy. The purpose of the short-term electricity demand prediction is to forecast in advance the system load. The basic idea of this project is to determine the load of a user and alert the user in order to reduce consumption of electricity through a web interface accordingly. An efficient electricity predicton model is needed to minimize the electricity bills. Here we are using web application through which we will interact with the user. Power demand forecasting is important for economically efficient operation and effective control of power systems and enables to plan the load of generating unit. A precise load forecasting is required to avoid high generation cost and the spinning reserve capacity. Under-prediction of the demands leads to an insufficient reserve capacity preparation and can threaten the system stability, on the other hand, over-prediction leads to an unnecessarily large reserve that leads to a high cost preparations. Multiple linear regressions are the earliest technique of load forecasting methods. Here, unit of electricity is the main target(dependent) variable that influence the load. The other influential variables are identified on the basis of correlation analysis with load. This study uses the linear static parameter estimation technique as they apply to the twenty four hour off- line forecasting problem.The results of the developed system is a convenient way of monitoring and forecasting electricty usage through the use of web application.

Robust System Separation Strategy Considering Online Wide-Area Coherency Identification and Uncertainties of Renewable Energy Sources

With the fast growth of renewable energy sources (RES), more and more uncertainties are involved and influencing the stable operation of power systems. Controlled islanding is the last measure to prevent power system blackouts, thus this paper aims to propose a novel model of system separation based on Online Coherency Identification and Adjustable Robust Optimization Programming (OCI-AROP) for minimizing load shedding considering the uncertainties of RES. First, Fuzzy C-Means (FCM) clustering method with F -statistics is utilized to identify the coherent generator groups with the frequency data measured by Phasor Measurement Units (PMUs). Then, the OCI-AROP model considering coherent group constraints, connectivity constraints and robustness constraints about RES are presented. Finally, the case studies on IEEE-39 bus system and WECC-179 bus system are employed to demonstrate the effectiveness of the proposed OCI-AROP model, and comparisons among the OCI-AROP model and the other models are also given to show its superiority.

Power failure evolution model considering voltage stability

Voltage instability is often associated with power system blackout. In order to study the evolution mechanism of power failure and voltage instability in power failure, this paper proposes a new kind of power failure evolution model that considers voltage stability. The theory of static voltage stability analysis is introduced into the modelling. According to the simulation results of power failure in an IEEE39 system, the model proposed in this paper verifies the self-organized criticality of power network. It was also found that voltage instability lead to the increase of the frequency and scale of power failure. Furthermore, the frequency of load nodes in voltage instability accidents is calculated and the set of weak nodes is obtained.

The preventive measure of power system blackout considering the thermal stability and voltage stability

In order to ensure the reliable transmission of electric energy, some preventive measures are adopted to improve the stability of the power system. The thermal stability of the system can be improved by increasing the transmission capacity. And the voltage stability of the system can be improved by installing reactive power compensation device. In this paper, the above two methods are integrated. Firstly, the objective functions of thermal stability, voltage stability and economy of the system are established. Then the multi-objective particle swarm optimization (MOPSO) algorithm is used to solve the optimal configuration scheme of the thyristor controlled series capacitor (TCSC) and the static var compensator (SVC). Finally, according to the simulation results of the classical power failure model, the effectiveness of the proposed method is verified.

Improvement strategy for robustness of power grid complex network by distributed photovoltaic power system

The robustness of power grids plays a very important role in reality. The blackout of power system can make a serious damage for the normal running of whole city or whole area and the loss caused by it is inestimable. This paper aims to propose a simulation method to evaluate the robustness of a power grid and some effective strategies to improve the robustness. Complex network keeps the most important characters of a power grid, and makes the simulation process concise. This paper studies how characters influence the robustness and proposes some conclusions and suggestions for enhancing effectively the robustness by changing these characters. In this paper, for the robustness of a network, it can be found that some parameters like tolerance coefficient are more dominant than others. Some parameters interact obviously, like capacity addition ratio and addition station ratio. Improper capacity addition strategy may have a negative effect on the network. And load node with big degree and small flow is likely an excellent choice to be added capacity by distributed photovoltaic power station. The simulation method and improvement strategy can be used in the engineering of power grid and establishment of distributed photovoltaic power system.

