A critical review of the states art schemes for under voltage load shedding

A blackout is usually the result of increasing load beyond the transmission capacity of the power system. One of the main reasons for power blackouts is voltage collapse. To avoid this problem, the proper corrective measures called load shedding is required. In critical and extreme emergencies, under voltage load shedding (UVLS) is performed as a final remedy to avoid a larger scale voltage collapse. Therefore, UVLS is considered state of the art to achieve voltage stability. This review summarizes and updates the important aspects of UVLS; it also provides principle understanding of UVLS, which are critical in planning such defense schemes. Moreover, this article provides a discussion on recent state-of-art UVLS schemes applied in various power industries. Additionally, the pros and cons of the conventional and computational intelligence techniques are discussed. It is envisioned that this work will serve as one-stop information for power system engineers, designers, and researches

An improved algorithm for optimal load shedding in power systems

A blackout is usually the result of load increasing beyond the transmission capacity of the power system. A collapsing system enters a contingency state before the blackout. This contingency state is characterized by a decline in the bus voltage magnitudes. To avoid blackouts, power systems may start shedding load when a contingency state occurs called under voltage load shedding (UVLS). The success of a UVLS scheme in arresting the contingency state depends on shedding the optimum amount of load at the optimum time and location. This paper proposes a hybrid algorithm based on genetic algorithms (GA) and particle swarm optimization (PSO). The proposed algorithm can be used to find the optimal amount of load shed for systems under stress (overloaded) in smart grids. The proposed algorithm uses the fast voltage stability index (FVSI) to determine the weak buses in the system and then calculates the optimal amount of load shed to recover a collapsing system. The performance analysis shows that the proposed algorithm can improve the voltage profile by 0.022 per units with up to 75% less load shedding and a convergence time that is 53% faster than GA

Critical Node Detection for Voltage Collapse Mitigation in Modern Power Systems: A Network Topological-Based Approach

Quick detection of critical nodes has become a great concern to most investors and utilities recently due to its influence on prevention of the frequent occurrence of voltage collapse within a power network. This paper, therefore presents an option for detecting critical nodes, an approach which is based on the network topological characteristics of power networks. The mathematical formulations of the approach from the basic circuit theory laws were revisited. A Normalized Eigenvalue (NEV) index using eigenvalue and eigenvector analyses was then developed using MATLAB 2019b as the simulation tool. A simple 10-bus network was used to test the effectiveness of the NEV index method suggested in this paper. The NEV for all the network buses was determined and ranked in decreasing value of NEV to measure the criticality and vulnerability of each load node to voltage collapse within the system. Buses 6 had the highest value of NEV index (1.00) while bus 4 had the lowest NEV index (0.00) value. This suggested that there is a possibility of occurrence of over-voltage at bus 6 and undervoltage at bus 4. Therefore, buses 4 and 6 were identified as the critical buses, where placement of the reactive power support will be most beneficial. The results obtained were compared with those obtained using other methods documented in the literature. The comparison showed the effectiveness of the approach in quick identification of critical parts of the network most especially during critical outages

Power System Stability Assessment and Enhancement using Computational Intelligence

