Istemihan Genc

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• Doctor of Science (D.Sc.)
• Professor (Full) at Istanbul Technical University

This study addresses the problem of ensuring accurate online transient stability prediction in modern power systems that are increasingly dependent on smart grid technology and are thus susceptible to cyber-attacks. Despite technological advancements, a considerable gap remains in the resilience of machine learning algorithms for stability predicti...

This paper proposes an emergency control scheme based on decentralized model predictive control (MPC) to prevent transient instabilities occurring in power systems. After an onset of transient instability due to a contingency, the control scheme acts on the intercept valves of the steam turbines and the excitation systems of the generators that are...

Precise forecasting of renewable energy generation is crucial for ensuring grid stability and enhancing the efficiency of energy management systems. This research develops and rigorously evaluates a range of deep learning models—such as Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRUs), and Bidir...

This work presents a novel approach for real-time transient stability prediction in power systems, combining synchrophasor data with model-driven dynamic state estimations. The proposed method integrates dynamic state information, obtained through the Square Root Unscented Kalman Filter (SR-UKF), with an ensemble of machine learning (ML) algorithms...

Maximizing Power Point Tracking (MPPT) is an essential technique in photovoltaic (PV) systems that guarantees the highest potential conversion of sunlight energy under any irradiance changes. Efficient and reliable MPPT technique is a challenge faced by researchers due to factors such as fluctuations in irradiance and the presence of partial shadin...

In forecasting the future energy consumption and generation at the national level, strategic planning for both the medium and long term becomes imperative. The trajectory of renewable energy contribution to the smart grid, whether in the short or long term, significantly influences the grid's operational dynamics. This study is dedicated to the dev...

Cyber-attacks can degrade the performance of online transient stability prediction (TSP) and assessment functions offered for power systems. This paper proposes a cyber-resilient real-time TSP framework created by considering both perspectives, of the attacker, and of the smart grid operator. From the attacker’s viewpoint, to minimize the attack co...

Microgrids offer superiorities such as reducing energy costs and increasing the quality of energy, with the use of renewable energy sources and the effective use of energy storage unit created with innovative batteries. Furthermore, this structure, which helps to reduce the carbon footprint, will become undeniably critical to use in near future wit...

Precise electric load forecasting at different time horizons is an essential aspect for electricity producers and consumers who participate in energy markets in order to maximize their economic efficiency. Moreover, accurate prediction of the electric load contributes toward robust and resilient power grids due to the error minimization of generato...

In the future, the load demand due to charging of large numbers of electric vehicles (EVs) will be at such a high level that existing networks in some regions may not afford. Therefore, radical changes modernizing the grid will be required to overcome the technical and economic problems besides bureaucratic issues. Amendments to be made in the regu...

Increased integration of renewable energy resources into the grid may create new difficulties for ensuring a sustainable power grid which drives electric utilities to use a number of cost-effective techniques such as Dynamic line rating (DLR) that enable them to run power networks more efficiently and reliably. DLR forecasting is a technique devise...

The expansion of power systems over large geographical areas renders centralized processing inefficient. Therefore, the distributed operation is increasingly adopted. This work introduces a new type of attack against distributed state estimation of power systems, which operates on inter-area boundary buses. We show that the developed attack can cir...

This paper proposes a novel optimization method for wind power investment to find the optimal location and sizing of multiple wind farms considering both the economic and security aspects of power system operation and planning. The proposed approach maintains the system's security against transient instabilities, while improving the voltage profile...

Transient stability assessment involves the determination of whether all generators in the power system would maintain synchronous operation when it is subjected to a large disturbance. Assessing the transient stability status of a power system in an accurate and timely manner is of high importance in order to plan and initiate relevant corrective...

In this paper, we propose a novel methodology based on Koopman mode analysis to predict the transient stability of a power system in real-time. The method assesses the stability of the system based on a sliding sampling window of PMU measurements, and it detects the evolving instabilities by predicting future samples and investigating the computed...

Smart grid's objective is to enable electricity and information to flow two-way while providing effective, robust, computerized, and decentralized energy delivery. This necessitates the use of state estimation-based techniques and real-time analysis to ensure that effective controls are deployed properly. However, the reliance on communication tech...

Transient stability prediction is essential for maintaining the reliability and integrity of power systems. In recent studies, deep learning models have been proposed and extensively utilized in predicting the transient stability for both off-line and online purposes with less emphasis on the quality of training datasets used in their methods. Howe...

A massive challenge for future cities is being environmentally sustainable by incorporating renewable energy resources (RES). At the same time, future smart cities need to support resilient environments against cyber-threats on their supported information and communication technologies (ICT). Therefore, the cybersecurity of future smart cities and...

In this study, a novel methodology based on signal processing and machine learning approaches is proposed for real-time transient stability prediction (TSP) in power systems where the signals obtained from PMUs are utilized. The proposed method for TSP takes the computed energy of PMU signals in a window of measurements, as an input to a classifier...

