Mitsubishi Denki Kabushiki Kaisha

Other

The Tokyo Electric Power Company, Incorporated

Yoshio Izui

Hideo Tanaka

Yoshiakira Akimoto

Hiromi Ogi

Yasuhiro Kojima

Masahiko Tanimoto

Hiroyuki Hashimoto

Masashi Kitayama

Toshiaki Sakaguchi

Tadahiro Goda

Tetsuo Sasaki

Shizuka Nakamura

Hisao Taoka

Sumie Kyomoto

Kenichi Deno

Naoto Fukuta

Kenji Iba

Kazumi Mori

Yasuyuki Kowada

An electric power demand adjusting system restrains fluctuation in an incentive for adjusting the demand for electric power or fluctuation in an electric power demand adjustment amount caused by an incentive. The system includes a central apparatus, lower units, and computing machine systems of consumers, which are connected through a communication network. The computing machine system of the central apparatus has a device for predicting a demand, a device for determining the electric power demand adjustment amounts to be sent to the lower units on the basis of the predicted demand and information that includes historical data regarding the electric power demand adjustments of the lower units, and a device for transmitting the determined electric power demand adjustment amounts to the lower units. The arrangement restrains the fluctuation in incentives required for adjusting the demand for electric power.

Electric power demand adjusting system

A power generator controller includes a power system monitor and a power generator output optimizing part. The power system monitor keeps check on current status of a power system and puts out an electricity demand. The power generator output optimizing part solves an objective function for determining outputs of power generators of the power system at an interval under a supply and demand balancing constraint which imposes agreement between electric power demanded and electric power supplied. The objective function minimizes total costs in power generation. The power generator output optimizing part solves the objective function further considering an automatic frequency control capacity constraint based on an upper limit of rates of change of outputs of power generators of the power system.

Power generator controller

A desired value of reactive power flow between a to-be-controlled power system and an adjoining power system is determined according to measured values of the reactive power flow and an effective power flow and desired voltage values of the to-be-controlled power system and of the adjoining power system. Thereafter, a required value of a reactive power of the to-be-controlled power system is calculated according to the desired value of the reactive power flow, the measured values of the reactive power flow and the effective power flow, and the desired voltage value and a measured voltage value in the to-be-controlled power system. Thereafter, a control apparatus having an electric capacity near to the required value of the reactive power is selected from control apparatus arranged in the to-be-controlled power system to make the selected control apparatus adjust a voltage of the to-be-controlled power system. Therefore, cooperation of the to-be-controlled power system with the adjoining power system can be performed by collecting locally-existing-information, and a voltage fluctuation and/or a reactive power fluctuation in the to-be-controlled power system can be immediately suppressed.

Power system control apparatus and power system control method

A command value decision unit capable of solving a problem of a conventional system in that when using a static fuel consuming characteristic model obtained by the least squares method as a fuel consuming characteristic model, it is sometimes difficult to decide the output command value P.sub.m * of each plant at high accuracy owing to the deviation of collected data and the response delay of the plants. The command value decision unit generates an output decision model of each plant by extracting items that affect the fuel consumption at the present time from among the items of the fuel consuming characteristic model of each plant, which is formed by taking account of hysteresis characteristics of the plant, and decides the output command value of the plant on the basis of the output decision model.

Command value decision unit

A neural network system includes input, intermediate and output layers, each layer containing at least one neural network element, each having an input and output, for simulating a neuron; and a plurality of inter-layer connections between neural elements wherein each input layer element has a connection to at least one intermediate layer element, and each intermediate layer element has a connection to at least one output layer element. Each inter-layer connection has a connecting weight. The system further includes sampling data and teaming data. The sampling data has pairs of values, each pair including an input value and corresponding output value, the input value having regular intervals. The learning data has at least three pairs of values, each pair including an input value and corresponding desired output value, the input values having irregular intervals. The intermediate layer elements are assigned unique sampling data value pairs and have unique sampling functions derived by translating original sampling functions by sampling data input values assigned to the neural elements. The sampling function defines a relationship between the input and output of the neural element. The connecting weight for each connection between an intermediate layer element and an output layer element is set to the sampling data output value assigned to the intermediate layer element. The system further includes a training mechanism that adjusts the connecting weights to minimize errors between learning data output values and actual output values obtained by applying the learning data input values to the neural network.

Neural network system with sampling data

A neural network apparatus and method for use in applications such as in a voltage/reactive-power controller in which a neuro control-object simulator and a neuro controller pre-learn so as to make input-output relations of the controller match the input-output relations of a control unit and so as to make input-output relations of the simulator match input-output relations of a control object. The controller re-learns so as to make the output of the simulator match an input corresponding to a desired output of the control object. After re-learning, the controller controls the control-object.

Control method using neural networks and a voltage/reactive-power

A monitoring diagnostic apparatus for detecting an abnormality occurring in an object being monitored such as electrical equipment and determining the cause of the abnormality is disclosed. First of all, vibration or partial discharge occurring in the monitored object is detected by using a sensor installed in close proximity to the monitored object. A detection signal output by the sensor then undergoes predetermined signal processing such as the Fourier transform and normalization. After the predetermined signal processing is completed, a neural network identifies the abnormality occurring in the monitored object and determining the cause of the abnormality. The neural network carries out a learning process based on causes of abnormalities occurring in the monitored object, outputting signals corresponding to the causes of the abnormalities.

Monitoring diagnostic apparatus using neural network

A monitoring diagnostic device for an electrical appliance such as gas insulated switchgear includes a sensor, such as an acceleration sensor, and a neural network including an input layer, an intermediate layer, and an output layer, each consisting of a plurality of neural elements. The input, intermediate and output layers are coupled to each other via a plurality of connection weights. The output of the sensor is first processed and then is supplied to the neural elements of the input layer. The connection weights are adjusted by means of learning data such that the output from the neural elements of the output layer of the neural network correctly identifies the causes of abnormality of the electrical appliance.

Growing community of inventors

Hyogo, Japan

Yoshio Izui