The main objective of the dissertation is to develop a fast and robust tool for assessment of power system stability and design a framework for enhancing system stability. The proposed framework is - based on the investigation of the dynamic behavior of the system - a market based rescheduling strategy that increases the stability margin. The dissertation specifically puts emphasis on the following approached: Power System Stability Evaluation: System stability is investigated by simulating a set of critical contingencies to determine whether the disturbances will result in any unsafe operating conditions and extract the necessary information to classify system states. The classification is based on the computation of the critical fault clearing time (CCT) for transient stability assessment (TSA) and the minimum damping of oscillation (MDO) for power system oscillatory stability assessment (OSA). The customary method of power system transient stability analysis including time-domain simulation (TDS) is used to compute the CCT at each critical contingency and Prony analysis as an efficient identification technique to estimate the mode parameters from the actual time response. The use of Prony analysis is to account for the effects of the change in location of the small disturbances as well as the increase in system nonlinearity on oscillating modes. Fast Power System Stability Assessment Tool: An artificial neural network (ANN) is designed to serve as accurate and fast tool for dynamic stability assessment (DSA). Fast response of ANN allows system operators to take suitable control actions to enhance the system stability and to forestall any possible impending breakup of the system. Two offline trained ANN are designed to map the dynamic behavior by relating the selected input features and the calculated CCT (as indicator for transient stability) and MDO (as indicator for oscillatory stability). Input features of ANN are selected to characterize the following: Changes in system topology and power distributions due to outage of major equipment such as transmission line, generation unit or large load Change in fault location and the severity of the fault Variation in loading levels and load allocation among market participants The features are generated for a wide range of loading at each expected system topology. Initial feature sets are pre-selected by engineering judgment based on experience in power system operation. In order to improve the accuracy of ANN to map the power system dynamic behavior, final selection is performed in the following three steps. In the first step, the generators terminal voltage drops immediately after fault are selected features to characterize the severity of the contingency with respect to the generators and to detect the fault location. In the second step, new features based on the inertia constant and the generated power in each area are calculated to characterize the changes in system topology and power flow pattern during normal and abnormal operation. In the third step, a systematic clustering feature selection technique is used to select the most important features that characterize the load levels and the power flow through lines from the mathematical viewpoint. The results prove the suitability of ANN in DSA with a reasonable degree of accuracy. Dynamic Stability Enhancement: To achieve online dynamic stability enhancement an online market based rescheduling strategy is proposed in the deregulated power systems. In case of power system operation by a centralized pool in vertically integrated electric utilities, generation rescheduling based sensitivity analysis is proposed. In the proposed market for deregulated power systems, the transactions among suppliers and consumers participating in the market are reallocated based on optional power bids to enhance system stability in case the available control actions are insufficient to enhance system stability. All participants are allowed to submit voluntary power bids to increase or decrease their scheduled level with equal chance. These bids represent the offered power quantity and the corresponding price. The goal of the framework is to enhance system stability with minimum additional and opportunity costs arising from the rescheduling. In case of vertically integrated electric utility, generation rescheduling based sensitivity analysis is used to enhance the system stability. The sensitivity analysis is based on the generators response following the most probable contingency. The generators are split into critical machines with positive sensitivity and non-critical machines with negative sensitivity. The change of the generation level among critical and non-critical machines provides the trajectories for stabilization procedure. The re-allocation