In recent years, machine learning (ML) techniques have gained popularity in facilitating real-time transient stability assessment and prediction for early detection of blackouts in power systems. Conventionally, synchrophasor measurements, as the real-time indicators of system dynamics, are fed into the ML-based models. However, if the quality of t...

Studies on transient stability constrained optimal power flow (TSCOPF) have become crucial for power systems to guarantee their dynamic securities against credible contingencies, while their optimum operations are to be continuously projected under changing conditions. However, the current approach to the TSCOPF problem is not sufficient to meet th...

Geçtiğimiz yıllarda, dünya çapında farklı güç sistemlerinde, çok sayıda geniş çaplı enerji kesintileri meydana gelmiş, bu kesintiler milyonlarca tüketicinin olumsuz etkilenmesine neden olmuş ve büyük miktarda mali zararlara neden olmuştur. Elektrik güç sistemi tasarımı ve işletmesinde kritik bir role sahip sistem kararlılığı, günümüzdeki önemini ko...

Online monitoring of the power system is a vital application for enhancing the situational awareness capabilities of the system. Rapid integration of phasor measurement units in the network enables transmission system operators to analyze the events in real time due to their high reporting rates. Real-time detection and classification of the fault...

A fast and accurate transient stability assessment is essential for maintaining the reliability and integrity of the power system where quick corrective control actions are to be taken to prevent cascading outages. In this work, two separate classifiers based on long short-term memory networks are proposed to predict the stability status of a power...

Recently, technological developments in PMUs have led to a widespread deployment of PMUs in electrical power systems to carry out many online applications, thanks to their high reporting rates and synchronized measurements. Since it may not be feasible to deploy a PMU on every bus in the system, a selected number of buses can be chosen for the depl...

In this paper, a new real-time defensive islanding method, which is adaptive to the operating conditions is proposed. In the method, a number of candidate islanding schemes are generated using both model- and measurement-based islanding algorithms after detecting a severe fault in the system by means of a new severity index based on generator bus v...

This paper represents a new methodology to improve the performance of population-based optimization algorithms designed for corrective and preventive control actions enhancing power system dynamic security. Unlike many adaptive approaches employing the parameters such as population size, crossover or mutation rates, the proposed method for the perf...

In this paper, the performance degradation of a generating unit in a multi-area power system regulated by an area-wise decentralized load frequency control is detected and isolated via a scheme of unknown input observers. This scheme is robust to the changes in the load demand, which are considered as the unknown inputs. The actuator faults studied...

Post-fault prediction of transient stability of power systems has a great impact on the performance of wide area monitoring, protection and control systems. Situational awareness capabilities of a power system are improved by fast detection of instabilities after severe fault occurrences. This allows sufficient time to take necessary corrective con...

In this paper, the dynamic security of a large power system against any critical contingency is predicted by a new type of radial basis function neural network, RBF-R NN, as it classifies the system’s transient stability status online. In order to keep the number of measurements limited, as well as to reduce the complexity of the NNs used, the mini...

Dynamic security assessment of a large power system operating over a wide range of conditions requires an intensive computation for evaluating the system's transient stability against a large number of contingencies. In this study, we investigate the application of multilabel learning for improving training and prediction time, along with the predi...

Due to extensive utilization of intermittent energy sources in recent years, deterministic approaches cannot provide an accurate security assessment for power systems under large uncertainties. Therefore, probabilistic approaches have become crucial for making decisions based on more reliable assessments. In this paper, a new method based on machin...

In this study, the effect of feature selection methods on the performance of multi-layer perceptrons used for the dynamic security assessment of electric power systems is investigated. The existence of many measurable parameters (features) characterizing the power system security status complicates the use of multi-layer perceptron both in terms of...

In this paper, a preventive control action that involves both generation rescheduling and load curtailment is proposed for enhancing the dynamic security of large interconnected power systems. The control action is formulated as a security-constrained optimization problem that is solved by mean-variance mapping optimization (MVMO) integrated with a...

This paper proposes a new methodology to improve the population based optimization techniques applied for preventive control actions enhancing power system security. The preventive control studied includes both generation rescheduling and load curtailment. We first investigate how the size of the search space affects and improves the best solution...

Among the power system corrective controls, defensive islanding is considered as the last resort to secure the system from severe cascading contingencies. The primary motive of defensive islanding is to limit the affected areas to maintain the stability of the resulting subsystems and to reduce the total loss of load in the system. The slow coheren...

In this paper, a preventive control method against transient instabilities due to a set of critical contingencies that could occur in a large power system is proposed. The generation rescheduling, adopted as the preventive control, is based on the coherency between the generators. The rescheduling is done by attempting to bring the generators’ roto...

Among the power system corrective controls, defensive islanding is considered as the last resort to secure the system from severe cascading contingencies. The primary motive of defensive islanding is to limit the affected areas as soon as possible to maintain the stability of the resulting subsystems and to reduce the total loss of load in the syst...

Among the power system corrective controls, defensive islanding is considered as the last resort to secure the system from severe cascading contingencies. The objective is to maintain the stability of the resulting subsystems and to reduce the total loss of load in the system. The slow coherency based islanding can successfully be applied for the d...

