Adeniyi A. Babalola

Real-Time Cascading Failures Prevention for Multiple Contingencies in Smart Grids Through a Multi-Agent System

Cascading failures (CF) is complex in nature and difficult to accurately model or solve mathematically. The current industry approach to preventing CF, which leads to blackout event, involves incurring losses. In this paper, a technique based on an adaptive multi-agent system algorithm is implemented to prevent CF without loss incurrence. The algorithm uses mathematical combinations heuristically selected through the use of sensitivities obtained from the economic dispatch history of the power system to redispatch the power from the generators. This approach enables the implementation of the algorithm on systems of any size. The algorithm is experimentally applied in real-time with the consideration of necessary constraints as it halts the occurrence of CF. The test system is an experimental set up of the generation and transmission side of the IEEE 30-bus system using a reconfigurable smart grid laboratory hardware developed for testing algorithms requiring two-way communication capabilities. It was first showed that the test system will experience CF if nothing is done to prevent CF after the occurrence of a contingency. A detailed experimental analysis of the ensuing blackout event is given. The algorithm was used to prevent CF in the system after the occurrence of N-1 and N-1-1 contingencies. The algorithm was also tested on a larger system, the IEEE 118-bus system, through simulation. The experimental and simulation results affirm the efficacy of the proposed algorithm for systems of any size. In fact, it was discovered from our results that the large number of generators in large systems helps the algorithm converge faster than it does for small systems, which have restricted resources and combinations.

Intelligent mitigation of blackout in real-time microgrids: Neural Network Approach

In this paper, a novel application of Artificial Neural Networks (ANN) is deployed to prevent blackout in a microgrid after N-1-1 contingency condition. In fact, microgrids are vulnerable to disturbances and abnormal conditions due to their inherent small inertia. Therefore, stability of microgrids after a disturbance turns into a challenge in power systems. The key contribution of this paper is to utilize the artificial intelligence concept to prevent cascading failure practically at early stages and to make microgrids more reliable and robust by intelligent and adaptive re-dispatch of power. The proposed ANN control approach is tested on an experimental testbed microgrid. Experimental results verify the robustness, accuracy, and effectiveness of the ANN method for preventing cascading failure in addition to providing voltage and frequency stability after initiation of a disturbance.

Experimental implementation of Multi-Agent System Algorithm to prevent Cascading Failure after N-1-1 contingency in smart grid systems

Cascading Failure and Blackout Event is one of the major concerns to all in the Power System industry. Recent literature mainly focused on predicting the occurrence of this catastrophic event and suggestions on ways to curtail the spread of Cascading Failure. No research, to our knowledge, as focused on preventing Cascading Failure without the use of high cost precautionary steps. This work utilizes an adaptive Multi-Agent System Algorithm in a smart grid system with two-way communication capability to successfully prevent Cascading Failure and Blackout Event after N-1-1 transmission line contingency condition with more than one transmission line overloaded. The proposed algorithm stops the cascading failure after the N-1-1 contingency by redispatching the power from the generators through the use of pre-stated mathematical combinations and the consideration of necessary constraints and factors. We obtained experimental results instead of the popular simulation results that has been the norm for publications on Cascading Failures and Blackout Event. The generation and transmission side of IEEE 30-bus system was used as the experimental test bed. It was ascertained that the test bed can experience a Cascading Failure and Blackout Event if no preventive measure was taken. We then utilized the algorithm to prevent Cascading Failure. The experimental results affirm the efficacy of the proposed algorithm.

Power system simulation and contingency ranking using load bus voltage index

Power system transmission network consists of thousands of transmission lines utilized to enable the system function as required. Components failure in the transmission network accounts for most of the transient instabilities and/or blackouts that have occurred in recent years. Therefore, it is very important to identify the components of the network which are capable of causing blackout in case of failure for proper transmission planning and operations. Various indices have been provided by some authors for contingency analysis in power system Dynamic Security Assessment (DSA) but most of which are computationally demanding. In this paper, a relatively easier index for identifying the critical elements in the transmission network is provided base on the load voltage profile. It is a common experience that when a fault occurs in a power system, the impacts are usually felt at the load centers. Therefore, the load bus voltage profile would provide a true measure of DSA. The index is compared to the generator angle profile to validate its effectiveness and it closely depicts the changes in the rotor angles of the machines. This index is applied and tested on the IEEE 14-Bus test system in MATLAB environment through the use of modelling and simulation.

Effect of Static VAR Compensator positioning on a grid-connected wind turbine-driven Squirrel Cage Induction Generator

Generation of electricity using wind power has received considerable attention worldwide in recent years as the power grid is being geared towards increased smartness. Various wind turbine generator models have been developed. Conventional wind turbines use Squirrel Cage Induction Generators. A Squirrel Cage Induction Generator (SCIG) always consumes reactive power. In most cases, this is undesirable, because it can lead to voltage instability, particularly in case of large turbines and weak grids. Therefore, it is pertinent to adequately compensate for the reactive power supply in the wind turbine squirrel cage induction generator systems. In this paper, the characteristics of a 2-mass model wind turbine connected to a SCIG are investigated. This investigation is conducted on a simulated model with the whole system connected to the grid and given a reactive power support through a Static VAR Compensator (SVC). The simulation result shows that the positioning of SVC alters the behavior of SCIG with SCIG generating some reactive power when SVC is connectedacross its terminal.