of power among generators in each group is calculated based on the generator capacities and inertia constant, which simplifies the optimization procedure and speeds up the iterative to find a feasible solution. The objective is to minimize the increase in the cost due to rescheduling process. Particle swarm optimization is used as an optimization tool to search for the optimal solution to enhance the system stability with a minimum cost. The handling of all system constraints including stability constraints is achieved using a self-adaptive penalty function. Comparison strategy for selecting the best individuals during the optimization process is proposed where the feasible solutions are ever preferable during selection of local and global best particles.Die Schwerpunkte der Dissertation liegen in der Entwicklung eines schnellen und robusten Echtzeit-Bewertungsinstruments für Stabilitätsuntersuchungen in elektrischen Energienetzen und in dem Entwurf von Rahmenbedingungen zur Verbesserung der Systemstabilität. Basierend auf Untersuchungen bezüglich des dynamischen Verhaltens von elektrischen Energienetzen ist das Ziel der vorgeschlagenen Rahmenbedingungen, eine Planungsstrategie zu entwickeln, die marktwirtschaftlich ausgerichtet ist, um so die Stabilitätsgrenze zu verbessern und die erforderliche Systemsicherheit zu gewährleisten. Die dynamische Stabilität von elektrischen Energienetzen wurde bezogen auf die transiente und oszillatorische Stabilität untersucht, welche zur Beurteilung des dynamischen Verhaltens des Systems während Netzstörungen genutzt wird. Das Ziel der Dissertation ist die folgenden Aspekte zu untersuchen: Evaluierung der Dynamischen Stabilität: Die dynamische Stabilität ist durch die Simulation von kritischen Netzereignissen untersucht worden. Ziel war es, Störungen zu ermitteln, die zu kritischen oder gar unsicheren Betriebszuständen führen, und wichtige Beurteilungsparameter über den Zustand des Netzes auszuwählen. Die Beurteilungsparameter über den Zustand des elektrischen Energienetzes sind unter Verwendung der kritischen Fehlerklärungszeit als Indikator für die transiente Stabilität und der minimalen Dämpfung von Oszillationen als Indikator für die ozillatorische Stabilität ermittelt worden. Die übliche Methode bei einer transienten Stabilitätsanalyse in elektrischen Energienetzen basiert auf Simulationen im Zeitbereich und wird unter der Verwendung von vordefinierten netzkritischen Ereignissen genutzt, um die kritische Fehlerklärungszeit präzise zu berechnen. Die Prony-Analyse als eine effiziente Identifizierungstechnik wird zur Schätzung der Zustandsparameter auf eine einer Störung folgenden Zeitantwort verwendet. Der Gebrauch der Prony-Analyse erfasst die Veränderungen im Fehlerort von kleinen Störungen und einen Anstieg von Systemnichtlinearitäten im oszillatorischen Modus. Die mit Hilfe der Modalanalyse berechneten Parameter für den oszillatorischen Modus werden als Referenzsignale während des Abstimmens der Parameter der Prony-Analyse verwendet. Ziel ist die Verbesserung der Identifizierung des Systemmodus. Schnelles Bewertungswerkzeug für die dynamische Stabilität: Ein präzises und schnelles Werkzeug für die Bewertung von dynamischer Stabilität wurde mit Hilfe von künstlichen, neuronalen Netzen entwickelt. Die schnelle Antwort eines künstlichen, neuronalen Netzes ermöglicht es dem Netzbetreiber, geeignete fehlerbehebende Schalthandlungen während kritischer Netzereignisse durchzuführen. So kann die Stabilität des elektrischen Netzes gewährleistet und bevorstehende Netzausfälle verhindert werden. Zwei offline trainierte künstliche neuronale Netze sind entwickelt worden, um a) das dynamische Verhalten unter Verwendung ausgewählter Eingangseigenschaften und b) die berechnete kritische Fehlerklärungszeit als Indikator für die transiente Stabilität und die minimale Dämpfung der Oszillationen als Indikator für ozillatorische Stabilität abzubilden. Künstliche, neuronale Netze bieten vielversprechende Lösungen für schnelle Berechnungen bei online Anwendungen. Als Folge kann die hohe Anzahl an Berechnungen, die zur Untersuchung aller zu erwartenden kritischen Netzereignissen in elektrischen Energienetzen benötigt werden, schnell durchgeführt werden. Dies ermöglicht eine Bewertung der Systemzustände des elektrischen Netzes und eine Initiierung der zu erwartenden Schalthandlungen, um so die Systemstabilität zu verbessern. Für eine genaue Bewertung der dynamischen Stabilität sollten die Eingangseigenschaften für das künstliche, neuronale Netz sorgfältig ausgewählt werden. In dieser Arbeit sind die Eingangseigenschaften aus den gesamten Systemdaten ausgewählt worden, um die folgenden Eigenschaften kennzuzeichnen: i. Veränderungen in der Systemtopologie und des Lastflusses durch Ausfälle oder planmäßige Wartungen