Implementation of Adaptive Critic-Based Neurocontrollers for Turbogenerators in a Multimachine Power System

This paper presents the design and practical hardware implementation of optimal neurocontrollers that replace the conventional automatic voltage regulator (AVR) and the turbine governor of turbogenerators on multimachine power systems. The neurocontroller design uses a powerful technique of the adaptive critic design (ACD) family called dual heuristic programming (DHP). The DHP neurocontroller\u27s training and testing are implemented on the Innovative Integration M67 card consisting of the TMS320C6701 processor. The measured results show that the DHP neurocontrollers are robust and their performance does not degrade unlike the conventional controllers even when a power system stabilizer (PSS) is included, for changes in system operating conditions and configurations. This paper also shows that it is possible to design and implement optimal neurocontrollers for multiple turbogenerators in real time, without having to do continually online training of the neural networks, thus avoiding risks of instability

Wide-Area Measurement Application and Power System Dynamics

Frequency monitoring network (FNET) is a GPS-synchronized distribution-level phasor measurement system. It is a powerful synchronized monitoring network for large-area power systems that provides significant information and data for power system situational awareness, real time and post-event analysis, and other important aspects of bulk systems. This work explored FNET measurements and utilized them for different applications and power system analysis. An island system was built and validated with FNET measurements to study the stability of the OTEC integration. FNET measurements were also used to validate a large system model like the U.S. Eastern Interconnection. It tries to match the simulation result and frequency measurement of a real event by adjusting the simulation model. The system model is tuned with the combination of different impact factors for different confirmed actual events, and some general rules and specific tuning quantities were concluded from the model validation process. This work also investigated the behavior of the power system frequency during large-scale, synchronous societal events, like the World Cup, Super Bowl and Royal Wedding. It is apparent that large groups of people engaging in the same event at roughly the same time can have significant impacts on the power grid frequency. The systematic analysis of the accumulating and statistical FNET frequency data presents an incisive point of view on the power grid frequency behavior during such events. To better understanding of system events recorded by FNET, a visualization tool was developed to visualize major events that occurred in the North American power grid. The measurement plot combined with the geographical contour map provides intuitive visualization of the event. Finally, the EI system was simplified and clustered into four groups based on FNET measurements and simulation results of generator trip cases. The generation and load capacity of each cluster was calculated based on the clustering result and simulation model, and a flow diagram of this simplified EI system was demonstrated with clusters and power flow between them

Implementation of self-tuning control for turbine generators

PhD ThesisThis thesis documents the work that has been done towards the development of a 'practical' self-tuning controller for turbine generator plant. It has been shown by simulation studies and practical investigations using a micro-alternator system that a significant enhancement in the overall performance in terms of control and stability can be achieved by improving the primary controls of a turbine generator using self-tuning control. The self-tuning AVR is based on the Generalised Predictive Control strategy. The design of the controller has been done using standard off-the-shelf microprocessor hardware and structured software design techniques. The proposed design is thus flexible, cost-effective, and readily applicable to 'real' generating plant. Several practical issues have been tackled during the design of the self-tuning controller and techniques to improve the robustness of the measurement system, controller, and parameter estimator have been proposed and evaluated. A simple and robust measurement system for plant variables based on software techniques has been developed and its suitability for use in the self-tuning controller has been practically verified. The convergence, adaptability, and robustness aspects of the parameter estimator have been evaluated and shown to be suitable for long-term operation in 'real' self-tuning controllers. The self-tuning AVR has been extensively evaluated under normal and fault conditions of the turbine generator. It has been shown that this new controller is superior in performance when compared with a conventional lag-lead type of fixed-parameter digital AVR. The use of electrical power as a supplementary feedback signal in the new AVR is shown to further improve the dynamic stability of the system. The self-tuning AVR has been extended to a multivariable integrated self-tuning controller which combines the AVR and EHG functions. The flexibility of the new AVR to enable its expansion for more complex control applications has thus been demonstrated. Simple techniques to incorporate constraints on control inputs without upsetting the loop decoupling property of the multivariable controller have been proposed and evaluated. It is shown that a further improvement in control performance and stability can be achieved by the integrated controller.Parsons Turbine Generators Ltd

A Wide Area Hierarchical Voltage Control for Systems with High Wind Penetration and an HVDC Overlay

The modern power grid is undergoing a dramatic revolution. On the generation side, renewable resources are replacing fossil fuel in powering the system. On the transmission side, an AC-DC hybrid network has become increasingly popular to help reduce the transportation cost of electricity. Wind power, as one of the environmental friendly renewable resources, has taken a larger and larger share of the generation market. Due to the remote locations of wind plants, an HVDC overlay turns out to be attractive for transporting wind energy due to its superiority in long distance transmission of electricity. While reducing environmental concern, the increasing utilization of wind energy forces the power system to operate under a tighter operating margin. The limited reactive capability of wind turbines is insufficient to provide adequate voltage support under stressed system conditions. Moreover, the volatility of wind further aggravates the problem as it brings uncertainty to the available reactive resources and can cause undesirable voltage behavior in the system. The power electronics of the HVDC overlay may also destabilize the gird under abnormal voltage conditions. Such limitations of wind generation have undermined system security and made the power grid more vulnerable to disturbances. This dissertation proposes a Hierarchical Voltage Control (HVC) methodology to optimize the reactive reserve of a power system with high levels of wind penetration. The proposed control architecture consists of three layers. A tertiary Optimal Power Flow computes references for pilot bus voltages. Secondary voltage scheduling adjusts primary control variables to achieve the desired set points. The three levels of the proposed HVC scheme coordinate to optimize the voltage profile of the system and enhance system security. The proposed HVC is tested on an equivalent Western Electricity Coordinated Council (WECC) system modified by a multi-terminal HVDC overlay. The effectiveness of the proposed HVC is validated under a wide range of operating conditions. The capability to manage a future AC/DC hybrid network is studied to allow even higher levels of wind