Among the power system corrective controls, defensive islanding is considered as the last resort to secure the system from severe cascading contingencies. The objective is to maintain the stability of the resulting subsystems and to reduce the total loss of load in the system. The slow coherency based islanding can successfully be applied for the d...

This paper proposes a new method of optimization, the Big Bang–Big Crunch (BB–BC) method, for the optimization of preventive and corrective control actions to enhance the dynamic security of a power system against transient instabilities. The control actions, generation rescheduling and load shedding, are considered as constrained optimization prob...

This paper presents a methodology based on Artificial Neural Network (ANN) structures for the dynamic security assessment (DSA) of power systems. Proposed methodology involves, ANN approach for fast and accurate estimation of critical clearing time (CCT) values of credible faults occurring in the system, considering changes in the loading condition...

Power system stabilizers (PSSs) are used to enhance damping of electromechanical oscillations occurring in power systems after small or large disturbances. PSSs designed by classical control methods have been widely used to accomplish this task. In this study, a new method based on big bang-big crunch algorithm is proposed to optimize the parameter...

This paper presents big bang-big crunch (BB-BC) method for the optimization of preventive control action to enhance the dynamic security of the power system against transient instabilities. In the proposed approach, generation rescheduling is selected as a preventive control action and this control action is considered as a constrained optimization...

This paper presents a methodology for the design of optimal load shedding against transient instability of power systems. The proposed method is considered as a security constrained, multi dimensional optimization problem which is solved by Big Bang-Big Crunch (BBBC) method. The objective function to be minimized is the total weighted load shedding...

A robust actuator fault detection and isolation method is proposed to detect and isolate the governor faults in a multi-area power system in which the frequency is regulated by an area-wise decentralized load frequency control system. A bank of unknown input observers that are robust to the changes in the load demand is used while each unknown inpu...

In this paper, a new dynamic security assessment and generation rescheduling method utilizing genetic algorithms (GAs) which are integrated with probabilistic neural networks (PNNs) and adaptive neuro fuzzy inference systems (ANFISs) is proposed for the preventive control of large power systems against transient instabilities. By the proposed appro...

In this paper, the impact of wind power generation in power systems on electromechanical oscillations is studied while both deterministic and probabilistic approaches are used. A probabilistic small signal security index is proposed to investigate the relationship between the probability of having poorly damped inter-area oscillations and the size...

In this paper, a method of utilizing Probabilistic Neural Networks (PNNs) in the dynamic security assessment of power systems is proposed. The method involves an approach of a proper training data selection for a PNN which classifies the operating conditions of a power system with high accuracy. The classification is based on the power system secur...

In this paper, decision tree (DT)-based preventive and corrective control methods are proposed to enhance the dynamic security of power systems against the credible contingencies causing transient instabilities. Preventive and corrective controls such as generation rescheduling and load shedding schemes, respectively, are developed based on the sec...

In this paper, a method of decision tree-based computation of security regions and boundaries for power systems is proposed. These computations are used in dynamic security enhancement via generation rescheduling against transient instabilities. This work also involves an iterative method of generation rescheduling using the security regions calcul...

In this paper, a robust sensor and actuator fault detection and isolation (FDI) method based on unknown input observers (UIO) is adopted and applied to the load frequency control loops of interconnected power systems. Changes in load demand are regarded as the unknown disturbances in the system and the designed UIOs and thus the proposed method are...

The main purpose of the optimal control is to minimize H2 norm of the closed loop transfer function matrix under the pole placement and H2 norm performance constraints. In this paper, the problem of static state feedback H2 optimal control with regional pole constraint is formulated as an optimization problem involving Linear Matrix Inequalities (L...

In this paper, a linear optimal controller design with regional pole placement is proposed and employed on a power system model especially for damping electromechanical oscillations. The method utilizes a theorem which relates the weighting matrices of a linear quadratic optimal system to the bounds of the closed-loop eigenvalues defined by two hal...

In this paper, a robust sensor fault detection and isolation in load frequency control loop of interconnected power systems based on Unknown Input Observers (UIO) is designed and applied. The disturbances are assumed as unknown inputs to the UIO and the UIO is robust to disturbances. Simulations are performed for the dynamical model of a power cont...

The non-uniform texture of the bulk power transmission system significantly influences its dynamics and leads to a clustered structure of the system. In this article, we present an algorithm which clusters the system for the study of oscillatory stability. We analyze Hopf bifurcation related oscillatory behavior between the clusters formed in an in...

Integration of distributed generation is continually and gradually aecting the stability of intercon- nected power systems. In this paper, the impacts of distributed synchronous generators on oscillatory stability are studied. In various parameter sub-spaces of interest, feasibility regions can be calculated to determine the conditions to sustain t...

We analyze Hopf bifurcation related oscillatory behavior occurring between clusters of an interconnected power system. This phenomenon is discussed through feasibility boundary calculations associated with inter-cluster oscillatory modes of the system's real-power-angle dynamics. We demonstrate examples of Hopf bifurcation related instabilities occ...