von Hauptkomponenten des Systems, wie zum Beispiel Übertragungsleitungen, Erzeugereinheiten oder großen Lasten ii. Veränderungen des Fehlerortes und des Einflusses des Fehlers auf die elektrischen Komponenten iii. Laständerungen und Lastaufteilung zwischen Netzversorgern Die Eingangseigenschaften wurden für viele, unterschiedliche Lastszenarien in Verbindung mit den zu erwartenden Netztopologien erzeugt. Die Anfangsbedingungen sind auf Grund von Erfahrungen mit dem Betrieb von elektrischen Energienetzen und bedingt durch das zu schätzende Ziel vorausgewählt. Die endgültige Auswahl der Eingangseigenschaften ist in drei Schritte unterteilt, um so die Genauigkeit des künstlichen, neuronalen Netzes zu erhöhen, welches die dynamische Stabilität des Energienetzes abbildet. Im ersten Schritt sind die Generatorklemmenspannungseinbrüche direkt nach der Netzstörung die wichtigen ausgewählten Eigenschaften. Hierdurch wird die Schwere des kritischen Netzereignisses aus der Sicht der Erzeugungseinheit gekennzeichnet und die Fehlerstelle lokalisiert. In dem zweiten Schritt werden neue Eingangseigenschaften basierend auf der Massenträgheitskonstante des Systems und der erzeugten Leistung in jedem Gebiet berechnet. So können Veränderungen in der Netztopologie und des Lastflusses unter normalen und gestörten Betriebsbedingungen gekennzeichnet werden. Im dritten Schritt wird eine systematische Cluster-Bildung der Eigenschaften genutzt, um so die wichtigsten Eigenschaften auszuwählen, die Aussagen über die Lastzustände und den Lastfluss über die Leitungen zulassen. Alle ausgewählten Eigenschaften repräsentieren das Eingangsmuster, wobei das Ausgangsmuster der Index der dynamischen Stabilitätsanalyse ist. Die Ergebnisse stellen die Eignung des künstlichen, neuronalen Netzes bei der Bewertung der dynamischen Stabilität dar. Verbesserung der dynamischen Stabilität: Eine online Verbesserung der dynamischen Stabilität kann durch eine vorgeschlagene marktwirtschaftliche Neuplanung des deregulierten Energiesystems und durch eine Neuplanung der Erzeugungseinheiten basierend auf der Empfindlichkeitsanalyse im Falle des Betriebs des Energienetzes durch eine zentrale Einheit erreicht werden. In dem vorgeschlagenen Markt für deregulierte Energiesysteme wird im Falle, dass vorgesehenen Schalthandlungen das Netz nicht in einen stabilen Zustand zurückbringen kann, die Energie zwischen Versorgern und Verbrauchern basierend auf optionalen Leistungsgeboten umgeschichtet. Alle Erzeuger und Verbraucher sind berechtigt an diesem Markt durch freiwillige Leistungsgebote teilzunehmen, um so ihre geplante Menge chancengleich zu erhöhen oder zu verkleinern. Diese Gebote der Marktteilnehmer repräsentieren die angebotene Leistungsmenge und den darauf bezogenen Preis. Teilnehmer, von denen es verlangt ist, Erzeugung oder Verbrauch zu reduzieren, werden für diese Möglichkeit zur Reduzierung bezahlt. So kann der Verlust der Serviceleistung kompensiert werden, während Teilnehmer, deren Leistung erhöht wird, durch den Marktpreis plus zusätzlicher Kosten für zusätzliche Veränderungen entlohnt werden. Das Ziel dieser Rahmenbedingungen ist eine Verbesserung der Systemstabilität kombiniert mit einem Minimum an zusätzlichen Kosten auftretend durch die Neuplanung. Im Falle eines zentralen Energiemarktes wird die Neuplanung der Erzeuger basierend auf der Empfindlichkeitsanalyse durchgeführt, um so eine Verbesserung der Systemstabilität zu erreichen. Die Empfindlichkeitsanalyse bezieht sich auf die Systemantwort des Generators während des belastbarsten kritischen Netzereignisses. Dieses kritische Netzereignis trennt die Erzeugungseinheiten a) in kritische Maschinen, die eine positive Empfindlichkeit besitzen, und b) in nicht-kritische Maschinen mit einer negativen Empfindlichkeit. Die Einteilung in kritische und nicht-kritische Maschinen ermöglicht eine Lösung für die Stabilisierung des Systems. Die Verteilung der verschobenen Leistung zwischen den Generatoren in jeder Gruppe wird unter Verwendung der Generatorleistungen und der Massenträgheitskonstanten berechnet. Dies erleichtert den Optimierungsalgorithmus und beschleunigt das Erhalten einer möglichen Lösung. Das Ziel ist die Minimierung der Erhöhung der Kosten für die absolut erzeugte Leistung auf Grund der Abweichung vom wirtschaftlichen Arbeitspunkt. In dieser Arbeit wird die Particle Swarm Optimierung als Werkzeug verwendet, um damit eine optimale Lösung mit den minimalen Kosten zu erlangen. Dadurch kann eine Verbesserung der dynamischen Stabilität des elektrischen Energienetzes unter Berücksichtigung aller systembedingten Nebenbedingungen erlangt werden. Die Handhabung aller systembedingten Nebenbedingungen inklusive der Nebenbedingungen der dynamischen Stabilität kann durch eine selbstanpassende Straffunktion erreicht werden