Priority based Energy Distribution for Off-grid Rural Electrification

Rural off-grid electrification is always very challenging due to mostly using limited output renewable energy source such as solar power system. Owing to its nature of power generation that depends on weather condition, the reliability in power provision is often affected by uncontrolled overwhelming usage or bad weather condition. Total power system blackout that frequently happens not only disturb the night activity routine but also can be life threatening if the rural community is unable to initiate telephony communication with the outside world during state of emergency due to power outage. In order to reduce the frequency of total system blackout caused by the reasons mentioned, we proposed a priority-based energy distribution scheme to assist the off-grid standalone solar power system to improve the overall operating hours of the critical appliances in rural areas. The scheme takes into consideration of criticality of the home appliances as defined by the rural users, so that the system would distribute power supply based on the current state of the system with an objective to prolong the service availability of the critical appliances that matter the most to the users. The scheme has been evaluated under simulated scenario and has shown a 100% operation availability of the critical appliance is achievable even during bad weather season that has very low solar input.

An Effective Multi-Solution Approach for Power System Islanding

Controlled islanding is known as a last resort to prevent power system blackouts. The most important challenge in this context is the appropriate selection of separation points in a very short time considering the practical constraints. In this regard, several islanding methods have been proposed until now. Among them, the methods that provide the appropriate solutions are usually very complicated and difficult to implement in the real-time application of power system separation. Also, all methods result in one islanding solution, which may not be optimal due to the limitation of the commonly used objective function. To overcome these limitations and reach a comprehensive solution, this paper proposes a straightforward multi-solution approach through a suggested hierarchical spectral clustering algorithm. In this concept, the most desirable islanding scenario could be selected based on secondary criteria to reach more sustainable islands. In the proposed method, the hierarchical clustering algorithm, which has good records in other applications, is improved such that the generator coherency constraint can be considered in the clustering process. Meanwhile, the transmission lines without remote-controllable circuit breakers could be easily excluded from the islanding solutions. The proposed method is tested using the model of the IEEE 39-bus test system. Furthermore, to evaluate the computational effectiveness and accuracy of the method in a large-scale grid, the model of Khorasan Regional Electric Company (KREC) power system (which is the biggest part of Iran power system) is used. The comparative analysis with the state-of-the-art methods verifies the superiority of the proposed approach.

Dual Hidden Failure Model for Cyber Physical Power System

With the increasingly close coupling between the cyber network and the physical power grid in Cyber Physical Power System (CPPS), the failure of cyber network has become an important cause of blackout. Equipment failure or abnormal operation state will be appeared without alarm in complex cyber network, which brings hidden failures not been diagnosed and dealt with in time. The invisibility and harmfulness of hidden failures pose a significant threat to the safe, reliable, and stable operation of CPPS. The dual hidden cascading failure model for CPPS is proposed to reveal the failure characteristics of CPPS accurately, considering not only the hidden faults in the power grid, but also the hidden failures in the cyber network. With the different coupled relationships between power systems and cyber networks, the cyber network topology with corresponding failure threshold is obtained according to the simulation of the model with different hidden failure rates of cyber network nodes. When the hidden failure rate is lower than the failure threshold, the scale-free topology cyber network coupled mode is adopted between power grids and cyber networks, otherwise the small-world topology cyber network coupled mode is adopted, which can effectively reduce the risk of power system blackouts. The method proposed in this paper is tested by using IEEE-118 power system and its cyber network. The results show that the method is effective.

Application of Dual Artificial Neural Networks for Emergency Load Shedding Control

This paper proposes a new model in emergency control of load shedding based on the combination of dual Artificial Neural Network to implement the load shedding, restore the power system frequency and prevent the power system blackout. The first Artificial Neural Network (ANN1) quickly recognizes the state with or without load shedding when a short-circuit occurs in the electrical system. The second Artificial Neural Network (ANN2) identifies and controls the selection of load shedding strategies. These load shedding strategies include pre-designed rules which is built on the AHP algorithm to calculate the importance factor of the load units and select the priority of the load shedding. In case the ANN1 results in a load shedding, the load shedding control strategy is immediately implemented. Therefore, the decision making time is much shorter than the under frequency load shedding method. The effectiveness of the proposed method is tested on the IEEE 39-bus system which proves the effectiveness of this method.