RTDS Modeling of a Hybrid-Source Autonomous Electric Microgrid

Abstract Presented in this article is a scaled model of a hybrid-source autonomous electric microgrid, which is developed using a Real Time Digital Simulator, to provide a laboratory-level research facility on renewable energy-based electric microgrid systems. Three energy sources: a pico-hydropower emulator, a diesel engine emulator and a wind turbine emulator; are inter-connected at a point of common coupling, with the hybridized source supplying an aggregate of electrical load. A control scheme is developed that employs the synchronous machine-based pico-hydropower emulator as grid-forming source, while the other two sources feed the grid-former. Since models demonstrated with simulated weather data and load demand profiles may not accurately represent the operational behaviours of the actual system in the real-world scenario, a real-life wind speed profile is implemented on the wind turbine emulator to investigate the behaviours of the lab-microgrid under a near-real-world operating scenario. Balance reactive power flow is established among the three source emulators as a result of which the frequencies of the emulators are synchronized. A proper fault ride-through characteristic is also exhibited by the system. These show that the near-real-world behaviours of the lab-microgrid closely match the operational performance of the actual microgrid in the real-world scenario. The application could, therefore, be adopted as an affordable laboratory tool in research activities and as an instructional kit in teaching on the autonomous microgrid.

Experimental Transient Stability Analysis of MicroGrid systems: Lessons learned

A Transient Stability Analysis of a low inertia microgrid system is presented in this paper. The analysis is performed on experimental data collected for different case scenarios of grid disturbances. The test bed consists of a reconfigurable 100kW MicroGrid system with more than 10 rotating generators, PV, and Wind penetration capabilities. Both islanded and non-islanded situations are considered for a solid three phase fault analysis and comparison. The paper also presents the results of fault scenario when the rotating machines are pushed to an unstable region. The experimental results and system behavior observed show interesting phenomena. The MicroGrid showed higher resiliency for faults than expected even if the relative angles are more than 160° and even if the fault is sustained for a long time.

Real-time measurement of frequency using affordable rotary encoder and LabVIEW

The accuracy of measurements from a rotary encoder is of high importance and the level of accuracy desired will depend on the nature of the application in which the encoder is used. Due to the various errors in the rotary encoder measurement during real-time usage, this work has designed a LabVIEW program that exempts erroneous readings that are not within the chosen range, forms an array of readings within the acceptable range and finds the mean of the array once the size of the array is equal to the chosen size of the array stipulated in the program. This mean value is given as the actual measurement. This gives a more accurate real-time measurement than is the case without the program. A motor-generator set frequency measurements is used to validate the effectiveness of this program and frequency readings on the inverter driving the motor are compared with that displayed on the system through the program. Some analyses are carried out in order to ascertain the dependency of the accuracy of the measurements acquired on the array size utilized in the program.

Adaptive Immune System reinforcement Learning-Based algorithm for real-time Cascading Failures prevention

Abstract Artificial intelligent algorithms have found a wide-range of applications in power systems, especially in solving long-existing problems immune to non-intelligent algorithms. Cascading Failures (CF), one of such problems, require load shedding as a current industrial solution. Load shedding results in losses to all power system stakeholders. This work proposes the use of an Artificial Immune System (AIS) algorithm to intelligently adjust the power output of the generators in the power system relative to one another in real time to prevent CF. AIS gives the artificial intelligent algorithm reinforcement learning capability by enabling it to pick the appropriate combination(s) for a particular system state; hence, the algorithm is called Immune System Reinforcement Learning-Based (ISRL-Based) algorithm. The algorithm was trained offline using both static and dynamic power equations and the effectiveness of both approaches was evaluated through statistical deviation. Analyses showed that using dynamic equations resulted in a more accurate solution than the static equations. CF was dynamically simulated on the IEEE 118-Bus system after an N-2 contingency, the results obtained agrees with the results from the analysis of the 2003 Northeast USA CF event. The effectiveness of the algorithm and online training were also experimentally validated after an N-1-1 contingency in a nine-bus system.

Real-Time Cascading Failures Prevention Through MAS Algorithm and Immune System Reinforcement Learning

Abstract The current options power engineers have during a pending CFs event have been limited to those that require load shedding. In this work, a novel algorithm is proposed that avoids loss incurrence as it does not use load shedding and involves real-time power redispatch from the generators through an appropriate combination. This method requires that the system possesses MAS capabilities to intelligently effect the power changes. A tool is needed to select the right combination for each system state as the use of lookup table and heuristics would be ineffective for large systems. The innovative use of AIS, as a reinforcement learning tool and not an optimization tool, is proposed for selecting the appropriate combination. The results obtained when lookup table, heuristics, and AIS are used to select the combination for a real-time experimental system are compared. The test system is the generation and transmission side of IEEE 30-bus system; and the CFs prevention algorithm is activated once the system is in an N-1 contingency state. IEEE 118-Bus System was also used to test the efficacy of AIS-MAS algorithm on large systems. The AIS-MAS algorithm proposed showed great promise for preventing CFs.