AI Applications to Power Systems

Today, the flow of electricity is bidirectional, and not all electricity is centrally produced in large power plants. With the growing emergence of prosumers and microgrids, the amount of electricity produced by sources other than large, traditional power plants is ever-increasing. These alternative sources include photovoltaic (PV), wind turbine (WT), geothermal, and biomass renewable generation plants. Some renewable energy resources (solar PV and wind turbine generation) are highly dependent on natural processes and parameters (wind speed, wind direction, temperature, solar irradiation, humidity, etc.). Thus, the outputs are so stochastic in nature. New data-science-inspired real-time solutions are needed in order to co-develop digital twins of large intermittent renewable plants whose services can be globally delivered

Analysis and management of security constraints in overstressed power systems

Management of operational security constraints is one of the important tasks performed by system operators, which must be addressed properly for secure and economic operation. Constraint management is becoming an increasingly complex and challenging to execute in modern electricity networks for three main reasons. First, insufficient transmission capacity during peak and emergency conditions, which typically result in numerous constraint violations. Second, reduced fault levels, inertia and damping due to power electronic interfaced demand and stochastic renewable generation, which are making network more vulnerable to even small disturbances. Third, re-regulated electricity markets require the networks to operate much closer to their operational security limits, which typically result in stressed and overstressed operating conditions. Operational security constraints can be divided into static security limits (bus voltage and branch thermal limits) and dynamic security limits (voltage and angle stability limits). Security constraint management, in general, is formulated as a constrained, nonlinear, and nonconvex optimization problem. The problem is usually solved by conventional gradient-based nonlinear programming methods to devise optimal non-emergency or emergency corrective actions utilizing minimal system reserves. When the network is in emergency state with reduced/insufficient control capability, the solution space of the corresponding nonlinear optimization problem may be too small, or even infeasible. In such cases, conventional non-linear programming methods may fail to compute a feasible (corrective) control solution that mitigate all constraint violations or might fail to rationalize a large number of immediate post-contingency constraint violations into a smaller number of critical constraints. Although there exists some work on devising corrective actions for voltage and thermal congestion management, this has mostly focused on the alert state of the operation, not on the overstressed and emergency conditions, where, if appropriate control actions are not taken, network may lose its integrity. As it will be difficult for a system operator to manage a large number of constraint violations (e.g. more than ten) at one time, it is very important to rationalize the violated constraints to a minimum subset of critical constraints and then use information on their type and location to implement the right corrective actions at the right locations, requiring minimal system reserves and switching operations. Hence, network operators and network planners should be equipped with intelligent computational tools to “filter out” the most critical constraints when the feasible solution space is empty and to provide a feasible control solution when the solution space is too narrow. With an aim to address these operational difficulties and challenges, this PhD thesis presents three novel interdependent frameworks: Infeasibility Diagnosis and Resolution Framework (IDRF), Constraint Rationalization Framework (CRF) and Remedial Action Selection and Implementation Framework (RASIF). IDRF presents a metaheuristic methodology to localise and resolve infeasibility in constraint management problem formulations (in specific) and nonlinear optimization problem formulations (in general). CRF extends PIDRF and reduces many immediate post-contingency constraint violations into a small number of critical constraints, according to various operational priorities during overstressed operating conditions. Each operational priority is modelled as a separate objective function and the formulation can be easily extended to include other operational aspects. Based on the developed CRF, RASIF presents a methodology for optimal selection and implementation of the most effective remedial actions utilizing various ancillary services, such as distributed generation control, reactive power compensation, demand side management, load shedding strategies. The target buses for the implementation of the selected remedial actions are identified using bus active and reactive power injection sensitivity factors, corresponding to the overloaded lines and buses with excessive voltage violations (i.e. critical constraints). The RASIF is validated through both static and dynamic simulations to check the satisfiability of dynamic security constraints during the transition and static security constraints after the transition. The obtained results demonstrate that the framework for implementation of remedial actions allows the most secure transition between the pre-contingency and post-contingency stable equilibrium points

Study of Voltage Stability Using Intelligent Techniques

The continuous increase in the demand of active and reactive power in the power system network has limits as scope for network expansion many a times poses serious problems. The power system must be able to maintain acceptable voltage at all nodes in the system at a normal operating condition as well as post disturbance periods. Voltage instability is a serious issue in the system due to progressive and uncontrollable fall in voltage level. The research presented in this thesis is concerned with several facets of the voltage stability problem. The focus of this thesis is to improve the voltage stability of the system. The sensitivity analysis plays an important role as it monitors the nearness of the system towards the voltage collapse situation. The conventional offline data as well as the online data are processed to determine the weak areas are determined. As the system is having nonlinearities it is governed by differential and algebraic equations which are in turn solved by nonlinear techniques. In this work the system is analysed with steady state model. Once the system is represented in the form of differential equations and standard form is achieved advanced control techniques can be easily applied for its solution. The main focus of this thesis is aimed at placing FACTS device known as the Static compensator (STATCOM) at weak location of the system network to address the problem of voltage instability. With its unique capability to control reactive power flow in a transmission line as well as voltage at the bus where it is connected, this device significantly contribute to improve the power system. These features turn out to be even more prominent because STATCOM can allow loading of the transmission lines close to their thermal limits, forcing the power to flow through the desired paths. This opens up new avenues for the much needed flexibility in order to satisfy the demands. The voltage instability is improved with reactive power supports of optimal values at optimal locations. Also renewable energy sources offer better option than the conventional types and hence attempt has been made to include the wind energy for this study the wind generator is considered delivering constant output and is assumed as a substitute to the conventional power generators. Finally the system voltage stability is studied with design of a controller based on probabilistic neural network. The developed controller has provided much better performance under wide variations in the system loads and contingencies and shown a significant improvement in the static performance of the system. The proposed controller is tested under different scenarios of line outages and the load increase and found to be more effective than the existing ones. The research has revealed a veritable cornucopia of research opportunities, some of which are discussed in the thesis