An Online Search Method for Representative Risky Fault Chains Based on Reinforcement Learning and Knowledge Transfer

In the analysis of cascading outages and blackouts in power systems, risky cascading fault chains should be accurately identified in order to do further block or alleviate blackouts. However, the huge computational burden makes online analysis difficult. In this paper, an online search method for representative risky fault chains based on reinforcement learning and knowledge transfer is proposed. This method aims at promoting efficiency by exploiting similarities of adjacent power flow snapshots in operations. After the “representative risky fault chain” is defined, a framework of tree search based on Markov Decision Process and Q-learning is constructed. The knowledge in past runs is accumulated offline and then applied online, with a mechanism of knowledge transition and extension. The proposed learning based approach is verified on an illustrative 39-bus system with different loading levels, and simulations are carried out on a real-world 1000-bus power grid in China to show the effectiveness and efficiency of the proposed approach.

Fault Examination of a 400kV Switchyard of 500MW Thermal Power Plant

The present paper deals with Load flow and stability analysis of thermal power plant. In the thermal power plant switchyards plays vital role in various control, security and monitoring features that enable robust routing of power from generators to loads through a complex network of transmission lines. Analysis of such switchyard is the key and most important task for planning of the Generation plant. In the past there have been many widespread blackouts in interconnected power systems, so it is essential to assure that power system must operate more stablely and reliably. In this present discussion, the adopted system of 500MW Thermal power plant comprises a generator bus connected to 6 load buses and 1 utility bus interconnected with 400kV transmission lines. Power flow studies and fault analysis are simulated in Power World simulator environment. These studies include the operating points when faults occurred in the system for the system reliability, line outages and its impact on other transmission lines such as incoming or outgoing lines and also on bus sections. Prior to planning and construction of substation, these studies help to calculate indices that reflect the reliability performance of the system. These analyses give the detail overview for future growth of power generation and subsequent extension of switchyard.

Performance Evaluation of Neuro Fuzzy Clustering for Hydro Thermal Power System

To meet increasing power demands and reduce carbon emission additional renewable sources of power are added to existing thermal unit. Even network frequency change by 1% due to change in speed, may result in loss of synchronization with the rest of power system and finally resulting in power system black-out. Inter-connection of hydro units to existing thermal system can allow to transmission system to operate at full capacity. The paper investigates LFC by adding non-linearity’s to the thermal and hydro-thermal systems. Further PID and Neuro-Fuzzy Controllers are compared for these systems . A multi section steam turbine with re-heater was used for a single area system modeled in MATLAB, later two single area systems were combined to create a two area system, and its dynamics are studied by creating load perturbations. Form simulation studies it is shown that the proposed Neuro-Fuzzy controller was able to attain a setting time of 10 Sec which is comparatively lower than other existing speed controllers. The time domain response result of hydropower system proves that it provides more rapid output response and minimal overshoot.

A smart load management system based on the grasshopper optimization algorithm using the under-frequency load shedding approach

Load management represents one of the main constraints when considering smart grids. In addition, load management is a significant challenge for power system security operations. The smart techniques in load management endow the system with the ability to restore its normal or stable operation after being subjected to any disturbances. When the load exceeds the generation, the system stability is affected, which leads to cascade outages and shutdown of the major parts of the power system, causing the frequency decay effect. Fast load shedding (LS) is the best way to avoid cascading outages and power system blackouts. This paper proposes an innovative, accurate, reliable and fast under-frequency load shedding (UFLS) technique based on the grasshopper optimization algorithm (GOA). LS is considered in this research as a constrained optimization problem. The objective function is to minimize the amount of load shed while maximizing the lowest swing frequency at all stages. To validate the proposed GOA-based UFLS, a comparison with adaptive, particle swarm optimization (PSO) and genetic algorithm (GA) UFLS is carried out for different disturbances. Two different case study systems are considered to test the accuracy and reliability of the proposed algorithm: the IEEE 9-bus and 39-bus systems. The proposed GOA and PSO are coded using the MATLAB environment. Different operating cases involving outage of multiple generators and increasing load are implemented to validate the proposed GOA. The DigSilent power factory software is used as a platform for simulating the power system under study when subjected to different disturbance levels. The results verify the accuracy and reliability of GOA in minimizing the amount of load shed and maximizing the lowest swing frequency while satisfying the constraints. Moreover, GOA achieves a faster solution than PSO and GA.