A functional link network based adaptive power system stabilizer

An on-line identifier using Functional Link Network (FLN) and Pole-shift (PS) controller for power system stabilizer (PSS) application are presented in this thesis. To have the satisfactory performance of the PSS controller, over a wide range of operating conditions, it is desirable to adapt PSS parameters in real time. Artificial Neural Networks (ANNs) transform the inputs in a low-dimensional space to high-dimensional nonlinear hidden unit space and they have the ability to model the nonlinear characteristics of the power system. The ability of ANNs to learn makes them more suitable for use in adaptive control techniques. On-line identification obtains a mathematical model at each sampling period to track the dynamic behavior of the plant. The ANN identifier consisting of a Functional link Network (FLN) is used for identifying the model parameters. A FLN model eliminates the need of hidden layer while retaining the nonlinear mapping capability of the neural network by using enhanced inputs. This network may be conveniently used for function approximation with faster convergence rate and lesser computational load. The most commonly used Pole Assignment (PA) algorithm for adaptive control purposes assign the pole locations to fixed locations within the unit circle in the z-plane. It may not be optimum for different operating conditions. In this thesis, PS type of adaptive control algorithm is used. This algorithm, instead of assigning the closed-loop poles to fixed locations within the unit circle in the z-plane, this algorithm assumes that the pole characteristic polynomial of the closed-loop system has the same form as the pole characteristic of the open-loop system and shifts the open-loop poles radially towards the centre of the unit circle in the z-plane by a shifting factor α according to some rules. In this control algorithm, no coefficients need to be tuned manually, so manual parameter tuning (which is a drawback in conventional power system stabilizer) is minimized. The PS control algorithm uses the on-line updated ARMA parameters to calculate the new closed-loop poles of the system that are always inside the unit circle in the z-plane. Simulation studies on a single-machine infinite bus and on a multi-machine power system for various operating condition changes, verify the effectiveness of the combined model of FLN identifier and PS control in damping the local and multi-mode oscillations occurring in the system. Simulation studies prove that the APSSs have significant benefits over conventional PSSs: performance improvement and no requirement for parameter tuning

Benchmarking Product Service System of Generator Set Distributors

Power generation is an industry which is essential to sustain daily life. It makes a tight competition between generator set distributors. An improvement in product service system of the distributors is necessary to survive the competition. The purpose of this research is to develop tools to benchmark the Product Service System of generator set distributors. This benchmarking aims for identifying the gaps between one product with other competitors’ products. The methodology of conducting this research is by using Product Service System Board to visualize the current Product Service System of a generator set distributor, adopting PPIAF framework for assessing product performance and SERVQUAL framework for assessing service quality, and using AHP as weighting method. The survey leads to the idea of the improvement of current Product Service System of a generator set distributor. This research needs further studies in more detailed measures weighting and the implementation of Product Service System Board in assessing service quality

Test system requirements for modelling future power systems

This paper discusses the need for new test system models to be developed and made available to researchers. A number of features of such test systems are proposed. These include sufficient size and scope to allow control interactions to be studied but not so much that phenomena associated with new technologies cannot be understood. It is recalled that the performance of new technologies and their controls should be verified on a full system model that is as faithful to the real system and its parameters as possible and that this requires access to data often owned by generating companies to which system operators have access but do not feel able to disclose. Finally, arguments are presented as to why such data should be disclosed and it is recommended that regulatory authorities take steps to achieve it

Grid-forming power converters tuned through artificial intelligence to damp subsynchronous interactions in electrical grids

The integration of non-synchronous generation units and energy storage through power electronics is introducing new challenges in power system dynamics. Specifically, the rotor angle stability has been identified as one of the major obstacle with regards to power electronics dominated power systems. To date, conventional power system stabilizer (PSS) devices are used for damping electromechanical oscillations, which are only tuned sporadically leading to significant deterioration in performance against the ever-changing operating conditions. In this paper, an intelligent power oscillation damper (iPOD) is proposed for grid-forming converters to attenuate electromechanical inter-area power oscillation. In particular, the iPOD is applied to a synchronous power controller (SPC) based grid-forming power converter to increases gain of the active power control loop at the oscillatory frequency. Predictions regarding the mode frequency, corresponding to the current operating points, are given by an artificial intelligence ensemble model called Random Forests. The performance of the proposed controller is verified using the two area system considering symmetrical fault for random operating points. In addition, a comparison with PSS installed in each generator reveals the individual contribution with respect to the inter-area mode damping.This work was supported in part by the European Commission under Project FLEXITRANSTORE—-H2020-LCE-2016-2017-SGS774407, and in part by the Spanish Ministry of Science under Project ENE2017-88889-C2-1-R.Peer ReviewedPostprint (published version