Preventing Wide Area Blackouts in Transmission Systems: A New Approach for Intentional Controlled Islanding using Power Flow Tracing

A novel method to reduce the impact of wide area blackouts in transmission networks is presented. Millions of customers are affected each year due to blackouts. Splitting a transmission system into smaller islands could significantly reduce the effect of these blackouts. Large blackouts are typically a result of cascading faults which propagate throughout a network where Intentional Controlled Islanding (ICI) has the advantage of containing faults to smaller regions and stop them cascading further. Existing methodologies for ICI are typically calculated offline and will form pre-determined islands which can often lead to excessive splits. This thesis developed an ICI approach based on real time information which will calculate an islanding solution quickly in order to provide a ‘just-in-time’ strategy. The advantage of this method is that the island solution is designed based on the current operating point, but well also be designed for the particular disturbance location and hence will avoid unnecessary islanding. The new method will use a power flow tracing technique to find a boundary around a disturbance which forms the island that will be cut. The tracing method required only power flow information and so, can be computed quite quickly. The action of islanding itself can be a significant disturbance, therefore any islanding solution should aim to add as little stress as possible to the system. While methods which minimise the power imbalance and total power disrupted due to splitting are well documented, there has been little study into the effect islanding would have on voltage. There a new approach to consider the effects that islanding will have on the voltage stability of the system is developed. The ICI method is based on forming an island specific to a disturbance. If the location of a source is known along with information that a blackout is imminent, the methodology will find the best island in which to contain that disturbance. This is a slightly different approach to existing methods which will form islands independent of disturbance location knowledge. An area of influence is found around a node using power flow tracing, which consists of the strongly connected elements to the disturbance. Therefore, low power flows can be disconnected. This area of influence forms the island that will be disconnected, leaving the rest of the system intact. Hence minimising the number of islands formed. Finally the methodology is compared to the existing methods to show that the new tool developed in this thesis can find better solutions and that a new way of thinking about power system ICI can be put forward

Development and application of a new voltage stability index for on-line monitoring and shedding

During the past decades, voltage instability was the reason behind several major blackouts worldwide. Continuous assessment of the system voltage stability is vital to ensure a secured operation of the system. Several voltage stability indicators have been proposed and used in an attempt to quantify proximity to voltage collapse. Some of these are computationally expensive, and others are reported not to perform as expected under all conditions. In this work a new voltage stability indicator named the P-index is proposed. This index is based on normalized voltage and power sensitivities and as such, it provides an absolute measure of the system stability. It is robust and based on solid theoretical foundations. The index has been tested on static and dynamic test platforms, and for both platforms offered a correct assessment of proximity to voltage collapse and weakest system buses. Furthermore, a method for topology change detection suitable for online systems was proposed. Dynamic stability monitoring with PMU measurements was simulated in real-time on the well-known Kundur 10-bus system and the appropriate load shedding using the P-index was calculated. Compared to the another node-based indicator, the L-index, the results show that the P-index gives a better prediction of proximity to voltage collapse and is well suited for load shedding purposes

Power system performance improvement in the presence of renewable sources

Electromechanical oscillations is a phenomenon in which a generator oscillates against other generators in the power system, the damping of these oscillations has therefore become a priority objective, The objective of our work is to ensure maximum damping of low frequency oscillations and to guarantee the overall stability of the system for different operating points by the use of power stabilizers (PSSs). To achieve this goal, we developed an improved metaheuristic optimization method based on the crows search algorithm (CSA) applied on an objective function extracted from the eigenvalue analysis of the power system. A comparative study was made, with a classic stabilizer, genetic algorithm-based PSS (GA-PSS), a particle-swarm-based PSS (PSO-PSS) and other stabilizers based on recent algorithms. The performances of these optimization methods were evaluated on a single machine connected to an infinite bus (SMIB) via a linear model time domain simulation. On the other hand, the effect of integrating a photovoltaic PV generator on the stability of the power system is presented, as well as solutions to increase the amount of integration of the PV generator without losing the stability of the system